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pandas.DataFrame.to_latex\n+    <a class=\"reference internal\" href=\"pandas.read_stata.html\">\n+     pandas.read_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_stata.html\">\n      pandas.DataFrame.to_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_gbq.html\">\n-     pandas.DataFrame.to_gbq\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_records.html\">\n-     pandas.DataFrame.to_records\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_string.html\">\n-     pandas.DataFrame.to_string\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2 current active\">\n-    <a class=\"current reference internal\" href=\"#\">\n-     pandas.DataFrame.to_clipboard\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.data_label.html\">\n+     pandas.io.stata.StataReader.data_label\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_markdown.html\">\n-     pandas.DataFrame.to_markdown\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.value_labels.html\">\n+     pandas.io.stata.StataReader.value_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.style.html\">\n-     pandas.DataFrame.style\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.variable_labels.html\">\n+     pandas.io.stata.StataReader.variable_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.__dataframe__.html\">\n-     pandas.DataFrame.__dataframe__\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataWriter.write_file.html\">\n+     pandas.io.stata.StataWriter.write_file\n     </a>\n    </li>\n   </ul>\n  </li>\n  <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../general_functions.html\">\n+   General functions\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../series.html\">\n+   Series\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../frame.html\">\n+   DataFrame\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../arrays.html\">\n    pandas arrays, scalars, and data types\n   </a>\n  </li>\n  <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../indexing.html\">\n    Index objects\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -4,242 +4,73 @@\n     * _\bA_\bP_\bI_\b _\br_\be_\bf_\be_\br_\be_\bn_\bc_\be\n     * _\bD_\be_\bv_\be_\bl_\bo_\bp_\bm_\be_\bn_\bt\n     * _\bR_\be_\bl_\be_\ba_\bs_\be_\b 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_\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bt_\br_\bu_\be_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bp_\bo_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\be\n-          o 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_\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bf_\bo_\br_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\br_\bo_\bu_\bp_\bb_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bl_\bl_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\ba_\bn_\bd_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bw_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bb_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bn_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bl_\bi_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br_\bw_\bi_\bt_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bu_\bn_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\be_\bs_\bc_\br_\bi_\bb_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bf_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bv_\ba_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt_\bo_\bs_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bd_\bi_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bc_\bt_\b__\bc_\bh_\ba_\bn_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd_\bu_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\ba_\bn_\bt_\bi_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bu_\bn_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bk_\be_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bu_\bn_\bi_\bq_\bu_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\bl_\bu_\be_\b__\bc_\bo_\bu_\bn_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bp_\br_\be_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bs_\bu_\bf_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\be_\bt_\bw_\be_\be_\bn_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\b__\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq_\bu_\ba_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b__\bl_\bi_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\bn_\bc_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\ba_\bc_\bk_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bl_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt_\b__\bt_\ba_\bb_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bo_\br_\bd_\be_\br_\b__\bl_\be_\bv_\be_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bv_\ba_\bl_\bu_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bl_\ba_\br_\bg_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bs_\bm_\ba_\bl_\bl_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bn_\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\ba_\bx_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\bl_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bq_\bu_\be_\be_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\ba_\br_\br_\ba_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bT\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bs_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bp_\ba_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bj_\bo_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\br_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bp_\bd_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bf_\br_\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bo_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bh_\bi_\bf_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\be_\br_\bi_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bt_\bi_\bm_\be_\bs_\bt_\ba_\bm_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bc_\bo_\bn_\bv_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bl_\bo_\bc_\ba_\bl_\bi_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bF_\bl_\ba_\bg_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\bt_\br_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\ba_\br_\be_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\bo_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\be_\bx_\bb_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bk_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bl_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bp_\bi_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bs_\bc_\ba_\bt_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bo_\bx_\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\br_\bo_\bm_\b__\bs_\bp_\bm_\ba_\bt_\br_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bc_\bo_\bo\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bd_\be_\bn_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\bd_\bi_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+    * _\bI_\bn_\bp_\bu_\bt_\b/_\bo_\bu_\bt_\bp_\bu_\bt\u0010\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\bi_\bc_\bk_\bl_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bs_\bv\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bs_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bd_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bd_\bi_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\bw_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\be_\bx_\bc_\be_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bb_\bo_\bo_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bs_\bh_\be_\be_\bt_\b__\bn_\ba_\bm_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bp_\ba_\br_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bW_\br_\bi_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bj_\bs_\bo_\bn_\b__\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bj_\bs_\bo_\bn_\b._\bb_\bu_\bi_\bl_\bd_\b__\bt_\ba_\bb_\bl_\be_\b__\bs_\bc_\bh_\be_\bm_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bt_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bx_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bd_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bp_\bu_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\ba_\bp_\bp_\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bs_\be_\bl_\be_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bi_\bn_\bf_\bo\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bk_\be_\by_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\br_\bo_\bu_\bp_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bw_\ba_\bl_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\ba_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bp_\bs_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bq_\bu_\be_\br_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bg_\bb_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bt_\ba_\bt_\ba\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\ba_\bt_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bg_\bb_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\br_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bm_\ba_\br_\bk_\bd_\bo_\bw_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\by_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bd_\ba_\bt_\ba_\bf_\br_\ba_\bm_\be_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bd_\ba_\bt_\ba_\b__\bl_\ba_\bb_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\bl_\bu_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\br_\bi_\ba_\bb_\bl_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bW_\br_\bi_\bt_\be_\br_\b._\bw_\br_\bi_\bt_\be_\b__\bf_\bi_\bl_\be\n+    * _\bG_\be_\bn_\be_\br_\ba_\bl_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n+    * _\bS_\be_\br_\bi_\be_\bs\n+    * _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\n     * _\bp_\ba_\bn_\bd_\ba_\bs_\b _\ba_\br_\br_\ba_\by_\bs_\b,_\b _\bs_\bc_\ba_\bl_\ba_\br_\bs_\b,_\b _\ba_\bn_\bd_\b _\bd_\ba_\bt_\ba_\b _\bt_\by_\bp_\be_\bs\n     * _\bI_\bn_\bd_\be_\bx_\b _\bo_\bb_\bj_\be_\bc_\bt_\bs\n     * _\bD_\ba_\bt_\be_\b _\bo_\bf_\bf_\bs_\be_\bt_\bs\n     * _\bW_\bi_\bn_\bd_\bo_\bw\n     * _\bG_\br_\bo_\bu_\bp_\bB_\by\n     * _\bR_\be_\bs_\ba_\bm_\bp_\bl_\bi_\bn_\bg\n     * _\bS_\bt_\by_\bl_\be\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_csv.html", "source2": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_csv.html", "unified_diff": "@@ -152,1162 +152,317 @@\n             <div class=\"col-12 col-md-3 bd-sidebar\"><form class=\"bd-search d-flex align-items-center\" action=\"../../search.html\" method=\"get\">\n   <i class=\"icon fas fa-search\"></i>\n   <input type=\"search\" class=\"form-control\" name=\"q\" id=\"search-input\" placeholder=\"Search the docs ...\" aria-label=\"Search the docs ...\" autocomplete=\"off\" >\n </form><nav class=\"bd-links\" id=\"bd-docs-nav\" aria-label=\"Main navigation\">\n   <div class=\"bd-toc-item navbar-nav\">\n     \n     <ul class=\"current nav bd-sidenav\">\n- <li class=\"toctree-l1\">\n+ <li class=\"toctree-l1 current active has-children\">\n   <a class=\"reference internal\" href=\"../io.html\">\n    Input/output\n   </a>\n- </li>\n- <li class=\"toctree-l1\">\n-  <a class=\"reference internal\" href=\"../general_functions.html\">\n-   General functions\n-  </a>\n- </li>\n- <li class=\"toctree-l1\">\n-  <a class=\"reference internal\" href=\"../series.html\">\n-   Series\n-  </a>\n- </li>\n- <li class=\"toctree-l1 current active has-children\">\n-  <a class=\"reference internal\" href=\"../frame.html\">\n-   DataFrame\n-  </a>\n   <input checked=\"\" class=\"toctree-checkbox\" id=\"toctree-checkbox-1\" name=\"toctree-checkbox-1\" type=\"checkbox\"/>\n   <label for=\"toctree-checkbox-1\">\n    <i class=\"fas fa-chevron-down\">\n    </i>\n   </label>\n   <ul class=\"current\">\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.html\">\n-     pandas.DataFrame\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.index.html\">\n-     pandas.DataFrame.index\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.columns.html\">\n-     pandas.DataFrame.columns\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.dtypes.html\">\n-     pandas.DataFrame.dtypes\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.info.html\">\n-     pandas.DataFrame.info\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.select_dtypes.html\">\n-     pandas.DataFrame.select_dtypes\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.values.html\">\n-     pandas.DataFrame.values\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.axes.html\">\n-     pandas.DataFrame.axes\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" 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href=\"pandas.DataFrame.head.html\">\n-     pandas.DataFrame.head\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.at.html\">\n-     pandas.DataFrame.at\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.iat.html\">\n-     pandas.DataFrame.iat\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.loc.html\">\n-     pandas.DataFrame.loc\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.iloc.html\">\n-     pandas.DataFrame.iloc\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.insert.html\">\n-     pandas.DataFrame.insert\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.__iter__.html\">\n-     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class=\"reference internal\" href=\"pandas.DataFrame.__add__.html\">\n-     pandas.DataFrame.__add__\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.add.html\">\n-     pandas.DataFrame.add\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sub.html\">\n-     pandas.DataFrame.sub\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.mul.html\">\n-     pandas.DataFrame.mul\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.div.html\">\n-     pandas.DataFrame.div\n+    <a class=\"reference internal\" href=\"pandas.read_pickle.html\">\n+     pandas.read_pickle\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.truediv.html\">\n-     pandas.DataFrame.truediv\n-    </a>\n-   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</a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.from_dict.html\">\n-     pandas.DataFrame.from_dict\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_parquet.html\">\n+     pandas.DataFrame.to_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.from_records.html\">\n-     pandas.DataFrame.from_records\n+    <a class=\"reference internal\" href=\"pandas.read_orc.html\">\n+     pandas.read_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_orc.html\">\n      pandas.DataFrame.to_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_parquet.html\">\n-     pandas.DataFrame.to_parquet\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_pickle.html\">\n-     pandas.DataFrame.to_pickle\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2 current active\">\n-    <a class=\"current reference internal\" href=\"#\">\n-     pandas.DataFrame.to_csv\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_hdf.html\">\n-     pandas.DataFrame.to_hdf\n+    <a class=\"reference internal\" href=\"pandas.read_sas.html\">\n+     pandas.read_sas\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n-     pandas.DataFrame.to_sql\n+    <a class=\"reference internal\" href=\"pandas.read_spss.html\">\n+     pandas.read_spss\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_dict.html\">\n-     pandas.DataFrame.to_dict\n+    <a class=\"reference internal\" href=\"pandas.read_sql_table.html\">\n+     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class=\"reference internal\" href=\"pandas.read_gbq.html\">\n+     pandas.read_gbq\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_latex.html\">\n-     pandas.DataFrame.to_latex\n+    <a class=\"reference internal\" href=\"pandas.read_stata.html\">\n+     pandas.read_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_stata.html\">\n      pandas.DataFrame.to_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_gbq.html\">\n-     pandas.DataFrame.to_gbq\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_records.html\">\n-     pandas.DataFrame.to_records\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_string.html\">\n-     pandas.DataFrame.to_string\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_clipboard.html\">\n-     pandas.DataFrame.to_clipboard\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.data_label.html\">\n+     pandas.io.stata.StataReader.data_label\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_markdown.html\">\n-     pandas.DataFrame.to_markdown\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.value_labels.html\">\n+     pandas.io.stata.StataReader.value_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.style.html\">\n-     pandas.DataFrame.style\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.variable_labels.html\">\n+     pandas.io.stata.StataReader.variable_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.__dataframe__.html\">\n-     pandas.DataFrame.__dataframe__\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataWriter.write_file.html\">\n+     pandas.io.stata.StataWriter.write_file\n     </a>\n    </li>\n   </ul>\n  </li>\n  <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../general_functions.html\">\n+   General functions\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../series.html\">\n+   Series\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../frame.html\">\n+   DataFrame\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../arrays.html\">\n    pandas arrays, scalars, and data types\n   </a>\n  </li>\n  <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../indexing.html\">\n    Index objects\n", "details": [{"source1": 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_\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bm_\bp_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bf_\bl_\ba_\bg_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bt_\by_\bp_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bn_\bv_\be_\br_\bt_\b__\bd_\bt_\by_\bp_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bf_\be_\br_\b__\bo_\bb_\bj_\be_\bc_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bp_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bo_\bo_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bn_\bu_\bm_\bp_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\ba_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\bo_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bl_\bo_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bs_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bi_\bt_\be_\br_\b__\b_\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\bm_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\be_\by_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\br_\br_\bo_\bw_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\br_\bt_\bu_\bp_\bl_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bx_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\be_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bw_\bh_\be_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bs_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\be_\br_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\ba_\bd_\bd_\b__\b_\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bb\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bu_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\be_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bo_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bd_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bs_\bu_\bb\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bu_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bt_\br_\bu_\be_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bp_\bo_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be_\b__\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by_\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bp_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg_\br_\be_\bg_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bf_\bo_\br_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\br_\bo_\bu_\bp_\bb_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bl_\bl_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\ba_\bn_\bd_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bw_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bb_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bn_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bl_\bi_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br_\bw_\bi_\bt_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bu_\bn_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\be_\bs_\bc_\br_\bi_\bb_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bf_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bv_\ba_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt_\bo_\bs_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bd_\bi_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bc_\bt_\b__\bc_\bh_\ba_\bn_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd_\bu_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\ba_\bn_\bt_\bi_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bu_\bn_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bk_\be_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bu_\bn_\bi_\bq_\bu_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\bl_\bu_\be_\b__\bc_\bo_\bu_\bn_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bp_\br_\be_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bs_\bu_\bf_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\be_\bt_\bw_\be_\be_\bn_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\b__\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq_\bu_\ba_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b__\bl_\bi_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\bn_\bc_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\ba_\bc_\bk_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bl_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt_\b__\bt_\ba_\bb_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bo_\br_\bd_\be_\br_\b__\bl_\be_\bv_\be_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bv_\ba_\bl_\bu_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bl_\ba_\br_\bg_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bs_\bm_\ba_\bl_\bl_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bn_\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\ba_\bx_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\bl_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bq_\bu_\be_\be_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\ba_\br_\br_\ba_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bT\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bs_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bp_\ba_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bj_\bo_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\br_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bp_\bd_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bf_\br_\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bo_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bh_\bi_\bf_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\be_\br_\bi_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bt_\bi_\bm_\be_\bs_\bt_\ba_\bm_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bc_\bo_\bn_\bv_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bl_\bo_\bc_\ba_\bl_\bi_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bF_\bl_\ba_\bg_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\bt_\br_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\ba_\br_\be_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\bo_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\be_\bx_\bb_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bk_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bl_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bp_\bi_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bs_\bc_\ba_\bt_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bo_\bx_\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\br_\bo_\bm_\b__\bs_\bp_\bm_\ba_\bt_\br_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bc_\bo_\bo\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bd_\be_\bn_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\bd_\bi_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+    * _\bI_\bn_\bp_\bu_\bt_\b/_\bo_\bu_\bt_\bp_\bu_\bt\u0010\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\bi_\bc_\bk_\bl_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bs_\bv\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bs_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bd_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bd_\bi_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\bw_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\be_\bx_\bc_\be_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bb_\bo_\bo_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bs_\bh_\be_\be_\bt_\b__\bn_\ba_\bm_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bp_\ba_\br_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bW_\br_\bi_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bj_\bs_\bo_\bn_\b__\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bj_\bs_\bo_\bn_\b._\bb_\bu_\bi_\bl_\bd_\b__\bt_\ba_\bb_\bl_\be_\b__\bs_\bc_\bh_\be_\bm_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bt_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bx_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bd_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bp_\bu_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\ba_\bp_\bp_\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bs_\be_\bl_\be_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bi_\bn_\bf_\bo\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bk_\be_\by_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\br_\bo_\bu_\bp_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bw_\ba_\bl_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\ba_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bp_\bs_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bq_\bu_\be_\br_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bg_\bb_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bt_\ba_\bt_\ba\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\ba_\bt_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bg_\bb_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\br_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bm_\ba_\br_\bk_\bd_\bo_\bw_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\by_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bd_\ba_\bt_\ba_\bf_\br_\ba_\bm_\be_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bd_\ba_\bt_\ba_\b__\bl_\ba_\bb_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\bl_\bu_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\br_\bi_\ba_\bb_\bl_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bW_\br_\bi_\bt_\be_\br_\b._\bw_\br_\bi_\bt_\be_\b__\bf_\bi_\bl_\be\n+    * _\bG_\be_\bn_\be_\br_\ba_\bl_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n+    * _\bS_\be_\br_\bi_\be_\bs\n+    * _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\n     * _\bp_\ba_\bn_\bd_\ba_\bs_\b _\ba_\br_\br_\ba_\by_\bs_\b,_\b _\bs_\bc_\ba_\bl_\ba_\br_\bs_\b,_\b _\ba_\bn_\bd_\b _\bd_\ba_\bt_\ba_\b _\bt_\by_\bp_\be_\bs\n     * _\bI_\bn_\bd_\be_\bx_\b _\bo_\bb_\bj_\be_\bc_\bt_\bs\n     * _\bD_\ba_\bt_\be_\b _\bo_\bf_\bf_\bs_\be_\bt_\bs\n     * _\bW_\bi_\bn_\bd_\bo_\bw\n     * _\bG_\br_\bo_\bu_\bp_\bB_\by\n     * _\bR_\be_\bs_\ba_\bm_\bp_\bl_\bi_\bn_\bg\n     * _\bS_\bt_\by_\bl_\be\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_excel.html", "source2": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_excel.html", "unified_diff": "@@ -152,1162 +152,317 @@\n             <div class=\"col-12 col-md-3 bd-sidebar\"><form class=\"bd-search d-flex align-items-center\" action=\"../../search.html\" method=\"get\">\n   <i class=\"icon fas fa-search\"></i>\n   <input type=\"search\" class=\"form-control\" name=\"q\" id=\"search-input\" placeholder=\"Search the docs ...\" aria-label=\"Search the docs ...\" autocomplete=\"off\" >\n </form><nav class=\"bd-links\" id=\"bd-docs-nav\" aria-label=\"Main navigation\">\n   <div class=\"bd-toc-item navbar-nav\">\n     \n     <ul class=\"current nav bd-sidenav\">\n- <li class=\"toctree-l1\">\n+ <li class=\"toctree-l1 current active has-children\">\n   <a class=\"reference internal\" href=\"../io.html\">\n    Input/output\n   </a>\n- </li>\n- <li class=\"toctree-l1\">\n-  <a class=\"reference internal\" href=\"../general_functions.html\">\n-   General functions\n-  </a>\n- </li>\n- <li class=\"toctree-l1\">\n-  <a class=\"reference internal\" href=\"../series.html\">\n-   Series\n-  </a>\n- </li>\n- <li class=\"toctree-l1 current active has-children\">\n-  <a class=\"reference internal\" href=\"../frame.html\">\n-   DataFrame\n-  </a>\n   <input checked=\"\" class=\"toctree-checkbox\" id=\"toctree-checkbox-1\" name=\"toctree-checkbox-1\" type=\"checkbox\"/>\n   <label for=\"toctree-checkbox-1\">\n    <i class=\"fas fa-chevron-down\">\n    </i>\n   </label>\n   <ul class=\"current\">\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.html\">\n-     pandas.DataFrame\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.index.html\">\n-   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class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.transpose.html\">\n-     pandas.DataFrame.transpose\n+    <a class=\"reference internal\" href=\"pandas.read_csv.html\">\n+     pandas.read_csv\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.assign.html\">\n-     pandas.DataFrame.assign\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_csv.html\">\n+     pandas.DataFrame.to_csv\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.compare.html\">\n-     pandas.DataFrame.compare\n+    <a class=\"reference internal\" href=\"pandas.read_fwf.html\">\n+     pandas.read_fwf\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.join.html\">\n-     pandas.DataFrame.join\n+    <a class=\"reference internal\" href=\"pandas.read_clipboard.html\">\n+     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class=\"reference internal\" href=\"pandas.json_normalize.html\">\n+     pandas.json_normalize\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.tz_localize.html\">\n-     pandas.DataFrame.tz_localize\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_json.html\">\n+     pandas.DataFrame.to_json\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.Flags.html\">\n-     pandas.Flags\n+    <a class=\"reference internal\" href=\"pandas.io.json.build_table_schema.html\">\n+     pandas.io.json.build_table_schema\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.attrs.html\">\n-     pandas.DataFrame.attrs\n+    <a class=\"reference internal\" href=\"pandas.read_html.html\">\n+     pandas.read_html\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" 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internal\" href=\"pandas.HDFStore.walk.html\">\n+     pandas.HDFStore.walk\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.from_spmatrix.html\">\n-     pandas.DataFrame.sparse.from_spmatrix\n+    <a class=\"reference internal\" href=\"pandas.read_feather.html\">\n+     pandas.read_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.to_coo.html\">\n-     pandas.DataFrame.sparse.to_coo\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n+     pandas.DataFrame.to_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.to_dense.html\">\n-     pandas.DataFrame.sparse.to_dense\n+    <a class=\"reference internal\" href=\"pandas.read_parquet.html\">\n+     pandas.read_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.from_dict.html\">\n-     pandas.DataFrame.from_dict\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_parquet.html\">\n+     pandas.DataFrame.to_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.from_records.html\">\n-     pandas.DataFrame.from_records\n+    <a class=\"reference internal\" href=\"pandas.read_orc.html\">\n+     pandas.read_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_orc.html\">\n      pandas.DataFrame.to_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_parquet.html\">\n-     pandas.DataFrame.to_parquet\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_pickle.html\">\n-     pandas.DataFrame.to_pickle\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_csv.html\">\n-     pandas.DataFrame.to_csv\n+    <a class=\"reference internal\" href=\"pandas.read_sas.html\">\n+     pandas.read_sas\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_hdf.html\">\n-     pandas.DataFrame.to_hdf\n+    <a class=\"reference internal\" href=\"pandas.read_spss.html\">\n+     pandas.read_spss\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n-     pandas.DataFrame.to_sql\n+    <a class=\"reference internal\" href=\"pandas.read_sql_table.html\">\n+     pandas.read_sql_table\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_dict.html\">\n-     pandas.DataFrame.to_dict\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2 current active\">\n-    <a class=\"current reference internal\" href=\"#\">\n-     pandas.DataFrame.to_excel\n+    <a class=\"reference internal\" href=\"pandas.read_sql_query.html\">\n+     pandas.read_sql_query\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_json.html\">\n-     pandas.DataFrame.to_json\n+    <a class=\"reference internal\" href=\"pandas.read_sql.html\">\n+     pandas.read_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_html.html\">\n-     pandas.DataFrame.to_html\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n+     pandas.DataFrame.to_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n-     pandas.DataFrame.to_feather\n+    <a class=\"reference internal\" href=\"pandas.read_gbq.html\">\n+     pandas.read_gbq\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_latex.html\">\n-     pandas.DataFrame.to_latex\n+    <a class=\"reference internal\" href=\"pandas.read_stata.html\">\n+     pandas.read_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_stata.html\">\n      pandas.DataFrame.to_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_gbq.html\">\n-     pandas.DataFrame.to_gbq\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_records.html\">\n-     pandas.DataFrame.to_records\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_string.html\">\n-     pandas.DataFrame.to_string\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_clipboard.html\">\n-     pandas.DataFrame.to_clipboard\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.data_label.html\">\n+     pandas.io.stata.StataReader.data_label\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_markdown.html\">\n-     pandas.DataFrame.to_markdown\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.value_labels.html\">\n+     pandas.io.stata.StataReader.value_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.style.html\">\n-     pandas.DataFrame.style\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.variable_labels.html\">\n+     pandas.io.stata.StataReader.variable_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.__dataframe__.html\">\n-     pandas.DataFrame.__dataframe__\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataWriter.write_file.html\">\n+     pandas.io.stata.StataWriter.write_file\n     </a>\n    </li>\n   </ul>\n  </li>\n  <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../general_functions.html\">\n+   General functions\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../series.html\">\n+   Series\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../frame.html\">\n+   DataFrame\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../arrays.html\">\n    pandas arrays, scalars, and data types\n   </a>\n  </li>\n  <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../indexing.html\">\n    Index objects\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -4,242 +4,73 @@\n     * _\bA_\bP_\bI_\b 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o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\ba_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\bo_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bl_\bo_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bs_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bi_\bt_\be_\br_\b__\b_\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\bm_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\be_\by_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\br_\br_\bo_\bw_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\br_\bt_\bu_\bp_\bl_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bx_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\be_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bw_\bh_\be_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bs_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\be_\br_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\ba_\bd_\bd_\b__\b_\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bb\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bu_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\be_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bo_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bd_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bs_\bu_\bb\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bu_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bt_\br_\bu_\be_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bp_\bo_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be_\b__\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by_\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bp_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg_\br_\be_\bg_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bf_\bo_\br_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\br_\bo_\bu_\bp_\bb_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bl_\bl_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\ba_\bn_\bd_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bw_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bb_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bn_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bl_\bi_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br_\bw_\bi_\bt_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bu_\bn_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\be_\bs_\bc_\br_\bi_\bb_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bf_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bv_\ba_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt_\bo_\bs_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bd_\bi_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bc_\bt_\b__\bc_\bh_\ba_\bn_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd_\bu_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\ba_\bn_\bt_\bi_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bu_\bn_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bk_\be_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bu_\bn_\bi_\bq_\bu_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\bl_\bu_\be_\b__\bc_\bo_\bu_\bn_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bp_\br_\be_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bs_\bu_\bf_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\be_\bt_\bw_\be_\be_\bn_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\b__\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq_\bu_\ba_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b__\bl_\bi_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\bn_\bc_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\ba_\bc_\bk_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bl_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt_\b__\bt_\ba_\bb_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bo_\br_\bd_\be_\br_\b__\bl_\be_\bv_\be_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bv_\ba_\bl_\bu_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bl_\ba_\br_\bg_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bs_\bm_\ba_\bl_\bl_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bn_\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\ba_\bx_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\bl_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bq_\bu_\be_\be_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\ba_\br_\br_\ba_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bT\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bs_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bp_\ba_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bj_\bo_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\br_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bp_\bd_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bf_\br_\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bo_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bh_\bi_\bf_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\be_\br_\bi_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bt_\bi_\bm_\be_\bs_\bt_\ba_\bm_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bc_\bo_\bn_\bv_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bl_\bo_\bc_\ba_\bl_\bi_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bF_\bl_\ba_\bg_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\bt_\br_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\ba_\br_\be_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\bo_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\be_\bx_\bb_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bk_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bl_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bp_\bi_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bs_\bc_\ba_\bt_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bo_\bx_\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\br_\bo_\bm_\b__\bs_\bp_\bm_\ba_\bt_\br_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bc_\bo_\bo\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bd_\be_\bn_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\bd_\bi_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+    * _\bI_\bn_\bp_\bu_\bt_\b/_\bo_\bu_\bt_\bp_\bu_\bt\u0010\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\bi_\bc_\bk_\bl_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bs_\bv\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bs_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bd_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bd_\bi_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\bw_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\be_\bx_\bc_\be_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bb_\bo_\bo_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bs_\bh_\be_\be_\bt_\b__\bn_\ba_\bm_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bp_\ba_\br_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bW_\br_\bi_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bj_\bs_\bo_\bn_\b__\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bj_\bs_\bo_\bn_\b._\bb_\bu_\bi_\bl_\bd_\b__\bt_\ba_\bb_\bl_\be_\b__\bs_\bc_\bh_\be_\bm_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bt_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bx_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bd_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bp_\bu_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\ba_\bp_\bp_\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bs_\be_\bl_\be_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bi_\bn_\bf_\bo\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bk_\be_\by_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\br_\bo_\bu_\bp_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bw_\ba_\bl_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\ba_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bp_\bs_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bq_\bu_\be_\br_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bg_\bb_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bt_\ba_\bt_\ba\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\ba_\bt_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bg_\bb_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\br_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bm_\ba_\br_\bk_\bd_\bo_\bw_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\by_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bd_\ba_\bt_\ba_\bf_\br_\ba_\bm_\be_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bd_\ba_\bt_\ba_\b__\bl_\ba_\bb_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\bl_\bu_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\br_\bi_\ba_\bb_\bl_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bW_\br_\bi_\bt_\be_\br_\b._\bw_\br_\bi_\bt_\be_\b__\bf_\bi_\bl_\be\n+    * _\bG_\be_\bn_\be_\br_\ba_\bl_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n+    * _\bS_\be_\br_\bi_\be_\bs\n+    * _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\n     * _\bp_\ba_\bn_\bd_\ba_\bs_\b _\ba_\br_\br_\ba_\by_\bs_\b,_\b _\bs_\bc_\ba_\bl_\ba_\br_\bs_\b,_\b _\ba_\bn_\bd_\b _\bd_\ba_\bt_\ba_\b _\bt_\by_\bp_\be_\bs\n     * _\bI_\bn_\bd_\be_\bx_\b _\bo_\bb_\bj_\be_\bc_\bt_\bs\n     * _\bD_\ba_\bt_\be_\b _\bo_\bf_\bf_\bs_\be_\bt_\bs\n     * _\bW_\bi_\bn_\bd_\bo_\bw\n     * _\bG_\br_\bo_\bu_\bp_\bB_\by\n     * _\bR_\be_\bs_\ba_\bm_\bp_\bl_\bi_\bn_\bg\n     * _\bS_\bt_\by_\bl_\be\n"}]}, {"source1": 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href=\"pandas.json_normalize.html\">\n+     pandas.json_normalize\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.tz_localize.html\">\n-     pandas.DataFrame.tz_localize\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_json.html\">\n+     pandas.DataFrame.to_json\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.Flags.html\">\n-     pandas.Flags\n+    <a class=\"reference internal\" href=\"pandas.io.json.build_table_schema.html\">\n+     pandas.io.json.build_table_schema\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.attrs.html\">\n-     pandas.DataFrame.attrs\n+    <a class=\"reference internal\" href=\"pandas.read_html.html\">\n+     pandas.read_html\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.html\">\n-     pandas.DataFrame.plot\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_html.html\">\n+     pandas.DataFrame.to_html\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.area.html\">\n-     pandas.DataFrame.plot.area\n+    <a class=\"reference internal\" href=\"pandas.io.formats.style.Styler.to_html.html\">\n+     pandas.io.formats.style.Styler.to_html\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.bar.html\">\n-     pandas.DataFrame.plot.bar\n+    <a class=\"reference internal\" href=\"pandas.read_xml.html\">\n+     pandas.read_xml\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.barh.html\">\n-     pandas.DataFrame.plot.barh\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_xml.html\">\n+     pandas.DataFrame.to_xml\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.box.html\">\n-     pandas.DataFrame.plot.box\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_latex.html\">\n+     pandas.DataFrame.to_latex\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.density.html\">\n-     pandas.DataFrame.plot.density\n+    <a class=\"reference internal\" href=\"pandas.io.formats.style.Styler.to_latex.html\">\n+     pandas.io.formats.style.Styler.to_latex\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.hexbin.html\">\n-     pandas.DataFrame.plot.hexbin\n+    <a class=\"reference internal\" href=\"pandas.read_hdf.html\">\n+     pandas.read_hdf\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.hist.html\">\n-     pandas.DataFrame.plot.hist\n+    <a 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</a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.from_spmatrix.html\">\n-     pandas.DataFrame.sparse.from_spmatrix\n+    <a class=\"reference internal\" href=\"pandas.read_feather.html\">\n+     pandas.read_feather\n     </a>\n    </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.to_coo.html\">\n-     pandas.DataFrame.sparse.to_coo\n+   <li class=\"toctree-l2 current active\">\n+    <a class=\"current reference internal\" href=\"#\">\n+     pandas.DataFrame.to_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.to_dense.html\">\n-     pandas.DataFrame.sparse.to_dense\n+    <a class=\"reference internal\" href=\"pandas.read_parquet.html\">\n+     pandas.read_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.from_dict.html\">\n-     pandas.DataFrame.from_dict\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_parquet.html\">\n+     pandas.DataFrame.to_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.from_records.html\">\n-     pandas.DataFrame.from_records\n+    <a class=\"reference internal\" href=\"pandas.read_orc.html\">\n+     pandas.read_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_orc.html\">\n      pandas.DataFrame.to_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_parquet.html\">\n-     pandas.DataFrame.to_parquet\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_pickle.html\">\n-     pandas.DataFrame.to_pickle\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_csv.html\">\n-     pandas.DataFrame.to_csv\n+    <a class=\"reference internal\" href=\"pandas.read_sas.html\">\n+     pandas.read_sas\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_hdf.html\">\n-     pandas.DataFrame.to_hdf\n+    <a class=\"reference internal\" href=\"pandas.read_spss.html\">\n+     pandas.read_spss\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n-     pandas.DataFrame.to_sql\n+    <a class=\"reference internal\" href=\"pandas.read_sql_table.html\">\n+     pandas.read_sql_table\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_dict.html\">\n-     pandas.DataFrame.to_dict\n+    <a class=\"reference internal\" href=\"pandas.read_sql_query.html\">\n+     pandas.read_sql_query\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_excel.html\">\n-     pandas.DataFrame.to_excel\n+    <a class=\"reference internal\" href=\"pandas.read_sql.html\">\n+     pandas.read_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_json.html\">\n-     pandas.DataFrame.to_json\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n+     pandas.DataFrame.to_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_html.html\">\n-     pandas.DataFrame.to_html\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2 current active\">\n-    <a class=\"current reference internal\" href=\"#\">\n-     pandas.DataFrame.to_feather\n+    <a class=\"reference internal\" href=\"pandas.read_gbq.html\">\n+     pandas.read_gbq\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_latex.html\">\n-     pandas.DataFrame.to_latex\n+    <a class=\"reference internal\" href=\"pandas.read_stata.html\">\n+     pandas.read_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_stata.html\">\n      pandas.DataFrame.to_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_gbq.html\">\n-     pandas.DataFrame.to_gbq\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_records.html\">\n-     pandas.DataFrame.to_records\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_string.html\">\n-     pandas.DataFrame.to_string\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_clipboard.html\">\n-     pandas.DataFrame.to_clipboard\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.data_label.html\">\n+     pandas.io.stata.StataReader.data_label\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_markdown.html\">\n-     pandas.DataFrame.to_markdown\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.value_labels.html\">\n+     pandas.io.stata.StataReader.value_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.style.html\">\n-     pandas.DataFrame.style\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.variable_labels.html\">\n+     pandas.io.stata.StataReader.variable_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.__dataframe__.html\">\n-     pandas.DataFrame.__dataframe__\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataWriter.write_file.html\">\n+     pandas.io.stata.StataWriter.write_file\n     </a>\n    </li>\n   </ul>\n  </li>\n  <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../general_functions.html\">\n+   General functions\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../series.html\">\n+   Series\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../frame.html\">\n+   DataFrame\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../arrays.html\">\n    pandas arrays, scalars, and data types\n   </a>\n  </li>\n  <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../indexing.html\">\n    Index objects\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -4,242 +4,73 @@\n     * _\bA_\bP_\bI_\b 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_\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bu_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bt_\br_\bu_\be_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bp_\bo_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be_\b__\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by_\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bp_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg_\br_\be_\bg_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bf_\bo_\br_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\br_\bo_\bu_\bp_\bb_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bl_\bl_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\ba_\bn_\bd_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bw_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bb_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bn_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bl_\bi_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br_\bw_\bi_\bt_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bu_\bn_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\be_\bs_\bc_\br_\bi_\bb_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bf_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bv_\ba_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt_\bo_\bs_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bd_\bi_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bc_\bt_\b__\bc_\bh_\ba_\bn_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd_\bu_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\ba_\bn_\bt_\bi_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bu_\bn_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bk_\be_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bu_\bn_\bi_\bq_\bu_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\bl_\bu_\be_\b__\bc_\bo_\bu_\bn_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bp_\br_\be_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bs_\bu_\bf_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\be_\bt_\bw_\be_\be_\bn_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\b__\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq_\bu_\ba_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b__\bl_\bi_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\bn_\bc_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\ba_\bc_\bk_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bl_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt_\b__\bt_\ba_\bb_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bo_\br_\bd_\be_\br_\b__\bl_\be_\bv_\be_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bv_\ba_\bl_\bu_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bl_\ba_\br_\bg_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bs_\bm_\ba_\bl_\bl_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bn_\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\ba_\bx_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\bl_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bq_\bu_\be_\be_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\ba_\br_\br_\ba_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bT\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bs_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bp_\ba_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bj_\bo_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\br_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bp_\bd_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bf_\br_\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bo_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bh_\bi_\bf_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\be_\br_\bi_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bt_\bi_\bm_\be_\bs_\bt_\ba_\bm_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bc_\bo_\bn_\bv_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bl_\bo_\bc_\ba_\bl_\bi_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bF_\bl_\ba_\bg_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\bt_\br_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\ba_\br_\be_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\bo_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\be_\bx_\bb_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bk_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bl_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bp_\bi_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bs_\bc_\ba_\bt_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bo_\bx_\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\br_\bo_\bm_\b__\bs_\bp_\bm_\ba_\bt_\br_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bc_\bo_\bo\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bd_\be_\bn_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\bd_\bi_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+    * _\bI_\bn_\bp_\bu_\bt_\b/_\bo_\bu_\bt_\bp_\bu_\bt\u0010\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\bi_\bc_\bk_\bl_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bs_\bv\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bs_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bd_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bd_\bi_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\bw_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\be_\bx_\bc_\be_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bb_\bo_\bo_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bs_\bh_\be_\be_\bt_\b__\bn_\ba_\bm_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bp_\ba_\br_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bW_\br_\bi_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bj_\bs_\bo_\bn_\b__\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bj_\bs_\bo_\bn_\b._\bb_\bu_\bi_\bl_\bd_\b__\bt_\ba_\bb_\bl_\be_\b__\bs_\bc_\bh_\be_\bm_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bt_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bx_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bd_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bp_\bu_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\ba_\bp_\bp_\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bs_\be_\bl_\be_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bi_\bn_\bf_\bo\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bk_\be_\by_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\br_\bo_\bu_\bp_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bw_\ba_\bl_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\ba_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bp_\bs_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bq_\bu_\be_\br_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bg_\bb_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bt_\ba_\bt_\ba\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\ba_\bt_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bg_\bb_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\br_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bm_\ba_\br_\bk_\bd_\bo_\bw_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\by_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bd_\ba_\bt_\ba_\bf_\br_\ba_\bm_\be_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bd_\ba_\bt_\ba_\b__\bl_\ba_\bb_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\bl_\bu_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\br_\bi_\ba_\bb_\bl_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bW_\br_\bi_\bt_\be_\br_\b._\bw_\br_\bi_\bt_\be_\b__\bf_\bi_\bl_\be\n+    * _\bG_\be_\bn_\be_\br_\ba_\bl_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n+    * _\bS_\be_\br_\bi_\be_\bs\n+    * _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\n     * _\bp_\ba_\bn_\bd_\ba_\bs_\b _\ba_\br_\br_\ba_\by_\bs_\b,_\b _\bs_\bc_\ba_\bl_\ba_\br_\bs_\b,_\b _\ba_\bn_\bd_\b _\bd_\ba_\bt_\ba_\b _\bt_\by_\bp_\be_\bs\n     * _\bI_\bn_\bd_\be_\bx_\b _\bo_\bb_\bj_\be_\bc_\bt_\bs\n     * _\bD_\ba_\bt_\be_\b _\bo_\bf_\bf_\bs_\be_\bt_\bs\n     * _\bW_\bi_\bn_\bd_\bo_\bw\n     * _\bG_\br_\bo_\bu_\bp_\bB_\by\n     * _\bR_\be_\bs_\ba_\bm_\bp_\bl_\bi_\bn_\bg\n     * _\bS_\bt_\by_\bl_\be\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_html.html", "source2": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_html.html", "unified_diff": "@@ -152,1162 +152,317 @@\n             <div class=\"col-12 col-md-3 bd-sidebar\"><form class=\"bd-search d-flex align-items-center\" action=\"../../search.html\" method=\"get\">\n   <i class=\"icon fas fa-search\"></i>\n   <input type=\"search\" class=\"form-control\" name=\"q\" id=\"search-input\" placeholder=\"Search the docs ...\" aria-label=\"Search the docs ...\" autocomplete=\"off\" >\n </form><nav class=\"bd-links\" id=\"bd-docs-nav\" aria-label=\"Main navigation\">\n   <div class=\"bd-toc-item navbar-nav\">\n     \n     <ul class=\"current nav bd-sidenav\">\n- <li class=\"toctree-l1\">\n+ <li class=\"toctree-l1 current active has-children\">\n   <a class=\"reference internal\" href=\"../io.html\">\n    Input/output\n   </a>\n- </li>\n- <li class=\"toctree-l1\">\n-  <a class=\"reference internal\" href=\"../general_functions.html\">\n-   General functions\n-  </a>\n- </li>\n- <li class=\"toctree-l1\">\n-  <a class=\"reference internal\" href=\"../series.html\">\n-   Series\n-  </a>\n- </li>\n- <li class=\"toctree-l1 current active has-children\">\n-  <a class=\"reference internal\" href=\"../frame.html\">\n-   DataFrame\n-  </a>\n   <input checked=\"\" class=\"toctree-checkbox\" id=\"toctree-checkbox-1\" name=\"toctree-checkbox-1\" type=\"checkbox\"/>\n   <label for=\"toctree-checkbox-1\">\n    <i class=\"fas fa-chevron-down\">\n    </i>\n   </label>\n   <ul class=\"current\">\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.html\">\n-     pandas.DataFrame\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.index.html\">\n-     pandas.DataFrame.index\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" 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class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.aggregate.html\">\n-     pandas.DataFrame.aggregate\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.transform.html\">\n-     pandas.DataFrame.transform\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.groupby.html\">\n-     pandas.DataFrame.groupby\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.rolling.html\">\n-     pandas.DataFrame.rolling\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.expanding.html\">\n-     pandas.DataFrame.expanding\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.ewm.html\">\n-     pandas.DataFrame.ewm\n-    </a>\n-   </li>\n-   <li 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internal\" href=\"pandas.DataFrame.count.html\">\n-     pandas.DataFrame.count\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.cov.html\">\n-     pandas.DataFrame.cov\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.cummax.html\">\n-     pandas.DataFrame.cummax\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.cummin.html\">\n-     pandas.DataFrame.cummin\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.cumprod.html\">\n-     pandas.DataFrame.cumprod\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.cumsum.html\">\n-     pandas.DataFrame.cumsum\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" 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pandas.DataFrame.plot.hist\n+    <a class=\"reference internal\" href=\"pandas.HDFStore.put.html\">\n+     pandas.HDFStore.put\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.kde.html\">\n-     pandas.DataFrame.plot.kde\n+    <a class=\"reference internal\" href=\"pandas.HDFStore.append.html\">\n+     pandas.HDFStore.append\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.line.html\">\n-     pandas.DataFrame.plot.line\n+    <a class=\"reference internal\" href=\"pandas.HDFStore.get.html\">\n+     pandas.HDFStore.get\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.pie.html\">\n-     pandas.DataFrame.plot.pie\n+    <a class=\"reference internal\" href=\"pandas.HDFStore.select.html\">\n+     pandas.HDFStore.select\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.scatter.html\">\n-     pandas.DataFrame.plot.scatter\n+    <a class=\"reference internal\" href=\"pandas.HDFStore.info.html\">\n+     pandas.HDFStore.info\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.boxplot.html\">\n-     pandas.DataFrame.boxplot\n+    <a class=\"reference internal\" href=\"pandas.HDFStore.keys.html\">\n+     pandas.HDFStore.keys\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.hist.html\">\n-     pandas.DataFrame.hist\n+    <a class=\"reference internal\" href=\"pandas.HDFStore.groups.html\">\n+     pandas.HDFStore.groups\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.density.html\">\n-     pandas.DataFrame.sparse.density\n+    <a class=\"reference internal\" href=\"pandas.HDFStore.walk.html\">\n+     pandas.HDFStore.walk\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.from_spmatrix.html\">\n-     pandas.DataFrame.sparse.from_spmatrix\n+    <a class=\"reference internal\" href=\"pandas.read_feather.html\">\n+     pandas.read_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.to_coo.html\">\n-     pandas.DataFrame.sparse.to_coo\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n+     pandas.DataFrame.to_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.to_dense.html\">\n-     pandas.DataFrame.sparse.to_dense\n+    <a class=\"reference internal\" href=\"pandas.read_parquet.html\">\n+     pandas.read_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.from_dict.html\">\n-     pandas.DataFrame.from_dict\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_parquet.html\">\n+     pandas.DataFrame.to_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.from_records.html\">\n-     pandas.DataFrame.from_records\n+    <a class=\"reference internal\" href=\"pandas.read_orc.html\">\n+     pandas.read_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_orc.html\">\n      pandas.DataFrame.to_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_parquet.html\">\n-     pandas.DataFrame.to_parquet\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_pickle.html\">\n-     pandas.DataFrame.to_pickle\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_csv.html\">\n-     pandas.DataFrame.to_csv\n+    <a class=\"reference internal\" href=\"pandas.read_sas.html\">\n+     pandas.read_sas\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_hdf.html\">\n-     pandas.DataFrame.to_hdf\n+    <a class=\"reference internal\" href=\"pandas.read_spss.html\">\n+     pandas.read_spss\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n-     pandas.DataFrame.to_sql\n+    <a class=\"reference internal\" href=\"pandas.read_sql_table.html\">\n+     pandas.read_sql_table\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_dict.html\">\n-     pandas.DataFrame.to_dict\n+    <a class=\"reference internal\" href=\"pandas.read_sql_query.html\">\n+     pandas.read_sql_query\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_excel.html\">\n-     pandas.DataFrame.to_excel\n+    <a class=\"reference internal\" href=\"pandas.read_sql.html\">\n+     pandas.read_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_json.html\">\n-     pandas.DataFrame.to_json\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2 current active\">\n-    <a class=\"current reference internal\" href=\"#\">\n-     pandas.DataFrame.to_html\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n+     pandas.DataFrame.to_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n-     pandas.DataFrame.to_feather\n+    <a class=\"reference internal\" href=\"pandas.read_gbq.html\">\n+     pandas.read_gbq\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_latex.html\">\n-     pandas.DataFrame.to_latex\n+    <a class=\"reference internal\" href=\"pandas.read_stata.html\">\n+     pandas.read_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_stata.html\">\n      pandas.DataFrame.to_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_gbq.html\">\n-     pandas.DataFrame.to_gbq\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_records.html\">\n-     pandas.DataFrame.to_records\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_string.html\">\n-     pandas.DataFrame.to_string\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_clipboard.html\">\n-     pandas.DataFrame.to_clipboard\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.data_label.html\">\n+     pandas.io.stata.StataReader.data_label\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_markdown.html\">\n-     pandas.DataFrame.to_markdown\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.value_labels.html\">\n+     pandas.io.stata.StataReader.value_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.style.html\">\n-     pandas.DataFrame.style\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.variable_labels.html\">\n+     pandas.io.stata.StataReader.variable_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.__dataframe__.html\">\n-     pandas.DataFrame.__dataframe__\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataWriter.write_file.html\">\n+     pandas.io.stata.StataWriter.write_file\n     </a>\n    </li>\n   </ul>\n  </li>\n  <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../general_functions.html\">\n+   General functions\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../series.html\">\n+   Series\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../frame.html\">\n+   DataFrame\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../arrays.html\">\n    pandas arrays, scalars, and data types\n   </a>\n  </li>\n  <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../indexing.html\">\n    Index objects\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -4,242 +4,73 @@\n     * _\bA_\bP_\bI_\b 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_\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg_\br_\be_\bg_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bf_\bo_\br_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\br_\bo_\bu_\bp_\bb_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bl_\bl_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\ba_\bn_\bd_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bw_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bb_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bn_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bl_\bi_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br_\bw_\bi_\bt_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bu_\bn_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\be_\bs_\bc_\br_\bi_\bb_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bf_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bv_\ba_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt_\bo_\bs_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bd_\bi_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bc_\bt_\b__\bc_\bh_\ba_\bn_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd_\bu_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\ba_\bn_\bt_\bi_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bu_\bn_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bk_\be_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bu_\bn_\bi_\bq_\bu_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\bl_\bu_\be_\b__\bc_\bo_\bu_\bn_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bp_\br_\be_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bs_\bu_\bf_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\be_\bt_\bw_\be_\be_\bn_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\b__\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq_\bu_\ba_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b__\bl_\bi_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\bn_\bc_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\ba_\bc_\bk_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bl_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt_\b__\bt_\ba_\bb_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bo_\br_\bd_\be_\br_\b__\bl_\be_\bv_\be_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bv_\ba_\bl_\bu_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bl_\ba_\br_\bg_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bs_\bm_\ba_\bl_\bl_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bn_\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\ba_\bx_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\bl_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bq_\bu_\be_\be_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\ba_\br_\br_\ba_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bT\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bs_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bp_\ba_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bj_\bo_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\br_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bp_\bd_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bf_\br_\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bo_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bh_\bi_\bf_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\be_\br_\bi_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bt_\bi_\bm_\be_\bs_\bt_\ba_\bm_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bc_\bo_\bn_\bv_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bl_\bo_\bc_\ba_\bl_\bi_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bF_\bl_\ba_\bg_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\bt_\br_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\ba_\br_\be_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\bo_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\be_\bx_\bb_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bk_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bl_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bp_\bi_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bs_\bc_\ba_\bt_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bo_\bx_\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\br_\bo_\bm_\b__\bs_\bp_\bm_\ba_\bt_\br_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bc_\bo_\bo\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bd_\be_\bn_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\bd_\bi_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+    * _\bI_\bn_\bp_\bu_\bt_\b/_\bo_\bu_\bt_\bp_\bu_\bt\u0010\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\bi_\bc_\bk_\bl_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bs_\bv\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bs_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bd_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bd_\bi_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\bw_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\be_\bx_\bc_\be_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bb_\bo_\bo_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bs_\bh_\be_\be_\bt_\b__\bn_\ba_\bm_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bp_\ba_\br_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bW_\br_\bi_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bj_\bs_\bo_\bn_\b__\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bj_\bs_\bo_\bn_\b._\bb_\bu_\bi_\bl_\bd_\b__\bt_\ba_\bb_\bl_\be_\b__\bs_\bc_\bh_\be_\bm_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bt_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bx_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bd_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bp_\bu_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\ba_\bp_\bp_\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bs_\be_\bl_\be_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bi_\bn_\bf_\bo\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bk_\be_\by_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\br_\bo_\bu_\bp_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bw_\ba_\bl_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\ba_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bp_\bs_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bq_\bu_\be_\br_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bg_\bb_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bt_\ba_\bt_\ba\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\ba_\bt_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bg_\bb_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\br_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bm_\ba_\br_\bk_\bd_\bo_\bw_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\by_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bd_\ba_\bt_\ba_\bf_\br_\ba_\bm_\be_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bd_\ba_\bt_\ba_\b__\bl_\ba_\bb_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\bl_\bu_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\br_\bi_\ba_\bb_\bl_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bW_\br_\bi_\bt_\be_\br_\b._\bw_\br_\bi_\bt_\be_\b__\bf_\bi_\bl_\be\n+    * _\bG_\be_\bn_\be_\br_\ba_\bl_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n+    * _\bS_\be_\br_\bi_\be_\bs\n+    * _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\n     * _\bp_\ba_\bn_\bd_\ba_\bs_\b _\ba_\br_\br_\ba_\by_\bs_\b,_\b _\bs_\bc_\ba_\bl_\ba_\br_\bs_\b,_\b _\ba_\bn_\bd_\b _\bd_\ba_\bt_\ba_\b _\bt_\by_\bp_\be_\bs\n     * _\bI_\bn_\bd_\be_\bx_\b _\bo_\bb_\bj_\be_\bc_\bt_\bs\n     * _\bD_\ba_\bt_\be_\b _\bo_\bf_\bf_\bs_\be_\bt_\bs\n     * _\bW_\bi_\bn_\bd_\bo_\bw\n     * _\bG_\br_\bo_\bu_\bp_\bB_\by\n     * _\bR_\be_\bs_\ba_\bm_\bp_\bl_\bi_\bn_\bg\n     * _\bS_\bt_\by_\bl_\be\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_json.html", "source2": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_json.html", "unified_diff": "@@ -152,1162 +152,317 @@\n             <div class=\"col-12 col-md-3 bd-sidebar\"><form class=\"bd-search d-flex align-items-center\" action=\"../../search.html\" method=\"get\">\n   <i class=\"icon fas fa-search\"></i>\n   <input type=\"search\" class=\"form-control\" name=\"q\" id=\"search-input\" placeholder=\"Search the docs ...\" aria-label=\"Search the docs ...\" autocomplete=\"off\" >\n </form><nav class=\"bd-links\" id=\"bd-docs-nav\" aria-label=\"Main navigation\">\n   <div class=\"bd-toc-item navbar-nav\">\n     \n     <ul class=\"current nav bd-sidenav\">\n- <li class=\"toctree-l1\">\n+ <li class=\"toctree-l1 current active has-children\">\n   <a class=\"reference internal\" href=\"../io.html\">\n    Input/output\n   </a>\n- </li>\n- <li class=\"toctree-l1\">\n-  <a 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internal\" href=\"pandas.HDFStore.walk.html\">\n+     pandas.HDFStore.walk\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.from_spmatrix.html\">\n-     pandas.DataFrame.sparse.from_spmatrix\n+    <a class=\"reference internal\" href=\"pandas.read_feather.html\">\n+     pandas.read_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.to_coo.html\">\n-     pandas.DataFrame.sparse.to_coo\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n+     pandas.DataFrame.to_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.to_dense.html\">\n-     pandas.DataFrame.sparse.to_dense\n+    <a class=\"reference internal\" href=\"pandas.read_parquet.html\">\n+     pandas.read_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.from_dict.html\">\n-     pandas.DataFrame.from_dict\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_parquet.html\">\n+     pandas.DataFrame.to_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.from_records.html\">\n-     pandas.DataFrame.from_records\n+    <a class=\"reference internal\" href=\"pandas.read_orc.html\">\n+     pandas.read_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_orc.html\">\n      pandas.DataFrame.to_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_parquet.html\">\n-     pandas.DataFrame.to_parquet\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_pickle.html\">\n-     pandas.DataFrame.to_pickle\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_csv.html\">\n-     pandas.DataFrame.to_csv\n+    <a class=\"reference internal\" href=\"pandas.read_sas.html\">\n+     pandas.read_sas\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_hdf.html\">\n-     pandas.DataFrame.to_hdf\n+    <a class=\"reference internal\" href=\"pandas.read_spss.html\">\n+     pandas.read_spss\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n-     pandas.DataFrame.to_sql\n+    <a class=\"reference internal\" href=\"pandas.read_sql_table.html\">\n+     pandas.read_sql_table\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_dict.html\">\n-     pandas.DataFrame.to_dict\n+    <a class=\"reference internal\" href=\"pandas.read_sql_query.html\">\n+     pandas.read_sql_query\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_excel.html\">\n-     pandas.DataFrame.to_excel\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2 current active\">\n-    <a class=\"current reference internal\" href=\"#\">\n-     pandas.DataFrame.to_json\n+    <a class=\"reference internal\" href=\"pandas.read_sql.html\">\n+     pandas.read_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_html.html\">\n-     pandas.DataFrame.to_html\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n+     pandas.DataFrame.to_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n-     pandas.DataFrame.to_feather\n+    <a class=\"reference internal\" href=\"pandas.read_gbq.html\">\n+     pandas.read_gbq\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_latex.html\">\n-     pandas.DataFrame.to_latex\n+    <a class=\"reference internal\" href=\"pandas.read_stata.html\">\n+     pandas.read_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_stata.html\">\n      pandas.DataFrame.to_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_gbq.html\">\n-     pandas.DataFrame.to_gbq\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_records.html\">\n-     pandas.DataFrame.to_records\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_string.html\">\n-     pandas.DataFrame.to_string\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_clipboard.html\">\n-     pandas.DataFrame.to_clipboard\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.data_label.html\">\n+     pandas.io.stata.StataReader.data_label\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_markdown.html\">\n-     pandas.DataFrame.to_markdown\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.value_labels.html\">\n+     pandas.io.stata.StataReader.value_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.style.html\">\n-     pandas.DataFrame.style\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.variable_labels.html\">\n+     pandas.io.stata.StataReader.variable_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.__dataframe__.html\">\n-     pandas.DataFrame.__dataframe__\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataWriter.write_file.html\">\n+     pandas.io.stata.StataWriter.write_file\n     </a>\n    </li>\n   </ul>\n  </li>\n  <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../general_functions.html\">\n+   General functions\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../series.html\">\n+   Series\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../frame.html\">\n+   DataFrame\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../arrays.html\">\n    pandas arrays, scalars, and data types\n   </a>\n  </li>\n  <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../indexing.html\">\n    Index objects\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -4,242 +4,73 @@\n     * _\bA_\bP_\bI_\b 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o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\ba_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\bo_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bl_\bo_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bs_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bi_\bt_\be_\br_\b__\b_\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\bm_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\be_\by_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\br_\br_\bo_\bw_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\br_\bt_\bu_\bp_\bl_\be_\bs\n-          o 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_\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bu_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bt_\br_\bu_\be_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bp_\bo_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be_\b__\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by_\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bp_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg_\br_\be_\bg_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bf_\bo_\br_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\br_\bo_\bu_\bp_\bb_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bl_\bl_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\ba_\bn_\bd_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bw_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bb_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bn_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bl_\bi_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br_\bw_\bi_\bt_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bu_\bn_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\be_\bs_\bc_\br_\bi_\bb_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bf_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bv_\ba_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt_\bo_\bs_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bd_\bi_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bc_\bt_\b__\bc_\bh_\ba_\bn_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd_\bu_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\ba_\bn_\bt_\bi_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bu_\bn_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bk_\be_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bu_\bn_\bi_\bq_\bu_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\bl_\bu_\be_\b__\bc_\bo_\bu_\bn_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bp_\br_\be_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bs_\bu_\bf_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\be_\bt_\bw_\be_\be_\bn_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\b__\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq_\bu_\ba_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b__\bl_\bi_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\bn_\bc_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\ba_\bc_\bk_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bl_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt_\b__\bt_\ba_\bb_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bo_\br_\bd_\be_\br_\b__\bl_\be_\bv_\be_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bv_\ba_\bl_\bu_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bl_\ba_\br_\bg_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bs_\bm_\ba_\bl_\bl_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bn_\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\ba_\bx_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\bl_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bq_\bu_\be_\be_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\ba_\br_\br_\ba_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bT\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bs_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bp_\ba_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bj_\bo_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\br_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bp_\bd_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bf_\br_\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bo_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bh_\bi_\bf_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\be_\br_\bi_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bt_\bi_\bm_\be_\bs_\bt_\ba_\bm_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bc_\bo_\bn_\bv_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bl_\bo_\bc_\ba_\bl_\bi_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bF_\bl_\ba_\bg_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\bt_\br_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\ba_\br_\be_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\bo_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\be_\bx_\bb_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bk_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bl_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bp_\bi_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bs_\bc_\ba_\bt_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bo_\bx_\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\br_\bo_\bm_\b__\bs_\bp_\bm_\ba_\bt_\br_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bc_\bo_\bo\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bd_\be_\bn_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\bd_\bi_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+    * _\bI_\bn_\bp_\bu_\bt_\b/_\bo_\bu_\bt_\bp_\bu_\bt\u0010\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\bi_\bc_\bk_\bl_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bs_\bv\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bs_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bd_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bd_\bi_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\bw_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\be_\bx_\bc_\be_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bb_\bo_\bo_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bs_\bh_\be_\be_\bt_\b__\bn_\ba_\bm_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bp_\ba_\br_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bW_\br_\bi_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bj_\bs_\bo_\bn_\b__\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bj_\bs_\bo_\bn_\b._\bb_\bu_\bi_\bl_\bd_\b__\bt_\ba_\bb_\bl_\be_\b__\bs_\bc_\bh_\be_\bm_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bt_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bx_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bd_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bp_\bu_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\ba_\bp_\bp_\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bs_\be_\bl_\be_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bi_\bn_\bf_\bo\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bk_\be_\by_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\br_\bo_\bu_\bp_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bw_\ba_\bl_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\ba_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bp_\bs_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bq_\bu_\be_\br_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bg_\bb_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bt_\ba_\bt_\ba\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\ba_\bt_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bg_\bb_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\br_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bm_\ba_\br_\bk_\bd_\bo_\bw_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\by_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bd_\ba_\bt_\ba_\bf_\br_\ba_\bm_\be_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bd_\ba_\bt_\ba_\b__\bl_\ba_\bb_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\bl_\bu_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\br_\bi_\ba_\bb_\bl_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bW_\br_\bi_\bt_\be_\br_\b._\bw_\br_\bi_\bt_\be_\b__\bf_\bi_\bl_\be\n+    * _\bG_\be_\bn_\be_\br_\ba_\bl_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n+    * _\bS_\be_\br_\bi_\be_\bs\n+    * _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\n     * _\bp_\ba_\bn_\bd_\ba_\bs_\b _\ba_\br_\br_\ba_\by_\bs_\b,_\b _\bs_\bc_\ba_\bl_\ba_\br_\bs_\b,_\b _\ba_\bn_\bd_\b _\bd_\ba_\bt_\ba_\b _\bt_\by_\bp_\be_\bs\n     * _\bI_\bn_\bd_\be_\bx_\b _\bo_\bb_\bj_\be_\bc_\bt_\bs\n     * _\bD_\ba_\bt_\be_\b _\bo_\bf_\bf_\bs_\be_\bt_\bs\n     * _\bW_\bi_\bn_\bd_\bo_\bw\n     * _\bG_\br_\bo_\bu_\bp_\bB_\by\n     * _\bR_\be_\bs_\ba_\bm_\bp_\bl_\bi_\bn_\bg\n     * _\bS_\bt_\by_\bl_\be\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_latex.html", "source2": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_latex.html", "unified_diff": "@@ -152,1162 +152,317 @@\n             <div class=\"col-12 col-md-3 bd-sidebar\"><form class=\"bd-search d-flex align-items-center\" action=\"../../search.html\" method=\"get\">\n   <i class=\"icon fas fa-search\"></i>\n   <input type=\"search\" class=\"form-control\" name=\"q\" id=\"search-input\" placeholder=\"Search the docs ...\" aria-label=\"Search the docs ...