--- /srv/reproducible-results/rbuild-debian/r-b-build.ZGf3tTlJ/b1/pandas_2.2.3+dfsg-8_amd64.changes +++ /srv/reproducible-results/rbuild-debian/r-b-build.ZGf3tTlJ/b2/pandas_2.2.3+dfsg-8_amd64.changes ├── Files │ @@ -1,5 +1,5 @@ │ │ - 6b97b409cc7646b0c8d0e963ec08c4c0 10794448 doc optional python-pandas-doc_2.2.3+dfsg-8_all.deb │ + c40ce271b5771565565e4c5f5d9e97b6 10795780 doc optional python-pandas-doc_2.2.3+dfsg-8_all.deb │ becffddde344b2e6fa22306930989191 35987996 debug optional python3-pandas-lib-dbgsym_2.2.3+dfsg-8_amd64.deb │ 69a552ffe39e45363503df097e2d5e56 4595916 python optional python3-pandas-lib_2.2.3+dfsg-8_amd64.deb │ 1e5595970bd1bbf475de7213b75d2d1d 3096900 python optional python3-pandas_2.2.3+dfsg-8_all.deb ├── python-pandas-doc_2.2.3+dfsg-8_all.deb │ ├── file list │ │ @@ -1,3 +1,3 @@ │ │ -rw-r--r-- 0 0 0 4 2025-02-01 18:39:17.000000 debian-binary │ │ -rw-r--r-- 0 0 0 147384 2025-02-01 18:39:17.000000 control.tar.xz │ │ --rw-r--r-- 0 0 0 10646872 2025-02-01 18:39:17.000000 data.tar.xz │ │ +-rw-r--r-- 0 0 0 10648204 2025-02-01 18:39:17.000000 data.tar.xz │ ├── control.tar.xz │ │ ├── control.tar │ │ │ ├── ./control │ │ │ │ @@ -1,13 +1,13 @@ │ │ │ │ Package: python-pandas-doc │ │ │ │ Source: pandas │ │ │ │ Version: 2.2.3+dfsg-8 │ │ │ │ Architecture: all │ │ │ │ Maintainer: Debian Science Team │ │ │ │ -Installed-Size: 209896 │ │ │ │ +Installed-Size: 209901 │ │ │ │ Depends: libjs-sphinxdoc (>= 8.1), libjs-mathjax │ │ │ │ Suggests: python3-pandas │ │ │ │ Section: doc │ │ │ │ Priority: optional │ │ │ │ Multi-Arch: foreign │ │ │ │ Homepage: https://pandas.pydata.org/ │ │ │ │ Description: data structures for "relational" or "labeled" data - documentation │ │ │ ├── ./md5sums │ │ │ │ ├── ./md5sums │ │ │ │ │┄ Files differ │ ├── data.tar.xz │ │ ├── data.tar │ │ │ ├── file list │ │ │ │ @@ -6256,84 +6256,84 @@ │ │ │ │ -rw-r--r-- 0 root (0) root (0) 210184 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/reference/series.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 48665 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/reference/style.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 48657 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/reference/testing.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 53295 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/reference/window.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 244 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/release.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 269 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/reshaping.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 17010 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/search.html │ │ │ │ --rw-r--r-- 0 root (0) root (0) 2358753 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/searchindex.js │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 2358749 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/searchindex.js │ │ │ │ -rw-r--r-- 0 root (0) root (0) 259 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/sparse.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 244 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/style.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 255 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/text.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 256 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/timedeltas.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 277 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/timeseries.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 272 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/tutorials.html │ │ │ │ drwxr-xr-x 0 root (0) root (0) 0 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/ │ │ │ │ -rw-r--r-- 0 root (0) root (0) 171380 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/10min.html │ │ │ │ --rw-r--r-- 0 root (0) root (0) 283977 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/advanced.html │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 283826 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/advanced.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 436075 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/basics.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 36646 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/boolean.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 217515 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/categorical.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 18313 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/cookbook.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 66125 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/copy_on_write.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 160414 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./usr/share/doc/python-pandas-doc/html/user_guide/indexing.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 41778 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/integer_na.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 1145820 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/io.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 208885 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/merging.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 178690 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/missing_data.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 112153 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/options.