\" autocomplete=\"off\" >\n </form><nav class=\"bd-links\" id=\"bd-docs-nav\" aria-label=\"Main navigation\">\n   <div class=\"bd-toc-item navbar-nav\">\n     \n     <ul class=\"current nav bd-sidenav\">\n- <li class=\"toctree-l1\">\n+ <li class=\"toctree-l1 current active has-children\">\n   <a class=\"reference internal\" href=\"../io.html\">\n    Input/output\n   </a>\n- </li>\n- <li class=\"toctree-l1\">\n-  <a class=\"reference internal\" href=\"../general_functions.html\">\n-   General functions\n-  </a>\n- </li>\n- <li class=\"toctree-l1\">\n-  <a class=\"reference internal\" href=\"../series.html\">\n-   Series\n-  </a>\n- </li>\n- <li class=\"toctree-l1 current active has-children\">\n-  <a class=\"reference internal\" href=\"../frame.html\">\n-   DataFrame\n-  </a>\n   <input checked=\"\" class=\"toctree-checkbox\" id=\"toctree-checkbox-1\" name=\"toctree-checkbox-1\" type=\"checkbox\"/>\n   <label for=\"toctree-checkbox-1\">\n    <i class=\"fas fa-chevron-down\">\n    </i>\n   </label>\n   <ul class=\"current\">\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.html\">\n-     pandas.DataFrame\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.index.html\">\n-     pandas.DataFrame.index\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" 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href=\"pandas.DataFrame.head.html\">\n-     pandas.DataFrame.head\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.at.html\">\n-     pandas.DataFrame.at\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.iat.html\">\n-     pandas.DataFrame.iat\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.loc.html\">\n-     pandas.DataFrame.loc\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.iloc.html\">\n-     pandas.DataFrame.iloc\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.insert.html\">\n-     pandas.DataFrame.insert\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.__iter__.html\">\n-     pandas.DataFrame.__iter__\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.items.html\">\n-     pandas.DataFrame.items\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.keys.html\">\n-     pandas.DataFrame.keys\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.iterrows.html\">\n-     pandas.DataFrame.iterrows\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.itertuples.html\">\n-     pandas.DataFrame.itertuples\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.pop.html\">\n-     pandas.DataFrame.pop\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.tail.html\">\n-     pandas.DataFrame.tail\n-    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pandas.read_sql_query\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_excel.html\">\n-     pandas.DataFrame.to_excel\n+    <a class=\"reference internal\" href=\"pandas.read_sql.html\">\n+     pandas.read_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_json.html\">\n-     pandas.DataFrame.to_json\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n+     pandas.DataFrame.to_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_html.html\">\n-     pandas.DataFrame.to_html\n+    <a class=\"reference internal\" href=\"pandas.read_gbq.html\">\n+     pandas.read_gbq\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n-     pandas.DataFrame.to_feather\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2 current active\">\n-    <a class=\"current reference internal\" href=\"#\">\n-     pandas.DataFrame.to_latex\n+    <a class=\"reference internal\" href=\"pandas.read_stata.html\">\n+     pandas.read_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_stata.html\">\n      pandas.DataFrame.to_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_gbq.html\">\n-     pandas.DataFrame.to_gbq\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_records.html\">\n-     pandas.DataFrame.to_records\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_string.html\">\n-     pandas.DataFrame.to_string\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_clipboard.html\">\n-     pandas.DataFrame.to_clipboard\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.data_label.html\">\n+     pandas.io.stata.StataReader.data_label\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_markdown.html\">\n-     pandas.DataFrame.to_markdown\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.value_labels.html\">\n+     pandas.io.stata.StataReader.value_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.style.html\">\n-     pandas.DataFrame.style\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.variable_labels.html\">\n+     pandas.io.stata.StataReader.variable_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.__dataframe__.html\">\n-     pandas.DataFrame.__dataframe__\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataWriter.write_file.html\">\n+     pandas.io.stata.StataWriter.write_file\n     </a>\n    </li>\n   </ul>\n  </li>\n  <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../general_functions.html\">\n+   General functions\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../series.html\">\n+   Series\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../frame.html\">\n+   DataFrame\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../arrays.html\">\n    pandas arrays, scalars, and data types\n   </a>\n  </li>\n  <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../indexing.html\">\n    Index objects\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -4,242 +4,73 @@\n     * _\bA_\bP_\bI_\b 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_\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bc_\bt_\b__\bc_\bh_\ba_\bn_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd_\bu_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\ba_\bn_\bt_\bi_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bu_\bn_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bk_\be_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bu_\bn_\bi_\bq_\bu_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\bl_\bu_\be_\b__\bc_\bo_\bu_\bn_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bp_\br_\be_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bs_\bu_\bf_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\be_\bt_\bw_\be_\be_\bn_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\b__\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq_\bu_\ba_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b__\bl_\bi_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\bn_\bc_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\ba_\bc_\bk_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bl_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt_\b__\bt_\ba_\bb_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bo_\br_\bd_\be_\br_\b__\bl_\be_\bv_\be_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bv_\ba_\bl_\bu_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bl_\ba_\br_\bg_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bs_\bm_\ba_\bl_\bl_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bn_\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\ba_\bx_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\bl_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bq_\bu_\be_\be_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\ba_\br_\br_\ba_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bT\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bs_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bp_\ba_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bj_\bo_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\br_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bp_\bd_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bf_\br_\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bo_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bh_\bi_\bf_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\be_\br_\bi_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bt_\bi_\bm_\be_\bs_\bt_\ba_\bm_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bc_\bo_\bn_\bv_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bl_\bo_\bc_\ba_\bl_\bi_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bF_\bl_\ba_\bg_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\bt_\br_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\ba_\br_\be_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\bo_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\be_\bx_\bb_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bk_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bl_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bp_\bi_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bs_\bc_\ba_\bt_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bo_\bx_\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\br_\bo_\bm_\b__\bs_\bp_\bm_\ba_\bt_\br_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bc_\bo_\bo\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bd_\be_\bn_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\bd_\bi_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+    * _\bI_\bn_\bp_\bu_\bt_\b/_\bo_\bu_\bt_\bp_\bu_\bt\u0010\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\bi_\bc_\bk_\bl_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bs_\bv\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bs_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bd_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bd_\bi_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\bw_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\be_\bx_\bc_\be_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bb_\bo_\bo_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bs_\bh_\be_\be_\bt_\b__\bn_\ba_\bm_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bp_\ba_\br_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bW_\br_\bi_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bj_\bs_\bo_\bn_\b__\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bj_\bs_\bo_\bn_\b._\bb_\bu_\bi_\bl_\bd_\b__\bt_\ba_\bb_\bl_\be_\b__\bs_\bc_\bh_\be_\bm_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bt_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bx_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bd_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bp_\bu_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\ba_\bp_\bp_\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bs_\be_\bl_\be_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bi_\bn_\bf_\bo\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bk_\be_\by_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\br_\bo_\bu_\bp_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bw_\ba_\bl_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\ba_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bp_\bs_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bq_\bu_\be_\br_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bg_\bb_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bt_\ba_\bt_\ba\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\ba_\bt_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bg_\bb_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\br_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bm_\ba_\br_\bk_\bd_\bo_\bw_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\by_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bd_\ba_\bt_\ba_\bf_\br_\ba_\bm_\be_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bd_\ba_\bt_\ba_\b__\bl_\ba_\bb_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\bl_\bu_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\br_\bi_\ba_\bb_\bl_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bW_\br_\bi_\bt_\be_\br_\b._\bw_\br_\bi_\bt_\be_\b__\bf_\bi_\bl_\be\n+    * _\bG_\be_\bn_\be_\br_\ba_\bl_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n+    * _\bS_\be_\br_\bi_\be_\bs\n+    * _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\n     * _\bp_\ba_\bn_\bd_\ba_\bs_\b _\ba_\br_\br_\ba_\by_\bs_\b,_\b _\bs_\bc_\ba_\bl_\ba_\br_\bs_\b,_\b _\ba_\bn_\bd_\b _\bd_\ba_\bt_\ba_\b _\bt_\by_\bp_\be_\bs\n     * _\bI_\bn_\bd_\be_\bx_\b _\bo_\bb_\bj_\be_\bc_\bt_\bs\n     * _\bD_\ba_\bt_\be_\b _\bo_\bf_\bf_\bs_\be_\bt_\bs\n     * _\bW_\bi_\bn_\bd_\bo_\bw\n     * _\bG_\br_\bo_\bu_\bp_\bB_\by\n     * _\bR_\be_\bs_\ba_\bm_\bp_\bl_\bi_\bn_\bg\n     * _\bS_\bt_\by_\bl_\be\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_orc.html", "source2": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_orc.html", "unified_diff": "@@ -152,1162 +152,317 @@\n             <div class=\"col-12 col-md-3 bd-sidebar\"><form class=\"bd-search d-flex align-items-center\" action=\"../../search.html\" method=\"get\">\n   <i class=\"icon fas fa-search\"></i>\n   <input type=\"search\" class=\"form-control\" name=\"q\" id=\"search-input\" placeholder=\"Search the docs ...\" aria-label=\"Search the docs ...\" autocomplete=\"off\" >\n </form><nav class=\"bd-links\" id=\"bd-docs-nav\" aria-label=\"Main navigation\">\n   <div class=\"bd-toc-item navbar-nav\">\n     \n     <ul class=\"current nav bd-sidenav\">\n- <li class=\"toctree-l1\">\n+ <li class=\"toctree-l1 current active has-children\">\n   <a class=\"reference internal\" href=\"../io.html\">\n    Input/output\n   </a>\n- </li>\n- <li class=\"toctree-l1\">\n-  <a class=\"reference internal\" href=\"../general_functions.html\">\n-   General functions\n-  </a>\n- </li>\n- <li class=\"toctree-l1\">\n-  <a class=\"reference internal\" href=\"../series.html\">\n-   Series\n-  </a>\n- </li>\n- <li class=\"toctree-l1 current active has-children\">\n-  <a class=\"reference internal\" href=\"../frame.html\">\n-   DataFrame\n-  </a>\n   <input checked=\"\" class=\"toctree-checkbox\" id=\"toctree-checkbox-1\" name=\"toctree-checkbox-1\" type=\"checkbox\"/>\n   <label for=\"toctree-checkbox-1\">\n    <i class=\"fas fa-chevron-down\">\n    </i>\n   </label>\n   <ul class=\"current\">\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.html\">\n-     pandas.DataFrame\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.index.html\">\n-     pandas.DataFrame.index\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" 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</a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.from_spmatrix.html\">\n-     pandas.DataFrame.sparse.from_spmatrix\n+    <a class=\"reference internal\" href=\"pandas.read_feather.html\">\n+     pandas.read_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.to_coo.html\">\n-     pandas.DataFrame.sparse.to_coo\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n+     pandas.DataFrame.to_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.to_dense.html\">\n-     pandas.DataFrame.sparse.to_dense\n+    <a class=\"reference internal\" href=\"pandas.read_parquet.html\">\n+     pandas.read_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.from_dict.html\">\n-     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internal\" href=\"pandas.DataFrame.to_latex.html\">\n-     pandas.DataFrame.to_latex\n+    <a class=\"reference internal\" href=\"pandas.read_stata.html\">\n+     pandas.read_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_stata.html\">\n      pandas.DataFrame.to_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_gbq.html\">\n-     pandas.DataFrame.to_gbq\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_records.html\">\n-     pandas.DataFrame.to_records\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_string.html\">\n-     pandas.DataFrame.to_string\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_clipboard.html\">\n-     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href=\"pandas.io.stata.StataWriter.write_file.html\">\n+     pandas.io.stata.StataWriter.write_file\n     </a>\n    </li>\n   </ul>\n  </li>\n  <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../general_functions.html\">\n+   General functions\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../series.html\">\n+   Series\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../frame.html\">\n+   DataFrame\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../arrays.html\">\n    pandas arrays, scalars, and data types\n   </a>\n  </li>\n  <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../indexing.html\">\n    Index objects\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -4,242 +4,73 @@\n     * _\bA_\bP_\bI_\b _\br_\be_\bf_\be_\br_\be_\bn_\bc_\be\n     * 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_\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bl_\be_\bc_\bt_\b__\bd_\bt_\by_\bp_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\bl_\bu_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bx_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bd_\bi_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bi_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bh_\ba_\bp_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bm_\bo_\br_\by_\b__\bu_\bs_\ba_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bm_\bp_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bf_\bl_\ba_\bg_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bt_\by_\bp_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bn_\bv_\be_\br_\bt_\b__\bd_\bt_\by_\bp_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bf_\be_\br_\b__\bo_\bb_\bj_\be_\bc_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bp_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bo_\bo_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bn_\bu_\bm_\bp_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\ba_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\bo_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bl_\bo_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bs_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bi_\bt_\be_\br_\b__\b_\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\bm_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\be_\by_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\br_\br_\bo_\bw_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\br_\bt_\bu_\bp_\bl_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bx_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\be_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bw_\bh_\be_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bs_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\be_\br_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\ba_\bd_\bd_\b__\b_\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bb\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bu_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\be_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bo_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bd_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bs_\bu_\bb\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bu_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bt_\br_\bu_\be_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bp_\bo_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be_\b__\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by_\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bp_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg_\br_\be_\bg_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bf_\bo_\br_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\br_\bo_\bu_\bp_\bb_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bl_\bl_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\ba_\bn_\bd_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bw_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bb_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bn_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bl_\bi_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br_\bw_\bi_\bt_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bu_\bn_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\be_\bs_\bc_\br_\bi_\bb_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bf_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bv_\ba_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt_\bo_\bs_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bd_\bi_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bc_\bt_\b__\bc_\bh_\ba_\bn_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd_\bu_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\ba_\bn_\bt_\bi_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bu_\bn_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bk_\be_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bu_\bn_\bi_\bq_\bu_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\bl_\bu_\be_\b__\bc_\bo_\bu_\bn_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bp_\br_\be_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bs_\bu_\bf_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\be_\bt_\bw_\be_\be_\bn_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\b__\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq_\bu_\ba_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b__\bl_\bi_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\bn_\bc_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\ba_\bc_\bk_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bl_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt_\b__\bt_\ba_\bb_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bo_\br_\bd_\be_\br_\b__\bl_\be_\bv_\be_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bv_\ba_\bl_\bu_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bl_\ba_\br_\bg_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bs_\bm_\ba_\bl_\bl_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bn_\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\ba_\bx_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\bl_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bq_\bu_\be_\be_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\ba_\br_\br_\ba_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bT\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bs_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bp_\ba_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bj_\bo_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\br_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bp_\bd_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bf_\br_\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bo_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bh_\bi_\bf_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\be_\br_\bi_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bt_\bi_\bm_\be_\bs_\bt_\ba_\bm_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bc_\bo_\bn_\bv_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bl_\bo_\bc_\ba_\bl_\bi_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bF_\bl_\ba_\bg_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\bt_\br_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\ba_\br_\be_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\bo_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\be_\bx_\bb_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bk_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bl_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bp_\bi_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bs_\bc_\ba_\bt_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bo_\bx_\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\br_\bo_\bm_\b__\bs_\bp_\bm_\ba_\bt_\br_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bc_\bo_\bo\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bd_\be_\bn_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\bd_\bi_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+    * _\bI_\bn_\bp_\bu_\bt_\b/_\bo_\bu_\bt_\bp_\bu_\bt\u0010\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\bi_\bc_\bk_\bl_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bs_\bv\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bs_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bd_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bd_\bi_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\bw_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\be_\bx_\bc_\be_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bb_\bo_\bo_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bs_\bh_\be_\be_\bt_\b__\bn_\ba_\bm_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bp_\ba_\br_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bW_\br_\bi_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bj_\bs_\bo_\bn_\b__\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bj_\bs_\bo_\bn_\b._\bb_\bu_\bi_\bl_\bd_\b__\bt_\ba_\bb_\bl_\be_\b__\bs_\bc_\bh_\be_\bm_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bt_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bx_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bd_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bp_\bu_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\ba_\bp_\bp_\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bs_\be_\bl_\be_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bi_\bn_\bf_\bo\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bk_\be_\by_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\br_\bo_\bu_\bp_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bw_\ba_\bl_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\ba_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bp_\bs_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bq_\bu_\be_\br_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bg_\bb_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bt_\ba_\bt_\ba\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\ba_\bt_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bg_\bb_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\br_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bm_\ba_\br_\bk_\bd_\bo_\bw_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\by_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bd_\ba_\bt_\ba_\bf_\br_\ba_\bm_\be_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bd_\ba_\bt_\ba_\b__\bl_\ba_\bb_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\bl_\bu_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\br_\bi_\ba_\bb_\bl_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bW_\br_\bi_\bt_\be_\br_\b._\bw_\br_\bi_\bt_\be_\b__\bf_\bi_\bl_\be\n+    * _\bG_\be_\bn_\be_\br_\ba_\bl_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n+    * _\bS_\be_\br_\bi_\be_\bs\n+    * _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\n     * _\bp_\ba_\bn_\bd_\ba_\bs_\b _\ba_\br_\br_\ba_\by_\bs_\b,_\b _\bs_\bc_\ba_\bl_\ba_\br_\bs_\b,_\b _\ba_\bn_\bd_\b _\bd_\ba_\bt_\ba_\b _\bt_\by_\bp_\be_\bs\n     * _\bI_\bn_\bd_\be_\bx_\b _\bo_\bb_\bj_\be_\bc_\bt_\bs\n     * _\bD_\ba_\bt_\be_\b _\bo_\bf_\bf_\bs_\be_\bt_\bs\n     * _\bW_\bi_\bn_\bd_\bo_\bw\n     * _\bG_\br_\bo_\bu_\bp_\bB_\by\n     * _\bR_\be_\bs_\ba_\bm_\bp_\bl_\bi_\bn_\bg\n     * _\bS_\bt_\by_\bl_\be\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_parquet.html", "source2": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_parquet.html", "unified_diff": "@@ -152,1162 +152,317 @@\n             <div class=\"col-12 col-md-3 bd-sidebar\"><form class=\"bd-search d-flex align-items-center\" action=\"../../search.html\" method=\"get\">\n   <i class=\"icon fas fa-search\"></i>\n   <input type=\"search\" class=\"form-control\" name=\"q\" id=\"search-input\" placeholder=\"Search the docs ...\" aria-label=\"Search the docs ...\" autocomplete=\"off\" >\n </form><nav class=\"bd-links\" id=\"bd-docs-nav\" aria-label=\"Main navigation\">\n   <div class=\"bd-toc-item navbar-nav\">\n     \n     <ul class=\"current nav bd-sidenav\">\n- <li class=\"toctree-l1\">\n+ <li class=\"toctree-l1 current active has-children\">\n   <a class=\"reference internal\" href=\"../io.html\">\n    Input/output\n   </a>\n- </li>\n- <li class=\"toctree-l1\">\n-  <a class=\"reference internal\" href=\"../general_functions.html\">\n-   General functions\n-  </a>\n- </li>\n- <li class=\"toctree-l1\">\n-  <a class=\"reference internal\" href=\"../series.html\">\n-   Series\n-  </a>\n- </li>\n- <li class=\"toctree-l1 current active has-children\">\n-  <a class=\"reference internal\" href=\"../frame.html\">\n-   DataFrame\n-  </a>\n   <input checked=\"\" class=\"toctree-checkbox\" id=\"toctree-checkbox-1\" name=\"toctree-checkbox-1\" type=\"checkbox\"/>\n   <label for=\"toctree-checkbox-1\">\n    <i class=\"fas fa-chevron-down\">\n    </i>\n   </label>\n   <ul class=\"current\">\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.html\">\n-     pandas.DataFrame\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.index.html\">\n-     pandas.DataFrame.index\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.columns.html\">\n-     pandas.DataFrame.columns\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.dtypes.html\">\n-     pandas.DataFrame.dtypes\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.info.html\">\n-     pandas.DataFrame.info\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.select_dtypes.html\">\n-     pandas.DataFrame.select_dtypes\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.values.html\">\n-     pandas.DataFrame.values\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.axes.html\">\n-     pandas.DataFrame.axes\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" 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pandas.DataFrame.from_records\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n+     pandas.DataFrame.to_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_orc.html\">\n-     pandas.DataFrame.to_orc\n+    <a class=\"reference internal\" href=\"pandas.read_parquet.html\">\n+     pandas.read_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2 current active\">\n     <a class=\"current reference internal\" href=\"#\">\n      pandas.DataFrame.to_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_pickle.html\">\n-     pandas.DataFrame.to_pickle\n+    <a class=\"reference internal\" href=\"pandas.read_orc.html\">\n+     pandas.read_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_csv.html\">\n-     pandas.DataFrame.to_csv\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_orc.html\">\n+     pandas.DataFrame.to_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_hdf.html\">\n-     pandas.DataFrame.to_hdf\n+    <a class=\"reference internal\" href=\"pandas.read_sas.html\">\n+     pandas.read_sas\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n-     pandas.DataFrame.to_sql\n+    <a class=\"reference internal\" href=\"pandas.read_spss.html\">\n+     pandas.read_spss\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_dict.html\">\n-     pandas.DataFrame.to_dict\n+    <a class=\"reference internal\" href=\"pandas.read_sql_table.html\">\n+     pandas.read_sql_table\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_excel.html\">\n-     pandas.DataFrame.to_excel\n+    <a class=\"reference internal\" href=\"pandas.read_sql_query.html\">\n+     pandas.read_sql_query\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_json.html\">\n-     pandas.DataFrame.to_json\n+    <a class=\"reference internal\" href=\"pandas.read_sql.html\">\n+     pandas.read_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_html.html\">\n-     pandas.DataFrame.to_html\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n+     pandas.DataFrame.to_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n-     pandas.DataFrame.to_feather\n+    <a class=\"reference internal\" href=\"pandas.read_gbq.html\">\n+     pandas.read_gbq\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_latex.html\">\n-     pandas.DataFrame.to_latex\n+    <a class=\"reference internal\" href=\"pandas.read_stata.html\">\n+     pandas.read_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_stata.html\">\n      pandas.DataFrame.to_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_gbq.html\">\n-     pandas.DataFrame.to_gbq\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_records.html\">\n-     pandas.DataFrame.to_records\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_string.html\">\n-     pandas.DataFrame.to_string\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_clipboard.html\">\n-     pandas.DataFrame.to_clipboard\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.data_label.html\">\n+     pandas.io.stata.StataReader.data_label\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_markdown.html\">\n-     pandas.DataFrame.to_markdown\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.value_labels.html\">\n+     pandas.io.stata.StataReader.value_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.style.html\">\n-     pandas.DataFrame.style\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.variable_labels.html\">\n+     pandas.io.stata.StataReader.variable_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.__dataframe__.html\">\n-     pandas.DataFrame.__dataframe__\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataWriter.write_file.html\">\n+     pandas.io.stata.StataWriter.write_file\n     </a>\n    </li>\n   </ul>\n  </li>\n  <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../general_functions.html\">\n+   General functions\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../series.html\">\n+   Series\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../frame.html\">\n+   DataFrame\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../arrays.html\">\n    pandas arrays, scalars, and data types\n   </a>\n  </li>\n  <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../indexing.html\">\n    Index objects\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -4,242 +4,73 @@\n     * _\bA_\bP_\bI_\b 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o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\ba_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\bo_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bl_\bo_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bs_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bi_\bt_\be_\br_\b__\b_\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\bm_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\be_\by_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\br_\br_\bo_\bw_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bt_\be_\br_\bt_\bu_\bp_\bl_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bx_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\be_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bw_\bh_\be_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bs_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\be_\br_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\ba_\bd_\bd_\b__\b_\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bb\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bu_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\be_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bo_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bd_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bs_\bu_\bb\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bu_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bt_\br_\bu_\be_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bp_\bo_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be_\b__\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by_\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bp_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg_\br_\be_\bg_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bf_\bo_\br_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\br_\bo_\bu_\bp_\bb_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bl_\bl_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\ba_\bn_\bd_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bw_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bb_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bn_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bl_\bi_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br_\bw_\bi_\bt_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bu_\bn_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\be_\bs_\bc_\br_\bi_\bb_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bf_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bv_\ba_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt_\bo_\bs_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bd_\bi_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bc_\bt_\b__\bc_\bh_\ba_\bn_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd_\bu_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\ba_\bn_\bt_\bi_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bu_\bn_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bk_\be_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bu_\bn_\bi_\bq_\bu_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\bl_\bu_\be_\b__\bc_\bo_\bu_\bn_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bp_\br_\be_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bs_\bu_\bf_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\be_\bt_\bw_\be_\be_\bn_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\b__\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq_\bu_\ba_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b__\bl_\bi_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\bn_\bc_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\ba_\bc_\bk_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bl_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt_\b__\bt_\ba_\bb_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bo_\br_\bd_\be_\br_\b__\bl_\be_\bv_\be_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bv_\ba_\bl_\bu_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bl_\ba_\br_\bg_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bs_\bm_\ba_\bl_\bl_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bn_\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\ba_\bx_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\bl_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bq_\bu_\be_\be_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\ba_\br_\br_\ba_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bT\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bs_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bp_\ba_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bj_\bo_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\br_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bp_\bd_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bf_\br_\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bo_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bh_\bi_\bf_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\be_\br_\bi_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bt_\bi_\bm_\be_\bs_\bt_\ba_\bm_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bc_\bo_\bn_\bv_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bl_\bo_\bc_\ba_\bl_\bi_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bF_\bl_\ba_\bg_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\bt_\br_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\ba_\br_\be_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\bo_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\be_\bx_\bb_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bk_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bl_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bp_\bi_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bs_\bc_\ba_\bt_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bo_\bx_\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\br_\bo_\bm_\b__\bs_\bp_\bm_\ba_\bt_\br_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bc_\bo_\bo\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bd_\be_\bn_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\bd_\bi_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+    * _\bI_\bn_\bp_\bu_\bt_\b/_\bo_\bu_\bt_\bp_\bu_\bt\u0010\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\bi_\bc_\bk_\bl_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bs_\bv\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bs_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bd_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bd_\bi_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\bw_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\be_\bx_\bc_\be_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bb_\bo_\bo_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bs_\bh_\be_\be_\bt_\b__\bn_\ba_\bm_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bp_\ba_\br_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bW_\br_\bi_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bj_\bs_\bo_\bn_\b__\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bj_\bs_\bo_\bn_\b._\bb_\bu_\bi_\bl_\bd_\b__\bt_\ba_\bb_\bl_\be_\b__\bs_\bc_\bh_\be_\bm_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bt_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bx_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bd_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bp_\bu_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\ba_\bp_\bp_\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bs_\be_\bl_\be_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bi_\bn_\bf_\bo\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bk_\be_\by_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\br_\bo_\bu_\bp_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bw_\ba_\bl_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\ba_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bp_\bs_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bq_\bu_\be_\br_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bg_\bb_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bt_\ba_\bt_\ba\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\ba_\bt_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bg_\bb_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\br_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bm_\ba_\br_\bk_\bd_\bo_\bw_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\by_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bd_\ba_\bt_\ba_\bf_\br_\ba_\bm_\be_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bd_\ba_\bt_\ba_\b__\bl_\ba_\bb_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\bl_\bu_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\br_\bi_\ba_\bb_\bl_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bW_\br_\bi_\bt_\be_\br_\b._\bw_\br_\bi_\bt_\be_\b__\bf_\bi_\bl_\be\n+    * _\bG_\be_\bn_\be_\br_\ba_\bl_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n+    * _\bS_\be_\br_\bi_\be_\bs\n+    * _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\n     * _\bp_\ba_\bn_\bd_\ba_\bs_\b _\ba_\br_\br_\ba_\by_\bs_\b,_\b _\bs_\bc_\ba_\bl_\ba_\br_\bs_\b,_\b _\ba_\bn_\bd_\b _\bd_\ba_\bt_\ba_\b _\bt_\by_\bp_\be_\bs\n     * _\bI_\bn_\bd_\be_\bx_\b _\bo_\bb_\bj_\be_\bc_\bt_\bs\n     * _\bD_\ba_\bt_\be_\b _\bo_\bf_\bf_\bs_\be_\bt_\bs\n     * _\bW_\bi_\bn_\bd_\bo_\bw\n     * _\bG_\br_\bo_\bu_\bp_\bB_\by\n     * _\bR_\be_\bs_\ba_\bm_\bp_\bl_\bi_\bn_\bg\n     * _\bS_\bt_\by_\bl_\be\n"}]}, {"source1": 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 pandas.DataFrame.plot\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_html.html\">\n+     pandas.DataFrame.to_html\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.area.html\">\n-     pandas.DataFrame.plot.area\n+    <a class=\"reference internal\" href=\"pandas.io.formats.style.Styler.to_html.html\">\n+     pandas.io.formats.style.Styler.to_html\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.bar.html\">\n-     pandas.DataFrame.plot.bar\n+    <a class=\"reference internal\" href=\"pandas.read_xml.html\">\n+     pandas.read_xml\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.barh.html\">\n-     pandas.DataFrame.plot.barh\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_xml.html\">\n+     pandas.DataFrame.to_xml\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.box.html\">\n-     pandas.DataFrame.plot.box\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_latex.html\">\n+     pandas.DataFrame.to_latex\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.density.html\">\n-     pandas.DataFrame.plot.density\n+    <a class=\"reference internal\" href=\"pandas.io.formats.style.Styler.to_latex.html\">\n+     pandas.io.formats.style.Styler.to_latex\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.hexbin.html\">\n-     pandas.DataFrame.plot.hexbin\n+    <a class=\"reference internal\" href=\"pandas.read_hdf.html\">\n+     pandas.read_hdf\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.hist.html\">\n-     pandas.DataFrame.plot.hist\n+    <a 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</a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.from_spmatrix.html\">\n-     pandas.DataFrame.sparse.from_spmatrix\n+    <a class=\"reference internal\" href=\"pandas.read_feather.html\">\n+     pandas.read_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.to_coo.html\">\n-     pandas.DataFrame.sparse.to_coo\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n+     pandas.DataFrame.to_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.to_dense.html\">\n-     pandas.DataFrame.sparse.to_dense\n+    <a class=\"reference internal\" href=\"pandas.read_parquet.html\">\n+     pandas.read_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.from_dict.html\">\n-     pandas.DataFrame.from_dict\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_parquet.html\">\n+     pandas.DataFrame.to_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.from_records.html\">\n-     pandas.DataFrame.from_records\n+    <a class=\"reference internal\" href=\"pandas.read_orc.html\">\n+     pandas.read_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_orc.html\">\n      pandas.DataFrame.to_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_parquet.html\">\n-     pandas.DataFrame.to_parquet\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2 current active\">\n-    <a class=\"current reference internal\" href=\"#\">\n-     pandas.DataFrame.to_pickle\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_csv.html\">\n-     pandas.DataFrame.to_csv\n+    <a class=\"reference internal\" href=\"pandas.read_sas.html\">\n+     pandas.read_sas\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_hdf.html\">\n-     pandas.DataFrame.to_hdf\n+    <a class=\"reference internal\" href=\"pandas.read_spss.html\">\n+     pandas.read_spss\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n-     pandas.DataFrame.to_sql\n+    <a class=\"reference internal\" href=\"pandas.read_sql_table.html\">\n+     pandas.read_sql_table\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_dict.html\">\n-     pandas.DataFrame.to_dict\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_excel.html\">\n-     pandas.DataFrame.to_excel\n+    <a class=\"reference internal\" href=\"pandas.read_sql_query.html\">\n+     pandas.read_sql_query\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_json.html\">\n-     pandas.DataFrame.to_json\n+    <a class=\"reference internal\" href=\"pandas.read_sql.html\">\n+     pandas.read_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_html.html\">\n-     pandas.DataFrame.to_html\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n+     pandas.DataFrame.to_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n-     pandas.DataFrame.to_feather\n+    <a class=\"reference internal\" href=\"pandas.read_gbq.html\">\n+     pandas.read_gbq\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_latex.html\">\n-     pandas.DataFrame.to_latex\n+    <a class=\"reference internal\" href=\"pandas.read_stata.html\">\n+     pandas.read_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_stata.html\">\n      pandas.DataFrame.to_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_gbq.html\">\n-     pandas.DataFrame.to_gbq\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_records.html\">\n-     pandas.DataFrame.to_records\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_string.html\">\n-     pandas.DataFrame.to_string\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_clipboard.html\">\n-     pandas.DataFrame.to_clipboard\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.data_label.html\">\n+     pandas.io.stata.StataReader.data_label\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_markdown.html\">\n-     pandas.DataFrame.to_markdown\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.value_labels.html\">\n+     pandas.io.stata.StataReader.value_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.style.html\">\n-     pandas.DataFrame.style\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.variable_labels.html\">\n+     pandas.io.stata.StataReader.variable_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.__dataframe__.html\">\n-     pandas.DataFrame.__dataframe__\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataWriter.write_file.html\">\n+     pandas.io.stata.StataWriter.write_file\n     </a>\n    </li>\n   </ul>\n  </li>\n  <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../general_functions.html\">\n+   General functions\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../series.html\">\n+   Series\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../frame.html\">\n+   DataFrame\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../arrays.html\">\n    pandas arrays, scalars, and data types\n   </a>\n  </li>\n  <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../indexing.html\">\n    Index objects\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -4,242 +4,73 @@\n     * _\bA_\bP_\bI_\b _\br_\be_\bf_\be_\br_\be_\bn_\bc_\be\n     * 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_\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bu_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\be_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bo_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bd_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bs_\bu_\bb\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bu_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bt_\br_\bu_\be_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bm_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bp_\bo_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be_\b__\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by_\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bp_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg_\br_\be_\bg_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bf_\bo_\br_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\br_\bo_\bu_\bp_\bb_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bl_\bl_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\ba_\bn_\bd_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bw_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bb_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bn_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bl_\bi_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br_\bw_\bi_\bt_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bu_\bn_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\be_\bs_\bc_\br_\bi_\bb_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bf_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bv_\ba_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt_\bo_\bs_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bd_\bi_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bc_\bt_\b__\bc_\bh_\ba_\bn_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd_\bu_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\ba_\bn_\bt_\bi_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bu_\bn_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bk_\be_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bu_\bn_\bi_\bq_\bu_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\bl_\bu_\be_\b__\bc_\bo_\bu_\bn_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bp_\br_\be_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bs_\bu_\bf_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\be_\bt_\bw_\be_\be_\bn_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\b__\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq_\bu_\ba_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b__\bl_\bi_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\bn_\bc_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\ba_\bc_\bk_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bl_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n-          o 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_\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\bt_\br_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\ba_\br_\be_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\bo_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\be_\bx_\bb_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bk_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bl_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bp_\bi_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bs_\bc_\ba_\bt_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bo_\bx_\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\br_\bo_\bm_\b__\bs_\bp_\bm_\ba_\bt_\br_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bc_\bo_\bo\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bd_\be_\bn_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\bd_\bi_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+    * _\bI_\bn_\bp_\bu_\bt_\b/_\bo_\bu_\bt_\bp_\bu_\bt\u0010\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\bi_\bc_\bk_\bl_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bs_\bv\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bs_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bd_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bd_\bi_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\bw_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\be_\bx_\bc_\be_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bb_\bo_\bo_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bs_\bh_\be_\be_\bt_\b__\bn_\ba_\bm_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bp_\ba_\br_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bW_\br_\bi_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bj_\bs_\bo_\bn_\b__\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bj_\bs_\bo_\bn_\b._\bb_\bu_\bi_\bl_\bd_\b__\bt_\ba_\bb_\bl_\be_\b__\bs_\bc_\bh_\be_\bm_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bt_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bx_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bd_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bp_\bu_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\ba_\bp_\bp_\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bs_\be_\bl_\be_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bi_\bn_\bf_\bo\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bk_\be_\by_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\br_\bo_\bu_\bp_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bw_\ba_\bl_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\ba_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bp_\bs_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bq_\bu_\be_\br_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bg_\bb_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bt_\ba_\bt_\ba\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\ba_\bt_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bg_\bb_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\br_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bm_\ba_\br_\bk_\bd_\bo_\bw_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\by_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bd_\ba_\bt_\ba_\bf_\br_\ba_\bm_\be_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bd_\ba_\bt_\ba_\b__\bl_\ba_\bb_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\bl_\bu_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\br_\bi_\ba_\bb_\bl_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bW_\br_\bi_\bt_\be_\br_\b._\bw_\br_\bi_\bt_\be_\b__\bf_\bi_\bl_\be\n+    * _\bG_\be_\bn_\be_\br_\ba_\bl_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n+    * _\bS_\be_\br_\bi_\be_\bs\n+    * _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\n     * _\bp_\ba_\bn_\bd_\ba_\bs_\b _\ba_\br_\br_\ba_\by_\bs_\b,_\b _\bs_\bc_\ba_\bl_\ba_\br_\bs_\b,_\b _\ba_\bn_\bd_\b _\bd_\ba_\bt_\ba_\b _\bt_\by_\bp_\be_\bs\n     * _\bI_\bn_\bd_\be_\bx_\b _\bo_\bb_\bj_\be_\bc_\bt_\bs\n     * _\bD_\ba_\bt_\be_\b _\bo_\bf_\bf_\bs_\be_\bt_\bs\n     * _\bW_\bi_\bn_\bd_\bo_\bw\n     * _\bG_\br_\bo_\bu_\bp_\bB_\by\n     * _\bR_\be_\bs_\ba_\bm_\bp_\bl_\bi_\bn_\bg\n     * _\bS_\bt_\by_\bl_\be\n"}]}, {"source1": 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href=\"pandas.io.formats.style.Styler.to_excel.html\">\n+     pandas.io.formats.style.Styler.to_excel\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.resample.html\">\n-     pandas.DataFrame.resample\n+    <a class=\"reference internal\" href=\"pandas.ExcelWriter.html\">\n+     pandas.ExcelWriter\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_period.html\">\n-     pandas.DataFrame.to_period\n+    <a class=\"reference internal\" href=\"pandas.read_json.html\">\n+     pandas.read_json\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_timestamp.html\">\n-     pandas.DataFrame.to_timestamp\n+    <a class=\"reference internal\" href=\"pandas.json_normalize.html\">\n+     pandas.json_normalize\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" 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</a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.html\">\n-     pandas.DataFrame.plot\n+    <a class=\"reference internal\" href=\"pandas.io.formats.style.Styler.to_html.html\">\n+     pandas.io.formats.style.Styler.to_html\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.area.html\">\n-     pandas.DataFrame.plot.area\n+    <a class=\"reference internal\" href=\"pandas.read_xml.html\">\n+     pandas.read_xml\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.bar.html\">\n-     pandas.DataFrame.plot.bar\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_xml.html\">\n+     pandas.DataFrame.to_xml\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.barh.html\">\n-     pandas.DataFrame.plot.barh\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_latex.html\">\n+     pandas.DataFrame.to_latex\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.box.html\">\n-     pandas.DataFrame.plot.box\n+    <a class=\"reference internal\" href=\"pandas.io.formats.style.Styler.to_latex.html\">\n+     pandas.io.formats.style.Styler.to_latex\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.density.html\">\n-     pandas.DataFrame.plot.density\n+    <a class=\"reference internal\" href=\"pandas.read_hdf.html\">\n+     pandas.read_hdf\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.plot.hexbin.html\">\n-     pandas.DataFrame.plot.hexbin\n+    <a class=\"reference internal\" href=\"pandas.HDFStore.put.html\">\n+     pandas.HDFStore.put\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-  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pandas.DataFrame.sparse.density\n+    <a class=\"reference internal\" href=\"pandas.read_feather.html\">\n+     pandas.read_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.from_spmatrix.html\">\n-     pandas.DataFrame.sparse.from_spmatrix\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n+     pandas.DataFrame.to_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.to_coo.html\">\n-     pandas.DataFrame.sparse.to_coo\n+    <a class=\"reference internal\" href=\"pandas.read_parquet.html\">\n+     pandas.read_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.sparse.to_dense.html\">\n-     pandas.DataFrame.sparse.to_dense\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_parquet.html\">\n+     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class=\"reference internal\" href=\"pandas.read_spss.html\">\n+     pandas.read_spss\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_pickle.html\">\n-     pandas.DataFrame.to_pickle\n+    <a class=\"reference internal\" href=\"pandas.read_sql_table.html\">\n+     pandas.read_sql_table\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_csv.html\">\n-     pandas.DataFrame.to_csv\n+    <a class=\"reference internal\" href=\"pandas.read_sql_query.html\">\n+     pandas.read_sql_query\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_hdf.html\">\n-     pandas.DataFrame.to_hdf\n+    <a class=\"reference internal\" href=\"pandas.read_sql.html\">\n+     pandas.read_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2 current active\">\n     <a class=\"current reference internal\" href=\"#\">\n      pandas.DataFrame.to_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_dict.html\">\n-     pandas.DataFrame.to_dict\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_excel.html\">\n-     pandas.DataFrame.to_excel\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_json.html\">\n-     pandas.DataFrame.to_json\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_html.html\">\n-     pandas.DataFrame.to_html\n+    <a class=\"reference internal\" href=\"pandas.read_gbq.html\">\n+     pandas.read_gbq\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n-     pandas.DataFrame.to_feather\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_latex.html\">\n-     pandas.DataFrame.to_latex\n+    <a class=\"reference internal\" href=\"pandas.read_stata.html\">\n+     pandas.read_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_stata.html\">\n      pandas.DataFrame.to_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_gbq.html\">\n-     pandas.DataFrame.to_gbq\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_records.html\">\n-     pandas.DataFrame.to_records\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_string.html\">\n-     pandas.DataFrame.to_string\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_clipboard.html\">\n-     pandas.DataFrame.to_clipboard\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.data_label.html\">\n+     pandas.io.stata.StataReader.data_label\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_markdown.html\">\n-     pandas.DataFrame.to_markdown\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.value_labels.html\">\n+     pandas.io.stata.StataReader.value_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.style.html\">\n-     pandas.DataFrame.style\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.variable_labels.html\">\n+     pandas.io.stata.StataReader.variable_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.__dataframe__.html\">\n-     pandas.DataFrame.__dataframe__\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataWriter.write_file.html\">\n+     pandas.io.stata.StataWriter.write_file\n     </a>\n    </li>\n   </ul>\n  </li>\n  <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../general_functions.html\">\n+   General functions\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../series.html\">\n+   Series\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../frame.html\">\n+   DataFrame\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../arrays.html\">\n    pandas arrays, scalars, and data types\n   </a>\n  </li>\n  <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../indexing.html\">\n    Index objects\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -4,242 +4,73 @@\n     * _\bA_\bP_\bI_\b 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_\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be_\b__\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by_\bm_\ba_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bp_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg_\br_\be_\bg_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bf_\bo_\br_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\br_\bo_\bu_\bp_\bb_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bl_\bl_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\ba_\bn_\bd_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bw_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bb_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bn_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bl_\bi_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br_\bw_\bi_\bt_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bu_\bn_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\be_\bs_\bc_\br_\bi_\bb_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bf_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bv_\ba_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt_\bo_\bs_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bd_\bi_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bc_\bt_\b__\bc_\bh_\ba_\bn_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd_\bu_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\ba_\bn_\bt_\bi_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bu_\bn_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bk_\be_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bu_\bn_\bi_\bq_\bu_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\bl_\bu_\be_\b__\bc_\bo_\bu_\bn_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bp_\br_\be_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bs_\bu_\bf_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\be_\bt_\bw_\be_\be_\bn_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\b__\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq_\bu_\ba_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b__\bl_\bi_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\bn_\bc_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\ba_\bc_\bk_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bl_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt_\b__\bt_\ba_\bb_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bo_\br_\bd_\be_\br_\b__\bl_\be_\bv_\be_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bv_\ba_\bl_\bu_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bl_\ba_\br_\bg_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bs_\bm_\ba_\bl_\bl_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bn_\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\ba_\bx_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\bl_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bq_\bu_\be_\be_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\ba_\br_\br_\ba_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bT\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bs_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bp_\ba_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bj_\bo_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\br_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bp_\bd_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bf_\br_\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bo_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bh_\bi_\bf_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\be_\br_\bi_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bt_\bi_\bm_\be_\bs_\bt_\ba_\bm_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bc_\bo_\bn_\bv_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bl_\bo_\bc_\ba_\bl_\bi_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bF_\bl_\ba_\bg_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\bt_\br_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\ba_\br_\be_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\bo_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\be_\bx_\bb_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bk_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bl_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bp_\bi_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bs_\bc_\ba_\bt_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bo_\bx_\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\br_\bo_\bm_\b__\bs_\bp_\bm_\ba_\bt_\br_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bc_\bo_\bo\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bd_\be_\bn_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\bd_\bi_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+    * _\bI_\bn_\bp_\bu_\bt_\b/_\bo_\bu_\bt_\bp_\bu_\bt\u0010\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\bi_\bc_\bk_\bl_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bs_\bv\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bs_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bd_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bd_\bi_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\bw_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\be_\bx_\bc_\be_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bb_\bo_\bo_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bs_\bh_\be_\be_\bt_\b__\bn_\ba_\bm_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bp_\ba_\br_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bW_\br_\bi_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bj_\bs_\bo_\bn_\b__\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bj_\bs_\bo_\bn_\b._\bb_\bu_\bi_\bl_\bd_\b__\bt_\ba_\bb_\bl_\be_\b__\bs_\bc_\bh_\be_\bm_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bt_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bx_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bd_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bp_\bu_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\ba_\bp_\bp_\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bs_\be_\bl_\be_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bi_\bn_\bf_\bo\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bk_\be_\by_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\br_\bo_\bu_\bp_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bw_\ba_\bl_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\ba_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bp_\bs_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bq_\bu_\be_\br_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bg_\bb_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bt_\ba_\bt_\ba\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\ba_\bt_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bg_\bb_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\br_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bm_\ba_\br_\bk_\bd_\bo_\bw_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\by_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bd_\ba_\bt_\ba_\bf_\br_\ba_\bm_\be_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bd_\ba_\bt_\ba_\b__\bl_\ba_\bb_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\bl_\bu_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\br_\bi_\ba_\bb_\bl_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bW_\br_\bi_\bt_\be_\br_\b._\bw_\br_\bi_\bt_\be_\b__\bf_\bi_\bl_\be\n+    * _\bG_\be_\bn_\be_\br_\ba_\bl_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n+    * _\bS_\be_\br_\bi_\be_\bs\n+    * _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\n     * _\bp_\ba_\bn_\bd_\ba_\bs_\b _\ba_\br_\br_\ba_\by_\bs_\b,_\b _\bs_\bc_\ba_\bl_\ba_\br_\bs_\b,_\b _\ba_\bn_\bd_\b _\bd_\ba_\bt_\ba_\b _\bt_\by_\bp_\be_\bs\n     * _\bI_\bn_\bd_\be_\bx_\b _\bo_\bb_\bj_\be_\bc_\bt_\bs\n     * _\bD_\ba_\bt_\be_\b _\bo_\bf_\bf_\bs_\be_\bt_\bs\n     * _\bW_\bi_\bn_\bd_\bo_\bw\n     * _\bG_\br_\bo_\bu_\bp_\bB_\by\n     * _\bR_\be_\bs_\ba_\bm_\bp_\bl_\bi_\bn_\bg\n     * _\bS_\bt_\by_\bl_\be\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_stata.html", "source2": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.DataFrame.to_stata.html", "unified_diff": "@@ -152,1162 +152,317 @@\n             <div class=\"col-12 col-md-3 bd-sidebar\"><form class=\"bd-search d-flex align-items-center\" action=\"../../search.html\" method=\"get\">\n   <i class=\"icon fas fa-search\"></i>\n   <input type=\"search\" class=\"form-control\" name=\"q\" id=\"search-input\" placeholder=\"Search the docs ...\" aria-label=\"Search the docs ...\" autocomplete=\"off\" >\n </form><nav class=\"bd-links\" id=\"bd-docs-nav\" aria-label=\"Main navigation\">\n   <div class=\"bd-toc-item navbar-nav\">\n     \n     <ul class=\"current nav bd-sidenav\">\n- <li class=\"toctree-l1\">\n+ <li class=\"toctree-l1 current active has-children\">\n   <a class=\"reference internal\" href=\"../io.html\">\n    Input/output\n   </a>\n- </li>\n- <li class=\"toctree-l1\">\n-  <a 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pandas.DataFrame.from_records\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n+     pandas.DataFrame.to_feather\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_orc.html\">\n-     pandas.DataFrame.to_orc\n+    <a class=\"reference internal\" href=\"pandas.read_parquet.html\">\n+     pandas.read_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n     <a class=\"reference internal\" href=\"pandas.DataFrame.to_parquet.html\">\n      pandas.DataFrame.to_parquet\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_pickle.html\">\n-     pandas.DataFrame.to_pickle\n+    <a class=\"reference internal\" href=\"pandas.read_orc.html\">\n+     pandas.read_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_csv.html\">\n-     pandas.DataFrame.to_csv\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_orc.html\">\n+     pandas.DataFrame.to_orc\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_hdf.html\">\n-     pandas.DataFrame.to_hdf\n+    <a class=\"reference internal\" href=\"pandas.read_sas.html\">\n+     pandas.read_sas\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n-     pandas.DataFrame.to_sql\n+    <a class=\"reference internal\" href=\"pandas.read_spss.html\">\n+     pandas.read_spss\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_dict.html\">\n-     pandas.DataFrame.to_dict\n+    <a class=\"reference internal\" href=\"pandas.read_sql_table.html\">\n+     pandas.read_sql_table\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_excel.html\">\n-     pandas.DataFrame.to_excel\n+    <a class=\"reference internal\" href=\"pandas.read_sql_query.html\">\n+     pandas.read_sql_query\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_json.html\">\n-     pandas.DataFrame.to_json\n+    <a class=\"reference internal\" href=\"pandas.read_sql.html\">\n+     pandas.read_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_html.html\">\n-     pandas.DataFrame.to_html\n+    <a class=\"reference internal\" href=\"pandas.DataFrame.to_sql.html\">\n+     pandas.DataFrame.to_sql\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_feather.html\">\n-     pandas.DataFrame.to_feather\n+    <a class=\"reference internal\" href=\"pandas.read_gbq.html\">\n+     pandas.read_gbq\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_latex.html\">\n-     pandas.DataFrame.to_latex\n+    <a class=\"reference internal\" href=\"pandas.read_stata.html\">\n+     pandas.read_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2 current active\">\n     <a class=\"current reference internal\" href=\"#\">\n      pandas.DataFrame.to_stata\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_gbq.html\">\n-     pandas.DataFrame.to_gbq\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_records.html\">\n-     pandas.DataFrame.to_records\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_string.html\">\n-     pandas.DataFrame.to_string\n-    </a>\n-   </li>\n-   <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_clipboard.html\">\n-     pandas.DataFrame.to_clipboard\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.data_label.html\">\n+     pandas.io.stata.StataReader.data_label\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.to_markdown.html\">\n-     pandas.DataFrame.to_markdown\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.value_labels.html\">\n+     pandas.io.stata.StataReader.value_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.style.html\">\n-     pandas.DataFrame.style\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataReader.variable_labels.html\">\n+     pandas.io.stata.StataReader.variable_labels\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.__dataframe__.html\">\n-     pandas.DataFrame.__dataframe__\n+    <a class=\"reference internal\" href=\"pandas.io.stata.StataWriter.write_file.html\">\n+     pandas.io.stata.StataWriter.write_file\n     </a>\n    </li>\n   </ul>\n  </li>\n  <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../general_functions.html\">\n+   General functions\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../series.html\">\n+   Series\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../frame.html\">\n+   DataFrame\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../arrays.html\">\n    pandas arrays, scalars, and data types\n   </a>\n  </li>\n  <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../indexing.html\">\n    Index objects\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -4,242 +4,73 @@\n     * _\bA_\bP_\bI_\b 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_\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bg_\bg_\br_\be_\bg_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bf_\bo_\br_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\br_\bo_\bu_\bp_\bb_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bl_\bl_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\ba_\bn_\bd_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bw_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bb_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bn_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bl_\bi_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\br_\br_\bw_\bi_\bt_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bu_\bn_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bu_\bm_\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\be_\bs_\bc_\br_\bi_\bb_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bi_\bf_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bv_\ba_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bk_\bu_\br_\bt_\bo_\bs_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bd_\bi_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bc_\bt_\b__\bc_\bh_\ba_\bn_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd_\bu_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\ba_\bn_\bt_\bi_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bu_\bn_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bk_\be_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bu_\bn_\bi_\bq_\bu_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\bl_\bu_\be_\b__\bc_\bo_\bu_\bn_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bp_\br_\be_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bs_\bu_\bf_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\be_\bt_\bw_\be_\be_\bn_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\b__\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq_\bu_\ba_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b__\bl_\bi_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\bn_\bc_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\ba_\bc_\bk_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bl_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt_\b__\bt_\ba_\bb_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bo_\br_\bd_\be_\br_\b__\bl_\be_\bv_\be_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bv_\ba_\bl_\bu_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bl_\ba_\br_\bg_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bs_\bm_\ba_\bl_\bl_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bn_\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\ba_\bx_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\bl_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bq_\bu_\be_\be_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\ba_\br_\br_\ba_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bT\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bs_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bp_\ba_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bj_\bo_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\br_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bp_\bd_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bf_\br_\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bo_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bh_\bi_\bf_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\be_\br_\bi_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bt_\bi_\bm_\be_\bs_\bt_\ba_\bm_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bc_\bo_\bn_\bv_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bl_\bo_\bc_\ba_\bl_\bi_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bF_\bl_\ba_\bg_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\bt_\br_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\ba_\br_\be_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\bo_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\be_\bx_\bb_\bi_\bn\n-          o 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_\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\br_\bo_\bm_\b__\bs_\bp_\bm_\ba_\bt_\br_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bc_\bo_\bo\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bd_\be_\bn_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\bd_\bi_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+    * _\bI_\bn_\bp_\bu_\bt_\b/_\bo_\bu_\bt_\bp_\bu_\bt\u0010\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\bi_\bc_\bk_\bl_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bs_\bv\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bs_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bd_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bd_\bi_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\bw_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\be_\bx_\bc_\be_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bb_\bo_\bo_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bs_\bh_\be_\be_\bt_\b__\bn_\ba_\bm_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bF_\bi_\bl_\be_\b._\bp_\ba_\br_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bE_\bx_\bc_\be_\bl_\bW_\br_\bi_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bj_\bs_\bo_\bn_\b__\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bj_\bs_\bo_\bn_\b._\bb_\bu_\bi_\bl_\bd_\b__\bt_\ba_\bb_\bl_\be_\b__\bs_\bc_\bh_\be_\bm_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bt_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bx_\bm_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\bm_\bl\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bf_\bo_\br_\bm_\ba_\bt_\bs_\b._\bs_\bt_\by_\bl_\be_\b._\bS_\bt_\by_\bl_\be_\br_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bh_\bd_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bp_\bu_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\ba_\bp_\bp_\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bs_\be_\bl_\be_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bi_\bn_\bf_\bo\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bk_\be_\by_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bg_\br_\bo_\bu_\bp_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bH_\bD_\bF_\bS_\bt_\bo_\br_\be_\b._\bw_\ba_\bl_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\ba_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bp_\bs_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bt_\ba_\bb_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bq_\bu_\be_\br_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bg_\bb_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bs_\bt_\ba_\bt_\ba\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\ba_\bt_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bg_\bb_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\br_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bm_\ba_\br_\bk_\bd_\bo_\bw_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\by_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bd_\ba_\bt_\ba_\bf_\br_\ba_\bm_\be_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bd_\ba_\bt_\ba_\b__\bl_\ba_\bb_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\bl_\bu_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bR_\be_\ba_\bd_\be_\br_\b._\bv_\ba_\br_\bi_\ba_\bb_\bl_\be_\b__\bl_\ba_\bb_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bi_\bo_\b._\bs_\bt_\ba_\bt_\ba_\b._\bS_\bt_\ba_\bt_\ba_\bW_\br_\bi_\bt_\be_\br_\b._\bw_\br_\bi_\bt_\be_\b__\bf_\bi_\bl_\be\n+    * _\bG_\be_\bn_\be_\br_\ba_\bl_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n+    * _\bS_\be_\br_\bi_\be_\bs\n+    * _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\n     * _\bp_\ba_\bn_\bd_\ba_\bs_\b _\ba_\br_\br_\ba_\by_\bs_\b,_\b _\bs_\bc_\ba_\bl_\ba_\br_\bs_\b,_\b _\ba_\bn_\bd_\b _\bd_\ba_\bt_\ba_\b _\bt_\by_\bp_\be_\bs\n     * _\bI_\bn_\bd_\be_\bx_\b _\bo_\bb_\bj_\be_\bc_\bt_\bs\n     * _\bD_\ba_\bt_\be_\b _\bo_\bf_\bf_\bs_\be_\bt_\bs\n     * _\bW_\bi_\bn_\bd_\bo_\bw\n     * _\bG_\br_\bo_\bu_\bp_\bB_\by\n     * _\bR_\be_\bs_\ba_\bm_\bp_\bl_\bi_\bn_\bg\n     * _\bS_\bt_\by_\bl_\be\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.Flags.html", "source2": "./usr/share/doc/python-pandas-doc/html/reference/api/pandas.Flags.html", "unified_diff": "@@ -162,1152 +162,1767 @@\n   </a>\n  </li>\n  <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../general_functions.html\">\n    General functions\n   </a>\n  </li>\n- <li class=\"toctree-l1\">\n+ <li class=\"toctree-l1 current active has-children\">\n   <a class=\"reference internal\" href=\"../series.html\">\n    Series\n   </a>\n- </li>\n- <li class=\"toctree-l1 current active has-children\">\n-  <a class=\"reference internal\" href=\"../frame.html\">\n-   DataFrame\n-  </a>\n   <input checked=\"\" class=\"toctree-checkbox\" id=\"toctree-checkbox-1\" name=\"toctree-checkbox-1\" type=\"checkbox\"/>\n   <label for=\"toctree-checkbox-1\">\n    <i class=\"fas fa-chevron-down\">\n    </i>\n   </label>\n   <ul class=\"current\">\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.html\">\n-     pandas.DataFrame\n+    <a class=\"reference internal\" href=\"pandas.Series.html\">\n+     pandas.Series\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.index.html\">\n-     pandas.DataFrame.index\n+    <a class=\"reference internal\" href=\"pandas.Series.index.html\">\n+     pandas.Series.index\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.columns.html\">\n-     pandas.DataFrame.columns\n+    <a class=\"reference internal\" href=\"pandas.Series.array.html\">\n+     pandas.Series.array\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.dtypes.html\">\n-     pandas.DataFrame.dtypes\n+    <a class=\"reference internal\" href=\"pandas.Series.values.html\">\n+     pandas.Series.values\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.info.html\">\n-     pandas.DataFrame.info\n+    <a class=\"reference internal\" href=\"pandas.Series.dtype.html\">\n+     pandas.Series.dtype\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.select_dtypes.html\">\n-     pandas.DataFrame.select_dtypes\n+    <a class=\"reference internal\" href=\"pandas.Series.shape.html\">\n+     pandas.Series.shape\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.values.html\">\n-     pandas.DataFrame.values\n+    <a class=\"reference internal\" href=\"pandas.Series.nbytes.html\">\n+     pandas.Series.nbytes\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.axes.html\">\n-     pandas.DataFrame.axes\n+    <a class=\"reference internal\" href=\"pandas.Series.ndim.html\">\n+     pandas.Series.ndim\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.ndim.html\">\n-     pandas.DataFrame.ndim\n+    <a class=\"reference internal\" href=\"pandas.Series.size.html\">\n+     pandas.Series.size\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.size.html\">\n-     pandas.DataFrame.size\n+    <a class=\"reference internal\" href=\"pandas.Series.T.html\">\n+     pandas.Series.T\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.shape.html\">\n-     pandas.DataFrame.shape\n+    <a class=\"reference internal\" href=\"pandas.Series.memory_usage.html\">\n+     pandas.Series.memory_usage\n     </a>\n    </li>\n    <li class=\"toctree-l2\">\n-    <a class=\"reference internal\" href=\"pandas.DataFrame.memory_usage.html\">\n-     pandas.DataFrame.memory_usage\n+    <a class=\"reference internal\" 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href=\"pandas.Series.to_csv.html\">\n+     pandas.Series.to_csv\n+    </a>\n+   </li>\n+   <li class=\"toctree-l2\">\n+    <a class=\"reference internal\" href=\"pandas.Series.to_dict.html\">\n+     pandas.Series.to_dict\n+    </a>\n+   </li>\n+   <li class=\"toctree-l2\">\n+    <a class=\"reference internal\" href=\"pandas.Series.to_excel.html\">\n+     pandas.Series.to_excel\n+    </a>\n+   </li>\n+   <li class=\"toctree-l2\">\n+    <a class=\"reference internal\" href=\"pandas.Series.to_frame.html\">\n+     pandas.Series.to_frame\n+    </a>\n+   </li>\n+   <li class=\"toctree-l2\">\n+    <a class=\"reference internal\" href=\"pandas.Series.to_xarray.html\">\n+     pandas.Series.to_xarray\n+    </a>\n+   </li>\n+   <li class=\"toctree-l2\">\n+    <a class=\"reference internal\" href=\"pandas.Series.to_hdf.html\">\n+     pandas.Series.to_hdf\n+    </a>\n+   </li>\n+   <li class=\"toctree-l2\">\n+    <a class=\"reference internal\" href=\"pandas.Series.to_sql.html\">\n+     pandas.Series.to_sql\n+    </a>\n+   </li>\n+   <li class=\"toctree-l2\">\n+    <a class=\"reference internal\" href=\"pandas.Series.to_json.html\">\n+     pandas.Series.to_json\n+    </a>\n+   </li>\n+   <li class=\"toctree-l2\">\n+    <a class=\"reference internal\" href=\"pandas.Series.to_string.html\">\n+     pandas.Series.to_string\n+    </a>\n+   </li>\n+   <li class=\"toctree-l2\">\n+    <a class=\"reference internal\" href=\"pandas.Series.to_clipboard.html\">\n+     pandas.Series.to_clipboard\n+    </a>\n+   </li>\n+   <li class=\"toctree-l2\">\n+    <a class=\"reference internal\" href=\"pandas.Series.to_latex.html\">\n+     pandas.Series.to_latex\n+    </a>\n+   </li>\n+   <li class=\"toctree-l2\">\n+    <a class=\"reference internal\" href=\"pandas.Series.to_markdown.html\">\n+     pandas.Series.to_markdown\n     </a>\n    </li>\n   </ul>\n  </li>\n  <li class=\"toctree-l1\">\n+  <a class=\"reference internal\" href=\"../frame.html\">\n+   DataFrame\n+  </a>\n+ </li>\n+ <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../arrays.html\">\n    pandas arrays, scalars, and data types\n   </a>\n  </li>\n  <li class=\"toctree-l1\">\n   <a class=\"reference internal\" href=\"../indexing.html\">\n    Index objects\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -6,240 +6,363 @@\n     * _\bR_\be_\bl_\be_\ba_\bs_\be_\b _\bn_\bo_\bt_\be_\bs\n     * _\bG_\bi_\bt_\bH_\bu_\bb\n     * _\bT_\bw_\bi_\bt_\bt_\be_\br\n     * _\bM_\ba_\bs_\bt_\bo_\bd_\bo_\bn\n [Unknown INPUT type]\n     * _\bI_\bn_\bp_\bu_\bt_\b/_\bo_\bu_\bt_\bp_\bu_\bt\n     * _\bG_\be_\bn_\be_\br_\ba_\bl_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n-    * _\bS_\be_\br_\bi_\be_\bs\n-    * _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\u0010\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bd_\be_\bx\n-          o 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_\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bd_\bi_\ba_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bc_\bt_\b__\bc_\bh_\ba_\bn_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\br_\bo_\bd_\bu_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bq_\bu_\ba_\bn_\bt_\bi_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\bo_\bu_\bn_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bk_\be_\bw\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bu_\bm\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bu_\bn_\bi_\bq_\bu_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bv_\ba_\bl_\bu_\be_\b__\bc_\bo_\bu_\bn_\bt_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bp_\br_\be_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bd_\bd_\b__\bs_\bu_\bf_\bf_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bl_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\be_\bt_\bw_\be_\be_\bn_\b__\bt_\bi_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\b__\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bq_\bu_\ba_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\be_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\ba_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bd_\bx_\bm_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b__\bl_\bi_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\ba_\bx_\bi_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bi_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\ba_\bk_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\bu_\bn_\bc_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\ba_\bc_\bk_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bf_\bi_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\bl_\bl_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bs_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bo_\bt_\bn_\bu_\bl_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\ba_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bd_\br_\bo_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bi_\bv_\bo_\bt_\b__\bt_\ba_\bb_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bo_\br_\bd_\be_\br_\b__\bl_\be_\bv_\be_\bl_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bv_\ba_\bl_\bu_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bl_\ba_\br_\bg_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bn_\bs_\bm_\ba_\bl_\bl_\be_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\bl_\be_\bv_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bn_\bs_\bt_\ba_\bc_\bk\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bw_\ba_\bp_\ba_\bx_\be_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\bl_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\be_\bx_\bp_\bl_\bo_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bq_\bu_\be_\be_\bz_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bx_\ba_\br_\br_\ba_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bT\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bs_\bi_\bg_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bc_\bo_\bm_\bp_\ba_\br_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bj_\bo_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\br_\bg_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bu_\bp_\bd_\ba_\bt_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bf_\br_\be_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bo_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bh_\bi_\bf_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\bi_\br_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bl_\ba_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bs_\ba_\bm_\bp_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\be_\br_\bi_\bo_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bt_\bi_\bm_\be_\bs_\bt_\ba_\bm_\bp\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bc_\bo_\bn_\bv_\be_\br_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bz_\b__\bl_\bo_\bc_\ba_\bl_\bi_\bz_\be\n+    * _\bS_\be_\br_\bi_\be_\bs\u0010\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bn_\bd_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\br_\br_\ba_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bv_\ba_\bl_\bu_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\by_\bp_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bh_\ba_\bp_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bn_\bb_\by_\bt_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bn_\bd_\bi_\bm\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bi_\bz_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bT\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bm_\be_\bm_\bo_\br_\by_\b__\bu_\bs_\ba_\bg_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bh_\ba_\bs_\bn_\ba_\bn_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\be_\bm_\bp_\bt_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\by_\bp_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bn_\ba_\bm_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bf_\bl_\ba_\bg_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\be_\bt_\b__\bf_\bl_\ba_\bg_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bs_\bt_\by_\bp_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\bo_\bn_\bv_\be_\br_\bt_\b__\bd_\bt_\by_\bp_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bn_\bf_\be_\br_\b__\bo_\bb_\bj_\be_\bc_\bt_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\bo_\bp_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bb_\bo_\bo_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bn_\bu_\bm_\bp_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bp_\be_\br_\bi_\bo_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bt_\bi_\bm_\be_\bs_\bt_\ba_\bm_\bp\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bl_\bi_\bs_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\b__\b__\ba_\br_\br_\ba_\by_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bg_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\ba_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bl_\bo_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bl_\bo_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\b__\b__\bi_\bt_\be_\br_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bt_\be_\bm_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bk_\be_\by_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\bo_\bp\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bt_\be_\bm\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bx_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bd_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bu_\bb\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bm_\bu_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bi_\bv\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\br_\bu_\be_\bd_\bi_\bv\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bm_\bo_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\bo_\bw\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\ba_\bd_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\bs_\bu_\bb\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\bm_\bu_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\bd_\bi_\bv\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\bt_\br_\bu_\be_\bd_\bi_\bv\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\bf_\bl_\bo_\bo_\br_\bd_\bi_\bv\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\bm_\bo_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\bp_\bo_\bw\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\bo_\bm_\bb_\bi_\bn_\be_\b__\bf_\bi_\br_\bs_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\bo_\bu_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bl_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bg_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bg_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bn_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\be_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\br_\bo_\bd_\bu_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bo_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bp_\bp_\bl_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bg_\bg\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bg_\bg_\br_\be_\bg_\ba_\bt_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\br_\ba_\bn_\bs_\bf_\bo_\br_\bm\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bm_\ba_\bp\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bg_\br_\bo_\bu_\bp_\bb_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\bo_\bl_\bl_\bi_\bn_\bg\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\be_\bx_\bp_\ba_\bn_\bd_\bi_\bn_\bg\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\be_\bw_\bm\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\bi_\bp_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bb_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bl_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bn_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bu_\bt_\bo_\bc_\bo_\br_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bb_\be_\bt_\bw_\be_\be_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\bl_\bi_\bp\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\bo_\br_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\bo_\bu_\bn_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\bo_\bv\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\bu_\bm_\bm_\ba_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\bu_\bm_\bm_\bi_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\bu_\bm_\bp_\br_\bo_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\bu_\bm_\bs_\bu_\bm\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\be_\bs_\bc_\br_\bi_\bb_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bi_\bf_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bf_\ba_\bc_\bt_\bo_\br_\bi_\bz_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bk_\bu_\br_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bm_\ba_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bm_\be_\ba_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bm_\be_\bd_\bi_\ba_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bm_\bi_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bm_\bo_\bd_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bn_\bl_\ba_\br_\bg_\be_\bs_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bn_\bs_\bm_\ba_\bl_\bl_\be_\bs_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\bc_\bt_\b__\bc_\bh_\ba_\bn_\bg_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\br_\bo_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bq_\bu_\ba_\bn_\bt_\bi_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\ba_\bn_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\be_\bm\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bk_\be_\bw\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bu_\bm\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bv_\ba_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bk_\bu_\br_\bt_\bo_\bs_\bi_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bu_\bn_\bi_\bq_\bu_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bn_\bu_\bn_\bi_\bq_\bu_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bs_\b__\bu_\bn_\bi_\bq_\bu_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bs_\b__\bm_\bo_\bn_\bo_\bt_\bo_\bn_\bi_\bc_\b__\bi_\bn_\bc_\br_\be_\ba_\bs_\bi_\bn_\bg\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bs_\b__\bm_\bo_\bn_\bo_\bt_\bo_\bn_\bi_\bc_\b__\bd_\be_\bc_\br_\be_\ba_\bs_\bi_\bn_\bg\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bv_\ba_\bl_\bu_\be_\b__\bc_\bo_\bu_\bn_\bt_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bl_\bi_\bg_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\ba_\bs_\be_\b__\bw_\bh_\be_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\br_\bo_\bp\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\br_\bo_\bp_\bl_\be_\bv_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\br_\bo_\bp_\b__\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\be_\bq_\bu_\ba_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bf_\bi_\br_\bs_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bh_\be_\ba_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bd_\bx_\bm_\ba_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bd_\bx_\bm_\bi_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bs_\bi_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bl_\ba_\bs_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\be_\bi_\bn_\bd_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b__\bl_\bi_\bk_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\be_\bn_\ba_\bm_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\be_\bn_\ba_\bm_\be_\b__\ba_\bx_\bi_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\be_\bs_\be_\bt_\b__\bi_\bn_\bd_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\ba_\bm_\bp_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\be_\bt_\b__\ba_\bx_\bi_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\ba_\bk_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\ba_\bi_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\br_\bu_\bn_\bc_\ba_\bt_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bw_\bh_\be_\br_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bm_\ba_\bs_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bd_\bd_\b__\bp_\br_\be_\bf_\bi_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bd_\bd_\b__\bs_\bu_\bf_\bf_\bi_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bf_\bi_\bl_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bb_\ba_\bc_\bk_\bf_\bi_\bl_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bb_\bf_\bi_\bl_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\br_\bo_\bp_\bn_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bf_\bf_\bi_\bl_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bf_\bi_\bl_\bl_\bn_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bs_\bn_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bs_\bn_\bu_\bl_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bn_\bo_\bt_\bn_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bn_\bo_\bt_\bn_\bu_\bl_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\ba_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\br_\bg_\bs_\bo_\br_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\br_\bg_\bm_\bi_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\br_\bg_\bm_\ba_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\be_\bo_\br_\bd_\be_\br_\b__\bl_\be_\bv_\be_\bl_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bo_\br_\bt_\b__\bv_\ba_\bl_\bu_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bo_\br_\bt_\b__\bi_\bn_\bd_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bw_\ba_\bp_\bl_\be_\bv_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bu_\bn_\bs_\bt_\ba_\bc_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\be_\bx_\bp_\bl_\bo_\bd_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\be_\ba_\br_\bc_\bh_\bs_\bo_\br_\bt_\be_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\ba_\bv_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\be_\bp_\be_\ba_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bq_\bu_\be_\be_\bz_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bv_\bi_\be_\bw\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\bo_\bm_\bp_\ba_\br_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bu_\bp_\bd_\ba_\bt_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bs_\bf_\br_\be_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bs_\bo_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bh_\bi_\bf_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bf_\bi_\br_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bl_\ba_\bs_\bt_\b__\bv_\ba_\bl_\bi_\bd_\b__\bi_\bn_\bd_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\br_\be_\bs_\ba_\bm_\bp_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bz_\b__\bc_\bo_\bn_\bv_\be_\br_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bz_\b__\bl_\bo_\bc_\ba_\bl_\bi_\bz_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bt_\b__\bt_\bi_\bm_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bb_\be_\bt_\bw_\be_\be_\bn_\b__\bt_\bi_\bm_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\ba_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bp_\ba_\br_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bI_\bn_\bd_\be_\bx_\b._\bs_\bt_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bd_\ba_\bt_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bt_\bi_\bm_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bt_\bi_\bm_\be_\bt_\bz\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\by_\be_\ba_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bm_\bo_\bn_\bt_\bh\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bd_\ba_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bh_\bo_\bu_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bm_\bi_\bn_\bu_\bt_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bs_\be_\bc_\bo_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bm_\bi_\bc_\br_\bo_\bs_\be_\bc_\bo_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bn_\ba_\bn_\bo_\bs_\be_\bc_\bo_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bd_\ba_\by_\bo_\bf_\bw_\be_\be_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bd_\ba_\by_\b__\bo_\bf_\b__\bw_\be_\be_\bk\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bw_\be_\be_\bk_\bd_\ba_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bd_\ba_\by_\bo_\bf_\by_\be_\ba_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bd_\ba_\by_\b__\bo_\bf_\b__\by_\be_\ba_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bd_\ba_\by_\bs_\b__\bi_\bn_\b__\bm_\bo_\bn_\bt_\bh\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bq_\bu_\ba_\br_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bi_\bs_\b__\bm_\bo_\bn_\bt_\bh_\b__\bs_\bt_\ba_\br_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bi_\bs_\b__\bm_\bo_\bn_\bt_\bh_\b__\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bi_\bs_\b__\bq_\bu_\ba_\br_\bt_\be_\br_\b__\bs_\bt_\ba_\br_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bi_\bs_\b__\bq_\bu_\ba_\br_\bt_\be_\br_\b__\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bi_\bs_\b__\by_\be_\ba_\br_\b__\bs_\bt_\ba_\br_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bi_\bs_\b__\by_\be_\ba_\br_\b__\be_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bi_\bs_\b__\bl_\be_\ba_\bp_\b__\by_\be_\ba_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bd_\ba_\by_\bs_\bi_\bn_\bm_\bo_\bn_\bt_\bh\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bd_\ba_\by_\bs_\b__\bi_\bn_\b__\bm_\bo_\bn_\bt_\bh\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bt_\bz\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bf_\br_\be_\bq\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bu_\bn_\bi_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bi_\bs_\bo_\bc_\ba_\bl_\be_\bn_\bd_\ba_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bt_\bo_\b__\bp_\be_\br_\bi_\bo_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bt_\bo_\b__\bp_\by_\bd_\ba_\bt_\be_\bt_\bi_\bm_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bt_\bz_\b__\bl_\bo_\bc_\ba_\bl_\bi_\bz_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bt_\bz_\b__\bc_\bo_\bn_\bv_\be_\br_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bs_\bt_\br_\bf_\bt_\bi_\bm_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\br_\bo_\bu_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bf_\bl_\bo_\bo_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bc_\be_\bi_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bm_\bo_\bn_\bt_\bh_\b__\bn_\ba_\bm_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bd_\ba_\by_\b__\bn_\ba_\bm_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\ba_\bs_\b__\bu_\bn_\bi_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bq_\by_\be_\ba_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bs_\bt_\ba_\br_\bt_\b__\bt_\bi_\bm_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\be_\bn_\bd_\b__\bt_\bi_\bm_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bd_\ba_\by_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bs_\be_\bc_\bo_\bn_\bd_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bm_\bi_\bc_\br_\bo_\bs_\be_\bc_\bo_\bn_\bd_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bn_\ba_\bn_\bo_\bs_\be_\bc_\bo_\bn_\bd_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bc_\bo_\bm_\bp_\bo_\bn_\be_\bn_\bt_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bu_\bn_\bi_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bt_\bo_\b__\bp_\by_\bt_\bi_\bm_\be_\bd_\be_\bl_\bt_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\bt_\bo_\bt_\ba_\bl_\b__\bs_\be_\bc_\bo_\bn_\bd_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bd_\bt_\b._\ba_\bs_\b__\bu_\bn_\bi_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bc_\ba_\bp_\bi_\bt_\ba_\bl_\bi_\bz_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bc_\ba_\bs_\be_\bf_\bo_\bl_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bc_\ba_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bc_\be_\bn_\bt_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bc_\bo_\bn_\bt_\ba_\bi_\bn_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bc_\bo_\bu_\bn_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bd_\be_\bc_\bo_\bd_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\be_\bn_\bc_\bo_\bd_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\be_\bn_\bd_\bs_\bw_\bi_\bt_\bh\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\be_\bx_\bt_\br_\ba_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\be_\bx_\bt_\br_\ba_\bc_\bt_\ba_\bl_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bf_\bi_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bf_\bi_\bn_\bd_\ba_\bl_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bf_\bu_\bl_\bl_\bm_\ba_\bt_\bc_\bh\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bg_\be_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bi_\bn_\bd_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bj_\bo_\bi_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bl_\be_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bl_\bj_\bu_\bs_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bl_\bo_\bw_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bl_\bs_\bt_\br_\bi_\bp\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bm_\ba_\bt_\bc_\bh\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bp_\ba_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bp_\ba_\br_\bt_\bi_\bt_\bi_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\br_\be_\bm_\bo_\bv_\be_\bp_\br_\be_\bf_\bi_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\br_\be_\bm_\bo_\bv_\be_\bs_\bu_\bf_\bf_\bi_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\br_\be_\bp_\be_\ba_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\br_\be_\bp_\bl_\ba_\bc_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\br_\bf_\bi_\bn_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\br_\bi_\bn_\bd_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\br_\bj_\bu_\bs_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\br_\bp_\ba_\br_\bt_\bi_\bt_\bi_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\br_\bs_\bt_\br_\bi_\bp\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bs_\bl_\bi_\bc_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bs_\bl_\bi_\bc_\be_\b__\br_\be_\bp_\bl_\ba_\bc_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bs_\bp_\bl_\bi_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\br_\bs_\bp_\bl_\bi_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bs_\bt_\ba_\br_\bt_\bs_\bw_\bi_\bt_\bh\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bs_\bt_\br_\bi_\bp\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bs_\bw_\ba_\bp_\bc_\ba_\bs_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bt_\bi_\bt_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bt_\br_\ba_\bn_\bs_\bl_\ba_\bt_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bu_\bp_\bp_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bw_\br_\ba_\bp\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bz_\bf_\bi_\bl_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bi_\bs_\ba_\bl_\bn_\bu_\bm\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bi_\bs_\ba_\bl_\bp_\bh_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bi_\bs_\bd_\bi_\bg_\bi_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bi_\bs_\bs_\bp_\ba_\bc_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bi_\bs_\bl_\bo_\bw_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bi_\bs_\bu_\bp_\bp_\be_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bi_\bs_\bt_\bi_\bt_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bi_\bs_\bn_\bu_\bm_\be_\br_\bi_\bc\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bi_\bs_\bd_\be_\bc_\bi_\bm_\ba_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bg_\be_\bt_\b__\bd_\bu_\bm_\bm_\bi_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\ba_\bt_\b._\bc_\ba_\bt_\be_\bg_\bo_\br_\bi_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\ba_\bt_\b._\bo_\br_\bd_\be_\br_\be_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\ba_\bt_\b._\bc_\bo_\bd_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\ba_\bt_\b._\br_\be_\bn_\ba_\bm_\be_\b__\bc_\ba_\bt_\be_\bg_\bo_\br_\bi_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\ba_\bt_\b._\br_\be_\bo_\br_\bd_\be_\br_\b__\bc_\ba_\bt_\be_\bg_\bo_\br_\bi_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\ba_\bt_\b._\ba_\bd_\bd_\b__\bc_\ba_\bt_\be_\bg_\bo_\br_\bi_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\ba_\bt_\b._\br_\be_\bm_\bo_\bv_\be_\b__\bc_\ba_\bt_\be_\bg_\bo_\br_\bi_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\ba_\bt_\b._\br_\be_\bm_\bo_\bv_\be_\b__\bu_\bn_\bu_\bs_\be_\bd_\b__\bc_\ba_\bt_\be_\bg_\bo_\br_\bi_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\ba_\bt_\b._\bs_\be_\bt_\b__\bc_\ba_\bt_\be_\bg_\bo_\br_\bi_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\ba_\bt_\b._\ba_\bs_\b__\bo_\br_\bd_\be_\br_\be_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bc_\ba_\bt_\b._\ba_\bs_\b__\bu_\bn_\bo_\br_\bd_\be_\br_\be_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bn_\bp_\bo_\bi_\bn_\bt_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\bi_\bl_\bl_\b__\bv_\ba_\bl_\bu_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bs_\bp_\b__\bv_\ba_\bl_\bu_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\br_\bo_\bm_\b__\bc_\bo_\bo\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bc_\bo_\bo\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bl_\bi_\bs_\bt_\b._\bf_\bl_\ba_\bt_\bt_\be_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bl_\bi_\bs_\bt_\b._\bl_\be_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bl_\bi_\bs_\bt_\b._\b__\b__\bg_\be_\bt_\bi_\bt_\be_\bm_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\bu_\bc_\bt_\b._\bd_\bt_\by_\bp_\be_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\bu_\bc_\bt_\b._\bf_\bi_\be_\bl_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\bu_\bc_\bt_\b._\be_\bx_\bp_\bl_\bo_\bd_\be\n           o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bF_\bl_\ba_\bg_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bt_\bt_\br_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\ba_\br_\be_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br_\bh\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bb_\bo_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\be_\bx_\bb_\bi_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bk_\bd_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bl_\bi_\bn_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bp_\bi_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bp_\bl_\bo_\bt_\b._\bs_\bc_\ba_\bt_\bt_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bb_\bo_\bx_\bp_\bl_\bo_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bh_\bi_\bs_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bf_\br_\bo_\bm_\b__\bs_\bp_\bm_\ba_\bt_\br_\bi_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bc_\bo_\bo\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bp_\ba_\br_\bs_\be_\b._\bt_\bo_\b__\bd_\be_\bn_\bs_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\bd_\bi_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bf_\br_\bo_\bm_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bo_\br_\bc\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\ba_\br_\bq_\bu_\be_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bs_\bv\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bd_\bf\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bq_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bd_\bi_\bc_\bt\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bj_\bs_\bo_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bh_\bt_\bm_\bl\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bf_\be_\ba_\bt_\bh_\be_\br\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\ba_\bt_\ba\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bg_\bb_\bq\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\br_\be_\bc_\bo_\br_\bd_\bs\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bs_\bt_\br_\bi_\bn_\bg\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bm_\ba_\br_\bk_\bd_\bo_\bw_\bn\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bt_\by_\bl_\be\n-          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\b__\b__\bd_\ba_\bt_\ba_\bf_\br_\ba_\bm_\be_\b__\b_\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\ba_\bt_\bt_\br_\bs\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\bl_\bo_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\bl_\bo_\bt_\b._\ba_\br_\be_\ba\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\bl_\bo_\bt_\b._\bb_\ba_\br_\bh\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\bl_\bo_\bt_\b._\bb_\bo_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\bl_\bo_\bt_\b._\bd_\be_\bn_\bs_\bi_\bt_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\bl_\bo_\bt_\b._\bh_\bi_\bs_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\bl_\bo_\bt_\b._\bk_\bd_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\bl_\bo_\bt_\b._\bl_\bi_\bn_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bp_\bl_\bo_\bt_\b._\bp_\bi_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bh_\bi_\bs_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bc_\bs_\bv\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bd_\bi_\bc_\bt\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\be_\bx_\bc_\be_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bf_\br_\ba_\bm_\be\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bx_\ba_\br_\br_\ba_\by\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bh_\bd_\bf\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bs_\bq_\bl\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bj_\bs_\bo_\bn\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bs_\bt_\br_\bi_\bn_\bg\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bc_\bl_\bi_\bp_\bb_\bo_\ba_\br_\bd\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bl_\ba_\bt_\be_\bx\n+          o _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bm_\ba_\br_\bk_\bd_\bo_\bw_\bn\n+    * _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be\n     * _\bp_\ba_\bn_\bd_\ba_\bs_\b _\ba_\br_\br_\ba_\by_\bs_\b,_\b _\bs_\bc_\ba_\bl_\ba_\br_\bs_\b,_\b _\ba_\bn_\bd_\b _\bd_\ba_\bt_\ba_\b _\bt_\by_\bp_\be_\bs\n     * _\bI_\bn_\bd_\be_\bx_\b _\bo_\bb_\bj_\be_\bc_\bt_\bs\n     * _\bD_\ba_\bt_\be_\b _\bo_\bf_\bf_\bs_\be_\bt_\bs\n     * _\bW_\bi_\bn_\bd_\bo_\bw\n     * _\bG_\br_\bo_\bu_\bp_\bB_\by\n     * _\bR_\be_\bs_\ba_\bm_\bp_\bl_\bi_\bn_\bg\n     * _\bS_\bt_\by_\bl_\be\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/searchindex.js", "source2": "./usr/share/doc/python-pandas-doc/html/searchindex.js", "unified_diff": null, "details": [{"source1": "js-beautify {}", "source2": "js-beautify {}", "unified_diff": "@@ -21,32 +21,32 @@\n         \"5. If every byte counts use string replacement\": [\n             [2207, \"5.-If-every-byte-counts-use-string-replacement\"]\n         ],\n         \"ADBC Driver support in to_sql and read_sql\": [\n             [2307, \"adbc-driver-support-in-to-sql-and-read-sql\"]\n         ],\n         \"API changes\": [\n-            [2263, \"api-changes\"],\n-            [2264, \"api-changes\"],\n             [2247, \"api-changes\"],\n-            [2231, \"api-changes\"],\n+            [2264, \"api-changes\"],\n+            [2263, \"api-changes\"],\n             [2232, \"api-changes\"],\n-            [2224, \"api-changes\"],\n+            [2227, \"api-changes\"],\n+            [2229, \"api-changes\"],\n             [2219, \"api-changes\"],\n             [2220, \"api-changes\"],\n-            [2226, \"api-changes\"],\n-            [2214, \"api-changes\"],\n+            [2217, \"api-changes\"],\n             [2216, \"api-changes\"],\n-            [2227, \"api-changes\"],\n+            [2214, \"api-changes\"],\n             [2215, \"api-changes\"],\n+            [2218, \"api-changes\"],\n+            [2224, \"api-changes\"],\n+            [2226, \"api-changes\"],\n+            [2231, \"api-changes\"],\n             [2223, \"api-changes\"],\n-            [2229, \"api-changes\"],\n-            [2221, \"api-changes\"],\n-            [2217, \"api-changes\"],\n-            [2218, \"api-changes\"]\n+            [2221, \"api-changes\"]\n         ],\n         \"API changes to integer indexing\": [\n             [2257, \"api-changes-to-integer-indexing\"]\n         ],\n         \"API reference\": [\n             [2172, \"api-reference\"]\n         ],\n@@ -129,18 +129,18 @@\n         \"Aggregating with a dict\": [\n             [2186, \"aggregating-with-a-dict\"]\n         ],\n         \"Aggregating with multiple functions\": [\n             [2186, \"aggregating-with-multiple-functions\"]\n         ],\n         \"Aggregation\": [\n-            [16, \"aggregation\"],\n             [19, \"aggregation\"],\n-            [2210, \"aggregation\"],\n-            [2195, \"aggregation\"]\n+            [16, \"aggregation\"],\n+            [2195, \"aggregation\"],\n+            [2210, \"aggregation\"]\n         ],\n         \"Aggregation API\": [\n             [2186, \"aggregation-api\"]\n         ],\n         \"Aggregation with User-Defined Functions\": [\n             [2195, \"aggregation-with-user-defined-functions\"]\n         ],\n@@ -266,31 +266,31 @@\n         \"Backward resample\": [\n             [2210, \"backward-resample\"]\n         ],\n         \"Backwards compatibility\": [\n             [2, \"backwards-compatibility\"]\n         ],\n         \"Backwards incompatible API changes\": [\n-            [2302, \"backwards-incompatible-api-changes\"],\n-            [2271, \"backwards-incompatible-api-changes\"],\n+            [2249, \"backwards-incompatible-api-changes\"],\n+            [2246, \"backwards-incompatible-api-changes\"],\n             [2265, \"backwards-incompatible-api-changes\"],\n+            [2271, \"backwards-incompatible-api-changes\"],\n+            [2289, \"backwards-incompatible-api-changes\"],\n             [2283, \"backwards-incompatible-api-changes\"],\n             [2298, \"backwards-incompatible-api-changes\"],\n+            [2302, \"backwards-incompatible-api-changes\"],\n             [2294, \"backwards-incompatible-api-changes\"],\n-            [2289, \"backwards-incompatible-api-changes\"],\n+            [2241, \"backwards-incompatible-api-changes\"],\n+            [2222, \"backwards-incompatible-api-changes\"],\n+            [2228, \"backwards-incompatible-api-changes\"],\n+            [2230, \"backwards-incompatible-api-changes\"],\n             [2240, \"backwards-incompatible-api-changes\"],\n             [2238, \"backwards-incompatible-api-changes\"],\n-            [2249, \"backwards-incompatible-api-changes\"],\n-            [2241, \"backwards-incompatible-api-changes\"],\n             [2235, \"backwards-incompatible-api-changes\"],\n-            [2246, \"backwards-incompatible-api-changes\"],\n-            [2230, \"backwards-incompatible-api-changes\"],\n-            [2225, \"backwards-incompatible-api-changes\"],\n-            [2228, \"backwards-incompatible-api-changes\"],\n-            [2222, \"backwards-incompatible-api-changes\"]\n+            [2225, \"backwards-incompatible-api-changes\"]\n         ],\n         \"Bar charts\": [\n             [2207, \"Bar-charts\"]\n         ],\n         \"Bar plots\": [\n             [2211, \"bar-plots\"]\n         ],\n@@ -337,16 +337,16 @@\n         \"Better support for compressed URLs in read_csv\": [\n             [2235, \"better-support-for-compressed-urls-in-read-csv\"]\n         ],\n         \"Binary Excel (.xlsb) files\": [\n             [2199, \"binary-excel-xlsb-files\"]\n         ],\n         \"Binary operator functions\": [\n-            [2180, \"binary-operator-functions\"],\n-            [2169, \"binary-operator-functions\"]\n+            [2169, \"binary-operator-functions\"],\n+            [2180, \"binary-operator-functions\"]\n         ],\n         \"Binary ufuncs on Series now align\": [\n             [2249, \"binary-ufuncs-on-series-now-align\"]\n         ],\n         \"Binary window functions\": [\n             [2212, \"binary-window-functions\"]\n         ],\n@@ -378,108 +378,108 @@\n         \"Box plots\": [\n             [2211, \"box-plots\"]\n         ],\n         \"Breaking changes\": [\n             [2222, \"breaking-changes\"]\n         ],\n         \"Bug fixes\": [\n-            [2302, \"bug-fixes\"],\n-            [2307, \"bug-fixes\"],\n-            [2309, \"bug-fixes\"],\n-            [2303, \"bug-fixes\"],\n-            [2308, \"bug-fixes\"],\n-            [2301, \"bug-fixes\"],\n-            [2300, \"bug-fixes\"],\n-            [2305, \"bug-fixes\"],\n-            [2304, \"bug-fixes\"],\n-            [2306, \"bug-fixes\"],\n-            [2275, 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[2265, \"build-changes\"],\n-            [2243, \"build-changes\"],\n             [2249, \"build-changes\"],\n+            [2246, \"build-changes\"],\n+            [2265, \"build-changes\"],\n             [2241, \"build-changes\"],\n-            [2246, \"build-changes\"]\n+            [2243, \"build-changes\"]\n         ],\n         \"Building main branch documentation\": [\n             [4, \"building-main-branch-documentation\"]\n         ],\n         \"Building the documentation\": [\n             [4, \"building-the-documentation\"]\n         ],\n@@ -513,16 +513,16 @@\n         \"BusinessMonthEnd\": [\n             [2176, \"businessmonthend\"]\n         ],\n         \"By default Categorical.min() now returns the minimum instead of np.nan\": [\n             [2265, \"by-default-categorical-min-now-returns-the-minimum-instead-of-np-nan\"]\n         ],\n         \"By group processing\": [\n-            [16, \"by-group-processing\"],\n-            [19, \"by-group-processing\"]\n+            [19, \"by-group-processing\"],\n+            [16, \"by-group-processing\"]\n         ],\n         \"By index\": [\n             [2186, \"by-index\"]\n         ],\n         \"By indexes and values\": [\n             [2186, \"by-indexes-and-values\"]\n         ],\n@@ -563,48 +563,48 @@\n         \"Can I come back to a previous workspace?\": [\n             [6, \"can-i-come-back-to-a-previous-workspace\"]\n         ],\n         \"Can I install additional VSCode extensions?\": [\n             [6, \"can-i-install-additional-vscode-extensions\"]\n         ],\n         \"Categorical\": [\n-            [2302, \"categorical\"],\n-            [2307, \"categorical\"],\n-            [2271, \"categorical\"],\n+            [2249, \"categorical\"],\n+            [2246, \"categorical\"],\n             [2265, \"categorical\"],\n+            [2250, \"categorical\"],\n+            [2271, \"categorical\"],\n+            [2307, \"categorical\"],\n+            [2289, \"categorical\"],\n             [2283, \"categorical\"],\n-            [2298, \"categorical\"],\n             [2277, \"categorical\"],\n+            [2298, \"categorical\"],\n+            [2302, \"categorical\"],\n             [2294, \"categorical\"],\n-            [2289, \"categorical\"],\n-            [2250, \"categorical\"],\n-            [2237, \"categorical\"],\n+            [2241, \"categorical\"],\n+            [2236, \"categorical\"],\n             [2238, \"categorical\"],\n             [2239, \"categorical\"],\n-            [2249, \"categorical\"],\n-            [2241, \"categorical\"],\n-            [2246, \"categorical\"],\n-            [2236, \"categorical\"]\n+            [2237, \"categorical\"]\n         ],\n         \"Categorical accessor\": [\n             [2180, \"categorical-accessor\"]\n         ],\n         \"Categorical changes\": [\n             [2225, \"categorical-changes\"]\n         ],\n         \"Categorical components\": [\n             [2173, \"categorical-components\"]\n         ],\n         \"Categorical concatenation\": [\n             [2232, \"categorical-concatenation\"]\n         ],\n         \"Categorical data\": [\n-            [2188, \"categorical-data\"],\n             [2199, \"categorical-data\"],\n-            [2199, \"io-stata-categorical\"]\n+            [2199, \"io-stata-categorical\"],\n+            [2188, \"categorical-data\"]\n         ],\n         \"Categorical dtypes are preserved during GroupBy\": [\n             [2249, \"categorical-dtypes-are-preserved-during-groupby\"]\n         ],\n         \"Categorical index\": [\n             [2188, \"categorical-index\"]\n         ],\n@@ -711,17 +711,17 @@\n         \"Changes to str.extract\": [\n             [2230, \"changes-to-str-extract\"]\n         ],\n         \"Changes to to_datetime and to_timedelta\": [\n             [2228, \"changes-to-to-datetime-and-to-timedelta\"]\n         ],\n         \"Changing case\": [\n-            [16, \"changing-case\"],\n             [19, \"changing-case\"],\n-            [17, \"changing-case\"]\n+            [17, \"changing-case\"],\n+            [16, \"changing-case\"]\n         ],\n         \"Changing the sort parameter for Index set operations\": [\n             [2247, \"changing-the-sort-parameter-for-index-set-operations\"]\n         ],\n         \"Chart visualization\": [\n             [2211, \"chart-visualization\"]\n         ],\n@@ -754,16 +754,16 @@\n         \"Column order is preserved when passing a list of dicts to DataFrame\": [\n             [2249, \"column-order-is-preserved-when-passing-a-list-of-dicts-to-dataframe\"]\n         ],\n         \"Column selection, addition, deletion\": [\n             [2191, \"column-selection-addition-deletion\"]\n         ],\n         \"Combining / comparing / joining / merging\": [\n-            [2180, \"combining-comparing-joining-merging\"],\n-            [2169, \"combining-comparing-joining-merging\"]\n+            [2169, \"combining-comparing-joining-merging\"],\n+            [2180, \"combining-comparing-joining-merging\"]\n         ],\n         \"Combining / joining / set operations\": [\n             [2173, \"combining-joining-set-operations\"]\n         ],\n         \"Combining aliases\": [\n             [2210, \"combining-aliases\"]\n         ],\n@@ -846,17 +846,17 @@\n             [22, \"compression\"],\n             [2199, \"compression\"]\n         ],\n         \"Computation\": [\n             [22, \"computation\"]\n         ],\n         \"Computations / descriptive stats\": [\n+            [2169, \"computations-descriptive-stats\"],\n             [2179, \"computations-descriptive-stats\"],\n-            [2180, \"computations-descriptive-stats\"],\n-            [2169, \"computations-descriptive-stats\"]\n+            [2180, \"computations-descriptive-stats\"]\n         ],\n         \"Computing rolling pairwise covariances and correlations\": [\n             [2212, \"computing-rolling-pairwise-covariances-and-correlations\"]\n         ],\n         \"Concat\": [\n             [2184, \"concat\"]\n         ],\n@@ -926,27 +926,27 @@\n         \"Console display\": [\n             [2191, \"console-display\"]\n         ],\n         \"Consortium Standard\": [\n             [22, \"consortium-standard\"]\n         ],\n         \"Constructing a DataFrame from values\": [\n-            [16, \"constructing-a-dataframe-from-values\"],\n             [19, \"constructing-a-dataframe-from-values\"],\n-            [17, \"constructing-a-dataframe-from-values\"]\n+            [17, \"constructing-a-dataframe-from-values\"],\n+            [16, \"constructing-a-dataframe-from-values\"]\n         ],\n         \"Construction\": [\n             [2198, \"construction\"]\n         ],\n         \"Construction with datetime64 or timedelta64 dtype with unsupported resolution\": [\n             [2298, \"construction-with-datetime64-or-timedelta64-dtype-with-unsupported-resolution\"]\n         ],\n         \"Constructor\": [\n-            [2180, 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\"conventions-for-the-examples\"]\n         ],\n         \"Conversion\": [\n-            [2302, \"conversion\"],\n-            [2307, \"conversion\"],\n-            [2271, \"conversion\"],\n+            [2249, \"conversion\"],\n+            [2246, \"conversion\"],\n             [2265, \"conversion\"],\n+            [2250, \"conversion\"],\n+            [2271, \"conversion\"],\n+            [2307, \"conversion\"],\n+            [2289, \"conversion\"],\n             [2283, \"conversion\"],\n-            [2298, \"conversion\"],\n             [2277, \"conversion\"],\n+            [2298, \"conversion\"],\n+            [2302, \"conversion\"],\n             [2294, \"conversion\"],\n-            [2289, \"conversion\"],\n-            [2250, \"conversion\"],\n-            [2237, \"conversion\"],\n-            [2238, \"conversion\"],\n-            [2239, \"conversion\"],\n-            [2249, \"conversion\"],\n-            [2235, \"conversion\"],\n-            [2246, \"conversion\"],\n-            [2236, \"conversion\"],\n             [2173, \"conversion\"],\n             [2173, \"id4\"],\n             [2173, \"id5\"],\n-            [2180, \"conversion\"],\n             [2169, \"conversion\"],\n+            [2236, \"conversion\"],\n+            [2238, \"conversion\"],\n+            [2239, \"conversion\"],\n+            [2237, \"conversion\"],\n+            [2235, \"conversion\"],\n+            [2180, \"conversion\"],\n             [2201, \"conversion\"]\n         ],\n         \"Conversions\": [\n             [2209, \"conversions\"]\n         ],\n         \"Converting between representations\": [\n             [2210, \"converting-between-representations\"]\n@@ -1115,34 +1115,34 @@\n         \"Converting to markdown\": [\n             [2265, \"converting-to-markdown\"]\n         ],\n         \"Converting to timestamps\": [\n             [2210, \"converting-to-timestamps\"]\n         ],\n         \"Copies vs. in place operations\": [\n-            [18, \"copies-vs-in-place-operations\"],\n-            [16, \"copies-vs-in-place-operations\"],\n             [19, \"copies-vs-in-place-operations\"],\n-            [17, \"copies-vs-in-place-operations\"]\n+            [17, \"copies-vs-in-place-operations\"],\n+            [18, \"copies-vs-in-place-operations\"],\n+            [16, \"copies-vs-in-place-operations\"]\n         ],\n         \"Copy on Write\": [\n             [2294, \"copy-on-write\"]\n         ],\n         \"Copy on write\": [\n             [7, \"copy-on-write\"]\n         ],\n         \"Copy-on-Write\": [\n             [2307, \"copy-on-write\"]\n         ],\n         \"Copy-on-Write (CoW)\": [\n             [2190, \"copy-on-write-cow\"]\n         ],\n         \"Copy-on-Write improvements\": [\n-            [2302, \"copy-on-write-improvements\"],\n-            [2298, \"copy-on-write-improvements\"]\n+            [2298, \"copy-on-write-improvements\"],\n+            [2302, \"copy-on-write-improvements\"]\n         ],\n         \"Copy-on-Write optimizations\": [\n             [2190, \"copy-on-write-optimizations\"]\n         ],\n         \"Copying\": [\n             [2186, \"copying\"]\n         ],\n@@ -1225,45 +1225,45 @@\n         \"Data alignment and using reindex\": [\n             [2185, \"data-alignment-and-using-reindex\"]\n         ],\n         \"Data conversion\": [\n             [2199, \"data-conversion\"]\n         ],\n         \"Data input / output\": [\n-            [16, \"data-input-output\"],\n             [19, \"data-input-output\"],\n-            [17, \"data-input-output\"]\n+            [17, \"data-input-output\"],\n+            [16, \"data-input-output\"]\n         ],\n         \"Data interop\": [\n             [16, \"data-interop\"]\n         ],\n         \"Data manipulations\": [\n             [2170, \"data-manipulations\"]\n         ],\n         \"Data munging\": [\n             [2188, \"data-munging\"]\n         ],\n         \"Data operations\": [\n-            [16, \"data-operations\"],\n             [19, \"data-operations\"],\n-            [17, \"data-operations\"]\n+            [17, \"data-operations\"],\n+            [16, \"data-operations\"]\n         ],\n         \"Data structures\": [\n-            [16, \"data-structures\"],\n+            [34, \"data-structures\"],\n             [19, \"data-structures\"],\n             [17, \"data-structures\"],\n-            [34, \"data-structures\"]\n+            [16, \"data-structures\"]\n         ],\n         \"Data type introspection\": [\n             [2167, \"data-type-introspection\"]\n         ],\n         \"DataFrame\": [\n-            [16, \"dataframe\"],\n             [19, \"dataframe\"],\n             [17, \"dataframe\"],\n+            [16, \"dataframe\"],\n             [2169, \"dataframe\"],\n             [2191, \"dataframe\"]\n         ],\n         \"DataFrame GroupBy ffill/bfill no longer return group labels\": [\n             [2249, \"dataframe-groupby-ffill-bfill-no-longer-return-group-labels\"]\n         ],\n         \"DataFrame arithmetic operations broadcasting changes\": [\n@@ -1356,17 +1356,17 @@\n         \"DataTypes\": [\n             [2199, \"datatypes\"]\n         ],\n         \"Date Handling\": [\n             [2199, \"date-handling\"]\n         ],\n         \"Date functionality\": [\n-            [16, \"date-functionality\"],\n             [19, \"date-functionality\"],\n-            [17, \"date-functionality\"]\n+            [17, \"date-functionality\"],\n+            [16, \"date-functionality\"]\n         ],\n         \"Date handling\": [\n             [2199, \"id1\"]\n         ],\n         \"Date offsets\": [\n             [2176, \"date-offsets\"]\n         ],\n@@ -1403,30 +1403,30 @@\n         \"Datetime-like\": [\n             [2277, \"datetime-like\"]\n         ],\n         \"DatetimeIndex\": [\n             [2173, \"datetimeindex\"]\n         ],\n         \"Datetimelike\": [\n-            [2302, \"datetimelike\"],\n-            [2307, \"datetimelike\"],\n-            [2271, \"datetimelike\"],\n+            [2249, \"datetimelike\"],\n+            [2246, \"datetimelike\"],\n             [2265, \"datetimelike\"],\n+            [2250, \"datetimelike\"],\n+            [2271, \"datetimelike\"],\n+            [2307, \"datetimelike\"],\n+            [2289, \"datetimelike\"],\n             [2283, \"datetimelike\"],\n             [2298, \"datetimelike\"],\n+            [2302, \"datetimelike\"],\n             [2294, \"datetimelike\"],\n-            [2289, \"datetimelike\"],\n-            [2250, \"datetimelike\"],\n-            [2249, \"datetimelike\"],\n-            [2241, \"datetimelike\"],\n-            [2246, \"datetimelike\"]\n+            [2241, \"datetimelike\"]\n         ],\n         \"Datetimelike API changes\": [\n-            [2241, \"datetimelike-api-changes\"],\n-            [2246, \"datetimelike-api-changes\"]\n+            [2246, \"datetimelike-api-changes\"],\n+            [2241, \"datetimelike-api-changes\"]\n         ],\n         \"Datetimelike properties\": [\n             [2180, \"datetimelike-properties\"]\n         ],\n         \"Datetimelike rounding\": [\n             [2230, \"datetimelike-rounding\"]\n         ],\n@@ -1484,16 +1484,16 @@\n         \"Density plot\": [\n             [2211, \"density-plot\"]\n         ],\n         \"Dependencies\": [\n             [22, \"dependencies\"]\n         ],\n         \"Dependencies have increased minimum versions\": [\n-            [2238, \"dependencies-have-increased-minimum-versions\"],\n-            [2241, \"dependencies-have-increased-minimum-versions\"]\n+            [2241, \"dependencies-have-increased-minimum-versions\"],\n+            [2238, \"dependencies-have-increased-minimum-versions\"]\n         ],\n         \"Deprecate .ix\": [\n             [2235, \"deprecate-ix\"]\n         ],\n         \"Deprecate .plotting\": [\n             [2235, \"deprecate-plotting\"]\n         ],\n@@ -1522,46 +1522,46 @@\n         \"Deprecated parsing datetimes with mixed time zones\": [\n             [2302, \"deprecated-parsing-datetimes-with-mixed-time-zones\"]\n         ],\n         \"Deprecated silent upcasting in setitem-like Series operations\": [\n             [2302, \"deprecated-silent-upcasting-in-setitem-like-series-operations\"]\n         ],\n         \"Deprecations\": [\n-            [2302, \"deprecations\"],\n-            [2307, \"deprecations\"],\n-            [2304, \"deprecations\"],\n+            [2249, \"deprecations\"],\n+            [2246, \"deprecations\"],\n+            [2265, \"deprecations\"],\n             [2266, \"deprecations\"],\n             [2271, \"deprecations\"],\n-            [2265, \"deprecations\"],\n+            [2307, \"deprecations\"],\n+            [2289, \"deprecations\"],\n             [2283, \"deprecations\"],\n-            [2298, \"deprecations\"],\n             [2277, \"deprecations\"],\n+            [2304, \"deprecations\"],\n+            [2298, \"deprecations\"],\n+            [2302, \"deprecations\"],\n             [2294, \"deprecations\"],\n-            [2289, \"deprecations\"],\n-            [2231, \"deprecations\"],\n-            [2238, \"deprecations\"],\n             [2232, \"deprecations\"],\n-            [2239, \"deprecations\"],\n-            [2249, \"deprecations\"],\n             [2241, \"deprecations\"],\n-            [2235, \"deprecations\"],\n-            [2246, \"deprecations\"],\n+            [2229, \"deprecations\"],\n+            [2222, \"deprecations\"],\n             [2219, \"deprecations\"],\n             [2220, \"deprecations\"],\n+            [2228, \"deprecations\"],\n             [2230, \"deprecations\"],\n+            [2218, \"deprecations\"],\n             [2226, \"deprecations\"],\n-            [2225, \"deprecations\"],\n-            [2228, \"deprecations\"],\n-            [2229, \"deprecations\"],\n-            [2222, \"deprecations\"],\n-            [2218, \"deprecations\"]\n+            [2231, \"deprecations\"],\n+            [2238, \"deprecations\"],\n+            [2239, \"deprecations\"],\n+            [2235, \"deprecations\"],\n+            [2225, \"deprecations\"]\n         ],\n         \"Description\": [\n-            [2188, \"description\"],\n-            [2190, \"description\"]\n+            [2190, \"description\"],\n+            [2188, \"description\"]\n         ],\n         \"Descriptive statistics\": [\n             [2186, \"descriptive-statistics\"]\n         ],\n         \"Developer\": [\n             [9, \"developer\"]\n         ],\n@@ -1604,29 +1604,29 @@\n         \"Disallowing Duplicate Labels\": [\n             [2192, \"disallowing-duplicate-labels\"]\n         ],\n         \"Discretization and quantiling\": [\n             [2186, \"discretization-and-quantiling\"]\n         ],\n         \"Disk vs memory\": [\n-            [16, \"disk-vs-memory\"],\n-            [19, \"disk-vs-memory\"]\n+            [19, \"disk-vs-memory\"],\n+            [16, \"disk-vs-memory\"]\n         ],\n         \"Display alignment with Unicode East Asian width\": [\n             [2228, \"display-alignment-with-unicode-east-asian-width\"]\n         ],\n         \"Display changes\": [\n             [2220, \"display-changes\"]\n         ],\n         \"Do something with a DataFrame or Series\": [\n             [23, \"do-something-with-a-dataframe-or-series\"]\n         ],\n         \"Documentation changes\": [\n-            [2238, \"documentation-changes\"],\n-            [2241, \"documentation-changes\"]\n+            [2241, \"documentation-changes\"],\n+            [2238, \"documentation-changes\"]\n         ],\n         \"Documentation improvements\": [\n             [2265, \"documentation-improvements\"]\n         ],\n         \"Documentation:\": [\n             [4, \"documentation\"]\n         ],\n@@ -1639,21 +1639,21 @@\n         \"Downsampling\": [\n             [2230, \"downsampling\"]\n         ],\n         \"Drop Duplicates\": [\n             [17, \"drop-duplicates\"]\n         ],\n         \"Drop a column\": [\n-            [16, \"drop-a-column\"],\n             [19, \"drop-a-column\"],\n-            [17, \"drop-a-column\"]\n+            [17, \"drop-a-column\"],\n+            [16, \"drop-a-column\"]\n         ],\n         \"Drop rows with missing values\": [\n-            [16, \"drop-rows-with-missing-values\"],\n-            [19, \"drop-rows-with-missing-values\"]\n+            [19, \"drop-rows-with-missing-values\"],\n+            [16, \"drop-rows-with-missing-values\"]\n         ],\n         \"Dropping labels from an axis\": [\n             [2186, \"dropping-labels-from-an-axis\"]\n         ],\n         \"Dropping missing data\": [\n             [2201, \"dropping-missing-data\"]\n         ],\n@@ -1702,39 +1702,39 @@\n         \"Endpoints are inclusive\": [\n             [2185, \"endpoints-are-inclusive\"]\n         ],\n         \"Engine connection examples\": [\n             [2199, \"engine-connection-examples\"]\n         ],\n         \"Enhancements\": [\n-            [2302, \"enhancements\"],\n-            [2307, \"enhancements\"],\n-            [2308, \"enhancements\"],\n-            [2271, \"enhancements\"],\n+            [2249, \"enhancements\"],\n+            [2246, \"enhancements\"],\n             [2274, \"enhancements\"],\n             [2265, \"enhancements\"],\n+            [2271, \"enhancements\"],\n+            [2307, \"enhancements\"],\n+            [2289, \"enhancements\"],\n             [2283, \"enhancements\"],\n-            [2298, \"enhancements\"],\n             [2277, \"enhancements\"],\n+            [2298, \"enhancements\"],\n+            [2302, \"enhancements\"],\n             [2294, \"enhancements\"],\n-            [2289, \"enhancements\"],\n+            [2308, \"enhancements\"],\n+            [2229, \"enhancements\"],\n+            [2222, \"enhancements\"],\n             [2234, \"enhancements\"],\n-            [2249, \"enhancements\"],\n-            [2246, \"enhancements\"],\n-            [2236, \"enhancements\"],\n-            [2224, \"enhancements\"],\n             [2219, \"enhancements\"],\n             [2220, \"enhancements\"],\n-            [2226, \"enhancements\"],\n             [2216, \"enhancements\"],\n+            [2218, \"enhancements\"],\n+            [2224, \"enhancements\"],\n+            [2226, \"enhancements\"],\n+            [2236, \"enhancements\"],\n             [2223, \"enhancements\"],\n-            [2229, \"enhancements\"],\n-            [2221, \"enhancements\"],\n-            [2222, \"enhancements\"],\n-            [2218, \"enhancements\"]\n+            [2221, \"enhancements\"]\n         ],\n         \"Enhancing performance\": [\n             [2193, \"enhancing-performance\"]\n         ],\n         \"Enumerate group items\": [\n             [2195, \"enumerate-group-items\"]\n         ],\n@@ -1804,16 +1804,16 @@\n         ],\n         \"Expanding window functions\": [\n             [2183, \"expanding-window-functions\"]\n         ],\n         \"Experimental\": [\n             [2219, \"experimental\"],\n             [2220, \"experimental\"],\n-            [2221, \"experimental\"],\n-            [2218, \"experimental\"]\n+            [2218, \"experimental\"],\n+            [2221, \"experimental\"]\n         ],\n         \"Experimental NA scalar to denote missing values\": [\n             [2265, \"experimental-na-scalar-to-denote-missing-values\"]\n         ],\n         \"Experimental features\": [\n             [2217, \"experimental-features\"]\n         ],\n@@ -1832,17 +1832,17 @@\n         \"Export to Excel\": [\n             [2207, \"Export-to-Excel\"]\n         ],\n         \"Export to LaTeX\": [\n             [2207, \"Export-to-LaTeX\"]\n         ],\n         \"Exporting data\": [\n-            [16, \"exporting-data\"],\n             [19, \"exporting-data\"],\n-            [17, \"exporting-data\"]\n+            [17, \"exporting-data\"],\n+            [16, \"exporting-data\"]\n         ],\n         \"Expression evaluation limitations with numexpr\": [\n             [2193, \"expression-evaluation-limitations-with-numexpr\"]\n         ],\n         \"Expression evaluation via eval()\": [\n             [2193, \"expression-evaluation-via-eval\"]\n         ],\n@@ -1861,24 +1861,24 @@\n         \"Extension type changes\": [\n             [2246, \"extension-type-changes\"]\n         ],\n         \"Extension types\": [\n             [10, \"extension-types\"]\n         ],\n         \"ExtensionArray\": [\n-            [2302, \"extensionarray\"],\n-            [10, \"extensionarray\"],\n-            [2271, \"extensionarray\"],\n+            [2249, \"extensionarray\"],\n             [2265, \"extensionarray\"],\n+            [2271, \"extensionarray\"],\n+            [10, \"extensionarray\"],\n+            [2289, \"extensionarray\"],\n             [2283, \"extensionarray\"],\n-            [2298, \"extensionarray\"],\n             [2277, \"extensionarray\"],\n-            [2294, \"extensionarray\"],\n-            [2289, \"extensionarray\"],\n-            [2249, \"extensionarray\"]\n+            [2298, \"extensionarray\"],\n+            [2302, \"extensionarray\"],\n+            [2294, \"extensionarray\"]\n         ],\n         \"ExtensionArray operator support\": [\n             [10, \"extensionarray-operator-support\"]\n         ],\n         \"ExtensionDtype\": [\n             [10, \"extensiondtype\"]\n         ],\n@@ -1888,22 +1888,22 @@\n         \"Extract all matches in each subject (extractall)\": [\n             [2208, \"extract-all-matches-in-each-subject-extractall\"]\n         ],\n         \"Extract first match in each subject (extract)\": [\n             [2208, \"extract-first-match-in-each-subject-extract\"]\n         ],\n         \"Extracting nth word\": [\n-            [16, \"extracting-nth-word\"],\n             [19, \"extracting-nth-word\"],\n-            [17, \"extracting-nth-word\"]\n+            [17, \"extracting-nth-word\"],\n+            [16, \"extracting-nth-word\"]\n         ],\n         \"Extracting substring by position\": [\n-            [16, \"extracting-substring-by-position\"],\n             [19, \"extracting-substring-by-position\"],\n-            [17, \"extracting-substring-by-position\"]\n+            [17, \"extracting-substring-by-position\"],\n+            [16, \"extracting-substring-by-position\"]\n         ],\n         \"Extracting substrings\": [\n             [2208, \"extracting-substrings\"]\n         ],\n         \"Extraction of matching patterns from strings\": [\n             [2241, \"extraction-of-matching-patterns-from-strings\"]\n         ],\n@@ -1956,95 +1956,95 @@\n         \"Filling missing data\": [\n             [2201, \"filling-missing-data\"]\n         ],\n         \"Filling while reindexing\": [\n             [2186, \"filling-while-reindexing\"]\n         ],\n         \"Filtering\": [\n-            [16, \"filtering\"],\n             [19, \"filtering\"],\n-            [17, \"filtering\"]\n+            [17, \"filtering\"],\n+            [16, \"filtering\"]\n         ],\n         \"Filtering columns (usecols)\": [\n             [2199, \"filtering-columns-usecols\"]\n         ],\n         \"Filtration\": [\n             [2195, \"filtration\"]\n         ],\n         \"Find and Replace\": [\n             [17, \"find-and-replace\"]\n         ],\n         \"Finding an issue to contribute to\": [\n             [1, \"finding-an-issue-to-contribute-to\"]\n         ],\n         \"Finding length of string\": [\n-            [16, \"finding-length-of-string\"],\n             [19, \"finding-length-of-string\"],\n-            [17, \"finding-length-of-string\"]\n+            [17, \"finding-length-of-string\"],\n+            [16, \"finding-length-of-string\"]\n         ],\n         \"Finding position of substring\": [\n-            [16, \"finding-position-of-substring\"],\n             [19, \"finding-position-of-substring\"],\n-            [17, \"finding-position-of-substring\"]\n+            [17, \"finding-position-of-substring\"],\n+            [16, \"finding-position-of-substring\"]\n         ],\n         \"Fine-grained NumPy errstate\": [\n             [2232, \"fine-grained-numpy-errstate\"]\n         ],\n         \"Finer Control with Slicing\": [\n             [2207, \"Finer-Control-with-Slicing\"]\n         ],\n         \"Fixed format\": [\n             [2199, \"fixed-format\"]\n         ],\n         \"Fixed regressions\": [\n-            [2309, \"fixed-regressions\"],\n-            [2303, \"fixed-regressions\"],\n-            [2308, \"fixed-regressions\"],\n-            [2301, \"fixed-regressions\"],\n-            [2300, \"fixed-regressions\"],\n-            [2305, \"fixed-regressions\"],\n-            [2304, \"fixed-regressions\"],\n-            [2306, \"fixed-regressions\"],\n-            [2275, \"fixed-regressions\"],\n-            [2267, \"fixed-regressions\"],\n-            [2266, \"fixed-regressions\"],\n-            [2273, \"fixed-regressions\"],\n-            [2268, \"fixed-regressions\"],\n+            [2245, \"fixed-regressions\"],\n+            [2274, \"fixed-regressions\"],\n             [2276, \"fixed-regressions\"],\n-            [2269, \"fixed-regressions\"],\n+            [2248, \"fixed-regressions\"],\n             [2272, \"fixed-regressions\"],\n-            [2274, \"fixed-regressions\"],\n+            [2266, \"fixed-regressions\"],\n+            [2247, \"fixed-regressions\"],\n+            [2267, \"fixed-regressions\"],\n+            [2269, \"fixed-regressions\"],\n             [2270, \"fixed-regressions\"],\n-            [2293, \"fixed-regressions\"],\n-            [2286, \"fixed-regressions\"],\n+            [2275, \"fixed-regressions\"],\n+            [2268, \"fixed-regressions\"],\n+            [2273, \"fixed-regressions\"],\n             [2290, \"fixed-regressions\"],\n-            [2287, \"fixed-regressions\"],\n+            [2296, \"fixed-regressions\"],\n+            [2309, \"fixed-regressions\"],\n             [2282, \"fixed-regressions\"],\n-            [2299, \"fixed-regressions\"],\n-            [2288, \"fixed-regressions\"],\n-            [2285, \"fixed-regressions\"],\n-            [2279, \"fixed-regressions\"],\n-            [2291, \"fixed-regressions\"],\n+            [2306, \"fixed-regressions\"],\n+            [2292, \"fixed-regressions\"],\n             [2295, \"fixed-regressions\"],\n-            [2280, \"fixed-regressions\"],\n-            [2297, \"fixed-regressions\"],\n-            [2296, \"fixed-regressions\"],\n+            [2304, \"fixed-regressions\"],\n+            [2278, \"fixed-regressions\"],\n+            [2293, \"fixed-regressions\"],\n+            [2305, \"fixed-regressions\"],\n+            [2301, \"fixed-regressions\"],\n             [2281, \"fixed-regressions\"],\n+            [2279, \"fixed-regressions\"],\n+            [2303, \"fixed-regressions\"],\n+            [2297, \"fixed-regressions\"],\n+            [2287, \"fixed-regressions\"],\n+            [2299, \"fixed-regressions\"],\n+            [2286, \"fixed-regressions\"],\n             [2284, \"fixed-regressions\"],\n-            [2292, \"fixed-regressions\"],\n-            [2278, \"fixed-regressions\"],\n-            [2247, \"fixed-regressions\"],\n-            [2242, \"fixed-regressions\"],\n-            [2245, \"fixed-regressions\"],\n-            [2248, \"fixed-regressions\"],\n-            [2243, \"fixed-regressions\"]\n+            [2308, \"fixed-regressions\"],\n+            [2285, \"fixed-regressions\"],\n+            [2288, \"fixed-regressions\"],\n+            [2280, \"fixed-regressions\"],\n+            [2291, \"fixed-regressions\"],\n+            [2300, \"fixed-regressions\"],\n+            [2243, \"fixed-regressions\"],\n+            [2242, \"fixed-regressions\"]\n         ],\n         \"Flags\": [\n-            [2180, \"flags\"],\n-            [2169, \"flags\"]\n+            [2169, \"flags\"],\n+            [2180, \"flags\"]\n         ],\n         \"Flat file\": [\n             [2174, \"flat-file\"]\n         ],\n         \"Flexible apply\": [\n             [2195, \"flexible-apply\"]\n         ],\n@@ -2075,23 +2075,23 @@\n         \"Formatting of integers in FloatIndex\": [\n             [2230, \"formatting-of-integers-in-floatindex\"]\n         ],\n         \"Formatting the Display\": [\n             [2207, \"Formatting-the-Display\"]\n         ],\n         \"Forward fill from previous rows\": [\n-            [16, \"forward-fill-from-previous-rows\"],\n-            [19, \"forward-fill-from-previous-rows\"]\n+            [19, \"forward-fill-from-previous-rows\"],\n+            [16, \"forward-fill-from-previous-rows\"]\n         ],\n         \"Frequencies\": [\n             [2176, \"frequencies\"]\n         ],\n         \"Frequency conversion\": [\n-            [2209, \"frequency-conversion\"],\n-            [2210, \"frequency-conversion\"]\n+            [2210, \"frequency-conversion\"],\n+            [2209, \"frequency-conversion\"]\n         ],\n         \"Frequency conversion and resampling with PeriodIndex\": [\n             [2210, \"frequency-conversion-and-resampling-with-periodindex\"]\n         ],\n         \"Frequently Asked Questions (FAQ)\": [\n             [2194, \"frequently-asked-questions-faq\"]\n         ],\n@@ -2133,16 +2133,16 @@\n             [2179, \"function-application\"],\n             [2186, \"function-application\"]\n         ],\n         \"Function application helper\": [\n             [2171, \"function-application-helper\"]\n         ],\n         \"Function application, GroupBy & window\": [\n-            [2180, \"function-application-groupby-window\"],\n-            [2169, \"function-application-groupby-window\"]\n+            [2169, \"function-application-groupby-window\"],\n+            [2180, \"function-application-groupby-window\"]\n         ],\n         \"Function get_dummies now supports dtype argument\": [\n             [2241, \"function-get-dummies-now-supports-dtype-argument\"]\n         ],\n         \"Function merge_asof for asof-style time-series joining\": [\n             [2232, \"function-merge-asof-for-asof-style-time-series-joining\"]\n         ],\n@@ -2167,17 +2167,17 @@\n         \"General plot style arguments\": [\n             [2211, \"general-plot-style-arguments\"]\n         ],\n         \"General rules\": [\n             [3, \"general-rules\"]\n         ],\n         \"General terminology translation\": [\n-            [16, \"general-terminology-translation\"],\n             [19, \"general-terminology-translation\"],\n-            [17, \"general-terminology-translation\"]\n+            [17, \"general-terminology-translation\"],\n+            [16, \"general-terminology-translation\"]\n         ],\n         \"Generating ranges of intervals\": [\n             [2185, \"generating-ranges-of-intervals\"]\n         ],\n         \"Generating ranges of time deltas\": [\n             [2209, \"generating-ranges-of-time-deltas\"]\n         ],\n@@ -2229,16 +2229,16 @@\n             [2184, \"gotchas\"],\n             [2188, \"gotchas\"]\n         ],\n         \"Group by: split-apply-combine\": [\n             [2195, \"group-by-split-apply-combine\"]\n         ],\n         \"GroupBy\": [\n-            [16, \"groupby\"],\n             [19, \"groupby\"],\n+            [16, \"groupby\"],\n             [2171, \"groupby\"]\n         ],\n         \"GroupBy API changes\": [\n             [2220, \"groupby-api-changes\"]\n         ],\n         \"GroupBy aggregation with multiple lambdas\": [\n             [2249, \"groupby-aggregation-with-multiple-lambdas\"]\n@@ -2273,44 +2273,44 @@\n         \"GroupBy with MultiIndex\": [\n             [2195, \"groupby-with-multiindex\"]\n         ],\n         \"GroupBy.apply on DataFrame evaluates first group only once\": [\n             [2249, \"groupby-apply-on-dataframe-evaluates-first-group-only-once\"]\n         ],\n         \"GroupBy/resample/rolling\": [\n-            [2271, \"groupby-resample-rolling\"],\n+            [2249, \"groupby-resample-rolling\"],\n+            [2246, \"groupby-resample-rolling\"],\n+            [2252, \"groupby-resample-rolling\"],\n             [2265, \"groupby-resample-rolling\"],\n+            [2251, \"groupby-resample-rolling\"],\n             [2250, \"groupby-resample-rolling\"],\n-            [2238, \"groupby-resample-rolling\"],\n-            [2252, \"groupby-resample-rolling\"],\n-            [2239, \"groupby-resample-rolling\"],\n-            [2249, \"groupby-resample-rolling\"],\n+            [2271, \"groupby-resample-rolling\"],\n             [2241, \"groupby-resample-rolling\"],\n-            [2235, \"groupby-resample-rolling\"],\n-            [2246, \"groupby-resample-rolling\"],\n             [2236, \"groupby-resample-rolling\"],\n-            [2251, \"groupby-resample-rolling\"]\n+            [2238, \"groupby-resample-rolling\"],\n+            [2239, \"groupby-resample-rolling\"],\n+            [2235, \"groupby-resample-rolling\"]\n         ],\n         \"Groupby by indexer to \\u2018resample\\u2019 data\": [\n             [2195, \"groupby-by-indexer-to-resample-data\"]\n         ],\n         \"Groupby methods agg and transform no longer changes return dtype for callables\": [\n             [2283, \"groupby-methods-agg-and-transform-no-longer-changes-return-dtype-for-callables\"]\n         ],\n         \"Groupby positional indexing\": [\n             [2289, \"groupby-positional-indexing\"]\n         ],\n         \"Groupby/resample/rolling\": [\n-            [2302, \"groupby-resample-rolling\"],\n             [2307, \"groupby-resample-rolling\"],\n+            [2289, \"groupby-resample-rolling\"],\n             [2283, \"groupby-resample-rolling\"],\n-            [2298, \"groupby-resample-rolling\"],\n             [2277, \"groupby-resample-rolling\"],\n-            [2294, \"groupby-resample-rolling\"],\n-            [2289, \"groupby-resample-rolling\"]\n+            [2298, \"groupby-resample-rolling\"],\n+            [2302, \"groupby-resample-rolling\"],\n+            [2294, \"groupby-resample-rolling\"]\n         ],\n         \"Grouper and resample now supports the arguments origin and offset\": [\n             [2271, \"grouper-and-resample-now-supports-the-arguments-origin-and-offset\"]\n         ],\n         \"Grouping\": [\n             [2184, \"grouping\"]\n         ],\n@@ -2469,55 +2469,55 @@\n         \"I authenticated through GitHub but I still cannot commit to the repository through Gitpod.\": [\n             [6, \"i-authenticated-through-github-but-i-still-cannot-commit-to-the-repository-through-gitpod\"]\n         ],\n         \"I registered on Gitpod but I still cannot see a Gitpod button in my repositories.\": [\n             [6, \"i-registered-on-gitpod-but-i-still-cannot-see-a-gitpod-button-in-my-repositories\"]\n         ],\n         \"I/O\": [\n-            [2302, \"i-o\"],\n             [2307, \"i-o\"],\n+            [2289, \"i-o\"],\n             [2283, \"i-o\"],\n-            [2298, \"i-o\"],\n             [2277, \"i-o\"],\n-            [2294, \"i-o\"],\n-            [2289, \"i-o\"]\n+            [2298, \"i-o\"],\n+            [2302, \"i-o\"],\n+            [2294, \"i-o\"]\n         ],\n         \"I/O Reading\": [\n             [2203, \"i-o-reading\"]\n         ],\n         \"INNER JOIN\": [\n             [18, \"inner-join\"]\n         ],\n         \"IO\": [\n-            [2271, \"io\"],\n+            [2249, \"io\"],\n+            [2246, \"io\"],\n             [2265, \"io\"],\n+            [2251, \"io\"],\n             [2250, \"io\"],\n-            [2237, \"io\"],\n-            [2238, \"io\"],\n-            [2239, \"io\"],\n-            [2249, \"io\"],\n+            [2271, \"io\"],\n             [2241, \"io\"],\n-            [2235, \"io\"],\n-            [2246, \"io\"],\n             [2236, \"io\"],\n-            [2251, \"io\"]\n+            [2238, \"io\"],\n+            [2239, \"io\"],\n+            [2237, \"io\"],\n+            [2235, \"io\"]\n         ],\n         \"IO and LZMA\": [\n             [2250, \"io-and-lzma\"]\n         ],\n         \"IO enhancements\": [\n             [2217, \"io-enhancements\"]\n         ],\n         \"IO tools (text, CSV, HDF5, \\u2026)\": [\n             [2199, \"io-tools-text-csv-hdf5\"]\n         ],\n         \"If/then logic\": [\n-            [16, \"if-then-logic\"],\n             [19, \"if-then-logic\"],\n-            [17, \"if-then-logic\"]\n+            [17, \"if-then-logic\"],\n+            [16, \"if-then-logic\"]\n         ],\n         \"Ignoring dtypes in concat with empty or all-NA columns\": [\n             [2289, \"ignoring-dtypes-in-concat-with-empty-or-all-na-columns\"]\n         ],\n         \"Ignoring indexes on the concatenation axis\": [\n             [2200, \"ignoring-indexes-on-the-concatenation-axis\"]\n         ],\n@@ -2548,36 +2548,36 @@\n         \"Incompatible Index type unions\": [\n             [2249, \"incompatible-index-type-unions\"]\n         ],\n         \"Inconsistent date string parsing\": [\n             [2289, \"inconsistent-date-string-parsing\"]\n         ],\n         \"Increased minimum version for Python\": [\n-            [2302, \"increased-minimum-version-for-python\"],\n             [2265, \"increased-minimum-version-for-python\"],\n+            [2289, \"increased-minimum-version-for-python\"],\n             [2277, \"increased-minimum-version-for-python\"],\n-            [2289, \"increased-minimum-version-for-python\"]\n+            [2302, \"increased-minimum-version-for-python\"]\n         ],\n         \"Increased minimum versions for dependencies\": [\n-            [2302, \"increased-minimum-versions-for-dependencies\"],\n-            [2307, \"increased-minimum-versions-for-dependencies\"],\n-            [2271, \"increased-minimum-versions-for-dependencies\"],\n+            [2249, \"increased-minimum-versions-for-dependencies\"],\n+            [2246, \"increased-minimum-versions-for-dependencies\"],\n             [2265, \"increased-minimum-versions-for-dependencies\"],\n+            [2271, \"increased-minimum-versions-for-dependencies\"],\n+            [2307, \"increased-minimum-versions-for-dependencies\"],\n+            [2289, \"increased-minimum-versions-for-dependencies\"],\n             [2283, \"increased-minimum-versions-for-dependencies\"],\n-            [2298, \"increased-minimum-versions-for-dependencies\"],\n             [2277, \"increased-minimum-versions-for-dependencies\"],\n-            [2294, \"increased-minimum-versions-for-dependencies\"],\n-            [2289, \"increased-minimum-versions-for-dependencies\"],\n-            [2249, \"increased-minimum-versions-for-dependencies\"],\n-            [2246, \"increased-minimum-versions-for-dependencies\"]\n+            [2298, \"increased-minimum-versions-for-dependencies\"],\n+            [2302, \"increased-minimum-versions-for-dependencies\"],\n+            [2294, \"increased-minimum-versions-for-dependencies\"]\n         ],\n         \"Index\": [\n-            [16, \"index\"],\n             [19, \"index\"],\n             [17, \"index\"],\n+            [16, \"index\"],\n             [2173, \"index\"]\n         ],\n         \"Index + / - no longer used for set operations\": [\n             [2232, \"index-no-longer-used-for-set-operations\"]\n         ],\n         \"Index can hold arbitrary ExtensionArrays\": [\n             [2289, \"index-can-hold-arbitrary-extensionarrays\"]\n@@ -2622,36 +2622,36 @@\n         \"Indexer dtype changes\": [\n             [2232, \"indexer-dtype-changes\"]\n         ],\n         \"Indexes\": [\n             [2199, \"indexes\"]\n         ],\n         \"Indexing\": [\n-            [2302, \"indexing\"],\n-            [2307, \"indexing\"],\n-            [12, \"indexing\"],\n-            [2271, \"indexing\"],\n+            [2249, \"indexing\"],\n+            [2246, \"indexing\"],\n             [2265, \"indexing\"],\n+            [2251, \"indexing\"],\n+            [2250, \"indexing\"],\n+            [2271, \"indexing\"],\n+            [12, \"indexing\"],\n+            [2307, \"indexing\"],\n+            [2289, \"indexing\"],\n             [2283, \"indexing\"],\n-            [2298, \"indexing\"],\n             [2277, \"indexing\"],\n+            [2298, \"indexing\"],\n+            [2302, \"indexing\"],\n             [2294, \"indexing\"],\n-            [2289, \"indexing\"],\n-            [2250, \"indexing\"],\n-            [2237, \"indexing\"],\n+            [2241, \"indexing\"],\n+            [2236, \"indexing\"],\n             [2238, \"indexing\"],\n             [2239, \"indexing\"],\n-            [2249, \"indexing\"],\n-            [2241, \"indexing\"],\n+            [2237, \"indexing\"],\n             [2235, \"indexing\"],\n-            [2246, \"indexing\"],\n-            [2236, \"indexing\"],\n-            [2251, \"indexing\"],\n-            [2210, \"indexing\"],\n-            [2199, \"indexing\"]\n+            [2199, \"indexing\"],\n+            [2210, \"indexing\"]\n         ],\n         \"Indexing / selection\": [\n             [2191, \"indexing-selection\"]\n         ],\n         \"Indexing API changes\": [\n             [2218, \"indexing-api-changes\"]\n         ],\n@@ -2691,18 +2691,18 @@\n         \"Indexing with isin\": [\n             [2197, \"indexing-with-isin\"]\n         ],\n         \"Indexing with nullable boolean arrays\": [\n             [2267, \"indexing-with-nullable-boolean-arrays\"]\n         ],\n         \"Indexing, iteration\": [\n+            [2169, \"indexing-iteration\"],\n             [2171, \"indexing-iteration\"],\n             [2179, \"indexing-iteration\"],\n-            [2180, \"indexing-iteration\"],\n-            [2169, \"indexing-iteration\"]\n+            [2180, \"indexing-iteration\"]\n         ],\n         \"Inferring datetime format\": [\n             [2199, \"inferring-datetime-format\"]\n         ],\n         \"Infinity\": [\n             [2199, \"infinity\"]\n         ],\n@@ -2715,16 +2715,16 @@\n         \"Inserting missing data\": [\n             [2201, \"inserting-missing-data\"]\n         ],\n         \"Insertion method\": [\n             [2199, \"insertion-method\"]\n         ],\n         \"Installation\": [\n-            [22, \"installation\"],\n-            [21, \"installation\"]\n+            [21, \"installation\"],\n+            [22, \"installation\"]\n         ],\n         \"Installing from PyPI\": [\n             [22, \"installing-from-pypi\"]\n         ],\n         \"Installing from source\": [\n             [22, \"installing-from-source\"]\n         ],\n@@ -2775,30 +2775,30 @@\n         \"Interpolation\": [\n             [2201, \"interpolation\"]\n         ],\n         \"Interpolation limits\": [\n             [2201, \"interpolation-limits\"]\n         ],\n         \"Interval\": [\n-            [2302, \"interval\"],\n-            [2307, \"interval\"],\n-            [2271, \"interval\"],\n+            [2249, \"interval\"],\n+            [2246, \"interval\"],\n             [2265, \"interval\"],\n+            [2250, \"interval\"],\n+            [2271, \"interval\"],\n+            [2307, \"interval\"],\n+            [2289, \"interval\"],\n             [2283, \"interval\"],\n-            [2298, \"interval\"],\n             [2277, \"interval\"],\n-            [2294, \"interval\"],\n-            [2289, \"interval\"],\n-            [2250, \"interval\"],\n-            [2249, \"interval\"],\n-            [2246, \"interval\"]\n+            [2298, \"interval\"],\n+            [2302, \"interval\"],\n+            [2294, \"interval\"]\n         ],\n         \"IntervalIndex\": [\n-            [2235, \"intervalindex\"],\n             [2173, \"intervalindex\"],\n+            [2235, \"intervalindex\"],\n             [2185, \"intervalindex\"]\n         ],\n         \"IntervalIndex components\": [\n             [2173, \"intervalindex-components\"]\n         ],\n         \"Intervals\": [\n             [2167, \"intervals\"]\n@@ -2821,16 +2821,16 @@\n         \"Iterable introspection\": [\n             [2167, \"iterable-introspection\"]\n         ],\n         \"Iterating through files chunk by chunk\": [\n             [2199, \"iterating-through-files-chunk-by-chunk\"]\n         ],\n         \"Iterating through groups\": [\n-            [2210, \"iterating-through-groups\"],\n-            [2195, \"iterating-through-groups\"]\n+            [2195, \"iterating-through-groups\"],\n+            [2210, \"iterating-through-groups\"]\n         ],\n         \"Iteration\": [\n             [2199, \"iteration\"],\n             [2186, \"iteration\"]\n         ],\n         \"Iteration of Series/Index will now return Python scalars\": [\n             [2238, \"iteration-of-series-index-will-now-return-python-scalars\"]\n@@ -2872,17 +2872,17 @@\n         \"Joining with two multi-indexes\": [\n             [2246, \"joining-with-two-multi-indexes\"]\n         ],\n         \"Joyful pandas\": [\n             [35, \"joyful-pandas\"]\n         ],\n         \"Keep certain columns\": [\n-            [16, \"keep-certain-columns\"],\n             [19, \"keep-certain-columns\"],\n-            [17, \"keep-certain-columns\"]\n+            [17, \"keep-certain-columns\"],\n+            [16, \"keep-certain-columns\"]\n         ],\n         \"KeyErrors raised by loc specify missing labels\": [\n             [2271, \"keyerrors-raised-by-loc-specify-missing-labels\"]\n         ],\n         \"Keyword argument dtype for data IO\": [\n             [2235, \"keyword-argument-dtype-for-data-io\"]\n         ],\n@@ -2922,17 +2922,17 @@\n         \"License\": [\n             [34, \"license\"]\n         ],\n         \"Limitations\": [\n             [2207, \"Limitations\"]\n         ],\n         \"Limiting output\": [\n-            [16, \"limiting-output\"],\n             [19, \"limiting-output\"],\n-            [17, \"limiting-output\"]\n+            [17, \"limiting-output\"],\n+            [16, \"limiting-output\"]\n         ],\n         \"Limits on filling while reindexing\": [\n             [2186, \"limits-on-filling-while-reindexing\"]\n         ],\n         \"Line delimited json\": [\n             [2199, \"line-delimited-json\"]\n         ],\n@@ -2997,17 +2997,17 @@\n         \"Merge types\": [\n             [2200, \"merge-types\"]\n         ],\n         \"Merge, join, concatenate and compare\": [\n             [2200, \"merge-join-concatenate-and-compare\"]\n         ],\n         \"Merging\": [\n-            [16, \"merging\"],\n             [19, \"merging\"],\n-            [17, \"merging\"]\n+            [17, \"merging\"],\n+            [16, \"merging\"]\n         ],\n         \"Merging / concatenation\": [\n             [2188, \"merging-concatenation\"]\n         ],\n         \"Merging changes\": [\n             [2232, \"merging-changes\"]\n         ],\n@@ -3015,19 +3015,19 @@\n             [2241, \"merging-on-a-combination-of-columns-and-index-levels\"],\n             [2200, \"merging-on-a-combination-of-columns-and-index-levels\"]\n         ],\n         \"Merging pull requests\": [\n             [13, \"merging-pull-requests\"]\n         ],\n         \"Metadata\": [\n-            [2302, \"metadata\"],\n             [2298, \"metadata\"],\n+            [2302, \"metadata\"],\n             [2294, \"metadata\"],\n-            [2180, \"metadata\"],\n-            [2169, \"metadata\"]\n+            [2169, \"metadata\"],\n+            [2180, \"metadata\"]\n         ],\n         \"Method .assign() accepts dependent arguments\": [\n             [2241, \"method-assign-accepts-dependent-arguments\"]\n         ],\n         \"Method .describe() changes\": [\n             [2232, \"method-describe-changes\"]\n         ],\n@@ -3076,20 +3076,20 @@\n         \"Method to_datetime error changes\": [\n             [2231, \"method-to-datetime-error-changes\"]\n         ],\n         \"Method to_xarray\": [\n             [2230, \"method-to-xarray\"]\n         ],\n         \"Methods\": [\n-            [2167, \"methods\"],\n-            [2167, \"id2\"],\n-            [2167, \"id4\"],\n             [2173, \"methods\"],\n             [2173, \"id6\"],\n             [2173, \"id8\"],\n+            [2167, \"methods\"],\n+            [2167, \"id2\"],\n+            [2167, \"id4\"],\n             [2176, \"methods\"],\n             [2176, \"id2\"],\n             [2176, \"id4\"],\n             [2176, \"id6\"],\n             [2176, \"id8\"],\n             [2176, \"id10\"],\n             [2176, \"id12\"],\n@@ -3146,40 +3146,40 @@\n         \"Minute\": [\n             [2176, \"minute\"]\n         ],\n         \"Miscellaneous indexing FAQ\": [\n             [2185, \"miscellaneous-indexing-faq\"]\n         ],\n         \"Missing\": [\n-            [2302, \"missing\"],\n-            [2307, \"missing\"],\n-            [2271, \"missing\"],\n+            [2249, \"missing\"],\n+            [2246, \"missing\"],\n             [2265, \"missing\"],\n+            [2250, \"missing\"],\n+            [2271, \"missing\"],\n+            [2307, \"missing\"],\n+            [2289, \"missing\"],\n             [2283, \"missing\"],\n-            [2298, \"missing\"],\n             [2277, \"missing\"],\n-            [2294, \"missing\"],\n-            [2289, \"missing\"],\n-            [2250, \"missing\"],\n-            [2249, \"missing\"],\n-            [2246, \"missing\"]\n+            [2298, \"missing\"],\n+            [2302, \"missing\"],\n+            [2294, \"missing\"]\n         ],\n         \"Missing data\": [\n-            [16, \"missing-data\"],\n             [19, \"missing-data\"],\n+            [16, \"missing-data\"],\n             [2184, \"missing-data\"],\n-            [2188, \"missing-data\"],\n-            [2204, \"missing-data\"]\n+            [2204, \"missing-data\"],\n+            [2188, \"missing-data\"]\n         ],\n         \"Missing data / operations with fill values\": [\n             [2186, \"missing-data-operations-with-fill-values\"]\n         ],\n         \"Missing data handling\": [\n-            [2180, \"missing-data-handling\"],\n-            [2169, \"missing-data-handling\"]\n+            [2169, \"missing-data-handling\"],\n+            [2180, \"missing-data-handling\"]\n         ],\n         \"Missing value representation for NumPy types\": [\n             [2194, \"missing-value-representation-for-numpy-types\"]\n         ],\n         \"Missing values\": [\n             [2173, \"missing-values\"],\n             [2175, \"missing-values\"],\n@@ -3213,28 +3213,28 @@\n         \"Multi-indexing using slicers\": [\n             [2220, \"multi-indexing-using-slicers\"]\n         ],\n         \"Multi-threaded CSV reading with a new CSV Engine based on pyarrow\": [\n             [2289, \"multi-threaded-csv-reading-with-a-new-csv-engine-based-on-pyarrow\"]\n         ],\n         \"MultiIndex\": [\n-            [2302, \"multiindex\"],\n-            [2307, \"multiindex\"],\n-            [12, \"multiindex\"],\n-            [2271, \"multiindex\"],\n+            [2249, \"multiindex\"],\n+            [2246, \"multiindex\"],\n             [2265, \"multiindex\"],\n+            [2271, \"multiindex\"],\n+            [12, \"multiindex\"],\n+            [2307, \"multiindex\"],\n+            [2289, \"multiindex\"],\n             [2283, \"multiindex\"],\n-            [2298, \"multiindex\"],\n             [2277, \"multiindex\"],\n+            [2298, \"multiindex\"],\n+            [2302, \"multiindex\"],\n             [2294, \"multiindex\"],\n-            [2289, \"multiindex\"],\n-            [2249, \"multiindex\"],\n-            [2241, \"multiindex\"],\n-            [2246, \"multiindex\"],\n-            [2173, \"multiindex\"]\n+            [2173, \"multiindex\"],\n+            [2241, \"multiindex\"]\n         ],\n         \"MultiIndex / advanced indexing\": [\n             [2185, \"multiindex-advanced-indexing\"]\n         ],\n         \"MultiIndex components\": [\n             [2173, \"multiindex-components\"]\n         ],\n@@ -3337,39 +3337,39 @@\n         \"New contributor meeting\": [\n             [0, \"new-contributor-meeting\"]\n         ],\n         \"New deprecation policy\": [\n             [2265, \"new-deprecation-policy\"]\n         ],\n         \"New features\": [\n-            [2259, \"new-features\"],\n-            [2257, \"new-features\"],\n             [2255, \"new-features\"],\n             [2260, \"new-features\"],\n-            [2263, \"new-features\"],\n-            [2258, \"new-features\"],\n-            [2262, \"new-features\"],\n             [2264, \"new-features\"],\n+            [2253, \"new-features\"],\n+            [2263, \"new-features\"],\n             [2256, \"new-features\"],\n+            [2262, \"new-features\"],\n+            [2257, \"new-features\"],\n             [2254, \"new-features\"],\n-            [2231, \"new-features\"],\n-            [2238, \"new-features\"],\n+            [2259, \"new-features\"],\n+            [2258, \"new-features\"],\n             [2232, \"new-features\"],\n-            [2239, \"new-features\"],\n-            [2253, \"new-features\"],\n+            [2227, \"new-features\"],\n             [2241, \"new-features\"],\n-            [2235, \"new-features\"],\n+            [2229, \"new-features\"],\n+            [2222, \"new-features\"],\n+            [2228, \"new-features\"],\n             [2230, \"new-features\"],\n             [2214, \"new-features\"],\n-            [2227, \"new-features\"],\n             [2215, \"new-features\"],\n-            [2225, \"new-features\"],\n-            [2228, \"new-features\"],\n-            [2229, \"new-features\"],\n-            [2222, \"new-features\"]\n+            [2231, \"new-features\"],\n+            [2238, \"new-features\"],\n+            [2239, \"new-features\"],\n+            [2235, \"new-features\"],\n+            [2225, \"new-features\"]\n         ],\n         \"New functions or methods\": [\n             [2238, \"new-functions-or-methods\"]\n         ],\n         \"New implementation of DataFrame.stack()\": [\n             [2302, \"new-implementation-of-dataframe-stack\"]\n         ],\n@@ -3398,21 +3398,21 @@\n             [2210, \"nonexistent-times-when-localizing\"]\n         ],\n         \"Normalization\": [\n             [2199, \"normalization\"],\n             [2204, \"normalization\"]\n         ],\n         \"Notable bug fixes\": [\n-            [2307, \"notable-bug-fixes\"],\n             [2271, \"notable-bug-fixes\"],\n+            [2307, \"notable-bug-fixes\"],\n+            [2289, \"notable-bug-fixes\"],\n             [2283, \"notable-bug-fixes\"],\n-            [2298, \"notable-bug-fixes\"],\n             [2277, \"notable-bug-fixes\"],\n-            [2294, \"notable-bug-fixes\"],\n-            [2289, \"notable-bug-fixes\"]\n+            [2298, \"notable-bug-fixes\"],\n+            [2294, \"notable-bug-fixes\"]\n         ],\n         \"Notes & caveats\": [\n             [2199, \"notes-caveats\"]\n         ],\n         \"Null-values are no longer coerced to NaN-value in value_counts and mode\": [\n             [2289, \"null-values-are-no-longer-coerced-to-nan-value-in-value-counts-and-mode\"]\n         ],\n@@ -3452,31 +3452,31 @@\n         \"Numba engine\": [\n             [2212, \"numba-engine\"]\n         ],\n         \"Number formatting\": [\n             [2202, \"number-formatting\"]\n         ],\n         \"Numeric\": [\n-            [2302, \"numeric\"],\n-            [2307, \"numeric\"],\n-            [2271, \"numeric\"],\n+            [2249, \"numeric\"],\n+            [2246, \"numeric\"],\n             [2265, \"numeric\"],\n+            [2250, \"numeric\"],\n+            [2271, \"numeric\"],\n+            [2307, \"numeric\"],\n+            [2289, \"numeric\"],\n             [2283, \"numeric\"],\n-            [2298, \"numeric\"],\n             [2277, \"numeric\"],\n+            [2298, \"numeric\"],\n+            [2302, \"numeric\"],\n             [2294, \"numeric\"],\n-            [2289, \"numeric\"],\n-            [2250, \"numeric\"],\n+            [2241, \"numeric\"],\n+            [2236, \"numeric\"],\n             [2238, \"numeric\"],\n             [2239, \"numeric\"],\n-            [2249, \"numeric\"],\n-            [2241, \"numeric\"],\n-            [2235, \"numeric\"],\n-            [2246, \"numeric\"],\n-            [2236, \"numeric\"]\n+            [2235, \"numeric\"]\n         ],\n         \"Numeric Index\": [\n             [2173, \"numeric-index\"]\n         ],\n         \"Numeric formatting\": [\n             [2177, \"numeric-formatting\"]\n         ],\n@@ -3491,32 +3491,32 @@\n         \"Objects\": [\n             [2167, \"objects\"]\n         ],\n         \"Offset aliases\": [\n             [2210, \"offset-aliases\"]\n         ],\n         \"Offsets\": [\n-            [2241, \"offsets\"],\n-            [2246, \"offsets\"]\n+            [2246, \"offsets\"],\n+            [2241, \"offsets\"]\n         ],\n         \"OpenDocument Spreadsheets\": [\n             [2199, \"opendocument-spreadsheets\"]\n         ],\n         \"Operations\": [\n+            [2198, \"operations\"],\n+            [2203, \"operations\"],\n             [2184, \"operations\"],\n-            [2209, \"operations\"],\n-            [2209, \"id1\"],\n             [2188, \"operations\"],\n-            [2198, \"operations\"],\n-            [2203, \"operations\"]\n+            [2209, \"operations\"],\n+            [2209, \"id1\"]\n         ],\n         \"Operations on columns\": [\n-            [16, \"operations-on-columns\"],\n             [19, \"operations-on-columns\"],\n-            [17, \"operations-on-columns\"]\n+            [17, \"operations-on-columns\"],\n+            [16, \"operations-on-columns\"]\n         ],\n         \"Operators now preserve dtypes\": [\n             [2232, \"operators-now-preserve-dtypes\"]\n         ],\n         \"Optimization\": [\n             [2207, \"Optimization\"]\n         ],\n@@ -3552,122 +3552,122 @@\n             [2177, \"options-and-settings\"],\n             [2202, \"options-and-settings\"]\n         ],\n         \"Orient options\": [\n             [2199, \"orient-options\"]\n         ],\n         \"Other\": [\n-            [2302, \"other\"],\n-            [2307, \"other\"],\n-            [2309, \"other\"],\n-            [2303, \"other\"],\n-            [2308, \"other\"],\n-            [2301, \"other\"],\n-            [2300, \"other\"],\n-            [2304, \"other\"],\n-            [2273, \"other\"],\n-            [2276, \"other\"],\n-            [2271, \"other\"],\n+            [2249, \"other\"],\n+            [2246, \"other\"],\n             [2274, \"other\"],\n+            [2276, \"other\"],\n             [2265, \"other\"],\n-            [2283, \"other\"],\n-            [2298, \"other\"],\n-            [2293, \"other\"],\n+            [2251, \"other\"],\n+            [2250, \"other\"],\n+            [2271, \"other\"],\n+            [2273, \"other\"],\n+            [2307, \"other\"],\n             [2290, \"other\"],\n-            [2277, \"other\"],\n-            [2287, \"other\"],\n-            [2299, \"other\"],\n-            [2294, \"other\"],\n-            [2295, \"other\"],\n-            [2297, \"other\"],\n             [2296, \"other\"],\n-            [2289, \"other\"],\n+            [2309, \"other\"],\n             [2292, \"other\"],\n+            [2289, \"other\"],\n+            [2295, \"other\"],\n+            [2283, \"other\"],\n+            [2277, \"other\"],\n+            [2304, \"other\"],\n             [2278, \"other\"],\n-            [2250, \"other\"],\n-            [2238, \"other\"],\n-            [2249, \"other\"],\n+            [2293, \"other\"],\n+            [2298, \"other\"],\n+            [2301, \"other\"],\n+            [2302, \"other\"],\n+            [2303, \"other\"],\n+            [2297, \"other\"],\n+            [2294, \"other\"],\n+            [2287, \"other\"],\n+            [2299, \"other\"],\n+            [2308, \"other\"],\n+            [2300, \"other\"],\n             [2241, \"other\"],\n-            [2235, \"other\"],\n-            [2246, \"other\"],\n             [2236, \"other\"],\n-            [2251, \"other\"]\n+            [2238, \"other\"],\n+            [2235, \"other\"]\n         ],\n         \"Other API changes\": [\n-            [2302, \"other-api-changes\"],\n-            [2307, \"other-api-changes\"],\n-            [2257, \"other-api-changes\"],\n+            [2249, \"other-api-changes\"],\n+            [2246, \"other-api-changes\"],\n             [2260, \"other-api-changes\"],\n-            [2261, \"other-api-changes\"],\n             [2265, \"other-api-changes\"],\n+            [2261, \"other-api-changes\"],\n+            [2257, \"other-api-changes\"],\n+            [2307, \"other-api-changes\"],\n+            [2289, \"other-api-changes\"],\n             [2283, \"other-api-changes\"],\n-            [2298, \"other-api-changes\"],\n             [2277, \"other-api-changes\"],\n+            [2298, \"other-api-changes\"],\n+            [2302, \"other-api-changes\"],\n             [2294, \"other-api-changes\"],\n-            [2289, \"other-api-changes\"],\n-            [2231, \"other-api-changes\"],\n-            [2238, \"other-api-changes\"],\n             [2232, \"other-api-changes\"],\n-            [2249, \"other-api-changes\"],\n             [2241, \"other-api-changes\"],\n-            [2235, \"other-api-changes\"],\n-            [2246, \"other-api-changes\"],\n+            [2228, \"other-api-changes\"],\n             [2230, \"other-api-changes\"],\n-            [2225, \"other-api-changes\"],\n-            [2228, \"other-api-changes\"]\n+            [2231, \"other-api-changes\"],\n+            [2238, \"other-api-changes\"],\n+            [2235, \"other-api-changes\"],\n+            [2225, \"other-api-changes\"]\n         ],\n         \"Other Deprecations\": [\n-            [2302, \"other-deprecations\"],\n             [2307, \"other-deprecations\"],\n+            [2289, \"other-deprecations\"],\n             [2283, \"other-deprecations\"],\n-            [2294, \"other-deprecations\"],\n-            [2289, \"other-deprecations\"]\n+            [2302, \"other-deprecations\"],\n+            [2294, \"other-deprecations\"]\n         ],\n         \"Other Fun and Useful Stuff\": [\n             [2207, \"Other-Fun-and-Useful-Stuff\"]\n         ],\n         \"Other considerations\": [\n-            [16, \"other-considerations\"],\n             [19, \"other-considerations\"],\n-            [17, \"other-considerations\"]\n+            [17, \"other-considerations\"],\n+            [16, \"other-considerations\"]\n         ],\n         \"Other data sources\": [\n             [22, \"other-data-sources\"]\n         ],\n         \"Other deprecations\": [\n             [2249, \"other-deprecations\"],\n             [2235, \"other-deprecations\"]\n         ],\n         \"Other development changes\": [\n             [2235, \"other-development-changes\"]\n         ],\n         \"Other enhancements\": [\n-            [2302, \"other-enhancements\"],\n-            [2307, \"other-enhancements\"],\n-            [2271, \"other-enhancements\"],\n+            [2249, \"other-enhancements\"],\n+            [2246, \"other-enhancements\"],\n             [2265, \"other-enhancements\"],\n+            [2271, \"other-enhancements\"],\n+            [2307, \"other-enhancements\"],\n+            [2289, \"other-enhancements\"],\n             [2283, \"other-enhancements\"],\n-            [2298, \"other-enhancements\"],\n             [2277, \"other-enhancements\"],\n+            [2298, \"other-enhancements\"],\n+            [2302, \"other-enhancements\"],\n             [2294, \"other-enhancements\"],\n-            [2289, \"other-enhancements\"],\n-            [2231, \"other-enhancements\"],\n-            [2238, \"other-enhancements\"],\n             [2232, \"other-enhancements\"],\n-            [2239, \"other-enhancements\"],\n-            [2249, \"other-enhancements\"],\n+            [2227, \"other-enhancements\"],\n             [2241, \"other-enhancements\"],\n-            [2235, \"other-enhancements\"],\n-            [2246, \"other-enhancements\"],\n+            [2228, \"other-enhancements\"],\n+            [2217, \"other-enhancements\"],\n             [2230, \"other-enhancements\"],\n             [2226, \"other-enhancements\"],\n-            [2227, \"other-enhancements\"],\n-            [2225, \"other-enhancements\"],\n-            [2228, \"other-enhancements\"],\n-            [2217, \"other-enhancements\"]\n+            [2231, \"other-enhancements\"],\n+            [2238, \"other-enhancements\"],\n+            [2239, \"other-enhancements\"],\n+            [2235, \"other-enhancements\"],\n+            [2225, \"other-enhancements\"]\n         ],\n         \"Other file formats\": [\n             [2199, \"other-file-formats\"]\n         ],\n         \"Other new features\": [\n             [2261, \"other-new-features\"]\n         ],\n@@ -3756,78 +3756,78 @@\n         \"Passing integer data and a timezone to DatetimeIndex\": [\n             [2246, \"passing-integer-data-and-a-timezone-to-datetimeindex\"]\n         ],\n         \"Patterns to avoid\": [\n             [2190, \"patterns-to-avoid\"]\n         ],\n         \"Performance\": [\n-            [2224, \"performance\"],\n+            [2222, \"performance\"],\n             [2219, \"performance\"],\n             [2220, \"performance\"],\n+            [2224, \"performance\"],\n             [2221, \"performance\"],\n-            [2222, \"performance\"],\n             [2199, \"performance\"]\n         ],\n         \"Performance considerations\": [\n             [2199, \"performance-considerations\"]\n         ],\n         \"Performance dependencies (recommended)\": [\n             [22, \"performance-dependencies-recommended\"]\n         ],\n         \"Performance enhancements\": [\n             [2255, \"performance-enhancements\"],\n-            [2254, \"performance-enhancements\"],\n-            [2253, \"performance-enhancements\"]\n+            [2253, \"performance-enhancements\"],\n+            [2254, \"performance-enhancements\"]\n         ],\n         \"Performance improvements\": [\n-            [2302, \"performance-improvements\"],\n-            [2307, \"performance-improvements\"],\n-            [2259, \"performance-improvements\"],\n+            [2249, \"performance-improvements\"],\n+            [2246, \"performance-improvements\"],\n+            [2265, \"performance-improvements\"],\n+            [2256, \"performance-improvements\"],\n+            [2262, \"performance-improvements\"],\n             [2257, \"performance-improvements\"],\n+            [2259, \"performance-improvements\"],\n             [2271, \"performance-improvements\"],\n             [2258, \"performance-improvements\"],\n-            [2262, \"performance-improvements\"],\n-            [2265, \"performance-improvements\"],\n-            [2256, \"performance-improvements\"],\n+            [2307, \"performance-improvements\"],\n+            [2289, \"performance-improvements\"],\n             [2283, \"performance-improvements\"],\n-            [2298, \"performance-improvements\"],\n-            [2286, \"performance-improvements\"],\n             [2277, \"performance-improvements\"],\n+            [2298, \"performance-improvements\"],\n+            [2302, \"performance-improvements\"],\n             [2294, \"performance-improvements\"],\n-            [2289, \"performance-improvements\"],\n-            [2242, \"performance-improvements\"],\n-            [2231, \"performance-improvements\"],\n+            [2286, \"performance-improvements\"],\n             [2233, \"performance-improvements\"],\n-            [2234, \"performance-improvements\"],\n-            [2238, \"performance-improvements\"],\n             [2232, \"performance-improvements\"],\n-            [2239, \"performance-improvements\"],\n-            [2249, \"performance-improvements\"],\n+            [2227, \"performance-improvements\"],\n             [2241, \"performance-improvements\"],\n-            [2235, \"performance-improvements\"],\n-            [2246, \"performance-improvements\"],\n-            [2236, \"performance-improvements\"],\n+            [2229, \"performance-improvements\"],\n+            [2234, \"performance-improvements\"],\n+            [2228, \"performance-improvements\"],\n             [2230, \"performance-improvements\"],\n             [2226, \"performance-improvements\"],\n-            [2227, \"performance-improvements\"],\n+            [2236, \"performance-improvements\"],\n+            [2231, \"performance-improvements\"],\n+            [2238, \"performance-improvements\"],\n+            [2239, \"performance-improvements\"],\n+            [2235, \"performance-improvements\"],\n             [2225, \"performance-improvements\"],\n-            [2228, \"performance-improvements\"],\n-            [2229, \"performance-improvements\"]\n+            [2242, \"performance-improvements\"]\n         ],\n         \"Performance of query()\": [\n             [2197, \"performance-of-query\"]\n         ],\n         \"Period\": [\n-            [2302, \"period\"],\n             [2307, \"period\"],\n+            [2289, \"period\"],\n             [2283, \"period\"],\n-            [2298, \"period\"],\n             [2277, \"period\"],\n+            [2298, \"period\"],\n+            [2302, \"period\"],\n             [2294, \"period\"],\n-            [2289, \"period\"],\n             [2167, \"period\"],\n             [2210, \"period\"]\n         ],\n         \"Period aliases\": [\n             [2210, \"period-aliases\"]\n         ],\n         \"Period changes\": [\n@@ -3903,38 +3903,38 @@\n         \"Plot submethods\": [\n             [2228, \"plot-submethods\"]\n         ],\n         \"Plots in examples\": [\n             [3, \"plots-in-examples\"]\n         ],\n         \"Plotting\": [\n-            [2302, \"plotting\"],\n-            [2307, \"plotting\"],\n-            [2271, \"plotting\"],\n+            [2249, \"plotting\"],\n+            [2246, \"plotting\"],\n             [2265, \"plotting\"],\n+            [2250, \"plotting\"],\n+            [2271, \"plotting\"],\n+            [2307, \"plotting\"],\n+            [2289, \"plotting\"],\n             [2283, \"plotting\"],\n-            [2298, \"plotting\"],\n             [2277, \"plotting\"],\n+            [2298, \"plotting\"],\n+            [2302, \"plotting\"],\n             [2294, \"plotting\"],\n-            [2289, \"plotting\"],\n-            [2250, \"plotting\"],\n-            [2237, \"plotting\"],\n+            [2169, \"plotting\"],\n+            [2241, \"plotting\"],\n+            [2220, \"plotting\"],\n+            [2236, \"plotting\"],\n             [2238, \"plotting\"],\n             [2239, \"plotting\"],\n-            [2249, \"plotting\"],\n-            [2241, \"plotting\"],\n+            [2237, \"plotting\"],\n             [2235, \"plotting\"],\n-            [2246, \"plotting\"],\n-            [2236, \"plotting\"],\n-            [2180, \"plotting\"],\n-            [2169, \"plotting\"],\n-            [2184, \"plotting\"],\n             [2178, \"plotting\"],\n-            [2220, \"plotting\"],\n-            [2195, \"plotting\"]\n+            [2180, \"plotting\"],\n+            [2195, \"plotting\"],\n+            [2184, \"plotting\"]\n         ],\n         \"Plotting and visualization\": [\n             [2171, \"plotting-and-visualization\"]\n         ],\n         \"Plotting backends\": [\n             [10, \"plotting-backends\"],\n             [2211, \"plotting-backends\"]\n@@ -4007,20 +4007,20 @@\n         \"Propagation in arithmetic and comparison operations\": [\n             [2201, \"propagation-in-arithmetic-and-comparison-operations\"]\n         ],\n         \"Proper handling of np.nan in a string data-typed column with the Python engine\": [\n             [2246, \"proper-handling-of-np-nan-in-a-string-data-typed-column-with-the-python-engine\"]\n         ],\n         \"Properties\": [\n+            [2173, \"properties\"],\n+            [2173, \"id7\"],\n             [2167, \"properties\"],\n             [2167, \"id1\"],\n             [2167, \"id3\"],\n             [2167, \"id5\"],\n-            [2173, \"properties\"],\n-            [2173, \"id7\"],\n             [2176, \"properties\"],\n             [2176, \"id1\"],\n             [2176, \"id3\"],\n             [2176, \"id5\"],\n             [2176, \"id7\"],\n             [2176, \"id9\"],\n             [2176, \"id11\"],\n@@ -4171,17 +4171,17 @@\n         \"Reading columns with a MultiIndex\": [\n             [2199, \"reading-columns-with-a-multiindex\"]\n         ],\n         \"Reading directly from TAR archives\": [\n             [2294, \"reading-directly-from-tar-archives\"]\n         ],\n         \"Reading external data\": [\n-            [16, \"reading-external-data\"],\n             [19, \"reading-external-data\"],\n-            [17, \"reading-external-data\"]\n+            [17, \"reading-external-data\"],\n+            [16, \"reading-external-data\"]\n         ],\n         \"Reading from Stata format\": [\n             [2199, \"reading-from-stata-format\"]\n         ],\n         \"Reading multiple files to create a single DataFrame\": [\n             [2199, \"reading-multiple-files-to-create-a-single-dataframe\"]\n         ],\n@@ -4209,16 +4209,16 @@\n         \"Regular expression replacement\": [\n             [2201, \"regular-expression-replacement\"]\n         ],\n         \"Reindexing\": [\n             [2197, \"reindexing\"]\n         ],\n         \"Reindexing / selection / label manipulation\": [\n-            [2180, \"reindexing-selection-label-manipulation\"],\n-            [2169, \"reindexing-selection-label-manipulation\"]\n+            [2169, \"reindexing-selection-label-manipulation\"],\n+            [2180, \"reindexing-selection-label-manipulation\"]\n         ],\n         \"Reindexing and altering labels\": [\n             [2186, \"reindexing-and-altering-labels\"]\n         ],\n         \"Reindexing to align with another object\": [\n             [2186, \"reindexing-to-align-with-another-object\"]\n         ],\n@@ -4234,40 +4234,40 @@\n         \"Releasing the GIL\": [\n             [2228, \"releasing-the-gil\"]\n         ],\n         \"Removal of deprecated float indexers\": [\n             [2230, \"removal-of-deprecated-float-indexers\"]\n         ],\n         \"Removal of prior version deprecations/changes\": [\n+            [2249, \"removal-of-prior-version-deprecations-changes\"],\n+            [2246, \"removal-of-prior-version-deprecations-changes\"],\n             [2265, \"removal-of-prior-version-deprecations-changes\"],\n             [2298, \"removal-of-prior-version-deprecations-changes\"],\n-            [2238, \"removal-of-prior-version-deprecations-changes\"],\n             [2232, \"removal-of-prior-version-deprecations-changes\"],\n-            [2249, \"removal-of-prior-version-deprecations-changes\"],\n             [2241, \"removal-of-prior-version-deprecations-changes\"],\n-            [2235, \"removal-of-prior-version-deprecations-changes\"],\n-            [2246, \"removal-of-prior-version-deprecations-changes\"],\n-            [2230, \"removal-of-prior-version-deprecations-changes\"],\n-            [2225, \"removal-of-prior-version-deprecations-changes\"],\n+            [2222, \"removal-of-prior-version-deprecations-changes\"],\n             [2228, \"removal-of-prior-version-deprecations-changes\"],\n-            [2222, \"removal-of-prior-version-deprecations-changes\"]\n+            [2230, \"removal-of-prior-version-deprecations-changes\"],\n+            [2238, \"removal-of-prior-version-deprecations-changes\"],\n+            [2235, \"removal-of-prior-version-deprecations-changes\"],\n+            [2225, \"removal-of-prior-version-deprecations-changes\"]\n         ],\n         \"Removed artificial truncation in rolling variance and standard deviation\": [\n             [2283, \"removed-artificial-truncation-in-rolling-variance-and-standard-deviation\"]\n         ],\n         \"Removing categories\": [\n             [2188, \"removing-categories\"]\n         ],\n         \"Removing unused categories\": [\n             [2188, \"removing-unused-categories\"]\n         ],\n         \"Rename a column\": [\n-            [16, \"rename-a-column\"],\n             [19, \"rename-a-column\"],\n-            [17, \"rename-a-column\"]\n+            [17, \"rename-a-column\"],\n+            [16, \"rename-a-column\"]\n         ],\n         \"Renaming / mapping labels\": [\n             [2186, \"renaming-mapping-labels\"]\n         ],\n         \"Renaming categories\": [\n             [2188, \"renaming-categories\"]\n         ],\n@@ -4286,16 +4286,16 @@\n         \"Reordering levels with reorder_levels\": [\n             [2185, \"reordering-levels-with-reorder-levels\"]\n         ],\n         \"Reorganization of the library: privacy changes\": [\n             [2235, \"reorganization-of-the-library-privacy-changes\"]\n         ],\n         \"Replace missing values with a specified value\": [\n-            [16, \"replace-missing-values-with-a-specified-value\"],\n-            [19, \"replace-missing-values-with-a-specified-value\"]\n+            [19, \"replace-missing-values-with-a-specified-value\"],\n+            [16, \"replace-missing-values-with-a-specified-value\"]\n         ],\n         \"Replacing values\": [\n             [2201, \"replacing-values\"]\n         ],\n         \"Representing out-of-bounds spans\": [\n             [2210, \"representing-out-of-bounds-spans\"]\n         ],\n@@ -4312,39 +4312,39 @@\n             [2230, \"resample-api\"]\n         ],\n         \"Resample a time series to another frequency\": [\n             [31, \"resample-a-time-series-to-another-frequency\"]\n         ],\n         \"Resampling\": [\n             [2179, \"resampling\"],\n-            [2209, \"resampling\"],\n-            [2210, \"resampling\"]\n+            [2210, \"resampling\"],\n+            [2209, \"resampling\"]\n         ],\n         \"Reset the index\": [\n             [2197, \"reset-the-index\"]\n         ],\n         \"Reshaping\": [\n-            [2302, \"reshaping\"],\n-            [2307, \"reshaping\"],\n-            [2271, \"reshaping\"],\n+            [2249, \"reshaping\"],\n+            [2246, \"reshaping\"],\n             [2265, \"reshaping\"],\n+            [2250, \"reshaping\"],\n+            [2271, \"reshaping\"],\n+            [2307, \"reshaping\"],\n+            [2289, \"reshaping\"],\n             [2283, \"reshaping\"],\n-            [2298, \"reshaping\"],\n             [2277, \"reshaping\"],\n+            [2298, \"reshaping\"],\n+            [2302, \"reshaping\"],\n             [2294, \"reshaping\"],\n-            [2289, \"reshaping\"],\n-            [2250, \"reshaping\"],\n-            [2237, \"reshaping\"],\n+            [2241, \"reshaping\"],\n+            [2236, \"reshaping\"],\n             [2238, \"reshaping\"],\n             [2239, \"reshaping\"],\n-            [2249, \"reshaping\"],\n-            [2241, \"reshaping\"],\n+            [2237, \"reshaping\"],\n             [2235, \"reshaping\"],\n-            [2246, \"reshaping\"],\n-            [2236, \"reshaping\"],\n             [2184, \"reshaping\"]\n         ],\n         \"Reshaping and pivot tables\": [\n             [2204, \"reshaping-and-pivot-tables\"]\n         ],\n         \"Reshaping, sorting\": [\n             [2180, \"reshaping-sorting\"]\n@@ -4531,38 +4531,38 @@\n         \"Selection choices\": [\n             [2216, \"selection-choices\"]\n         ],\n         \"Selection deprecations\": [\n             [2216, \"selection-deprecations\"]\n         ],\n         \"Selection of columns\": [\n-            [16, \"selection-of-columns\"],\n             [19, \"selection-of-columns\"],\n-            [17, \"selection-of-columns\"]\n+            [17, \"selection-of-columns\"],\n+            [16, \"selection-of-columns\"]\n         ],\n         \"Semi-month offsets\": [\n             [2232, \"semi-month-offsets\"]\n         ],\n         \"SemiMonthBegin\": [\n             [2176, \"semimonthbegin\"]\n         ],\n         \"SemiMonthEnd\": [\n             [2176, \"semimonthend\"]\n         ],\n         \"Serialization / IO / conversion\": [\n-            [2180, \"serialization-io-conversion\"],\n-            [2169, \"serialization-io-conversion\"]\n+            [2169, \"serialization-io-conversion\"],\n+            [2180, \"serialization-io-conversion\"]\n         ],\n         \"Serializing tz-naive Timestamps with to_json() with iso_dates=True\": [\n             [2294, \"serializing-tz-naive-timestamps-with-to-json-with-iso-dates-true\"]\n         ],\n         \"Series\": [\n-            [16, \"series\"],\n             [19, \"series\"],\n             [17, \"series\"],\n+            [16, \"series\"],\n             [2180, \"series\"],\n             [2191, \"series\"]\n         ],\n         \"Series and Index data-dtype incompatibilities\": [\n             [2246, \"series-and-index-data-dtype-incompatibilities\"]\n         ],\n         \"Series creation\": [\n@@ -4700,50 +4700,50 @@\n         \"Sorting by a MultiIndex column\": [\n             [2186, \"sorting-by-a-multiindex-column\"]\n         ],\n         \"Sorting by a combination of columns and index levels\": [\n             [2241, \"sorting-by-a-combination-of-columns-and-index-levels\"]\n         ],\n         \"Sorting by values\": [\n-            [16, \"sorting-by-values\"],\n             [19, \"sorting-by-values\"],\n-            [17, \"sorting-by-values\"]\n+            [17, \"sorting-by-values\"],\n+            [16, \"sorting-by-values\"]\n         ],\n         \"Sorting with keys\": [\n             [2271, \"sorting-with-keys\"]\n         ],\n         \"Sparse\": [\n-            [2302, \"sparse\"],\n-            [2307, \"sparse\"],\n-            [2271, \"sparse\"],\n+            [2249, \"sparse\"],\n+            [2246, \"sparse\"],\n             [2265, \"sparse\"],\n+            [2250, \"sparse\"],\n+            [2271, \"sparse\"],\n+            [2307, \"sparse\"],\n+            [2289, \"sparse\"],\n             [2283, \"sparse\"],\n             [2298, \"sparse\"],\n+            [2302, \"sparse\"],\n             [2294, \"sparse\"],\n-            [2289, \"sparse\"],\n-            [2250, \"sparse\"],\n-            [2238, \"sparse\"],\n-            [2249, \"sparse\"],\n+            [2167, \"sparse\"],\n             [2241, \"sparse\"],\n-            [2235, \"sparse\"],\n-            [2246, \"sparse\"],\n             [2236, \"sparse\"],\n-            [2167, \"sparse\"]\n+            [2238, \"sparse\"],\n+            [2235, \"sparse\"]\n         ],\n         \"Sparse accessor\": [\n-            [2180, \"sparse-accessor\"],\n             [2169, \"sparse-accessor\"],\n+            [2180, \"sparse-accessor\"],\n             [2206, \"sparse-accessor\"]\n         ],\n         \"Sparse calculation\": [\n             [2206, \"sparse-calculation\"]\n         ],\n         \"Sparse changes\": [\n-            [2231, \"sparse-changes\"],\n-            [2232, \"sparse-changes\"]\n+            [2232, \"sparse-changes\"],\n+            [2231, \"sparse-changes\"]\n         ],\n         \"Sparse data structure refactor\": [\n             [2246, \"sparse-data-structure-refactor\"]\n         ],\n         \"Sparse data structures\": [\n             [2206, \"sparse-data-structures\"]\n         ],\n@@ -4850,32 +4850,32 @@\n             [2208, \"string-methods\"]\n         ],\n         \"String methods enhancements\": [\n             [2226, \"string-methods-enhancements\"],\n             [2225, \"string-methods-enhancements\"]\n         ],\n         \"String processing\": [\n-            [16, \"string-processing\"],\n             [19, \"string-processing\"],\n-            [17, \"string-processing\"]\n+            [17, \"string-processing\"],\n+            [16, \"string-processing\"]\n         ],\n         \"Strings\": [\n-            [2302, \"strings\"],\n-            [2307, \"strings\"],\n-            [2271, \"strings\"],\n+            [2249, \"strings\"],\n+            [2246, \"strings\"],\n             [2265, \"strings\"],\n+            [2271, \"strings\"],\n+            [2307, \"strings\"],\n+            [2289, \"strings\"],\n             [2283, \"strings\"],\n-            [2298, \"strings\"],\n             [2277, \"strings\"],\n+            [2298, \"strings\"],\n+            [2302, \"strings\"],\n             [2294, \"strings\"],\n-            [2289, \"strings\"],\n-            [2249, \"strings\"],\n-            [2241, \"strings\"],\n-            [2246, \"strings\"],\n-            [2167, \"strings\"]\n+            [2167, \"strings\"],\n+            [2241, \"strings\"]\n         ],\n         \"Struct accessor\": [\n             [2180, \"struct-accessor\"]\n         ],\n         \"Style\": [\n             [2246, \"style\"],\n             [2181, \"style\"]\n@@ -4889,22 +4889,22 @@\n         \"Style export and import\": [\n             [2181, \"style-export-and-import\"]\n         ],\n         \"Style guidelines\": [\n             [2, \"style-guidelines\"]\n         ],\n         \"Styler\": [\n-            [2302, \"styler\"],\n+            [2289, \"styler\"],\n+            [2289, \"id2\"],\n             [2283, \"styler\"],\n-            [2298, \"styler\"],\n             [2277, \"styler\"],\n+            [2298, \"styler\"],\n+            [2302, \"styler\"],\n             [2294, \"styler\"],\n-            [2294, \"id1\"],\n-            [2289, \"styler\"],\n-            [2289, \"id2\"]\n+            [2294, \"id1\"]\n         ],\n         \"Styler Functions\": [\n             [2207, \"Styler-Functions\"]\n         ],\n         \"Styler Object and Customising the Display\": [\n             [2207, \"Styler-Object-and-Customising-the-Display\"]\n         ],\n@@ -5095,23 +5095,23 @@\n         \"Tick DateOffset normalize restrictions\": [\n             [2246, \"tick-dateoffset-normalize-restrictions\"]\n         ],\n         \"Time Series changes and improvements\": [\n             [2261, \"time-series-changes-and-improvements\"]\n         ],\n         \"Time Series-related\": [\n-            [2180, \"time-series-related\"],\n-            [2169, \"time-series-related\"]\n+            [2169, \"time-series-related\"],\n+            [2180, \"time-series-related\"]\n         ],\n         \"Time Series-related instance methods\": [\n             [2210, \"time-series-related-instance-methods\"]\n         ],\n         \"Time Zones\": [\n-            [2294, \"time-zones\"],\n-            [2289, \"time-zones\"]\n+            [2289, \"time-zones\"],\n+            [2294, \"time-zones\"]\n         ],\n         \"Time deltas\": [\n             [2209, \"time-deltas\"]\n         ],\n         \"Time series\": [\n             [2184, \"time-series\"]\n         ],\n@@ -5135,26 +5135,26 @@\n             [2173, \"id3\"]\n         ],\n         \"Time/date components\": [\n             [2173, \"time-date-components\"],\n             [2210, \"time-date-components\"]\n         ],\n         \"Timedelta\": [\n-            [2302, \"timedelta\"],\n-            [2307, \"timedelta\"],\n-            [2271, \"timedelta\"],\n+            [2249, \"timedelta\"],\n+            [2246, \"timedelta\"],\n             [2265, \"timedelta\"],\n+            [2271, \"timedelta\"],\n+            [2307, \"timedelta\"],\n+            [2289, \"timedelta\"],\n             [2283, \"timedelta\"],\n-            [2298, \"timedelta\"],\n             [2277, \"timedelta\"],\n+            [2298, \"timedelta\"],\n+            [2302, \"timedelta\"],\n             [2294, \"timedelta\"],\n-            [2289, \"timedelta\"],\n-            [2249, \"timedelta\"],\n-            [2241, \"timedelta\"],\n-            [2246, \"timedelta\"]\n+            [2241, \"timedelta\"]\n         ],\n         \"Timedelta limitations\": [\n             [2209, \"timedelta-limitations\"]\n         ],\n         \"Timedelta methods\": [\n             [2180, \"timedelta-methods\"]\n         ],\n@@ -5180,25 +5180,25 @@\n         \"Timestamps vs. time spans\": [\n             [2210, \"timestamps-vs-time-spans\"]\n         ],\n         \"Timezone handling improvements\": [\n             [2222, \"timezone-handling-improvements\"]\n         ],\n         \"Timezones\": [\n-            [2302, \"timezones\"],\n-            [2307, \"timezones\"],\n-            [2271, \"timezones\"],\n+            [2249, \"timezones\"],\n+            [2246, \"timezones\"],\n             [2265, \"timezones\"],\n+            [2250, \"timezones\"],\n+            [2271, \"timezones\"],\n+            [2307, \"timezones\"],\n             [2283, \"timezones\"],\n-            [2298, \"timezones\"],\n             [2277, \"timezones\"],\n-            [2250, \"timezones\"],\n-            [2249, \"timezones\"],\n-            [2241, \"timezones\"],\n-            [2246, \"timezones\"]\n+            [2298, \"timezones\"],\n+            [2302, \"timezones\"],\n+            [2241, \"timezones\"]\n         ],\n         \"Tips for a successful pull request\": [\n             [1, \"tips-for-a-successful-pull-request\"]\n         ],\n         \"Tips for getting your examples pass the doctests\": [\n             [3, \"tips-for-getting-your-examples-pass-the-doctests\"]\n         ],\n@@ -5229,16 +5229,16 @@\n         \"Transform API\": [\n             [2186, \"transform-api\"]\n         ],\n         \"Transform with multiple functions\": [\n             [2186, \"transform-with-multiple-functions\"]\n         ],\n         \"Transformation\": [\n-            [16, \"transformation\"],\n             [19, \"transformation\"],\n+            [16, \"transformation\"],\n             [2195, \"transformation\"]\n         ],\n         \"Transforming\": [\n             [15, \"transforming\"]\n         ],\n         \"Transforming with a dict\": [\n             [2186, \"transforming-with-a-dict\"]\n@@ -5294,16 +5294,16 @@\n         \"Updating the development environment\": [\n             [1, \"updating-the-development-environment\"]\n         ],\n         \"Updating your pull request\": [\n             [1, \"updating-your-pull-request\"]\n         ],\n         \"Upsampling\": [\n-            [2179, \"upsampling\"],\n             [2230, \"upsampling\"],\n+            [2179, \"upsampling\"],\n             [2210, \"upsampling\"]\n         ],\n         \"Use Other Libraries\": [\n             [2205, \"use-other-libraries\"]\n         ],\n         \"Use chunking\": [\n             [2205, \"use-chunking\"]\n@@ -21476,15 +21476,15 @@\n         \"000830\": 2214,\n         \"000895\": 2195,\n         \"000951\": 2186,\n         \"000k\": 1489,\n         \"000m\": 1489,\n         \"000n\": 1489,\n         \"000z\": 2294,\n-        \"001\": [532, 874, 1467, 2193, 2232, 2264],\n+        \"001\": [532, 874, 1467, 2232, 2264],\n         \"001000\": [917, 919, 922, 929, 1876, 2209],\n         \"001294\": 2210,\n         \"001372\": 2207,\n         \"001376\": 2207,\n         \"001427\": 2214,\n         \"001438\": 2195,\n         \"001486\": [102, 1158],\n@@ -21514,15 +21514,15 @@\n         \"004194\": 2186,\n         \"004201\": 2186,\n         \"004229\": 2186,\n         \"004474\": 2184,\n         \"004580\": 2210,\n         \"00486\": 30,\n         \"004956\": 2207,\n-        \"005\": [2193, 2209],\n+        \"005\": 2209,\n         \"005000\": 2218,\n         \"005361\": 2207,\n         \"005383\": 2220,\n         \"005446\": 2219,\n         \"005462\": 2191,\n         \"005977\": 2199,\n         \"005979\": 2186,\n@@ -21540,15 +21540,14 @@\n         \"007200\": 2184,\n         \"007207\": [2184, 2214],\n         \"007717\": 2199,\n         \"007824\": 15,\n         \"007952\": 2207,\n         \"007996\": 2186,\n         \"007f\": 203,\n-        \"008\": 2193,\n         \"008182\": 2204,\n         \"008298\": 2186,\n         \"008344\": 2207,\n         \"008358\": 2207,\n         \"008500\": 15,\n         \"008543\": [102, 1158],\n         \"008943\": [102, 1158],\n@@ -21582,14 +21581,15 @@\n         \"011374\": 2195,\n         \"011470\": 2207,\n         \"011736\": 2186,\n         \"011829\": 2207,\n         \"01183\": 2229,\n         \"011860\": [182, 760],\n         \"011975\": 2207,\n+        \"012\": 2193,\n         \"012108\": 2207,\n         \"012299\": 2207,\n         \"0123456789123456\": [2164, 2165],\n         \"012549\": 2207,\n         \"012694\": 2199,\n         \"012922\": 2219,\n         \"013086\": 15,\n@@ -21617,14 +21617,15 @@\n         \"015083\": 2186,\n         \"015420\": 2195,\n         \"015458\": 2207,\n         \"015696\": [2220, 2228, 2230],\n         \"015906\": 2186,\n         \"015962\": [2184, 2214],\n         \"015988\": 2186,\n+        \"016\": 2193,\n         \"016009\": 15,\n         \"016287\": 2210,\n         \"016331\": 2210,\n         \"016424\": [16, 19],\n         \"016692\": [2184, 2195, 2214],\n         \"01685762652715874\": [624, 1215],\n         \"017\": 2193,\n@@ -21653,15 +21654,14 @@\n         \"01t03\": 2210,\n         \"01t05\": [909, 2210, 2235],\n         \"01t07\": 1280,\n         \"01t10\": 1005,\n         \"01t12\": 953,\n         \"01t23\": [893, 2186, 2246],\n         \"02\": [13, 16, 17, 19, 26, 27, 29, 31, 79, 80, 82, 133, 182, 183, 202, 207, 208, 213, 218, 230, 261, 271, 276, 277, 278, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 299, 301, 304, 305, 306, 307, 310, 312, 313, 314, 318, 319, 320, 321, 322, 323, 324, 326, 327, 329, 330, 331, 332, 345, 362, 363, 423, 519, 534, 536, 542, 543, 544, 545, 546, 547, 548, 549, 557, 558, 562, 563, 564, 565, 566, 575, 591, 592, 593, 637, 639, 640, 641, 642, 643, 644, 645, 646, 647, 649, 650, 651, 652, 654, 656, 657, 658, 659, 665, 666, 667, 673, 674, 675, 677, 678, 679, 680, 684, 685, 686, 688, 708, 760, 761, 781, 782, 788, 793, 804, 893, 899, 902, 903, 904, 919, 939, 940, 943, 945, 948, 949, 953, 957, 970, 997, 1014, 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\"026458\": 2216,\n         \"0266708\": 2202,\n         \"026692\": 2207,\n         \"0267\": 2202,\n         \"027496\": 2207,\n         \"027778\": [69, 109, 129, 171, 173, 199, 204, 206, 215, 216, 217, 220, 221, 222, 244, 275],\n-        \"028\": 2193,\n         \"028096\": 2210,\n         \"028152\": 2207,\n         \"028166\": 15,\n         \"028182\": 2207,\n         \"028578\": 2207,\n         \"028603\": 2195,\n         \"028662\": 28,\n@@ -21732,15 +21731,15 @@\n         \"029630\": 2195,\n         \"029766\": 2197,\n         \"02d\": 2205,\n         \"02t00\": [2199, 2210, 2235, 2261],\n         \"02t02\": 2235,\n         \"02t05\": [909, 2210],\n         \"03\": [26, 27, 29, 31, 79, 80, 82, 121, 182, 207, 213, 218, 219, 230, 264, 278, 286, 287, 290, 291, 292, 294, 296, 298, 301, 302, 304, 305, 306, 307, 310, 313, 314, 318, 321, 322, 326, 330, 331, 332, 362, 420, 423, 512, 517, 518, 519, 522, 524, 530, 534, 536, 543, 544, 545, 546, 547, 548, 549, 551, 557, 558, 562, 563, 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     \"030522\": 2204,\n@@ -21990,15 +21989,14 @@\n         \"063850\": 2207,\n         \"063922\": 2184,\n         \"063933\": 2207,\n         \"064\": 2207,\n         \"064034\": [15, 2191],\n         \"064423\": 2207,\n         \"064434\": 2207,\n-        \"065\": 2193,\n         \"065587\": 2218,\n         \"065761\": 2207,\n         \"065818\": [2204, 2207],\n         \"065934\": [182, 760],\n         \"066126\": 2207,\n         \"066510\": 2210,\n         \"066533\": 2210,\n@@ -22023,15 +22021,16 @@\n         \"069486\": 2230,\n         \"069546\": 2199,\n         \"069718\": 2186,\n         \"069887\": 2207,\n         \"069908\": 2207,\n         \"069949\": 2207,\n         \"06t00\": 2261,\n-        \"07\": [26, 27, 29, 30, 31, 187, 202, 207, 213, 230, 273, 277, 292, 294, 330, 332, 345, 644, 646, 685, 688, 763, 781, 788, 804, 900, 903, 1075, 1280, 1344, 1441, 1442, 1449, 1450, 1452, 1598, 1677, 1720, 2184, 2186, 2193, 2195, 2197, 2199, 2201, 2204, 2205, 2207, 2209, 2210, 2212, 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2193, 2195, 2197, 2199, 2200, 2201, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2230, 2232, 2235, 2246, 2249],\n+        \"102\": [1491, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2230, 2232, 2235, 2246, 2249],\n         \"1020\": 2185,\n         \"10209\": 2227,\n         \"1021\": [2185, 2197, 2231],\n         \"10214\": [2227, 2228],\n         \"10217\": 2227,\n         \"10218\": 2228,\n         \"1022\": [16, 17, 18, 19, 2185, 2199, 2203, 2205, 2232, 2235, 2298],\n@@ -22378,15 +22377,15 @@\n         \"102889\": 18,\n         \"10289\": 2227,\n         \"1029\": 2199,\n         \"10291\": 2230,\n         \"10292\": 2227,\n         \"10295\": 2228,\n         \"10299\": 2229,\n-        \"103\": [139, 140, 1174, 1175, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2222, 2227, 2230, 2232, 2235, 2246, 2255],\n+        \"103\": [139, 140, 1174, 1175, 2184, 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646, 652, 655, 660, 661, 666, 670, 673, 688, 689, 708, 738, 761, 763, 764, 765, 766, 768, 781, 782, 788, 799, 873, 886, 890, 893, 895, 923, 926, 940, 943, 948, 953, 976, 980, 987, 1017, 1075, 1154, 1158, 1162, 1164, 1169, 1189, 1192, 1195, 1205, 1219, 1221, 1226, 1250, 1253, 1256, 1267, 1274, 1276, 1290, 1292, 1336, 1344, 1392, 1431, 1433, 1452, 1482, 1487, 1497, 1498, 1560, 1578, 1598, 1620, 1637, 1657, 1677, 1699, 1720, 1758, 1793, 1815, 1839, 1876, 1894, 1912, 1930, 1964, 2018, 2127, 2145, 2184, 2185, 2186, 2188, 2190, 2191, 2192, 2193, 2194, 2195, 2197, 2198, 2199, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2215, 2216, 2218, 2219, 2220, 2221, 2222, 2223, 2225, 2226, 2228, 2229, 2230, 2231, 2232, 2235, 2238, 2240, 2241, 2246, 2249, 2257, 2261, 2264, 2265, 2271, 2277, 2283, 2289, 2294, 2298, 2302, 2307],\n-        \"120\": [15, 78, 162, 273, 359, 360, 587, 588, 900, 930, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2204, 2208, 2209, 2210, 2211, 2220, 2230, 2232],\n+        \"120\": [15, 78, 162, 273, 359, 360, 587, 588, 900, 930, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2204, 2208, 2209, 2210, 2211, 2220, 2230, 2232],\n         \"12000\": [2185, 2220],\n         \"12004\": 2265,\n         \"120055\": 2228,\n         \"12011\": [176, 179],\n         \"12014\": 2230,\n         \"12017\": 2230,\n         \"12019\": 2230,\n@@ -23124,15 +23123,15 @@\n         \"12182\": 2230,\n         \"12185\": 2232,\n         \"1219\": 2298,\n         \"12190\": 2238,\n         \"121950\": 2207,\n         \"12198\": 2230,\n         \"121991\": 2207,\n-        \"122\": [2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2204, 2207, 2208, 2209, 2210, 2211, 2220, 2232],\n+        \"122\": [2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2204, 2207, 2208, 2209, 2210, 2211, 2220, 2232],\n         \"1220\": 2193,\n         \"12202\": 2230,\n         \"12203\": 2231,\n         \"1221\": 2298,\n         \"12211\": 2231,\n         \"12213\": 2265,\n         \"12216\": 2232,\n@@ -23578,15 +23577,15 @@\n         \"13382\": 2232,\n         \"13383\": 2232,\n         \"13386\": 2241,\n         \"13389\": 2232,\n         \"13393\": 2239,\n         \"13395\": 2232,\n         \"13398\": 2232,\n-        \"134\": [2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2208, 2210, 2211, 2232, 2235, 2249, 2259, 2283],\n+        \"134\": [2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2208, 2210, 2211, 2232, 2235, 2249, 2259, 2283],\n         \"13402\": 2232,\n         \"13407\": 2241,\n         \"13410\": 2235,\n         \"134105\": 2207,\n         \"13411\": 2232,\n         \"13412\": 2234,\n         \"134146\": 15,\n@@ -23670,15 +23669,15 @@\n         \"13589\": 2235,\n         \"13590\": 2232,\n         \"13592\": 2232,\n         \"13593\": 2241,\n         \"13595\": 2238,\n         \"13598\": 2232,\n         \"13599\": 2232,\n-        \"136\": [2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2208, 2210, 2211, 2232, 2235, 2249],\n+        \"136\": [2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2208, 2210, 2211, 2232, 2235, 2249],\n         \"1360\": [2199, 2203, 2212, 2298],\n         \"13601\": 2265,\n         \"13602\": 2235,\n         \"13603\": 2232,\n         \"136056\": 2207,\n         \"13607\": 2232,\n         \"13611\": [2232, 2241],\n@@ -23817,16 +23816,14 @@\n         \"13971\": 2238,\n         \"13972\": 2232,\n         \"13977\": 2232,\n         \"13980\": 2232,\n         \"13981\": 2232,\n         \"13985\": 2232,\n         \"139853\": 2207,\n-        \"139875956514064\": 2246,\n-        \"139875956514160\": 2246,\n         \"13988\": 2232,\n         \"13990\": 2232,\n         \"13994\": 2232,\n         \"139976\": 2186,\n         \"13999\": 2232,\n         \"139999\": 1372,\n         \"14\": [3, 6, 15, 16, 17, 18, 19, 25, 26, 28, 29, 30, 31, 32, 133, 187, 190, 193, 197, 208, 213, 245, 268, 277, 345, 420, 632, 708, 718, 763, 766, 768, 782, 788, 799, 879, 903, 955, 956, 957, 958, 963, 964, 965, 966, 967, 968, 970, 973, 975, 976, 977, 978, 979, 980, 981, 992, 994, 995, 997, 999, 1009, 1013, 1014, 1018, 1023, 1025, 1195, 1256, 1292, 1336, 1433, 1437, 1438, 1439, 1598, 1657, 1677, 1815, 1876, 1894, 1912, 1964, 2018, 2054, 2184, 2185, 2186, 2188, 2190, 2191, 2192, 2193, 2194, 2195, 2197, 2198, 2199, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2215, 2216, 2217, 2218, 2219, 2222, 2223, 2225, 2226, 2228, 2229, 2230, 2231, 2232, 2235, 2238, 2240, 2241, 2242, 2246, 2249, 2257, 2261, 2265, 2271, 2277, 2283, 2289, 2294, 2298, 2302, 2307],\n@@ -23835,14 +23832,15 @@\n         \"14001\": 2238,\n         \"140069\": 2229,\n         \"14007\": 2241,\n         \"14012\": 2232,\n         \"14013\": 2241,\n         \"14015\": 2235,\n         \"14021\": 2232,\n+        \"140225432699600\": 2246,\n         \"140249\": 2207,\n         \"14039\": 2232,\n         \"14041\": 2232,\n         \"140528\": 2207,\n         \"14058\": 2232,\n         \"14065\": 2232,\n         \"14066\": 2232,\n@@ -23853,15 +23851,15 @@\n         \"1409\": [2185, 2197],\n         \"14093\": 2283,\n         \"14094\": [2232, 2246],\n         \"14095\": 2232,\n         \"14096\": 2241,\n         \"140983\": 2207,\n         \"140min\": 2210,\n-        \"141\": [2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2203, 2210, 2211, 2212, 2231, 2232, 2253, 2298],\n+        \"141\": [2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2203, 2210, 2211, 2212, 2231, 2232, 2253, 2298],\n         \"1410\": [2185, 2197],\n         \"14101\": 2232,\n         \"14105\": 2246,\n         \"1411\": [2185, 2197],\n         \"14113\": 2241,\n         \"141155\": 2207,\n         \"141185\": 15,\n@@ -25547,15 +25545,15 @@\n         \"1977\": 2199,\n         \"197720\": 2204,\n         \"197727\": 2207,\n         \"197749\": 2210,\n         \"19776\": 2242,\n         \"19788\": 2242,\n         \"19789\": 2241,\n-        \"198\": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211, 2231, 2249],\n+        \"198\": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2210, 2211, 2231, 2249],\n         \"1980\": [182, 760, 2166, 2204, 2218],\n         \"198053\": 2219,\n         \"19806\": 2241,\n         \"19810\": 2241,\n         \"19819\": 2241,\n         \"19820\": 2241,\n         \"19822\": 2241,\n@@ -25740,18 +25738,17 @@\n         \"2020q1\": 1008,\n         \"2021\": [288, 296, 318, 639, 652, 673, 940, 943, 948, 957, 970, 997, 1542, 2201, 2207, 2213, 2277, 2289, 2294],\n         \"2022\": [5, 22, 523, 525, 528, 537, 982, 1185, 1246, 1288, 1491, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1542, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1560, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1578, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1598, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1620, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1637, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1657, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1677, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1699, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1720, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1758, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1776, 1780, 1781, 1782, 1783, 1784, 1785, 1786, 1793, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1815, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1839, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1857, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1876, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1894, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1912, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1930, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1947, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1964, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1982, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 2000, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2018, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2036, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2054, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2094, 2095, 2096, 2097, 2098, 2099, 2100, 2108, 2112, 2113, 2114, 2115, 2116, 2117, 2118, 2127, 2131, 2132, 2133, 2134, 2135, 2136, 2137, 2145, 2149, 2150, 2151, 2152, 2153, 2154, 2155, 2186, 2203, 2213, 2227, 2298, 2302, 2307],\n         \"2022a\": 2294,\n         \"2023\": [34, 270, 298, 301, 320, 363, 511, 519, 526, 533, 543, 544, 545, 546, 547, 548, 549, 551, 554, 555, 556, 557, 558, 560, 563, 564, 565, 566, 567, 651, 894, 898, 954, 959, 960, 982, 984, 1000, 1001, 1003, 1004, 1005, 1011, 1016, 1020, 1021, 1024, 1122, 1141, 1147, 1157, 1170, 1171, 1176, 1180, 1185, 1195, 1197, 1206, 1214, 1227, 1228, 1233, 1239, 1245, 1246, 1256, 1258, 1268, 1271, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1283, 1284, 1285, 1287, 1288, 1290, 1291, 1292, 1293, 1294, 1295, 1297, 1501, 1620, 1930, 2090, 2127, 2145, 2213],\n         \"202380\": 2207,\n         \"20239\": [2241, 2265],\n-        \"2024\": [36, 270, 544, 546, 555, 567, 894, 898, 2127, 2213],\n+        \"2024\": [36, 270, 544, 546, 555, 567, 894, 898, 2127, 2213, 2228],\n         \"2025\": [544, 546, 555, 567, 894, 898, 2228],\n         \"20251\": 2307,\n-        \"2026\": 2228,\n         \"202602\": 2205,\n         \"202646\": 2230,\n         \"20271\": 2241,\n         \"202872\": [2184, 2214],\n         \"202946\": 2207,\n         \"203\": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211, 2231, 2253],\n         \"2030\": 2265,\n@@ -25827,15 +25824,15 @@\n         \"20585\": 2246,\n         \"205899\": 2199,\n         \"20591\": 2246,\n         \"205917\": 2207,\n         \"20593\": 2229,\n         \"20594\": 2277,\n         \"205944\": [2199, 2235],\n-        \"206\": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2210, 2211, 2220],\n+        \"206\": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211, 2220],\n         \"20601\": 2246,\n         \"206053\": [2185, 2191, 2197],\n         \"2061\": [2199, 2204],\n         \"20611\": 2246,\n         \"20612\": [2265, 2289],\n         \"206122\": 2193,\n         \"20613\": 2265,\n@@ -26062,15 +26059,15 @@\n         \"213588\": [2185, 2191, 2197],\n         \"2136\": 2204,\n         \"21365\": 2246,\n         \"21369\": 2246,\n         \"21372\": 2246,\n         \"21380\": 2243,\n         \"21390\": 2243,\n-        \"214\": [16, 17, 18, 19, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2210, 2211, 2212, 2235, 2254],\n+        \"214\": [16, 17, 18, 19, 2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211, 2212, 2235, 2254],\n         \"21400\": 2265,\n         \"21404\": 2246,\n         \"214041\": 2207,\n         \"214154\": 2207,\n         \"21417\": 2249,\n         \"21422\": 2298,\n         \"214235\": 2207,\n@@ -26480,15 +26477,15 @@\n         \"23273\": 2246,\n         \"23282\": 2246,\n         \"23283\": 2246,\n         \"23284\": 2246,\n         \"23293\": 2249,\n         \"23294\": 2246,\n         \"23296\": 2246,\n-        \"233\": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2203, 2210, 2220, 2254, 2298],\n+        \"233\": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2203, 2205, 2210, 2220, 2254, 2298],\n         \"233070\": 2207,\n         \"23309\": 2246,\n         \"233103\": 2184,\n         \"233141\": 2185,\n         \"23316\": 2289,\n         \"233203\": 2197,\n         \"23348\": 2265,\n@@ -26596,15 +26593,15 @@\n         \"237723\": 2195,\n         \"23774\": 2246,\n         \"23779\": 2265,\n         \"23784\": 2271,\n         \"23785\": 2249,\n         \"23788\": 2246,\n         \"237881\": 2185,\n-        \"238\": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2210, 2220, 2298],\n+        \"238\": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2220, 2298],\n         \"238000\": [2185, 2220],\n         \"23801\": 2246,\n         \"23803\": 2277,\n         \"23807\": 2246,\n         \"238075\": 2197,\n         \"23809\": 2271,\n         \"23814\": 2246,\n@@ -26737,15 +26734,15 @@\n         \"243678\": 2210,\n         \"2437\": 2216,\n         \"24371\": 2246,\n         \"24372\": 2246,\n         \"24382\": 2271,\n         \"2439\": [196, 771],\n         \"24398\": 2246,\n-        \"244\": [268, 745, 2185, 2186, 2188, 2195, 2197, 2199, 2203, 2210, 2220, 2222, 2224, 2246, 2254, 2298],\n+        \"244\": [268, 745, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2203, 2210, 2220, 2222, 2224, 2246, 2254, 2298],\n         \"24405\": 2246,\n         \"24408\": 2246,\n         \"244140625\": 2298,\n         \"24415\": 2246,\n         \"24416\": 2249,\n         \"24435\": [2283, 2298],\n         \"244413\": 2199,\n@@ -26929,15 +26926,15 @@\n         \"253128\": 2191,\n         \"25317\": 2248,\n         \"25318\": 2248,\n         \"253355\": 2210,\n         \"25338\": 2248,\n         \"253495\": 2207,\n         \"253881\": 2229,\n-        \"254\": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2220],\n+        \"254\": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2210, 2220],\n         \"254000\": [2185, 2220],\n         \"25403\": 2248,\n         \"25405\": 2249,\n         \"25409\": 2248,\n         \"254161\": [2184, 2195, 2214],\n         \"25433\": 2249,\n         \"25435\": 2249,\n@@ -27415,14 +27412,15 @@\n         \"276183\": 2257,\n         \"2762\": [2184, 2186, 2191],\n         \"276232\": [15, 2184, 2185, 2186, 2191, 2197, 2199, 2202, 2210, 2214, 2215, 2216, 2218, 2225, 2231, 2241, 2264],\n         \"27636\": 2250,\n         \"276386\": 2207,\n         \"27642\": 2250,\n         \"276464\": 2230,\n+        \"2765\": 2193,\n         \"27656\": [2294, 2298],\n         \"27660\": 2265,\n         \"2766617129497566\": 2257,\n         \"276662\": [2185, 2197, 2199, 2202, 2215, 2257],\n         \"27668\": 2265,\n         \"2767\": 2191,\n         \"27676\": 2265,\n@@ -27562,15 +27560,15 @@\n         \"283627\": 2229,\n         \"28368\": 2265,\n         \"2837\": 2216,\n         \"28375\": 2271,\n         \"28383\": 2265,\n         \"28385\": 2298,\n         \"28394\": 2277,\n-        \"284\": [16, 17, 18, 19, 2185, 2186, 2197, 2199, 2210, 2235],\n+        \"284\": [16, 17, 18, 19, 2186, 2197, 2199, 2210, 2235],\n         \"28406\": 2265,\n         \"28410\": 2265,\n         \"28425\": 2265,\n         \"28426\": 2265,\n         \"28427\": 2265,\n         \"284319\": 2202,\n         \"2846\": 2185,\n@@ -27742,15 +27740,15 @@\n         \"2936\": [2199, 2249],\n         \"293601\": 2199,\n         \"293786\": 2191,\n         \"2938\": 2199,\n         \"29388\": 2265,\n         \"293906\": 2207,\n         \"293926\": 2204,\n-        \"294\": [2186, 2197, 2199, 2210, 2218],\n+        \"294\": [2186, 2197, 2199, 2210],\n         \"29403\": 2265,\n         \"29405\": 2298,\n         \"294122\": 2199,\n         \"294348\": 2199,\n         \"294389\": 2207,\n         \"29442\": 2275,\n         \"2945\": 2191,\n@@ -28969,15 +28967,15 @@\n         \"35566\": 2277,\n         \"35574\": 2272,\n         \"35579\": 2277,\n         \"35584\": 2277,\n         \"35588\": 2272,\n         \"35596\": 2277,\n         \"35598\": 2272,\n-        \"356\": [78, 162, 2186, 2197, 2199, 2210, 2298],\n+        \"356\": [78, 162, 2185, 2186, 2193, 2197, 2199, 2210, 2298],\n         \"35606\": 2272,\n         \"35607\": 2277,\n         \"3561\": 2217,\n         \"35612\": 2283,\n         \"35614\": 2277,\n         \"3562\": 2217,\n         \"35625\": 2277,\n@@ -29543,15 +29541,15 @@\n         \"38433\": [2283, 2298],\n         \"38439\": 2283,\n         \"38453\": 2289,\n         \"38454\": 2289,\n         \"384724\": 2197,\n         \"3849\": 2218,\n         \"384941\": 2207,\n-        \"385\": [16, 17, 18, 19, 2186, 2193, 2197, 2199, 2210, 2235],\n+        \"385\": [16, 17, 18, 19, 2186, 2197, 2199, 2210, 2235],\n         \"38502\": 2283,\n         \"385062\": 2207,\n         \"38516\": 2283,\n         \"38521\": 2283,\n         \"38522\": 30,\n         \"38523\": 2283,\n         \"38525\": 2277,\n@@ -30029,15 +30027,15 @@\n         \"40585\": 2283,\n         \"40589\": 2294,\n         \"405906\": 2207,\n         \"405919\": 2195,\n         \"406\": [2186, 2199, 2210],\n         \"4060\": 2222,\n         \"40606\": 2283,\n-        \"4062\": [2193, 2217],\n+        \"4062\": 2217,\n         \"40628\": [2283, 2298],\n         \"4063\": 2217,\n         \"406345\": 2207,\n         \"40638\": 2298,\n         \"4065\": 2218,\n         \"40660\": 2283,\n         \"40662\": 2281,\n@@ -30293,15 +30291,15 @@\n         \"41770\": [2283, 2298],\n         \"417752\": 2207,\n         \"41777\": 2283,\n         \"41778\": 2282,\n         \"41779\": 2289,\n         \"41789\": 2298,\n         \"41797\": 2282,\n-        \"418\": [2186, 2199, 2210],\n+        \"418\": [2186, 2193, 2199, 2210],\n         \"41812\": 2283,\n         \"418176\": 2207,\n         \"41821\": 2283,\n         \"41828\": 2289,\n         \"41831\": 2283,\n         \"41836\": 2283,\n         \"4184\": 2257,\n@@ -31199,15 +31197,15 @@\n         \"45577\": 2294,\n         \"4558\": 2220,\n         \"45580\": 2294,\n         \"45582\": 2294,\n         \"45585\": 2294,\n         \"455893\": 2207,\n         \"45598\": 2294,\n-        \"456\": [833, 2199, 2210, 2218],\n+        \"456\": [833, 2199, 2210],\n         \"45601\": 2291,\n         \"45603\": 2290,\n         \"45608\": 2294,\n         \"456086\": 2197,\n         \"4561\": 2218,\n         \"45615\": 2290,\n         \"45618\": 2290,\n@@ -31361,14 +31359,15 @@\n         \"462824\": 2186,\n         \"46288\": 2294,\n         \"462887\": 2207,\n         \"46297\": 2292,\n         \"462989\": 2207,\n         \"463\": [2199, 2210, 2256],\n         \"46306\": 2291,\n+        \"463094\": 2228,\n         \"46312\": 2294,\n         \"46317\": 2291,\n         \"46319\": 2298,\n         \"4633\": 2218,\n         \"46330\": 2294,\n         \"463376\": 2207,\n         \"46357\": 2294,\n@@ -31427,14 +31426,15 @@\n         \"46589\": 2292,\n         \"466\": [2199, 2210],\n         \"46601\": 2294,\n         \"466039\": 2199,\n         \"466122\": 2207,\n         \"46613\": 2294,\n         \"46622\": 2294,\n+        \"466336\": 2228,\n         \"46634\": 2292,\n         \"4664\": [2186, 2227],\n         \"46653\": 2294,\n         \"46658\": 2294,\n         \"4667\": 2218,\n         \"46671\": 2293,\n         \"46673\": 2294,\n@@ -31583,15 +31583,14 @@\n         \"4746\": 2218,\n         \"47477\": 2294,\n         \"47480\": 2298,\n         \"47487\": 2294,\n         \"475\": [2184, 2199, 2205, 2210],\n         \"47500\": [2294, 2298],\n         \"47501\": 2293,\n-        \"475173\": 2228,\n         \"47521\": 2210,\n         \"47527\": 2294,\n         \"475308\": 2199,\n         \"47538\": 2294,\n         \"47557\": 2294,\n         \"47561\": 2298,\n         \"47563\": [2294, 2298],\n@@ -31620,15 +31619,14 @@\n         \"477014\": 2207,\n         \"47705\": 2298,\n         \"4771\": 2218,\n         \"477102\": 2207,\n         \"47713\": 2294,\n         \"47718\": 2294,\n         \"47728\": [2294, 2298],\n-        \"477316\": 2228,\n         \"477394\": 2207,\n         \"4774\": 2218,\n         \"477441\": 2207,\n         \"477495\": 2207,\n         \"47753\": 2294,\n         \"47761\": 2298,\n         \"47762\": 2293,\n@@ -31870,15 +31868,15 @@\n         \"489811\": 2219,\n         \"48996\": 2296,\n         \"489961\": 2207,\n         \"489981\": 2207,\n         \"48e\": [2186, 2227],\n         \"48hr\": 234,\n         \"49\": [15, 17, 19, 28, 242, 283, 817, 910, 1323, 1433, 2184, 2185, 2186, 2188, 2190, 2191, 2193, 2194, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2218, 2220, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2238, 2241, 2246, 2249, 2265, 2271, 2283],\n-        \"490\": [16, 17, 18, 19, 2185, 2194, 2199, 2201, 2203, 2210, 2235, 2249, 2283, 2294, 2298, 2307],\n+        \"490\": [16, 17, 18, 19, 2194, 2199, 2201, 2203, 2210, 2235, 2249, 2283, 2294, 2298, 2307],\n         \"49008\": 2298,\n         \"49010\": 2298,\n         \"49014\": 2298,\n         \"4902\": 2218,\n         \"49021\": 2298,\n         \"49037\": 2298,\n         \"490482\": 2199,\n@@ -32437,15 +32435,15 @@\n         \"517603\": 2207,\n         \"51764\": 2302,\n         \"51777\": 2302,\n         \"51778\": 2302,\n         \"51787\": 2302,\n         \"517871\": 2199,\n         \"51790\": 2302,\n-        \"518\": [16, 17, 18, 19, 2194, 2199, 2201, 2203, 2235, 2238, 2283, 2294, 2307],\n+        \"518\": [16, 17, 18, 19, 2193, 2194, 2199, 2201, 2203, 2235, 2238, 2283, 2294, 2307],\n         \"51800\": 2302,\n         \"51809\": 2302,\n         \"518162\": 2207,\n         \"51828\": 2302,\n         \"5185\": 2218,\n         \"51853\": 2298,\n         \"51856\": 2302,\n@@ -32592,15 +32590,15 @@\n         \"52555\": 2299,\n         \"52564\": 2302,\n         \"52568\": 2299,\n         \"52570\": 2299,\n         \"52586\": 2302,\n         \"52588\": 2299,\n         \"52590\": 2300,\n-        \"526\": [1444, 2199, 2257],\n+        \"526\": [1444, 2193, 2199, 2257],\n         \"52607\": 2302,\n         \"526206\": 2195,\n         \"526246\": 2195,\n         \"52625\": 2299,\n         \"52628\": 2302,\n         \"52629\": 2299,\n         \"52632\": 2302,\n@@ -32714,14 +32712,15 @@\n         \"53195\": 2300,\n         \"53199\": 2302,\n         \"532\": [16, 17, 18, 19, 2199, 2235],\n         \"53200\": 2300,\n         \"53212\": 2302,\n         \"53220\": 2302,\n         \"53229\": 2302,\n+        \"5323\": 2193,\n         \"53231\": 2301,\n         \"53232\": 2300,\n         \"532359\": 2207,\n         \"5324\": 2218,\n         \"532532\": [2184, 2214],\n         \"53255\": 2302,\n         \"53258\": 2302,\n@@ -34024,15 +34023,15 @@\n         \"6418\": 2220,\n         \"641817\": 2216,\n         \"642\": [2197, 2199],\n         \"6423\": 2221,\n         \"642343\": 2195,\n         \"6424\": 2235,\n         \"642994\": 2199,\n-        \"643\": [2197, 2199, 2205],\n+        \"643\": [2197, 2199],\n         \"643342\": 2210,\n         \"643563\": [2185, 2191, 2197, 2199, 2215, 2216, 2218, 2219, 2235, 2241, 2257],\n         \"6436\": 2191,\n         \"643703\": 2207,\n         \"6439\": 2220,\n         \"643950\": 2186,\n         \"644\": [29, 2197, 2199],\n@@ -34282,14 +34281,15 @@\n         \"671496\": 2207,\n         \"6715\": 2220,\n         \"6716\": 2220,\n         \"671824\": 2207,\n         \"6719\": 2220,\n         \"672808\": 2201,\n         \"672848\": 2207,\n+        \"673\": 2218,\n         \"673145\": 2215,\n         \"6732\": 2220,\n         \"6733\": 2220,\n         \"6734\": 2220,\n         \"6736\": 2226,\n         \"673601\": 2195,\n         \"6736897080883706\": 2197,\n@@ -34431,14 +34431,15 @@\n         \"687716\": 2207,\n         \"687738\": [15, 2185, 2191, 2197, 2199, 2202, 2204],\n         \"6877383895\": 2199,\n         \"687758\": 2184,\n         \"6878\": [2185, 2197, 2294],\n         \"6879\": [2185, 2197],\n         \"687921\": 2207,\n+        \"688\": 2205,\n         \"6880\": [2185, 2197],\n         \"6881\": [2185, 2197],\n         \"688223\": 2229,\n         \"6883\": 2185,\n         \"6884\": 2185,\n         \"6885\": [2185, 2220],\n         \"6886\": [2185, 2220],\n@@ -34920,15 +34921,15 @@\n         \"7485\": 2221,\n         \"748572\": 2191,\n         \"749\": 2207,\n         \"749014\": 2207,\n         \"7496\": 2221,\n         \"7498\": 2221,\n         \"749892\": 2186,\n-        \"75\": [23, 28, 107, 133, 144, 208, 213, 252, 266, 629, 708, 776, 782, 788, 1164, 1221, 1314, 1397, 1419, 1430, 1447, 1467, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2205, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2249, 2257, 2260, 2271],\n+        \"75\": [23, 28, 107, 133, 144, 208, 213, 252, 266, 629, 708, 776, 782, 788, 1164, 1221, 1314, 1397, 1419, 1430, 1447, 1467, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 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2218, 2220, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271],\n         \"770\": [2193, 2207],\n         \"7701\": 2221,\n         \"770309\": 2207,\n         \"7704\": 2222,\n         \"770555\": 2204,\n         \"770743\": 2207,\n         \"7708\": 2222,\n@@ -35218,15 +35219,15 @@\n         \"7890\": 2222,\n         \"7892\": 2222,\n         \"789461\": 2191,\n         \"789478\": 2207,\n         \"7897\": 2222,\n         \"789963\": 2186,\n         \"78e\": [2186, 2227],\n-        \"79\": [29, 1433, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271],\n+        \"79\": [29, 1433, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271],\n         \"7900\": 2222,\n         \"790087\": 2207,\n         \"7901\": 2222,\n    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15,\n@@ -35888,15 +35889,14 @@\n         \"881376\": 2204,\n         \"882\": [27, 2259],\n         \"8822\": 2226,\n         \"8823\": 2226,\n         \"8825\": 2235,\n         \"8826\": 2246,\n         \"882641\": 2230,\n-        \"883\": 2193,\n         \"8831\": 2224,\n         \"8833\": 2224,\n         \"883333\": 2222,\n         \"883362\": 2207,\n         \"8835\": 2224,\n         \"8836\": 2224,\n         \"883656\": 2184,\n@@ -35957,14 +35957,15 @@\n         \"8909\": 2224,\n         \"891\": [24, 25, 28, 32, 2197, 2199],\n         \"8910\": [2243, 2246],\n         \"891236\": 2193,\n         \"8917\": 2246,\n         \"891773\": 2166,\n         \"891939\": 2197,\n+        \"892\": 2193,\n         \"892029\": 2235,\n         \"892516\": 2186,\n         \"892667\": 2207,\n         \"892858\": 2218,\n         \"893\": [2186, 2197, 2259],\n         \"8932\": 2246,\n         \"893220\": 2207,\n@@ -36171,14 +36172,15 @@\n         \"920379\": 2230,\n         \"9204\": 2225,\n         \"9205\": 2225,\n         \"920733\": 2207,\n         \"9208\": 2246,\n         \"920830\": 2216,\n         \"9209\": 2202,\n+        \"921\": 2205,\n         \"9210\": 2225,\n         \"921208\": 2207,\n         \"921215\": 2207,\n         \"921297\": [102, 1158],\n         \"921494\": 15,\n         \"9217\": 2235,\n         \"9218\": 2228,\n@@ -36286,15 +36288,15 @@\n         \"938819\": 2204,\n         \"939\": 2230,\n         \"939036\": 2207,\n         \"939145\": 2207,\n         \"939470\": 2199,\n         \"939652\": 2207,\n         \"9398\": 2225,\n-        \"94\": [15, 282, 2184, 2185, 2186, 2188, 2191, 2192, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2226, 2230, 2232, 2235, 2246],\n+        \"94\": [15, 282, 2184, 2185, 2186, 2188, 2191, 2192, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2226, 2230, 2232, 2235, 2246],\n         \"9402\": 2228,\n         \"941248\": 2199,\n         \"9413\": 2238,\n         \"941451\": 2210,\n         \"9416\": 2228,\n         \"9422\": 2238,\n         \"942321\": 2207,\n@@ -36317,14 +36319,15 @@\n         \"9440\": 2225,\n         \"9442\": 2228,\n         \"9443\": 2225,\n         \"9444\": 2225,\n         \"944446\": 2207,\n         \"9445\": 2225,\n         \"944953\": 2210,\n+        \"945\": 2205,\n         \"9450\": 2225,\n         \"9452\": 2225,\n         \"9455\": 2231,\n         \"945553\": 2235,\n         \"9456\": 2227,\n         \"945867\": [2184, 2195, 2214],\n         \"945m\": 2202,\n@@ -36410,15 +36413,14 @@\n         \"957208\": 2205,\n         \"9573\": 2228,\n         \"957427\": 1318,\n         \"957545\": 2207,\n         \"9576\": 2228,\n         \"957923\": 2197,\n         \"957963\": 2207,\n-        \"958\": 2193,\n         \"958139\": 2230,\n         \"9586\": 2307,\n         \"958621\": [218, 793],\n         \"9589\": 2228,\n         \"9596\": 2226,\n         \"9597255933\": 2199,\n         \"959726\": [2185, 2197, 2199, 2202],\n@@ -36773,15 +36775,15 @@\n         \"__array_wrap__\": [2294, 2298],\n         \"__arrow_array__\": 10,\n         \"__bool__\": 2201,\n         \"__builtin__\": 2259,\n         \"__bytes__\": [2217, 2249],\n         \"__call__\": [2202, 2271, 2298],\n         \"__contain\": 2249,\n-        \"__contains__\": [2218, 2224, 2225, 2230, 2241, 2249, 2273, 2277, 2283, 2302],\n+        \"__contains__\": [2193, 2218, 2224, 2225, 2230, 2241, 2249, 2273, 2277, 2283, 2302],\n         \"__dataframe__\": [1077, 2294, 2307, 2309],\n         \"__deepcopy__\": 2235,\n         \"__delitem__\": 2283,\n         \"__dir__\": [2226, 2271, 2289],\n         \"__doc__\": [3, 1054],\n         \"__enter__\": 2224,\n         \"__eq__\": [1031, 1068, 2186, 2246, 2289, 2307],\n@@ -36964,15 +36966,14 @@\n         \"_kei\": 2192,\n         \"_l\": 2200,\n         \"_left\": [169, 1446],\n         \"_lexsort_depth\": 2185,\n         \"_lib\": [2, 5, 2185, 2191, 2194, 2199, 2201, 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2240, 2249, 2261, 2265, 2277, 2283, 2289, 2290, 2294, 2302, 2307],\n         \"behavior\": [0, 2, 3, 10, 12, 13, 14, 34, 72, 73, 74, 77, 81, 82, 94, 98, 99, 143, 146, 160, 169, 200, 201, 207, 208, 209, 210, 212, 213, 225, 226, 227, 242, 245, 255, 258, 263, 264, 270, 273, 274, 276, 277, 278, 283, 288, 296, 318, 427, 575, 581, 582, 583, 586, 593, 621, 622, 639, 652, 673, 681, 719, 720, 738, 774, 775, 781, 782, 783, 784, 787, 788, 800, 801, 802, 817, 873, 879, 880, 889, 894, 898, 900, 902, 903, 904, 910, 940, 943, 948, 957, 970, 997, 999, 1014, 1018, 1031, 1068, 1118, 1148, 1149, 1152, 1155, 1168, 1202, 1203, 1207, 1208, 1211, 1213, 1225, 1263, 1264, 1269, 1270, 1304, 1321, 1345, 1391, 1446, 1469, 1470, 1475, 1477, 1478, 1486, 1487, 1488, 1490, 1497, 1498, 2177, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2201, 2202, 2206, 2207, 2210, 2211, 2212, 2213, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2222, 2223, 2224, 2225, 2226, 2231, 2232, 2235, 2238, 2240, 2241, 2242, 2246, 2247, 2249, 2257, 2260, 2265, 2266, 2271, 2277, 2283, 2289, 2294, 2297, 2298, 2302, 2308],\n         \"behaviour\": [18, 75, 77, 97, 98, 169, 205, 242, 247, 584, 620, 621, 634, 778, 808, 817, 864, 880, 1123, 1345, 1391, 1419, 1446, 1468, 1469, 1470, 1471, 1472, 1475, 1476, 1477, 1478, 1481, 1482, 1483, 1484, 1486, 1487, 1488, 1490, 1498, 1499, 2186, 2188, 2199, 2201, 2202, 2206, 2221, 2222, 2223, 2224, 2225, 2226, 2231, 2235, 2241, 2243, 2246, 2249, 2265, 2271, 2277, 2278, 2289, 2294, 2298, 2302, 2307],\n         \"behind\": [2197, 2207, 2218, 2302, 2307],\n         \"behr\": 32,\n         \"beij\": [1145, 2207],\n-        \"being\": [1, 2, 3, 4, 10, 13, 17, 141, 150, 152, 160, 188, 189, 209, 212, 214, 223, 241, 253, 257, 259, 262, 263, 269, 276, 346, 352, 375, 376, 563, 617, 699, 717, 738, 764, 765, 783, 787, 798, 830, 835, 858, 859, 864, 886, 890, 902, 1035, 1076, 1117, 1192, 1253, 1387, 1388, 1431, 1433, 1469, 1472, 1475, 1486, 1487, 1493, 1494, 1495, 1496, 1498, 2186, 2188, 2191, 2194, 2195, 2197, 2199, 2201, 2204, 2206, 2210, 2211, 2212, 2214, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2225, 2226, 2228, 2229, 2230, 2231, 2232, 2233, 2234, 2235, 2237, 2238, 2239, 2241, 2242, 2246, 2249, 2250, 2261, 2265, 2266, 2267, 2271, 2275, 2277, 2278, 2283, 2286, 2287, 2289, 2294, 2296, 2298, 2302, 2304, 2307, 2308],\n+        \"being\": [1, 2, 3, 4, 10, 13, 17, 141, 150, 152, 160, 188, 189, 209, 212, 214, 223, 241, 253, 257, 259, 262, 263, 269, 276, 346, 352, 375, 376, 563, 617, 699, 717, 738, 764, 765, 783, 787, 798, 830, 835, 858, 859, 864, 886, 890, 902, 1035, 1076, 1117, 1192, 1253, 1387, 1388, 1431, 1433, 1469, 1472, 1475, 1486, 1487, 1493, 1494, 1495, 1496, 1498, 2186, 2188, 2191, 2193, 2194, 2195, 2197, 2199, 2201, 2204, 2206, 2210, 2211, 2212, 2214, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2225, 2226, 2228, 2229, 2230, 2231, 2232, 2233, 2234, 2235, 2237, 2238, 2239, 2241, 2242, 2246, 2249, 2250, 2261, 2265, 2266, 2267, 2271, 2275, 2277, 2278, 2283, 2286, 2287, 2289, 2294, 2296, 2298, 2302, 2304, 2307, 2308],\n         \"belal01\": 30,\n         \"belhb23\": 30,\n         \"belld01\": 30,\n         \"belld02\": 30,\n         \"belong\": [2, 150, 303, 445, 555, 655, 2195, 2210, 2211, 2217, 2222, 2228, 2232],\n         \"below\": [1, 3, 5, 6, 9, 10, 13, 15, 16, 17, 19, 22, 79, 92, 98, 102, 107, 117, 160, 196, 213, 252, 276, 378, 380, 465, 489, 591, 616, 621, 629, 693, 738, 771, 788, 902, 1121, 1146, 1148, 1149, 1152, 1158, 1164, 1203, 1207, 1208, 1211, 1221, 1264, 1309, 1323, 1326, 1328, 1343, 1344, 1345, 1354, 1391, 1397, 1403, 1421, 1430, 1433, 1488, 1490, 1498, 1657, 1677, 1699, 1720, 1793, 1815, 2167, 2175, 2184, 2185, 2186, 2188, 2194, 2195, 2197, 2199, 2202, 2206, 2207, 2208, 2210, 2211, 2212, 2218, 2221, 2228, 2231, 2232, 2235, 2241, 2249, 2265, 2271, 2275, 2277, 2283, 2289, 2294, 2298, 2302, 2307],\n         \"belr833\": 30,\n@@ -38255,15 +38256,15 @@\n         \"cheat\": [21, 2234],\n         \"check\": [1, 2, 4, 5, 6, 8, 12, 13, 18, 21, 22, 23, 24, 25, 26, 27, 30, 32, 36, 62, 75, 80, 81, 147, 153, 163, 169, 228, 256, 284, 346, 384, 386, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 420, 445, 447, 448, 453, 454, 455, 461, 469, 473, 478, 500, 501, 584, 592, 603, 615, 741, 799, 836, 837, 838, 839, 840, 841, 842, 843, 844, 888, 912, 976, 977, 978, 979, 1076, 1079, 1081, 1082, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1093, 1095, 1097, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1110, 1111, 1112, 1113, 1114, 1115, 1127, 1136, 1141, 1146, 1184, 1345, 1354, 1370, 1391, 1441, 1442, 1446, 1449, 1450, 1475, 1482, 1483, 1488, 1490, 1493, 1494, 1495, 1496, 1499, 1512, 1530, 1548, 1566, 1586, 1607, 1626, 1643, 1665, 1686, 1707, 1728, 1747, 1765, 1782, 1801, 1823, 1846, 1863, 1883, 1901, 1919, 1936, 1953, 1971, 1988, 2006, 2025, 2043, 2061, 2079, 2096, 2114, 2133, 2151, 2168, 2185, 2186, 2188, 2190, 2191, 2192, 2193, 2194, 2195, 2197, 2199, 2200, 2201, 2202, 2208, 2211, 2217, 2218, 2220, 2222, 2224, 2225, 2227, 2228, 2229, 2230, 2231, 2232, 2234, 2235, 2238, 2240, 2241, 2246, 2253, 2255, 2261, 2265, 2271, 2277, 2279, 2283, 2289, 2294, 2298, 2302, 2307, 2308],\n         \"check_array_index\": 2172,\n         \"check_categor\": [1494, 1495, 1496, 2242],\n         \"check_category_ord\": 1496,\n         \"check_column_typ\": 1494,\n         \"check_datetimelike_compat\": [1494, 1496],\n-        \"check_dict_or_set_index\": 2197,\n+        \"check_dict_or_set_index\": [2193, 2197],\n         \"check_dtyp\": [1493, 1494, 1496, 2271, 2272, 2299],\n         \"check_dtype_backend\": 2199,\n         \"check_exact\": [1493, 1494, 1495, 1496, 2272, 2277, 2307, 2308],\n         \"check_extens\": 2294,\n         \"check_flag\": [1494, 1496, 2290],\n         \"check_frame_typ\": 1494,\n         \"check_freq\": [1494, 1496, 2278],\n@@ -38699,15 +38700,15 @@\n         \"correctli\": [6, 7, 1042, 1345, 1391, 1400, 1433, 1469, 1475, 1486, 1488, 1490, 2168, 2186, 2199, 2202, 2215, 2217, 2218, 2220, 2221, 2222, 2223, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2233, 2235, 2238, 2239, 2242, 2243, 2246, 2249, 2250, 2265, 2267, 2277, 2283, 2284, 2285, 2286, 2289, 2290, 2293, 2298, 2301, 2302, 2303, 2304, 2307],\n         \"correl\": [99, 100, 102, 197, 597, 622, 1155, 1156, 1158, 1213, 1298, 1306, 1323, 1433, 1463, 2220, 2229, 2235, 2246, 2256, 2286, 2294, 2295],\n         \"correspond\": [2, 13, 21, 27, 30, 32, 35, 56, 65, 69, 79, 109, 111, 119, 121, 129, 131, 144, 163, 171, 173, 183, 186, 192, 199, 204, 206, 207, 210, 215, 216, 217, 220, 221, 222, 244, 249, 269, 272, 275, 280, 284, 285, 286, 330, 350, 363, 378, 380, 383, 405, 420, 455, 462, 465, 489, 510, 532, 540, 578, 591, 599, 631, 685, 694, 695, 696, 706, 707, 710, 734, 739, 740, 741, 747, 749, 750, 753, 761, 762, 773, 777, 780, 781, 783, 784, 790, 791, 792, 795, 796, 797, 799, 821, 830, 834, 835, 856, 858, 859, 876, 877, 878, 882, 901, 907, 912, 913, 938, 953, 972, 1042, 1061, 1128, 1188, 1202, 1249, 1338, 1339, 1340, 1341, 1387, 1397, 1403, 1404, 1421, 1430, 1439, 1441, 1442, 1449, 1450, 1455, 1456, 1469, 1470, 1476, 1480, 1482, 1483, 1484, 1486, 1498, 1506, 1524, 1815, 1982, 2000, 2167, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2208, 2209, 2210, 2211, 2212, 2217, 2220, 2222, 2228, 2230, 2232, 2241, 2246, 2249, 2253, 2271, 2277, 2283, 2289, 2294, 2298, 2302],\n         \"corrupt\": [2199, 2220, 2232, 2241, 2265, 2278, 2279, 2282, 2298, 2307],\n         \"corrwith\": [99, 597, 622, 1155, 1213, 2241, 2246, 2271, 2294, 2295, 2302],\n         \"cosh\": [2193, 2228],\n         \"cost\": [3, 13, 118, 132, 135, 144, 159, 161, 175, 1473, 2186, 2197, 2241, 2295],\n-        \"could\": [1, 2, 3, 5, 12, 13, 15, 16, 17, 18, 19, 22, 102, 162, 184, 197, 212, 251, 258, 265, 268, 272, 481, 787, 884, 889, 895, 1117, 1158, 1343, 1373, 1453, 1469, 1470, 1471, 1472, 1476, 1477, 1478, 1479, 1480, 1484, 1485, 1486, 1487, 2166, 2185, 2186, 2188, 2192, 2194, 2195, 2197, 2199, 2210, 2211, 2212, 2218, 2220, 2225, 2226, 2227, 2228, 2229, 2230, 2232, 2233, 2234, 2235, 2238, 2239, 2241, 2246, 2247, 2248, 2249, 2250, 2252, 2260, 2265, 2271, 2277, 2278, 2283, 2284, 2289, 2293, 2294, 2295, 2298, 2302, 2307, 2308],\n+        \"could\": [1, 2, 3, 5, 12, 13, 15, 16, 17, 18, 19, 22, 102, 162, 184, 197, 212, 251, 258, 265, 268, 272, 481, 787, 884, 889, 895, 1117, 1158, 1343, 1373, 1453, 1469, 1470, 1471, 1472, 1476, 1477, 1478, 1479, 1480, 1484, 1485, 1486, 1487, 2166, 2185, 2186, 2188, 2192, 2193, 2194, 2195, 2197, 2199, 2210, 2211, 2212, 2218, 2220, 2225, 2226, 2227, 2228, 2229, 2230, 2232, 2233, 2234, 2235, 2238, 2239, 2241, 2246, 2247, 2248, 2249, 2250, 2252, 2260, 2265, 2271, 2277, 2278, 2283, 2284, 2289, 2293, 2294, 2295, 2298, 2302, 2307, 2308],\n         \"couldn\": [22, 2277, 2286, 2298],\n         \"count\": [16, 18, 21, 23, 24, 107, 112, 123, 144, 172, 180, 281, 414, 436, 629, 748, 758, 831, 908, 1164, 1182, 1183, 1184, 1194, 1204, 1221, 1241, 1244, 1255, 1345, 1382, 1391, 1400, 1470, 1488, 1490, 2188, 2191, 2194, 2195, 2199, 2202, 2204, 2205, 2208, 2211, 2215, 2216, 2218, 2219, 2220, 2222, 2223, 2225, 2228, 2229, 2230, 2231, 2232, 2235, 2239, 2241, 2246, 2249, 2254, 2255, 2256, 2257, 2260, 2265, 2271, 2277, 2279, 2283, 2289, 2294, 2302],\n         \"counter\": [3, 1416, 2235],\n         \"counterexampl\": 2,\n         \"counterpart\": [98, 621, 2206, 2225, 2231, 2238, 2265, 2277, 2289, 2294],\n         \"countess\": 32,\n         \"counti\": [1443, 2199],\n@@ -39806,15 +39807,15 @@\n         \"farmer\": 2199,\n         \"farthest\": [91, 1458],\n         \"fashion\": [34, 39, 46, 2221, 2246, 2283],\n         \"fast\": [5, 15, 34, 83, 141, 256, 351, 594, 717, 888, 1203, 1264, 1469, 1470, 1476, 1486, 2184, 2186, 2192, 2193, 2195, 2196, 2199, 2210, 2222, 2226, 2235, 2246, 2249, 2253, 2254, 2255, 2256],\n         \"fast_path\": 2199,\n         \"fastavro\": [1473, 2249],\n         \"faster\": [4, 5, 15, 16, 34, 62, 151, 162, 251, 258, 262, 263, 265, 268, 272, 390, 615, 754, 757, 815, 884, 889, 895, 1152, 1211, 1242, 1243, 1469, 1486, 1498, 2163, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2208, 2211, 2214, 2215, 2216, 2219, 2220, 2222, 2232, 2238, 2246, 2249, 2253, 2255, 2256, 2277, 2289, 2302, 2307],\n-        \"fastest\": [2186, 2197, 2199],\n+        \"fastest\": [2186, 2193, 2197, 2199, 2218],\n         \"fastparquet\": [22, 263, 1345, 1391, 1478, 1488, 1490, 2184, 2199, 2202, 2205, 2238, 2246, 2249, 2265, 2271, 2277, 2278, 2283, 2286, 2289, 2294, 2298, 2302, 2307],\n         \"fastparquetimpl\": 2199,\n         \"fastpath\": [39, 573, 2194, 2201, 2203, 2246, 2265, 2271, 2283, 2294, 2298, 2302, 2307],\n         \"fatal\": 2229,\n         \"fault\": [2228, 2235, 2239, 2246, 2249, 2271, 2275, 2289],\n         \"faulti\": 2220,\n         \"favor\": [34, 2220, 2222, 2225, 2226, 2228, 2230, 2231, 2232, 2235, 2238, 2239, 2241, 2246, 2249, 2265, 2266, 2283, 2289, 2294, 2298],\n@@ -40887,15 +40888,15 @@\n         \"interchang\": [66, 246, 916, 953, 2172, 2299, 2300, 2302, 2307, 2308],\n         \"interchange_object\": [66, 1077],\n         \"interest\": [1, 2, 3, 13, 23, 24, 25, 28, 29, 32, 34, 35, 789, 2186, 2193, 2197, 2199, 2207, 2210, 2212, 2217, 2219, 2307, 2308],\n         \"interest_r\": 3,\n         \"interf\": 2265,\n         \"interfac\": [2, 10, 12, 13, 16, 17, 18, 19, 40, 77, 119, 695, 914, 1031, 1068, 1090, 2167, 2186, 2199, 2203, 2207, 2210, 2211, 2218, 2220, 2225, 2227, 2228, 2230, 2235, 2246, 2261, 2271, 2298, 2307],\n         \"interleav\": 2199,\n-        \"intermedi\": [7, 2172, 2195, 2205, 2210, 2212, 2253, 2307],\n+        \"intermedi\": [7, 2172, 2193, 2195, 2205, 2210, 2212, 2218, 2253, 2307],\n         \"intermix\": 2186,\n         \"intern\": [0, 7, 11, 22, 191, 194, 203, 268, 286, 364, 376, 430, 622, 624, 699, 767, 769, 873, 932, 938, 1031, 1044, 1123, 1124, 1140, 1148, 1149, 1203, 1207, 1208, 1213, 1215, 1264, 1280, 1345, 1361, 1364, 1388, 1391, 1422, 1423, 1433, 1469, 1486, 1488, 1490, 1493, 1494, 1495, 1496, 1499, 2186, 2188, 2193, 2194, 2195, 2197, 2202, 2207, 2210, 2213, 2216, 2217, 2219, 2220, 2230, 2232, 2235, 2238, 2246, 2249, 2253, 2261, 2263, 2265, 2267, 2271, 2274, 2277, 2280, 2289, 2293, 2298, 2307],\n         \"internal_cach\": 10,\n         \"internet\": 2,\n         \"interoper\": [2167, 2186, 2201, 2203, 2302],\n         \"interp1d\": [146, 720, 1280],\n         \"interp_\": 2201,\n@@ -41504,15 +41505,15 @@\n         \"logx\": [186, 762, 1188, 1249, 2211, 2215, 2249],\n         \"lon\": [10, 1069, 1071, 1072],\n         \"london\": [26, 27, 29, 30, 31, 586, 2210, 2221, 2271],\n         \"london_mg_per_cub\": 27,\n         \"long\": [0, 1, 2, 3, 21, 31, 119, 123, 167, 184, 185, 230, 241, 263, 695, 698, 804, 808, 873, 1345, 1391, 1444, 1445, 1453, 1454, 1469, 1486, 1487, 1488, 1490, 2163, 2166, 2185, 2188, 2190, 2199, 2202, 2204, 2205, 2208, 2210, 2214, 2216, 2218, 2220, 2222, 2225, 2228, 2229, 2230, 2231, 2232, 2235, 2238, 2239, 2240, 2241, 2243, 2246, 2249, 2277, 2278, 2289, 2302, 2307, 2308],\n         \"long_seri\": 2186,\n         \"longdoubl\": 2186,\n-        \"longer\": [1, 2, 5, 98, 134, 522, 533, 563, 621, 709, 873, 874, 1118, 1178, 1179, 1180, 1181, 1189, 1200, 1237, 1238, 1239, 1240, 1250, 1261, 1284, 1290, 1295, 1469, 1486, 2191, 2197, 2199, 2202, 2210, 2214, 2215, 2217, 2218, 2219, 2220, 2221, 2222, 2224, 2225, 2226, 2228, 2230, 2231, 2233, 2235, 2238, 2242, 2243, 2246, 2247, 2257, 2261, 2263, 2264, 2265, 2266, 2271, 2275, 2277, 2278, 2292, 2294, 2295, 2298, 2302],\n+        \"longer\": [1, 2, 5, 98, 134, 522, 533, 563, 621, 709, 873, 874, 1118, 1178, 1179, 1180, 1181, 1189, 1200, 1237, 1238, 1239, 1240, 1250, 1261, 1284, 1290, 1295, 1469, 1486, 2191, 2193, 2197, 2199, 2202, 2210, 2214, 2215, 2217, 2218, 2219, 2220, 2221, 2222, 2224, 2225, 2226, 2228, 2230, 2231, 2233, 2235, 2238, 2242, 2243, 2246, 2247, 2257, 2261, 2263, 2264, 2265, 2266, 2271, 2275, 2277, 2278, 2292, 2294, 2295, 2298, 2302],\n         \"longest\": [32, 923, 2217, 2272],\n         \"longitud\": [10, 30, 197, 1069, 1071, 1072],\n         \"longlong\": 2186,\n         \"longpanel\": [2228, 2246, 2257],\n         \"longtabl\": [259, 890, 1345, 1391, 1433, 1488, 1490, 2202, 2220, 2230, 2239, 2277, 2289, 2291, 2298],\n         \"longtablebuild\": 2277,\n         \"longtim\": 2228,\n@@ -43953,14 +43954,15 @@\n         \"slight\": [3, 2195],\n         \"slightli\": [3, 13, 203, 862, 866, 1387, 2185, 2197, 2199, 2217, 2228, 2277, 2294],\n         \"slinear\": [146, 720, 1280, 2218],\n         \"sln\": 2191,\n         \"sloper\": 25,\n         \"slow\": [2, 22, 1345, 1391, 1488, 1490, 1492, 1498, 2186, 2193, 2199, 2202, 2217, 2222, 2232, 2238, 2241, 2253, 2307],\n         \"slower\": [1152, 1211, 2193, 2197, 2199, 2202, 2210, 2218, 2228],\n+        \"slowest\": [2193, 2218],\n         \"slshape\": 1433,\n         \"sm\": [1275, 2186, 2210, 2227, 2232, 2307],\n         \"small\": [3, 13, 16, 17, 18, 19, 29, 111, 185, 190, 191, 194, 754, 757, 766, 767, 769, 1242, 1243, 1454, 2185, 2186, 2193, 2195, 2199, 2205, 2207, 2210, 2216, 2218, 2219, 2220, 2221, 2222, 2223, 2224, 2225, 2226, 2228, 2230, 2232, 2233, 2234, 2236, 2237, 2239, 2241, 2242, 2243, 2245, 2249, 2271, 2277, 2283, 2289, 2294, 2298, 2302],\n         \"smaller\": [0, 94, 144, 268, 745, 1345, 1391, 1488, 1490, 1499, 2186, 2188, 2193, 2202, 2207, 2208, 2210, 2211, 2243, 2249],\n         \"smallest\": [176, 179, 360, 588, 754, 757, 1191, 1194, 1242, 1243, 1252, 1255, 1499, 2199, 2205, 2235, 2246, 2264, 2294],\n         \"smallint\": [2199, 2307],\n         \"smart\": [22, 2186, 2277],\n@@ -44790,15 +44792,15 @@\n         \"tolist\": [15, 432, 891, 2199, 2222, 2238, 2246, 2289, 2298, 2302],\n         \"tolong\": 2241,\n         \"tom\": [13, 35, 2199, 2247, 2248, 2294],\n         \"tomaugsburg\": 2231,\n         \"tomaugspurg\": [13, 35],\n         \"toml\": [2, 22, 2238, 2265],\n         \"too\": [2, 3, 233, 807, 831, 1196, 1257, 1358, 1469, 1470, 1486, 2197, 2199, 2205, 2207, 2211, 2215, 2217, 2220, 2231, 2241, 2249, 2257, 2274, 2277, 2283, 2289, 2293, 2294, 2298, 2308],\n-        \"took\": [2199, 2223, 2241],\n+        \"took\": [2193, 2199, 2218, 2223, 2241],\n         \"tool\": [2, 5, 6, 8, 10, 15, 21, 22, 34, 36, 1146, 1469, 1472, 1486, 2184, 2185, 2186, 2191, 2193, 2195, 2196, 2210, 2220, 2225, 2226, 2232, 2235, 2241, 2246, 2260, 2283, 2298, 2307],\n         \"tooltip\": [1402, 1423, 2196, 2283],\n         \"toordin\": 2302,\n         \"top\": [22, 34, 91, 107, 148, 149, 177, 178, 185, 186, 203, 205, 212, 214, 241, 259, 341, 348, 376, 402, 413, 629, 699, 725, 726, 755, 756, 762, 778, 787, 890, 905, 1036, 1051, 1164, 1188, 1191, 1221, 1249, 1252, 1345, 1387, 1388, 1391, 1400, 1433, 1454, 1458, 1488, 1490, 2167, 2172, 2184, 2186, 2188, 2193, 2195, 2199, 2202, 2204, 2207, 2209, 2211, 2217, 2218, 2220, 2222, 2227, 2230, 2232, 2235, 2238, 2241, 2260, 2264, 2265, 2283, 2289, 2302],\n         \"topic\": [0, 4, 13, 35, 2185, 2196],\n         \"topmost\": 2204,\n         \"toprul\": [259, 890, 1433, 2277],\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/user_guide/advanced.html", "source2": "./usr/share/doc/python-pandas-doc/html/user_guide/advanced.html", "unified_diff": "@@ -1849,25 +1849,25 @@\n <span class=\"gp\">In [141]: </span><span class=\"n\">indexer</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">10000</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [142]: </span><span class=\"n\">random</span><span class=\"o\">.</span><span class=\"n\">shuffle</span><span class=\"p\">(</span><span class=\"n\">indexer</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [143]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> arr[indexer]\n <span class=\"gp\">   .....: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> arr.take(indexer, axis=0)\n <span class=\"gp\">   .....: </span>\n-<span class=\"go\">284 us +- 68.4 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>\n-<span class=\"go\">95.8 us +- 2.13 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)</span>\n+<span class=\"go\">356 us +- 21 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>\n+<span class=\"go\">105 us +- 2.38 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [144]: </span><span class=\"n\">ser</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">(</span><span class=\"n\">arr</span><span class=\"p\">[:,</span> <span class=\"mi\">0</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [145]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> ser.iloc[indexer]\n <span class=\"gp\">   .....: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> ser.take(indexer)\n <span class=\"gp\">   .....: </span>\n-<span class=\"go\">147 us +- 136 ns per loop (mean +- std. dev. of 7 runs, 10,000 loops each)</span>\n-<span class=\"go\">133 us +- 490 ns per loop (mean +- std. dev. of 7 runs, 10,000 loops each)</span>\n+<span class=\"go\">215 us +- 2.55 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>\n+<span class=\"go\">198 us +- 2.83 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>\n </pre></div>\n </div>\n </section>\n <section id=\"index-types\">\n <span id=\"advanced-index-types\"></span><h2>Index types<a class=\"headerlink\" href=\"#index-types\" title=\"Link to this heading\">\u00b6</a></h2>\n <p>We have discussed <code class=\"docutils literal notranslate\"><span class=\"pre\">MultiIndex</span></code> in the previous sections pretty extensively.\n Documentation about <code class=\"docutils literal notranslate\"><span class=\"pre\">DatetimeIndex</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">PeriodIndex</span></code> are shown <a class=\"reference internal\" href=\"timeseries.html#timeseries-overview\"><span class=\"std std-ref\">here</span></a>,\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -1257,23 +1257,23 @@\n In [141]: indexer = np.arange(10000)\n \n In [142]: random.shuffle(indexer)\n \n In [143]: %timeit arr[indexer]\n    .....: %timeit arr.take(indexer, axis=0)\n    .....:\n-284 us +- 68.4 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)\n-95.8 us +- 2.13 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)\n+356 us +- 21 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)\n+105 us +- 2.38 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)\n In [144]: ser = pd.Series(arr[:, 0])\n \n In [145]: %timeit ser.iloc[indexer]\n    .....: %timeit ser.take(indexer)\n    .....:\n-147 us +- 136 ns per loop (mean +- std. dev. of 7 runs, 10,000 loops each)\n-133 us +- 490 ns per loop (mean +- std. dev. of 7 runs, 10,000 loops each)\n+215 us +- 2.55 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)\n+198 us +- 2.83 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)\n *\b**\b**\b**\b**\b* I\bIn\bnd\bde\bex\bx t\bty\byp\bpe\bes\bs_\b?\b\u00b6 *\b**\b**\b**\b**\b*\n We have discussed MultiIndex in the previous sections pretty extensively.\n Documentation about DatetimeIndex and PeriodIndex are shown _\bh_\be_\br_\be, and\n documentation about TimedeltaIndex is found _\bh_\be_\br_\be.\n In the following sub-sections we will highlight some other index types.\n *\b**\b**\b**\b* C\bCa\bat\bte\beg\bgo\bor\bri\bic\bca\bal\blI\bIn\bnd\bde\bex\bx_\b?\b\u00b6 *\b**\b**\b**\b*\n _\bC_\ba_\bt_\be_\bg_\bo_\br_\bi_\bc_\ba_\bl_\bI_\bn_\bd_\be_\bx is a type of index that is useful for supporting indexing with\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/user_guide/enhancingperf.html", "source2": "./usr/share/doc/python-pandas-doc/html/user_guide/enhancingperf.html", "unified_diff": "@@ -529,31 +529,32 @@\n <span class=\"gp\">   ...: </span>        <span class=\"n\">s</span> <span class=\"o\">+=</span> <span class=\"n\">f</span><span class=\"p\">(</span><span class=\"n\">a</span> <span class=\"o\">+</span> <span class=\"n\">i</span> <span class=\"o\">*</span> <span class=\"n\">dx</span><span class=\"p\">)</span>\n <span class=\"gp\">   ...: </span>    <span class=\"k\">return</span> <span class=\"n\">s</span> <span class=\"o\">*</span> <span class=\"n\">dx</span>\n <span class=\"gp\">   ...: </span>\n </pre></div>\n </div>\n <p>We achieve our result by using <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.apply.html#pandas.DataFrame.apply\" title=\"pandas.DataFrame.apply\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">DataFrame.apply()</span></code></a> (row-wise):</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [5]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> df.apply(lambda x: integrate_f(x[&quot;a&quot;], x[&quot;b&quot;], x[&quot;N&quot;]), axis=1)\n-<span class=\"go\">102 ms +- 28.6 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n+<span class=\"go\">The slowest run took 4.94 times longer than the fastest. This could mean that an intermediate result is being cached.</span>\n+<span class=\"go\">356 ms +- 244 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n </pre></div>\n </div>\n <p>Let\u2019s take a look and see where the time is spent during this operation\n using the <a class=\"reference external\" href=\"https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-prun\">prun ipython magic function</a>:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"go\"># most time consuming 4 calls</span>\n <span class=\"gp\">In [6]: </span><span class=\"o\">%</span><span class=\"k\">prun</span> -l 4 df.apply(lambda x: integrate_f(x[&quot;a&quot;], x[&quot;b&quot;], x[&quot;N&quot;]), axis=1)  # noqa E999\n-<span class=\"go\">         605946 function calls (605928 primitive calls) in 0.238 seconds</span>\n+<span class=\"go\">         605946 function calls (605928 primitive calls) in 5.198 seconds</span>\n \n <span class=\"go\">   Ordered by: internal time</span>\n <span class=\"go\">   List reduced from 159 to 4 due to restriction &lt;4&gt;</span>\n \n <span class=\"go\">   ncalls  tottime  percall  cumtime  percall filename:lineno(function)</span>\n-<span class=\"go\">     1000    0.141    0.000    0.206    0.000 &lt;ipython-input-4-c2a74e076cf0&gt;:1(integrate_f)</span>\n-<span class=\"go\">   552423    0.065    0.000    0.065    0.000 &lt;ipython-input-3-c138bdd570e3&gt;:1(f)</span>\n-<span class=\"go\">     3000    0.005    0.000    0.020    0.000 series.py:1095(__getitem__)</span>\n-<span class=\"go\">     3000    0.004    0.000    0.009    0.000 series.py:1220(_get_value)</span>\n+<span class=\"go\">     1000    2.526    0.003    4.418    0.004 &lt;ipython-input-4-c2a74e076cf0&gt;:1(integrate_f)</span>\n+<span class=\"go\">   552423    1.892    0.000    1.892    0.000 &lt;ipython-input-3-c138bdd570e3&gt;:1(f)</span>\n+<span class=\"go\">     3000    0.136    0.000    0.518    0.000 series.py:1095(__getitem__)</span>\n+<span class=\"go\">     3000    0.120    0.000    0.122    0.000 base.py:3777(get_loc)</span>\n </pre></div>\n </div>\n <p>By far the majority of time is spend inside either <code class=\"docutils literal notranslate\"><span class=\"pre\">integrate_f</span></code> or <code class=\"docutils literal notranslate\"><span class=\"pre\">f</span></code>,\n hence we\u2019ll concentrate our efforts cythonizing these two functions.</p>\n </section>\n <section id=\"plain-cython\">\n <span id=\"enhancingperf-plain\"></span><h3>Plain Cython<a class=\"headerlink\" href=\"#plain-cython\" title=\"Link to this heading\">\u00b6</a></h3>\n@@ -571,15 +572,15 @@\n <span class=\"gp\">   ...: </span>    for i in range(N):\n <span class=\"gp\">   ...: </span>        s += f_plain(a + i * dx)\n <span class=\"gp\">   ...: </span>    return s * dx\n <span class=\"gp\">   ...: </span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [9]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> df.apply(lambda x: integrate_f_plain(x[&quot;a&quot;], x[&quot;b&quot;], x[&quot;N&quot;]), axis=1)\n-<span class=\"go\">93 ms +- 4.77 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n+<span class=\"go\">347 ms +- 120 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n </pre></div>\n </div>\n <p>This has improved the performance compared to the pure Python approach by one-third.