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 147524 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/pyarrow.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 162660 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/reshaping.html │ │ │ │ --rw-r--r-- 0 root (0) root (0) 115581 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/scale.html │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 115574 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/scale.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 65546 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/sparse.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 698240 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/style.html │ │ │ │ --rw-r--r-- 0 root (0) root (0) 87825 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/style.ipynb.gz │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 88222 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/style.ipynb.gz │ │ │ │ -rw-r--r-- 0 root (0) root (0) 165302 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/text.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 100947 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/timedeltas.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 486621 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/timeseries.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 204341 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/visualization.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 141947 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/window.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 270 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/visualization.html │ │ │ │ drwxr-xr-x 0 root (0) root (0) 0 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/ │ │ │ │ -rw-r--r-- 0 root (0) root (0) 107681 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/index.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 10569 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/index.html.gz │ │ │ │ -rw-r--r-- 0 root (0) root (0) 83987 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.10.0.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 66492 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.10.1.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 82312 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.11.0.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 104316 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.12.0.html │ │ │ │ --rw-r--r-- 0 root (0) root (0) 222518 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.13.0.html │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 222514 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.13.0.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 89385 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.13.1.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 243730 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.14.0.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 83262 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.14.1.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 252303 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.15.0.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 68280 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.15.1.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 75115 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.15.2.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 145199 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.16.0.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 115518 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.16.1.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 64656 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.16.2.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 231394 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.17.0.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 95028 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.17.1.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 224091 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.18.0.html │ │ │ │ --rw-r--r-- 0 root (0) root (0) 171419 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.18.1.html │ │ │ │ --rw-r--r-- 0 root (0) root (0) 349360 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.19.0.html │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 171888 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.18.1.html │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 350916 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.19.0.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 45179 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.19.1.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 48525 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.19.2.html │ │ │ │ --rw-r--r-- 0 root (0) root (0) 406081 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.20.0.html │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 407596 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.20.0.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 52898 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.20.2.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 43404 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.20.3.html │ │ │ │ --rw-r--r-- 0 root (0) root (0) 255116 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.21.0.html │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 255811 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.21.0.