</p>\n </section>\n <section id=\"declaring-c-types\">\n <span id=\"enhancingperf-type\"></span><h3>Declaring C types<a class=\"headerlink\" href=\"#declaring-c-types\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>We can annotate the function variables and return types as well as use <code class=\"docutils literal notranslate\"><span class=\"pre\">cdef</span></code>\n@@ -595,36 +596,36 @@\n <span class=\"gp\">   ....: </span>    for i in range(N):\n <span class=\"gp\">   ....: </span>        s += f_typed(a + i * dx)\n <span class=\"gp\">   ....: </span>    return s * dx\n <span class=\"gp\">   ....: </span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [11]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> df.apply(lambda x: integrate_f_typed(x[&quot;a&quot;], x[&quot;b&quot;], x[&quot;N&quot;]), axis=1)\n-<span class=\"go\">11.3 ms +- 1.15 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n+<span class=\"go\">26.3 ms +- 2.79 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n </pre></div>\n </div>\n <p>Annotating the functions with C types yields an over ten times performance improvement compared to\n the original Python implementation.</p>\n </section>\n <section id=\"using-ndarray\">\n <span id=\"enhancingperf-ndarray\"></span><h3>Using ndarray<a class=\"headerlink\" href=\"#using-ndarray\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>When re-profiling, time is spent creating a <a class=\"reference internal\" href=\"../reference/api/pandas.Series.html#pandas.Series\" title=\"pandas.Series\"><code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">Series</span></code></a> from each row, and calling <code class=\"docutils literal notranslate\"><span class=\"pre\">__getitem__</span></code> from both\n the index and the series (three times for each row). These Python function calls are expensive and\n can be improved by passing an <code class=\"docutils literal notranslate\"><span class=\"pre\">np.ndarray</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [12]: </span><span class=\"o\">%</span><span class=\"k\">prun</span> -l 4 df.apply(lambda x: integrate_f_typed(x[&quot;a&quot;], x[&quot;b&quot;], x[&quot;N&quot;]), axis=1)\n-<span class=\"go\">         52523 function calls (52505 primitive calls) in 0.028 seconds</span>\n+<span class=\"go\">         52523 function calls (52505 primitive calls) in 0.103 seconds</span>\n \n <span class=\"go\">   Ordered by: internal time</span>\n <span class=\"go\">   List reduced from 157 to 4 due to restriction &lt;4&gt;</span>\n \n <span class=\"go\">   ncalls  tottime  percall  cumtime  percall filename:lineno(function)</span>\n-<span class=\"go\">     3000    0.005    0.000    0.017    0.000 series.py:1095(__getitem__)</span>\n-<span class=\"go\">     3000    0.003    0.000    0.008    0.000 series.py:1220(_get_value)</span>\n-<span class=\"go\">    16098    0.003    0.000    0.004    0.000 {built-in method builtins.isinstance}</span>\n-<span class=\"go\">     3000    0.002    0.000    0.003    0.000 base.py:3777(get_loc)</span>\n+<span class=\"go\">     3000    0.016    0.000    0.070    0.000 series.py:1095(__getitem__)</span>\n+<span class=\"go\">    16098    0.012    0.000    0.016    0.000 {built-in method builtins.isinstance}</span>\n+<span class=\"go\">     3000    0.012    0.000    0.030    0.000 series.py:1220(_get_value)</span>\n+<span class=\"go\">     3000    0.009    0.000    0.017    0.000 indexing.py:2765(check_dict_or_set_indexers)</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [13]: </span><span class=\"o\">%%</span><span class=\"k\">cython</span>\n <span class=\"gp\">   ....: </span>cimport numpy as np\n <span class=\"gp\">   ....: </span>import numpy as np\n <span class=\"gp\">   ....: </span>cdef double f_typed(double x) except? -2:\n <span class=\"gp\">   ....: </span>    return x * (x - 1)\n@@ -659,15 +660,15 @@\n </div>\n <p>This implementation creates an array of zeros and inserts the result\n of <code class=\"docutils literal notranslate\"><span class=\"pre\">integrate_f_typed</span></code> applied over each row. Looping over an <code class=\"docutils literal notranslate\"><span class=\"pre\">ndarray</span></code> is faster\n in Cython than looping over a <a class=\"reference internal\" href=\"../reference/api/pandas.Series.html#pandas.Series\" title=\"pandas.Series\"><code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">Series</span></code></a> object.</p>\n <p>Since <code class=\"docutils literal notranslate\"><span class=\"pre\">apply_integrate_f</span></code> is typed to accept an <code class=\"docutils literal notranslate\"><span class=\"pre\">np.ndarray</span></code>, <a class=\"reference internal\" href=\"../reference/api/pandas.Series.to_numpy.html#pandas.Series.to_numpy\" title=\"pandas.Series.to_numpy\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">Series.to_numpy()</span></code></a>\n calls are needed to utilize this function.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [14]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> apply_integrate_f(df[&quot;a&quot;].to_numpy(), df[&quot;b&quot;].to_numpy(), df[&quot;N&quot;].to_numpy())\n-<span class=\"go\">1.3 ms +- 3.86 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>\n+<span class=\"go\">1.84 ms +- 15.1 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n </pre></div>\n </div>\n <p>Performance has improved from the prior implementation by almost ten times.</p>\n </section>\n <section id=\"disabling-compiler-directives\">\n <span id=\"enhancingperf-boundswrap\"></span><h3>Disabling compiler directives<a class=\"headerlink\" href=\"#disabling-compiler-directives\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>The majority of the time is now spent in <code class=\"docutils literal notranslate\"><span class=\"pre\">apply_integrate_f</span></code>. Disabling Cython\u2019s <code class=\"docutils literal notranslate\"><span class=\"pre\">boundscheck</span></code>\n@@ -675,18 +676,18 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [15]: </span><span class=\"o\">%</span><span class=\"k\">prun</span> -l 4 apply_integrate_f(df[&quot;a&quot;].to_numpy(), df[&quot;b&quot;].to_numpy(), df[&quot;N&quot;].to_numpy())\n <span class=\"go\">         78 function calls in 0.002 seconds</span>\n \n <span class=\"go\">   Ordered by: internal time</span>\n <span class=\"go\">   List reduced from 21 to 4 due to restriction &lt;4&gt;</span>\n \n <span class=\"go\">   ncalls  tottime  percall  cumtime  percall filename:lineno(function)</span>\n-<span class=\"go\">        1    0.001    0.001    0.002    0.002 &lt;string&gt;:1(&lt;module&gt;)</span>\n-<span class=\"go\">        1    0.000    0.000    0.000    0.000 {method &#39;disable&#39; of &#39;_lsprof.Profiler&#39; objects}</span>\n+<span class=\"go\">        1    0.002    0.002    0.002    0.002 &lt;string&gt;:1(&lt;module&gt;)</span>\n+<span class=\"go\">        3    0.000    0.000    0.000    0.000 base.py:5323(__contains__)</span>\n <span class=\"go\">        1    0.000    0.000    0.002    0.002 {built-in method builtins.exec}</span>\n-<span class=\"go\">        3    0.000    0.000    0.000    0.000 frame.py:4062(__getitem__)</span>\n+<span class=\"go\">       18    0.000    0.000    0.000    0.000 {built-in method builtins.isinstance}</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [16]: </span><span class=\"o\">%%</span><span class=\"k\">cython</span>\n <span class=\"gp\">   ....: </span>cimport cython\n <span class=\"gp\">   ....: </span>cimport numpy as np\n <span class=\"gp\">   ....: </span>import numpy as np\n <span class=\"gp\">   ....: </span>cdef np.float64_t f_typed(np.float64_t x) except? -2:\n@@ -719,15 +720,15 @@\n <span class=\"go\">                 from /build/reproducible-path/pandas-2.2.2+dfsg/buildtmp/.cache/ipython/cython/_cython_magic_73b4b9b50a65184309e4abf64d4849b0ed40427a.c:1251:</span>\n <span class=\"go\">/usr/lib/python3/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning &quot;Using deprecated NumPy API, disable it with &quot; &quot;#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION&quot; [-Wcpp]</span>\n <span class=\"go\">   17 | #warning &quot;Using deprecated NumPy API, disable it with &quot; \\</span>\n <span class=\"go\">      |  ^~~~~~~</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [17]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> apply_integrate_f_wrap(df[&quot;a&quot;].to_numpy(), df[&quot;b&quot;].to_numpy(), df[&quot;N&quot;].to_numpy())\n-<span class=\"go\">1.22 ms +- 214 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>\n+<span class=\"go\">1.64 ms +- 35 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>\n </pre></div>\n </div>\n <p>However, a loop indexer <code class=\"docutils literal notranslate\"><span class=\"pre\">i</span></code> accessing an invalid location in an array would cause a segfault because memory access isn\u2019t checked.\n For more about <code class=\"docutils literal notranslate\"><span class=\"pre\">boundscheck</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">wraparound</span></code>, see the Cython docs on\n <a class=\"reference external\" href=\"https://cython.readthedocs.io/en/latest/src/userguide/source_files_and_compilation.html#compiler-directives\">compiler directives</a>.</p>\n </section>\n </section>\n@@ -1085,19 +1086,19 @@\n compared to standard Python syntax for large <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.html#pandas.DataFrame\" title=\"pandas.DataFrame\"><code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code></a>. This engine requires the\n optional dependency <code class=\"docutils literal notranslate\"><span class=\"pre\">numexpr</span></code> to be installed.</p>\n <p>The <code class=\"docutils literal notranslate\"><span class=\"pre\">'python'</span></code> engine is generally <em>not</em> useful except for testing\n other evaluation engines against it. You will achieve <strong>no</strong> performance\n benefits using <a class=\"reference internal\" href=\"../reference/api/pandas.eval.html#pandas.eval\" title=\"pandas.eval\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">eval()</span></code></a> with <code class=\"docutils literal notranslate\"><span class=\"pre\">engine='python'</span></code> and may\n incur a performance hit.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [40]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> df1 + df2 + df3 + df4\n-<span class=\"go\">27.3 ms +- 1.75 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n+<span class=\"go\">55.2 ms +- 3.23 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [41]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> pd.eval(&quot;df1 + df2 + df3 + df4&quot;, engine=&quot;python&quot;)\n-<span class=\"go\">14.2 ms +- 3.41 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n+<span class=\"go\">58.3 ms +- 3.18 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n </pre></div>\n </div>\n </section>\n <section id=\"the-dataframe-eval-method\">\n <h3>The <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.eval.html#pandas.DataFrame.eval\" title=\"pandas.DataFrame.eval\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">DataFrame.eval()</span></code></a> method<a class=\"headerlink\" href=\"#the-dataframe-eval-method\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>In addition to the top level <a class=\"reference internal\" href=\"../reference/api/pandas.eval.html#pandas.eval\" title=\"pandas.eval\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">pandas.eval()</span></code></a> function you can also\n evaluate an expression in the \u201ccontext\u201d of a <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.html#pandas.DataFrame\" title=\"pandas.DataFrame\"><code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code></a>.</p>\n@@ -1212,39 +1213,39 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [58]: </span><span class=\"n\">nrows</span><span class=\"p\">,</span> <span class=\"n\">ncols</span> <span class=\"o\">=</span> <span class=\"mi\">20000</span><span class=\"p\">,</span> <span class=\"mi\">100</span>\n \n <span class=\"gp\">In [59]: </span><span class=\"n\">df1</span><span class=\"p\">,</span> <span class=\"n\">df2</span><span class=\"p\">,</span> <span class=\"n\">df3</span><span class=\"p\">,</span> <span class=\"n\">df4</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">(</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">random</span><span class=\"o\">.</span><span class=\"n\">randn</span><span class=\"p\">(</span><span class=\"n\">nrows</span><span class=\"p\">,</span> <span class=\"n\">ncols</span><span class=\"p\">))</span> <span class=\"k\">for</span> <span class=\"n\">_</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mi\">4</span><span class=\"p\">)]</span>\n </pre></div>\n </div>\n <p><a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.html#pandas.DataFrame\" title=\"pandas.DataFrame\"><code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code></a> arithmetic:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [60]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> df1 + df2 + df3 + df4\n-<span class=\"go\">29.7 ms +- 958 us per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n+<span class=\"go\">56 ms +- 3.6 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [61]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> pd.eval(&quot;df1 + df2 + df3 + df4&quot;)\n-<span class=\"go\">11.5 ms +- 385 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n+<span class=\"go\">15.4 ms +- 1.1 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n </pre></div>\n </div>\n <p><a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.html#pandas.DataFrame\" title=\"pandas.DataFrame\"><code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code></a> comparison:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [62]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> (df1 &gt; 0) &amp; (df2 &gt; 0) &amp; (df3 &gt; 0) &amp; (df4 &gt; 0)\n-<span class=\"go\">12.9 ms +- 3.07 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n+<span class=\"go\">27.2 ms +- 1.26 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [63]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> pd.eval(&quot;(df1 &gt; 0) &amp; (df2 &gt; 0) &amp; (df3 &gt; 0) &amp; (df4 &gt; 0)&quot;)\n-<span class=\"go\">8.75 ms +- 883 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n+<span class=\"go\">13.3 ms +- 838 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n </pre></div>\n </div>\n <p><a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.html#pandas.DataFrame\" title=\"pandas.DataFrame\"><code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code></a> arithmetic with unaligned axes.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [64]: </span><span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">(</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">random</span><span class=\"o\">.</span><span class=\"n\">randn</span><span class=\"p\">(</span><span class=\"mi\">50</span><span class=\"p\">))</span>\n \n <span class=\"gp\">In [65]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> df1 + df2 + df3 + df4 + s\n-<span class=\"go\">20.9 ms +- 1.19 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n+<span class=\"go\">85.2 ms +- 2.93 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [66]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> pd.eval(&quot;df1 + df2 + df3 + df4 + s&quot;)\n-<span class=\"go\">6.08 ms +- 134 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n+<span class=\"go\">15.7 ms +- 254 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n </pre></div>\n </div>\n <div class=\"admonition note\">\n <p class=\"admonition-title\">Note</p>\n <p>Operations such as</p>\n <div class=\"highlight-python notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"mi\">1</span> <span class=\"ow\">and</span> <span class=\"mi\">2</span>  <span class=\"c1\"># would parse to 1 &amp; 2, but should evaluate to 2</span>\n <span class=\"mi\">3</span> <span class=\"ow\">or</span> <span class=\"mi\">4</span>  <span class=\"c1\"># would parse to 3 | 4, but should evaluate to 3</span>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -113,32 +113,34 @@\n    ...:     dx = (b - a) / N\n    ...:     for i in range(N):\n    ...:         s += f(a + i * dx)\n    ...:     return s * dx\n    ...:\n We achieve our result by using _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by_\b(_\b) (row-wise):\n In [5]: %timeit df.apply(lambda x: integrate_f(x[\"a\"], x[\"b\"], x[\"N\"]), axis=1)\n-102 ms +- 28.6 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+The slowest run took 4.94 times longer than the fastest. This could mean that\n+an intermediate result is being cached.\n+356 ms +- 244 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n Let\u2019s take a look and see where the time is spent during this operation using\n the _\bp_\br_\bu_\bn_\b _\bi_\bp_\by_\bt_\bh_\bo_\bn_\b _\bm_\ba_\bg_\bi_\bc_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn:\n # most time consuming 4 calls\n In [6]: %prun -l 4 df.apply(lambda x: integrate_f(x[\"a\"], x[\"b\"], x[\"N\"]),\n axis=1)  # noqa E999\n-         605946 function calls (605928 primitive calls) in 0.238 seconds\n+         605946 function calls (605928 primitive calls) in 5.198 seconds\n \n    Ordered by: internal time\n    List reduced from 159 to 4 due to restriction <4>\n \n    ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n-     1000    0.141    0.000    0.206    0.000 <ipython-input-4-c2a74e076cf0>:1\n+     1000    2.526    0.003    4.418    0.004 <ipython-input-4-c2a74e076cf0>:1\n (integrate_f)\n-   552423    0.065    0.000    0.065    0.000 <ipython-input-3-c138bdd570e3>:1\n+   552423    1.892    0.000    1.892    0.000 <ipython-input-3-c138bdd570e3>:1\n (f)\n-     3000    0.005    0.000    0.020    0.000 series.py:1095(__getitem__)\n-     3000    0.004    0.000    0.009    0.000 series.py:1220(_get_value)\n+     3000    0.136    0.000    0.518    0.000 series.py:1095(__getitem__)\n+     3000    0.120    0.000    0.122    0.000 base.py:3777(get_loc)\n By far the majority of time is spend inside either integrate_f or f, hence\n we\u2019ll concentrate our efforts cythonizing these two functions.\n *\b**\b**\b**\b* P\bPl\bla\bai\bin\bn C\bCy\byt\bth\bho\bon\bn_\b?\b\u00b6 *\b**\b**\b**\b*\n First we\u2019re going to need to import the Cython magic function to IPython:\n In [7]: %load_ext Cython\n Now, let\u2019s simply copy our functions over to Cython:\n In [8]: %%cython\n@@ -149,15 +151,15 @@\n    ...:     dx = (b - a) / N\n    ...:     for i in range(N):\n    ...:         s += f_plain(a + i * dx)\n    ...:     return s * dx\n    ...:\n In [9]: %timeit df.apply(lambda x: integrate_f_plain(x[\"a\"], x[\"b\"], x[\"N\"]),\n axis=1)\n-93 ms +- 4.77 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+347 ms +- 120 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n This has improved the performance compared to the pure Python approach by one-\n third.\n *\b**\b**\b**\b* D\bDe\bec\bcl\bla\bar\bri\bin\bng\bg C\bC t\bty\byp\bpe\bes\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n We can annotate the function variables and return types as well as use cdef and\n cpdef to improve performance:\n In [10]: %%cython\n    ....: cdef double f_typed(double x) except? -2:\n@@ -169,35 +171,36 @@\n    ....:     dx = (b - a) / N\n    ....:     for i in range(N):\n    ....:         s += f_typed(a + i * dx)\n    ....:     return s * dx\n    ....:\n In [11]: %timeit df.apply(lambda x: integrate_f_typed(x[\"a\"], x[\"b\"], x[\"N\"]),\n axis=1)\n-11.3 ms +- 1.15 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+26.3 ms +- 2.79 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n Annotating the functions with C types yields an over ten times performance\n improvement compared to the original Python implementation.\n *\b**\b**\b**\b* U\bUs\bsi\bin\bng\bg n\bnd\bda\bar\brr\bra\bay\by_\b?\b\u00b6 *\b**\b**\b**\b*\n When re-profiling, time is spent creating a _\bS_\be_\br_\bi_\be_\bs from each row, and calling\n __getitem__ from both the index and the series (three times for each row).\n These Python function calls are expensive and can be improved by passing an\n np.ndarray.\n In [12]: %prun -l 4 df.apply(lambda x: integrate_f_typed(x[\"a\"], x[\"b\"], x\n [\"N\"]), axis=1)\n-         52523 function calls (52505 primitive calls) in 0.028 seconds\n+         52523 function calls (52505 primitive calls) in 0.103 seconds\n \n    Ordered by: internal time\n    List reduced from 157 to 4 due to restriction <4>\n \n    ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n-     3000    0.005    0.000    0.017    0.000 series.py:1095(__getitem__)\n-     3000    0.003    0.000    0.008    0.000 series.py:1220(_get_value)\n-    16098    0.003    0.000    0.004    0.000 {built-in method\n+     3000    0.016    0.000    0.070    0.000 series.py:1095(__getitem__)\n+    16098    0.012    0.000    0.016    0.000 {built-in method\n builtins.isinstance}\n-     3000    0.002    0.000    0.003    0.000 base.py:3777(get_loc)\n+     3000    0.012    0.000    0.030    0.000 series.py:1220(_get_value)\n+     3000    0.009    0.000    0.017    0.000 indexing.py:2765\n+(check_dict_or_set_indexers)\n In [13]: %%cython\n    ....: cimport numpy as np\n    ....: import numpy as np\n    ....: cdef double f_typed(double x) except? -2:\n    ....:     return x * (x - 1)\n    ....: cpdef double integrate_f_typed(double a, double b, int N):\n    ....:     cdef int i\n@@ -238,32 +241,32 @@\n This implementation creates an array of zeros and inserts the result of\n integrate_f_typed applied over each row. Looping over an ndarray is faster in\n Cython than looping over a _\bS_\be_\br_\bi_\be_\bs object.\n Since apply_integrate_f is typed to accept an np.ndarray, _\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bn_\bu_\bm_\bp_\by_\b(_\b)\n calls are needed to utilize this function.\n In [14]: %timeit apply_integrate_f(df[\"a\"].to_numpy(), df[\"b\"].to_numpy(), df\n [\"N\"].to_numpy())\n-1.3 ms +- 3.86 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)\n+1.84 ms +- 15.1 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n Performance has improved from the prior implementation by almost ten times.\n *\b**\b**\b**\b* D\bDi\bis\bsa\bab\bbl\bli\bin\bng\bg c\bco\bom\bmp\bpi\bil\ble\ber\br d\bdi\bir\bre\bec\bct\bti\biv\bve\bes\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n The majority of the time is now spent in apply_integrate_f. Disabling Cython\u2019s\n boundscheck and wraparound checks can yield more performance.\n In [15]: %prun -l 4 apply_integrate_f(df[\"a\"].to_numpy(), df[\"b\"].to_numpy(),\n df[\"N\"].to_numpy())\n          78 function calls in 0.002 seconds\n \n    Ordered by: internal time\n    List reduced from 21 to 4 due to restriction <4>\n \n    ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n-        1    0.001    0.001    0.002    0.002 <string>:1(<module>)\n-        1    0.000    0.000    0.000    0.000 {method 'disable' of\n-'_lsprof.Profiler' objects}\n+        1    0.002    0.002    0.002    0.002 <string>:1(<module>)\n+        3    0.000    0.000    0.000    0.000 base.py:5323(__contains__)\n         1    0.000    0.000    0.002    0.002 {built-in method builtins.exec}\n-        3    0.000    0.000    0.000    0.000 frame.py:4062(__getitem__)\n+       18    0.000    0.000    0.000    0.000 {built-in method\n+builtins.isinstance}\n In [16]: %%cython\n    ....: cimport cython\n    ....: cimport numpy as np\n    ....: import numpy as np\n    ....: cdef np.float64_t f_typed(np.float64_t x) except? -2:\n    ....:     return x * (x - 1)\n    ....: cpdef np.float64_t integrate_f_typed(np.float64_t a, np.float64_t b,\n@@ -301,15 +304,15 @@\n /usr/lib/python3/dist-packages/numpy/core/include/numpy/\n npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API,\n disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n    17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n       |  ^~~~~~~\n In [17]: %timeit apply_integrate_f_wrap(df[\"a\"].to_numpy(), df[\"b\"].to_numpy(),\n df[\"N\"].to_numpy())\n-1.22 ms +- 214 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)\n+1.64 ms +- 35 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)\n However, a loop indexer i accessing an invalid location in an array would cause\n a segfault because memory access isn\u2019t checked. For more about boundscheck and\n wraparound, see the Cython docs on _\bc_\bo_\bm_\bp_\bi_\bl_\be_\br_\b _\bd_\bi_\br_\be_\bc_\bt_\bi_\bv_\be_\bs.\n *\b**\b**\b**\b**\b* N\bNu\bum\bmb\bba\ba (\b(J\bJI\bIT\bT c\bco\bom\bmp\bpi\bil\bla\bat\bti\bio\bon\bn)\b)_\b?\b\u00b6 *\b**\b**\b**\b**\b*\n An alternative to statically compiling Cython code is to use a dynamic just-in-\n time (JIT) compiler with _\bN_\bu_\bm_\bb_\ba.\n Numba allows you to write a pure Python function which can be JIT compiled to\n@@ -612,17 +615,17 @@\n The 'numexpr' engine is the more performant engine that can yield performance\n improvements compared to standard Python syntax for large _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be. This\n engine requires the optional dependency numexpr to be installed.\n The 'python' engine is generally n\bno\bot\bt useful except for testing other evaluation\n engines against it. You will achieve n\bno\bo performance benefits using _\be_\bv_\ba_\bl_\b(_\b) with\n engine='python' and may incur a performance hit.\n In [40]: %timeit df1 + df2 + df3 + df4\n-27.3 ms +- 1.75 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+55.2 ms +- 3.23 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n In [41]: %timeit pd.eval(\"df1 + df2 + df3 + df4\", engine=\"python\")\n-14.2 ms +- 3.41 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+58.3 ms +- 3.18 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n *\b**\b**\b**\b* T\bTh\bhe\be _\bD\bD_\ba\ba_\bt\bt_\ba\ba_\bF\bF_\br\br_\ba\ba_\bm\bm_\be\be_\b.\b._\be\be_\bv\bv_\ba\ba_\bl\bl_\b(\b(_\b)\b) m\bme\bet\bth\bho\bod\bd_\b?\b\u00b6 *\b**\b**\b**\b*\n In addition to the top level _\bp_\ba_\bn_\bd_\ba_\bs_\b._\be_\bv_\ba_\bl_\b(_\b) function you can also evaluate an\n expression in the \u201ccontext\u201d of a _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be.\n In [42]: df = pd.DataFrame(np.random.randn(5, 2), columns=[\"a\", \"b\"])\n \n In [43]: df.eval(\"a + b\")\n Out[43]:\n@@ -719,29 +722,29 @@\n _\bp_\ba_\bn_\bd_\ba_\bs_\b._\be_\bv_\ba_\bl_\b(_\b) works well with expressions containing large arrays.\n In [58]: nrows, ncols = 20000, 100\n \n In [59]: df1, df2, df3, df4 = [pd.DataFrame(np.random.randn(nrows, ncols)) for\n _ in range(4)]\n _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be arithmetic:\n In [60]: %timeit df1 + df2 + df3 + df4\n-29.7 ms +- 958 us per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+56 ms +- 3.6 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n In [61]: %timeit pd.eval(\"df1 + df2 + df3 + df4\")\n-11.5 ms +- 385 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+15.4 ms +- 1.1 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)\n _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be comparison:\n In [62]: %timeit (df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)\n-12.9 ms +- 3.07 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+27.2 ms +- 1.26 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n In [63]: %timeit pd.eval(\"(df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)\")\n-8.75 ms +- 883 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+13.3 ms +- 838 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be arithmetic with unaligned axes.\n In [64]: s = pd.Series(np.random.randn(50))\n \n In [65]: %timeit df1 + df2 + df3 + df4 + s\n-20.9 ms +- 1.19 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+85.2 ms +- 2.93 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n In [66]: %timeit pd.eval(\"df1 + df2 + df3 + df4 + s\")\n-6.08 ms +- 134 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+15.7 ms +- 254 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n Note\n Operations such as\n 1 and 2  # would parse to 1 & 2, but should evaluate to 2\n 3 or 4  # would parse to 3 | 4, but should evaluate to 3\n ~1  # this is okay, but slower when using eval\n should be performed in Python. An exception will be raised if you try to\n perform any boolean/bitwise operations with scalar operands that are not of\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/user_guide/scale.html", "source2": "./usr/share/doc/python-pandas-doc/html/user_guide/scale.html", "unified_diff": "@@ -916,16 +916,16 @@\n <span class=\"gp\">   ....: </span><span class=\"n\">files</span> <span class=\"o\">=</span> <span class=\"n\">pathlib</span><span class=\"o\">.</span><span class=\"n\">Path</span><span class=\"p\">(</span><span class=\"s2\">&quot;data/timeseries/&quot;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">glob</span><span class=\"p\">(</span><span class=\"s2\">&quot;ts*.parquet&quot;</span><span class=\"p\">)</span>\n <span class=\"gp\">   ....: </span><span class=\"n\">counts</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">(</span><span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"nb\">int</span><span class=\"p\">)</span>\n <span class=\"gp\">   ....: </span><span class=\"k\">for</span> <span class=\"n\">path</span> <span class=\"ow\">in</span> <span class=\"n\">files</span><span class=\"p\">:</span>\n <span class=\"gp\">   ....: </span>    <span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_parquet</span><span class=\"p\">(</span><span class=\"n\">path</span><span class=\"p\">)</span>\n <span class=\"gp\">   ....: </span>    <span class=\"n\">counts</span> <span class=\"o\">=</span> <span class=\"n\">counts</span><span class=\"o\">.</span><span class=\"n\">add</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">[</span><span class=\"s2\">&quot;name&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">value_counts</span><span class=\"p\">(),</span> <span class=\"n\">fill_value</span><span class=\"o\">=</span><span class=\"mi\">0</span><span class=\"p\">)</span>\n <span class=\"gp\">   ....: </span><span class=\"n\">counts</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">)</span>\n <span class=\"gp\">   ....: </span>\n-<span class=\"go\">CPU times: user 643 us, sys: 0 ns, total: 643 us</span>\n-<span class=\"go\">Wall time: 655 us</span>\n+<span class=\"go\">CPU times: user 688 us, sys: 233 us, total: 921 us</span>\n+<span class=\"go\">Wall time: 945 us</span>\n <span class=\"gh\">Out[32]: </span><span class=\"go\">Series([], dtype: int64)</span>\n </pre></div>\n </div>\n <p>Some readers, like <a class=\"reference internal\" href=\"../reference/api/pandas.read_csv.html#pandas.read_csv\" title=\"pandas.read_csv\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">pandas.read_csv()</span></code></a>, offer parameters to control the\n <code class=\"docutils literal notranslate\"><span class=\"pre\">chunksize</span></code> when reading a single file.</p>\n <p>Manually chunking is an OK option for workflows that don\u2019t\n require too sophisticated of operations. Some operations, like <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.groupby.html#pandas.DataFrame.groupby\" title=\"pandas.DataFrame.groupby\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">pandas.DataFrame.groupby()</span></code></a>, are\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -633,16 +633,16 @@\n    ....: files = pathlib.Path(\"data/timeseries/\").glob(\"ts*.parquet\")\n    ....: counts = pd.Series(dtype=int)\n    ....: for path in files:\n    ....:     df = pd.read_parquet(path)\n    ....:     counts = counts.add(df[\"name\"].value_counts(), fill_value=0)\n    ....: counts.astype(int)\n    ....:\n-CPU times: user 643 us, sys: 0 ns, total: 643 us\n-Wall time: 655 us\n+CPU times: user 688 us, sys: 233 us, total: 921 us\n+Wall time: 945 us\n Out[32]: Series([], dtype: int64)\n Some readers, like _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bs_\bv_\b(_\b), offer parameters to control the chunksize\n when reading a single file.\n Manually chunking is an OK option for workflows that don\u2019t require too\n sophisticated of operations. Some operations, like _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\br_\bo_\bu_\bp_\bb_\by_\b(_\b),\n are much harder to do chunkwise. In these cases, you may be better switching to\n a different library that implements these out-of-core algorithms for you.\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/user_guide/style.ipynb.gz", "source2": "./usr/share/doc/python-pandas-doc/html/user_guide/style.ipynb.gz", "unified_diff": null, "details": [{"source1": "style.ipynb", "source2": "style.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9985610875706213%", "Differences: {\"'cells'\": \"{1: {'metadata': {'execution': {'iopub.execute_input': '2024-07-01T18:06:12.575620Z', \"", "            \"'iopub.status.busy': '2024-07-01T18:06:12.575254Z', 'iopub.status.idle': \"", "            \"'2024-07-01T18:06:13.269368Z', 'shell.execute_reply': \"", "            \"'2024-07-01T18:06:13.268230Z'}}}, 3: {'metadata': {'execution': \"", "            \"{'iopub.execute_input': '2024-07-01T18:06:13.274683Z', 'iopub.status.busy': \"", "            \"'2024-07-01T18:06:13.274184Z', 'iopub.status.idle': '2024-07-01T18:06:1 [\u2026]"], "unified_diff": "@@ -39,18 +39,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 1,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:21.334403Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:21.333840Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:21.771989Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:21.771306Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:12.575620Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:12.575254Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:13.269368Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:13.268230Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"import matplotlib.pyplot\\n\",\n                 \"# We have this here to trigger matplotlib's font cache stuff.\\n\",\n@@ -77,36 +77,36 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 2,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:21.775369Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:21.775017Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.006155Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.005376Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:13.274683Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:13.274184Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:13.610495Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:13.609421Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"import pandas as pd\\n\",\n                 \"import numpy as np\\n\",\n                 \"import matplotlib as mpl\\n\"\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 3,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.009628Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.009021Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.091068Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.090424Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:13.615263Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:13.614766Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:13.706144Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:13.705150Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"# For reproducibility - this doesn't respect uuid_len or positionally-passed uuid but the places here that use that coincidentally bypass this anyway\\n\",\n                 \"from pandas.io.formats.style import Styler\\n\",\n@@ -123,18 +123,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 4,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.094364Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.093853Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.103770Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.103216Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:13.711196Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:13.710727Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:13.726282Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:13.725390Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -193,18 +193,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 5,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.142465Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.141883Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.153682Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.153127Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:13.803108Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:13.802075Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:13.819656Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:13.818675Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<div>\\n\",\n@@ -327,18 +327,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 6,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.156364Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.155832Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.167704Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.167131Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:13.823495Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:13.823153Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:13.839905Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:13.838922Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -448,18 +448,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 7,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.170493Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.169993Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.177301Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.176775Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:13.844343Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:13.843990Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:13.855794Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:13.854816Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -510,18 +510,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 8,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.179973Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.179536Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.186622Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.186080Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:13.859928Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:13.859601Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:13.874376Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:13.873090Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -585,18 +585,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 9,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.189209Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.188808Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.200937Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.200383Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:13.881012Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:13.879502Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:13.906054Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:13.905043Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -705,18 +705,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 10,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.203732Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.203249Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.213288Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.212507Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:13.910496Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:13.910127Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:13.925807Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:13.924815Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -787,18 +787,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 11,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.216195Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.215595Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.227613Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.226796Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:13.930058Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:13.929715Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:13.948619Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:13.947602Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"# Hidden cell to just create the below example: code is covered throughout the guide.\\n\",\n                 \"s = df.style\\\\\\n\",\n@@ -861,18 +861,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 12,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.230572Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.229896Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.239342Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.238535Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:13.955266Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:13.954894Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:13.969228Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:13.968231Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -1012,18 +1012,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 13,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.242075Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.241653Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.252957Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.252173Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:13.973401Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:13.973009Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:13.988898Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:13.988029Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -1083,18 +1083,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 14,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.255757Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.255180Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.262231Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.261445Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:13.992369Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:13.992044Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.001643Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.000718Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -1202,18 +1202,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 15,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.265102Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.264532Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.272313Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.271529Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.005199Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.004845Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.016082Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.015205Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -1296,18 +1296,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 16,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.274893Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.274482Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.281523Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.280735Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.019405Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.019090Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.028804Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.027845Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -1387,18 +1387,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 17,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.284338Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.283779Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.291513Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.290709Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.038383Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.038035Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.049984Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.048979Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -1483,18 +1483,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 18,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.294208Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.293645Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.300891Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.300048Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.054401Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.054015Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.065651Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.064591Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -1586,18 +1586,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 19,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.303607Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.303181Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.312896Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.312063Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.069919Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.069558Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.083455Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.082512Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -1687,18 +1687,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 20,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.315988Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.315386Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.323422Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.322579Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.087889Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.087506Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.098589Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.097652Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -1802,18 +1802,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 21,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.326584Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.325933Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.333810Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.332970Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.102850Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.102489Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.113320Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.112282Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"name\": \"stdout\",\n                     \"output_type\": \"stream\",\n                     \"text\": [\n@@ -1844,18 +1844,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 22,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.336879Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.336431Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.347713Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.346850Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.118566Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.118201Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.134832Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.133872Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -1956,18 +1956,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 23,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.350589Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.350138Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.357971Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.357176Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.140717Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.140336Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.152054Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.151098Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -2081,18 +2081,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 24,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.360897Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.360323Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.367628Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.366829Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.156930Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.156617Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.168399Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.167386Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -2204,18 +2204,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 25,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.370332Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.369917Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.379058Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.378273Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.172782Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.172423Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.187138Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.186165Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -2332,18 +2332,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 26,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.381860Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.381277Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.389541Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.388748Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.191404Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.191037Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.204685Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.203660Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -2465,18 +2465,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 27,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.392489Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.391900Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.402787Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.402004Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.209122Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.208737Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.225308Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.224309Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -2595,18 +2595,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 28,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.405327Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.404909Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.414690Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.413908Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.229490Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.229120Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.244677Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.243761Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -2732,18 +2732,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 29,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.417479Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.416919Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.430579Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.429798Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.249562Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.249155Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.271762Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.270629Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -2884,18 +2884,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 30,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.433276Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.432845Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.446204Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.445432Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.276081Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.275722Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.297315Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.296081Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -3069,18 +3069,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 31,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.449093Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.448514Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.464927Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.464148Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.301835Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.301454Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.327822Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.326797Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -3244,18 +3244,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 32,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.467789Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.467193Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.474748Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.473947Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.332780Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.331788Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.344197Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.343120Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -3357,18 +3357,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 33,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.477446Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.477034Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.484226Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.483437Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.348426Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.348028Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.359793Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.358817Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -3477,18 +3477,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 34,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.486902Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.486483Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.497258Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.496468Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.364927Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.364556Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.381453Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.380441Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -3615,18 +3615,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 35,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.500079Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.499465Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.508654Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.507873Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.385960Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.385584Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.400294Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.399346Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -3757,18 +3757,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 36,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.511394Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.510936Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.523957Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.523172Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.404889Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.404545Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.425939Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.424876Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -3904,18 +3904,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 37,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.526573Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.526148Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.535328Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.534533Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.430325Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.429954Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.445469Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.444505Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -4062,18 +4062,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 38,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.538123Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.537562Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.549581Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.548793Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.450137Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.449313Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.466661Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.465631Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<div>\\n\",\n@@ -4164,18 +4164,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 39,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.552452Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.551849Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.562518Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.561723Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.471802Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.471459Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.488005Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.487029Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -4255,18 +4255,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 40,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.565153Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.564732Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.577649Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.576870Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.492632Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.492250Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.511825Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.510818Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -4347,18 +4347,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 41,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.580532Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.579939Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.590802Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.590014Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.516102Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.515730Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.533476Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.532467Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -4440,18 +4440,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 42,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.593691Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.593121Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.605221Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.604438Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.537933Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.537555Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.555708Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.554786Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -4571,18 +4571,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 43,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.607947Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.607525Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.611375Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.610575Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.559998Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.559681Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.564815Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.563865Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"df4 = pd.DataFrame([[1,2],[3,4]])\\n\",\n                 \"s4 = df4.style\"\n             ]\n@@ -4599,18 +4599,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 44,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.614160Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.613597Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.617316Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.616527Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.569509Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.569150Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.574538Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.573587Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"from pandas.io.formats.style import Styler\\n\",\n                 \"s4 = Styler(df4, uuid_len=0, cell_ids=False)\"\n             ]\n@@ -4636,18 +4636,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 45,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.620172Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.619572Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.626661Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.625881Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.579077Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.578689Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.589844Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.588791Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -4705,18 +4705,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 46,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.629475Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.628882Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.634748Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.633957Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.594533Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.594090Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.604313Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.603083Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -4782,18 +4782,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 47,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.637622Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.637045Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.649412Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.648624Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.609144Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.608705Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.630768Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.629603Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -4931,18 +4931,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 48,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.652287Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.651714Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.664380Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.663599Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.636285Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.635138Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.658768Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.657639Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -5083,18 +5083,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 49,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.667065Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.666637Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.672427Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.671657Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.663360Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.662997Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.672869Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.671855Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"name\": \"stdout\",\n                     \"output_type\": \"stream\",\n                     \"text\": [\n@@ -5158,18 +5158,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 50,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.675221Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.674612Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.680244Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.679454Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.677385Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.676897Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.685694Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.684681Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -5260,18 +5260,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 51,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.683124Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.682537Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.690116Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.689309Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.690310Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.689953Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.702146Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.701119Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -5351,18 +5351,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 52,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.692963Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.692361Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.703361Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.702566Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.707124Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.706724Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.724867Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.723800Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -5443,18 +5443,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 53,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.706062Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.705636Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.720197Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.719392Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.729764Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.729424Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.751945Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.750905Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -5535,18 +5535,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 54,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.722907Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.722464Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:22.732312Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:22.731521Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.756504Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.756074Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:14.772036Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:14.771093Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -5631,18 +5631,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 55,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:22.735166Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:22.734570Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.302245Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.301659Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:14.777598Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:14.776405Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:15.578604Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:15.576546Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -5880,18 +5880,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 56,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.305218Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.304704Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.314867Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.314335Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:15.582711Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:15.582190Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:15.604780Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:15.603755Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -6112,18 +6112,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 57,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.317554Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.317086Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.323553Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.322998Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:15.609168Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:15.608723Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:15.624179Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:15.623182Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -6212,18 +6212,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 58,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.326202Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.325727Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.333583Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.333044Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:15.628151Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:15.627776Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:15.645621Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:15.644059Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -6415,18 +6415,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 59,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.336140Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.335736Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.353101Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.352568Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:15.650117Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:15.649680Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:15.685678Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:15.684675Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -6810,18 +6810,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 60,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.355764Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.355219Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.413633Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.413076Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:15.689877Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:15.689463Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:15.790840Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:15.788129Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"# Hide the construction of the display chart from the user\\n\",\n                 \"from IPython.display import HTML\\n\",\n@@ -6864,18 +6864,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 61,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.416187Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.415691Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.419766Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.419223Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:15.798498Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:15.798076Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:15.808934Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:15.807902Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"\\n\",\n@@ -7847,18 +7847,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 62,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.422273Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.421867Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.429709Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.429162Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:15.814034Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:15.813166Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:15.831933Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:15.830786Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -7968,18 +7968,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 63,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.432205Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.431794Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.439222Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.438671Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:15.835843Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:15.835486Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:15.852305Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:15.851282Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -8084,18 +8084,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 64,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.441808Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.441407Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.646711Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.646140Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:15.856757Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:15.856397Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:16.312807Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:16.311704Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"ModuleNotFoundError\",\n                     \"evalue\": \"No module named 'ipywidgets'\",\n                     \"output_type\": \"error\",\n@@ -8125,18 +8125,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 65,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.649813Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.649213Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.653011Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.652488Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:16.319711Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:16.319277Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:16.327209Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:16.326029Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"def magnify():\\n\",\n                 \"    return [dict(selector=\\\"th\\\",\\n\",\n                 \"                 props=[(\\\"font-size\\\", \\\"4pt\\\")]),\\n\",\n@@ -8151,18 +8151,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 66,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.655566Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.655063Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.704255Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.703678Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:16.330892Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:16.330519Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:16.446338Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:16.445283Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -10009,18 +10009,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 67,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.707218Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.706714Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.739997Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.739398Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:16.450613Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:16.450215Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:16.531403Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:16.528555Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -11817,18 +11817,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 68,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.743557Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.743026Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.779660Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.779046Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:16.537576Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:16.536321Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:16.623712Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:16.622571Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -13655,18 +13655,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 69,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.783202Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.782666Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.788199Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.787658Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:16.630397Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:16.630014Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:16.642120Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:16.641143Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -13705,18 +13705,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 70,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.790799Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.790315Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.795428Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.794882Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:16.645940Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:16.645564Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:16.658303Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:16.657296Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -13754,18 +13754,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 71,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.797878Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.797474Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.802513Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.801915Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:16.662571Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:16.662212Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:16.674898Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:16.673835Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -13836,18 +13836,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 72,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.805688Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.805369Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.952601Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.951945Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:16.679374Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:16.679021Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:16.929090Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:16.927957Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"df2.style.\\\\\\n\",\n                 \"    map(style_negative, props='color:red;').\\\\\\n\",\n                 \"    highlight_max(axis=0).\\\\\\n\",\n@@ -13915,18 +13915,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 73,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.956189Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.955565Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.961410Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.960853Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:16.934287Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:16.933722Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:16.945377Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:16.944220Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"name\": \"stdout\",\n                     \"output_type\": \"stream\",\n                     \"text\": [\n@@ -13971,18 +13971,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 74,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.964075Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.963657Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.969806Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.969252Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:16.949195Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:16.948721Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:16.962892Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:16.961726Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14022,18 +14022,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 75,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.973381Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.972888Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.978509Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.977984Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:16.968690Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:16.968314Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:16.980968Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:16.980042Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14088,18 +14088,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 76,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.981110Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.980697Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.985967Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.985439Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:16.984714Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:16.984361Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:16.996823Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:16.995833Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14148,18 +14148,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 77,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.988586Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.988096Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:23.996388Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:23.995693Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.000979Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.000599Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.014496Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.013547Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14213,18 +14213,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 78,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:23.999312Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:23.998743Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:24.005913Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:24.005369Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.018237Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.017868Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.032520Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.031496Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14284,18 +14284,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 79,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:24.008269Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:24.008037Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:24.014160Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:24.013623Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.036912Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.036551Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.050448Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.048230Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14373,36 +14373,36 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 80,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:24.016748Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:24.016341Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:24.019251Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:24.018692Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.054824Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.054447Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.060950Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.059975Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"from jinja2 import Environment, ChoiceLoader, FileSystemLoader\\n\",\n                 \"from IPython.display import HTML\\n\",\n                 \"from pandas.io.formats.style import Styler\"\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 81,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:24.021765Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:24.021362Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:24.024003Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:24.023471Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.064713Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.064328Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.070159Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.069202Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"# For reproducibility\\n\",\n                 \"Styler = StylerReproducible\\n\"\n@@ -14416,18 +14416,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 82,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:24.026439Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:24.026042Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:24.029448Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:24.028903Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.074142Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.073767Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.080908Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.079930Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"name\": \"stdout\",\n                     \"output_type\": \"stream\",\n                     \"text\": [\n@@ -14454,18 +14454,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 83,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:24.031951Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:24.031545Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:24.037110Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:24.036562Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.084633Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.084257Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.094288Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.093297Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"class MyStyler(Styler):\\n\",\n                 \"    env = Environment(\\n\",\n                 \"        loader=ChoiceLoader([\\n\",\n@@ -14488,18 +14488,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 84,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:24.039586Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:24.039184Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:24.059155Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:24.058618Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.098718Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.098347Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.142797Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.140416Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14661,18 +14661,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 85,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:24.061715Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:24.061293Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:24.067153Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:24.066656Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.147028Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.146653Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.160350Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.159356Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14834,18 +14834,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 86,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:24.069701Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:24.069280Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:24.089943Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:24.089397Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.165421Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.165020Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.212254Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.211250Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -15017,36 +15017,36 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 87,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:24.092646Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:24.092159Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:24.095217Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:24.094657Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.216218Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.215856Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.223265Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.222250Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"with open(\\\"templates/html_style_structure.html\\\") as f:\\n\",\n                 \"    style_structure = f.read()\"\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 88,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:24.097655Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:24.097244Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:24.101042Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:24.100516Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.227573Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.227195Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.237210Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.236166Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<!--\\n\",\n@@ -15106,36 +15106,36 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 89,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:24.103596Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:24.103206Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:24.106144Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:24.105605Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.241216Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.240824Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.247372Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.245107Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"with open(\\\"templates/html_table_structure.html\\\") as f:\\n\",\n                 \"    table_structure = f.read()\"\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 90,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:24.108695Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:24.108203Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:24.111954Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:24.111427Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.251152Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.250780Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.259976Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.259007Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<!--\\n\",\n@@ -15208,18 +15208,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 91,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-08-03T22:17:24.114497Z\",\n-                    \"iopub.status.busy\": \"2025-08-03T22:17:24.114035Z\",\n-                    \"iopub.status.idle\": \"2025-08-03T22:17:24.116715Z\",\n-                    \"shell.execute_reply\": \"2025-08-03T22:17:24.116182Z\"\n+                    \"iopub.execute_input\": \"2024-07-01T18:06:17.264773Z\",\n+                    \"iopub.status.busy\": \"2024-07-01T18:06:17.264400Z\",\n+                    \"iopub.status.idle\": \"2024-07-01T18:06:17.270337Z\",\n+                    \"shell.execute_reply\": \"2024-07-01T18:06:17.269295Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"# # Hack to get the same style in the notebook as the\\n\",\n                 \"# # main site. This is hidden in the docs.\\n\",\n"}]}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.13.0.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.13.0.html", "unified_diff": "@@ -1758,20 +1758,21 @@\n <span class=\"gp\">In [94]: </span><span class=\"n\">df1</span><span class=\"p\">,</span> <span class=\"n\">df2</span><span class=\"p\">,</span> <span class=\"n\">df3</span><span class=\"p\">,</span> <span class=\"n\">df4</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">(</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">random</span><span class=\"o\">.</span><span class=\"n\">randn</span><span class=\"p\">(</span><span class=\"n\">nrows</span><span class=\"p\">,</span> <span class=\"n\">ncols</span><span class=\"p\">))</span>\n <span class=\"gp\">   ....: </span>                      <span class=\"k\">for</span> <span class=\"n\">_</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mi\">4</span><span class=\"p\">)]</span>\n <span class=\"gp\">   ....: </span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"go\"># eval with NumExpr backend</span>\n <span class=\"gp\">In [95]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> pd.eval(&#39;df1 + df2 + df3 + df4&#39;)\n-<span class=\"go\">4.16 ms +- 294 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n+<span class=\"go\">13.5 ms +- 673 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"go\"># pure Python evaluation</span>\n <span class=\"gp\">In [96]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> df1 + df2 + df3 + df4\n-<span class=\"go\">4.17 ms +- 456 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n+<span class=\"go\">The slowest run took 4.51 times longer than the fastest. This could mean that an intermediate result is being cached.</span>\n+<span class=\"go\">64.4 ms +- 25.8 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n </pre></div>\n </div>\n <p>For more details, see the <a class=\"reference internal\" href=\"../user_guide/enhancingperf.html#enhancingperf-eval\"><span class=\"std std-ref\">the docs</span></a></p>\n </li>\n <li><p>Similar to <code class=\"docutils literal notranslate\"><span class=\"pre\">pandas.eval</span></code>, <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.html#pandas.DataFrame\" title=\"pandas.DataFrame\"><code class=\"xref py py-class docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code></a> has a new\n <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame.eval</span></code> method that evaluates an expression in the context of\n the <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code>. For example,</p>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -961,18 +961,20 @@\n \n       In [94]: df1, df2, df3, df4 = [pd.DataFrame(np.random.randn(nrows,\n       ncols))\n          ....:                       for _ in range(4)]\n          ....:\n       # eval with NumExpr backend\n       In [95]: %timeit pd.eval('df1 + df2 + df3 + df4')\n-      4.16 ms +- 294 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+      13.5 ms +- 673 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n       # pure Python evaluation\n       In [96]: %timeit df1 + df2 + df3 + df4\n-      4.17 ms +- 456 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+      The slowest run took 4.51 times longer than the fastest. This could mean\n+      that an intermediate result is being cached.