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 61789 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.21.1.html │ │ │ │ --rw-r--r-- 0 root (0) root (0) 59841 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.22.0.html │ │ │ │ --rw-r--r-- 0 root (0) root (0) 401704 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.23.0.html │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 59896 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.22.0.html │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 402831 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.23.0.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 59871 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.23.1.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 52005 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.23.2.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 32373 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.23.3.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 35785 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.23.4.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 520683 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.24.0.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 44717 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.24.1.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 49347 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.24.2.html │ │ │ ├── ./usr/share/doc/python-pandas-doc/html/searchindex.js │ │ │ │ ├── js-beautify {} │ │ │ │ │ @@ -21510,28 +21510,28 @@ │ │ │ │ │ "003494": 15, │ │ │ │ │ "003507": [2209, 2218], │ │ │ │ │ "003556": 2207, │ │ │ │ │ "00360": 2294, │ │ │ │ │ "003733": 2207, │ │ │ │ │ "003932": 2216, │ │ │ │ │ "003945": 2210, │ │ │ │ │ - "004": [2186, 2193, 2227], │ │ │ │ │ + "004": [2186, 2227], │ │ │ │ │ "004000": 2232, │ │ │ │ │ "004005006": [287, 939], │ │ │ │ │ "004054": 2229, │ │ │ │ │ "004091": [2204, 2257], │ │ │ │ │ "004127": 2207, │ │ │ │ │ "004194": 2186, │ │ │ │ │ "004201": 2186, │ │ │ │ │ "004229": 2186, │ │ │ │ │ "004474": 2184, │ │ │ │ │ "004580": 2210, │ │ │ │ │ "00486": 30, │ │ │ │ │ "004956": 2207, │ │ │ │ │ - "005": [2193, 2209], │ │ │ │ │ + "005": 2209, │ │ │ │ │ "005000": 2218, │ │ │ │ │ "005361": 2207, │ │ │ │ │ "005383": 2220, │ │ │ │ │ "005446": 2219, │ │ │ │ │ "005462": 2191, │ │ │ │ │ "005977": 2199, │ │ │ │ │ "005979": 2186, │ │ │ │ │ @@ -21549,23 +21549,21 @@ │ │ │ │ │ "007200": 2184, │ │ │ │ │ "007207": [2184, 2214], │ │ │ │ │ "007717": 2199, │ │ │ │ │ "007824": 15, │ │ │ │ │ "007952": 2207, │ │ │ │ │ "007996": 2186, │ │ │ │ │ "007f": 203, │ │ │ │ │ - "008": 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213, 233, 273, 303, 345, 359, 360, 427, 577, 587, 588, 620, 621, 655, 709, 717, 760, 781, 787, 788, 900, 1345, 1391, 1398, 1447, 1457, 1472, 1473, 1488, 1490, 2184, 2185, 2186, 2188, 2190, 2191, 2193, 2194, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2223, 2225, 2226, 2230, 2231, 2232, 2235, 2241, 2242, 2246, 2249, 2302, 2307], │ │ │ │ │ "1000": [9, 10, 15, 24, 25, 28, 29, 32, 102, 141, 183, 191, 193, 194, 427, 717, 761, 767, 768, 769, 874, 1154, 1158, 1456, 1465, 1467, 1876, 1964, 2184, 2185, 2186, 2188, 2193, 2195, 2199, 2205, 2206, 2207, 2210, 2211, 2220, 2223, 2229, 2230, 2235, 2238, 2246, 2249, 2261, 2294], │ │ │ │ │ "10000": [192, 1485, 2185, 2201, 2206, 2210, 2220, 2228, 2266], │ │ │ │ │ "100000": [1354, 1372, 2199, 2201, 2210], │ │ │ │ │ "1000000": [144, 2199, 2228], │ │ │ │ │ "1000000000000000": 1039, │ │ │ │ │ "100000d": 1497, │ │ │ │ │ "100001": 1497, │ │ │ │ │ "10001": 2232, │ │ │ │ │ "10008": [2231, 2232], │ │ │ │ │ - "1001": [2195, 2199], │ │ │ │ │ + "1001": [2193, 2195, 2199], │ │ │ │ │ "100123": 2225, │ │ │ │ │ "1001m": [917, 919, 922, 929], │ │ │ │ │ "1002": [16, 17, 18, 19, 2199, 2205, 2235], │ │ │ │ │ "10022": 2226, │ │ │ │ │ "100230": 2184, │ │ │ │ │ "10024": 2226, │ │ │ │ │ "10025": 2226, │ │ │ │ │ @@ -22386,15 +22386,15 @@ │ │ │ │ │ "102889": 18, │ │ │ │ │ "10289": 2227, │ │ │ │ │ "1029": 2199, │ │ │ │ │ "10291": 2230, │ │ │ │ │ "10292": 2227, │ │ │ │ │ "10295": 2228, │ │ │ │ │ "10299": 2229, │ │ │ │ │ - "103": [139, 140, 1174, 1175, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2222, 2227, 2230, 2232, 2235, 2246, 2255], │ │ │ │ │ + "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], │ │ │ │ │ "1030": 2199, │ │ │ │ │ "10303": 2227, │ │ │ │ │ "1031": 2199, │ │ │ │ │ "103104": 2235, │ │ │ │ │ "10317": 2227, │ │ │ │ │ "10319": 2277, │ │ │ │ │ "103219": 2207, │ │ │ │ │ @@ -23214,14 +23214,15 @@ │ │ │ │ │ "124124": 2207, │ │ │ │ │ "12424": 2232, │ │ │ │ │ "12425": 2241, │ │ │ │ │ "12448": 2230, │ │ │ │ │ "124518": 2230, │ │ │ │ │ "12467": 2231, │ │ │ │ │ "12468": 2199, │ │ │ │ │ + "1247": 2193, │ │ │ │ │ "12471": 2230, │ │ │ │ │ "12473": 2231, │ │ │ │ │ "12486": 2231, │ │ │ │ │ "124862": 2191, │ │ │ │ │ "12489": 2230, │ │ │ │ │ "12492": 2230, │ │ │ │ │ "12493": 2231, │ │ │ │ │ @@ -23256,15 +23257,15 @@ │ │ │ │ │ "12577": 2231, │ │ │ │ │ "125798": 28, │ │ │ │ │ "12585": 2289, │ │ │ │ │ "12588": 2235, │ │ │ │ │ "12591": [2231, 2238], │ │ │ │ │ "125929": 2207, │ │ │ │ │ "125934": 2199, │ │ │ │ │ - "126": [2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2203, 2204, 2208, 2210, 2211, 2220, 2225, 2232, 2283], │ │ │ │ │ + "126": [2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2203, 2204, 2208, 2210, 2211, 2220, 2225, 2232, 2283], │ │ │ │ │ "12600": 2231, │ │ │ │ │ "12601": 2246, │ │ │ │ │ "12610": 2231, │ │ │ │ │ "12615": 2231, │ │ │ │ │ "12617": 2231, │ │ │ │ │ "12619": 2246, │ │ │ │ │ "12620": 2231, │ │ │ │ │ @@ -23592,15 +23593,15 @@ │ │ │ │ │ "13382": 2232, │ │ │ │ │ "13383": 2232, │ │ │ │ │ "13386": 2241, │ │ │ │ │ "13389": 2232, │ │ │ │ │ "13393": 2239, │ │ │ │ │ "13395": 2232, │ │ │ │ │ "13398": 2232, │ │ │ │ │ - "134": [2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2208, 2210, 2211, 2232, 2235, 2249, 2259, 2283], │ │ │ │ │ + "134": [2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2208, 2210, 2211, 2232, 2235, 2249, 2259, 2283], │ │ │ │ │ "13402": 2232, │ │ │ │ │ "13407": 2241, │ │ │ │ │ "13410": 2235, │ │ │ │ │ "134105": 2207, │ │ │ │ │ "13411": 2232, │ │ │ │ │ "13412": 2234, │ │ │ │ │ "134146": 15, │ │ │ │ │ @@ -23824,33 +23825,32 @@ │ │ │ │ │ "13971": 2238, │ │ │ │ │ "13972": 2232, │ │ │ │ │ "13977": 2232, │ │ │ │ │ "13980": 2232, │ │ │ │ │ "13981": 2232, │ │ │ │ │ "13985": 2232, │ │ │ │ │ "139853": 2207, │ │ │ │ │ + "139868208517392": 2246, │ │ │ │ │ "13988": 2232, │ │ │ │ │ "13990": 2232, │ │ │ │ │ "13994": 2232, │ │ │ │ │ "139976": 2186, │ │ │ │ │ "13999": 2232, │ │ │ │ │ "139999": 1372, │ │ │ │ │ "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], │ │ │ │ │ - "140": [213, 788, 1433, 2184, 2185, 2186, 2188, 2195, 2197, 2199, 2200, 2201, 2203, 2208, 2210, 2211, 2212, 2231, 2232, 2298], │ │ │ │ │ + "140": [213, 788, 1433, 2184, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2201, 2203, 2208, 2210, 2211, 2212, 2231, 2232, 2298], │ │ │ │ │ "14000": [2185, 2220, 2232], │ │ │ │ │ "14001": 2238, │ │ │ │ │ "140069": 2229, │ │ │ │ │ "14007": 2241, │ │ │ │ │ "14012": 2232, │ │ │ │ │ "14013": 2241, │ │ │ │ │ "14015": 2235, │ │ │ │ │ "14021": 2232, │ │ │ │ │ "140249": 2207, │ │ │ │ │ - "140263739805968": 2246, │ │ │ │ │ - "140263739808464": 2246, │ │ │ │ │ "14039": 2232, │ │ │ │ │ "14041": 2232, │ │ │ │ │ "140528": 2207, │ │ │ │ │ "14058": 2232, │ │ │ │ │ "14065": 2232, │ │ │ │ │ "14066": 2232, │ │ │ │ │ "14068": [2232, 2233], │ │ │ │ │ @@ -23860,15 +23860,15 @@ │ │ │ │ │ "1409": [2185, 2197], │ │ │ │ │ "14093": 2283, │ │ │ │ │ "14094": [2232, 2246], │ │ │ │ │ "14095": 2232, │ │ │ │ │ "14096": 2241, │ │ │ │ │ "140983": 2207, │ │ │ │ │ "140min": 2210, │ │ │ │ │ - "141": [2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2203, 2210, 2211, 2212, 2231, 2232, 2253, 2298], │ │ │ │ │ + "141": [2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2203, 2210, 2211, 2212, 2231, 2232, 2253, 2298], │ │ │ │ │ "1410": [2185, 2197], │ │ │ │ │ "14101": 2232, │ │ │ │ │ "14105": 2246, │ │ │ │ │ "1411": [2185, 2197], │ │ │ │ │ "14113": 2241, │ │ │ │ │ "141155": 2207, │ │ │ │ │ "141185": 15, │ │ │ │ │ @@ -23893,15 +23893,15 @@ │ │ │ │ │ "14173": 2232, │ │ │ │ │ "141809": 2214, │ │ │ │ │ "14187": 2246, │ │ │ │ │ "14189": 2235, │ │ │ │ │ "14190": 2232, │ │ │ │ │ "141915": 2207, │ │ │ │ │ "14194": 2241, │ │ │ │ │ - "142": [2185, 2186, 2188, 2195, 2197, 2199, 2200, 2201, 2203, 2210, 2211, 2212, 2232, 2253, 2298], │ │ │ │ │ + "142": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2201, 2203, 2210, 2211, 2212, 2232, 2253, 2298], │ │ │ │ │ "1420043460": 2231, │ │ │ │ │ "14203": 2234, │ │ │ │ │ "14204": 2233, │ │ │ │ │ "14207": [2238, 2241], │ │ │ │ │ "14208": 2232, │ │ │ │ │ "14216": 2238, │ │ │ │ │ "14218": 2235, │ │ │ │ │ @@ -24042,15 +24042,15 @@ │ │ │ │ │ "14684": 2234, │ │ │ │ │ "14685": 2234, │ │ │ │ │ "14686": 2246, │ │ │ │ │ "14687": 2234, │ │ │ │ │ "14689": 2234, │ │ │ │ │ "14696": 2238, │ │ │ │ │ "14699": 2235, │ │ │ │ │ - "147": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2201, 2210, 2211, 2232], │ │ │ │ │ + "147": [2185, 2186, 2188, 2195, 2197, 2199, 2200, 2201, 2210, 2211, 2232], │ │ │ │ │ "1470": [16, 17, 18, 19, 2199, 2235], │ │ │ │ │ "14704": 2289, │ │ │ │ │ "14711": 2238, │ │ │ │ │ "14712": 2234, │ │ │ │ │ "14714": 2235, │ │ │ │ │ "1472": [16, 17, 18, 19, 2199, 2235], │ │ │ │ │ "14721": 2235, │ │ │ │ │ @@ -24494,16 +24494,15 @@ │ │ │ │ │ "16059": 2246, │ │ │ │ │ "16063": 2294, │ │ │ │ │ "16071": 2235, │ │ │ │ │ "16073": 2241, │ │ │ │ │ "16078": 2238, │ │ │ │ │ "160910": 2207, │ │ │ │ │ "160915": 2186, │ │ │ │ │ - "16098": 2193, │ │ │ │ │ - "161": [2185, 2186, 2188, 2195, 2197, 2199, 2201, 2210, 2211], │ │ │ │ │ + "161": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2201, 2210, 2211], │ │ │ │ │ "161007": 2207, │ │ │ │ │ "161099": 2193, │ │ │ │ │ "16111": 2235, │ │ │ │ │ "16112": 2238, │ │ │ │ │ "161137": 2235, │ │ │ │ │ "16120": 2235, │ │ │ │ │ "16122": 2238, │ │ │ │ │ @@ -24792,15 +24791,15 @@ │ │ │ │ │ "17060": 2238, │ │ │ │ │ "17066": 2246, │ │ │ │ │ "170667": 2207, │ │ │ │ │ "17095": 2238, │ │ │ │ │ "17097": 2238, │ │ │ │ │ "170972": 2207, │ │ │ │ │ "17099": 2238, │ │ │ │ │ - "171": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2210, 2211, 2283], │ │ │ │ │ + "171": [2185, 2186, 2188, 2195, 2197, 2199, 2200, 2210, 2211, 2283], │ │ │ │ │ "17105": 2241, │ │ │ │ │ "17108": 2238, │ │ │ │ │ "171092": 2199, │ │ │ │ │ "17116": 2238, │ │ │ │ │ "1712": [139, 140, 1174, 1175], │ │ │ │ │ "17125": 2238, │ │ │ │ │ "17126": 2298, │ │ │ │ │ @@ -25059,14 +25058,15 @@ │ │ │ │ │ "18069": 2239, │ │ │ │ │ "18071": 2239, │ │ │ │ │ "18079": 2241, │ │ │ │ │ "1809": 2263, │ │ │ │ │ "18092": 2241, │ │ │ │ │ "18099": 2241, │ │ │ │ │ "181": [69, 109, 129, 171, 173, 199, 204, 206, 215, 216, 217, 220, 221, 222, 244, 259, 275, 890, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2210, 2211, 2298], │ │ │ │ │ + "181091": 2228, │ │ │ │ │ "18113": 2241, │ │ │ │ │ "18116": 2239, │ │ │ │ │ "181231": 2195, │ │ │ │ │ "18146": 2246, │ │ │ │ │ "181507": 2207, │ │ │ │ │ "18154": 2239, │ │ │ │ │ "18159": 2239, │ │ │ │ │ @@ -25078,15 +25078,15 @@ │ │ │ │ │ "18178": 2239, │ │ │ │ │ "1818": 2217, │ │ │ │ │ "18184": 2241, │ │ │ │ │ "18186": 2239, │ │ │ │ │ "18187": 2239, │ │ │ │ │ "181873": 2207, │ │ │ │ │ "18198": 2294, │ │ │ │ │ - "182": [176, 179, 2185, 2186, 2188, 2195, 2197, 2199, 2200, 2210, 2211, 2298], │ │ │ │ │ + "182": [176, 179, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2210, 2211, 2298], │ │ │ │ │ "18203": 2239, │ │ │ │ │ "18213": 2241, │ │ │ │ │ "18216": 2239, │ │ │ │ │ "18217": [2241, 2265], │ │ │ │ │ "18218": 2241, │ │ │ │ │ "18221": 2241, │ │ │ │ │ "18222": 2265, │ │ │ │ │ @@ -25174,15 +25174,15 @@ │ │ │ │ │ "18478": 2241, │ │ │ │ │ "1848": 2220, │ │ │ │ │ "18480": 2241, │ │ │ │ │ "18482": 2241, │ │ │ │ │ "18485": 2241, │ │ │ │ │ "18489": 2241, │ │ │ │ │ "18493": 2239, │ │ │ │ │ - "185": [134, 709, 2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211, 2212], │ │ │ │ │ + "185": [134, 709, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2210, 2211, 2212], │ │ │ │ │ "18501": 2241, │ │ │ │ │ "18502": 2249, │ │ │ │ │ "185043": 2195, │ │ │ │ │ "18505": 2241, │ │ │ │ │ "18509": 2241, │ │ │ │ │ "18510": 2241, │ │ │ │ │ "18515": 2241, │ │ │ │ │ @@ -25524,15 +25524,15 @@ │ │ │ │ │ "196569": 2207, │ │ │ │ │ "196591": 2207, │ │ │ │ │ "19671": 2241, │ │ │ │ │ "19682": 2241, │ │ │ │ │ "19686": 2241, │ │ │ │ │ "196903": 2204, │ │ │ │ │ "19699": 2241, │ │ │ │ │ - "197": [22, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2210, 2211], │ │ │ │ │ + "197": [22, 2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211], │ │ │ │ │ "1970": [213, 345, 788, 1498, 2166, 2199, 2201, 2204, 2210, 2215, 2218, 2232, 2235, 2238, 2271], │ │ │ │ │ "19700": 2246, │ │ │ │ │ "197035": 2210, │ │ │ │ │ "19708": 2302, │ │ │ │ │ "19711": 2265, │ │ │ │ │ "197138": 2207, │ │ │ │ │ "19714": 2241, │ │ │ │ │ @@ -25736,31 +25736,31 @@ │ │ │ │ │ "2017q4": 2238, │ │ │ │ │ "2018": [13, 35, 80, 84, 88, 127, 157, 187, 213, 271, 277, 278, 288, 291, 296, 298, 302, 304, 305, 308, 309, 314, 318, 322, 327, 331, 418, 421, 445, 512, 513, 515, 517, 518, 522, 524, 529, 530, 534, 535, 536, 551, 562, 592, 595, 600, 639, 643, 652, 656, 657, 660, 661, 667, 673, 677, 681, 686, 703, 732, 763, 788, 899, 903, 904, 940, 943, 944, 948, 1109, 1145, 1272, 1275, 1286, 1296, 1344, 1452, 1498, 2185, 2199, 2210, 2212, 2213, 2238, 2246, 2298], │ │ │ │ │ "20180101": [1272, 1275, 1286, 1296], │ │ │ │ │ "20180310": [115, 681], │ │ │ │ │ "2018q1": [529, 2238], │ │ │ │ │ "2018q2": 2238, │ │ │ │ │ "2019": [13, 26, 27, 29, 30, 31, 418, 421, 1344, 1487, 1560, 2199, 2210, 2213, 2241, 2242, 2243, 2245, 2271, 2302], │ │ │ │ │ + "201922": 2228, │ │ │ │ │ "202": [2184, 2185, 2186, 2188, 2195, 2197, 2199, 2207, 2210, 2211], │ │ │ │ │ "2020": [22, 82, 121, 218, 230, 268, 286, 287, 289, 293, 295, 298, 300, 317, 323, 324, 329, 519, 521, 523, 542, 547, 548, 549, 551, 593, 641, 645, 647, 649, 650, 651, 671, 678, 679, 684, 696, 793, 804, 939, 955, 956, 957, 958, 962, 963, 964, 965, 966, 967, 968, 970, 972, 973, 975, 976, 977, 978, 979, 980, 981, 983, 990, 992, 993, 994, 995, 997, 999, 1002, 1006, 1007, 1008, 1009, 1010, 1013, 1014, 1017, 1018, 1019, 1023, 1025, 1075, 1392, 1459, 1464, 1498, 1506, 1524, 1542, 1560, 2199, 2201, 2204, 2210, 2212, 2213, 2283, 2289, 2294, 2298, 2302, 2307], │ │ │ │ │ "20200101": [82, 593], │ │ │ │ │ "2020q1": 1008, │ │ │ │ │ "2021": [288, 296, 318, 639, 652, 673, 940, 943, 948, 957, 970, 997, 1542, 2201, 2207, 2213, 2277, 2289, 2294], │ │ │ │ │ "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], │ │ │ │ │ "2022a": 2294, │ │ │ │ │ "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], │ │ │ │ │ "202380": 2207, │ │ │ │ │ "20239": [2241, 2265], │ │ │ │ │ "2024": [270, 544, 546, 555, 567, 894, 898, 2127, 2213], │ │ │ │ │ - "2025": [36, 544, 546, 555, 567, 894, 898], │ │ │ │ │ + "2025": [36, 544, 546, 555, 567, 894, 898, 2228], │ │ │ │ │ "20251": 2307, │ │ │ │ │ "2026": 2228, │ │ │ │ │ "202602": 2205, │ │ │ │ │ "202646": 2230, │ │ │ │ │ - "2027": 2228, │ │ │ │ │ "20271": 2241, │ │ │ │ │ "202872": [2184, 2214], │ │ │ │ │ "202946": 2207, │ │ │ │ │ "203": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211, 2231, 2253], │ │ │ │ │ "2030": 2265, │ │ │ │ │ "20303": 2265, │ │ │ │ │ "20306": 2302, │ │ │ │ │ @@ -25920,15 +25920,15 @@ │ │ │ │ │ "20854": 2243, │ │ │ │ │ "208564": 2207, │ │ │ │ │ "20859": 2241, │ │ │ │ │ "20868": 2294, │ │ │ │ │ "20869": 2246, │ │ │ │ │ "208707": 2199, │ │ │ │ │ "208843": [2184, 2214], │ │ │ │ │ - "209": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2210, 2211, 2212, 2253], │ │ │ │ │ + "209": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211, 2212, 2253], │ │ │ │ │ "209013": 15, │ │ │ │ │ "20902": 2241, │ │ │ │ │ "209097": 2207, │ │ │ │ │ "20911": 2246, │ │ │ │ │ "209138": 2185, │ │ │ │ │ "20920": 2241, │ │ │ │ │ "20921": 2241, │ │ │ │ │ @@ -25975,15 +25975,15 @@ │ │ │ │ │ "21071": 2242, │ │ │ │ │ "21078": 2242, │ │ │ │ │ "21083": 2242, │ │ │ │ │ "2109": 2264, │ │ │ │ │ "21090": 2271, │ │ │ │ │ "210945": 2195, │ │ │ │ │ "21097": 2242, │ │ │ │ │ - "211": [2185, 2186, 2188, 2195, 2197, 2199, 2203, 2210, 2211, 2212, 2254], │ │ │ │ │ + "211": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2203, 2210, 2211, 2212, 2254], │ │ │ │ │ "2110": 2264, │ │ │ │ │ "21101": 2242, │ │ │ │ │ "21103": 2242, │ │ │ │ │ "21104": 2243, │ │ │ │ │ "211056": 2197, │ │ │ │ │ "21106": 2242, │ │ │ │ │ "21107": 2242, │ │ │ │ │ @@ -26325,15 +26325,15 @@ │ │ │ │ │ "224824": 2207, │ │ │ │ │ "224826": 2210, │ │ │ │ │ "22484": [2246, 2249], │ │ │ │ │ "22487": 2246, │ │ │ │ │ "2249": [2194, 2201, 2203, 2294, 2302, 2307], │ │ │ │ │ "224904": 2230, │ │ │ │ │ "22492": 2246, │ │ │ │ │ - "225": [118, 132, 135, 159, 161, 175, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2210, 2227], │ │ │ │ │ + "225": [118, 132, 135, 159, 161, 175, 2185, 2186, 2188, 2195, 2197, 2199, 2210, 2227], │ │ │ │ │ "2250": [2194, 2201, 2203, 2294, 2302, 2307], │ │ │ │ │ "225000": [121, 696], │ │ │ │ │ "22501": 2249, │ │ │ │ │ "22508": 2246, │ │ │ │ │ "2251": [2194, 2201, 2203, 2294, 2302, 2307], │ │ │ │ │ "22519": 2246, │ │ │ │ │ "2252": [2194, 2201, 2203, 2294, 2302, 2307], │ │ │ │ │ @@ -26409,15 +26409,15 @@ │ │ │ │ │ "22818": [2283, 2298], │ │ │ │ │ "22835": 2246, │ │ │ │ │ "22858": 2246, │ │ │ │ │ "22860": 2246, │ │ │ │ │ "22862": 2246, │ │ │ │ │ "22880": 2246, │ │ │ │ │ "22887": 2246, │ │ │ │ │ - "229": [2185, 2186, 2188, 2195, 2197, 2199, 2210], │ │ │ │ │ + "229": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2210], │ │ │ │ │ "22903": 2246, │ │ │ │ │ "22905": 2246, │ │ │ │ │ "22912": 2246, │ │ │ │ │ "22922": 2246, │ │ │ │ │ "229349": 2207, │ │ │ │ │ "22938": 2246, │ │ │ │ │ "229453": 2197, │ │ │ │ │ @@ -26500,15 +26500,15 @@ │ │ │ │ │ "23316": 2289, │ │ │ │ │ "233203": 2197, │ │ │ │ │ "23348": 2265, │ │ │ │ │ "23352": 2246, │ │ │ │ │ "233686": [121, 696, 2212], │ │ │ │ │ "23372": 2246, │ │ │ │ │ "233881": 2199, │ │ │ │ │ - "234": [233, 2185, 2186, 2188, 2195, 2197, 2199, 2203, 2210, 2220, 2254, 2298], │ │ │ │ │ + "234": [233, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2203, 2210, 2220, 2254, 2298], │ │ │ │ │ "23404": 2246, │ │ │ │ │ "234178": 2207, │ │ │ │ │ "23424": 2246, │ │ │ │ │ "23451": 2246, │ │ │ │ │ "23455": 2246, │ │ │ │ │ "234564": 2195, │ │ │ │ │ "23466": 2246, │ │ │ │ │ @@ -26652,15 +26652,15 @@ │ │ │ │ │ "23980": 2246, │ │ │ │ │ "239885": 2186, │ │ │ │ │ "23990": [2246, 2265], │ │ │ │ │ "23998": 2289, │ │ │ │ │ "239990": 2235, │ │ │ │ │ "23h30min": [213, 345, 788, 2210], │ │ │ │ │ "24": [3, 15, 17, 18, 19, 25, 29, 30, 31, 32, 35, 101, 133, 134, 198, 208, 213, 214, 249, 271, 282, 341, 345, 407, 411, 532, 632, 708, 745, 751, 782, 788, 882, 899, 938, 1198, 1202, 1263, 1344, 1397, 1430, 1491, 1506, 1524, 1542, 1560, 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, 2222, 2223, 2225, 2226, 2228, 2230, 2231, 2232, 2235, 2238, 2241, 2249, 2265, 2271, 2277, 2283, 2287, 2289, 2294, 2297, 2298, 2302, 2307], │ │ │ │ │ - "240": [1302, 1433, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2210, 2220, 2231, 2238, 2246, 2298], │ │ │ │ │ + "240": [1302, 1433, 2185, 2186, 2188, 2195, 2197, 2199, 2210, 2220, 2231, 2238, 2246, 2298], │ │ │ │ │ "24008": 2223, │ │ │ │ │ "24009288": 2199, │ │ │ │ │ "24011": 2249, │ │ │ │ │ "24014": 2249, │ │ │ │ │ "24023": 2246, │ │ │ │ │ "24024": 2246, │ │ │ │ │ "24025": 2246, │ │ │ │ │ @@ -27435,15 +27435,15 @@ │ │ │ │ │ "2767": 2191, │ │ │ │ │ "27676": 2265, │ │ │ │ │ "27679": 2269, │ │ │ │ │ "276829": 2185, │ │ │ │ │ "27686": 2265, │ │ │ │ │ "27692": 2271, │ │ │ │ │ "276923": 2212, │ │ │ │ │ - "277": [2186, 2195, 2197, 2199, 2205, 2210], │ │ │ │ │ + "277": [2186, 2195, 2197, 2199, 2210], │ │ │ │ │ "277052": 2207, │ │ │ │ │ "27709": 2283, │ │ │ │ │ "277155": 2186, │ │ │ │ │ "27720": 2250, │ │ │ │ │ "277264": 2207, │ │ │ │ │ "277320": 1301, │ │ │ │ │ "27733": 2250, │ │ │ │ │ @@ -28274,15 +28274,15 @@ │ │ │ │ │ "320444": 2207, │ │ │ │ │ "3205": 2199, │ │ │ │ │ "3206": 2199, │ │ │ │ │ "320690": 2191, │ │ │ │ │ "32073": 2277, │ │ │ │ │ "3208": 2199, │ │ │ │ │ "3209": 2199, │ │ │ │ │ - "321": [2186, 2197, 2199, 2210, 2255], │ │ │ │ │ + "321": [2186, 2193, 2197, 2199, 2210, 2255], │ │ │ │ │ "3210": 2199, │ │ │ │ │ "321153": 2195, │ │ │ │ │ "321158": 2230, │ │ │ │ │ "32117": 2267, │ │ │ │ │ "321219": 2191, │ │ │ │ │ "32123": 2267, │ │ │ │ │ "321243": 2186, │ │ │ │ │ @@ -28308,15 +28308,15 @@ │ │ │ │ │ "32259": 2283, │ │ │ │ │ "32262": 2283, │ │ │ │ │ "32264": 2294, │ │ │ │ │ "32265": 2277, │ │ │ │ │ "32276": 2271, │ │ │ │ │ "32287": 2267, │ │ │ │ │ "32289": 2271, │ │ │ │ │ - "323": [2186, 2197, 2199, 2210], │ │ │ │ │ + "323": [2185, 2186, 2197, 2199, 2210], │ │ │ │ │ "3230": 2217, │ │ │ │ │ "3232": 2249, │ │ │ │ │ "3232235777": 2241, │ │ │ │ │ "323321": 2197, │ │ │ │ │ "32334": 2277, │ │ │ │ │ "32346": 2294, │ │ │ │ │ "323510": 2207, │ │ │ │ │ @@ -28404,15 +28404,15 @@ │ │ │ │ │ "32766": 30, │ │ │ │ │ "327710": 2191, │ │ │ │ │ "32779": 2271, │ │ │ │ │ "32782": 2271, │ │ │ │ │ "327863": 2186, │ │ │ │ │ "3279": 2199, │ │ │ │ │ "32792": 2271, │ │ │ │ │ - "328": [2184, 2186, 2191, 2197, 2199, 2205, 2210, 2218, 2246], │ │ │ │ │ + "328": [2184, 2186, 2191, 2197, 2199, 2205, 2210, 2246], │ │ │ │ │ "3280": 2199, │ │ │ │ │ "32800": 2269, │ │ │ │ │ "32803": 2289, │ │ │ │ │ "32806": 2271, │ │ │ │ │ "32809": 2271, │ │ │ │ │ "3281": 2199, │ │ │ │ │ "32815": 2271, │ │ │ │ │ @@ -28541,15 +28541,15 @@ │ │ │ │ │ "333758": 2193, │ │ │ │ │ "333828": 2186, │ │ │ │ │ "33385": [2271, 2298], │ │ │ │ │ "33388": 2271, │ │ │ │ │ "33389": 2271, │ │ │ │ │ "333945": 2212, │ │ │ │ │ "33396": [2289, 2298], │ │ │ │ │ - "334": [15, 2186, 2197, 2199, 2207, 2210, 2254], │ │ │ │ │ + "334": [15, 2186, 2193, 2197, 2199, 2207, 2210, 2254], │ │ │ │ │ "33401": 2283, │ │ │ │ │ "334077": [2230, 2231], │ │ │ │ │ "33410": 2271, │ │ │ │ │ "3342": 2206, │ │ │ │ │ "3342113401317768": 2206, │ │ │ │ │ "33422": 2271, │ │ │ │ │ "33425": 2271, │ │ │ │ │ @@ -28672,15 +28672,15 @@ │ │ │ │ │ "339770": 2195, │ │ │ │ │ "33980": 2271, │ │ │ │ │ "339846": 2230, │ │ │ │ │ "33987": 2277, │ │ │ │ │ "339969": [2184, 2214], │ │ │ │ │ "33ff85": 1433, │ │ │ │ │ "34": [15, 17, 18, 19, 29, 1017, 1403, 1404, 2184, 2185, 2186, 2188, 2190, 2191, 2193, 2194, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2216, 2217, 2218, 2219, 2220, 2222, 2225, 2226, 2227, 2228, 2230, 2231, 2232, 2235, 