\n+      64.4 ms +- 25.8 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)\n       For more details, see the _\bt_\bh_\be_\b _\bd_\bo_\bc_\bs\n     * Similar to pandas.eval, _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be has a new DataFrame.eval method that\n       evaluates an expression in the context of the DataFrame. For example,\n       In [97]: df = pd.DataFrame(np.random.randn(10, 2), columns=['a', 'b'])\n \n       In [98]: df.eval('a + b')\n       Out[98]:\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.17.0.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.17.0.html", "unified_diff": "@@ -1736,18 +1736,18 @@\n </div>\n <div class=\"admonition note\">\n <p class=\"admonition-title\">Note</p>\n <p>If you want to perform calculations based on today\u2019s date, use <code class=\"docutils literal notranslate\"><span class=\"pre\">Timestamp.now()</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">pandas.tseries.offsets</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [65]: </span><span class=\"kn\">import</span> <span class=\"nn\">pandas.tseries.offsets</span> <span class=\"k\">as</span> <span class=\"nn\">offsets</span>\n \n <span class=\"gp\">In [66]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Timestamp</span><span class=\"o\">.</span><span class=\"n\">now</span><span class=\"p\">()</span>\n-<span class=\"gh\">Out[66]: </span><span class=\"go\">Timestamp(&#39;2025-08-03 10:15:04.475173&#39;)</span>\n+<span class=\"gh\">Out[66]: </span><span class=\"go\">Timestamp(&#39;2024-07-02 08:03:44.463094&#39;)</span>\n \n <span class=\"gp\">In [67]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Timestamp</span><span class=\"o\">.</span><span class=\"n\">now</span><span class=\"p\">()</span> <span class=\"o\">+</span> <span class=\"n\">offsets</span><span class=\"o\">.</span><span class=\"n\">DateOffset</span><span class=\"p\">(</span><span class=\"n\">years</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n-<span class=\"gh\">Out[67]: </span><span class=\"go\">Timestamp(&#39;2026-08-03 10:15:04.477316&#39;)</span>\n+<span class=\"gh\">Out[67]: </span><span class=\"go\">Timestamp(&#39;2025-07-02 08:03:44.466336&#39;)</span>\n </pre></div>\n </div>\n </div>\n </section>\n </section>\n <section id=\"changes-to-index-comparisons\">\n <h3>Changes to Index comparisons<a class=\"headerlink\" href=\"#changes-to-index-comparisons\" title=\"Link to this heading\">\u00b6</a></h3>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -829,18 +829,18 @@\n freq=None)\n Note\n If you want to perform calculations based on today\u2019s date, use Timestamp.now()\n and pandas.tseries.offsets.\n In [65]: import pandas.tseries.offsets as offsets\n \n In [66]: pd.Timestamp.now()\n-Out[66]: Timestamp('2025-08-03 10:15:04.475173')\n+Out[66]: Timestamp('2024-07-02 08:03:44.463094')\n \n In [67]: pd.Timestamp.now() + offsets.DateOffset(years=1)\n-Out[67]: Timestamp('2026-08-03 10:15:04.477316')\n+Out[67]: Timestamp('2025-07-02 08:03:44.466336')\n *\b**\b**\b**\b* C\bCh\bha\ban\bng\bge\bes\bs t\bto\bo I\bIn\bnd\bde\bex\bx c\bco\bom\bmp\bpa\bar\bri\bis\bso\bon\bns\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n Operator equal on Index should behavior similarly to Series (_\bG_\bH_\b _\b9_\b9_\b4_\b7, _\bG_\bH_\b _\b1_\b0_\b6_\b3_\b7)\n Starting in v0.17.0, comparing Index objects of different lengths will raise a\n ValueError. This is to be consistent with the behavior of Series.\n Previous behavior:\n In [2]: pd.Index([1, 2, 3]) == pd.Index([1, 4, 5])\n Out[2]: array([ True, False, False], dtype=bool)\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.18.1.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.18.1.html", "unified_diff": "@@ -1110,14 +1110,16 @@\n <span class=\"gh\">Out[12]: </span>\n <span class=\"go\">            group  val</span>\n <span class=\"go\">date                  </span>\n <span class=\"go\">2016-01-03      1    5</span>\n <span class=\"go\">2016-01-10      1    6</span>\n <span class=\"go\">2016-01-17      2    7</span>\n <span class=\"go\">2016-01-24      2    8</span>\n+\n+<span class=\"go\">[4 rows x 2 columns]</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"n\">In</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]:</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"s2\">&quot;group&quot;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"n\">x</span><span class=\"o\">.</span><span class=\"n\">resample</span><span class=\"p\">(</span><span class=\"s2\">&quot;1D&quot;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">ffill</span><span class=\"p\">())</span>\n <span class=\"n\">Out</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]:</span>\n                   <span class=\"n\">group</span>  <span class=\"n\">val</span>\n <span class=\"n\">group</span> <span class=\"n\">date</span>\n <span class=\"mi\">1</span>     <span class=\"mi\">2016</span><span class=\"o\">-</span><span class=\"mi\">01</span><span class=\"o\">-</span><span class=\"mi\">03</span>      <span class=\"mi\">1</span>    <span class=\"mi\">5</span>\n@@ -1180,48 +1182,54 @@\n \n <span class=\"gp\">In [14]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">where</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"n\">x</span> <span class=\"o\">&gt;</span> <span class=\"mi\">4</span><span class=\"p\">,</span> <span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"n\">x</span> <span class=\"o\">+</span> <span class=\"mi\">10</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[14]: </span>\n <span class=\"go\">    A   B  C</span>\n <span class=\"go\">0  11  14  7</span>\n <span class=\"go\">1  12   5  8</span>\n <span class=\"go\">2  13   6  9</span>\n+\n+<span class=\"go\">[3 rows x 3 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"methods-loc-iloc-ix\">\n <h4>Methods <code class=\"docutils literal notranslate\"><span class=\"pre\">.loc[]</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">.iloc[]</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">.ix[]</span></code><a class=\"headerlink\" href=\"#methods-loc-iloc-ix\" title=\"Link to this heading\">\u00b6</a></h4>\n <p>These can accept a callable, and a tuple of callable as a slicer. The callable\n can return a valid boolean indexer or anything which is valid for these indexer\u2019s input.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"go\"># callable returns bool indexer</span>\n <span class=\"gp\">In [15]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"n\">x</span><span class=\"o\">.</span><span class=\"n\">A</span> <span class=\"o\">&gt;=</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"n\">x</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span> <span class=\"o\">&gt;</span> <span class=\"mi\">10</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[15]: </span>\n <span class=\"go\">   B  C</span>\n <span class=\"go\">1  5  8</span>\n <span class=\"go\">2  6  9</span>\n \n+<span class=\"go\">[2 rows x 2 columns]</span>\n+\n <span class=\"go\"># callable returns list of labels</span>\n <span class=\"gp\">In [16]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">],</span> <span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"s2\">&quot;A&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;B&quot;</span><span class=\"p\">]]</span>\n <span class=\"gh\">Out[16]: </span>\n <span class=\"go\">   A  B</span>\n <span class=\"go\">1  2  5</span>\n <span class=\"go\">2  3  6</span>\n+\n+<span class=\"go\">[2 rows x 2 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"indexing-with\">\n <h4>Indexing with <code class=\"docutils literal notranslate\"><span class=\"pre\">[]</span></code><a class=\"headerlink\" href=\"#indexing-with\" title=\"Link to this heading\">\u00b6</a></h4>\n <p>Finally, you can use a callable in <code class=\"docutils literal notranslate\"><span class=\"pre\">[]</span></code> indexing of Series, DataFrame and Panel.\n The callable must return a valid input for <code class=\"docutils literal notranslate\"><span class=\"pre\">[]</span></code> indexing depending on its\n class and index type.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [17]: </span><span class=\"n\">df</span><span class=\"p\">[</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"s2\">&quot;A&quot;</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[17]: </span>\n <span class=\"go\">0    1</span>\n <span class=\"go\">1    2</span>\n <span class=\"go\">2    3</span>\n-<span class=\"go\">Name: A, dtype: int64</span>\n+<span class=\"go\">Name: A, Length: 3, dtype: int64</span>\n </pre></div>\n </div>\n <p>Using these methods / indexers, you can chain data selection operations\n without using temporary variable.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [18]: </span><span class=\"n\">bb</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_csv</span><span class=\"p\">(</span><span class=\"s2\">&quot;data/baseball.csv&quot;</span><span class=\"p\">,</span> <span class=\"n\">index_col</span><span class=\"o\">=</span><span class=\"s2\">&quot;id&quot;</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [19]: </span><span class=\"p\">(</span><span class=\"n\">bb</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">([</span><span class=\"s2\">&quot;year&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;team&quot;</span><span class=\"p\">])</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">(</span><span class=\"n\">numeric_only</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"k\">lambda</span> <span class=\"n\">df</span><span class=\"p\">:</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">r</span> <span class=\"o\">&gt;</span> <span class=\"mi\">100</span><span class=\"p\">])</span>\n@@ -1325,30 +1333,32 @@\n <span class=\"gp\">   ....: </span>\n \n <span class=\"gp\">In [21]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[21]: </span>\n <span class=\"go\">   year  month  day  hour</span>\n <span class=\"go\">0  2015      2    4     2</span>\n <span class=\"go\">1  2016      3    5     3</span>\n+\n+<span class=\"go\">[2 rows x 4 columns]</span>\n </pre></div>\n </div>\n <p>Assembling using the passed frame.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [22]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">to_datetime</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[22]: </span>\n <span class=\"go\">0   2015-02-04 02:00:00</span>\n <span class=\"go\">1   2016-03-05 03:00:00</span>\n-<span class=\"go\">dtype: datetime64[ns]</span>\n+<span class=\"go\">Length: 2, dtype: datetime64[ns]</span>\n </pre></div>\n </div>\n <p>You can pass only the columns that you need to assemble.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [23]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">to_datetime</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">[[</span><span class=\"s2\">&quot;year&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;month&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;day&quot;</span><span class=\"p\">]])</span>\n <span class=\"gh\">Out[23]: </span>\n <span class=\"go\">0   2015-02-04</span>\n <span class=\"go\">1   2016-03-05</span>\n-<span class=\"go\">dtype: datetime64[ns]</span>\n+<span class=\"go\">Length: 2, dtype: datetime64[ns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"other-enhancements\">\n <span id=\"whatsnew-0181-other\"></span><h3>Other enhancements<a class=\"headerlink\" href=\"#other-enhancements\" title=\"Link to this heading\">\u00b6</a></h3>\n <ul>\n <li><p><code class=\"docutils literal notranslate\"><span class=\"pre\">pd.read_csv()</span></code> now supports <code class=\"docutils literal notranslate\"><span class=\"pre\">delim_whitespace=True</span></code> for the Python engine (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/12958\">GH 12958</a>)</p></li>\n@@ -1433,14 +1443,16 @@\n \n <span class=\"gp\">In [30]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[30]: </span>\n <span class=\"go\">   A  B</span>\n <span class=\"go\">0  a  1</span>\n <span class=\"go\">1  b  2</span>\n <span class=\"go\">2  a  3</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [3]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">,</span> <span class=\"n\">as_index</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)[</span><span class=\"s1\">&#39;B&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">nth</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[3]:</span>\n <span class=\"go\">0    1</span>\n <span class=\"go\">1    2</span>\n@@ -1454,21 +1466,21 @@\n </pre></div>\n </div>\n <p>New behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [31]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"s2\">&quot;A&quot;</span><span class=\"p\">,</span> <span class=\"n\">as_index</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)[</span><span class=\"s2\">&quot;B&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">nth</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[31]: </span>\n <span class=\"go\">0    1</span>\n <span class=\"go\">1    2</span>\n-<span class=\"go\">Name: B, dtype: int64</span>\n+<span class=\"go\">Name: B, Length: 2, dtype: int64</span>\n \n <span class=\"gp\">In [32]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"s2\">&quot;A&quot;</span><span class=\"p\">,</span> <span class=\"n\">as_index</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)[</span><span class=\"s2\">&quot;B&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">nth</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[32]: </span>\n <span class=\"go\">0    1</span>\n <span class=\"go\">1    2</span>\n-<span class=\"go\">Name: B, dtype: int64</span>\n+<span class=\"go\">Name: B, Length: 2, dtype: int64</span>\n </pre></div>\n </div>\n <p>Furthermore, previously, a <code class=\"docutils literal notranslate\"><span class=\"pre\">.groupby</span></code> would always sort, regardless if <code class=\"docutils literal notranslate\"><span class=\"pre\">sort=False</span></code> was passed with <code class=\"docutils literal notranslate\"><span class=\"pre\">.nth()</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [33]: </span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">random</span><span class=\"o\">.</span><span class=\"n\">seed</span><span class=\"p\">(</span><span class=\"mi\">1234</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [34]: </span><span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">(</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">random</span><span class=\"o\">.</span><span class=\"n\">randn</span><span class=\"p\">(</span><span class=\"mi\">100</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">),</span> <span class=\"n\">columns</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s2\">&quot;a&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;b&quot;</span><span class=\"p\">])</span>\n \n@@ -1500,21 +1512,25 @@\n <span class=\"gh\">Out[36]: </span>\n <span class=\"go\">           a         b  c</span>\n <span class=\"go\">2  -0.720589  0.887163  2</span>\n <span class=\"go\">3   0.859588 -0.636524  3</span>\n <span class=\"go\">7  -0.334077  0.002118  0</span>\n <span class=\"go\">21  0.036142 -2.074978  1</span>\n \n+<span class=\"go\">[4 rows x 3 columns]</span>\n+\n <span class=\"gp\">In [37]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"s2\">&quot;c&quot;</span><span class=\"p\">,</span> <span class=\"n\">sort</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">nth</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[37]: </span>\n <span class=\"go\">           a         b  c</span>\n <span class=\"go\">2  -0.720589  0.887163  2</span>\n <span class=\"go\">3   0.859588 -0.636524  3</span>\n <span class=\"go\">7  -0.334077  0.002118  0</span>\n <span class=\"go\">21  0.036142 -2.074978  1</span>\n+\n+<span class=\"go\">[4 rows x 3 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"numpy-function-compatibility\">\n <span id=\"whatsnew-0181-numpy-compatibility\"></span><h3>NumPy function compatibility<a class=\"headerlink\" href=\"#numpy-function-compatibility\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>Compatibility between pandas array-like methods (e.g. <code class=\"docutils literal notranslate\"><span class=\"pre\">sum</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">take</span></code>) and their <code class=\"docutils literal notranslate\"><span class=\"pre\">numpy</span></code>\n counterparts has been greatly increased by augmenting the signatures of the <code class=\"docutils literal notranslate\"><span class=\"pre\">pandas</span></code> methods so\n@@ -1549,14 +1565,16 @@\n <span class=\"gp\">   ....: </span>\n \n <span class=\"gp\">In [39]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[39]: </span>\n <span class=\"go\">        date  value</span>\n <span class=\"go\">0 2000-10-10     10</span>\n <span class=\"go\">1 2000-11-10     13</span>\n+\n+<span class=\"go\">[2 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [1]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">TimeGrouper</span><span class=\"p\">(</span><span class=\"n\">key</span><span class=\"o\">=</span><span class=\"s1\">&#39;date&#39;</span><span class=\"p\">,</span>\n <span class=\"gp\">   ...: </span>                          <span class=\"n\">freq</span><span class=\"o\">=</span><span class=\"s1\">&#39;M&#39;</span><span class=\"p\">))</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"n\">x</span><span class=\"o\">.</span><span class=\"n\">value</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">())</span>\n <span class=\"gh\">Out[1]:</span>\n <span class=\"go\">...</span>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -287,14 +287,16 @@\n Out[12]:\n             group  val\n date\n 2016-01-03      1    5\n 2016-01-10      1    6\n 2016-01-17      2    7\n 2016-01-24      2    8\n+\n+[4 rows x 2 columns]\n In[1]: df.groupby(\"group\").apply(lambda x: x.resample(\"1D\").ffill())\n Out[1]:\n                   group  val\n group date\n 1     2016-01-03      1    5\n       2016-01-04      1    5\n       2016-01-05      1    5\n@@ -344,41 +346,47 @@\n \n In [14]: df.where(lambda x: x > 4, lambda x: x + 10)\n Out[14]:\n     A   B  C\n 0  11  14  7\n 1  12   5  8\n 2  13   6  9\n+\n+[3 rows x 3 columns]\n *\b**\b**\b* M\bMe\bet\bth\bho\bod\bds\bs .\b.l\blo\boc\bc[\b[]\b],\b, .\b.i\bil\blo\boc\bc[\b[]\b],\b, .\b.i\bix\bx[\b[]\b]_\b?\b\u00b6 *\b**\b**\b*\n These can accept a callable, and a tuple of callable as a slicer. The callable\n can return a valid boolean indexer or anything which is valid for these\n indexer\u2019s input.\n # callable returns bool indexer\n In [15]: df.loc[lambda x: x.A >= 2, lambda x: x.sum() > 10]\n Out[15]:\n    B  C\n 1  5  8\n 2  6  9\n \n+[2 rows x 2 columns]\n+\n # callable returns list of labels\n In [16]: df.loc[lambda x: [1, 2], lambda x: [\"A\", \"B\"]]\n Out[16]:\n    A  B\n 1  2  5\n 2  3  6\n+\n+[2 rows x 2 columns]\n *\b**\b**\b* I\bIn\bnd\bde\bex\bxi\bin\bng\bg w\bwi\bit\bth\bh [\b[]\b]_\b?\b\u00b6 *\b**\b**\b*\n Finally, you can use a callable in [] indexing of Series, DataFrame and Panel.\n The callable must return a valid input for [] indexing depending on its class\n and index type.\n In [17]: df[lambda x: \"A\"]\n Out[17]:\n 0    1\n 1    2\n 2    3\n-Name: A, dtype: int64\n+Name: A, Length: 3, dtype: int64\n Using these methods / indexers, you can chain data selection operations without\n using temporary variable.\n In [18]: bb = pd.read_csv(\"data/baseball.csv\", index_col=\"id\")\n \n In [19]: (bb.groupby([\"year\", \"team\"]).sum(numeric_only=True).loc[lambda df:\n df.r > 100])\n Out[19]:\n@@ -484,26 +492,28 @@\n    ....:\n \n In [21]: df\n Out[21]:\n    year  month  day  hour\n 0  2015      2    4     2\n 1  2016      3    5     3\n+\n+[2 rows x 4 columns]\n Assembling using the passed frame.\n In [22]: pd.to_datetime(df)\n Out[22]:\n 0   2015-02-04 02:00:00\n 1   2016-03-05 03:00:00\n-dtype: datetime64[ns]\n+Length: 2, dtype: datetime64[ns]\n You can pass only the columns that you need to assemble.\n In [23]: pd.to_datetime(df[[\"year\", \"month\", \"day\"]])\n Out[23]:\n 0   2015-02-04\n 1   2016-03-05\n-dtype: datetime64[ns]\n+Length: 2, dtype: datetime64[ns]\n *\b**\b**\b**\b* O\bOt\bth\bhe\ber\br e\ben\bnh\bha\ban\bnc\bce\bem\bme\ben\bnt\bts\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n     * pd.read_csv() now supports delim_whitespace=True for the Python engine\n       (_\bG_\bH_\b _\b1_\b2_\b9_\b5_\b8)\n     * pd.read_csv() now supports opening ZIP files that contains a single CSV,\n       via extension inference or explicit compression='zip' (_\bG_\bH_\b _\b1_\b2_\b1_\b7_\b5)\n     * pd.read_csv() now supports opening files using xz compression, via\n       extension inference or explicit compression='xz' is specified; xz\n@@ -595,14 +605,16 @@\n \n In [30]: df\n Out[30]:\n    A  B\n 0  a  1\n 1  b  2\n 2  a  3\n+\n+[3 rows x 2 columns]\n Previous behavior:\n In [3]: df.groupby('A', as_index=True)['B'].nth(0)\n Out[3]:\n 0    1\n 1    2\n Name: B, dtype: int64\n \n@@ -612,21 +624,21 @@\n 1    2\n Name: B, dtype: int64\n New behavior:\n In [31]: df.groupby(\"A\", as_index=True)[\"B\"].nth(0)\n Out[31]:\n 0    1\n 1    2\n-Name: B, dtype: int64\n+Name: B, Length: 2, dtype: int64\n \n In [32]: df.groupby(\"A\", as_index=False)[\"B\"].nth(0)\n Out[32]:\n 0    1\n 1    2\n-Name: B, dtype: int64\n+Name: B, Length: 2, dtype: int64\n Furthermore, previously, a .groupby would always sort, regardless if sort=False\n was passed with .nth().\n In [33]: np.random.seed(1234)\n \n In [34]: df = pd.DataFrame(np.random.randn(100, 2), columns=[\"a\", \"b\"])\n \n In [35]: df[\"c\"] = np.random.randint(0, 4, 100)\n@@ -653,21 +665,25 @@\n Out[36]:\n            a         b  c\n 2  -0.720589  0.887163  2\n 3   0.859588 -0.636524  3\n 7  -0.334077  0.002118  0\n 21  0.036142 -2.074978  1\n \n+[4 rows x 3 columns]\n+\n In [37]: df.groupby(\"c\", sort=False).nth(1)\n Out[37]:\n            a         b  c\n 2  -0.720589  0.887163  2\n 3   0.859588 -0.636524  3\n 7  -0.334077  0.002118  0\n 21  0.036142 -2.074978  1\n+\n+[4 rows x 3 columns]\n *\b**\b**\b**\b* N\bNu\bum\bmP\bPy\by f\bfu\bun\bnc\bct\bti\bio\bon\bn c\bco\bom\bmp\bpa\bat\bti\bib\bbi\bil\bli\bit\bty\by_\b?\b\u00b6 *\b**\b**\b**\b*\n Compatibility between pandas array-like methods (e.g. sum and take) and their\n numpy counterparts has been greatly increased by augmenting the signatures of\n the pandas methods so as to accept arguments that can be passed in from numpy,\n even if they are not necessarily used in the pandas implementation (_\bG_\bH_\b _\b1_\b2_\b6_\b4_\b4,\n _\bG_\bH_\b _\b1_\b2_\b6_\b3_\b8, _\bG_\bH_\b _\b1_\b2_\b6_\b8_\b7)\n     * .searchsorted() for Index and TimedeltaIndex now accept a sorter argument\n@@ -693,14 +709,16 @@\n    ....:\n \n In [39]: df\n Out[39]:\n         date  value\n 0 2000-10-10     10\n 1 2000-11-10     13\n+\n+[2 rows x 2 columns]\n Previous behavior:\n In [1]: df.groupby(pd.TimeGrouper(key='date',\n    ...:                           freq='M')).apply(lambda x: x.value.sum())\n Out[1]:\n ...\n TypeError: cannot concatenate a non-NDFrame object\n \n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.19.0.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.19.0.html", "unified_diff": "@@ -1250,41 +1250,49 @@\n <span class=\"gp\">In [3]: </span><span class=\"n\">left</span>\n <span class=\"gh\">Out[3]: </span>\n <span class=\"go\">    a left_val</span>\n <span class=\"go\">0   1        a</span>\n <span class=\"go\">1   5        b</span>\n <span class=\"go\">2  10        c</span>\n \n+<span class=\"go\">[3 rows x 2 columns]</span>\n+\n <span class=\"gp\">In [4]: </span><span class=\"n\">right</span>\n <span class=\"gh\">Out[4]: </span>\n <span class=\"go\">   a  right_val</span>\n <span class=\"go\">0  1          1</span>\n <span class=\"go\">1  2          2</span>\n <span class=\"go\">2  3          3</span>\n <span class=\"go\">3  6          6</span>\n <span class=\"go\">4  7          7</span>\n+\n+<span class=\"go\">[5 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>We typically want to match exactly when possible, and use the most\n recent value otherwise.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [5]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">merge_asof</span><span class=\"p\">(</span><span class=\"n\">left</span><span class=\"p\">,</span> <span class=\"n\">right</span><span class=\"p\">,</span> <span class=\"n\">on</span><span class=\"o\">=</span><span class=\"s2\">&quot;a&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[5]: </span>\n <span class=\"go\">    a left_val  right_val</span>\n <span class=\"go\">0   1        a          1</span>\n <span class=\"go\">1   5        b          3</span>\n <span class=\"go\">2  10        c          7</span>\n+\n+<span class=\"go\">[3 rows x 3 columns]</span>\n </pre></div>\n </div>\n <p>We can also match rows ONLY with prior data, and not an exact match.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [6]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">merge_asof</span><span class=\"p\">(</span><span class=\"n\">left</span><span class=\"p\">,</span> <span class=\"n\">right</span><span class=\"p\">,</span> <span class=\"n\">on</span><span class=\"o\">=</span><span class=\"s2\">&quot;a&quot;</span><span class=\"p\">,</span> <span class=\"n\">allow_exact_matches</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[6]: </span>\n <span class=\"go\">    a left_val  right_val</span>\n <span class=\"go\">0   1        a        NaN</span>\n <span class=\"go\">1   5        b        3.0</span>\n <span class=\"go\">2  10        c        7.0</span>\n+\n+<span class=\"go\">[3 rows x 3 columns]</span>\n </pre></div>\n </div>\n <p>In a typical time-series example, we have <code class=\"docutils literal notranslate\"><span class=\"pre\">trades</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">quotes</span></code> and we want to <code class=\"docutils literal notranslate\"><span class=\"pre\">asof-join</span></code> them.\n This also illustrates using the <code class=\"docutils literal notranslate\"><span class=\"pre\">by</span></code> parameter to group data before merging.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [7]: </span><span class=\"n\">trades</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">(</span>\n <span class=\"gp\">   ...: </span>    <span class=\"p\">{</span>\n <span class=\"gp\">   ...: </span>        <span class=\"s2\">&quot;time&quot;</span><span class=\"p\">:</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">to_datetime</span><span class=\"p\">(</span>\n@@ -1332,38 +1340,44 @@\n <span class=\"go\">                     time ticker   price  quantity</span>\n <span class=\"go\">0 2016-05-25 13:30:00.023   MSFT   51.95        75</span>\n <span class=\"go\">1 2016-05-25 13:30:00.038   MSFT   51.95       155</span>\n <span class=\"go\">2 2016-05-25 13:30:00.048   GOOG  720.77       100</span>\n <span class=\"go\">3 2016-05-25 13:30:00.048   GOOG  720.92       100</span>\n <span class=\"go\">4 2016-05-25 13:30:00.048   AAPL   98.00       100</span>\n \n+<span class=\"go\">[5 rows x 4 columns]</span>\n+\n <span class=\"gp\">In [10]: </span><span class=\"n\">quotes</span>\n <span class=\"gh\">Out[10]: </span>\n <span class=\"go\">                     time ticker     bid     ask</span>\n <span class=\"go\">0 2016-05-25 13:30:00.023   GOOG  720.50  720.93</span>\n <span class=\"go\">1 2016-05-25 13:30:00.023   MSFT   51.95   51.96</span>\n <span class=\"go\">2 2016-05-25 13:30:00.030   MSFT   51.97   51.98</span>\n <span class=\"go\">3 2016-05-25 13:30:00.041   MSFT   51.99   52.00</span>\n <span class=\"go\">4 2016-05-25 13:30:00.048   GOOG  720.50  720.93</span>\n <span class=\"go\">5 2016-05-25 13:30:00.049   AAPL   97.99   98.01</span>\n <span class=\"go\">6 2016-05-25 13:30:00.072   GOOG  720.50  720.88</span>\n <span class=\"go\">7 2016-05-25 13:30:00.075   MSFT   52.01   52.03</span>\n+\n+<span class=\"go\">[8 rows x 4 columns]</span>\n </pre></div>\n </div>\n <p>An asof merge joins on the <code class=\"docutils literal notranslate\"><span class=\"pre\">on</span></code>, typically a datetimelike field, which is ordered, and\n in this case we are using a grouper in the <code class=\"docutils literal notranslate\"><span class=\"pre\">by</span></code> field. This is like a left-outer join, except\n that forward filling happens automatically taking the most recent non-NaN value.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [11]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">merge_asof</span><span class=\"p\">(</span><span class=\"n\">trades</span><span class=\"p\">,</span> <span class=\"n\">quotes</span><span class=\"p\">,</span> <span class=\"n\">on</span><span class=\"o\">=</span><span class=\"s2\">&quot;time&quot;</span><span class=\"p\">,</span> <span class=\"n\">by</span><span class=\"o\">=</span><span class=\"s2\">&quot;ticker&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[11]: </span>\n <span class=\"go\">                     time ticker   price  quantity     bid     ask</span>\n <span class=\"go\">0 2016-05-25 13:30:00.023   MSFT   51.95        75   51.95   51.96</span>\n <span class=\"go\">1 2016-05-25 13:30:00.038   MSFT   51.95       155   51.97   51.98</span>\n <span class=\"go\">2 2016-05-25 13:30:00.048   GOOG  720.77       100  720.50  720.93</span>\n <span class=\"go\">3 2016-05-25 13:30:00.048   GOOG  720.92       100  720.50  720.93</span>\n <span class=\"go\">4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN</span>\n+\n+<span class=\"go\">[5 rows x 6 columns]</span>\n </pre></div>\n </div>\n <p>This returns a merged DataFrame with the entries in the same order as the original left\n passed DataFrame (<code class=\"docutils literal notranslate\"><span class=\"pre\">trades</span></code> in this case), with the fields of the <code class=\"docutils literal notranslate\"><span class=\"pre\">quotes</span></code> merged.</p>\n </section>\n <section id=\"method-rolling-is-now-time-series-aware\">\n <span id=\"whatsnew-0190-enhancements-rolling-ts\"></span><h3>Method <code class=\"docutils literal notranslate\"><span class=\"pre\">.rolling()</span></code> is now time-series aware<a class=\"headerlink\" href=\"#method-rolling-is-now-time-series-aware\" title=\"Link to this heading\">\u00b6</a></h3>\n@@ -1379,45 +1393,53 @@\n <span class=\"gh\">Out[13]: </span>\n <span class=\"go\">                       B</span>\n <span class=\"go\">2013-01-01 09:00:00  0.0</span>\n <span class=\"go\">2013-01-01 09:00:01  1.0</span>\n <span class=\"go\">2013-01-01 09:00:02  2.0</span>\n <span class=\"go\">2013-01-01 09:00:03  NaN</span>\n <span class=\"go\">2013-01-01 09:00:04  4.0</span>\n+\n+<span class=\"go\">[5 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p>This is a regular frequency index. Using an integer window parameter works to roll along the window frequency.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [14]: </span><span class=\"n\">dft</span><span class=\"o\">.</span><span class=\"n\">rolling</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[14]: </span>\n <span class=\"go\">                       B</span>\n <span class=\"go\">2013-01-01 09:00:00  NaN</span>\n <span class=\"go\">2013-01-01 09:00:01  1.0</span>\n <span class=\"go\">2013-01-01 09:00:02  3.0</span>\n <span class=\"go\">2013-01-01 09:00:03  NaN</span>\n <span class=\"go\">2013-01-01 09:00:04  NaN</span>\n \n+<span class=\"go\">[5 rows x 1 columns]</span>\n+\n <span class=\"gp\">In [15]: </span><span class=\"n\">dft</span><span class=\"o\">.</span><span class=\"n\">rolling</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">min_periods</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[15]: </span>\n <span class=\"go\">                       B</span>\n <span class=\"go\">2013-01-01 09:00:00  0.0</span>\n <span class=\"go\">2013-01-01 09:00:01  1.0</span>\n <span class=\"go\">2013-01-01 09:00:02  3.0</span>\n <span class=\"go\">2013-01-01 09:00:03  2.0</span>\n <span class=\"go\">2013-01-01 09:00:04  4.0</span>\n+\n+<span class=\"go\">[5 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p>Specifying an offset allows a more intuitive specification of the rolling frequency.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [16]: </span><span class=\"n\">dft</span><span class=\"o\">.</span><span class=\"n\">rolling</span><span class=\"p\">(</span><span class=\"s2\">&quot;2s&quot;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[16]: </span>\n <span class=\"go\">                       B</span>\n <span class=\"go\">2013-01-01 09:00:00  0.0</span>\n <span class=\"go\">2013-01-01 09:00:01  1.0</span>\n <span class=\"go\">2013-01-01 09:00:02  3.0</span>\n <span class=\"go\">2013-01-01 09:00:03  2.0</span>\n <span class=\"go\">2013-01-01 09:00:04  4.0</span>\n+\n+<span class=\"go\">[5 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p>Using a non-regular, but still monotonic index, rolling with an integer window does not impart any special calculation.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [17]: </span><span class=\"n\">dft</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">(</span>\n <span class=\"gp\">   ....: </span>    <span class=\"p\">{</span><span class=\"s2\">&quot;B&quot;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">nan</span><span class=\"p\">,</span> <span class=\"mi\">4</span><span class=\"p\">]},</span>\n <span class=\"gp\">   ....: </span>    <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Index</span><span class=\"p\">(</span>\n <span class=\"gp\">   ....: </span>        <span class=\"p\">[</span>\n@@ -1438,35 +1460,41 @@\n <span class=\"go\">foo                     </span>\n <span class=\"go\">2013-01-01 09:00:00  0.0</span>\n <span class=\"go\">2013-01-01 09:00:02  1.0</span>\n <span class=\"go\">2013-01-01 09:00:03  2.0</span>\n <span class=\"go\">2013-01-01 09:00:05  NaN</span>\n <span class=\"go\">2013-01-01 09:00:06  4.0</span>\n \n+<span class=\"go\">[5 rows x 1 columns]</span>\n+\n <span class=\"gp\">In [19]: </span><span class=\"n\">dft</span><span class=\"o\">.</span><span class=\"n\">rolling</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[19]: </span>\n <span class=\"go\">                       B</span>\n <span class=\"go\">foo                     </span>\n <span class=\"go\">2013-01-01 09:00:00  NaN</span>\n <span class=\"go\">2013-01-01 09:00:02  1.0</span>\n <span class=\"go\">2013-01-01 09:00:03  3.0</span>\n <span class=\"go\">2013-01-01 09:00:05  NaN</span>\n <span class=\"go\">2013-01-01 09:00:06  NaN</span>\n+\n+<span class=\"go\">[5 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p>Using the time-specification generates variable windows for this sparse data.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [20]: </span><span class=\"n\">dft</span><span class=\"o\">.</span><span class=\"n\">rolling</span><span class=\"p\">(</span><span class=\"s2\">&quot;2s&quot;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[20]: </span>\n <span class=\"go\">                       B</span>\n <span class=\"go\">foo                     </span>\n <span class=\"go\">2013-01-01 09:00:00  0.0</span>\n <span class=\"go\">2013-01-01 09:00:02  1.0</span>\n <span class=\"go\">2013-01-01 09:00:03  3.0</span>\n <span class=\"go\">2013-01-01 09:00:05  NaN</span>\n <span class=\"go\">2013-01-01 09:00:06  4.0</span>\n+\n+<span class=\"go\">[5 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p>Furthermore, we now allow an optional <code class=\"docutils literal notranslate\"><span class=\"pre\">on</span></code> parameter to specify a column (rather than the\n default of the index) in a DataFrame.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [21]: </span><span class=\"n\">dft</span> <span class=\"o\">=</span> <span class=\"n\">dft</span><span class=\"o\">.</span><span class=\"n\">reset_index</span><span class=\"p\">()</span>\n \n <span class=\"gp\">In [22]: </span><span class=\"n\">dft</span>\n@@ -1474,22 +1502,26 @@\n <span class=\"go\">                  foo    B</span>\n <span class=\"go\">0 2013-01-01 09:00:00  0.0</span>\n <span class=\"go\">1 2013-01-01 09:00:02  1.0</span>\n <span class=\"go\">2 2013-01-01 09:00:03  2.0</span>\n <span class=\"go\">3 2013-01-01 09:00:05  NaN</span>\n <span class=\"go\">4 2013-01-01 09:00:06  4.0</span>\n \n+<span class=\"go\">[5 rows x 2 columns]</span>\n+\n <span class=\"gp\">In [23]: </span><span class=\"n\">dft</span><span class=\"o\">.</span><span class=\"n\">rolling</span><span class=\"p\">(</span><span class=\"s2\">&quot;2s&quot;</span><span class=\"p\">,</span> <span class=\"n\">on</span><span class=\"o\">=</span><span class=\"s2\">&quot;foo&quot;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[23]: </span>\n <span class=\"go\">                  foo    B</span>\n <span class=\"go\">0 2013-01-01 09:00:00  0.0</span>\n <span class=\"go\">1 2013-01-01 09:00:02  1.0</span>\n <span class=\"go\">2 2013-01-01 09:00:03  3.0</span>\n <span class=\"go\">3 2013-01-01 09:00:05  NaN</span>\n <span class=\"go\">4 2013-01-01 09:00:06  4.0</span>\n+\n+<span class=\"go\">[5 rows x 2 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"method-read-csv-has-improved-support-for-duplicate-column-names\">\n <span id=\"whatsnew-0190-enhancements-read-csv-dupe-col-names-support\"></span><h3>Method <code class=\"docutils literal notranslate\"><span class=\"pre\">read_csv</span></code> has improved support for duplicate column names<a class=\"headerlink\" href=\"#method-read-csv-has-improved-support-for-duplicate-column-names\" title=\"Link to this heading\">\u00b6</a></h3>\n <p><a class=\"reference internal\" href=\"../user_guide/io.html#io-dupe-names\"><span class=\"std std-ref\">Duplicate column names</span></a> are now supported in <a class=\"reference internal\" href=\"../reference/api/pandas.read_csv.html#pandas.read_csv\" title=\"pandas.read_csv\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">read_csv()</span></code></a> whether\n they are in the file or passed in as the <code class=\"docutils literal notranslate\"><span class=\"pre\">names</span></code> parameter (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/7160\">GH 7160</a>, <a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/9424\">GH 9424</a>)</p>\n@@ -1562,36 +1594,38 @@\n <span class=\"gp\">In [28]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_csv</span><span class=\"p\">(</span><span class=\"n\">StringIO</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">))</span>\n <span class=\"gh\">Out[28]: </span>\n <span class=\"go\">  col1 col2  col3</span>\n <span class=\"go\">0    a    b     1</span>\n <span class=\"go\">1    a    b     2</span>\n <span class=\"go\">2    c    d     3</span>\n \n+<span class=\"go\">[3 rows x 3 columns]</span>\n+\n <span class=\"gp\">In [29]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_csv</span><span class=\"p\">(</span><span class=\"n\">StringIO</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">))</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[29]: </span>\n <span class=\"go\">col1    object</span>\n <span class=\"go\">col2    object</span>\n <span class=\"go\">col3     int64</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n \n <span class=\"gp\">In [30]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_csv</span><span class=\"p\">(</span><span class=\"n\">StringIO</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">),</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"s2\">&quot;category&quot;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[30]: </span>\n <span class=\"go\">col1    category</span>\n <span class=\"go\">col2    category</span>\n <span class=\"go\">col3    category</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n </pre></div>\n </div>\n <p>Individual columns can be parsed as a <code class=\"docutils literal notranslate\"><span class=\"pre\">Categorical</span></code> using a dict specification</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [31]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_csv</span><span class=\"p\">(</span><span class=\"n\">StringIO</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">),</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"p\">{</span><span class=\"s2\">&quot;col1&quot;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;category&quot;</span><span class=\"p\">})</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[31]: </span>\n <span class=\"go\">col1    category</span>\n <span class=\"go\">col2      object</span>\n <span class=\"go\">col3       int64</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n </pre></div>\n </div>\n <div class=\"admonition note\">\n <p class=\"admonition-title\">Note</p>\n <p>The resulting categories will always be parsed as strings (object dtype).\n If the categories are numeric they can be converted using the\n <a class=\"reference internal\" href=\"../reference/api/pandas.to_numeric.html#pandas.to_numeric\" title=\"pandas.to_numeric\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">to_numeric()</span></code></a> function, or as appropriate, another converter\n@@ -1599,34 +1633,34 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [32]: </span><span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_csv</span><span class=\"p\">(</span><span class=\"n\">StringIO</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">),</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"s2\">&quot;category&quot;</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [33]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[33]: </span>\n <span class=\"go\">col1    category</span>\n <span class=\"go\">col2    category</span>\n <span class=\"go\">col3    category</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n \n <span class=\"gp\">In [34]: </span><span class=\"n\">df</span><span class=\"p\">[</span><span class=\"s2\">&quot;col3&quot;</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[34]: </span>\n <span class=\"go\">0    1</span>\n <span class=\"go\">1    2</span>\n <span class=\"go\">2    3</span>\n-<span class=\"go\">Name: col3, dtype: category</span>\n+<span class=\"go\">Name: col3, Length: 3, dtype: category</span>\n <span class=\"go\">Categories (3, object): [&#39;1&#39;, &#39;2&#39;, &#39;3&#39;]</span>\n \n <span class=\"gp\">In [35]: </span><span class=\"n\">new_categories</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">to_numeric</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">[</span><span class=\"s2\">&quot;col3&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">cat</span><span class=\"o\">.</span><span class=\"n\">categories</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [36]: </span><span class=\"n\">df</span><span class=\"p\">[</span><span class=\"s2\">&quot;col3&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"p\">[</span><span class=\"s2\">&quot;col3&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">cat</span><span class=\"o\">.</span><span class=\"n\">rename_categories</span><span class=\"p\">(</span><span class=\"n\">new_categories</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [37]: </span><span class=\"n\">df</span><span class=\"p\">[</span><span class=\"s2\">&quot;col3&quot;</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[37]: </span>\n <span class=\"go\">0    1</span>\n <span class=\"go\">1    2</span>\n <span class=\"go\">2    3</span>\n-<span class=\"go\">Name: col3, dtype: category</span>\n+<span class=\"go\">Name: col3, Length: 3, dtype: category</span>\n <span class=\"go\">Categories (3, int64): [1, 2, 3]</span>\n </pre></div>\n </div>\n </div>\n </section>\n <section id=\"categorical-concatenation\">\n <span id=\"whatsnew-0190-enhancements-union-categoricals\"></span><h3>Categorical concatenation<a class=\"headerlink\" href=\"#categorical-concatenation\" title=\"Link to this heading\">\u00b6</a></h3>\n@@ -1661,15 +1695,15 @@\n <p><strong>New behavior</strong>:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [44]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">concat</span><span class=\"p\">([</span><span class=\"n\">s1</span><span class=\"p\">,</span> <span class=\"n\">s2</span><span class=\"p\">])</span>\n <span class=\"gh\">Out[44]: </span>\n <span class=\"go\">0    a</span>\n <span class=\"go\">1    b</span>\n <span class=\"go\">0    b</span>\n <span class=\"go\">1    c</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 4, dtype: object</span>\n </pre></div>\n </div>\n </section>\n <section id=\"semi-month-offsets\">\n <span id=\"whatsnew-0190-enhancements-semi-month-offsets\"></span><h3>Semi-month offsets<a class=\"headerlink\" href=\"#semi-month-offsets\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>pandas has gained new frequency offsets, <code class=\"docutils literal notranslate\"><span class=\"pre\">SemiMonthEnd</span></code> (\u2018SM\u2019) and <code class=\"docutils literal notranslate\"><span class=\"pre\">SemiMonthBegin</span></code> (\u2018SMS\u2019).\n These provide date offsets anchored (by default) to the 15th and end of month, and 15th and 1st of month respectively.\n@@ -1751,14 +1785,16 @@\n <span class=\"gp\">In [57]: </span><span class=\"n\">idx</span><span class=\"o\">.</span><span class=\"n\">str</span><span class=\"o\">.</span><span class=\"n\">extractall</span><span class=\"p\">(</span><span class=\"sa\">r</span><span class=\"s2\">&quot;[ab](?P&lt;digit&gt;\\d)&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[57]: </span>\n <span class=\"go\">        digit</span>\n <span class=\"go\">  match      </span>\n <span class=\"go\">0 0         1</span>\n <span class=\"go\">  1         2</span>\n <span class=\"go\">1 0         1</span>\n+\n+<span class=\"go\">[3 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p><code class=\"docutils literal notranslate\"><span class=\"pre\">Index.astype()</span></code> now accepts an optional boolean argument <code class=\"docutils literal notranslate\"><span class=\"pre\">copy</span></code>, which allows optional copying if the requirements on dtype are satisfied (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/13209\">GH 13209</a>)</p>\n </section>\n <section id=\"google-bigquery-enhancements\">\n <span id=\"whatsnew-0190-gbq\"></span><h3>Google BigQuery enhancements<a class=\"headerlink\" href=\"#google-bigquery-enhancements\" title=\"Link to this heading\">\u00b6</a></h3>\n <ul class=\"simple\">\n@@ -1786,15 +1822,15 @@\n </div>\n <p><strong>New behavior</strong>:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [58]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">get_dummies</span><span class=\"p\">([</span><span class=\"s2\">&quot;a&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;b&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;a&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;c&quot;</span><span class=\"p\">])</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[58]: </span>\n <span class=\"go\">a    bool</span>\n <span class=\"go\">b    bool</span>\n <span class=\"go\">c    bool</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n </pre></div>\n </div>\n </section>\n <section id=\"downcast-values-to-smallest-possible-dtype-in-to-numeric\">\n <span id=\"whatsnew-0190-enhancements-to-numeric-downcast\"></span><h3>Downcast values to smallest possible dtype in <code class=\"docutils literal notranslate\"><span class=\"pre\">to_numeric</span></code><a class=\"headerlink\" href=\"#downcast-values-to-smallest-possible-dtype-in-to-numeric\" title=\"Link to this heading\">\u00b6</a></h3>\n <p><code class=\"docutils literal notranslate\"><span class=\"pre\">pd.to_numeric()</span></code> now accepts a <code class=\"docutils literal notranslate\"><span class=\"pre\">downcast</span></code> parameter, which will downcast the data if possible to smallest specified numerical dtype (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/13352\">GH 13352</a>)</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [59]: </span><span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"s2\">&quot;1&quot;</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">]</span>\n@@ -1901,14 +1937,16 @@\n <span class=\"go\">                   date  a</span>\n <span class=\"go\">v d                       </span>\n <span class=\"go\">1 2015-01-04 2015-01-04  0</span>\n <span class=\"go\">2 2015-01-11 2015-01-11  1</span>\n <span class=\"go\">3 2015-01-18 2015-01-18  2</span>\n <span class=\"go\">4 2015-01-25 2015-01-25  3</span>\n <span class=\"go\">5 2015-02-01 2015-02-01  4</span>\n+\n+<span class=\"go\">[5 rows x 2 columns]</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [74]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">resample</span><span class=\"p\">(</span><span class=\"s2\">&quot;M&quot;</span><span class=\"p\">,</span> <span class=\"n\">on</span><span class=\"o\">=</span><span class=\"s2\">&quot;date&quot;</span><span class=\"p\">)[[</span><span class=\"s2\">&quot;a&quot;</span><span class=\"p\">]]</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[74]:</span>\n <span class=\"go\">            a</span>\n <span class=\"go\">date</span>\n <span class=\"go\">2015-01-31  6</span>\n@@ -1952,19 +1990,23 @@\n \n <span class=\"gp\">In [71]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[71]: </span>\n <span class=\"go\">      A  B  C</span>\n <span class=\"go\">row1  2  3  4</span>\n <span class=\"go\">row2  7  5  8</span>\n \n+<span class=\"go\">[2 rows x 3 columns]</span>\n+\n <span class=\"gp\">In [72]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">sort_values</span><span class=\"p\">(</span><span class=\"n\">by</span><span class=\"o\">=</span><span class=\"s2\">&quot;row2&quot;</span><span class=\"p\">,</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[72]: </span>\n <span class=\"go\">      B  A  C</span>\n <span class=\"go\">row1  3  2  4</span>\n <span class=\"go\">row2  5  7  8</span>\n+\n+<span class=\"go\">[2 rows x 3 columns]</span>\n </pre></div>\n </div>\n </li>\n <li><p>Added documentation to <a class=\"reference internal\" href=\"../user_guide/io.html#io-dtypes\"><span class=\"std std-ref\">I/O</span></a> regarding the perils of reading in columns with mixed dtypes and how to handle it (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/13746\">GH 13746</a>)</p></li>\n <li><p><a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.to_html.html#pandas.DataFrame.to_html\" title=\"pandas.DataFrame.to_html\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">to_html()</span></code></a> now has a <code class=\"docutils literal notranslate\"><span class=\"pre\">border</span></code> argument to control the value in the opening <code class=\"docutils literal notranslate\"><span class=\"pre\">&lt;table&gt;</span></code> tag. The default is the value of the <code class=\"docutils literal notranslate\"><span class=\"pre\">html.border</span></code> option, which defaults to 1. This also affects the notebook HTML repr, but since Jupyter\u2019s CSS includes a border-width attribute, the visual effect is the same. (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/11563\">GH 11563</a>).</p></li>\n <li><p>Raise <code class=\"docutils literal notranslate\"><span class=\"pre\">ImportError</span></code> in the sql functions when <code class=\"docutils literal notranslate\"><span class=\"pre\">sqlalchemy</span></code> is not installed and a connection string is used (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/11920\">GH 11920</a>).</p></li>\n <li><p>Compatibility with matplotlib 2.0. Older versions of pandas should also work with matplotlib 2.0 (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/13333\">GH 13333</a>)</p></li>\n@@ -2025,27 +2067,29 @@\n \n <span class=\"gp\">In [77]: </span><span class=\"n\">s1</span> <span class=\"o\">+</span> <span class=\"n\">s2</span>\n <span class=\"gh\">Out[77]: </span>\n <span class=\"go\">A    3.0</span>\n <span class=\"go\">B    4.0</span>\n <span class=\"go\">C    NaN</span>\n <span class=\"go\">D    NaN</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 4, dtype: float64</span>\n \n <span class=\"gp\">In [78]: </span><span class=\"n\">df1</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">([</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">],</span> <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"s2\">&quot;ABC&quot;</span><span class=\"p\">))</span>\n \n <span class=\"gp\">In [79]: </span><span class=\"n\">df2</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">([</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">],</span> <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"s2\">&quot;ABD&quot;</span><span class=\"p\">))</span>\n \n <span class=\"gp\">In [80]: </span><span class=\"n\">df1</span> <span class=\"o\">+</span> <span class=\"n\">df2</span>\n <span class=\"gh\">Out[80]: </span>\n <span class=\"go\">     0</span>\n <span class=\"go\">A  3.0</span>\n <span class=\"go\">B  4.0</span>\n <span class=\"go\">C  NaN</span>\n <span class=\"go\">D  NaN</span>\n+\n+<span class=\"go\">[4 rows x 1 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"comparison-operators\">\n <h4>Comparison operators<a class=\"headerlink\" href=\"#comparison-operators\" title=\"Link to this heading\">\u00b6</a></h4>\n <p>Comparison operators raise <code class=\"docutils literal notranslate\"><span class=\"pre\">ValueError</span></code> when <code class=\"docutils literal notranslate\"><span class=\"pre\">.index</span></code> are different.</p>\n <p><strong>Previous behavior</strong> (<code class=\"docutils literal notranslate\"><span class=\"pre\">Series</span></code>):</p>\n@@ -2074,15 +2118,15 @@\n <p>If you want to compare <code class=\"docutils literal notranslate\"><span class=\"pre\">Series</span></code> aligning its <code class=\"docutils literal notranslate\"><span class=\"pre\">.index</span></code>, see flexible comparison methods section below:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [82]: </span><span class=\"n\">s1</span><span class=\"o\">.</span><span class=\"n\">eq</span><span class=\"p\">(</span><span class=\"n\">s2</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[82]: </span>\n <span class=\"go\">A    False</span>\n <span class=\"go\">B     True</span>\n <span class=\"go\">C    False</span>\n <span class=\"go\">D    False</span>\n-<span class=\"go\">dtype: bool</span>\n+<span class=\"go\">Length: 4, dtype: bool</span>\n </pre></div>\n </div>\n </div>\n <p><strong>Current behavior</strong> (<code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code>, no change):</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [3]: </span><span class=\"n\">df1</span> <span class=\"o\">==</span> <span class=\"n\">df2</span>\n <span class=\"gh\">Out[3]:</span>\n <span class=\"go\">ValueError: Can only compare identically-labeled DataFrame objects</span>\n@@ -2110,30 +2154,30 @@\n \n <span class=\"gp\">In [85]: </span><span class=\"n\">s1</span> <span class=\"o\">&amp;</span> <span class=\"n\">s2</span>\n <span class=\"gh\">Out[85]: </span>\n <span class=\"go\">A     True</span>\n <span class=\"go\">B    False</span>\n <span class=\"go\">C    False</span>\n <span class=\"go\">D    False</span>\n-<span class=\"go\">dtype: bool</span>\n+<span class=\"go\">Length: 4, dtype: bool</span>\n </pre></div>\n </div>\n <div class=\"admonition note\">\n <p class=\"admonition-title\">Note</p>\n <p><code class=\"docutils literal notranslate\"><span class=\"pre\">Series</span></code> logical operators fill a <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code> result with <code class=\"docutils literal notranslate\"><span class=\"pre\">False</span></code>.</p>\n </div>\n <div class=\"admonition note\">\n <p class=\"admonition-title\">Note</p>\n <p>To achieve the same result as previous versions (compare values based on only left hand side index), you can use <code class=\"docutils literal notranslate\"><span class=\"pre\">reindex_like</span></code>:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [86]: </span><span class=\"n\">s1</span> <span class=\"o\">&amp;</span> <span class=\"n\">s2</span><span class=\"o\">.</span><span class=\"n\">reindex_like</span><span class=\"p\">(</span><span class=\"n\">s1</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[86]: </span>\n <span class=\"go\">A     True</span>\n <span class=\"go\">B    False</span>\n <span class=\"go\">C    False</span>\n-<span class=\"go\">dtype: bool</span>\n+<span class=\"go\">Length: 3, dtype: bool</span>\n </pre></div>\n </div>\n </div>\n <p><strong>Current behavior</strong> (<code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code>, no change):</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [87]: </span><span class=\"n\">df1</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">([</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"kc\">True</span><span class=\"p\">],</span> <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"s2\">&quot;ABC&quot;</span><span class=\"p\">))</span>\n \n <span class=\"gp\">In [88]: </span><span class=\"n\">df2</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">([</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"kc\">True</span><span class=\"p\">],</span> <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"s2\">&quot;ABD&quot;</span><span class=\"p\">))</span>\n@@ -2141,14 +2185,16 @@\n <span class=\"gp\">In [89]: </span><span class=\"n\">df1</span> <span class=\"o\">&amp;</span> <span class=\"n\">df2</span>\n <span class=\"gh\">Out[89]: </span>\n <span class=\"go\">       0</span>\n <span class=\"go\">A   True</span>\n <span class=\"go\">B  False</span>\n <span class=\"go\">C  False</span>\n <span class=\"go\">D  False</span>\n+\n+<span class=\"go\">[4 rows x 1 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"flexible-comparison-methods\">\n <h4>Flexible comparison methods<a class=\"headerlink\" href=\"#flexible-comparison-methods\" title=\"Link to this heading\">\u00b6</a></h4>\n <p><code class=\"docutils literal notranslate\"><span class=\"pre\">Series</span></code> flexible comparison methods like <code class=\"docutils literal notranslate\"><span class=\"pre\">eq</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">ne</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">le</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">lt</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">ge</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">gt</span></code> now align both <code class=\"docutils literal notranslate\"><span class=\"pre\">index</span></code>. Use these operators if you want to compare two <code class=\"docutils literal notranslate\"><span class=\"pre\">Series</span></code>\n which has the different <code class=\"docutils literal notranslate\"><span class=\"pre\">index</span></code>.</p>\n@@ -2158,23 +2204,23 @@\n \n <span class=\"gp\">In [92]: </span><span class=\"n\">s1</span><span class=\"o\">.</span><span class=\"n\">eq</span><span class=\"p\">(</span><span class=\"n\">s2</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[92]: </span>\n <span class=\"go\">a    False</span>\n <span class=\"go\">b     True</span>\n <span class=\"go\">c    False</span>\n <span class=\"go\">d    False</span>\n-<span class=\"go\">dtype: bool</span>\n+<span class=\"go\">Length: 4, dtype: bool</span>\n \n <span class=\"gp\">In [93]: </span><span class=\"n\">s1</span><span class=\"o\">.</span><span class=\"n\">ge</span><span class=\"p\">(</span><span class=\"n\">s2</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[93]: </span>\n <span class=\"go\">a    False</span>\n <span class=\"go\">b     True</span>\n <span class=\"go\">c     True</span>\n <span class=\"go\">d    False</span>\n-<span class=\"go\">dtype: bool</span>\n+<span class=\"go\">Length: 4, dtype: bool</span>\n </pre></div>\n </div>\n <p>Previously, this worked the same as comparison operators (see above).</p>\n </section>\n </section>\n <section id=\"series-type-promotion-on-assignment\">\n <span id=\"whatsnew-0190-api-promote\"></span><h3><code class=\"docutils literal notranslate\"><span class=\"pre\">Series</span></code> type promotion on assignment<a class=\"headerlink\" href=\"#series-type-promotion-on-assignment\" title=\"Link to this heading\">\u00b6</a></h3>\n@@ -2194,15 +2240,15 @@\n \n <span class=\"gp\">In [96]: </span><span class=\"n\">s</span><span class=\"p\">[</span><span class=\"s2\">&quot;b&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">3.0</span>\n \n <span class=\"gp\">In [97]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[97]: </span>\n <span class=\"go\">a    2016-01-01 00:00:00</span>\n <span class=\"go\">b                    3.0</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 2, dtype: object</span>\n \n <span class=\"gp\">In [98]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">dtype</span>\n <span class=\"gh\">Out[98]: </span><span class=\"go\">dtype(&#39;O&#39;)</span>\n </pre></div>\n </div>\n </section>\n <section id=\"function-to-datetime-changes\">\n@@ -2234,21 +2280,25 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [100]: </span><span class=\"n\">df1</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">({</span><span class=\"s2\">&quot;key&quot;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">],</span> <span class=\"s2\">&quot;v1&quot;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">10</span><span class=\"p\">]})</span>\n \n <span class=\"gp\">In [101]: </span><span class=\"n\">df1</span>\n <span class=\"gh\">Out[101]: </span>\n <span class=\"go\">   key  v1</span>\n <span class=\"go\">0    1  10</span>\n \n+<span class=\"go\">[1 rows x 2 columns]</span>\n+\n <span class=\"gp\">In [102]: </span><span class=\"n\">df2</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">({</span><span class=\"s2\">&quot;key&quot;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">],</span> <span class=\"s2\">&quot;v1&quot;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">20</span><span class=\"p\">,</span> <span class=\"mi\">30</span><span class=\"p\">]})</span>\n \n <span class=\"gp\">In [103]: </span><span class=\"n\">df2</span>\n <span class=\"gh\">Out[103]: </span>\n <span class=\"go\">   key  v1</span>\n <span class=\"go\">0    1  20</span>\n <span class=\"go\">1    2  30</span>\n+\n+<span class=\"go\">[2 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p><strong>Previous behavior</strong>:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [5]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">merge</span><span class=\"p\">(</span><span class=\"n\">df1</span><span class=\"p\">,</span> <span class=\"n\">df2</span><span class=\"p\">,</span> <span class=\"n\">how</span><span class=\"o\">=</span><span class=\"s1\">&#39;outer&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[5]:</span>\n <span class=\"go\">   key    v1</span>\n <span class=\"go\">0  1.0  10.0</span>\n@@ -2267,35 +2317,39 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [104]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">merge</span><span class=\"p\">(</span><span class=\"n\">df1</span><span class=\"p\">,</span> <span class=\"n\">df2</span><span class=\"p\">,</span> <span class=\"n\">how</span><span class=\"o\">=</span><span class=\"s2\">&quot;outer&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[104]: </span>\n <span class=\"go\">   key  v1</span>\n <span class=\"go\">0    1  10</span>\n <span class=\"go\">1    1  20</span>\n <span class=\"go\">2    2  30</span>\n \n+<span class=\"go\">[3 rows x 2 columns]</span>\n+\n <span class=\"gp\">In [105]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">merge</span><span class=\"p\">(</span><span class=\"n\">df1</span><span class=\"p\">,</span> <span class=\"n\">df2</span><span class=\"p\">,</span> <span class=\"n\">how</span><span class=\"o\">=</span><span class=\"s2\">&quot;outer&quot;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[105]: </span>\n <span class=\"go\">key    int64</span>\n <span class=\"go\">v1     int64</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 2, dtype: object</span>\n </pre></div>\n </div>\n <p>Of course if you have missing values that are introduced, then the\n resulting dtype will be upcast, which is unchanged from previous.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [106]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">merge</span><span class=\"p\">(</span><span class=\"n\">df1</span><span class=\"p\">,</span> <span class=\"n\">df2</span><span class=\"p\">,</span> <span class=\"n\">how</span><span class=\"o\">=</span><span class=\"s2\">&quot;outer&quot;</span><span class=\"p\">,</span> <span class=\"n\">on</span><span class=\"o\">=</span><span class=\"s2\">&quot;key&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[106]: </span>\n <span class=\"go\">   key  v1_x  v1_y</span>\n <span class=\"go\">0    1  10.0    20</span>\n <span class=\"go\">1    2   NaN    30</span>\n \n+<span class=\"go\">[2 rows x 3 columns]</span>\n+\n <span class=\"gp\">In [107]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">merge</span><span class=\"p\">(</span><span class=\"n\">df1</span><span class=\"p\">,</span> <span class=\"n\">df2</span><span class=\"p\">,</span> <span class=\"n\">how</span><span class=\"o\">=</span><span class=\"s2\">&quot;outer&quot;</span><span class=\"p\">,</span> <span class=\"n\">on</span><span class=\"o\">=</span><span class=\"s2\">&quot;key&quot;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[107]: </span>\n <span class=\"go\">key       int64</span>\n <span class=\"go\">v1_x    float64</span>\n <span class=\"go\">v1_y      int64</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n </pre></div>\n </div>\n </section>\n <section id=\"method-describe-changes\">\n <span id=\"whatsnew-0190-api-describe\"></span><h3>Method <code class=\"docutils literal notranslate\"><span class=\"pre\">.describe()</span></code> changes<a class=\"headerlink\" href=\"#method-describe-changes\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>Percentile identifiers in the index of a <code class=\"docutils literal notranslate\"><span class=\"pre\">.describe()</span></code> output will now be rounded to the least precision that keeps them distinct (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/13104\">GH 13104</a>)</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [108]: </span><span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">,</span> <span class=\"mi\">4</span><span class=\"p\">])</span>\n@@ -2338,15 +2392,15 @@\n <span class=\"go\">0.05%     0.002000</span>\n <span class=\"go\">0.1%      0.004000</span>\n <span class=\"go\">50%       2.000000</span>\n <span class=\"go\">99.9%     3.996000</span>\n <span class=\"go\">99.95%    3.998000</span>\n <span class=\"go\">99.99%    3.999600</span>\n <span class=\"go\">max       4.000000</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 12, dtype: float64</span>\n \n <span class=\"gp\">In [111]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">describe</span><span class=\"p\">(</span><span class=\"n\">percentiles</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"mf\">0.0001</span><span class=\"p\">,</span> <span class=\"mf\">0.0005</span><span class=\"p\">,</span> <span class=\"mf\">0.001</span><span class=\"p\">,</span> <span class=\"mf\">0.999</span><span class=\"p\">,</span> <span class=\"mf\">0.9995</span><span class=\"p\">,</span> <span class=\"mf\">0.9999</span><span class=\"p\">])</span>\n <span class=\"gh\">Out[111]: </span>\n <span class=\"go\">               0</span>\n <span class=\"go\">count   5.000000</span>\n <span class=\"go\">mean    2.000000</span>\n <span class=\"go\">std     1.581139</span>\n@@ -2355,14 +2409,16 @@\n <span class=\"go\">0.05%   0.002000</span>\n <span class=\"go\">0.1%    0.004000</span>\n <span class=\"go\">50%     2.000000</span>\n <span class=\"go\">99.9%   3.996000</span>\n <span class=\"go\">99.95%  3.998000</span>\n <span class=\"go\">99.99%  3.999600</span>\n <span class=\"go\">max     4.000000</span>\n+\n+<span class=\"go\">[12 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p>Furthermore:</p>\n <ul class=\"simple\">\n <li><p>Passing duplicated <code class=\"docutils literal notranslate\"><span class=\"pre\">percentiles</span></code> will now raise a <code class=\"docutils literal notranslate\"><span class=\"pre\">ValueError</span></code>.</p></li>\n <li><p>Bug in <code class=\"docutils literal notranslate\"><span class=\"pre\">.describe()</span></code> on a DataFrame with a mixed-dtype column index, which would previously raise a <code class=\"docutils literal notranslate\"><span class=\"pre\">TypeError</span></code> (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/13288\">GH 13288</a>)</p></li>\n </ul>\n@@ -2631,25 +2687,25 @@\n <span class=\"gh\">Out[142]: </span><span class=\"go\">CategoricalIndex([&#39;b&#39;, &#39;a&#39;, &#39;c&#39;], categories=[&#39;b&#39;, &#39;a&#39;, &#39;c&#39;], ordered=False, dtype=&#39;category&#39;, name=&#39;C&#39;)</span>\n \n <span class=\"gp\">In [143]: </span><span class=\"n\">df_grouped</span><span class=\"o\">.</span><span class=\"n\">reset_index</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[143]: </span>\n <span class=\"go\">A       int64</span>\n <span class=\"go\">C    category</span>\n <span class=\"go\">B     float64</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n \n <span class=\"gp\">In [144]: </span><span class=\"n\">df_set_idx</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"o\">.</span><span class=\"n\">levels</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[144]: </span><span class=\"go\">CategoricalIndex([&#39;b&#39;, &#39;a&#39;, &#39;c&#39;], categories=[&#39;b&#39;, &#39;a&#39;, &#39;c&#39;], ordered=False, dtype=&#39;category&#39;, name=&#39;C&#39;)</span>\n \n <span class=\"gp\">In [145]: </span><span class=\"n\">df_set_idx</span><span class=\"o\">.</span><span class=\"n\">reset_index</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[145]: </span>\n <span class=\"go\">A       int64</span>\n <span class=\"go\">C    category</span>\n <span class=\"go\">B       int64</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n </pre></div>\n </div>\n </section>\n <section id=\"function-read-csv-will-progressively-enumerate-chunks\">\n <span id=\"whatsnew-0190-api-autogenerated-chunksize-index\"></span><h3>Function <code class=\"docutils literal notranslate\"><span class=\"pre\">read_csv</span></code> will progressively enumerate chunks<a class=\"headerlink\" href=\"#function-read-csv-will-progressively-enumerate-chunks\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>When <a class=\"reference internal\" href=\"../reference/api/pandas.read_csv.html#pandas.read_csv\" title=\"pandas.read_csv\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">read_csv()</span></code></a> is called with <code class=\"docutils literal notranslate\"><span class=\"pre\">chunksize=n</span></code> and without specifying an index,\n each chunk used to have an independently generated index from <code class=\"docutils literal notranslate\"><span class=\"pre\">0</span></code> to <code class=\"docutils literal notranslate\"><span class=\"pre\">n-1</span></code>.\n@@ -2674,14 +2730,16 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [147]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">concat</span><span class=\"p\">(</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_csv</span><span class=\"p\">(</span><span class=\"n\">StringIO</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">),</span> <span class=\"n\">chunksize</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">))</span>\n <span class=\"gh\">Out[147]: </span>\n <span class=\"go\">   A  B</span>\n <span class=\"go\">0  0  1</span>\n <span class=\"go\">1  2  3</span>\n <span class=\"go\">2  4  5</span>\n <span class=\"go\">3  6  7</span>\n+\n+<span class=\"go\">[4 rows x 2 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"sparse-changes\">\n <span id=\"whatsnew-0190-sparse\"></span><h3>Sparse changes<a class=\"headerlink\" href=\"#sparse-changes\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>These changes allow pandas to handle sparse data with more dtypes, and for work to make a smoother experience with data handling.</p>\n <section id=\"types-int64-and-bool-support-enhancements\">\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -237,37 +237,45 @@\n In [3]: left\n Out[3]:\n     a left_val\n 0   1        a\n 1   5        b\n 2  10        c\n \n+[3 rows x 2 columns]\n+\n In [4]: right\n Out[4]:\n    a  right_val\n 0  1          1\n 1  2          2\n 2  3          3\n 3  6          6\n 4  7          7\n+\n+[5 rows x 2 columns]\n We typically want to match exactly when possible, and use the most recent value\n otherwise.\n In [5]: pd.merge_asof(left, right, on=\"a\")\n Out[5]:\n     a left_val  right_val\n 0   1        a          1\n 1   5        b          3\n 2  10        c          7\n+\n+[3 rows x 3 columns]\n We can also match rows ONLY with prior data, and not an exact match.\n In [6]: pd.merge_asof(left, right, on=\"a\", allow_exact_matches=False)\n Out[6]:\n     a left_val  right_val\n 0   1        a        NaN\n 1   5        b        3.0\n 2  10        c        7.0\n+\n+[3 rows x 3 columns]\n In a typical time-series example, we have trades and quotes and we want to\n asof-join them. This also illustrates using the by parameter to group data\n before merging.\n In [7]: trades = pd.DataFrame(\n    ...:     {\n    ...:         \"time\": pd.to_datetime(\n    ...:             [\n@@ -315,37 +323,43 @@\n                      time ticker   price  quantity\n 0 2016-05-25 13:30:00.023   MSFT   51.95        75\n 1 2016-05-25 13:30:00.038   MSFT   51.95       155\n 2 2016-05-25 13:30:00.048   GOOG  720.77       100\n 3 2016-05-25 13:30:00.048   GOOG  720.92       100\n 4 2016-05-25 13:30:00.048   AAPL   98.00       100\n \n+[5 rows x 4 columns]\n+\n In [10]: quotes\n Out[10]:\n                      time ticker     bid     ask\n 0 2016-05-25 13:30:00.023   GOOG  720.50  720.93\n 1 2016-05-25 13:30:00.023   MSFT   51.95   51.96\n 2 2016-05-25 13:30:00.030   MSFT   51.97   51.98\n 3 2016-05-25 13:30:00.041   MSFT   51.99   52.00\n 4 2016-05-25 13:30:00.048   GOOG  720.50  720.93\n 5 2016-05-25 13:30:00.049   AAPL   97.99   98.01\n 6 2016-05-25 13:30:00.072   GOOG  720.50  720.88\n 7 2016-05-25 13:30:00.075   MSFT   52.01   52.03\n+\n+[8 rows x 4 columns]\n An asof merge joins on the on, typically a datetimelike field, which is\n ordered, and in this case we are using a grouper in the by field. This is like\n a left-outer join, except that forward filling happens automatically taking the\n most recent non-NaN value.\n In [11]: pd.merge_asof(trades, quotes, on=\"time\", by=\"ticker\")\n Out[11]:\n                      time ticker   price  quantity     bid     ask\n 0 2016-05-25 13:30:00.023   MSFT   51.95        75   51.95   51.96\n 1 2016-05-25 13:30:00.038   MSFT   51.95       155   51.97   51.98\n 2 2016-05-25 13:30:00.048   GOOG  720.77       100  720.50  720.93\n 3 2016-05-25 13:30:00.048   GOOG  720.92       100  720.50  720.93\n 4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN\n+\n+[5 rows x 6 columns]\n This returns a merged DataFrame with the entries in the same order as the\n original left passed DataFrame (trades in this case), with the fields of the\n quotes merged.\n *\b**\b**\b**\b* M\bMe\bet\bth\bho\bod\bd .\b.r\bro\bol\bll\bli\bin\bng\bg(\b()\b) i\bis\bs n\bno\bow\bw t\bti\bim\bme\be-\b-s\bse\ber\bri\bie\bes\bs a\baw\bwa\bar\bre\be_\b?\b\u00b6 *\b**\b**\b**\b*\n .rolling() objects are now time-series aware and can accept a time-series\n offset (or convertible) for the window argument (_\bG_\bH_\b _\b1_\b3_\b3_\b2_\b7, _\bG_\bH_\b _\b1_\b2_\b9_\b9_\b5). See the\n full documentation _\bh_\be_\br_\be.\n@@ -359,43 +373,51 @@\n Out[13]:\n                        B\n 2013-01-01 09:00:00  0.0\n 2013-01-01 09:00:01  1.0\n 2013-01-01 09:00:02  2.0\n 2013-01-01 09:00:03  NaN\n 2013-01-01 09:00:04  4.0\n+\n+[5 rows x 1 columns]\n This is a regular frequency index. Using an integer window parameter works to\n roll along the window frequency.\n In [14]: dft.rolling(2).sum()\n Out[14]:\n                        B\n 2013-01-01 09:00:00  NaN\n 2013-01-01 09:00:01  1.0\n 2013-01-01 09:00:02  3.0\n 2013-01-01 09:00:03  NaN\n 2013-01-01 09:00:04  NaN\n \n+[5 rows x 1 columns]\n+\n In [15]: dft.rolling(2, min_periods=1).sum()\n Out[15]:\n                        B\n 2013-01-01 09:00:00  0.0\n 2013-01-01 09:00:01  1.0\n 2013-01-01 09:00:02  3.0\n 2013-01-01 09:00:03  2.0\n 2013-01-01 09:00:04  4.0\n+\n+[5 rows x 1 columns]\n Specifying an offset allows a more intuitive specification of the rolling\n frequency.\n In [16]: dft.rolling(\"2s\").sum()\n Out[16]:\n                        B\n 2013-01-01 09:00:00  0.0\n 2013-01-01 09:00:01  1.0\n 2013-01-01 09:00:02  3.0\n 2013-01-01 09:00:03  2.0\n 2013-01-01 09:00:04  4.0\n+\n+[5 rows x 1 columns]\n Using a non-regular, but still monotonic index, rolling with an integer window\n does not impart any special calculation.\n In [17]: dft = pd.DataFrame(\n    ....:     {\"B\": [0, 1, 2, np.nan, 4]},\n    ....:     index=pd.Index(\n    ....:         [\n    ....:             pd.Timestamp(\"20130101 09:00:00\"),\n@@ -415,54 +437,64 @@\n foo\n 2013-01-01 09:00:00  0.0\n 2013-01-01 09:00:02  1.0\n 2013-01-01 09:00:03  2.0\n 2013-01-01 09:00:05  NaN\n 2013-01-01 09:00:06  4.0\n \n+[5 rows x 1 columns]\n+\n In [19]: dft.rolling(2).sum()\n Out[19]:\n                        B\n foo\n 2013-01-01 09:00:00  NaN\n 2013-01-01 09:00:02  1.0\n 2013-01-01 09:00:03  3.0\n 2013-01-01 09:00:05  NaN\n 2013-01-01 09:00:06  NaN\n+\n+[5 rows x 1 columns]\n Using the time-specification generates variable windows for this sparse data.\n In [20]: dft.rolling(\"2s\").sum()\n Out[20]:\n                        B\n foo\n 2013-01-01 09:00:00  0.0\n 2013-01-01 09:00:02  1.0\n 2013-01-01 09:00:03  3.0\n 2013-01-01 09:00:05  NaN\n 2013-01-01 09:00:06  4.0\n+\n+[5 rows x 1 columns]\n Furthermore, we now allow an optional on parameter to specify a column (rather\n than the default of the index) in a DataFrame.\n In [21]: dft = dft.reset_index()\n \n In [22]: dft\n Out[22]:\n                   foo    B\n 0 2013-01-01 09:00:00  0.0\n 1 2013-01-01 09:00:02  1.0\n 2 2013-01-01 09:00:03  2.0\n 3 2013-01-01 09:00:05  NaN\n 4 2013-01-01 09:00:06  4.0\n \n+[5 rows x 2 columns]\n+\n In [23]: dft.rolling(\"2s\", on=\"foo\").sum()\n Out[23]:\n                   foo    B\n 0 2013-01-01 09:00:00  0.0\n 1 2013-01-01 09:00:02  1.0\n 2 2013-01-01 09:00:03  3.0\n 3 2013-01-01 09:00:05  NaN\n 4 2013-01-01 09:00:06  4.0\n+\n+[5 rows x 2 columns]\n *\b**\b**\b**\b* M\bMe\bet\bth\bho\bod\bd r\bre\bea\bad\bd_\b_c\bcs\bsv\bv h\bha\bas\bs i\bim\bmp\bpr\bro\bov\bve\bed\bd s\bsu\bup\bpp\bpo\bor\brt\bt f\bfo\bor\br d\bdu\bup\bpl\bli\bic\bca\bat\bte\be c\bco\bol\blu\bum\bmn\bn n\bna\bam\bme\bes\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n _\bD_\bu_\bp_\bl_\bi_\bc_\ba_\bt_\be_\b _\bc_\bo_\bl_\bu_\bm_\bn_\b _\bn_\ba_\bm_\be_\bs are now supported in _\br_\be_\ba_\bd_\b__\bc_\bs_\bv_\b(_\b) whether they are in the\n file or passed in as the names parameter (_\bG_\bH_\b _\b7_\b1_\b6_\b0, _\bG_\bH_\b _\b9_\b4_\b2_\b4)\n In [24]: data = \"0,1,2\\n3,4,5\"\n \n In [25]: names = [\"a\", \"b\", \"a\"]\n P\bPr\bre\bev\bvi\bio\bou\bus\bs b\bbe\beh\bha\bav\bvi\bio\bor\br:\n@@ -535,65 +567,67 @@\n In [28]: pd.read_csv(StringIO(data))\n Out[28]:\n   col1 col2  col3\n 0    a    b     1\n 1    a    b     2\n 2    c    d     3\n \n+[3 rows x 3 columns]\n+\n In [29]: pd.read_csv(StringIO(data)).dtypes\n Out[29]:\n col1    object\n col2    object\n col3     int64\n-dtype: object\n+Length: 3, dtype: object\n \n In [30]: pd.read_csv(StringIO(data), dtype=\"category\").dtypes\n Out[30]:\n col1    category\n col2    category\n col3    category\n-dtype: object\n+Length: 3, dtype: object\n Individual columns can be parsed as a Categorical using a dict specification\n In [31]: pd.read_csv(StringIO(data), dtype={\"col1\": \"category\"}).dtypes\n Out[31]:\n col1    category\n col2      object\n col3       int64\n-dtype: object\n+Length: 3, dtype: object\n Note\n The resulting categories will always be parsed as strings (object dtype). If\n the categories are numeric they can be converted using the _\bt_\bo_\b__\bn_\bu_\bm_\be_\br_\bi_\bc_\b(_\b)\n function, or as appropriate, another converter such as _\bt_\bo_\b__\bd_\ba_\bt_\be_\bt_\bi_\bm_\be_\b(_\b).\n In [32]: df = pd.read_csv(StringIO(data), dtype=\"category\")\n \n In [33]: df.dtypes\n Out[33]:\n col1    category\n col2    category\n col3    category\n-dtype: object\n+Length: 3, dtype: object\n \n In [34]: df[\"col3\"]\n Out[34]:\n 0    1\n 1    2\n 2    3\n-Name: col3, dtype: category\n+Name: col3, Length: 3, dtype: category\n Categories (3, object): ['1', '2', '3']\n \n In [35]: new_categories = pd.to_numeric(df[\"col3\"].cat.categories)\n \n In [36]: df[\"col3\"] = df[\"col3\"].cat.rename_categories(new_categories)\n \n In [37]: df[\"col3\"]\n Out[37]:\n 0    1\n 1    2\n 2    3\n-Name: col3, dtype: category\n+Name: col3, Length: 3, dtype: category\n Categories (3, int64): [1, 2, 3]\n *\b**\b**\b**\b* C\bCa\bat\bte\beg\bgo\bor\bri\bic\bca\bal\bl c\bco\bon\bnc\bca\bat\bte\ben\bna\bat\bti\bio\bon\bn_\b?\b\u00b6 *\b**\b**\b**\b*\n     * A function union_categoricals() has been added for combining\n       categoricals, see _\bU_\bn_\bi_\bo_\bn_\bi_\bn_\bg_\b _\bC_\ba_\bt_\be_\bg_\bo_\br_\bi_\bc_\ba_\bl_\bs (_\bG_\bH_\b _\b1_\b3_\b3_\b6_\b1, _\bG_\bH_\b _\b1_\b3_\b7_\b6_\b3, _\bG_\bH_\b _\b1_\b3_\b8_\b4_\b6, _\bG_\bH\n       _\b1_\b4_\b1_\b7_\b3)\n       In [38]: from pandas.api.types import union_categoricals\n \n@@ -616,15 +650,15 @@\n N\bNe\bew\bw b\bbe\beh\bha\bav\bvi\bio\bor\br:\n In [44]: pd.concat([s1, s2])\n Out[44]:\n 0    a\n 1    b\n 0    b\n 1    c\n-dtype: object\n+Length: 4, dtype: object\n *\b**\b**\b**\b* S\bSe\bem\bmi\bi-\b-m\bmo\bon\bnt\bth\bh o\bof\bff\bfs\bse\bet\bts\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n pandas has gained new frequency offsets, SemiMonthEnd (\u2018SM\u2019) and SemiMonthBegin\n (\u2018SMS\u2019). These provide date offsets anchored (by default) to the 15th and end\n of month, and 15th and 1st of month respectively. (_\bG_\bH_\b _\b1_\b5_\b4_\b3)\n In [45]: from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin\n S\bSe\bem\bmi\biM\bMo\bon\bnt\bth\bhE\bEn\bnd\bd:\n In [46]: pd.Timestamp(\"2016-01-01\") + SemiMonthEnd()\n@@ -694,14 +728,16 @@\n In [57]: idx.str.extractall(r\"[ab](?P<digit>\\d)\")\n Out[57]:\n         digit\n   match\n 0 0         1\n   1         2\n 1 0         1\n+\n+[3 rows x 1 columns]\n Index.astype() now accepts an optional boolean argument copy, which allows\n optional copying if the requirements on dtype are satisfied (_\bG_\bH_\b _\b1_\b3_\b2_\b0_\b9)\n *\b**\b**\b**\b* G\bGo\boo\bog\bgl\ble\be B\bBi\big\bgQ\bQu\bue\ber\bry\by e\ben\bnh\bha\ban\bnc\bce\bem\bme\ben\bnt\bts\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n     * The _\br_\be_\ba_\bd_\b__\bg_\bb_\bq_\b(_\b) method has gained the dialect argument to allow users to\n       specify whether to use BigQuery\u2019s legacy SQL or BigQuery\u2019s standard SQL.\n       See the _\bd_\bo_\bc_\bs for more details (_\bG_\bH_\b _\b1_\b3_\b6_\b1_\b5).\n     * The _\bt_\bo_\b__\bg_\bb_\bq_\b(_\b) method now allows the DataFrame column order to differ from\n@@ -734,15 +770,15 @@\n dtype: object\n N\bNe\bew\bw b\bbe\beh\bha\bav\bvi\bio\bor\br:\n In [58]: pd.get_dummies([\"a\", \"b\", \"a\", \"c\"]).dtypes\n Out[58]:\n a    bool\n b    bool\n c    bool\n-dtype: object\n+Length: 3, dtype: object\n *\b**\b**\b**\b* D\bDo\bow\bwn\bnc\bca\bas\bst\bt v\bva\bal\blu\bue\bes\bs t\bto\bo s\bsm\bma\bal\bll\ble\bes\bst\bt p\bpo\bos\bss\bsi\bib\bbl\ble\be d\bdt\bty\byp\bpe\be i\bin\bn t\bto\bo_\b_n\bnu\bum\bme\ber\bri\bic\bc_\b?\b\u00b6 *\b**\b**\b**\b*\n pd.to_numeric() now accepts a downcast parameter, which will downcast the data\n if possible to smallest specified numerical dtype (_\bG_\bH_\b _\b1_\b3_\b3_\b5_\b2)\n In [59]: s = [\"1\", 2, 3]\n \n In [60]: pd.to_numeric(s, downcast=\"unsigned\")\n Out[60]: array([1, 2, 3], dtype=uint8)\n@@ -838,14 +874,16 @@\n                          date  a\n       v d\n       1 2015-01-04 2015-01-04  0\n       2 2015-01-11 2015-01-11  1\n       3 2015-01-18 2015-01-18  2\n       4 2015-01-25 2015-01-25  3\n       5 2015-02-01 2015-02-01  4\n+\n+      [5 rows x 2 columns]\n       In [74]: df.resample(\"M\", on=\"date\")[[\"a\"]].sum()\n       Out[74]:\n                   a\n       date\n       2015-01-31  6\n       2015-02-28  4\n \n@@ -904,19 +942,23 @@\n \n       In [71]: df\n       Out[71]:\n             A  B  C\n       row1  2  3  4\n       row2  7  5  8\n \n+      [2 rows x 3 columns]\n+\n       In [72]: df.sort_values(by=\"row2\", axis=1)\n       Out[72]:\n             B  A  C\n       row1  3  2  4\n       row2  5  7  8\n+\n+      [2 rows x 3 columns]\n     * Added documentation to _\bI_\b/_\bO regarding the perils of reading in columns\n       with mixed dtypes and how to handle it (_\bG_\bH_\b _\b1_\b3_\b7_\b4_\b6)\n     * _\bt_\bo_\b__\bh_\bt_\bm_\bl_\b(_\b) now has a border argument to control the value in the opening\n       <table> tag. The default is the value of the html.border option, which\n       defaults to 1. This also affects the notebook HTML repr, but since\n       Jupyter\u2019s CSS includes a border-width attribute, the visual effect is the\n       same. (_\bG_\bH_\b _\b1_\b1_\b5_\b6_\b3).\n@@ -978,27 +1020,29 @@\n \n In [77]: s1 + s2\n Out[77]:\n A    3.0\n B    4.0\n C    NaN\n D    NaN\n-dtype: float64\n+Length: 4, dtype: float64\n \n In [78]: df1 = pd.DataFrame([1, 2, 3], index=list(\"ABC\"))\n \n In [79]: df2 = pd.DataFrame([2, 2, 2], index=list(\"ABD\"))\n \n In [80]: df1 + df2\n Out[80]:\n      0\n A  3.0\n B  4.0\n C  NaN\n D  NaN\n+\n+[4 rows x 1 columns]\n *\b**\b**\b* C\bCo\bom\bmp\bpa\bar\bri\bis\bso\bon\bn o\bop\bpe\ber\bra\bat\bto\bor\brs\bs_\b?\b\u00b6 *\b**\b**\b*\n Comparison operators raise ValueError when .index are different.\n P\bPr\bre\bev\bvi\bio\bou\bus\bs b\bbe\beh\bha\bav\bvi\bio\bor\br (Series):\n Series compared values ignoring the .index as long as both had the same length:\n In [1]: s1 == s2\n Out[1]:\n A    False\n@@ -1018,15 +1062,15 @@\n methods section below:\n In [82]: s1.eq(s2)\n Out[82]:\n A    False\n B     True\n C    False\n D    False\n-dtype: bool\n+Length: 4, dtype: bool\n C\bCu\bur\brr\bre\ben\bnt\bt b\bbe\beh\bha\bav\bvi\bio\bor\br (DataFrame, no change):\n In [3]: df1 == df2\n Out[3]:\n ValueError: Can only compare identically-labeled DataFrame objects\n *\b**\b**\b* L\bLo\bog\bgi\bic\bca\bal\bl o\bop\bpe\ber\bra\bat\bto\bor\brs\bs_\b?\b\u00b6 *\b**\b**\b*\n Logical operators align both .index of left and right hand side.\n P\bPr\bre\bev\bvi\bio\bou\bus\bs b\bbe\beh\bha\bav\bvi\bio\bor\br (Series), only left hand side index was kept:\n@@ -1045,61 +1089,63 @@\n \n In [85]: s1 & s2\n Out[85]:\n A     True\n B    False\n C    False\n D    False\n-dtype: bool\n+Length: 4, dtype: bool\n Note\n Series logical operators fill a NaN result with False.\n Note\n To achieve the same result as previous versions (compare values based on only\n left hand side index), you can use reindex_like:\n In [86]: s1 & s2.reindex_like(s1)\n Out[86]:\n A     True\n B    False\n C    False\n-dtype: bool\n+Length: 3, dtype: bool\n C\bCu\bur\brr\bre\ben\bnt\bt b\bbe\beh\bha\bav\bvi\bio\bor\br (DataFrame, no change):\n In [87]: df1 = pd.DataFrame([True, False, True], index=list(\"ABC\"))\n \n In [88]: df2 = pd.DataFrame([True, True, True], index=list(\"ABD\"))\n \n In [89]: df1 & df2\n Out[89]:\n        0\n A   True\n B  False\n C  False\n D  False\n+\n+[4 rows x 1 columns]\n *\b**\b**\b* F\bFl\ble\bex\bxi\bib\bbl\ble\be c\bco\bom\bmp\bpa\bar\bri\bis\bso\bon\bn m\bme\bet\bth\bho\bod\bds\bs_\b?\b\u00b6 *\b**\b**\b*\n Series flexible comparison methods like eq, ne, le, lt, ge and gt now align\n both index. Use these operators if you want to compare two Series which has the\n different index.\n In [90]: s1 = pd.Series([1, 2, 3], index=[\"a\", \"b\", \"c\"])\n \n In [91]: s2 = pd.Series([2, 2, 2], index=[\"b\", \"c\", \"d\"])\n \n In [92]: s1.eq(s2)\n Out[92]:\n a    False\n b     True\n c    False\n d    False\n-dtype: bool\n+Length: 4, dtype: bool\n \n In [93]: s1.ge(s2)\n Out[93]:\n a    False\n b     True\n c     True\n d    False\n-dtype: bool\n+Length: 4, dtype: bool\n Previously, this worked the same as comparison operators (see above).\n *\b**\b**\b**\b* S\bSe\ber\bri\bie\bes\bs t\bty\byp\bpe\be p\bpr\bro\bom\bmo\bot\bti\bio\bon\bn o\bon\bn a\bas\bss\bsi\big\bgn\bnm\bme\ben\bnt\bt_\b?\b\u00b6 *\b**\b**\b**\b*\n A Series will now correctly promote its dtype for assignment with incompat\n values to the current dtype (_\bG_\bH_\b _\b1_\b3_\b2_\b3_\b4)\n In [94]: s = pd.Series()\n P\bPr\bre\bev\bvi\bio\bou\bus\bs b\bbe\beh\bha\bav\bvi\bio\bor\br:\n In [2]: s[\"a\"] = pd.Timestamp(\"2016-01-01\")\n@@ -1111,15 +1157,15 @@\n \n In [96]: s[\"b\"] = 3.0\n \n In [97]: s\n Out[97]:\n a    2016-01-01 00:00:00\n b                    3.0\n-dtype: object\n+Length: 2, dtype: object\n \n In [98]: s.dtype\n Out[98]: dtype('O')\n *\b**\b**\b**\b* F\bFu\bun\bnc\bct\bti\bio\bon\bn .\b.t\bto\bo_\b_d\bda\bat\bte\bet\bti\bim\bme\be(\b()\b) c\bch\bha\ban\bng\bge\bes\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n Previously if .to_datetime() encountered mixed integers/floats and strings, but\n no datetimes with errors='coerce' it would convert all to NaT.\n P\bPr\bre\bev\bvi\bio\bou\bus\bs b\bbe\beh\bha\bav\bvi\bio\bor\br:\n@@ -1146,21 +1192,25 @@\n In [100]: df1 = pd.DataFrame({\"key\": [1], \"v1\": [10]})\n \n In [101]: df1\n Out[101]:\n    key  v1\n 0    1  10\n \n+[1 rows x 2 columns]\n+\n In [102]: df2 = pd.DataFrame({\"key\": [1, 2], \"v1\": [20, 30]})\n \n In [103]: df2\n Out[103]:\n    key  v1\n 0    1  20\n 1    2  30\n+\n+[2 rows x 2 columns]\n P\bPr\bre\bev\bvi\bio\bou\bus\bs b\bbe\beh\bha\bav\bvi\bio\bor\br:\n In [5]: pd.merge(df1, df2, how='outer')\n Out[5]:\n    key    v1\n 0  1.0  10.0\n 1  1.0  20.0\n 2  2.0  30.0\n@@ -1175,33 +1225,37 @@\n In [104]: pd.merge(df1, df2, how=\"outer\")\n Out[104]:\n    key  v1\n 0    1  10\n 1    1  20\n 2    2  30\n \n+[3 rows x 2 columns]\n+\n In [105]: pd.merge(df1, df2, how=\"outer\").dtypes\n Out[105]:\n key    int64\n v1     int64\n-dtype: object\n+Length: 2, dtype: object\n Of course if you have missing values that are introduced, then the resulting\n dtype will be upcast, which is unchanged from previous.\n In [106]: pd.merge(df1, df2, how=\"outer\", on=\"key\")\n Out[106]:\n    key  v1_x  v1_y\n 0    1  10.0    20\n 1    2   NaN    30\n \n+[2 rows x 3 columns]\n+\n In [107]: pd.merge(df1, df2, how=\"outer\", on=\"key\").dtypes\n Out[107]:\n key       int64\n v1_x    float64\n v1_y      int64\n-dtype: object\n+Length: 3, dtype: object\n *\b**\b**\b**\b* M\bMe\bet\bth\bho\bod\bd .\b.d\bde\bes\bsc\bcr\bri\bib\bbe\be(\b()\b) c\bch\bha\ban\bng\bge\bes\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n Percentile identifiers in the index of a .describe() output will now be rounded\n to the least precision that keeps them distinct (_\bG_\bH_\b _\b1_\b3_\b1_\b0_\b4)\n In [108]: s = pd.Series([0, 1, 2, 3, 4])\n \n In [109]: df = pd.DataFrame([0, 1, 2, 3, 4])\n P\bPr\bre\bev\bvi\bio\bou\bus\bs b\bbe\beh\bha\bav\bvi\bio\bor\br:\n@@ -1239,15 +1293,15 @@\n 0.05%     0.002000\n 0.1%      0.004000\n 50%       2.000000\n 99.9%     3.996000\n 99.95%    3.998000\n 99.99%    3.999600\n max       4.000000\n-dtype: float64\n+Length: 12, dtype: float64\n \n In [111]: df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995,\n 0.9999])\n Out[111]:\n                0\n count   5.000000\n mean    2.000000\n@@ -1257,14 +1311,16 @@\n 0.05%   0.002000\n 0.1%    0.004000\n 50%     2.000000\n 99.9%   3.996000\n 99.95%  3.998000\n 99.99%  3.999600\n max     4.000000\n+\n+[12 rows x 1 columns]\n Furthermore:\n     * Passing duplicated percentiles will now raise a ValueError.\n     * Bug in .describe() on a DataFrame with a mixed-dtype column index, which\n       would previously raise a TypeError (_\bG_\bH_\b _\b1_\b3_\b2_\b8_\b8)\n *\b**\b**\b**\b* P\bPe\ber\bri\bio\bod\bd c\bch\bha\ban\bng\bge\bes\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n *\b**\b**\b* T\bTh\bhe\be P\bPe\ber\bri\bio\bod\bdI\bIn\bnd\bde\bex\bx n\bno\bow\bw h\bha\bas\bs p\bpe\ber\bri\bio\bod\bd d\bdt\bty\byp\bpe\be_\b?\b\u00b6 *\b**\b**\b*\n PeriodIndex now has its own period dtype. The period dtype is a pandas\n@@ -1478,26 +1534,26 @@\n ordered=False, dtype='category', name='C')\n \n In [143]: df_grouped.reset_index().dtypes\n Out[143]:\n A       int64\n C    category\n B     float64\n-dtype: object\n+Length: 3, dtype: object\n \n In [144]: df_set_idx.index.levels[1]\n Out[144]: CategoricalIndex(['b', 'a', 'c'], categories=['b', 'a', 'c'],\n ordered=False, dtype='category', name='C')\n \n In [145]: df_set_idx.reset_index().dtypes\n Out[145]:\n A       int64\n C    category\n B       int64\n-dtype: object\n+Length: 3, dtype: object\n *\b**\b**\b**\b* F\bFu\bun\bnc\bct\bti\bio\bon\bn r\bre\bea\bad\bd_\b_c\bcs\bsv\bv w\bwi\bil\bll\bl p\bpr\bro\bog\bgr\bre\bes\bss\bsi\biv\bve\bel\bly\by e\ben\bnu\bum\bme\ber\bra\bat\bte\be c\bch\bhu\bun\bnk\bks\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n When _\br_\be_\ba_\bd_\b__\bc_\bs_\bv_\b(_\b) is called with chunksize=n and without specifying an index,\n each chunk used to have an independently generated index from 0 to n-1. They\n are now given instead a progressive index, starting from 0 for the first chunk,\n from n for the second, and so on, so that, when concatenated, they are\n identical to the result of calling _\br_\be_\ba_\bd_\b__\bc_\bs_\bv_\b(_\b) without the chunksize= argument\n (_\bG_\bH_\b _\b1_\b2_\b1_\b8_\b5).