2238, 2241, 2246, 2249, 2265, 2271, 2283, 2294, 2298], │ │ │ │ │ - "340": [2186, 2188, 2191, 2197, 2199, 2210], │ │ │ │ │ + "340": [2186, 2188, 2191, 2193, 2197, 2199, 2210], │ │ │ │ │ "34002": 2283, │ │ │ │ │ "3401": 2219, │ │ │ │ │ "34010": 2269, │ │ │ │ │ "3403": 2191, │ │ │ │ │ "3403088497993827": 2197, │ │ │ │ │ "340309": [2185, 2197, 2199, 2202, 2204, 2215, 2257], │ │ │ │ │ "3404": 2232, │ │ │ │ │ @@ -29478,15 +29478,15 @@ │ │ │ │ │ "3807": [2185, 2191, 2194], │ │ │ │ │ "38071": 2277, │ │ │ │ │ "3808": [2185, 2191, 2194], │ │ │ │ │ "380863": [2204, 2257], │ │ │ │ │ "380871": 2191, │ │ │ │ │ "3809": [2185, 2191, 2194], │ │ │ │ │ "38098": 2277, │ │ │ │ │ - "381": [2185, 2186, 2197, 2199, 2210, 2255], │ │ │ │ │ + "381": [2186, 2197, 2199, 2210, 2255], │ │ │ │ │ "3810": [2185, 2191, 2194], │ │ │ │ │ "38100": 2289, │ │ │ │ │ "3811": [2185, 2191, 2194], │ │ │ │ │ "38111": 2277, │ │ │ │ │ "381137": 2207, │ │ │ │ │ "381160": 2191, │ │ │ │ │ "3812": [2185, 2191, 2194], │ │ │ │ │ @@ -30278,15 +30278,15 @@ │ │ │ │ │ "41647": 2283, │ │ │ │ │ "41653": 2283, │ │ │ │ │ "41662": 2298, │ │ │ │ │ "41670": 2283, │ │ │ │ │ "41673": 2283, │ │ │ │ │ "416988": 2191, │ │ │ │ │ "41699": 2298, │ │ │ │ │ - "417": [2185, 2186, 2199, 2205, 2210, 2227], │ │ │ │ │ + "417": [2185, 2186, 2199, 2210, 2227], │ │ │ │ │ "4170": 2218, │ │ │ │ │ "41700": 2199, │ │ │ │ │ "41707": 2283, │ │ │ │ │ "41710": 2294, │ │ │ │ │ "417200": 2207, │ │ │ │ │ "41731": 2298, │ │ │ │ │ "41733": 2298, │ │ │ │ │ @@ -30850,15 +30850,15 @@ │ │ │ │ │ "439872": 2199, │ │ │ │ │ "43988": 2289, │ │ │ │ │ "439895": 2193, │ │ │ │ │ "4399": 2197, │ │ │ │ │ "43997": 2289, │ │ │ │ │ "43999": 2302, │ │ │ │ │ "44": [15, 17, 19, 28, 31, 32, 213, 345, 788, 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, 2219, 2220, 2222, 2225, 2226, 2228, 2230, 2232, 2235, 2238, 2241, 2246, 2249, 2265, 2271, 2283, 2294], │ │ │ │ │ - "440": [1363, 2185, 2186, 2199, 2210], │ │ │ │ │ + "440": [1363, 2186, 2199, 2210], │ │ │ │ │ "4400": 2197, │ │ │ │ │ "44008": 2302, │ │ │ │ │ "44011": 2289, │ │ │ │ │ "44014": 2294, │ │ │ │ │ "44019": 2289, │ │ │ │ │ "4402": 2218, │ │ │ │ │ "44021": 2289, │ │ │ │ │ @@ -31533,15 +31533,15 @@ │ │ │ │ │ "471593": 2204, │ │ │ │ │ "47172": 2293, │ │ │ │ │ "47177": 2298, │ │ │ │ │ "4718": 2218, │ │ │ │ │ "47188": 2292, │ │ │ │ │ "47196": 2294, │ │ │ │ │ "471992": 2264, │ │ │ │ │ - "472": [2191, 2199, 2210], │ │ │ │ │ + "472": [2191, 2193, 2199, 2210], │ │ │ │ │ "47203": 2294, │ │ │ │ │ "472035": [2185, 2197, 2199, 2202, 2204, 2215, 2257], │ │ │ │ │ "47207": 2292, │ │ │ │ │ "47209": 2294, │ │ │ │ │ "47215": 2294, │ │ │ │ │ "47216": 2294, │ │ │ │ │ "47244": 2298, │ │ │ │ │ @@ -32180,15 +32180,15 @@ │ │ │ │ │ "50453": 2298, │ │ │ │ │ "50465": 2298, │ │ │ │ │ "50467": 2298, │ │ │ │ │ "50471": 2298, │ │ │ │ │ "5048": 2218, │ │ │ │ │ "50482": 2298, │ │ │ │ │ "50486": 2298, │ │ │ │ │ - "505": [16, 17, 18, 19, 2199, 2235], │ │ │ │ │ + "505": [16, 17, 18, 19, 2185, 2199, 2235], │ │ │ │ │ "505089": 2207, │ │ │ │ │ "50524": 2298, │ │ │ │ │ "50533": 2298, │ │ │ │ │ "505430": 2220, │ │ │ │ │ "505601": 2186, │ │ │ │ │ "50563": 2298, │ │ │ │ │ "505723": 2197, │ │ │ │ │ @@ -32453,15 +32453,15 @@ │ │ │ │ │ "51856": 2302, │ │ │ │ │ "51858": 2302, │ │ │ │ │ "51861": 2302, │ │ │ │ │ "51873": 2302, │ │ │ │ │ "518736": 2197, │ │ │ │ │ "51895": 2300, │ │ │ │ │ "51896": 2302, │ │ │ │ │ - "519": [2194, 2199, 2201, 2203, 2238, 2283, 2294, 2307], │ │ │ │ │ + "519": [2193, 2194, 2199, 2201, 2203, 2238, 2283, 2294, 2307], │ │ │ │ │ "51903": 2302, │ │ │ │ │ "5191": 2218, │ │ │ │ │ "519133": 2207, │ │ │ │ │ "51921": 2302, │ │ │ │ │ "51922": 2302, │ │ │ │ │ "51929": 2307, │ │ │ │ │ "51936": 2302, │ │ │ │ │ @@ -33570,15 +33570,15 @@ │ │ │ │ │ "587528": 2207, │ │ │ │ │ "587584": 2207, │ │ │ │ │ "5877": 2219, │ │ │ │ │ "58776": 2257, │ │ │ │ │ "5878": 2220, │ │ │ │ │ "587886": 2207, │ │ │ │ │ "5879": 2219, │ │ │ │ │ - "588": 2199, │ │ │ │ │ + "588": [2193, 2199], │ │ │ │ │ "5884": 2222, │ │ │ │ │ "588635": 2230, │ │ │ │ │ "588641": 2207, │ │ │ │ │ "589": [1193, 1254, 2199], │ │ │ │ │ "5890": 2219, │ │ │ │ │ "589168": 2197, │ │ │ │ │ "5892": [183, 761], │ │ │ │ │ @@ -33957,15 +33957,15 @@ │ │ │ │ │ "633": 2199, │ │ │ │ │ "633165": 2230, │ │ │ │ │ "6332": 2220, │ │ │ │ │ "633372": 2215, │ │ │ │ │ "6335": 2220, │ │ │ │ │ "633678": 2185, │ │ │ │ │ "6337": 2220, │ │ │ │ │ - "634": 2199, │ │ │ │ │ + "634": [2185, 2199], │ │ │ │ │ "6341": 2220, │ │ │ │ │ "6342": 2220, │ │ │ │ │ "634248": 2199, │ │ │ │ │ "6344": 2220, │ │ │ │ │ "6345": 2220, │ │ │ │ │ "634509": 2191, │ │ │ │ │ "634686": 2207, │ │ │ │ │ @@ -34489,15 +34489,15 @@ │ │ │ │ │ "693043": 2210, │ │ │ │ │ "6932": 2222, │ │ │ │ │ "693205": [2184, 2214], │ │ │ │ │ "693429": 28, │ │ │ │ │ "6937": 2221, │ │ │ │ │ "693884": 2210, │ │ │ │ │ "6939": 2220, │ │ │ │ │ - "694": [2199, 2205], │ │ │ │ │ + "694": 2199, │ │ │ │ │ "694268": 28, │ │ │ │ │ "6945": 2241, │ │ │ │ │ "694592": 2207, │ │ │ │ │ "695": 2199, │ │ │ │ │ "6951": 2220, │ │ │ │ │ "695148": 2186, │ │ │ │ │ "6952": 2220, │ │ │ │ │ @@ -34577,15 +34577,15 @@ │ │ │ │ │ "7034": [2199, 2220], │ │ │ │ │ "7035": 2199, │ │ │ │ │ "7036": 2199, │ │ │ │ │ "7037": 2199, │ │ │ │ │ "7038": 2199, │ │ │ │ │ "703846": 2201, │ │ │ │ │ "7039": 2199, │ │ │ │ │ - "704": [2199, 2203, 2205], │ │ │ │ │ + "704": [2199, 2203], │ │ │ │ │ "7040": [2199, 2220], │ │ │ │ │ "704118": 2207, │ │ │ │ │ "704261": 2230, │ │ │ │ │ "7043": 2220, │ │ │ │ │ "704581": 2230, │ │ │ │ │ "704907": [1148, 1149], │ │ │ │ │ "705": [1193, 1254, 2199], │ │ │ │ │ @@ -34631,15 +34631,15 @@ │ │ │ │ │ "709459": 2199, │ │ │ │ │ "7095": 