\n@@ -1514,14 +1570,16 @@\n In [147]: pd.concat(pd.read_csv(StringIO(data), chunksize=2))\n Out[147]:\n    A  B\n 0  0  1\n 1  2  3\n 2  4  5\n 3  6  7\n+\n+[4 rows x 2 columns]\n *\b**\b**\b**\b* S\bSp\bpa\bar\brs\bse\be c\bch\bha\ban\bng\bge\bes\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n These changes allow pandas to handle sparse data with more dtypes, and for work\n to make a smoother experience with data handling.\n *\b**\b**\b* T\bTy\byp\bpe\bes\bs i\bin\bnt\bt6\b64\b4 a\ban\bnd\bd b\bbo\boo\bol\bl s\bsu\bup\bpp\bpo\bor\brt\bt e\ben\bnh\bha\ban\bnc\bce\bem\bme\ben\bnt\bts\bs_\b?\b\u00b6 *\b**\b**\b*\n Sparse data structures now gained enhanced support of int64 and bool dtype (_\bG_\bH\n _\b6_\b6_\b7, _\bG_\bH_\b _\b1_\b3_\b8_\b4_\b9).\n Previously, sparse data were float64 dtype by default, even if all inputs were\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.20.0.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.20.0.html", "unified_diff": "@@ -1205,43 +1205,49 @@\n <span class=\"go\">2000-01-04       NaN       NaN       NaN</span>\n <span class=\"go\">2000-01-05       NaN       NaN       NaN</span>\n <span class=\"go\">2000-01-06       NaN       NaN       NaN</span>\n <span class=\"go\">2000-01-07       NaN       NaN       NaN</span>\n <span class=\"go\">2000-01-08  0.113648 -1.478427  0.524988</span>\n <span class=\"go\">2000-01-09  0.404705  0.577046 -1.715002</span>\n <span class=\"go\">2000-01-10 -1.039268 -0.370647 -1.157892</span>\n+\n+<span class=\"go\">[10 rows x 3 columns]</span>\n </pre></div>\n </div>\n <p>One can operate using string function names, callables, lists, or dictionaries of these.</p>\n <p>Using a single function is equivalent to <code class=\"docutils literal notranslate\"><span class=\"pre\">.apply</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [4]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">agg</span><span class=\"p\">(</span><span class=\"s1\">&#39;sum&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[4]: </span>\n <span class=\"go\">A   -1.068226</span>\n <span class=\"go\">B   -1.387015</span>\n <span class=\"go\">C   -4.892029</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 3, dtype: float64</span>\n </pre></div>\n </div>\n <p>Multiple aggregations with a list of functions.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [5]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">agg</span><span class=\"p\">([</span><span class=\"s1\">&#39;sum&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;min&#39;</span><span class=\"p\">])</span>\n <span class=\"gh\">Out[5]: </span>\n <span class=\"go\">            A         B         C</span>\n <span class=\"go\">sum -1.068226 -1.387015 -4.892029</span>\n <span class=\"go\">min -1.135632 -1.478427 -1.715002</span>\n+\n+<span class=\"go\">[2 rows x 3 columns]</span>\n </pre></div>\n </div>\n <p>Using a dict provides the ability to apply specific aggregations per column.\n You will get a matrix-like output of all of the aggregators. The output has one column\n per unique function. Those functions applied to a particular column will be <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code>:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [6]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">agg</span><span class=\"p\">({</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"s1\">&#39;sum&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;min&#39;</span><span class=\"p\">],</span> <span class=\"s1\">&#39;B&#39;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"s1\">&#39;min&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;max&#39;</span><span class=\"p\">]})</span>\n <span class=\"gh\">Out[6]: </span>\n <span class=\"go\">            A         B</span>\n <span class=\"go\">sum -1.068226       NaN</span>\n <span class=\"go\">min -1.135632 -1.478427</span>\n <span class=\"go\">max       NaN  1.212112</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>The API also supports a <code class=\"docutils literal notranslate\"><span class=\"pre\">.transform()</span></code> function for broadcasting results.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [7]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">transform</span><span class=\"p\">([</span><span class=\"s1\">&#39;abs&#39;</span><span class=\"p\">,</span> <span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"n\">x</span> <span class=\"o\">-</span> <span class=\"n\">x</span><span class=\"o\">.</span><span class=\"n\">min</span><span class=\"p\">()])</span>\n <span class=\"gh\">Out[7]: </span>\n <span class=\"go\">                   A                   B                   C          </span>\n <span class=\"go\">                 abs  &lt;lambda&gt;       abs  &lt;lambda&gt;       abs  &lt;lambda&gt;</span>\n@@ -1251,14 +1257,16 @@\n <span class=\"go\">2000-01-04       NaN       NaN       NaN       NaN       NaN       NaN</span>\n <span class=\"go\">2000-01-05       NaN       NaN       NaN       NaN       NaN       NaN</span>\n <span class=\"go\">2000-01-06       NaN       NaN       NaN       NaN       NaN       NaN</span>\n <span class=\"go\">2000-01-07       NaN       NaN       NaN       NaN       NaN       NaN</span>\n <span class=\"go\">2000-01-08  0.113648  1.249281  1.478427  0.000000  0.524988  2.239990</span>\n <span class=\"go\">2000-01-09  0.404705  1.540338  0.577046  2.055473  1.715002  0.000000</span>\n <span class=\"go\">2000-01-10  1.039268  0.096364  0.370647  1.107780  1.157892  0.557110</span>\n+\n+<span class=\"go\">[10 rows x 6 columns]</span>\n </pre></div>\n </div>\n <p>When presented with mixed dtypes that cannot be aggregated, <code class=\"docutils literal notranslate\"><span class=\"pre\">.agg()</span></code> will only take the valid\n aggregations. This is similar to how groupby <code class=\"docutils literal notranslate\"><span class=\"pre\">.agg()</span></code> works. (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/15015\">GH 15015</a>)</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [8]: </span><span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">({</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">],</span>\n <span class=\"gp\">   ...: </span>                   <span class=\"s1\">&#39;B&#39;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mf\">1.</span><span class=\"p\">,</span> <span class=\"mf\">2.</span><span class=\"p\">,</span> <span class=\"mf\">3.</span><span class=\"p\">],</span>\n <span class=\"gp\">   ...: </span>                   <span class=\"s1\">&#39;C&#39;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"s1\">&#39;foo&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;bar&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;baz&#39;</span><span class=\"p\">],</span>\n@@ -1267,15 +1275,15 @@\n \n <span class=\"gp\">In [9]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[9]: </span>\n <span class=\"go\">A             int64</span>\n <span class=\"go\">B           float64</span>\n <span class=\"go\">C            object</span>\n <span class=\"go\">D    datetime64[ns]</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 4, dtype: object</span>\n </pre></div>\n </div>\n <div class=\"highlight-python notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"n\">In</span> <span class=\"p\">[</span><span class=\"mi\">10</span><span class=\"p\">]:</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">agg</span><span class=\"p\">([</span><span class=\"s1\">&#39;min&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;sum&#39;</span><span class=\"p\">])</span>\n <span class=\"n\">Out</span><span class=\"p\">[</span><span class=\"mi\">10</span><span class=\"p\">]:</span>\n      <span class=\"n\">A</span>    <span class=\"n\">B</span>          <span class=\"n\">C</span>          <span class=\"n\">D</span>\n <span class=\"nb\">min</span>  <span class=\"mi\">1</span>  <span class=\"mf\">1.0</span>        <span class=\"n\">bar</span> <span class=\"mi\">2013</span><span class=\"o\">-</span><span class=\"mi\">01</span><span class=\"o\">-</span><span class=\"mi\">01</span>\n <span class=\"nb\">sum</span>  <span class=\"mi\">6</span>  <span class=\"mf\">6.0</span>  <span class=\"n\">foobarbaz</span>        <span class=\"n\">NaT</span>\n@@ -1288,21 +1296,21 @@\n fixed-width text files and <a class=\"reference internal\" href=\"../reference/api/pandas.read_excel.html#pandas.read_excel\" title=\"pandas.read_excel\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">read_excel()</span></code></a> for parsing Excel files, now accept the <code class=\"docutils literal notranslate\"><span class=\"pre\">dtype</span></code> keyword argument for specifying the types of specific columns (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/14295\">GH 14295</a>). See the <a class=\"reference internal\" href=\"../user_guide/io.html#io-dtypes\"><span class=\"std std-ref\">io docs</span></a> for more information.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [10]: </span><span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"s2\">&quot;a  b</span><span class=\"se\">\\n</span><span class=\"s2\">1  2</span><span class=\"se\">\\n</span><span class=\"s2\">3  4&quot;</span>\n \n <span class=\"gp\">In [11]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_fwf</span><span class=\"p\">(</span><span class=\"n\">StringIO</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">))</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[11]: </span>\n <span class=\"go\">a    int64</span>\n <span class=\"go\">b    int64</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 2, dtype: object</span>\n \n <span class=\"gp\">In [12]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_fwf</span><span class=\"p\">(</span><span class=\"n\">StringIO</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"p\">),</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"p\">{</span><span class=\"s1\">&#39;a&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;float64&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;b&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;object&#39;</span><span class=\"p\">})</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[12]: </span>\n <span class=\"go\">a    float64</span>\n <span class=\"go\">b     object</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 2, dtype: object</span>\n </pre></div>\n </div>\n </section>\n <section id=\"method-to-datetime-has-gained-an-origin-parameter\">\n <span id=\"whatsnew-0120-enhancements-datetime-origin\"></span><h3>Method <code class=\"docutils literal notranslate\"><span class=\"pre\">.to_datetime()</span></code> has gained an <code class=\"docutils literal notranslate\"><span class=\"pre\">origin</span></code> parameter<a class=\"headerlink\" href=\"#method-to-datetime-has-gained-an-origin-parameter\" title=\"Link to this heading\">\u00b6</a></h3>\n <p><a class=\"reference internal\" href=\"../reference/api/pandas.to_datetime.html#pandas.to_datetime\" title=\"pandas.to_datetime\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">to_datetime()</span></code></a> has gained a new parameter, <code class=\"docutils literal notranslate\"><span class=\"pre\">origin</span></code>, to define a reference date\n from where to compute the resulting timestamps when parsing numerical values with a specific <code class=\"docutils literal notranslate\"><span class=\"pre\">unit</span></code> specified. (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/11276\">GH 11276</a>, <a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/11745\">GH 11745</a>)</p>\n@@ -1341,24 +1349,28 @@\n <span class=\"go\">baz   one     1  2</span>\n <span class=\"go\">      two     1  3</span>\n <span class=\"go\">foo   one     2  4</span>\n <span class=\"go\">      two     2  5</span>\n <span class=\"go\">qux   one     3  6</span>\n <span class=\"go\">      two     3  7</span>\n \n+<span class=\"go\">[8 rows x 2 columns]</span>\n+\n <span class=\"gp\">In [19]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">([</span><span class=\"s1\">&#39;second&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;A&#39;</span><span class=\"p\">])</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[19]: </span>\n <span class=\"go\">          B</span>\n <span class=\"go\">second A   </span>\n <span class=\"go\">one    1  2</span>\n <span class=\"go\">       2  4</span>\n <span class=\"go\">       3  6</span>\n <span class=\"go\">two    1  4</span>\n <span class=\"go\">       2  5</span>\n <span class=\"go\">       3  7</span>\n+\n+<span class=\"go\">[6 rows x 1 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"better-support-for-compressed-urls-in-read-csv\">\n <span id=\"whatsnew-0200-enhancements-compressed-urls\"></span><h3>Better support for compressed URLs in <code class=\"docutils literal notranslate\"><span class=\"pre\">read_csv</span></code><a class=\"headerlink\" href=\"#better-support-for-compressed-urls-in-read-csv\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>The compression code was refactored (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/12688\">GH 12688</a>). As a result, reading\n dataframes from URLs in <a class=\"reference internal\" href=\"../reference/api/pandas.read_csv.html#pandas.read_csv\" title=\"pandas.read_csv\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">read_csv()</span></code></a> or <a class=\"reference internal\" href=\"../reference/api/pandas.read_table.html#pandas.read_table\" title=\"pandas.read_table\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">read_table()</span></code></a> now supports\n@@ -1718,14 +1730,16 @@\n \n <span class=\"gp\">In [23]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">head</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[23]: </span>\n <span class=\"go\">              A  B</span>\n <span class=\"go\">first second      </span>\n <span class=\"go\">bar   one     1  0</span>\n <span class=\"go\">      two     1  1</span>\n+\n+<span class=\"go\">[2 rows x 2 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"pickle-file-io-now-supports-compression\">\n <span id=\"whatsnew-0200-enhancements-pickle-compression\"></span><h3>Pickle file IO now supports compression<a class=\"headerlink\" href=\"#pickle-file-io-now-supports-compression\" title=\"Link to this heading\">\u00b6</a></h3>\n <p><a class=\"reference internal\" href=\"../reference/api/pandas.read_pickle.html#pandas.read_pickle\" title=\"pandas.read_pickle\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">read_pickle()</span></code></a>, <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.to_pickle.html#pandas.DataFrame.to_pickle\" title=\"pandas.DataFrame.to_pickle\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">DataFrame.to_pickle()</span></code></a> and <a class=\"reference internal\" href=\"../reference/api/pandas.Series.to_pickle.html#pandas.Series.to_pickle\" title=\"pandas.Series.to_pickle\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">Series.to_pickle()</span></code></a>\n can now read from and write to compressed pickle files. Compression methods\n@@ -1746,14 +1760,16 @@\n <span class=\"gh\">Out[27]: </span>\n <span class=\"go\">          A    B                   C</span>\n <span class=\"go\">0 -1.344312  foo 2013-01-01 00:00:00</span>\n <span class=\"go\">1  0.844885  foo 2013-01-01 00:00:01</span>\n <span class=\"go\">2  1.075770  foo 2013-01-01 00:00:02</span>\n <span class=\"go\">3 -0.109050  foo 2013-01-01 00:00:03</span>\n <span class=\"go\">4  1.643563  foo 2013-01-01 00:00:04</span>\n+\n+<span class=\"go\">[5 rows x 3 columns]</span>\n </pre></div>\n </div>\n <p>The default is to infer the compression type from the extension (<code class=\"docutils literal notranslate\"><span class=\"pre\">compression='infer'</span></code>):</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [28]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">to_pickle</span><span class=\"p\">(</span><span class=\"s2\">&quot;data.pkl.gz&quot;</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [29]: </span><span class=\"n\">rt</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_pickle</span><span class=\"p\">(</span><span class=\"s2\">&quot;data.pkl.gz&quot;</span><span class=\"p\">)</span>\n \n@@ -1762,26 +1778,28 @@\n <span class=\"go\">          A    B                   C</span>\n <span class=\"go\">0 -1.344312  foo 2013-01-01 00:00:00</span>\n <span class=\"go\">1  0.844885  foo 2013-01-01 00:00:01</span>\n <span class=\"go\">2  1.075770  foo 2013-01-01 00:00:02</span>\n <span class=\"go\">3 -0.109050  foo 2013-01-01 00:00:03</span>\n <span class=\"go\">4  1.643563  foo 2013-01-01 00:00:04</span>\n \n+<span class=\"go\">[5 rows x 3 columns]</span>\n+\n <span class=\"gp\">In [31]: </span><span class=\"n\">df</span><span class=\"p\">[</span><span class=\"s2\">&quot;A&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">to_pickle</span><span class=\"p\">(</span><span class=\"s2\">&quot;s1.pkl.bz2&quot;</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [32]: </span><span class=\"n\">rt</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_pickle</span><span class=\"p\">(</span><span class=\"s2\">&quot;s1.pkl.bz2&quot;</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [33]: </span><span class=\"n\">rt</span><span class=\"o\">.</span><span class=\"n\">head</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[33]: </span>\n <span class=\"go\">0   -1.344312</span>\n <span class=\"go\">1    0.844885</span>\n <span class=\"go\">2    1.075770</span>\n <span class=\"go\">3   -0.109050</span>\n <span class=\"go\">4    1.643563</span>\n-<span class=\"go\">Name: A, dtype: float64</span>\n+<span class=\"go\">Name: A, Length: 5, dtype: float64</span>\n </pre></div>\n </div>\n </section>\n <section id=\"uint64-support-improved\">\n <span id=\"whatsnew-0200-enhancements-uint64-support\"></span><h3>UInt64 support improved<a class=\"headerlink\" href=\"#uint64-support-improved\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>pandas has significantly improved support for operations involving unsigned,\n or purely non-negative, integers. Previously, handling these integers would\n@@ -1877,14 +1895,16 @@\n <span class=\"gh\">Out[39]: </span>\n <span class=\"go\">     A  B          C</span>\n <span class=\"go\">idx                 </span>\n <span class=\"go\">0    1  a 2016-01-01</span>\n <span class=\"go\">1    2  b 2016-01-02</span>\n <span class=\"go\">2    3  c 2016-01-03</span>\n \n+<span class=\"go\">[3 rows x 3 columns]</span>\n+\n <span class=\"gp\">In [40]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">to_json</span><span class=\"p\">(</span><span class=\"n\">orient</span><span class=\"o\">=</span><span class=\"s1\">&#39;table&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[40]: </span><span class=\"go\">&#39;{&quot;schema&quot;:{&quot;fields&quot;:[{&quot;name&quot;:&quot;idx&quot;,&quot;type&quot;:&quot;integer&quot;},{&quot;name&quot;:&quot;A&quot;,&quot;type&quot;:&quot;integer&quot;},{&quot;name&quot;:&quot;B&quot;,&quot;type&quot;:&quot;string&quot;},{&quot;name&quot;:&quot;C&quot;,&quot;type&quot;:&quot;datetime&quot;}],&quot;primaryKey&quot;:[&quot;idx&quot;],&quot;pandas_version&quot;:&quot;1.4.0&quot;},&quot;data&quot;:[{&quot;idx&quot;:0,&quot;A&quot;:1,&quot;B&quot;:&quot;a&quot;,&quot;C&quot;:&quot;2016-01-01T00:00:00.000&quot;},{&quot;idx&quot;:1,&quot;A&quot;:2,&quot;B&quot;:&quot;b&quot;,&quot;C&quot;:&quot;2016-01-02T00:00:00.000&quot;},{&quot;idx&quot;:2,&quot;A&quot;:3,&quot;B&quot;:&quot;c&quot;,&quot;C&quot;:&quot;2016-01-03T00:00:00.000&quot;}]}&#39;</span>\n </pre></div>\n </div>\n <p>See <a class=\"reference internal\" href=\"../user_guide/io.html#io-table-schema\"><span class=\"std std-ref\">IO: Table Schema for more information</span></a>.</p>\n <p>Additionally, the repr for <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">Series</span></code> can now publish\n this JSON Table schema representation of the Series or DataFrame if you are\n@@ -1939,14 +1959,16 @@\n <span class=\"go\">4   5.0  1.453425  1.057737  0.165562  0.515018</span>\n <span class=\"go\">5   6.0 -1.336936  0.562861  1.392855 -0.063328</span>\n <span class=\"go\">6   7.0  0.121668  1.207603 -0.002040  1.627796</span>\n <span class=\"go\">7   8.0  0.354493  1.037528 -0.385684  0.519818</span>\n <span class=\"go\">8   9.0  1.686583 -1.325963  1.428984 -2.089354</span>\n <span class=\"go\">9  10.0 -0.129820  0.631523 -0.586538  0.290720</span>\n \n+<span class=\"go\">[10 rows x 5 columns]</span>\n+\n <span class=\"gp\">In [46]: </span><span class=\"n\">styled</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">style</span>\n <span class=\"gp\">   ....: </span>          <span class=\"o\">.</span><span class=\"n\">applymap</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">val</span><span class=\"p\">:</span> <span class=\"s1\">&#39;color:red;&#39;</span> <span class=\"k\">if</span> <span class=\"n\">val</span> <span class=\"o\">&lt;</span> <span class=\"mi\">0</span> <span class=\"k\">else</span> <span class=\"s1\">&#39;color:black;&#39;</span><span class=\"p\">)</span>\n <span class=\"gp\">   ....: </span>          <span class=\"o\">.</span><span class=\"n\">highlight_max</span><span class=\"p\">())</span>\n <span class=\"gp\">   ....: </span>\n \n <span class=\"gp\">In [47]: </span><span class=\"n\">styled</span><span class=\"o\">.</span><span class=\"n\">to_excel</span><span class=\"p\">(</span><span class=\"s1\">&#39;styled.xlsx&#39;</span><span class=\"p\">,</span> <span class=\"n\">engine</span><span class=\"o\">=</span><span class=\"s1\">&#39;openpyxl&#39;</span><span class=\"p\">)</span>\n </pre></div>\n@@ -2006,31 +2028,35 @@\n <span class=\"gh\">Out[53]: </span>\n <span class=\"go\">               A</span>\n <span class=\"go\">B               </span>\n <span class=\"go\">(-0.003, 1.5]  0</span>\n <span class=\"go\">(1.5, 3.0]     1</span>\n <span class=\"go\">(-0.003, 1.5]  2</span>\n <span class=\"go\">(-0.003, 1.5]  3</span>\n+\n+<span class=\"go\">[4 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p>Selecting via a specific interval:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [54]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Interval</span><span class=\"p\">(</span><span class=\"mf\">1.5</span><span class=\"p\">,</span> <span class=\"mf\">3.0</span><span class=\"p\">)]</span>\n <span class=\"gh\">Out[54]: </span>\n <span class=\"go\">A    1</span>\n-<span class=\"go\">Name: (1.5, 3.0], dtype: int64</span>\n+<span class=\"go\">Name: (1.5, 3.0], Length: 1, dtype: int64</span>\n </pre></div>\n </div>\n <p>Selecting via a scalar value that is contained <em>in</em> the intervals.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [55]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[55]: </span>\n <span class=\"go\">               A</span>\n <span class=\"go\">B               </span>\n <span class=\"go\">(-0.003, 1.5]  0</span>\n <span class=\"go\">(-0.003, 1.5]  2</span>\n <span class=\"go\">(-0.003, 1.5]  3</span>\n+\n+<span class=\"go\">[3 rows x 1 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"other-enhancements\">\n <span id=\"whatsnew-0200-enhancements-other\"></span><h3>Other enhancements<a class=\"headerlink\" href=\"#other-enhancements\" title=\"Link to this heading\">\u00b6</a></h3>\n <ul class=\"simple\">\n <li><p><code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame.rolling()</span></code> now accepts the parameter <code class=\"docutils literal notranslate\"><span class=\"pre\">closed='right'|'left'|'both'|'neither'</span></code> to choose the rolling window-endpoint closedness. See the <a class=\"reference internal\" href=\"../user_guide/window.html#window-endpoints\"><span class=\"std std-ref\">documentation</span></a> (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/13965\">GH 13965</a>)</p></li>\n@@ -2362,36 +2388,36 @@\n <p>Previously, <code class=\"docutils literal notranslate\"><span class=\"pre\">concat</span></code> of multiple objects with different <code class=\"docutils literal notranslate\"><span class=\"pre\">float</span></code> dtypes would automatically upcast results to a dtype of <code class=\"docutils literal notranslate\"><span class=\"pre\">float64</span></code>.\n Now the smallest acceptable dtype will be used (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/13247\">GH 13247</a>)</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [71]: </span><span class=\"n\">df1</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">(</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">([</span><span class=\"mf\">1.0</span><span class=\"p\">],</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">float32</span><span class=\"p\">,</span> <span class=\"n\">ndmin</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">))</span>\n \n <span class=\"gp\">In [72]: </span><span class=\"n\">df1</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[72]: </span>\n <span class=\"go\">0    float32</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 1, dtype: object</span>\n \n <span class=\"gp\">In [73]: </span><span class=\"n\">df2</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">(</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">([</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">nan</span><span class=\"p\">],</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">float32</span><span class=\"p\">,</span> <span class=\"n\">ndmin</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">))</span>\n \n <span class=\"gp\">In [74]: </span><span class=\"n\">df2</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[74]: </span>\n <span class=\"go\">0    float32</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 1, dtype: object</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [7]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">concat</span><span class=\"p\">([</span><span class=\"n\">df1</span><span class=\"p\">,</span> <span class=\"n\">df2</span><span class=\"p\">])</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[7]:</span>\n <span class=\"go\">0    float64</span>\n <span class=\"go\">dtype: object</span>\n </pre></div>\n </div>\n <p>New behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [75]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">concat</span><span class=\"p\">([</span><span class=\"n\">df1</span><span class=\"p\">,</span> <span class=\"n\">df2</span><span class=\"p\">])</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[75]: </span>\n <span class=\"go\">0    float32</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 1, dtype: object</span>\n </pre></div>\n </div>\n </section>\n <section id=\"pandas-google-bigquery-support-has-moved\">\n <span id=\"whatsnew-0200-api-breaking-gbq\"></span><h3>pandas Google BigQuery support has moved<a class=\"headerlink\" href=\"#pandas-google-bigquery-support-has-moved\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>pandas has split off Google BigQuery support into a separate package <code class=\"docutils literal notranslate\"><span class=\"pre\">pandas-gbq</span></code>. You can <code class=\"docutils literal notranslate\"><span class=\"pre\">conda</span> <span class=\"pre\">install</span> <span class=\"pre\">pandas-gbq</span> <span class=\"pre\">-c</span> <span class=\"pre\">conda-forge</span></code> or\n <code class=\"docutils literal notranslate\"><span class=\"pre\">pip</span> <span class=\"pre\">install</span> <span class=\"pre\">pandas-gbq</span></code> to get it. The functionality of <a class=\"reference internal\" href=\"../reference/api/pandas.read_gbq.html#pandas.read_gbq\" title=\"pandas.read_gbq\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">read_gbq()</span></code></a> and <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.to_gbq.html#pandas.DataFrame.to_gbq\" title=\"pandas.DataFrame.to_gbq\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">DataFrame.to_gbq()</span></code></a> remain the same with the\n@@ -2459,14 +2485,16 @@\n <p>Sorting works as expected</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [76]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">sort_index</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[76]: </span>\n <span class=\"go\">                     a</span>\n <span class=\"go\">2011-12-31 23:59:59  1</span>\n <span class=\"go\">2012-01-01 00:00:00  2</span>\n <span class=\"go\">2012-01-01 00:00:01  3</span>\n+\n+<span class=\"go\">[3 rows x 1 columns]</span>\n </pre></div>\n </div>\n <div class=\"highlight-python notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"n\">In</span> <span class=\"p\">[</span><span class=\"mi\">90</span><span class=\"p\">]:</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">sort_index</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"o\">.</span><span class=\"n\">is_lexsorted</span><span class=\"p\">()</span>\n <span class=\"n\">Out</span><span class=\"p\">[</span><span class=\"mi\">90</span><span class=\"p\">]:</span> <span class=\"kc\">True</span>\n \n <span class=\"n\">In</span> <span class=\"p\">[</span><span class=\"mi\">91</span><span class=\"p\">]:</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">sort_index</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"o\">.</span><span class=\"n\">is_monotonic</span>\n <span class=\"n\">Out</span><span class=\"p\">[</span><span class=\"mi\">91</span><span class=\"p\">]:</span> <span class=\"kc\">True</span>\n@@ -2482,14 +2510,16 @@\n <span class=\"gp\">In [78]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[78]: </span>\n <span class=\"go\">      value</span>\n <span class=\"go\">a bb      1</span>\n <span class=\"go\">  aa      2</span>\n <span class=\"go\">b bb      3</span>\n <span class=\"go\">  aa      4</span>\n+\n+<span class=\"go\">[4 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <div class=\"highlight-python notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"n\">In</span> <span class=\"p\">[</span><span class=\"mi\">11</span><span class=\"p\">]:</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">sort_index</span><span class=\"p\">()</span>\n <span class=\"n\">Out</span><span class=\"p\">[</span><span class=\"mi\">11</span><span class=\"p\">]:</span>\n       <span class=\"n\">value</span>\n <span class=\"n\">a</span> <span class=\"n\">bb</span>      <span class=\"mi\">1</span>\n@@ -2567,21 +2597,25 @@\n <span class=\"gh\">Out[80]: </span>\n <span class=\"go\">      B                                          </span>\n <span class=\"go\">  count mean       std  min   25%  50%   75%  max</span>\n <span class=\"go\">A                                                </span>\n <span class=\"go\">1   2.0  1.5  0.707107  1.0  1.25  1.5  1.75  2.0</span>\n <span class=\"go\">2   2.0  3.5  0.707107  3.0  3.25  3.5  3.75  4.0</span>\n \n+<span class=\"go\">[2 rows x 8 columns]</span>\n+\n <span class=\"gp\">In [81]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">agg</span><span class=\"p\">([</span><span class=\"s2\">&quot;mean&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;std&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;min&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;max&quot;</span><span class=\"p\">])</span>\n <span class=\"gh\">Out[81]: </span>\n <span class=\"go\">     B                  </span>\n <span class=\"go\">  mean       std min max</span>\n <span class=\"go\">A                       </span>\n <span class=\"go\">1  1.5  0.707107   1   2</span>\n <span class=\"go\">2  3.5  0.707107   3   4</span>\n+\n+<span class=\"go\">[2 rows x 4 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"window-binary-corr-cov-operations-return-a-multiindex-dataframe\">\n <span id=\"whatsnew-0200-api-breaking-rolling-pairwise\"></span><h3>Window binary corr/cov operations return a MultiIndex DataFrame<a class=\"headerlink\" href=\"#window-binary-corr-cov-operations-return-a-multiindex-dataframe\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>A binary window operation, like <code class=\"docutils literal notranslate\"><span class=\"pre\">.corr()</span></code> or <code class=\"docutils literal notranslate\"><span class=\"pre\">.cov()</span></code>, when operating on a <code class=\"docutils literal notranslate\"><span class=\"pre\">.rolling(..)</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">.expanding(..)</span></code>, or <code class=\"docutils literal notranslate\"><span class=\"pre\">.ewm(..)</span></code> object,\n will now return a 2-level <code class=\"docutils literal notranslate\"><span class=\"pre\">MultiIndexed</span> <span class=\"pre\">DataFrame</span></code> rather than a <code class=\"docutils literal notranslate\"><span class=\"pre\">Panel</span></code>, as <code class=\"docutils literal notranslate\"><span class=\"pre\">Panel</span></code> is now deprecated,\n@@ -2601,14 +2635,16 @@\n <span class=\"go\">bar                A         B</span>\n <span class=\"go\">foo                           </span>\n <span class=\"go\">2016-04-05  0.640880  0.126205</span>\n <span class=\"go\">2016-04-06  0.171465  0.737086</span>\n <span class=\"go\">2016-04-07  0.127029  0.369650</span>\n <span class=\"go\">2016-04-08  0.604334  0.103104</span>\n <span class=\"go\">2016-04-09  0.802374  0.945553</span>\n+\n+<span class=\"go\">[5 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [2]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">rolling</span><span class=\"p\">(</span><span class=\"mi\">12</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">corr</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[2]:</span>\n <span class=\"go\">&lt;class &#39;pandas.core.panel.Panel&#39;&gt;</span>\n <span class=\"go\">Dimensions: 100 (items) x 2 (major_axis) x 2 (minor_axis)</span>\n@@ -2625,23 +2661,27 @@\n <span class=\"go\">bar                    A         B</span>\n <span class=\"go\">foo        bar                    </span>\n <span class=\"go\">2016-04-07 B   -0.132090  1.000000</span>\n <span class=\"go\">2016-04-08 A    1.000000 -0.145775</span>\n <span class=\"go\">           B   -0.145775  1.000000</span>\n <span class=\"go\">2016-04-09 A    1.000000  0.119645</span>\n <span class=\"go\">           B    0.119645  1.000000</span>\n+\n+<span class=\"go\">[5 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>Retrieving a correlation matrix for a cross-section</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [87]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">rolling</span><span class=\"p\">(</span><span class=\"mi\">12</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">corr</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"s1\">&#39;2016-04-07&#39;</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[87]: </span>\n <span class=\"go\">bar        A        B</span>\n <span class=\"go\">bar                  </span>\n <span class=\"go\">A    1.00000 -0.13209</span>\n <span class=\"go\">B   -0.13209  1.00000</span>\n+\n+<span class=\"go\">[2 rows x 2 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"hdfstore-where-string-comparison\">\n <span id=\"whatsnew-0200-api-breaking-hdfstore-where\"></span><h3>HDFStore where string comparison<a class=\"headerlink\" href=\"#hdfstore-where-string-comparison\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>In previous versions most types could be compared to string column in a <code class=\"docutils literal notranslate\"><span class=\"pre\">HDFStore</span></code>\n usually resulting in an invalid comparison, returning an empty result frame. These comparisons will now raise a\n@@ -2649,15 +2689,15 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [88]: </span><span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">({</span><span class=\"s1\">&#39;unparsed_date&#39;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"s1\">&#39;2014-01-01&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;2014-01-01&#39;</span><span class=\"p\">]})</span>\n \n <span class=\"gp\">In [89]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">to_hdf</span><span class=\"p\">(</span><span class=\"s1\">&#39;store.h5&#39;</span><span class=\"p\">,</span> <span class=\"n\">key</span><span class=\"o\">=</span><span class=\"s1\">&#39;key&#39;</span><span class=\"p\">,</span> <span class=\"nb\">format</span><span class=\"o\">=</span><span class=\"s1\">&#39;table&#39;</span><span class=\"p\">,</span> <span class=\"n\">data_columns</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [90]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[90]: </span>\n <span class=\"go\">unparsed_date    object</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 1, dtype: object</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [4]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_hdf</span><span class=\"p\">(</span><span class=\"s1\">&#39;store.h5&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;key&#39;</span><span class=\"p\">,</span> <span class=\"n\">where</span><span class=\"o\">=</span><span class=\"s1\">&#39;unparsed_date &gt; ts&#39;</span><span class=\"p\">)</span>\n <span class=\"go\">File &quot;&lt;string&gt;&quot;, line 1</span>\n <span class=\"go\">  (unparsed_date &gt; 1970-01-01 00:00:01.388552400)</span>\n <span class=\"go\">                        ^</span>\n@@ -2708,22 +2748,26 @@\n <span class=\"gp\">In [97]: </span><span class=\"n\">left</span>\n <span class=\"gh\">Out[97]: </span>\n <span class=\"go\">    a</span>\n <span class=\"go\">2  20</span>\n <span class=\"go\">1  10</span>\n <span class=\"go\">0   0</span>\n \n+<span class=\"go\">[3 rows x 1 columns]</span>\n+\n <span class=\"gp\">In [98]: </span><span class=\"n\">right</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">({</span><span class=\"s1\">&#39;b&#39;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">100</span><span class=\"p\">,</span> <span class=\"mi\">200</span><span class=\"p\">,</span> <span class=\"mi\">300</span><span class=\"p\">]},</span> <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [99]: </span><span class=\"n\">right</span>\n <span class=\"gh\">Out[99]: </span>\n <span class=\"go\">     b</span>\n <span class=\"go\">1  100</span>\n <span class=\"go\">2  200</span>\n <span class=\"go\">3  300</span>\n+\n+<span class=\"go\">[3 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [4]: </span><span class=\"n\">left</span><span class=\"o\">.</span><span class=\"n\">join</span><span class=\"p\">(</span><span class=\"n\">right</span><span class=\"p\">,</span> <span class=\"n\">how</span><span class=\"o\">=</span><span class=\"s1\">&#39;inner&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[4]:</span>\n <span class=\"go\">   a    b</span>\n <span class=\"go\">1  10  100</span>\n@@ -2732,14 +2776,16 @@\n </div>\n <p>New behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [100]: </span><span class=\"n\">left</span><span class=\"o\">.</span><span class=\"n\">join</span><span class=\"p\">(</span><span class=\"n\">right</span><span class=\"p\">,</span> <span class=\"n\">how</span><span class=\"o\">=</span><span class=\"s1\">&#39;inner&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[100]: </span>\n <span class=\"go\">    a    b</span>\n <span class=\"go\">2  20  200</span>\n <span class=\"go\">1  10  100</span>\n+\n+<span class=\"go\">[2 rows x 2 columns]</span>\n </pre></div>\n </div>\n </li>\n </ul>\n </section>\n <section id=\"pivot-table-always-returns-a-dataframe\">\n <span id=\"whatsnew-0200-api-breaking-pivot-table\"></span><h3>Pivot table always returns a DataFrame<a class=\"headerlink\" href=\"#pivot-table-always-returns-a-dataframe\" title=\"Link to this heading\">\u00b6</a></h3>\n@@ -2752,14 +2798,16 @@\n \n <span class=\"gp\">In [102]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[102]: </span>\n <span class=\"go\">   col1 col2  col3</span>\n <span class=\"go\">0     3    C     1</span>\n <span class=\"go\">1     4    D     3</span>\n <span class=\"go\">2     5    E     9</span>\n+\n+<span class=\"go\">[3 rows x 3 columns]</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [2]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">pivot_table</span><span class=\"p\">(</span><span class=\"s1\">&#39;col1&#39;</span><span class=\"p\">,</span> <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;col3&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;col2&#39;</span><span class=\"p\">],</span> <span class=\"n\">aggfunc</span><span class=\"o\">=</span><span class=\"s2\">&quot;sum&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[2]:</span>\n <span class=\"go\">col3  col2</span>\n <span class=\"go\">1     C       3</span>\n@@ -2772,14 +2820,16 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [103]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">pivot_table</span><span class=\"p\">(</span><span class=\"s1\">&#39;col1&#39;</span><span class=\"p\">,</span> <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;col3&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;col2&#39;</span><span class=\"p\">],</span> <span class=\"n\">aggfunc</span><span class=\"o\">=</span><span class=\"s2\">&quot;sum&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[103]: </span>\n <span class=\"go\">           col1</span>\n <span class=\"go\">col3 col2      </span>\n <span class=\"go\">1    C        3</span>\n <span class=\"go\">3    D        4</span>\n <span class=\"go\">9    E        5</span>\n+\n+<span class=\"go\">[3 rows x 1 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"other-api-changes\">\n <span id=\"whatsnew-0200-api\"></span><h3>Other API changes<a class=\"headerlink\" href=\"#other-api-changes\" title=\"Link to this heading\">\u00b6</a></h3>\n <ul class=\"simple\">\n <li><p><code class=\"docutils literal notranslate\"><span class=\"pre\">numexpr</span></code> version is now required to be &gt;= 2.4.6 and it will not be used at all if this requisite is not fulfilled (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/15213\">GH 15213</a>).</p></li>\n@@ -2991,14 +3041,16 @@\n \n <span class=\"gp\">In [105]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[105]: </span>\n <span class=\"go\">   A  B</span>\n <span class=\"go\">a  1  4</span>\n <span class=\"go\">b  2  5</span>\n <span class=\"go\">c  3  6</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>Previous behavior, where you wish to get the 0th and the 2nd elements from the index in the \u2018A\u2019 column.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [3]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">ix</span><span class=\"p\">[[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">],</span> <span class=\"s1\">&#39;A&#39;</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[3]:</span>\n <span class=\"go\">a    1</span>\n <span class=\"go\">c    3</span>\n@@ -3006,23 +3058,23 @@\n </pre></div>\n </div>\n <p>Using <code class=\"docutils literal notranslate\"><span class=\"pre\">.loc</span></code>. Here we will select the appropriate indexes from the index, then use <em>label</em> indexing.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [106]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"p\">[[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">]],</span> <span class=\"s1\">&#39;A&#39;</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[106]: </span>\n <span class=\"go\">a    1</span>\n <span class=\"go\">c    3</span>\n-<span class=\"go\">Name: A, dtype: int64</span>\n+<span class=\"go\">Name: A, Length: 2, dtype: int64</span>\n </pre></div>\n </div>\n <p>Using <code class=\"docutils literal notranslate\"><span class=\"pre\">.iloc</span></code>. Here we will get the location of the \u2018A\u2019 column, then use <em>positional</em> indexing to select things.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [107]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">iloc</span><span class=\"p\">[[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">],</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">columns</span><span class=\"o\">.</span><span class=\"n\">get_loc</span><span class=\"p\">(</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">)]</span>\n <span class=\"gh\">Out[107]: </span>\n <span class=\"go\">a    1</span>\n <span class=\"go\">c    3</span>\n-<span class=\"go\">Name: A, dtype: int64</span>\n+<span class=\"go\">Name: A, Length: 2, dtype: int64</span>\n </pre></div>\n </div>\n </section>\n <section id=\"deprecate-panel\">\n <span id=\"whatsnew-0200-api-breaking-deprecate-panel\"></span><h3>Deprecate Panel<a class=\"headerlink\" href=\"#deprecate-panel\" title=\"Link to this heading\">\u00b6</a></h3>\n <p><code class=\"docutils literal notranslate\"><span class=\"pre\">Panel</span></code> is deprecated and will be removed in a future version. The recommended way to represent 3-D data are\n with a <code class=\"docutils literal notranslate\"><span class=\"pre\">MultiIndex</span></code> on a <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code> via the <code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">to_frame()</span></code> or with the <a class=\"reference external\" href=\"http://xarray.pydata.org/en/stable/\">xarray package</a>. pandas\n@@ -3106,25 +3158,29 @@\n <span class=\"gh\">Out[109]: </span>\n <span class=\"go\">   A  B  C</span>\n <span class=\"go\">0  1  0  0</span>\n <span class=\"go\">1  1  1  1</span>\n <span class=\"go\">2  1  2  2</span>\n <span class=\"go\">3  2  3  3</span>\n <span class=\"go\">4  2  4  4</span>\n+\n+<span class=\"go\">[5 rows x 3 columns]</span>\n </pre></div>\n </div>\n <p>Here is a typical useful syntax for computing different aggregations for different columns. This\n is a natural, and useful syntax. We aggregate from the dict-to-list by taking the specified\n columns and applying the list of functions. This returns a <code class=\"docutils literal notranslate\"><span class=\"pre\">MultiIndex</span></code> for the columns (this is <em>not</em> deprecated).</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [110]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">agg</span><span class=\"p\">({</span><span class=\"s1\">&#39;B&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;sum&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;C&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;min&#39;</span><span class=\"p\">})</span>\n <span class=\"gh\">Out[110]: </span>\n <span class=\"go\">   B  C</span>\n <span class=\"go\">A      </span>\n <span class=\"go\">1  3  0</span>\n <span class=\"go\">2  7  3</span>\n+\n+<span class=\"go\">[2 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>Here\u2019s an example of the first deprecation, passing a dict to a grouped <code class=\"docutils literal notranslate\"><span class=\"pre\">Series</span></code>. This\n is a combination aggregation &amp; renaming:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [6]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">B</span><span class=\"o\">.</span><span class=\"n\">agg</span><span class=\"p\">({</span><span class=\"s1\">&#39;foo&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;count&#39;</span><span class=\"p\">})</span>\n <span class=\"go\">FutureWarning: using a dict on a Series for aggregation</span>\n <span class=\"go\">is deprecated and will be removed in a future version</span>\n@@ -3139,14 +3195,16 @@\n <p>You can accomplish the same operation, more idiomatically by:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [111]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">B</span><span class=\"o\">.</span><span class=\"n\">agg</span><span class=\"p\">([</span><span class=\"s1\">&#39;count&#39;</span><span class=\"p\">])</span><span class=\"o\">.</span><span class=\"n\">rename</span><span class=\"p\">(</span><span class=\"n\">columns</span><span class=\"o\">=</span><span class=\"p\">{</span><span class=\"s1\">&#39;count&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;foo&#39;</span><span class=\"p\">})</span>\n <span class=\"gh\">Out[111]: </span>\n <span class=\"go\">   foo</span>\n <span class=\"go\">A     </span>\n <span class=\"go\">1    3</span>\n <span class=\"go\">2    2</span>\n+\n+<span class=\"go\">[2 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p>Here\u2019s an example of the second deprecation, passing a dict-of-dict to a grouped <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code>:</p>\n <div class=\"highlight-python notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"n\">In</span> <span class=\"p\">[</span><span class=\"mi\">23</span><span class=\"p\">]:</span> <span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">)</span>\n     <span class=\"o\">...</span><span class=\"p\">:</span>    <span class=\"o\">.</span><span class=\"n\">agg</span><span class=\"p\">({</span><span class=\"s1\">&#39;B&#39;</span><span class=\"p\">:</span> <span class=\"p\">{</span><span class=\"s1\">&#39;foo&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;sum&#39;</span><span class=\"p\">},</span> <span class=\"s1\">&#39;C&#39;</span><span class=\"p\">:</span> <span class=\"p\">{</span><span class=\"s1\">&#39;bar&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;min&#39;</span><span class=\"p\">}})</span>\n     <span class=\"o\">...</span><span class=\"p\">:</span>  <span class=\"p\">)</span>\n <span class=\"ne\">FutureWarning</span><span class=\"p\">:</span> <span class=\"n\">using</span> <span class=\"n\">a</span> <span class=\"nb\">dict</span> <span class=\"k\">with</span> <span class=\"n\">renaming</span> <span class=\"ow\">is</span> <span class=\"n\">deprecated</span> <span class=\"ow\">and</span>\n@@ -3167,14 +3225,16 @@\n <span class=\"gp\">   .....: </span> <span class=\"p\">)</span>\n <span class=\"gp\">   .....: </span>\n <span class=\"gh\">Out[112]: </span>\n <span class=\"go\">   foo  bar</span>\n <span class=\"go\">A          </span>\n <span class=\"go\">1    3    0</span>\n <span class=\"go\">2    7    3</span>\n+\n+<span class=\"go\">[2 rows x 2 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"deprecate-plotting\">\n <span id=\"whatsnew-0200-privacy-deprecate-plotting\"></span><h3>Deprecate .plotting<a class=\"headerlink\" href=\"#deprecate-plotting\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>The <code class=\"docutils literal notranslate\"><span class=\"pre\">pandas.tools.plotting</span></code> module has been deprecated,  in favor of the top level <code class=\"docutils literal notranslate\"><span class=\"pre\">pandas.plotting</span></code> module. All the public plotting functions are now available\n from <code class=\"docutils literal notranslate\"><span class=\"pre\">pandas.plotting</span></code> (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/12548\">GH 12548</a>).</p>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -277,39 +277,45 @@\n 2000-01-04       NaN       NaN       NaN\n 2000-01-05       NaN       NaN       NaN\n 2000-01-06       NaN       NaN       NaN\n 2000-01-07       NaN       NaN       NaN\n 2000-01-08  0.113648 -1.478427  0.524988\n 2000-01-09  0.404705  0.577046 -1.715002\n 2000-01-10 -1.039268 -0.370647 -1.157892\n+\n+[10 rows x 3 columns]\n One can operate using string function names, callables, lists, or dictionaries\n of these.\n Using a single function is equivalent to .apply.\n In [4]: df.agg('sum')\n Out[4]:\n A   -1.068226\n B   -1.387015\n C   -4.892029\n-dtype: float64\n+Length: 3, dtype: float64\n Multiple aggregations with a list of functions.\n In [5]: df.agg(['sum', 'min'])\n Out[5]:\n             A         B         C\n sum -1.068226 -1.387015 -4.892029\n min -1.135632 -1.478427 -1.715002\n+\n+[2 rows x 3 columns]\n Using a dict provides the ability to apply specific aggregations per column.\n You will get a matrix-like output of all of the aggregators. The output has one\n column per unique function. Those functions applied to a particular column will\n be NaN:\n In [6]: df.agg({'A': ['sum', 'min'], 'B': ['min', 'max']})\n Out[6]:\n             A         B\n sum -1.068226       NaN\n min -1.135632 -1.478427\n max       NaN  1.212112\n+\n+[3 rows x 2 columns]\n The API also supports a .transform() function for broadcasting results.\n In [7]: df.transform(['abs', lambda x: x - x.min()])\n Out[7]:\n                    A                   B                   C\n                  abs  <lambda>       abs  <lambda>       abs  <lambda>\n 2000-01-01  0.469112  1.604745  0.282863  1.195563  1.509059  0.205944\n 2000-01-02  1.135632  0.000000  1.212112  2.690539  0.173215  1.541787\n@@ -317,14 +323,16 @@\n 2000-01-04       NaN       NaN       NaN       NaN       NaN       NaN\n 2000-01-05       NaN       NaN       NaN       NaN       NaN       NaN\n 2000-01-06       NaN       NaN       NaN       NaN       NaN       NaN\n 2000-01-07       NaN       NaN       NaN       NaN       NaN       NaN\n 2000-01-08  0.113648  1.249281  1.478427  0.000000  0.524988  2.239990\n 2000-01-09  0.404705  1.540338  0.577046  2.055473  1.715002  0.000000\n 2000-01-10  1.039268  0.096364  0.370647  1.107780  1.157892  0.557110\n+\n+[10 rows x 6 columns]\n When presented with mixed dtypes that cannot be aggregated, .agg() will only\n take the valid aggregations. This is similar to how groupby .agg() works. (_\bG_\bH\n _\b1_\b5_\b0_\b1_\b5)\n In [8]: df = pd.DataFrame({'A': [1, 2, 3],\n    ...:                    'B': [1., 2., 3.],\n    ...:                    'C': ['foo', 'bar', 'baz'],\n    ...:                    'D': pd.date_range('20130101', periods=3)})\n@@ -332,15 +340,15 @@\n \n In [9]: df.dtypes\n Out[9]:\n A             int64\n B           float64\n C            object\n D    datetime64[ns]\n-dtype: object\n+Length: 4, dtype: object\n In [10]: df.agg(['min', 'sum'])\n Out[10]:\n      A    B          C          D\n min  1  1.0        bar 2013-01-01\n sum  6  6.0  foobarbaz        NaT\n *\b**\b**\b**\b* K\bKe\bey\byw\bwo\bor\brd\bd a\bar\brg\bgu\bum\bme\ben\bnt\bt d\bdt\bty\byp\bpe\be f\bfo\bor\br d\bda\bat\bta\ba I\bIO\bO_\b?\b\u00b6 *\b**\b**\b**\b*\n The 'python' engine for _\br_\be_\ba_\bd_\b__\bc_\bs_\bv_\b(_\b), as well as the _\br_\be_\ba_\bd_\b__\bf_\bw_\bf_\b(_\b) function for\n@@ -349,22 +357,22 @@\n (_\bG_\bH_\b _\b1_\b4_\b2_\b9_\b5). See the _\bi_\bo_\b _\bd_\bo_\bc_\bs for more information.\n In [10]: data = \"a  b\\n1  2\\n3  4\"\n \n In [11]: pd.read_fwf(StringIO(data)).dtypes\n Out[11]:\n a    int64\n b    int64\n-dtype: object\n+Length: 2, dtype: object\n \n In [12]: pd.read_fwf(StringIO(data), dtype={'a': 'float64', 'b':\n 'object'}).dtypes\n Out[12]:\n a    float64\n b     object\n-dtype: object\n+Length: 2, dtype: object\n *\b**\b**\b**\b* M\bMe\bet\bth\bho\bod\bd .\b.t\bto\bo_\b_d\bda\bat\bte\bet\bti\bim\bme\be(\b()\b) h\bha\bas\bs g\bga\bai\bin\bne\bed\bd a\ban\bn o\bor\bri\big\bgi\bin\bn p\bpa\bar\bra\bam\bme\bet\bte\ber\br_\b?\b\u00b6 *\b**\b**\b**\b*\n _\bt_\bo_\b__\bd_\ba_\bt_\be_\bt_\bi_\bm_\be_\b(_\b) has gained a new parameter, origin, to define a reference date\n from where to compute the resulting timestamps when parsing numerical values\n with a specific unit specified. (_\bG_\bH_\b _\b1_\b1_\b2_\b7_\b6, _\bG_\bH_\b _\b1_\b1_\b7_\b4_\b5)\n For example, with 1960-01-01 as the starting date:\n In [13]: pd.to_datetime([1, 2, 3], unit='D', origin=pd.Timestamp('1960-01-01'))\n Out[13]: DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'],\n@@ -400,24 +408,28 @@\n baz   one     1  2\n       two     1  3\n foo   one     2  4\n       two     2  5\n qux   one     3  6\n       two     3  7\n \n+[8 rows x 2 columns]\n+\n In [19]: df.groupby(['second', 'A']).sum()\n Out[19]:\n           B\n second A\n one    1  2\n        2  4\n        3  6\n two    1  4\n        2  5\n        3  7\n+\n+[6 rows x 1 columns]\n *\b**\b**\b**\b* B\bBe\bet\btt\bte\ber\br s\bsu\bup\bpp\bpo\bor\brt\bt f\bfo\bor\br c\bco\bom\bmp\bpr\bre\bes\bss\bse\bed\bd U\bUR\bRL\bLs\bs i\bin\bn r\bre\bea\bad\bd_\b_c\bcs\bsv\bv_\b?\b\u00b6 *\b**\b**\b**\b*\n The compression code was refactored (_\bG_\bH_\b _\b1_\b2_\b6_\b8_\b8). As a result, reading dataframes\n from URLs in _\br_\be_\ba_\bd_\b__\bc_\bs_\bv_\b(_\b) or _\br_\be_\ba_\bd_\b__\bt_\ba_\bb_\bl_\be_\b(_\b) now supports additional compression\n methods: xz, bz2, and zip (_\bG_\bH_\b _\b1_\b4_\b5_\b7_\b0). Previously, only gzip compression was\n supported. By default, compression of URLs and paths are now inferred using\n their file extensions. Additionally, support for bz2 compression in the python\n 2 C-engine improved (_\bG_\bH_\b _\b1_\b4_\b8_\b7_\b4).\n@@ -839,14 +851,16 @@\n \n In [23]: df.head(2)\n Out[23]:\n               A  B\n first second\n bar   one     1  0\n       two     1  1\n+\n+[2 rows x 2 columns]\n *\b**\b**\b**\b* P\bPi\bic\bck\bkl\ble\be f\bfi\bil\ble\be I\bIO\bO n\bno\bow\bw s\bsu\bup\bpp\bpo\bor\brt\bts\bs c\bco\bom\bmp\bpr\bre\bes\bss\bsi\bio\bon\bn_\b?\b\u00b6 *\b**\b**\b**\b*\n _\br_\be_\ba_\bd_\b__\bp_\bi_\bc_\bk_\bl_\be_\b(_\b), _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be_\b(_\b) and _\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bp_\bi_\bc_\bk_\bl_\be_\b(_\b) can now read from\n and write to compressed pickle files. Compression methods can be an explicit\n parameter or be inferred from the file extension. See _\bt_\bh_\be_\b _\bd_\bo_\bc_\bs_\b _\bh_\be_\br_\be_\b.\n In [24]: df = pd.DataFrame({'A': np.random.randn(1000),\n    ....:                    'B': 'foo',\n    ....:                    'C': pd.date_range('20130101', periods=1000,\n@@ -861,14 +875,16 @@\n Out[27]:\n           A    B                   C\n 0 -1.344312  foo 2013-01-01 00:00:00\n 1  0.844885  foo 2013-01-01 00:00:01\n 2  1.075770  foo 2013-01-01 00:00:02\n 3 -0.109050  foo 2013-01-01 00:00:03\n 4  1.643563  foo 2013-01-01 00:00:04\n+\n+[5 rows x 3 columns]\n The default is to infer the compression type from the extension\n (compression='infer'):\n In [28]: df.to_pickle(\"data.pkl.gz\")\n \n In [29]: rt = pd.read_pickle(\"data.pkl.gz\")\n \n In [30]: rt.head()\n@@ -876,26 +892,28 @@\n           A    B                   C\n 0 -1.344312  foo 2013-01-01 00:00:00\n 1  0.844885  foo 2013-01-01 00:00:01\n 2  1.075770  foo 2013-01-01 00:00:02\n 3 -0.109050  foo 2013-01-01 00:00:03\n 4  1.643563  foo 2013-01-01 00:00:04\n \n+[5 rows x 3 columns]\n+\n In [31]: df[\"A\"].to_pickle(\"s1.pkl.bz2\")\n \n In [32]: rt = pd.read_pickle(\"s1.pkl.bz2\")\n \n In [33]: rt.head()\n Out[33]:\n 0   -1.344312\n 1    0.844885\n 2    1.075770\n 3   -0.109050\n 4    1.643563\n-Name: A, dtype: float64\n+Name: A, Length: 5, dtype: float64\n *\b**\b**\b**\b* U\bUI\bIn\bnt\bt6\b64\b4 s\bsu\bup\bpp\bpo\bor\brt\bt i\bim\bmp\bpr\bro\bov\bve\bed\bd_\b?\b\u00b6 *\b**\b**\b**\b*\n pandas has significantly improved support for operations involving unsigned, or\n purely non-negative, integers. Previously, handling these integers would result\n in improper rounding or data-type casting, leading to incorrect results.\n Notably, a new numerical index, UInt64Index, has been created (_\bG_\bH_\b _\b1_\b4_\b9_\b3_\b7)\n In [1]: idx = pd.UInt64Index([1, 2, 3])\n In [2]: df = pd.DataFrame({'A': ['a', 'b', 'c']}, index=idx)\n@@ -982,14 +1000,16 @@\n Out[39]:\n      A  B          C\n idx\n 0    1  a 2016-01-01\n 1    2  b 2016-01-02\n 2    3  c 2016-01-03\n \n+[3 rows x 3 columns]\n+\n In [40]: df.to_json(orient='table')\n Out[40]: '{\"schema\":{\"fields\":[{\"name\":\"idx\",\"type\":\"integer\"},{\"name\":\n \"A\",\"type\":\"integer\"},{\"name\":\"B\",\"type\":\"string\"},{\"name\":\"C\",\"type\":\n \"datetime\"}],\"primaryKey\":[\"idx\"],\"pandas_version\":\"1.4.0\"},\"data\":[{\"idx\":\n 0,\"A\":1,\"B\":\"a\",\"C\":\"2016-01-01T00:00:00.000\"},{\"idx\":1,\"A\":2,\"B\":\"b\",\"C\":\n \"2016-01-02T00:00:00.000\"},{\"idx\":2,\"A\":3,\"B\":\"c\",\"C\":\"2016-01-03T00:00:\n 00.000\"}]}'\n@@ -1042,14 +1062,16 @@\n 4   5.0  1.453425  1.057737  0.165562  0.515018\n 5   6.0 -1.336936  0.562861  1.392855 -0.063328\n 6   7.0  0.121668  1.207603 -0.002040  1.627796\n 7   8.0  0.354493  1.037528 -0.385684  0.519818\n 8   9.0  1.686583 -1.325963  1.428984 -2.089354\n 9  10.0 -0.129820  0.631523 -0.586538  0.290720\n \n+[10 rows x 5 columns]\n+\n In [46]: styled = (df.style\n    ....:           .applymap(lambda val: 'color:red;' if val < 0 else 'color:\n black;')\n    ....:           .highlight_max())\n    ....:\n \n In [47]: styled.to_excel('styled.xlsx', engine='openpyxl')\n@@ -1101,27 +1123,31 @@\n Out[53]:\n                A\n B\n (-0.003, 1.5]  0\n (1.5, 3.0]     1\n (-0.003, 1.5]  2\n (-0.003, 1.5]  3\n+\n+[4 rows x 1 columns]\n Selecting via a specific interval:\n In [54]: df.loc[pd.Interval(1.5, 3.0)]\n Out[54]:\n A    1\n-Name: (1.5, 3.0], dtype: int64\n+Name: (1.5, 3.0], Length: 1, dtype: int64\n Selecting via a scalar value that is contained i\bin\bn the intervals.\n In [55]: df.loc[0]\n Out[55]:\n                A\n B\n (-0.003, 1.5]  0\n (-0.003, 1.5]  2\n (-0.003, 1.5]  3\n+\n+[3 rows x 1 columns]\n *\b**\b**\b**\b* O\bOt\bth\bhe\ber\br e\ben\bnh\bha\ban\bnc\bce\bem\bme\ben\bnt\bts\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n     * DataFrame.rolling() now accepts the parameter\n       closed='right'|'left'|'both'|'neither' to choose the rolling window-\n       endpoint closedness. See the _\bd_\bo_\bc_\bu_\bm_\be_\bn_\bt_\ba_\bt_\bi_\bo_\bn (_\bG_\bH_\b _\b1_\b3_\b9_\b6_\b5)\n     * Integration with the feather-format, including a new top-level\n       pd.read_feather() and DataFrame.to_feather() method, see _\bh_\be_\br_\be.\n     * Series.str.replace() now accepts a callable, as replacement, which is\n@@ -1465,32 +1491,32 @@\n automatically upcast results to a dtype of float64. Now the smallest acceptable\n dtype will be used (_\bG_\bH_\b _\b1_\b3_\b2_\b4_\b7)\n In [71]: df1 = pd.DataFrame(np.array([1.0], dtype=np.float32, ndmin=2))\n \n In [72]: df1.dtypes\n Out[72]:\n 0    float32\n-dtype: object\n+Length: 1, dtype: object\n \n In [73]: df2 = pd.DataFrame(np.array([np.nan], dtype=np.float32, ndmin=2))\n \n In [74]: df2.dtypes\n Out[74]:\n 0    float32\n-dtype: object\n+Length: 1, dtype: object\n Previous behavior:\n In [7]: pd.concat([df1, df2]).dtypes\n Out[7]:\n 0    float64\n dtype: object\n New behavior:\n In [75]: pd.concat([df1, df2]).dtypes\n Out[75]:\n 0    float32\n-dtype: object\n+Length: 1, dtype: object\n *\b**\b**\b**\b* p\bpa\ban\bnd\bda\bas\bs G\bGo\boo\bog\bgl\ble\be B\bBi\big\bgQ\bQu\bue\ber\bry\by s\bsu\bup\bpp\bpo\bor\brt\bt h\bha\bas\bs m\bmo\bov\bve\bed\bd_\b?\b\u00b6 *\b**\b**\b**\b*\n pandas has split off Google BigQuery support into a separate package pandas-\n gbq. You can conda install pandas-gbq -c conda-forge or pip install pandas-gbq\n to get it. The functionality of _\br_\be_\ba_\bd_\b__\bg_\bb_\bq_\b(_\b) and _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bt_\bo_\b__\bg_\bb_\bq_\b(_\b) remain the\n same with the currently released version of pandas-gbq=0.1.4. Documentation is\n now hosted _\bh_\be_\br_\be (_\bG_\bH_\b _\b1_\b5_\b3_\b4_\b7)\n *\b**\b**\b**\b* M\bMe\bem\bmo\bor\bry\by u\bus\bsa\bag\bge\be f\bfo\bor\br I\bIn\bnd\bde\bex\bx i\bis\bs m\bmo\bor\bre\be a\bac\bcc\bcu\bur\bra\bat\bte\be_\b?\b\u00b6 *\b**\b**\b**\b*\n@@ -1550,14 +1576,16 @@\n Sorting works as expected\n In [76]: df.sort_index()\n Out[76]:\n                      a\n 2011-12-31 23:59:59  1\n 2012-01-01 00:00:00  2\n 2012-01-01 00:00:01  3\n+\n+[3 rows x 1 columns]\n In [90]: df.sort_index().index.is_lexsorted()\n Out[90]: True\n \n In [91]: df.sort_index().index.is_monotonic\n Out[91]: True\n However, this example, which has a non-monotonic 2nd level, doesn\u2019t behave as\n desired.\n@@ -1569,14 +1597,16 @@\n In [78]: df\n Out[78]:\n       value\n a bb      1\n   aa      2\n b bb      3\n   aa      4\n+\n+[4 rows x 1 columns]\n Previous behavior:\n In [11]: df.sort_index()\n Out[11]:\n       value\n a bb      1\n   aa      2\n b bb      3\n@@ -1645,21 +1675,25 @@\n Out[80]:\n       B\n   count mean       std  min   25%  50%   75%  max\n A\n 1   2.0  1.5  0.707107  1.0  1.25  1.5  1.75  2.0\n 2   2.0  3.5  0.707107  3.0  3.25  3.5  3.75  4.0\n \n+[2 rows x 8 columns]\n+\n In [81]: df.groupby('A').agg([\"mean\", \"std\", \"min\", \"max\"])\n Out[81]:\n      B\n   mean       std min max\n A\n 1  1.5  0.707107   1   2\n 2  3.5  0.707107   3   4\n+\n+[2 rows x 4 columns]\n *\b**\b**\b**\b* W\bWi\bin\bnd\bdo\bow\bw b\bbi\bin\bna\bar\bry\by c\bco\bor\brr\br/\b/c\bco\bov\bv o\bop\bpe\ber\bra\bat\bti\bio\bon\bns\bs r\bre\bet\btu\bur\brn\bn a\ba M\bMu\bul\blt\bti\biI\bIn\bnd\bde\bex\bx D\bDa\bat\bta\baF\bFr\bra\bam\bme\be_\b?\b\u00b6 *\b**\b**\b**\b*\n A binary window operation, like .corr() or .cov(), when operating on a .rolling\n (..), .expanding(..), or .ewm(..) object, will now return a 2-level\n MultiIndexed DataFrame rather than a Panel, as Panel is now deprecated, see\n _\bh_\be_\br_\be. These are equivalent in function, but a MultiIndexed DataFrame enjoys\n more support in pandas. See the section on _\bW_\bi_\bn_\bd_\bo_\bw_\be_\bd_\b _\bB_\bi_\bn_\ba_\br_\by_\b _\bO_\bp_\be_\br_\ba_\bt_\bi_\bo_\bn_\bs for more\n information. (_\bG_\bH_\b _\b1_\b5_\b6_\b7_\b7)\n@@ -1677,14 +1711,16 @@\n bar                A         B\n foo\n 2016-04-05  0.640880  0.126205\n 2016-04-06  0.171465  0.737086\n 2016-04-07  0.127029  0.369650\n 2016-04-08  0.604334  0.103104\n 2016-04-09  0.802374  0.945553\n+\n+[5 rows x 2 columns]\n Previous behavior:\n In [2]: df.rolling(12).corr()\n Out[2]:\n <class 'pandas.core.panel.Panel'>\n Dimensions: 100 (items) x 2 (major_axis) x 2 (minor_axis)\n Items axis: 2016-01-01 00:00:00 to 2016-04-09 00:00:00\n Major_axis axis: A to B\n@@ -1697,33 +1733,37 @@\n bar                    A         B\n foo        bar\n 2016-04-07 B   -0.132090  1.000000\n 2016-04-08 A    1.000000 -0.145775\n            B   -0.145775  1.000000\n 2016-04-09 A    1.000000  0.119645\n            B    0.119645  1.000000\n+\n+[5 rows x 2 columns]\n Retrieving a correlation matrix for a cross-section\n In [87]: df.rolling(12).corr().loc['2016-04-07']\n Out[87]:\n bar        A        B\n bar\n A    1.00000 -0.13209\n B   -0.13209  1.00000\n+\n+[2 rows x 2 columns]\n *\b**\b**\b**\b* H\bHD\bDF\bFS\bSt\bto\bor\bre\be w\bwh\bhe\ber\bre\be s\bst\btr\bri\bin\bng\bg c\bco\bom\bmp\bpa\bar\bri\bis\bso\bon\bn_\b?\b\u00b6 *\b**\b**\b**\b*\n In previous versions most types could be compared to string column in a\n HDFStore usually resulting in an invalid comparison, returning an empty result\n frame. These comparisons will now raise a TypeError (_\bG_\bH_\b _\b1_\b5_\b4_\b9_\b2)\n In [88]: df = pd.DataFrame({'unparsed_date': ['2014-01-01', '2014-01-01']})\n \n In [89]: df.to_hdf('store.h5', key='key', format='table', data_columns=True)\n \n In [90]: df.dtypes\n Out[90]:\n unparsed_date    object\n-dtype: object\n+Length: 1, dtype: object\n Previous behavior:\n In [4]: pd.read_hdf('store.h5', 'key', where='unparsed_date > ts')\n File \"<string>\", line 1\n   (unparsed_date > 1970-01-01 00:00:01.388552400)\n                         ^\n SyntaxError: invalid token\n New behavior:\n@@ -1759,34 +1799,40 @@\n       In [97]: left\n       Out[97]:\n           a\n       2  20\n       1  10\n       0   0\n \n+      [3 rows x 1 columns]\n+\n       In [98]: right = pd.DataFrame({'b': [100, 200, 300]}, index=[1, 2, 3])\n \n       In [99]: right\n       Out[99]:\n            b\n       1  100\n       2  200\n       3  300\n+\n+      [3 rows x 1 columns]\n       Previous behavior:\n       In [4]: left.join(right, how='inner')\n       Out[4]:\n          a    b\n       1  10  100\n       2  20  200\n       New behavior:\n       In [100]: left.join(right, how='inner')\n       Out[100]:\n           a    b\n       2  20  200\n       1  10  100\n+\n+      [2 rows x 2 columns]\n *\b**\b**\b**\b* P\bPi\biv\bvo\bot\bt t\bta\bab\bbl\ble\be a\bal\blw\bwa\bay\bys\bs r\bre\bet\btu\bur\brn\bns\bs a\ba D\bDa\bat\bta\baF\bFr\bra\bam\bme\be_\b?\b\u00b6 *\b**\b**\b**\b*\n The documentation for _\bp_\bi_\bv_\bo_\bt_\b__\bt_\ba_\bb_\bl_\be_\b(_\b) states that a DataFrame is a\bal\blw\bwa\bay\bys\bs returned.\n Here a bug is fixed that allowed this to return a Series under certain\n circumstance. (_\bG_\bH_\b _\b4_\b3_\b8_\b6)\n In [101]: df = pd.DataFrame({'col1': [3, 4, 5],\n    .....:                    'col2': ['C', 'D', 'E'],\n    .....:                    'col3': [1, 3, 9]})\n@@ -1794,14 +1840,16 @@\n \n In [102]: df\n Out[102]:\n    col1 col2  col3\n 0     3    C     1\n 1     4    D     3\n 2     5    E     9\n+\n+[3 rows x 3 columns]\n Previous behavior:\n In [2]: df.pivot_table('col1', index=['col3', 'col2'], aggfunc=\"sum\")\n Out[2]:\n col3  col2\n 1     C       3\n 3     D       4\n 9     E       5\n@@ -1810,14 +1858,16 @@\n In [103]: df.pivot_table('col1', index=['col3', 'col2'], aggfunc=\"sum\")\n Out[103]:\n            col1\n col3 col2\n 1    C        3\n 3    D        4\n 9    E        5\n+\n+[3 rows x 1 columns]\n *\b**\b**\b**\b* O\bOt\bth\bhe\ber\br A\bAP\bPI\bI c\bch\bha\ban\bng\bge\bes\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n     * numexpr version is now required to be >= 2.4.6 and it will not be used at\n       all if this requisite is not fulfilled (_\bG_\bH_\b _\b1_\b5_\b2_\b1_\b3).\n     * CParserError has been renamed to ParserError in pd.read_csv() and will be\n       removed in the future (_\bG_\bH_\b _\b1_\b2_\b6_\b6_\b5)\n     * SparseArray.cumsum() and SparseSeries.cumsum() will now always return\n       SparseArray and SparseSeries respectively (_\bG_\bH_\b _\b1_\b2_\b8_\b5_\b5)\n@@ -1964,35 +2014,37 @@\n \n In [105]: df\n Out[105]:\n    A  B\n a  1  4\n b  2  5\n c  3  6\n+\n+[3 rows x 2 columns]\n Previous behavior, where you wish to get the 0th and the 2nd elements from the\n index in the \u2018A\u2019 column.\n In [3]: df.ix[[0, 2], 'A']\n Out[3]:\n a    1\n c    3\n Name: A, dtype: int64\n Using .loc. Here we will select the appropriate indexes from the index, then\n use l\bla\bab\bbe\bel\bl indexing.\n In [106]: df.loc[df.index[[0, 2]], 'A']\n Out[106]:\n a    1\n c    3\n-Name: A, dtype: int64\n+Name: A, Length: 2, dtype: int64\n Using .iloc. Here we will get the location of the \u2018A\u2019 column, then use\n p\bpo\bos\bsi\bit\bti\bio\bon\bna\bal\bl indexing to select things.\n In [107]: df.iloc[[0, 2], df.columns.get_loc('A')]\n Out[107]:\n a    1\n c    3\n-Name: A, dtype: int64\n+Name: A, Length: 2, dtype: int64\n *\b**\b**\b**\b* D\bDe\bep\bpr\bre\bec\bca\bat\bte\be P\bPa\ban\bne\bel\bl_\b?\b\u00b6 *\b**\b**\b**\b*\n Panel is deprecated and will be removed in a future version. The recommended\n way to represent 3-D data are with a MultiIndex on a DataFrame via the to_frame\n () or with the _\bx_\ba_\br_\br_\ba_\by_\b _\bp_\ba_\bc_\bk_\ba_\bg_\be. pandas provides a to_xarray() method to automate\n this conversion (_\bG_\bH_\b _\b1_\b3_\b5_\b6_\b3).\n In [133]: import pandas._testing as tm\n \n@@ -2067,24 +2119,28 @@\n Out[109]:\n    A  B  C\n 0  1  0  0\n 1  1  1  1\n 2  1  2  2\n 3  2  3  3\n 4  2  4  4\n+\n+[5 rows x 3 columns]\n Here is a typical useful syntax for computing different aggregations for\n different columns. This is a natural, and useful syntax. We aggregate from the\n dict-to-list by taking the specified columns and applying the list of\n functions. This returns a MultiIndex for the columns (this is n\bno\bot\bt deprecated).\n In [110]: df.groupby('A').agg({'B': 'sum', 'C': 'min'})\n Out[110]:\n    B  C\n A\n 1  3  0\n 2  7  3\n+\n+[2 rows x 2 columns]\n Here\u2019s an example of the first deprecation, passing a dict to a grouped Series.\n This is a combination aggregation & renaming:\n In [6]: df.groupby('A').B.agg({'foo': 'count'})\n FutureWarning: using a dict on a Series for aggregation\n is deprecated and will be removed in a future version\n \n Out[6]:\n@@ -2095,14 +2151,16 @@\n You can accomplish the same operation, more idiomatically by:\n In [111]: df.groupby('A').B.agg(['count']).rename(columns={'count': 'foo'})\n Out[111]:\n    foo\n A\n 1    3\n 2    2\n+\n+[2 rows x 1 columns]\n Here\u2019s an example of the second deprecation, passing a dict-of-dict to a\n grouped DataFrame:\n In [23]: (df.groupby('A')\n     ...:    .agg({'B': {'foo': 'sum'}, 'C': {'bar': 'min'}})\n     ...:  )\n FutureWarning: using a dict with renaming is deprecated and\n will be removed in a future version\n@@ -2120,14 +2178,16 @@\n    .....:  )\n    .....:\n Out[112]:\n    foo  bar\n A\n 1    3    0\n 2    7    3\n+\n+[2 rows x 2 columns]\n *\b**\b**\b**\b* D\bDe\bep\bpr\bre\bec\bca\bat\bte\be .\b.p\bpl\blo\bot\btt\bti\bin\bng\bg_\b?\b\u00b6 *\b**\b**\b**\b*\n The pandas.tools.plotting module has been deprecated, in favor of the top level\n pandas.plotting module. All the public plotting functions are now available\n from pandas.plotting (_\bG_\bH_\b _\b1_\b2_\b5_\b4_\b8).\n Furthermore, the top-level pandas.scatter_matrix and pandas.plot_params are\n deprecated. Users can import these from pandas.plotting as well.\n Previous script:\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.21.0.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.21.0.html", "unified_diff": "@@ -1137,37 +1137,37 @@\n <span class=\"gp\">   ...: </span>\n \n <span class=\"gp\">In [2]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[2]: </span>\n <span class=\"go\">A     int64</span>\n <span class=\"go\">B    object</span>\n <span class=\"go\">C    object</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n \n <span class=\"gp\">In [3]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">infer_objects</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[3]: </span>\n <span class=\"go\">A     int64</span>\n <span class=\"go\">B     int64</span>\n <span class=\"go\">C    object</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n </pre></div>\n </div>\n <p>Note that column <code class=\"docutils literal notranslate\"><span class=\"pre\">'C'</span></code> was not converted - only scalar numeric types\n will be converted to a new type.  Other types of conversion should be accomplished\n using the <a class=\"reference internal\" href=\"../reference/api/pandas.to_numeric.html#pandas.to_numeric\" title=\"pandas.to_numeric\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">to_numeric()</span></code></a> function (or <a class=\"reference internal\" href=\"../reference/api/pandas.to_datetime.html#pandas.to_datetime\" title=\"pandas.to_datetime\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">to_datetime()</span></code></a>, <a class=\"reference internal\" href=\"../reference/api/pandas.to_timedelta.html#pandas.to_timedelta\" title=\"pandas.to_timedelta\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">to_timedelta()</span></code></a>).</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [4]: </span><span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">infer_objects</span><span class=\"p\">()</span>\n \n <span class=\"gp\">In [5]: </span><span class=\"n\">df</span><span class=\"p\">[</span><span class=\"s1\">&#39;C&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">to_numeric</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">[</span><span class=\"s1\">&#39;C&#39;</span><span class=\"p\">],</span> <span class=\"n\">errors</span><span class=\"o\">=</span><span class=\"s1\">&#39;coerce&#39;</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [6]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[6]: </span>\n <span class=\"go\">A    int64</span>\n <span class=\"go\">B    int64</span>\n <span class=\"go\">C    int64</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n </pre></div>\n </div>\n </section>\n <section id=\"improved-warnings-when-attempting-to-create-columns\">\n <span id=\"whatsnew-0210-enhancements-attribute-access\"></span><h3>Improved warnings when attempting to create columns<a class=\"headerlink\" href=\"#improved-warnings-when-attempting-to-create-columns\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>New users are often puzzled by the relationship between column operations and\n attribute access on <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code> instances (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/7175\">GH 7175</a>). One specific\n@@ -1200,26 +1200,32 @@\n \n <span class=\"gp\">In [8]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[8]: </span>\n <span class=\"go\">   A  B  C  D</span>\n <span class=\"go\">0  0  1  2  3</span>\n <span class=\"go\">1  4  5  6  7</span>\n \n+<span class=\"go\">[2 rows x 4 columns]</span>\n+\n <span class=\"gp\">In [9]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">drop</span><span class=\"p\">([</span><span class=\"s1\">&#39;B&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;C&#39;</span><span class=\"p\">],</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[9]: </span>\n <span class=\"go\">   A  D</span>\n <span class=\"go\">0  0  3</span>\n <span class=\"go\">1  4  7</span>\n \n+<span class=\"go\">[2 rows x 2 columns]</span>\n+\n <span class=\"go\"># the following is now equivalent</span>\n <span class=\"gp\">In [10]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">drop</span><span class=\"p\">(</span><span class=\"n\">columns</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;B&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;C&#39;</span><span class=\"p\">])</span>\n <span class=\"gh\">Out[10]: </span>\n <span class=\"go\">   A  D</span>\n <span class=\"go\">0  0  3</span>\n <span class=\"go\">1  4  7</span>\n+\n+<span class=\"go\">[2 rows x 2 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"methods-rename-reindex-now-also-accept-axis-keyword\">\n <span id=\"whatsnew-0210-enhancements-rename-reindex-axis\"></span><h3>Methods <code class=\"docutils literal notranslate\"><span class=\"pre\">rename</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">reindex</span></code> now also accept axis keyword<a class=\"headerlink\" href=\"#methods-rename-reindex-now-also-accept-axis-keyword\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>The <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.rename.html#pandas.DataFrame.rename\" title=\"pandas.DataFrame.rename\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">DataFrame.rename()</span></code></a> and <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.reindex.html#pandas.DataFrame.reindex\" title=\"pandas.DataFrame.reindex\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">DataFrame.reindex()</span></code></a> methods have gained\n the <code class=\"docutils literal notranslate\"><span class=\"pre\">axis</span></code> keyword to specify the axis to target with the operation\n@@ -1230,52 +1236,64 @@\n <span class=\"gp\">In [12]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">rename</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"o\">.</span><span class=\"n\">lower</span><span class=\"p\">,</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"s1\">&#39;columns&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[12]: </span>\n <span class=\"go\">   a  b</span>\n <span class=\"go\">0  1  4</span>\n <span class=\"go\">1  2  5</span>\n <span class=\"go\">2  3  6</span>\n \n+<span class=\"go\">[3 rows x 2 columns]</span>\n+\n <span class=\"gp\">In [13]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">rename</span><span class=\"p\">(</span><span class=\"nb\">id</span><span class=\"p\">,</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"s1\">&#39;index&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[13]: </span>\n <span class=\"go\">          A  B</span>\n <span class=\"go\">11739816  1  4</span>\n <span class=\"go\">11739848  2  5</span>\n <span class=\"go\">11739880  3  6</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>And <code class=\"docutils literal notranslate\"><span class=\"pre\">reindex</span></code>:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [14]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">reindex</span><span class=\"p\">([</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;B&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;C&#39;</span><span class=\"p\">],</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"s1\">&#39;columns&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[14]: </span>\n <span class=\"go\">   A  B   C</span>\n <span class=\"go\">0  1  4 NaN</span>\n <span class=\"go\">1  2  5 NaN</span>\n <span class=\"go\">2  3  6 NaN</span>\n \n+<span class=\"go\">[3 rows x 3 columns]</span>\n+\n <span class=\"gp\">In [15]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">reindex</span><span class=\"p\">([</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">],</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"s1\">&#39;index&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[15]: </span>\n <span class=\"go\">     A    B</span>\n <span class=\"go\">0  1.0  4.0</span>\n <span class=\"go\">1  2.0  5.0</span>\n <span class=\"go\">3  NaN  NaN</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>The \u201cindex, columns\u201d style continues to work as before.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [16]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">rename</span><span class=\"p\">(</span><span class=\"n\">index</span><span class=\"o\">=</span><span class=\"nb\">id</span><span class=\"p\">,</span> <span class=\"n\">columns</span><span class=\"o\">=</span><span class=\"nb\">str</span><span class=\"o\">.</span><span class=\"n\">lower</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[16]: </span>\n <span class=\"go\">          a  b</span>\n <span class=\"go\">11739816  1  4</span>\n <span class=\"go\">11739848  2  5</span>\n <span class=\"go\">11739880  3  6</span>\n \n+<span class=\"go\">[3 rows x 2 columns]</span>\n+\n <span class=\"gp\">In [17]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">reindex</span><span class=\"p\">(</span><span class=\"n\">index</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">],</span> <span class=\"n\">columns</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;B&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;C&#39;</span><span class=\"p\">])</span>\n <span class=\"gh\">Out[17]: </span>\n <span class=\"go\">     A    B   C</span>\n <span class=\"go\">0  1.0  4.0 NaN</span>\n <span class=\"go\">1  2.0  5.0 NaN</span>\n <span class=\"go\">3  NaN  NaN NaN</span>\n+\n+<span class=\"go\">[3 rows x 3 columns]</span>\n </pre></div>\n </div>\n <p>We <em>highly</em> encourage using named arguments to avoid confusion when using either\n style.</p>\n </section>\n <section id=\"categoricaldtype-for-specifying-categoricals\">\n <span id=\"whatsnew-0210-enhancements-categorical-dtype\"></span><h3><code class=\"docutils literal notranslate\"><span class=\"pre\">CategoricalDtype</span></code> for specifying categoricals<a class=\"headerlink\" href=\"#categoricaldtype-for-specifying-categoricals\" title=\"Link to this heading\">\u00b6</a></h3>\n@@ -1293,15 +1311,15 @@\n \n <span class=\"gp\">In [21]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"n\">dtype</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[21]: </span>\n <span class=\"go\">0    a</span>\n <span class=\"go\">1    b</span>\n <span class=\"go\">2    c</span>\n <span class=\"go\">3    a</span>\n-<span class=\"go\">dtype: category</span>\n+<span class=\"go\">Length: 4, dtype: category</span>\n <span class=\"go\">Categories (4, object): [&#39;a&#39; &lt; &#39;b&#39; &lt; &#39;c&#39; &lt; &#39;d&#39;]</span>\n </pre></div>\n </div>\n <p>One place that deserves special mention is in <a class=\"reference internal\" href=\"../reference/api/pandas.read_csv.html#pandas.read_csv\" title=\"pandas.read_csv\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">read_csv()</span></code></a>. Previously, with\n <code class=\"docutils literal notranslate\"><span class=\"pre\">dtype={'col':</span> <span class=\"pre\">'category'}</span></code>, the returned values and categories would always\n be strings.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [22]: </span><span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"s1\">&#39;A,B</span><span class=\"se\">\\n</span><span class=\"s1\">a,1</span><span class=\"se\">\\n</span><span class=\"s1\">b,2</span><span class=\"se\">\\n</span><span class=\"s1\">c,3&#39;</span>\n@@ -1353,26 +1371,30 @@\n <span class=\"gp\">   ....: </span>\n \n <span class=\"gp\">In [29]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">head</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[29]: </span>\n <span class=\"go\">     Store    Product  Revenue  Quantity</span>\n <span class=\"go\">0  Store_2  Product_2    32.09         7</span>\n <span class=\"go\">1  Store_1  Product_3    14.20         1</span>\n+\n+<span class=\"go\">[2 rows x 4 columns]</span>\n </pre></div>\n </div>\n <p>Now, to find prices per store/product, we can simply do:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [30]: </span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">([</span><span class=\"s1\">&#39;Store&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Product&#39;</span><span class=\"p\">])</span>\n <span class=\"gp\">   ....: </span>   <span class=\"o\">.</span><span class=\"n\">pipe</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">grp</span><span class=\"p\">:</span> <span class=\"n\">grp</span><span class=\"o\">.</span><span class=\"n\">Revenue</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span> <span class=\"o\">/</span> <span class=\"n\">grp</span><span class=\"o\">.</span><span class=\"n\">Quantity</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">())</span>\n <span class=\"gp\">   ....: </span>   <span class=\"o\">.</span><span class=\"n\">unstack</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"n\">round</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">))</span>\n <span class=\"gp\">   ....: </span>\n <span class=\"gh\">Out[30]: </span>\n <span class=\"go\">Product  Product_1  Product_2  Product_3</span>\n <span class=\"go\">Store                                   </span>\n <span class=\"go\">Store_1       6.73       6.72       7.14</span>\n <span class=\"go\">Store_2       7.59       6.98       7.23</span>\n+\n+<span class=\"go\">[2 rows x 3 columns]</span>\n </pre></div>\n </div>\n <p>See the <a class=\"reference internal\" href=\"../user_guide/groupby.html#groupby-pipe\"><span class=\"std std-ref\">documentation</span></a> for more.</p>\n </section>\n <section id=\"categorical-rename-categories-accepts-a-dict-like\">\n <span id=\"whatsnew-0210-enhancements-rename-categories\"></span><h3><code class=\"docutils literal notranslate\"><span class=\"pre\">Categorical.rename_categories</span></code> accepts a dict-like<a class=\"headerlink\" href=\"#categorical-rename-categories-accepts-a-dict-like\" title=\"Link to this heading\">\u00b6</a></h3>\n <p><a class=\"reference internal\" href=\"../reference/api/pandas.Series.cat.rename_categories.html#pandas.Series.cat.rename_categories\" title=\"pandas.Series.cat.rename_categories\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">rename_categories()</span></code></a> now accepts a dict-like argument for\n@@ -1542,15 +1564,15 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [35]: </span><span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [36]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[36]: </span>\n <span class=\"go\">0    1</span>\n <span class=\"go\">1    2</span>\n <span class=\"go\">2    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 3, dtype: int64</span>\n </pre></div>\n </div>\n <p>Previous behavior</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [4]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">]]</span>\n <span class=\"gh\">Out[4]:</span>\n <span class=\"go\">1    2.0</span>\n <span class=\"go\">2    3.0</span>\n@@ -1575,23 +1597,23 @@\n </div>\n <p>The idiomatic way to achieve selecting potentially not-found elements is via <code class=\"docutils literal notranslate\"><span class=\"pre\">.reindex()</span></code></p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [37]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">reindex</span><span class=\"p\">([</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">])</span>\n <span class=\"gh\">Out[37]: </span>\n <span class=\"go\">1    2.0</span>\n <span class=\"go\">2    3.0</span>\n <span class=\"go\">3    NaN</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 3, dtype: float64</span>\n </pre></div>\n </div>\n <p>Selection with all keys found is unchanged.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [38]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">]]</span>\n <span class=\"gh\">Out[38]: </span>\n <span class=\"go\">1    2</span>\n <span class=\"go\">2    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 2, dtype: int64</span>\n </pre></div>\n </div>\n </section>\n <section id=\"na-naming-changes\">\n <span id=\"whatsnew-0210-api-na-changes\"></span><h3>NA naming changes<a class=\"headerlink\" href=\"#na-naming-changes\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>In order to promote more consistency among the pandas API, we have added additional top-level\n functions <a class=\"reference internal\" href=\"../reference/api/pandas.isna.html#pandas.isna\" title=\"pandas.isna\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">isna()</span></code></a> and <a class=\"reference internal\" href=\"../reference/api/pandas.notna.html#pandas.notna\" title=\"pandas.notna\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">notna()</span></code></a> that are aliases for <a class=\"reference internal\" href=\"../reference/api/pandas.isnull.html#pandas.isnull\" title=\"pandas.isnull\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">isnull()</span></code></a> and <a class=\"reference internal\" href=\"../reference/api/pandas.notnull.html#pandas.notnull\" title=\"pandas.notnull\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">notnull()</span></code></a>.\n@@ -1607,15 +1629,15 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [39]: </span><span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [40]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[40]: </span>\n <span class=\"go\">0    1</span>\n <span class=\"go\">1    2</span>\n <span class=\"go\">2    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 3, dtype: int64</span>\n </pre></div>\n </div>\n <p>Previously:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [2]: </span><span class=\"nb\">type</span><span class=\"p\">(</span><span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"p\">)[</span><span class=\"mi\">0</span><span class=\"p\">])</span>\n <span class=\"gh\">Out[2]: </span><span class=\"go\">numpy.int64</span>\n </pre></div>\n </div>\n@@ -1651,15 +1673,15 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [45]: </span><span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">],</span> <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"kc\">False</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [46]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[46]: </span>\n <span class=\"go\">False    1</span>\n <span class=\"go\">True     2</span>\n <span class=\"go\">False    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 3, dtype: int64</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [59]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Index</span><span class=\"p\">([</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"kc\">True</span><span class=\"p\">])]</span>\n <span class=\"gh\">Out[59]:</span>\n <span class=\"go\">True     2</span>\n <span class=\"go\">False    1</span>\n <span class=\"go\">False    3</span>\n@@ -1668,40 +1690,40 @@\n </pre></div>\n </div>\n <p>Current behavior</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [47]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Index</span><span class=\"p\">([</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"kc\">True</span><span class=\"p\">])]</span>\n <span class=\"gh\">Out[47]: </span>\n <span class=\"go\">False    1</span>\n <span class=\"go\">False    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 2, dtype: int64</span>\n </pre></div>\n </div>\n <p>Furthermore, previously if you had an index that was non-numeric (e.g. strings), then a boolean Index would raise a <code class=\"docutils literal notranslate\"><span class=\"pre\">KeyError</span></code>.\n This will now be treated as a boolean indexer.</p>\n <p>Previously behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [48]: </span><span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">],</span> <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;a&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;b&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;c&#39;</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [49]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[49]: </span>\n <span class=\"go\">a    1</span>\n <span class=\"go\">b    2</span>\n <span class=\"go\">c    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 3, dtype: int64</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [39]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Index</span><span class=\"p\">([</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"kc\">True</span><span class=\"p\">])]</span>\n <span class=\"go\">KeyError: &quot;None of [Index([True, False, True], dtype=&#39;object&#39;)] are in the [index]&quot;</span>\n </pre></div>\n </div>\n <p>Current behavior</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [50]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Index</span><span class=\"p\">([</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"kc\">True</span><span class=\"p\">])]</span>\n <span class=\"gh\">Out[50]: </span>\n <span class=\"go\">a    1</span>\n <span class=\"go\">c    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 2, dtype: int64</span>\n </pre></div>\n </div>\n </section>\n <section id=\"periodindex-resampling\">\n <span id=\"whatsnew-0210-api-breaking-period-index-resampling\"></span><h3><code class=\"docutils literal notranslate\"><span class=\"pre\">PeriodIndex</span></code> resampling<a class=\"headerlink\" href=\"#periodindex-resampling\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>In previous versions of pandas, resampling a <code class=\"docutils literal notranslate\"><span class=\"pre\">Series</span></code>/<code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code> indexed by a <code class=\"docutils literal notranslate\"><span class=\"pre\">PeriodIndex</span></code> returned a <code class=\"docutils literal notranslate\"><span class=\"pre\">DatetimeIndex</span></code> in some cases (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/12884\">GH 12884</a>). Resampling to a multiplied frequency now returns a <code class=\"docutils literal notranslate\"><span class=\"pre\">PeriodIndex</span></code> (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/15944\">GH 15944</a>). As a minor enhancement, resampling a <code class=\"docutils literal notranslate\"><span class=\"pre\">PeriodIndex</span></code> can now handle <code class=\"docutils literal notranslate\"><span class=\"pre\">NaT</span></code> values (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/13224\">GH 13224</a>)</p>\n <p>Previous behavior:</p>\n@@ -1917,15 +1939,15 @@\n </div>\n <p>New behavior</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [56]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">to_datetime</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"p\">,</span> <span class=\"n\">utc</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[56]: </span>\n <span class=\"go\">0   2013-01-01 00:00:00+00:00</span>\n <span class=\"go\">1   2013-01-01 00:00:00+00:00</span>\n <span class=\"go\">2   2013-01-01 00:00:00+00:00</span>\n-<span class=\"go\">dtype: datetime64[ns, UTC]</span>\n+<span class=\"go\">Length: 3, dtype: datetime64[ns, UTC]</span>\n </pre></div>\n </div>\n <p>Additionally, DataFrames with datetime columns that were parsed by <a class=\"reference internal\" href=\"../reference/api/pandas.read_sql_table.html#pandas.read_sql_table\" title=\"pandas.read_sql_table\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">read_sql_table()</span></code></a> and <a class=\"reference internal\" href=\"../reference/api/pandas.read_sql_query.html#pandas.read_sql_query\" title=\"pandas.read_sql_query\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">read_sql_query()</span></code></a> will also be localized to UTC only if the original SQL columns were timezone aware datetime columns.</p>\n </section>\n <section id=\"consistency-of-range-functions\">\n <span id=\"whatsnew-0210-api-consistency-of-range-functions\"></span><h3>Consistency of range functions<a class=\"headerlink\" href=\"#consistency-of-range-functions\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>In previous versions, there were some inconsistencies between the various range functions: <a class=\"reference internal\" href=\"../reference/api/pandas.date_range.html#pandas.date_range\" title=\"pandas.date_range\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">date_range()</span></code></a>, <a class=\"reference internal\" href=\"../reference/api/pandas.bdate_range.html#pandas.bdate_range\" title=\"pandas.bdate_range\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">bdate_range()</span></code></a>, <a class=\"reference internal\" href=\"../reference/api/pandas.period_range.html#pandas.period_range\" title=\"pandas.period_range\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">period_range()</span></code></a>, <a class=\"reference internal\" href=\"../reference/api/pandas.timedelta_range.html#pandas.timedelta_range\" title=\"pandas.timedelta_range\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">timedelta_range()</span></code></a>, and <a class=\"reference internal\" href=\"../reference/api/pandas.interval_range.html#pandas.interval_range\" title=\"pandas.interval_range\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">interval_range()</span></code></a>. (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/17471\">GH 17471</a>).</p>\n@@ -2041,14 +2063,16 @@\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [59]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"o\">.</span><span class=\"n\">map</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"n\">x</span> <span class=\"ow\">in</span> <span class=\"p\">[</span><span class=\"s1\">&#39;bar&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;baz&#39;</span><span class=\"p\">])]</span>\n <span class=\"gh\">Out[59]: </span>\n <span class=\"go\">     A</span>\n <span class=\"go\">bar  2</span>\n <span class=\"go\">baz  3</span>\n+\n+<span class=\"go\">[2 rows x 1 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"series-argmax-and-series-argmin\">\n <span id=\"whatsnew-0210-deprecations-argmin-min\"></span><h3>Series.argmax and Series.argmin<a class=\"headerlink\" href=\"#series-argmax-and-series-argmin\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>The behavior of <a class=\"reference internal\" href=\"../reference/api/pandas.Series.argmax.html#pandas.Series.argmax\" title=\"pandas.Series.argmax\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">Series.argmax()</span></code></a> and <a class=\"reference internal\" href=\"../reference/api/pandas.Series.argmin.html#pandas.Series.argmin\" title=\"pandas.Series.argmin\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">Series.argmin()</span></code></a> have been deprecated in favor of <a class=\"reference internal\" href=\"../reference/api/pandas.Series.idxmax.html#pandas.Series.idxmax\" title=\"pandas.Series.idxmax\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">Series.idxmax()</span></code></a> and <a class=\"reference internal\" href=\"../reference/api/pandas.Series.idxmin.html#pandas.Series.idxmin\" title=\"pandas.Series.idxmin\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">Series.idxmin()</span></code></a>, respectively (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/16830\">GH 16830</a>).</p>\n <p>For compatibility with NumPy arrays, <code class=\"docutils literal notranslate\"><span class=\"pre\">pd.Series</span></code> implements <code class=\"docutils literal notranslate\"><span class=\"pre\">argmax</span></code> and\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -244,35 +244,35 @@\n    ...:\n \n In [2]: df.dtypes\n Out[2]:\n A     int64\n B    object\n C    object\n-dtype: object\n+Length: 3, dtype: object\n \n In [3]: df.infer_objects().dtypes\n Out[3]:\n A     int64\n B     int64\n C    object\n-dtype: object\n+Length: 3, dtype: object\n Note that column 'C' was not converted - only scalar numeric types will be\n converted to a new type. Other types of conversion should be accomplished using\n the _\bt_\bo_\b__\bn_\bu_\bm_\be_\br_\bi_\bc_\b(_\b) function (or _\bt_\bo_\b__\bd_\ba_\bt_\be_\bt_\bi_\bm_\be_\b(_\b), _\bt_\bo_\b__\bt_\bi_\bm_\be_\bd_\be_\bl_\bt_\ba_\b(_\b)).\n In [4]: df = df.infer_objects()\n \n In [5]: df['C'] = pd.to_numeric(df['C'], errors='coerce')\n \n In [6]: df.dtypes\n Out[6]:\n A    int64\n B    int64\n C    int64\n-dtype: object\n+Length: 3, dtype: object\n *\b**\b**\b**\b* I\bIm\bmp\bpr\bro\bov\bve\bed\bd w\bwa\bar\brn\bni\bin\bng\bgs\bs w\bwh\bhe\ben\bn a\bat\btt\bte\bem\bmp\bpt\bti\bin\bng\bg t\bto\bo c\bcr\bre\bea\bat\bte\be c\bco\bol\blu\bum\bmn\bns\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n New users are often puzzled by the relationship between column operations and\n attribute access on DataFrame instances (_\bG_\bH_\b _\b7_\b1_\b7_\b5). One specific instance of\n this confusion is attempting to create a new column by setting an attribute on\n the DataFrame:\n In [1]: df = pd.DataFrame({'one': [1., 2., 3.]})\n In [2]: df.two = [4, 5, 6]\n@@ -296,73 +296,91 @@\n \n In [8]: df\n Out[8]:\n    A  B  C  D\n 0  0  1  2  3\n 1  4  5  6  7\n \n+[2 rows x 4 columns]\n+\n In [9]: df.drop(['B', 'C'], axis=1)\n Out[9]:\n    A  D\n 0  0  3\n 1  4  7\n \n+[2 rows x 2 columns]\n+\n # the following is now equivalent\n In [10]: df.drop(columns=['B', 'C'])\n Out[10]:\n    A  D\n 0  0  3\n 1  4  7\n+\n+[2 rows x 2 columns]\n *\b**\b**\b**\b* M\bMe\bet\bth\bho\bod\bds\bs r\bre\ben\bna\bam\bme\be,\b, r\bre\bei\bin\bnd\bde\bex\bx n\bno\bow\bw a\bal\bls\bso\bo a\bac\bcc\bce\bep\bpt\bt a\bax\bxi\bis\bs k\bke\bey\byw\bwo\bor\brd\bd_\b?\b\u00b6 *\b**\b**\b**\b*\n The _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b(_\b) and _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bi_\bn_\bd_\be_\bx_\b(_\b) methods have gained the axis\n keyword to specify the axis to target with the operation (_\bG_\bH_\b _\b1_\b2_\b3_\b9_\b2).\n Here\u2019s rename:\n In [11]: df = pd.DataFrame({\"A\": [1, 2, 3], \"B\": [4, 5, 6]})\n \n In [12]: df.rename(str.lower, axis='columns')\n Out[12]:\n    a  b\n 0  1  4\n 1  2  5\n 2  3  6\n \n+[3 rows x 2 columns]\n+\n In [13]: df.rename(id, axis='index')\n Out[13]:\n           A  B\n 11739816  1  4\n 11739848  2  5\n 11739880  3  6\n+\n+[3 rows x 2 columns]\n And reindex:\n In [14]: df.reindex(['A', 'B', 'C'], axis='columns')\n Out[14]:\n    A  B   C\n 0  1  4 NaN\n 1  2  5 NaN\n 2  3  6 NaN\n \n+[3 rows x 3 columns]\n+\n In [15]: df.reindex([0, 1, 3], axis='index')\n Out[15]:\n      A    B\n 0  1.0  4.0\n 1  2.0  5.0\n 3  NaN  NaN\n+\n+[3 rows x 2 columns]\n The \u201cindex, columns\u201d style continues to work as before.\n In [16]: df.rename(index=id, columns=str.lower)\n Out[16]:\n           a  b\n 11739816  1  4\n 11739848  2  5\n 11739880  3  6\n \n+[3 rows x 2 columns]\n+\n In [17]: df.reindex(index=[0, 1, 3], columns=['A', 'B', 'C'])\n Out[17]:\n      A    B   C\n 0  1.0  4.0 NaN\n 1  2.0  5.0 NaN\n 3  NaN  NaN NaN\n+\n+[3 rows x 3 columns]\n We h\bhi\big\bgh\bhl\bly\by encourage using named arguments to avoid confusion when using either\n style.\n *\b**\b**\b**\b* C\bCa\bat\bte\beg\bgo\bor\bri\bic\bca\bal\blD\bDt\bty\byp\bpe\be f\bfo\bor\br s\bsp\bpe\bec\bci\bif\bfy\byi\bin\bng\bg c\bca\bat\bte\beg\bgo\bor\bri\bic\bca\bal\bls\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n pandas.api.types.CategoricalDtype has been added to the public API and expanded\n to include the categories and ordered attributes. A CategoricalDtype can be\n used to specify the set of categories and orderedness of an array, independent\n of the data. This can be useful for example, when converting string data to a\n@@ -376,15 +394,15 @@\n \n In [21]: s.astype(dtype)\n Out[21]:\n 0    a\n 1    b\n 2    c\n 3    a\n-dtype: category\n+Length: 4, dtype: category\n Categories (4, object): ['a' < 'b' < 'c' < 'd']\n One place that deserves special mention is in _\br_\be_\ba_\bd_\b__\bc_\bs_\bv_\b(_\b). Previously, with\n dtype={'col': 'category'}, the returned values and categories would always be\n strings.\n In [22]: data = 'A,B\\na,1\\nb,2\\nc,3'\n \n In [23]: pd.read_csv(StringIO(data), dtype={'B': 'category'}).B.cat.categories\n@@ -430,24 +448,28 @@\n    ....:\n \n In [29]: df.head(2)\n Out[29]:\n      Store    Product  Revenue  Quantity\n 0  Store_2  Product_2    32.09         7\n 1  Store_1  Product_3    14.20         1\n+\n+[2 rows x 4 columns]\n Now, to find prices per store/product, we can simply do:\n In [30]: (df.groupby(['Store', 'Product'])\n    ....:    .pipe(lambda grp: grp.Revenue.sum() / grp.Quantity.sum())\n    ....:    .unstack().round(2))\n    ....:\n Out[30]:\n Product  Product_1  Product_2  Product_3\n Store\n Store_1       6.73       6.72       7.14\n Store_2       7.59       6.98       7.23\n+\n+[2 rows x 3 columns]\n See the _\bd_\bo_\bc_\bu_\bm_\be_\bn_\bt_\ba_\bt_\bi_\bo_\bn for more.\n *\b**\b**\b**\b* C\bCa\bat\bte\beg\bgo\bor\bri\bic\bca\bal\bl.\b.r\bre\ben\bna\bam\bme\be_\b_c\bca\bat\bte\beg\bgo\bor\bri\bie\bes\bs a\bac\bcc\bce\bep\bpt\bts\bs a\ba d\bdi\bic\bct\bt-\b-l\bli\bik\bke\be_\b?\b\u00b6 *\b**\b**\b**\b*\n _\br_\be_\bn_\ba_\bm_\be_\b__\bc_\ba_\bt_\be_\bg_\bo_\br_\bi_\be_\bs_\b(_\b) now accepts a dict-like argument for new_categories. The\n previous categories are looked up in the dictionary\u2019s keys and replaced if\n found. The behavior of missing and extra keys is the same as in\n _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\be_\bn_\ba_\bm_\be_\b(_\b).\n In [31]: c = pd.Categorical(['a', 'a', 'b'])\n@@ -588,15 +610,15 @@\n In [35]: s = pd.Series([1, 2, 3])\n \n In [36]: s\n Out[36]:\n 0    1\n 1    2\n 2    3\n-dtype: int64\n+Length: 3, dtype: int64\n Previous behavior\n In [4]: s.loc[[1, 2, 3]]\n Out[4]:\n 1    2.0\n 2    3.0\n 3    NaN\n dtype: float64\n@@ -617,21 +639,21 @@\n The idiomatic way to achieve selecting potentially not-found elements is via\n .reindex()\n In [37]: s.reindex([1, 2, 3])\n Out[37]:\n 1    2.0\n 2    3.0\n 3    NaN\n-dtype: float64\n+Length: 3, dtype: float64\n Selection with all keys found is unchanged.\n In [38]: s.loc[[1, 2]]\n Out[38]:\n 1    2\n 2    3\n-dtype: int64\n+Length: 2, dtype: int64\n *\b**\b**\b**\b* N\bNA\bA n\bna\bam\bmi\bin\bng\bg c\bch\bha\ban\bng\bge\bes\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n In order to promote more consistency among the pandas API, we have added\n additional top-level functions _\bi_\bs_\bn_\ba_\b(_\b) and _\bn_\bo_\bt_\bn_\ba_\b(_\b) that are aliases for _\bi_\bs_\bn_\bu_\bl_\bl_\b(_\b)\n and _\bn_\bo_\bt_\bn_\bu_\bl_\bl_\b(_\b). The naming scheme is now more consistent with methods like\n .dropna() and .fillna(). Furthermore in all cases where .isnull() and .notnull\n () methods are defined, these have additional methods named .isna() and .notna\n (), these are included for classes Categorical, Index, Series, and DataFrame.\n@@ -648,15 +670,15 @@\n In [39]: s = pd.Series([1, 2, 3])\n \n In [40]: s\n Out[40]:\n 0    1\n 1    2\n 2    3\n-dtype: int64\n+Length: 3, dtype: int64\n Previously:\n In [2]: type(list(s)[0])\n Out[2]: numpy.int64\n New behavior:\n In [41]: type(list(s)[0])\n Out[41]: int\n Furthermore this will now correctly box the results of iteration for\n@@ -681,49 +703,49 @@\n In [45]: s = pd.Series([1, 2, 3], index=[False, True, False])\n \n In [46]: s\n Out[46]:\n False    1\n True     2\n False    3\n-dtype: int64\n+Length: 3, dtype: int64\n In [59]: s.loc[pd.Index([True, False, True])]\n Out[59]:\n True     2\n False    1\n False    3\n True     2\n dtype: int64\n Current behavior\n In [47]: s.loc[pd.Index([True, False, True])]\n Out[47]:\n False    1\n False    3\n-dtype: int64\n+Length: 2, dtype: int64\n Furthermore, previously if you had an index that was non-numeric (e.g.\n strings), then a boolean Index would raise a KeyError. This will now be treated\n as a boolean indexer.\n Previously behavior:\n In [48]: s = pd.Series([1, 2, 3], index=['a', 'b', 'c'])\n \n In [49]: s\n Out[49]:\n a    1\n b    2\n c    3\n-dtype: int64\n+Length: 3, dtype: int64\n In [39]: s.loc[pd.Index([True, False, True])]\n KeyError: \"None of [Index([True, False, True], dtype='object')] are in the\n [index]\"\n Current behavior\n In [50]: s.loc[pd.Index([True, False, True])]\n Out[50]:\n a    1\n c    3\n-dtype: int64\n+Length: 2, dtype: int64\n *\b**\b**\b**\b* P\bPe\ber\bri\bio\bod\bdI\bIn\bnd\bde\bex\bx r\bre\bes\bsa\bam\bmp\bpl\bli\bin\bng\bg_\b?\b\u00b6 *\b**\b**\b**\b*\n In previous versions of pandas, resampling a Series/DataFrame indexed by a\n PeriodIndex returned a DatetimeIndex in some cases (_\bG_\bH_\b _\b1_\b2_\b8_\b8_\b4). Resampling to a\n multiplied frequency now returns a PeriodIndex (_\bG_\bH_\b _\b1_\b5_\b9_\b4_\b4). As a minor\n enhancement, resampling a PeriodIndex can now handle NaT values (_\bG_\bH_\b _\b1_\b3_\b2_\b2_\b4)\n Previous behavior:\n In [1]: pi = pd.period_range('2017-01', periods=12, freq='M')\n@@ -907,15 +929,15 @@\n dtype: datetime64[ns]\n New behavior\n In [56]: pd.to_datetime(s, utc=True)\n Out[56]:\n 0   2013-01-01 00:00:00+00:00\n 1   2013-01-01 00:00:00+00:00\n 2   2013-01-01 00:00:00+00:00\n-dtype: datetime64[ns, UTC]\n+Length: 3, dtype: datetime64[ns, UTC]\n Additionally, DataFrames with datetime columns that were parsed by\n _\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bt_\ba_\bb_\bl_\be_\b(_\b) and _\br_\be_\ba_\bd_\b__\bs_\bq_\bl_\b__\bq_\bu_\be_\br_\by_\b(_\b) will also be localized to UTC only if the\n original SQL columns were timezone aware datetime columns.\n *\b**\b**\b**\b* C\bCo\bon\bns\bsi\bis\bst\bte\ben\bnc\bcy\by o\bof\bf r\bra\ban\bng\bge\be f\bfu\bun\bnc\bct\bti\bio\bon\bns\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n In previous versions, there were some inconsistencies between the various range\n functions: _\bd_\ba_\bt_\be_\b__\br_\ba_\bn_\bg_\be_\b(_\b), _\bb_\bd_\ba_\bt_\be_\b__\br_\ba_\bn_\bg_\be_\b(_\b), _\bp_\be_\br_\bi_\bo_\bd_\b__\br_\ba_\bn_\bg_\be_\b(_\b), _\bt_\bi_\bm_\be_\bd_\be_\bl_\bt_\ba_\b__\br_\ba_\bn_\bg_\be_\b(_\b), and\n _\bi_\bn_\bt_\be_\br_\bv_\ba_\bl_\b__\br_\ba_\bn_\bg_\be_\b(_\b). (_\bG_\bH_\b _\b1_\b7_\b4_\b7_\b1).\n@@ -1062,14 +1084,16 @@\n bar  2\n baz  3\n In [59]: df.loc[df.index.map(lambda x: x in ['bar', 'baz'])]\n Out[59]:\n      A\n bar  2\n baz  3\n+\n+[2 rows x 1 columns]\n *\b**\b**\b**\b* S\bSe\ber\bri\bie\bes\bs.\b.a\bar\brg\bgm\bma\bax\bx a\ban\bnd\bd S\bSe\ber\bri\bie\bes\bs.\b.a\bar\brg\bgm\bmi\bin\bn_\b?\b\u00b6 *\b**\b**\b**\b*\n The behavior of _\bS_\be_\br_\bi_\be_\bs_\b._\ba_\br_\bg_\bm_\ba_\bx_\b(_\b) and _\bS_\be_\br_\bi_\be_\bs_\b._\ba_\br_\bg_\bm_\bi_\bn_\b(_\b) have been deprecated in\n favor of _\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bd_\bx_\bm_\ba_\bx_\b(_\b) and _\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bd_\bx_\bm_\bi_\bn_\b(_\b), respectively (_\bG_\bH_\b _\b1_\b6_\b8_\b3_\b0).\n For compatibility with NumPy arrays, pd.Series implements argmax and argmin.\n Since pandas 0.13.0, argmax has been an alias for _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bd_\bx_\bm_\ba_\bx_\b(_\b), and\n argmin has been an alias for _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bS_\be_\br_\bi_\be_\bs_\b._\bi_\bd_\bx_\bm_\bi_\bn_\b(_\b). They return the l\bla\bab\bbe\bel\bl of\n the maximum or minimum, rather than the p\bpo\bos\bsi\bit\bti\bio\bon\bn.\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.22.0.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.22.0.html", "unified_diff": "@@ -867,24 +867,24 @@\n <p><em>pandas 0.22</em></p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [8]: </span><span class=\"n\">grouper</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Categorical</span><span class=\"p\">([</span><span class=\"s2\">&quot;a&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;a&quot;</span><span class=\"p\">],</span> <span class=\"n\">categories</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s2\">&quot;a&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;b&quot;</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [9]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">])</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"n\">grouper</span><span class=\"p\">,</span> <span class=\"n\">observed</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[9]: </span>\n <span class=\"go\">a    3</span>\n <span class=\"go\">b    0</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 2, dtype: int64</span>\n </pre></div>\n </div>\n <p>To restore the 0.21 behavior of returning <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code> for unobserved groups,\n use <code class=\"docutils literal notranslate\"><span class=\"pre\">min_count&gt;=1</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [10]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">])</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"n\">grouper</span><span class=\"p\">,</span> <span class=\"n\">observed</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">(</span><span class=\"n\">min_count</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[10]: </span>\n <span class=\"go\">a    3.0</span>\n <span class=\"go\">b    NaN</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 2, dtype: float64</span>\n </pre></div>\n </div>\n </section>\n <section id=\"resample\">\n <h3>Resample<a class=\"headerlink\" href=\"#resample\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>The sum and product of all-<em>NA</em> bins has changed from <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code> to <code class=\"docutils literal notranslate\"><span class=\"pre\">0</span></code> for\n sum and <code class=\"docutils literal notranslate\"><span class=\"pre\">1</span></code> for product.</p>\n@@ -909,23 +909,23 @@\n <p><em>pandas 0.22.0</em></p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [11]: </span><span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">nan</span><span class=\"p\">,</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">nan</span><span class=\"p\">],</span> <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">date_range</span><span class=\"p\">(</span><span class=\"s2\">&quot;2017&quot;</span><span class=\"p\">,</span> <span class=\"n\">periods</span><span class=\"o\">=</span><span class=\"mi\">4</span><span class=\"p\">))</span>\n \n <span class=\"gp\">In [12]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">resample</span><span class=\"p\">(</span><span class=\"s2\">&quot;2d&quot;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[12]: </span>\n <span class=\"go\">2017-01-01    2.0</span>\n <span class=\"go\">2017-01-03    0.0</span>\n-<span class=\"go\">Freq: 2D, dtype: float64</span>\n+<span class=\"go\">Freq: 2D, Length: 2, dtype: float64</span>\n </pre></div>\n </div>\n <p>To restore the 0.21 behavior of returning <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code>, use <code class=\"docutils literal notranslate\"><span class=\"pre\">min_count&gt;=1</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [13]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">resample</span><span class=\"p\">(</span><span class=\"s2\">&quot;2d&quot;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">(</span><span class=\"n\">min_count</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[13]: </span>\n <span class=\"go\">2017-01-01    2.0</span>\n <span class=\"go\">2017-01-03    NaN</span>\n-<span class=\"go\">Freq: 2D, dtype: float64</span>\n+<span class=\"go\">Freq: 2D, Length: 2, dtype: float64</span>\n </pre></div>\n </div>\n <p>In particular, upsampling and taking the sum or product is affected, as\n upsampling introduces missing values even if the original series was\n entirely valid.</p>\n <p><em>pandas 0.21.x</em></p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [14]: </span><span class=\"n\">idx</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DatetimeIndex</span><span class=\"p\">([</span><span class=\"s1\">&#39;2017-01-01&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;2017-01-02&#39;</span><span class=\"p\">])</span>\n@@ -978,15 +978,15 @@\n <p><em>pandas 0.22.0</em></p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [14]: </span><span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">nan</span><span class=\"p\">,</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">nan</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [15]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">rolling</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">min_periods</span><span class=\"o\">=</span><span class=\"mi\">0</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[15]: </span>\n <span class=\"go\">0    0.0</span>\n <span class=\"go\">1    0.0</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 2, dtype: float64</span>\n </pre></div>\n </div>\n <p>The default behavior of <code class=\"docutils literal notranslate\"><span class=\"pre\">min_periods=None</span></code>, implying that <code class=\"docutils literal notranslate\"><span class=\"pre\">min_periods</span></code>\n equals the window size, is unchanged.</p>\n </section>\n </section>\n <section id=\"compatibility\">\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -183,22 +183,22 @@\n p\bpa\ban\bnd\bda\bas\bs 0\b0.\b.2\b22\b2\n In [8]: grouper = pd.Categorical([\"a\", \"a\"], categories=[\"a\", \"b\"])\n \n In [9]: pd.Series([1, 2]).groupby(grouper, observed=False).sum()\n Out[9]:\n a    3\n b    0\n-dtype: int64\n+Length: 2, dtype: int64\n To restore the 0.21 behavior of returning NaN for unobserved groups, use\n min_count>=1.\n In [10]: pd.Series([1, 2]).groupby(grouper, observed=False).sum(min_count=1)\n Out[10]:\n a    3.0\n b    NaN\n-dtype: float64\n+Length: 2, dtype: float64\n *\b**\b**\b**\b* R\bRe\bes\bsa\bam\bmp\bpl\ble\be_\b?\b\u00b6 *\b**\b**\b**\b*\n The sum and product of all-N\bNA\bA bins has changed from NaN to 0 for sum and 1 for\n product.\n p\bpa\ban\bnd\bda\bas\bs 0\b0.\b.2\b21\b1.\b.x\bx\n In [11]: s = pd.Series([1, 1, np.nan, np.nan],\n    ....:               index=pd.date_range('2017', periods=4))\n    ....: s\n@@ -218,21 +218,21 @@\n In [11]: s = pd.Series([1, 1, np.nan, np.nan], index=pd.date_range(\"2017\",\n periods=4))\n \n In [12]: s.resample(\"2d\").sum()\n Out[12]:\n 2017-01-01    2.0\n 2017-01-03    0.0\n-Freq: 2D, dtype: float64\n+Freq: 2D, Length: 2, dtype: float64\n To restore the 0.21 behavior of returning NaN, use min_count>=1.\n In [13]: s.resample(\"2d\").sum(min_count=1)\n Out[13]:\n 2017-01-01    2.0\n 2017-01-03    NaN\n-Freq: 2D, dtype: float64\n+Freq: 2D, Length: 2, dtype: float64\n In particular, upsampling and taking the sum or product is affected, as\n upsampling introduces missing values even if the original series was entirely\n valid.\n p\bpa\ban\bnd\bda\bas\bs 0\b0.\b.2\b21\b1.\b.x\bx\n In [14]: idx = pd.DatetimeIndex(['2017-01-01', '2017-01-02'])\n \n In [15]: pd.Series([1, 2], index=idx).resample('12H').sum()\n@@ -272,15 +272,15 @@\n p\bpa\ban\bnd\bda\bas\bs 0\b0.\b.2\b22\b2.\b.0\b0\n In [14]: s = pd.Series([np.nan, np.nan])\n \n In [15]: s.rolling(2, min_periods=0).sum()\n Out[15]:\n 0    0.0\n 1    0.0\n-dtype: float64\n+Length: 2, dtype: float64\n The default behavior of min_periods=None, implying that min_periods equals the\n window size, is unchanged.\n *\b**\b**\b**\b**\b* C\bCo\bom\bmp\bpa\bat\bti\bib\bbi\bil\bli\bit\bty\by_\b?\b\u00b6 *\b**\b**\b**\b**\b*\n If you maintain a library that should work across pandas versions, it may be\n easiest to exclude pandas 0.21 from your requirements. Otherwise, all your sum\n () calls would need to check if the Series is empty before summing.\n With setuptools, in your setup.py use:\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.23.0.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.23.0.html", "unified_diff": "@@ -1210,42 +1210,46 @@\n <span class=\"go\">     foo bar        baz qux</span>\n <span class=\"go\">idx                        </span>\n <span class=\"go\">0      1   a 2018-01-01   a</span>\n <span class=\"go\">1      2   b 2018-01-02   b</span>\n <span class=\"go\">2      3   c 2018-01-03   c</span>\n <span class=\"go\">3      4   d 2018-01-04   c</span>\n \n+<span class=\"go\">[4 rows x 4 columns]</span>\n+\n <span class=\"gp\">In [3]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[3]: </span>\n <span class=\"go\">foo             int64</span>\n <span class=\"go\">bar            object</span>\n <span class=\"go\">baz    datetime64[ns]</span>\n <span class=\"go\">qux          category</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 4, dtype: object</span>\n \n <span class=\"gp\">In [4]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">to_json</span><span class=\"p\">(</span><span class=\"s1\">&#39;test.json&#39;</span><span class=\"p\">,</span> <span class=\"n\">orient</span><span class=\"o\">=</span><span class=\"s1\">&#39;table&#39;</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [5]: </span><span class=\"n\">new_df</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_json</span><span class=\"p\">(</span><span class=\"s1\">&#39;test.json&#39;</span><span class=\"p\">,</span> <span class=\"n\">orient</span><span class=\"o\">=</span><span class=\"s1\">&#39;table&#39;</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [6]: </span><span class=\"n\">new_df</span>\n <span class=\"gh\">Out[6]: </span>\n <span class=\"go\">     foo bar        baz qux</span>\n <span class=\"go\">idx                        </span>\n <span class=\"go\">0      1   a 2018-01-01   a</span>\n <span class=\"go\">1      2   b 2018-01-02   b</span>\n <span class=\"go\">2      3   c 2018-01-03   c</span>\n <span class=\"go\">3      4   d 2018-01-04   c</span>\n \n+<span class=\"go\">[4 rows x 4 columns]</span>\n+\n <span class=\"gp\">In [7]: </span><span class=\"n\">new_df</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[7]: </span>\n <span class=\"go\">foo             int64</span>\n <span class=\"go\">bar            object</span>\n <span class=\"go\">baz    datetime64[ns]</span>\n <span class=\"go\">qux          category</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 4, dtype: object</span>\n </pre></div>\n </div>\n <p>Please note that the string <code class=\"docutils literal notranslate\"><span class=\"pre\">index</span></code> is not supported with the round trip format, as it is used by default in <code class=\"docutils literal notranslate\"><span class=\"pre\">write_json</span></code> to indicate a missing index name.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [8]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"o\">.</span><span class=\"n\">name</span> <span class=\"o\">=</span> <span class=\"s1\">&#39;index&#39;</span>\n \n <span class=\"gp\">In [9]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">to_json</span><span class=\"p\">(</span><span class=\"s1\">&#39;test.json&#39;</span><span class=\"p\">,</span> <span class=\"n\">orient</span><span class=\"o\">=</span><span class=\"s1\">&#39;table&#39;</span><span class=\"p\">)</span>\n \n@@ -1255,21 +1259,23 @@\n <span class=\"gh\">Out[11]: </span>\n <span class=\"go\">   foo bar        baz qux</span>\n <span class=\"go\">0    1   a 2018-01-01   a</span>\n <span class=\"go\">1    2   b 2018-01-02   b</span>\n <span class=\"go\">2    3   c 2018-01-03   c</span>\n <span class=\"go\">3    4   d 2018-01-04   c</span>\n \n+<span class=\"go\">[4 rows x 4 columns]</span>\n+\n <span class=\"gp\">In [12]: </span><span class=\"n\">new_df</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[12]: </span>\n <span class=\"go\">foo             int64</span>\n <span class=\"go\">bar            object</span>\n <span class=\"go\">baz    datetime64[ns]</span>\n <span class=\"go\">qux          category</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 4, dtype: object</span>\n </pre></div>\n </div>\n </section>\n <section id=\"method-assign-accepts-dependent-arguments\">\n <span id=\"whatsnew-0230-enhancements-assign-dependent\"></span><h3>Method <code class=\"docutils literal notranslate\"><span class=\"pre\">.assign()</span></code> accepts dependent arguments<a class=\"headerlink\" href=\"#method-assign-accepts-dependent-arguments\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>The <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.assign.html#pandas.DataFrame.assign\" title=\"pandas.DataFrame.assign\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">DataFrame.assign()</span></code></a> now accepts dependent keyword arguments for python version later than 3.6 (see also <a class=\"reference external\" href=\"https://www.python.org/dev/peps/pep-0468/\">PEP 468</a>). Later keyword arguments may now refer to earlier ones if the argument is a callable. See the\n <a class=\"reference internal\" href=\"../user_guide/dsintro.html#dsintro-chained-assignment\"><span class=\"std std-ref\">documentation here</span></a> (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/14207\">GH 14207</a>)</p>\n@@ -1278,20 +1284,24 @@\n <span class=\"gp\">In [14]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[14]: </span>\n <span class=\"go\">   A</span>\n <span class=\"go\">0  1</span>\n <span class=\"go\">1  2</span>\n <span class=\"go\">2  3</span>\n \n+<span class=\"go\">[3 rows x 1 columns]</span>\n+\n <span class=\"gp\">In [15]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">assign</span><span class=\"p\">(</span><span class=\"n\">B</span><span class=\"o\">=</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">A</span><span class=\"p\">,</span> <span class=\"n\">C</span><span class=\"o\">=</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"n\">x</span><span class=\"p\">[</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">x</span><span class=\"p\">[</span><span class=\"s1\">&#39;B&#39;</span><span class=\"p\">])</span>\n <span class=\"gh\">Out[15]: </span>\n <span class=\"go\">   A  B  C</span>\n <span class=\"go\">0  1  1  2</span>\n <span class=\"go\">1  2  2  4</span>\n <span class=\"go\">2  3  3  6</span>\n+\n+<span class=\"go\">[3 rows x 3 columns]</span>\n </pre></div>\n </div>\n <div class=\"admonition warning\">\n <p class=\"admonition-title\">Warning</p>\n <p>This may subtly change the behavior of your code when you\u2019re\n using <code class=\"docutils literal notranslate\"><span class=\"pre\">.assign()</span></code> to update an existing column. Previously, callables\n referring to other variables being updated would get the \u201cold\u201d values</p>\n@@ -1309,14 +1319,16 @@\n <p>New behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [16]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">assign</span><span class=\"p\">(</span><span class=\"n\">A</span><span class=\"o\">=</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">A</span> <span class=\"o\">+</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">C</span><span class=\"o\">=</span><span class=\"k\">lambda</span> <span class=\"n\">df</span><span class=\"p\">:</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">A</span> <span class=\"o\">*</span> <span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[16]: </span>\n <span class=\"go\">   A  C</span>\n <span class=\"go\">0  2 -2</span>\n <span class=\"go\">1  3 -3</span>\n <span class=\"go\">2  4 -4</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n </div>\n </section>\n <section id=\"merging-on-a-combination-of-columns-and-index-levels\">\n <span id=\"whatsnew-0230-enhancements-merge-on-columns-and-levels\"></span><h3>Merging on a combination of columns and index levels<a class=\"headerlink\" href=\"#merging-on-a-combination-of-columns-and-index-levels\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>Strings passed to <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.merge.html#pandas.DataFrame.merge\" title=\"pandas.DataFrame.merge\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">DataFrame.merge()</span></code></a> as the <code class=\"docutils literal notranslate\"><span class=\"pre\">on</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">left_on</span></code>, and <code class=\"docutils literal notranslate\"><span class=\"pre\">right_on</span></code>\n@@ -1344,14 +1356,16 @@\n <span class=\"gp\">In [21]: </span><span class=\"n\">left</span><span class=\"o\">.</span><span class=\"n\">merge</span><span class=\"p\">(</span><span class=\"n\">right</span><span class=\"p\">,</span> <span class=\"n\">on</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;key1&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;key2&#39;</span><span class=\"p\">])</span>\n <span class=\"gh\">Out[21]: </span>\n <span class=\"go\">       A   B key2   C   D</span>\n <span class=\"go\">key1                     </span>\n <span class=\"go\">K0    A0  B0   K0  C0  D0</span>\n <span class=\"go\">K1    A2  B2   K0  C1  D1</span>\n <span class=\"go\">K2    A3  B3   K1  C3  D3</span>\n+\n+<span class=\"go\">[3 rows x 5 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"sorting-by-a-combination-of-columns-and-index-levels\">\n <span id=\"whatsnew-0230-enhancements-sort-by-columns-and-levels\"></span><h3>Sorting by a combination of columns and index levels<a class=\"headerlink\" href=\"#sorting-by-a-combination-of-columns-and-index-levels\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>Strings passed to <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.sort_values.html#pandas.DataFrame.sort_values\" title=\"pandas.DataFrame.sort_values\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">DataFrame.sort_values()</span></code></a> as the <code class=\"docutils literal notranslate\"><span class=\"pre\">by</span></code> parameter may\n now refer to either column names or index level names.  This enables sorting\n@@ -1377,25 +1391,29 @@\n <span class=\"go\">a     1       6</span>\n <span class=\"go\">      2       5</span>\n <span class=\"go\">      2       4</span>\n <span class=\"go\">b     2       3</span>\n <span class=\"go\">      1       2</span>\n <span class=\"go\">      1       1</span>\n \n+<span class=\"go\">[6 rows x 1 columns]</span>\n+\n <span class=\"go\"># Sort by &#39;second&#39; (index) and &#39;A&#39; (column)</span>\n <span class=\"gp\">In [26]: </span><span class=\"n\">df_multi</span><span class=\"o\">.</span><span class=\"n\">sort_values</span><span class=\"p\">(</span><span class=\"n\">by</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;second&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;A&#39;</span><span class=\"p\">])</span>\n <span class=\"gh\">Out[26]: </span>\n <span class=\"go\">              A</span>\n <span class=\"go\">first second   </span>\n <span class=\"go\">b     1       1</span>\n <span class=\"go\">      1       2</span>\n <span class=\"go\">a     1       6</span>\n <span class=\"go\">b     2       3</span>\n <span class=\"go\">a     2       4</span>\n <span class=\"go\">      2       5</span>\n+\n+<span class=\"go\">[6 rows x 1 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"extending-pandas-with-custom-types-experimental\">\n <span id=\"whatsnew-023-enhancements-extension\"></span><h3>Extending pandas with custom types (experimental)<a class=\"headerlink\" href=\"#extending-pandas-with-custom-types-experimental\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>pandas now supports storing array-like objects that aren\u2019t necessarily 1-D NumPy\n arrays as columns in a DataFrame or values in a Series. This allows third-party\n@@ -1461,14 +1479,16 @@\n <span class=\"gp\">In [31]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[31]: </span>\n <span class=\"go\">   A  B  values    C</span>\n <span class=\"go\">0  a  c       1  foo</span>\n <span class=\"go\">1  a  d       2  bar</span>\n <span class=\"go\">2  b  c       3  foo</span>\n <span class=\"go\">3  b  d       4  bar</span>\n+\n+<span class=\"go\">[4 rows x 4 columns]</span>\n </pre></div>\n </div>\n <p>To show all values, the previous behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [32]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">([</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;B&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;C&#39;</span><span class=\"p\">],</span> <span class=\"n\">observed</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">count</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[32]: </span>\n <span class=\"go\">         values</span>\n <span class=\"go\">A B C          </span>\n@@ -1492,14 +1512,16 @@\n <span class=\"gh\">Out[33]: </span>\n <span class=\"go\">         values</span>\n <span class=\"go\">A B C          </span>\n <span class=\"go\">a c foo       1</span>\n <span class=\"go\">  d bar       1</span>\n <span class=\"go\">b c foo       1</span>\n <span class=\"go\">  d bar       1</span>\n+\n+<span class=\"go\">[4 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p>For pivoting operations, this behavior is <em>already</em> controlled by the <code class=\"docutils literal notranslate\"><span class=\"pre\">dropna</span></code> keyword:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [34]: </span><span class=\"n\">cat1</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Categorical</span><span class=\"p\">([</span><span class=\"s2\">&quot;a&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;a&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;b&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;b&quot;</span><span class=\"p\">],</span>\n <span class=\"gp\">   ....: </span>                      <span class=\"n\">categories</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s2\">&quot;a&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;b&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;z&quot;</span><span class=\"p\">],</span> <span class=\"n\">ordered</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n <span class=\"gp\">   ....: </span>\n \n@@ -1512,14 +1534,16 @@\n <span class=\"gp\">In [37]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[37]: </span>\n <span class=\"go\">   A  B  values</span>\n <span class=\"go\">0  a  c       1</span>\n <span class=\"go\">1  a  d       2</span>\n <span class=\"go\">2  b  c       3</span>\n <span class=\"go\">3  b  d       4</span>\n+\n+<span class=\"go\">[4 rows x 3 columns]</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [1]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">pivot_table</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">values</span><span class=\"o\">=</span><span class=\"s1\">&#39;values&#39;</span><span class=\"p\">,</span> <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;B&#39;</span><span class=\"p\">],</span> <span class=\"n\">dropna</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n \n <span class=\"gh\">Out[1]:</span>\n <span class=\"go\">     values</span>\n <span class=\"go\">A B</span>\n@@ -1557,37 +1581,37 @@\n <span class=\"gp\">In [39]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[39]: </span>\n <span class=\"go\">1    0</span>\n <span class=\"go\">2    1</span>\n <span class=\"go\">3    2</span>\n <span class=\"go\">4    3</span>\n <span class=\"go\">5    4</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 5, dtype: int64</span>\n </pre></div>\n </div>\n <p>Pass a <code class=\"docutils literal notranslate\"><span class=\"pre\">Series</span></code>:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [40]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">rolling</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">min_periods</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"n\">x</span><span class=\"o\">.</span><span class=\"n\">iloc</span><span class=\"p\">[</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">],</span> <span class=\"n\">raw</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[40]: </span>\n <span class=\"go\">1    0.0</span>\n <span class=\"go\">2    1.0</span>\n <span class=\"go\">3    2.0</span>\n <span class=\"go\">4    3.0</span>\n <span class=\"go\">5    4.0</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 5, dtype: float64</span>\n </pre></div>\n </div>\n <p>Mimic the original behavior of passing a ndarray:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [41]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">rolling</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"n\">min_periods</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"n\">x</span><span class=\"p\">[</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">],</span> <span class=\"n\">raw</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[41]: </span>\n <span class=\"go\">1    0.0</span>\n <span class=\"go\">2    1.0</span>\n <span class=\"go\">3    2.0</span>\n <span class=\"go\">4    3.0</span>\n <span class=\"go\">5    4.0</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 5, dtype: float64</span>\n </pre></div>\n </div>\n </section>\n <section id=\"dataframe-interpolate-has-gained-the-limit-area-kwarg\">\n <span id=\"whatsnew-0210-enhancements-limit-area\"></span><h3><code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame.interpolate</span></code> has gained the <code class=\"docutils literal notranslate\"><span class=\"pre\">limit_area</span></code> kwarg<a class=\"headerlink\" href=\"#dataframe-interpolate-has-gained-the-limit-area-kwarg\" title=\"Link to this heading\">\u00b6</a></h3>\n <p><a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.interpolate.html#pandas.DataFrame.interpolate\" title=\"pandas.DataFrame.interpolate\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">DataFrame.interpolate()</span></code></a> has gained a <code class=\"docutils literal notranslate\"><span class=\"pre\">limit_area</span></code> parameter to allow further control of which <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code> s are replaced.\n Use <code class=\"docutils literal notranslate\"><span class=\"pre\">limit_area='inside'</span></code> to fill only NaNs surrounded by valid values or use <code class=\"docutils literal notranslate\"><span class=\"pre\">limit_area='outside'</span></code> to fill only <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code> s\n@@ -1603,83 +1627,83 @@\n <span class=\"go\">2     5.0</span>\n <span class=\"go\">3     NaN</span>\n <span class=\"go\">4     NaN</span>\n <span class=\"go\">5     NaN</span>\n <span class=\"go\">6    13.0</span>\n <span class=\"go\">7     NaN</span>\n <span class=\"go\">8     NaN</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 9, dtype: float64</span>\n </pre></div>\n </div>\n <p>Fill one consecutive inside value in both directions</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [44]: </span><span class=\"n\">ser</span><span class=\"o\">.</span><span class=\"n\">interpolate</span><span class=\"p\">(</span><span class=\"n\">limit_direction</span><span class=\"o\">=</span><span class=\"s1\">&#39;both&#39;</span><span class=\"p\">,</span> <span class=\"n\">limit_area</span><span class=\"o\">=</span><span class=\"s1\">&#39;inside&#39;</span><span class=\"p\">,</span> <span class=\"n\">limit</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[44]: </span>\n <span class=\"go\">0     NaN</span>\n <span class=\"go\">1     NaN</span>\n <span class=\"go\">2     5.0</span>\n <span class=\"go\">3     7.0</span>\n <span class=\"go\">4     NaN</span>\n <span class=\"go\">5    11.0</span>\n <span class=\"go\">6    13.0</span>\n <span class=\"go\">7     NaN</span>\n <span class=\"go\">8     NaN</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 9, dtype: float64</span>\n </pre></div>\n </div>\n <p>Fill all consecutive outside values backward</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [45]: </span><span class=\"n\">ser</span><span class=\"o\">.</span><span class=\"n\">interpolate</span><span class=\"p\">(</span><span class=\"n\">limit_direction</span><span class=\"o\">=</span><span class=\"s1\">&#39;backward&#39;</span><span class=\"p\">,</span> <span class=\"n\">limit_area</span><span class=\"o\">=</span><span class=\"s1\">&#39;outside&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[45]: </span>\n <span class=\"go\">0     5.0</span>\n <span class=\"go\">1     5.0</span>\n <span class=\"go\">2     5.0</span>\n <span class=\"go\">3     NaN</span>\n <span class=\"go\">4     NaN</span>\n <span class=\"go\">5     NaN</span>\n <span class=\"go\">6    13.0</span>\n <span class=\"go\">7     NaN</span>\n <span class=\"go\">8     NaN</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 9, dtype: float64</span>\n </pre></div>\n </div>\n <p>Fill all consecutive outside values in both directions</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [46]: </span><span class=\"n\">ser</span><span class=\"o\">.</span><span class=\"n\">interpolate</span><span class=\"p\">(</span><span class=\"n\">limit_direction</span><span class=\"o\">=</span><span class=\"s1\">&#39;both&#39;</span><span class=\"p\">,</span> <span class=\"n\">limit_area</span><span class=\"o\">=</span><span class=\"s1\">&#39;outside&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[46]: </span>\n <span class=\"go\">0     5.0</span>\n <span class=\"go\">1     5.0</span>\n <span class=\"go\">2     5.0</span>\n <span class=\"go\">3     NaN</span>\n <span class=\"go\">4     NaN</span>\n <span class=\"go\">5     NaN</span>\n <span class=\"go\">6    13.0</span>\n <span class=\"go\">7    13.0</span>\n <span class=\"go\">8    13.0</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 9, dtype: float64</span>\n </pre></div>\n </div>\n </section>\n <section id=\"function-get-dummies-now-supports-dtype-argument\">\n <span id=\"whatsnew-0210-enhancements-get-dummies-dtype\"></span><h3>Function <code class=\"docutils literal notranslate\"><span class=\"pre\">get_dummies</span></code> now supports <code class=\"docutils literal notranslate\"><span class=\"pre\">dtype</span></code> argument<a class=\"headerlink\" href=\"#function-get-dummies-now-supports-dtype-argument\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>The <a class=\"reference internal\" href=\"../reference/api/pandas.get_dummies.html#pandas.get_dummies\" title=\"pandas.get_dummies\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">get_dummies()</span></code></a> now accepts a <code class=\"docutils literal notranslate\"><span class=\"pre\">dtype</span></code> argument, which specifies a dtype for the new columns. The default remains uint8. (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/18330\">GH 18330</a>)</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [47]: </span><span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">({</span><span class=\"s1\">&#39;a&#39;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">],</span> <span class=\"s1\">&#39;b&#39;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">3</span><span class=\"p\">,</span> <span class=\"mi\">4</span><span class=\"p\">],</span> <span class=\"s1\">&#39;c&#39;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">5</span><span class=\"p\">,</span> <span class=\"mi\">6</span><span class=\"p\">]})</span>\n \n <span class=\"gp\">In [48]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">get_dummies</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">columns</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;c&#39;</span><span class=\"p\">])</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[48]: </span>\n <span class=\"go\">a      int64</span>\n <span class=\"go\">b      int64</span>\n <span class=\"go\">c_5     bool</span>\n <span class=\"go\">c_6     bool</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 4, dtype: object</span>\n \n <span class=\"gp\">In [49]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">get_dummies</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">columns</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;c&#39;</span><span class=\"p\">],</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"nb\">bool</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[49]: </span>\n <span class=\"go\">a      int64</span>\n <span class=\"go\">b      int64</span>\n <span class=\"go\">c_5     bool</span>\n <span class=\"go\">c_6     bool</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 4, dtype: object</span>\n </pre></div>\n </div>\n </section>\n <section id=\"timedelta-mod-method\">\n <span id=\"whatsnew-0230-enhancements-timedelta-mod\"></span><h3>Timedelta mod method<a class=\"headerlink\" href=\"#timedelta-mod-method\" title=\"Link to this heading\">\u00b6</a></h3>\n <p><code class=\"docutils literal notranslate\"><span class=\"pre\">mod</span></code> (%) and <code class=\"docutils literal notranslate\"><span class=\"pre\">divmod</span></code> operations are now defined on <code class=\"docutils literal notranslate\"><span class=\"pre\">Timedelta</span></code> objects\n when operating with either timedelta-like or with numeric arguments.\n@@ -1699,15 +1723,15 @@\n <span class=\"gp\">In [53]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[53]: </span>\n <span class=\"go\">0   -inf</span>\n <span class=\"go\">1    0.0</span>\n <span class=\"go\">2    1.0</span>\n <span class=\"go\">3    NaN</span>\n <span class=\"go\">4    inf</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 5, dtype: float64</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [11]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">rank</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[11]:</span>\n <span class=\"go\">0    1.0</span>\n <span class=\"go\">1    2.0</span>\n@@ -1721,27 +1745,27 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [54]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">rank</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[54]: </span>\n <span class=\"go\">0    1.0</span>\n <span class=\"go\">1    2.0</span>\n <span class=\"go\">2    3.0</span>\n <span class=\"go\">3    NaN</span>\n <span class=\"go\">4    4.0</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 5, dtype: float64</span>\n </pre></div>\n </div>\n <p>Furthermore, previously if you rank <code class=\"docutils literal notranslate\"><span class=\"pre\">inf</span></code> or <code class=\"docutils literal notranslate\"><span class=\"pre\">-inf</span></code> values together with <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code> values, the calculation won\u2019t distinguish <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code> from infinity when using \u2018top\u2019 or \u2018bottom\u2019 argument.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [55]: </span><span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">nan</span><span class=\"p\">,</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">nan</span><span class=\"p\">,</span> <span class=\"o\">-</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">inf</span><span class=\"p\">,</span> <span class=\"o\">-</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">inf</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [56]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[56]: </span>\n <span class=\"go\">0    NaN</span>\n <span class=\"go\">1    NaN</span>\n <span class=\"go\">2   -inf</span>\n <span class=\"go\">3   -inf</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 4, dtype: float64</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [15]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">rank</span><span class=\"p\">(</span><span class=\"n\">na_option</span><span class=\"o\">=</span><span class=\"s1\">&#39;top&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[15]:</span>\n <span class=\"go\">0    2.5</span>\n <span class=\"go\">1    2.5</span>\n@@ -1753,15 +1777,15 @@\n <p>Current behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [57]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">rank</span><span class=\"p\">(</span><span class=\"n\">na_option</span><span class=\"o\">=</span><span class=\"s1\">&#39;top&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[57]: </span>\n <span class=\"go\">0    1.5</span>\n <span class=\"go\">1    1.5</span>\n <span class=\"go\">2    3.5</span>\n <span class=\"go\">3    3.5</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 4, dtype: float64</span>\n </pre></div>\n </div>\n <p>These bugs were squashed:</p>\n <ul class=\"simple\">\n <li><p>Bug in <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.rank.html#pandas.DataFrame.rank\" title=\"pandas.DataFrame.rank\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">DataFrame.rank()</span></code></a> and <a class=\"reference internal\" href=\"../reference/api/pandas.Series.rank.html#pandas.Series.rank\" title=\"pandas.Series.rank\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">Series.rank()</span></code></a> when <code class=\"docutils literal notranslate\"><span class=\"pre\">method='dense'</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">pct=True</span></code> in which percentile ranks were not being used with the number of distinct observations (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/15630\">GH 15630</a>)</p></li>\n <li><p>Bug in <a class=\"reference internal\" href=\"../reference/api/pandas.Series.rank.html#pandas.Series.rank\" title=\"pandas.Series.rank\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">Series.rank()</span></code></a> and <a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.rank.html#pandas.DataFrame.rank\" title=\"pandas.DataFrame.rank\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">DataFrame.rank()</span></code></a> when <code class=\"docutils literal notranslate\"><span class=\"pre\">ascending='False'</span></code> failed to return correct ranks for infinity if <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code> were present (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/19538\">GH 19538</a>)</p></li>\n <li><p>Bug in <code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">DataFrameGroupBy.rank()</span></code> where ranks were incorrect when both infinity and <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code> were present (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/20561\">GH 20561</a>)</p></li>\n@@ -1778,23 +1802,23 @@\n \n <span class=\"gp\">In [60]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">str</span><span class=\"o\">.</span><span class=\"n\">cat</span><span class=\"p\">(</span><span class=\"n\">t</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[60]: </span>\n <span class=\"go\">0    NaN</span>\n <span class=\"go\">1     bb</span>\n <span class=\"go\">2     cc</span>\n <span class=\"go\">3     dd</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 4, dtype: object</span>\n \n <span class=\"gp\">In [61]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">str</span><span class=\"o\">.</span><span class=\"n\">cat</span><span class=\"p\">(</span><span class=\"n\">t</span><span class=\"p\">,</span> <span class=\"n\">join</span><span class=\"o\">=</span><span class=\"s1\">&#39;left&#39;</span><span class=\"p\">,</span> <span class=\"n\">na_rep</span><span class=\"o\">=</span><span class=\"s1\">&#39;-&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[61]: </span>\n <span class=\"go\">0    a-</span>\n <span class=\"go\">1    bb</span>\n <span class=\"go\">2    cc</span>\n <span class=\"go\">3    dd</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 4, dtype: object</span>\n </pre></div>\n </div>\n <p>Furthermore, <a class=\"reference internal\" href=\"../reference/api/pandas.Series.str.cat.html#pandas.Series.str.cat\" title=\"pandas.Series.str.cat\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">Series.str.cat()</span></code></a> now works for <code class=\"docutils literal notranslate\"><span class=\"pre\">CategoricalIndex</span></code> as well (previously raised a <code class=\"docutils literal notranslate\"><span class=\"pre\">ValueError</span></code>; see <a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/20842\">GH 20842</a>).</p>\n </section>\n <section id=\"dataframe-astype-performs-column-wise-conversion-to-categorical\">\n <span id=\"whatsnew-0230-enhancements-astype-category\"></span><h3><code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame.astype</span></code> performs column-wise conversion to <code class=\"docutils literal notranslate\"><span class=\"pre\">Categorical</span></code><a class=\"headerlink\" href=\"#dataframe-astype-performs-column-wise-conversion-to-categorical\" title=\"Link to this heading\">\u00b6</a></h3>\n <p><a class=\"reference internal\" href=\"../reference/api/pandas.DataFrame.astype.html#pandas.DataFrame.astype\" title=\"pandas.DataFrame.astype\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">DataFrame.astype()</span></code></a> can now perform column-wise conversion to <code class=\"docutils literal notranslate\"><span class=\"pre\">Categorical</span></code> by supplying the string <code class=\"docutils literal notranslate\"><span class=\"pre\">'category'</span></code> or\n@@ -1955,15 +1979,15 @@\n <span class=\"gp\">   ....: </span>           <span class=\"s1\">&#39;Net result&#39;</span><span class=\"p\">:</span> <span class=\"mi\">300</span><span class=\"p\">})</span>\n <span class=\"gp\">   ....: </span>\n <span class=\"gh\">Out[72]: </span>\n <span class=\"go\">Income        2000</span>\n <span class=\"go\">Expenses     -1500</span>\n <span class=\"go\">Taxes         -200</span>\n <span class=\"go\">Net result     300</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 4, dtype: int64</span>\n </pre></div>\n </div>\n <p>Notice that the Series is now ordered by insertion order. This new behavior is\n used for all relevant pandas types (<code class=\"docutils literal notranslate\"><span class=\"pre\">Series</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">SparseSeries</span></code>\n and <code class=\"docutils literal notranslate\"><span class=\"pre\">SparseDataFrame</span></code>).</p>\n <p>If you wish to retain the old behavior while using Python &gt;= 3.6, you can use\n <code class=\"docutils literal notranslate\"><span class=\"pre\">.sort_index()</span></code>:</p>\n@@ -1973,15 +1997,15 @@\n <span class=\"gp\">   ....: </span>           <span class=\"s1\">&#39;Net result&#39;</span><span class=\"p\">:</span> <span class=\"mi\">300</span><span class=\"p\">})</span><span class=\"o\">.</span><span class=\"n\">sort_index</span><span class=\"p\">()</span>\n <span class=\"gp\">   ....: </span>\n <span class=\"gh\">Out[73]: </span>\n <span class=\"go\">Expenses     -1500</span>\n <span class=\"go\">Income        2000</span>\n <span class=\"go\">Net result     300</span>\n <span class=\"go\">Taxes         -200</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 4, dtype: int64</span>\n </pre></div>\n </div>\n </section>\n <section id=\"deprecate-panel\">\n <span id=\"whatsnew-0230-api-breaking-deprecate-panel\"></span><h3>Deprecate Panel<a class=\"headerlink\" href=\"#deprecate-panel\" title=\"Link to this heading\">\u00b6</a></h3>\n <p><code class=\"docutils literal notranslate\"><span class=\"pre\">Panel</span></code> was deprecated in the 0.20.x release, showing as a <code class=\"docutils literal notranslate\"><span class=\"pre\">DeprecationWarning</span></code>. Using <code class=\"docutils literal notranslate\"><span class=\"pre\">Panel</span></code> will now show a <code class=\"docutils literal notranslate\"><span class=\"pre\">FutureWarning</span></code>. The recommended way to represent 3-D data are\n with a <code class=\"docutils literal notranslate\"><span class=\"pre\">MultiIndex</span></code> on a <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code> via the <code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">to_frame()</span></code> or with the <a class=\"reference external\" href=\"http://xarray.pydata.org/en/stable/\">xarray package</a>. pandas\n@@ -2068,14 +2092,16 @@\n <span class=\"go\">   A  B  C</span>\n <span class=\"go\">0  1  2  3</span>\n <span class=\"go\">1  1  2  3</span>\n <span class=\"go\">2  1  2  3</span>\n <span class=\"go\">3  1  2  3</span>\n <span class=\"go\">4  1  2  3</span>\n <span class=\"go\">5  1  2  3</span>\n+\n+<span class=\"go\">[6 rows x 3 columns]</span>\n </pre></div>\n </div>\n <p>Previous behavior: if the returned shape happened to match the length of original columns, this would return a <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code>.\n If the return shape did not match, a <code class=\"docutils literal notranslate\"><span class=\"pre\">Series</span></code> with lists was returned.</p>\n <div class=\"highlight-python notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"n\">In</span> <span class=\"p\">[</span><span class=\"mi\">3</span><span class=\"p\">]:</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">],</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"n\">Out</span><span class=\"p\">[</span><span class=\"mi\">3</span><span class=\"p\">]:</span>\n    <span class=\"n\">A</span>  <span class=\"n\">B</span>  <span class=\"n\">C</span>\n@@ -2102,63 +2128,69 @@\n <span class=\"gh\">Out[76]: </span>\n <span class=\"go\">0    [1, 2, 3]</span>\n <span class=\"go\">1    [1, 2, 3]</span>\n <span class=\"go\">2    [1, 2, 3]</span>\n <span class=\"go\">3    [1, 2, 3]</span>\n <span class=\"go\">4    [1, 2, 3]</span>\n <span class=\"go\">5    [1, 2, 3]</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 6, dtype: object</span>\n \n <span class=\"gp\">In [77]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">],</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[77]: </span>\n <span class=\"go\">0    [1, 2]</span>\n <span class=\"go\">1    [1, 2]</span>\n <span class=\"go\">2    [1, 2]</span>\n <span class=\"go\">3    [1, 2]</span>\n <span class=\"go\">4    [1, 2]</span>\n <span class=\"go\">5    [1, 2]</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 6, dtype: object</span>\n </pre></div>\n </div>\n <p>To have expanded columns, you can use <code class=\"docutils literal notranslate\"><span class=\"pre\">result_type='expand'</span></code></p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [78]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">],</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">result_type</span><span class=\"o\">=</span><span class=\"s1\">&#39;expand&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[78]: </span>\n <span class=\"go\">   0  1  2</span>\n <span class=\"go\">0  1  2  3</span>\n <span class=\"go\">1  1  2  3</span>\n <span class=\"go\">2  1  2  3</span>\n <span class=\"go\">3  1  2  3</span>\n <span class=\"go\">4  1  2  3</span>\n <span class=\"go\">5  1  2  3</span>\n+\n+<span class=\"go\">[6 rows x 3 columns]</span>\n </pre></div>\n </div>\n <p>To broadcast the result across the original columns (the old behaviour for\n list-likes of the correct length), you can use <code class=\"docutils literal notranslate\"><span class=\"pre\">result_type='broadcast'</span></code>.\n The shape must match the original columns.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [79]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">],</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">result_type</span><span class=\"o\">=</span><span class=\"s1\">&#39;broadcast&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[79]: </span>\n <span class=\"go\">   A  B  C</span>\n <span class=\"go\">0  1  2  3</span>\n <span class=\"go\">1  1  2  3</span>\n <span class=\"go\">2  1  2  3</span>\n <span class=\"go\">3  1  2  3</span>\n <span class=\"go\">4  1  2  3</span>\n <span class=\"go\">5  1  2  3</span>\n+\n+<span class=\"go\">[6 rows x 3 columns]</span>\n </pre></div>\n </div>\n <p>Returning a <code class=\"docutils literal notranslate\"><span class=\"pre\">Series</span></code> allows one to control the exact return structure and column names:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [80]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">],</span> <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;D&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;E&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;F&#39;</span><span class=\"p\">]),</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[80]: </span>\n <span class=\"go\">   D  E  F</span>\n <span class=\"go\">0  1  2  3</span>\n <span class=\"go\">1  1  2  3</span>\n <span class=\"go\">2  1  2  3</span>\n <span class=\"go\">3  1  2  3</span>\n <span class=\"go\">4  1  2  3</span>\n <span class=\"go\">5  1  2  3</span>\n+\n+<span class=\"go\">[6 rows x 3 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"concatenation-will-no-longer-sort\">\n <span id=\"whatsnew-0230-api-breaking-concat\"></span><h3>Concatenation will no longer sort<a class=\"headerlink\" href=\"#concatenation-will-no-longer-sort\" title=\"Link to this heading\">\u00b6</a></h3>\n <p>In a future version of pandas <a class=\"reference internal\" href=\"../reference/api/pandas.concat.html#pandas.concat\" title=\"pandas.concat\"><code class=\"xref py py-func docutils literal notranslate\"><span class=\"pre\">pandas.concat()</span></code></a> will no longer sort the non-concatenation axis when it is not already aligned.\n The current behavior is the same as the previous (sorting), but now a warning is issued when <code class=\"docutils literal notranslate\"><span class=\"pre\">sort</span></code> is not specified and the non-concatenation axis is not aligned (<a class=\"reference external\" href=\"https://github.com/pandas-dev/pandas/issues/4588\">GH 4588</a>).</p>\n@@ -2169,24 +2201,28 @@\n <span class=\"gp\">In [83]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">concat</span><span class=\"p\">([</span><span class=\"n\">df1</span><span class=\"p\">,</span> <span class=\"n\">df2</span><span class=\"p\">])</span>\n <span class=\"gh\">Out[83]: </span>\n <span class=\"go\">     b  a</span>\n <span class=\"go\">0  1.0  1</span>\n <span class=\"go\">1  2.0  2</span>\n <span class=\"go\">0  NaN  4</span>\n <span class=\"go\">1  NaN  5</span>\n+\n+<span class=\"go\">[4 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>To keep the previous behavior (sorting) and silence the warning, pass <code class=\"docutils literal notranslate\"><span class=\"pre\">sort=True</span></code></p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [84]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">concat</span><span class=\"p\">([</span><span class=\"n\">df1</span><span class=\"p\">,</span> <span class=\"n\">df2</span><span class=\"p\">],</span> <span class=\"n\">sort</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[84]: </span>\n <span class=\"go\">   a    b</span>\n <span class=\"go\">0  1  1.0</span>\n <span class=\"go\">1  2  2.0</span>\n <span class=\"go\">0  4  NaN</span>\n <span class=\"go\">1  5  NaN</span>\n+\n+<span class=\"go\">[4 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>To accept the future behavior (no sorting), pass <code class=\"docutils literal notranslate\"><span class=\"pre\">sort=False</span></code></p>\n <p>Note that this change also applies to <code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">DataFrame.append()</span></code>, which has also received a <code class=\"docutils literal notranslate\"><span class=\"pre\">sort</span></code> keyword for controlling this behavior.</p>\n </section>\n <section id=\"build-changes\">\n <span id=\"whatsnew-0230-api-breaking-build-changes\"></span><h3>Build changes<a class=\"headerlink\" href=\"#build-changes\" title=\"Link to this heading\">\u00b6</a></h3>\n@@ -2265,28 +2301,30 @@\n \n <span class=\"gp\">In [87]: </span><span class=\"n\">extracted</span>\n <span class=\"gh\">Out[87]: </span>\n <span class=\"go\">    0</span>\n <span class=\"go\">0  10</span>\n <span class=\"go\">1  12</span>\n \n+<span class=\"go\">[2 rows x 1 columns]</span>\n+\n <span class=\"gp\">In [88]: </span><span class=\"nb\">type</span><span class=\"p\">(</span><span class=\"n\">extracted</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[88]: </span><span class=\"go\">pandas.core.frame.DataFrame</span>\n </pre></div>\n </div>\n <p>To restore previous behavior, simply set <code class=\"docutils literal notranslate\"><span class=\"pre\">expand</span></code> to <code class=\"docutils literal notranslate\"><span class=\"pre\">False</span></code>:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [89]: </span><span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"s1\">&#39;number 10&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;12 eggs&#39;</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [90]: </span><span class=\"n\">extracted</span> <span class=\"o\">=</span> <span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">str</span><span class=\"o\">.</span><span class=\"n\">extract</span><span class=\"p\">(</span><span class=\"sa\">r</span><span class=\"s1\">&#39;.*(\\d\\d).*&#39;</span><span class=\"p\">,</span> <span class=\"n\">expand</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [91]: </span><span class=\"n\">extracted</span>\n <span class=\"gh\">Out[91]: </span>\n <span class=\"go\">0    10</span>\n <span class=\"go\">1    12</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 2, dtype: object</span>\n \n <span class=\"gp\">In [92]: </span><span class=\"nb\">type</span><span class=\"p\">(</span><span class=\"n\">extracted</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[92]: </span><span class=\"go\">pandas.core.series.Series</span>\n </pre></div>\n </div>\n </section>\n <section id=\"default-value-for-the-ordered-parameter-of-categoricaldtype\">\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -246,42 +246,46 @@\n      foo bar        baz qux\n idx\n 0      1   a 2018-01-01   a\n 1      2   b 2018-01-02   b\n 2      3   c 2018-01-03   c\n 3      4   d 2018-01-04   c\n \n+[4 rows x 4 columns]\n+\n In [3]: df.dtypes\n Out[3]:\n foo             int64\n bar            object\n baz    datetime64[ns]\n qux          category\n-dtype: object\n+Length: 4, dtype: object\n \n In [4]: df.to_json('test.json', orient='table')\n \n In [5]: new_df = pd.read_json('test.json', orient='table')\n \n In [6]: new_df\n Out[6]:\n      foo bar        baz qux\n idx\n 0      1   a 2018-01-01   a\n 1      2   b 2018-01-02   b\n 2      3   c 2018-01-03   c\n 3      4   d 2018-01-04   c\n \n+[4 rows x 4 columns]\n+\n In [7]: new_df.dtypes\n Out[7]:\n foo             int64\n bar            object\n baz    datetime64[ns]\n qux          category\n-dtype: object\n+Length: 4, dtype: object\n Please note that the string index is not supported with the round trip format,\n as it is used by default in write_json to indicate a missing index name.\n In [8]: df.index.name = 'index'\n \n In [9]: df.to_json('test.json', orient='table')\n \n In [10]: new_df = pd.read_json('test.json', orient='table')\n@@ -290,41 +294,47 @@\n Out[11]:\n    foo bar        baz qux\n 0    1   a 2018-01-01   a\n 1    2   b 2018-01-02   b\n 2    3   c 2018-01-03   c\n 3    4   d 2018-01-04   c\n \n+[4 rows x 4 columns]\n+\n In [12]: new_df.dtypes\n Out[12]:\n foo             int64\n bar            object\n baz    datetime64[ns]\n qux          category\n-dtype: object\n+Length: 4, dtype: object\n *\b**\b**\b**\b* M\bMe\bet\bth\bho\bod\bd .\b.a\bas\bss\bsi\big\bgn\bn(\b()\b) a\bac\bcc\bce\bep\bpt\bts\bs d\bde\bep\bpe\ben\bnd\bde\ben\bnt\bt a\bar\brg\bgu\bum\bme\ben\bnt\bts\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n The _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bs_\bi_\bg_\bn_\b(_\b) now accepts dependent keyword arguments for python\n version later than 3.6 (see also _\bP_\bE_\bP_\b _\b4_\b6_\b8). Later keyword arguments may now\n refer to earlier ones if the argument is a callable. See the _\bd_\bo_\bc_\bu_\bm_\be_\bn_\bt_\ba_\bt_\bi_\bo_\bn_\b _\bh_\be_\br_\be\n (_\bG_\bH_\b _\b1_\b4_\b2_\b0_\b7)\n In [13]: df = pd.DataFrame({'A': [1, 2, 3]})\n \n In [14]: df\n Out[14]:\n    A\n 0  1\n 1  2\n 2  3\n \n+[3 rows x 1 columns]\n+\n In [15]: df.assign(B=df.A, C=lambda x: x['A'] + x['B'])\n Out[15]:\n    A  B  C\n 0  1  1  2\n 1  2  2  4\n 2  3  3  6\n+\n+[3 rows x 3 columns]\n Warning\n This may subtly change the behavior of your code when you\u2019re using .assign() to\n update an existing column. Previously, callables referring to other variables\n being updated would get the \u201cold\u201d values\n Previous behavior:\n In [2]: df = pd.DataFrame({\"A\": [1, 2, 3]})\n \n@@ -337,14 +347,16 @@\n New behavior:\n In [16]: df.assign(A=df.A + 1, C=lambda df: df.A * -1)\n Out[16]:\n    A  C\n 0  2 -2\n 1  3 -3\n 2  4 -4\n+\n+[3 rows x 2 columns]\n *\b**\b**\b**\b* M\bMe\ber\brg\bgi\bin\bng\bg o\bon\bn a\ba c\bco\bom\bmb\bbi\bin\bna\bat\bti\bio\bon\bn o\bof\bf c\bco\bol\blu\bum\bmn\bns\bs a\ban\bnd\bd i\bin\bnd\bde\bex\bx l\ble\bev\bve\bel\bls\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n Strings passed to _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bm_\be_\br_\bg_\be_\b(_\b) as the on, left_on, and right_on parameters\n may now refer to either column names or index level names. This enables merging\n DataFrame instances on a combination of index levels and columns without\n resetting indexes. See the _\bM_\be_\br_\bg_\be_\b _\bo_\bn_\b _\bc_\bo_\bl_\bu_\bm_\bn_\bs_\b _\ba_\bn_\bd_\b _\bl_\be_\bv_\be_\bl_\bs documentation section.\n (_\bG_\bH_\b _\b1_\b4_\b3_\b5_\b5)\n In [17]: left_index = pd.Index(['K0', 'K0', 'K1', 'K2'], name='key1')\n@@ -366,14 +378,16 @@\n In [21]: left.merge(right, on=['key1', 'key2'])\n Out[21]:\n        A   B key2   C   D\n key1\n K0    A0  B0   K0  C0  D0\n K1    A2  B2   K0  C1  D1\n K2    A3  B3   K1  C3  D3\n+\n+[3 rows x 5 columns]\n *\b**\b**\b**\b* S\bSo\bor\brt\bti\bin\bng\bg b\bby\by a\ba c\bco\bom\bmb\bbi\bin\bna\bat\bti\bio\bon\bn o\bof\bf c\bco\bol\blu\bum\bmn\bns\bs a\ban\bnd\bd i\bin\bnd\bde\bex\bx l\ble\bev\bve\bel\bls\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n Strings passed to _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bs_\bo_\br_\bt_\b__\bv_\ba_\bl_\bu_\be_\bs_\b(_\b) as the by parameter may now refer to\n either column names or index level names. This enables sorting DataFrame\n instances by a combination of index levels and columns without resetting\n indexes. See the _\bS_\bo_\br_\bt_\bi_\bn_\bg_\b _\bb_\by_\b _\bI_\bn_\bd_\be_\bx_\be_\bs_\b _\ba_\bn_\bd_\b _\bV_\ba_\bl_\bu_\be_\bs documentation section. (_\bG_\bH\n _\b1_\b4_\b3_\b5_\b3)\n # Build MultiIndex\n@@ -395,25 +409,29 @@\n a     1       6\n       2       5\n       2       4\n b     2       3\n       1       2\n       1       1\n \n+[6 rows x 1 columns]\n+\n # Sort by 'second' (index) and 'A' (column)\n In [26]: df_multi.sort_values(by=['second', 'A'])\n Out[26]:\n               A\n first second\n b     1       1\n       1       2\n a     1       6\n b     2       3\n a     2       4\n       2       5\n+\n+[6 rows x 1 columns]\n *\b**\b**\b**\b* E\bEx\bxt\bte\ben\bnd\bdi\bin\bng\bg p\bpa\ban\bnd\bda\bas\bs w\bwi\bit\bth\bh c\bcu\bus\bst\bto\bom\bm t\bty\byp\bpe\bes\bs (\b(e\bex\bxp\bpe\ber\bri\bim\bme\ben\bnt\bta\bal\bl)\b)_\b?\b\u00b6 *\b**\b**\b**\b*\n pandas now supports storing array-like objects that aren\u2019t necessarily 1-\n D NumPy arrays as columns in a DataFrame or values in a Series. This allows\n third-party libraries to implement extensions to NumPy\u2019s types, similar to how\n pandas implemented categoricals, datetimes with timezones, periods, and\n intervals.\n As a demonstration, we\u2019ll use _\bc_\by_\bb_\be_\br_\bp_\ba_\bn_\bd_\ba_\bs, which provides an IPArray type for\n@@ -470,14 +488,16 @@\n In [31]: df\n Out[31]:\n    A  B  values    C\n 0  a  c       1  foo\n 1  a  d       2  bar\n 2  b  c       3  foo\n 3  b  d       4  bar\n+\n+[4 rows x 4 columns]\n To show all values, the previous behavior:\n In [32]: df.groupby(['A', 'B', 'C'], observed=False).count()\n Out[32]:\n          values\n A B C\n a c bar       0\n     foo       1\n@@ -497,14 +517,16 @@\n Out[33]:\n          values\n A B C\n a c foo       1\n   d bar       1\n b c foo       1\n   d bar       1\n+\n+[4 rows x 1 columns]\n For pivoting operations, this behavior is a\bal\blr\bre\bea\bad\bdy\by controlled by the dropna\n keyword:\n In [34]: cat1 = pd.Categorical([\"a\", \"a\", \"b\", \"b\"],\n    ....:                       categories=[\"a\", \"b\", \"z\"], ordered=True)\n    ....:\n \n In [35]: cat2 = pd.Categorical([\"c\", \"d\", \"c\", \"d\"],\n@@ -516,14 +538,16 @@\n In [37]: df\n Out[37]:\n    A  B  values\n 0  a  c       1\n 1  a  d       2\n 2  b  c       3\n 3  b  d       4\n+\n+[4 rows x 3 columns]\n In [1]: pd.pivot_table(df, values='values', index=['A', 'B'], dropna=True)\n \n Out[1]:\n      values\n A B\n a c     1.0\n   d     2.0\n@@ -558,33 +582,33 @@\n In [39]: s\n Out[39]:\n 1    0\n 2    1\n 3    2\n 4    3\n 5    4\n-dtype: int64\n+Length: 5, dtype: int64\n Pass a Series:\n In [40]: s.rolling(2, min_periods=1).apply(lambda x: x.iloc[-1], raw=False)\n Out[40]:\n 1    0.0\n 2    1.0\n 3    2.0\n 4    3.0\n 5    4.0\n-dtype: float64\n+Length: 5, dtype: float64\n Mimic the original behavior of passing a ndarray:\n In [41]: s.rolling(2, min_periods=1).apply(lambda x: x[-1], raw=True)\n Out[41]:\n 1    0.0\n 2    1.0\n 3    2.0\n 4    3.0\n 5    4.0\n-dtype: float64\n+Length: 5, dtype: float64\n *\b**\b**\b**\b* D\bDa\bat\bta\baF\bFr\bra\bam\bme\be.\b.i\bin\bnt\bte\ber\brp\bpo\bol\bla\bat\bte\be h\bha\bas\bs g\bga\bai\bin\bne\bed\bd t\bth\bhe\be l\bli\bim\bmi\bit\bt_\b_a\bar\bre\bea\ba k\bkw\bwa\bar\brg\bg_\b?\b\u00b6 *\b**\b**\b**\b*\n _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bi_\bn_\bt_\be_\br_\bp_\bo_\bl_\ba_\bt_\be_\b(_\b) has gained a limit_area parameter to allow further\n control of which NaN s are replaced. Use limit_area='inside' to fill only NaNs\n surrounded by valid values or use limit_area='outside' to fill only NaN s\n outside the existing valid values while preserving those inside. (_\bG_\bH_\b _\b1_\b6_\b2_\b8_\b4) See\n the _\bf_\bu_\bl_\bl_\b _\bd_\bo_\bc_\bu_\bm_\be_\bn_\bt_\ba_\bt_\bi_\bo_\bn_\b _\bh_\be_\br_\be.\n In [42]: ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan,\n@@ -598,74 +622,74 @@\n 2     5.0\n 3     NaN\n 4     NaN\n 5     NaN\n 6    13.0\n 7     NaN\n 8     NaN\n-dtype: float64\n+Length: 9, dtype: float64\n Fill one consecutive inside value in both directions\n In [44]: ser.interpolate(limit_direction='both', limit_area='inside', limit=1)\n Out[44]:\n 0     NaN\n 1     NaN\n 2     5.0\n 3     7.0\n 4     NaN\n 5    11.0\n 6    13.0\n 7     NaN\n 8     NaN\n-dtype: float64\n+Length: 9, dtype: float64\n Fill all consecutive outside values backward\n In [45]: ser.interpolate(limit_direction='backward', limit_area='outside')\n Out[45]:\n 0     5.0\n 1     5.0\n 2     5.0\n 3     NaN\n 4     NaN\n 5     NaN\n 6    13.0\n 7     NaN\n 8     NaN\n-dtype: float64\n+Length: 9, dtype: float64\n Fill all consecutive outside values in both directions\n In [46]: ser.interpolate(limit_direction='both', limit_area='outside')\n Out[46]:\n 0     5.0\n 1     5.0\n 2     5.0\n 3     NaN\n 4     NaN\n 5     NaN\n 6    13.0\n 7    13.0\n 8    13.0\n-dtype: float64\n+Length: 9, dtype: float64\n *\b**\b**\b**\b* F\bFu\bun\bnc\bct\bti\bio\bon\bn g\bge\bet\bt_\b_d\bdu\bum\bmm\bmi\bie\bes\bs n\bno\bow\bw s\bsu\bup\bpp\bpo\bor\brt\bts\bs d\bdt\bty\byp\bpe\be a\bar\brg\bgu\bum\bme\ben\bnt\bt_\b?\b\u00b6 *\b**\b**\b**\b*\n The _\bg_\be_\bt_\b__\bd_\bu_\bm_\bm_\bi_\be_\bs_\b(_\b) now accepts a dtype argument, which specifies a dtype for the\n new columns. The default remains uint8. (_\bG_\bH_\b _\b1_\b8_\b3_\b3_\b0)\n In [47]: df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [5, 6]})\n \n In [48]: pd.get_dummies(df, columns=['c']).dtypes\n Out[48]:\n a      int64\n b      int64\n c_5     bool\n c_6     bool\n-dtype: object\n+Length: 4, dtype: object\n \n In [49]: pd.get_dummies(df, columns=['c'], dtype=bool).dtypes\n Out[49]:\n a      int64\n b      int64\n c_5     bool\n c_6     bool\n-dtype: object\n+Length: 4, dtype: object\n *\b**\b**\b**\b* T\bTi\bim\bme\bed\bde\bel\blt\bta\ba m\bmo\bod\bd m\bme\bet\bth\bho\bod\bd_\b?\b\u00b6 *\b**\b**\b**\b*\n mod (%) and divmod operations are now defined on Timedelta objects when\n operating with either timedelta-like or with numeric arguments. See the\n _\bd_\bo_\bc_\bu_\bm_\be_\bn_\bt_\ba_\bt_\bi_\bo_\bn_\b _\bh_\be_\br_\be. (_\bG_\bH_\b _\b1_\b9_\b3_\b6_\b5)\n In [50]: td = pd.Timedelta(hours=37)\n \n In [51]: td % pd.Timedelta(minutes=45)\n@@ -678,15 +702,15 @@\n In [53]: s\n Out[53]:\n 0   -inf\n 1    0.0\n 2    1.0\n 3    NaN\n 4    inf\n-dtype: float64\n+Length: 5, dtype: float64\n Previous behavior:\n In [11]: s.rank()\n Out[11]:\n 0    1.0\n 1    2.0\n 2    3.0\n 3    NaN\n@@ -696,27 +720,27 @@\n In [54]: s.rank()\n Out[54]:\n 0    1.0\n 1    2.0\n 2    3.0\n 3    NaN\n 4    4.0\n-dtype: float64\n+Length: 5, dtype: float64\n Furthermore, previously if you rank inf or -inf values together with NaN\n values, the calculation won\u2019t distinguish NaN from infinity when using \u2018top\u2019 or\n \u2018bottom\u2019 argument.\n In [55]: s = pd.Series([np.nan, np.nan, -np.inf, -np.inf])\n \n In [56]: s\n Out[56]:\n 0    NaN\n 1    NaN\n 2   -inf\n 3   -inf\n-dtype: float64\n+Length: 4, dtype: float64\n Previous behavior:\n In [15]: s.rank(na_option='top')\n Out[15]:\n 0    2.5\n 1    2.5\n 2    2.5\n 3    2.5\n@@ -724,15 +748,15 @@\n Current behavior:\n In [57]: s.rank(na_option='top')\n Out[57]:\n 0    1.5\n 1    1.5\n 2    3.5\n 3    3.5\n-dtype: float64\n+Length: 4, dtype: float64\n These bugs were squashed:\n     * Bug in _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk_\b(_\b) and _\bS_\be_\br_\bi_\be_\bs_\b._\br_\ba_\bn_\bk_\b(_\b) when method='dense' and\n       pct=True in which percentile ranks were not being used with the number of\n       distinct observations (_\bG_\bH_\b _\b1_\b5_\b6_\b3_\b0)\n     * Bug in _\bS_\be_\br_\bi_\be_\bs_\b._\br_\ba_\bn_\bk_\b(_\b) and _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\br_\ba_\bn_\bk_\b(_\b) when ascending='False' failed\n       to return correct ranks for infinity if NaN were present (_\bG_\bH_\b _\b1_\b9_\b5_\b3_\b8)\n     * Bug in DataFrameGroupBy.rank() where ranks were incorrect when both\n@@ -750,23 +774,23 @@\n \n In [60]: s.str.cat(t)\n Out[60]:\n 0    NaN\n 1     bb\n 2     cc\n 3     dd\n-dtype: object\n+Length: 4, dtype: object\n \n In [61]: s.str.cat(t, join='left', na_rep='-')\n Out[61]:\n 0    a-\n 1    bb\n 2    cc\n 3    dd\n-dtype: object\n+Length: 4, dtype: object\n Furthermore, _\bS_\be_\br_\bi_\be_\bs_\b._\bs_\bt_\br_\b._\bc_\ba_\bt_\b(_\b) now works for CategoricalIndex as well\n (previously raised a ValueError; see _\bG_\bH_\b _\b2_\b0_\b8_\b4_\b2).\n *\b**\b**\b**\b* D\bDa\bat\bta\baF\bFr\bra\bam\bme\be.\b.a\bas\bst\bty\byp\bpe\be p\bpe\ber\brf\bfo\bor\brm\bms\bs c\bco\bol\blu\bum\bmn\bn-\b-w\bwi\bis\bse\be c\bco\bon\bnv\bve\ber\brs\bsi\bio\bon\bn t\bto\bo C\bCa\bat\bte\beg\bgo\bor\bri\bic\bca\bal\bl_\b?\b\u00b6 *\b**\b**\b**\b*\n _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bs_\bt_\by_\bp_\be_\b(_\b) can now perform column-wise conversion to Categorical by\n supplying the string 'category' or a CategoricalDtype. Previously, attempting\n this would raise a NotImplementedError. See the _\bO_\bb_\bj_\be_\bc_\bt_\b _\bc_\br_\be_\ba_\bt_\bi_\bo_\bn section of the\n documentation for more details and examples. (_\bG_\bH_\b _\b1_\b2_\b8_\b6_\b0, _\bG_\bH_\b _\b1_\b8_\b0_\b9_\b9)\n@@ -933,15 +957,15 @@\n    ....:            'Net result': 300})\n    ....:\n Out[72]:\n Income        2000\n Expenses     -1500\n Taxes         -200\n Net result     300\n-dtype: int64\n+Length: 4, dtype: int64\n Notice that the Series is now ordered by insertion order. This new behavior is\n used for all relevant pandas types (Series, DataFrame, SparseSeries and\n SparseDataFrame).\n If you wish to retain the old behavior while using Python >= 3.6, you can use\n .sort_index():\n In [73]: pd.Series({'Income': 2000,\n    ....:            'Expenses': -1500,\n@@ -949,15 +973,15 @@\n    ....:            'Net result': 300}).sort_index()\n    ....:\n Out[73]:\n Expenses     -1500\n Income        2000\n Net result     300\n Taxes         -200\n-dtype: int64\n+Length: 4, dtype: int64\n *\b**\b**\b**\b* D\bDe\bep\bpr\bre\bec\bca\bat\bte\be P\bPa\ban\bne\bel\bl_\b?\b\u00b6 *\b**\b**\b**\b*\n Panel was deprecated in the 0.20.x release, showing as a DeprecationWarning.\n Using Panel will now show a FutureWarning. The recommended way to represent 3-\n D data are with a MultiIndex on a DataFrame via the to_frame() or with the\n _\bx_\ba_\br_\br_\ba_\by_\b _\bp_\ba_\bc_\bk_\ba_\bg_\be. pandas provides a to_xarray() method to automate this\n conversion (_\bG_\bH_\b _\b1_\b3_\b5_\b6_\b3, _\bG_\bH_\b _\b1_\b8_\b3_\b2_\b4).\n In [75]: import pandas._testing as tm\n@@ -1034,14 +1058,16 @@\n    A  B  C\n 0  1  2  3\n 1  1  2  3\n 2  1  2  3\n 3  1  2  3\n 4  1  2  3\n 5  1  2  3\n+\n+[6 rows x 3 columns]\n Previous behavior: if the returned shape happened to match the length of\n original columns, this would return a DataFrame. If the return shape did not\n match, a Series with lists was returned.\n In [3]: df.apply(lambda x: [1, 2, 3], axis=1)\n Out[3]:\n    A  B  C\n 0  1  2  3\n@@ -1066,59 +1092,65 @@\n Out[76]:\n 0    [1, 2, 3]\n 1    [1, 2, 3]\n 2    [1, 2, 3]\n 3    [1, 2, 3]\n 4    [1, 2, 3]\n 5    [1, 2, 3]\n-dtype: object\n+Length: 6, dtype: object\n \n In [77]: df.apply(lambda x: [1, 2], axis=1)\n Out[77]:\n 0    [1, 2]\n 1    [1, 2]\n 2    [1, 2]\n 3    [1, 2]\n 4    [1, 2]\n 5    [1, 2]\n-dtype: object\n+Length: 6, dtype: object\n To have expanded columns, you can use result_type='expand'\n In [78]: df.apply(lambda x: [1, 2, 3], axis=1, result_type='expand')\n Out[78]:\n    0  1  2\n 0  1  2  3\n 1  1  2  3\n 2  1  2  3\n 3  1  2  3\n 4  1  2  3\n 5  1  2  3\n+\n+[6 rows x 3 columns]\n To broadcast the result across the original columns (the old behaviour for\n list-likes of the correct length), you can use result_type='broadcast'. The\n shape must match the original columns.\n In [79]: df.apply(lambda x: [1, 2, 3], axis=1, result_type='broadcast')\n Out[79]:\n    A  B  C\n 0  1  2  3\n 1  1  2  3\n 2  1  2  3\n 3  1  2  3\n 4  1  2  3\n 5  1  2  3\n+\n+[6 rows x 3 columns]\n Returning a Series allows one to control the exact return structure and column\n names:\n In [80]: df.apply(lambda x: pd.Series([1, 2, 3], index=['D', 'E', 'F']),\n axis=1)\n Out[80]:\n    D  E  F\n 0  1  2  3\n 1  1  2  3\n 2  1  2  3\n 3  1  2  3\n 4  1  2  3\n 5  1  2  3\n+\n+[6 rows x 3 columns]\n *\b**\b**\b**\b* C\bCo\bon\bnc\bca\bat\bte\ben\bna\bat\bti\bio\bon\bn w\bwi\bil\bll\bl n\bno\bo l\blo\bon\bng\bge\ber\br s\bso\bor\brt\bt_\b?\b\u00b6 *\b**\b**\b**\b*\n In a future version of pandas _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bc_\bo_\bn_\bc_\ba_\bt_\b(_\b) will no longer sort the non-\n concatenation axis when it is not already aligned. The current behavior is the\n same as the previous (sorting), but now a warning is issued when sort is not\n specified and the non-concatenation axis is not aligned (_\bG_\bH_\b _\b4_\b5_\b8_\b8).\n In [81]: df1 = pd.DataFrame({\"a\": [1, 2], \"b\": [1, 2]}, columns=['b', 'a'])\n \n@@ -1127,22 +1159,26 @@\n In [83]: pd.concat([df1, df2])\n Out[83]:\n      b  a\n 0  1.0  1\n 1  2.0  2\n 0  NaN  4\n 1  NaN  5\n+\n+[4 rows x 2 columns]\n To keep the previous behavior (sorting) and silence the warning, pass sort=True\n In [84]: pd.concat([df1, df2], sort=True)\n Out[84]:\n    a    b\n 0  1  1.0\n 1  2  2.0\n 0  4  NaN\n 1  5  NaN\n+\n+[4 rows x 2 columns]\n To accept the future behavior (no sorting), pass sort=False\n Note that this change also applies to DataFrame.append(), which has also\n received a sort keyword for controlling this behavior.\n *\b**\b**\b**\b* B\bBu\bui\bil\bld\bd c\bch\bha\ban\bng\bge\bes\bs_\b?\b\u00b6 *\b**\b**\b**\b*\n     * Building pandas for development now requires cython >= 0.24 (_\bG_\bH_\b _\b1_\b8_\b6_\b1_\b3)\n     * Building from source now explicitly requires setuptools in setup.py (_\bG_\bH\n       _\b1_\b8_\b1_\b1_\b3)\n@@ -1212,26 +1248,28 @@\n \n In [87]: extracted\n Out[87]:\n     0\n 0  10\n 1  12\n \n+[2 rows x 1 columns]\n+\n In [88]: type(extracted)\n Out[88]: pandas.core.frame.DataFrame\n To restore previous behavior, simply set expand to False:\n In [89]: s = pd.Series(['number 10', '12 eggs'])\n \n In [90]: extracted = s.str.extract(r'.*(\\d\\d).*', expand=False)\n \n In [91]: extracted\n Out[91]:\n 0    10\n 1    12\n-dtype: object\n+Length: 2, dtype: object\n \n In [92]: type(extracted)\n Out[92]: pandas.core.series.Series\n *\b**\b**\b**\b* D\bDe\bef\bfa\bau\bul\blt\bt v\bva\bal\blu\bue\be f\bfo\bor\br t\bth\bhe\be o\bor\brd\bde\ber\bre\bed\bd p\bpa\bar\bra\bam\bme\bet\bte\ber\br o\bof\bf C\bCa\bat\bte\beg\bgo\bor\bri\bic\bca\bal\blD\bDt\bty\byp\bpe\be_\b?\b\u00b6 *\b**\b**\b**\b*\n The default value of the ordered parameter for CategoricalDtype has changed\n from False to None to allow updating of categories without impacting ordered.\n Behavior should remain consistent for downstream objects, such as _\bC_\ba_\bt_\be_\bg_\bo_\br_\bi_\bc_\ba_\bl\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.24.0.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.24.0.html", "unified_diff": "@@ -1284,18 +1284,18 @@\n a new ndarray of period objects each time.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [18]: </span><span class=\"n\">idx</span><span class=\"o\">.</span><span class=\"n\">values</span>\n <span class=\"gh\">Out[18]: </span>\n <span class=\"go\">array([Period(&#39;2000-01-01&#39;, &#39;D&#39;), Period(&#39;2000-01-02&#39;, &#39;D&#39;),</span>\n <span class=\"go\">       Period(&#39;2000-01-03&#39;, &#39;D&#39;), Period(&#39;2000-01-04&#39;, &#39;D&#39;)], dtype=object)</span>\n \n <span class=\"gp\">In [19]: </span><span class=\"nb\">id</span><span class=\"p\">(</span><span class=\"n\">idx</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">)</span>\n-<span class=\"gh\">Out[19]: </span><span class=\"go\">139875956514064</span>\n+<span class=\"gh\">Out[19]: </span><span class=\"go\">140225432699600</span>\n \n <span class=\"gp\">In [20]: </span><span class=\"nb\">id</span><span class=\"p\">(</span><span class=\"n\">idx</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">)</span>\n-<span class=\"gh\">Out[20]: </span><span class=\"go\">139875956514160</span>\n+<span class=\"gh\">Out[20]: </span><span class=\"go\">140225432699600</span>\n </pre></div>\n </div>\n <p>If you need an actual NumPy array, use <a class=\"reference internal\" href=\"../reference/api/pandas.Series.to_numpy.html#pandas.Series.to_numpy\" title=\"pandas.Series.to_numpy\"><code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">Series.to_numpy()</span></code></a> or <code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">Index.to_numpy()</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [21]: </span><span class=\"n\">idx</span><span class=\"o\">.</span><span class=\"n\">to_numpy</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[21]: </span>\n <span class=\"go\">array([Period(&#39;2000-01-01&#39;, &#39;D&#39;), Period(&#39;2000-01-02&#39;, &#39;D&#39;),</span>\n <span class=\"go\">       Period(&#39;2000-01-03&#39;, &#39;D&#39;), Period(&#39;2000-01-04&#39;, &#39;D&#39;)], dtype=object)</span>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -313,18 +313,18 @@\n each time.\n In [18]: idx.values\n Out[18]:\n array([Period('2000-01-01', 'D'), Period('2000-01-02', 'D'),\n        Period('2000-01-03', 'D'), Period('2000-01-04', 'D')], dtype=object)\n \n In [19]: id(idx.values)\n-Out[19]: 139875956514064\n+Out[19]: 140225432699600\n \n In [20]: id(idx.values)\n-Out[20]: 139875956514160\n+Out[20]: 140225432699600\n If you need an actual NumPy array, use _\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bn_\bu_\bm_\bp_\by_\b(_\b) or Index.to_numpy().\n In [21]: idx.to_numpy()\n Out[21]:\n array([Period('2000-01-01', 'D'), Period('2000-01-02', 'D'),\n        Period('2000-01-03', 'D'), Period('2000-01-04', 'D')], dtype=object)\n \n In [22]: pd.Series(idx).to_numpy()\n"}]}]}]}]}]}