2228, │ │ │ │ │ "7096": 2232, │ │ │ │ │ "709661": [2184, 2214], │ │ │ │ │ "7097": 2222, │ │ │ │ │ "7098": 2220, │ │ │ │ │ "71": [15, 17, 24, 25, 28, 29, 32, 133, 208, 708, 718, 782, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2205, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2222, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271], │ │ │ │ │ - "710": 2199, │ │ │ │ │ + "710": [2193, 2199], │ │ │ │ │ "7101": 2220, │ │ │ │ │ "7103": 2222, │ │ │ │ │ "7105": 2220, │ │ │ │ │ "7106": 2220, │ │ │ │ │ "711": 2199, │ │ │ │ │ "711409": 2186, │ │ │ │ │ "7115": 2223, │ │ │ │ │ @@ -34700,15 +34700,15 @@ │ │ │ │ │ "719369": 2195, │ │ │ │ │ "7195": 2221, │ │ │ │ │ "719541": 2228, │ │ │ │ │ "7196": 2221, │ │ │ │ │ "7198": 2220, │ │ │ │ │ "7199": 2220, │ │ │ │ │ "719915": 2207, │ │ │ │ │ - "72": [17, 31, 190, 193, 766, 768, 1189, 1250, 1433, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2205, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2238, 2241, 2246, 2271], │ │ │ │ │ + "72": [17, 31, 190, 193, 766, 768, 1189, 1250, 1433, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2205, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2238, 2241, 2246, 2271], │ │ │ │ │ "720": [69, 109, 129, 171, 173, 199, 204, 206, 215, 216, 217, 220, 221, 222, 244, 275, 1447, 2200, 2232], │ │ │ │ │ "7200": 2210, │ │ │ │ │ "720000": [2191, 2225], │ │ │ │ │ "720521": 2210, │ │ │ │ │ "720589": [2220, 2228, 2230, 2231], │ │ │ │ │ "7206": 2220, │ │ │ │ │ "7207": 2222, │ │ │ │ │ @@ -34765,15 +34765,15 @@ │ │ │ │ │ "729": [16, 17, 18, 19, 2197, 2199, 2231, 2235], │ │ │ │ │ "729161": 2199, │ │ │ │ │ "7292": 2241, │ │ │ │ │ "7297": 2221, │ │ │ │ │ "7299": 2221, │ │ │ │ │ "729907": 2186, │ │ │ │ │ "72hr": 234, │ │ │ │ │ - "73": [15, 17, 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, 2238, 2241, 2246, 2271], │ │ │ │ │ + "73": [15, 17, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2205, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2238, 2241, 2246, 2271], │ │ │ │ │ "730": [16, 17, 18, 19, 2199, 2235], │ │ │ │ │ "7300": 2221, │ │ │ │ │ "730057": 2195, │ │ │ │ │ "7302": 2221, │ │ │ │ │ "7306": 2221, │ │ │ │ │ "7308": 2294, │ │ │ │ │ "730951": 2257, │ │ │ │ │ @@ -34995,15 +34995,15 @@ │ │ │ │ │ "759104": 2185, │ │ │ │ │ "7592": 2221, │ │ │ │ │ "759328": 2199, │ │ │ │ │ "759606": 2199, │ │ │ │ │ "759644": 2222, │ │ │ │ │ "7599": 2228, │ │ │ │ │ "75th": [107, 629, 1164, 1221], │ │ │ │ │ - "76": [18, 190, 193, 766, 768, 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], │ │ │ │ │ + "76": [18, 190, 193, 766, 768, 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], │ │ │ │ │ "760": [32, 2298], │ │ │ │ │ "7601": 2221, │ │ │ │ │ "760109": 2195, │ │ │ │ │ "7606": 2221, │ │ │ │ │ "760643": 2199, │ │ │ │ │ "7609": 2221, │ │ │ │ │ "760970": 2207, │ │ │ │ │ @@ -35051,15 +35051,15 @@ │ │ │ │ │ "7683": 2222, │ │ │ │ │ "768681": 2207, │ │ │ │ │ "7687": [2246, 2271], │ │ │ │ │ "7692": 2228, │ │ │ │ │ "769691": 2207, │ │ │ │ │ "7697": 2222, │ │ │ │ │ "769804": [2185, 2191, 2197, 2199, 2202, 2204], │ │ │ │ │ - "77": [15, 81, 1447, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2205, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271], │ │ │ │ │ + "77": [15, 81, 1447, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2205, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271], │ │ │ │ │ "770": [2193, 2207], │ │ │ │ │ "7701": 2221, │ │ │ │ │ "770309": 2207, │ │ │ │ │ "7704": 2222, │ │ │ │ │ "770555": 2204, │ │ │ │ │ "770743": 2207, │ │ │ │ │ "7708": 2222, │ │ │ │ │ @@ -35166,14 +35166,15 @@ │ │ │ │ │ "782": 2277, │ │ │ │ │ "7822": 2222, │ │ │ │ │ "782326": 2207, │ │ │ │ │ "782376": 2214, │ │ │ │ │ "7825": [2243, 2246], │ │ │ │ │ "7826": 2222, │ │ │ │ │ "782797": 2195, │ │ │ │ │ + "783": 2193, │ │ │ │ │ "783051": 2219, │ │ │ │ │ "783123": 2186, │ │ │ │ │ "783168": 2207, │ │ │ │ │ "7833": 2222, │ │ │ │ │ "783425": 2207, │ │ │ │ │ "7835": 2222, │ │ │ │ │ "7839": 2222, │ │ │ │ │ @@ -35455,23 +35456,23 @@ │ │ │ │ │ "8190": 2222, │ │ │ │ │ "819059": 2207, │ │ │ │ │ "8193": 2271, │ │ │ │ │ "819476": 2207, │ │ │ │ │ "819492": 2207, │ │ │ │ │ "8199": 2222, │ │ │ │ │ "82": [2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271], │ │ │ │ │ - "820": 2199, │ │ │ │ │ + "820": [2193, 2199], │ │ │ │ │ "820223": 2191, │ │ │ │ │ "820408": 2215, │ │ │ │ │ "820750": 2199, │ │ │ │ │ "8208": 2222, │ │ │ │ │ "820801": 2230, │ │ │ │ │ "8209": 2222, │ │ │ │ │ "820952": 2199, │ │ │ │ │ - "821": [2193, 2199], │ │ │ │ │ + "821": 2199, │ │ │ │ │ "821225": 2205, │ │ │ │ │ "821428": 2218, │ │ │ │ │ "8215": 2222, │ │ │ │ │ "8217": 2222, │ │ │ │ │ "822": 2199, │ │ │ │ │ "822162": 2207, │ │ │ │ │ "8222": 2235, │ │ │ │ │ @@ -35521,15 +35522,15 @@ │ │ │ │ │ "8285": 2225, │ │ │ │ │ "8287": 2232, │ │ │ │ │ "828904": 2191, │ │ │ │ │ "8292": 2232, │ │ │ │ │ "829645": 2207, │ │ │ │ │ "829678": 2191, │ │ │ │ │ "829721": 2212, │ │ │ │ │ - "83": [15, 24, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271], │ │ │ │ │ + "83": [15, 24, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271], │ │ │ │ │ "8302": 2224, │ │ │ │ │ "8303": 2222, │ │ │ │ │ "830429": 2207, │ │ │ │ │ "8305": 2222, │ │ │ │ │ "830545": 2199, │ │ │ │ │ "8306": [2243, 2246], │ │ │ │ │ "830957": 2207, │ │ │ │ │ @@ -35636,15 +35637,15 @@ │ │ │ │ │ "848896": 2193, │ │ │ │ │ "848974": 2197, │ │ │ │ │ "849": [16, 17, 18, 19, 2199, 2235], │ │ │ │ │ "8494": 2223, │ │ │ │ │ "8496": 2241, │ │ │ │ │ "84960": 2210, │ │ │ │ │ "849980": 2195, │ │ │ │ │ - "85": [182, 190, 193, 718, 760, 766, 768, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246], │ │ │ │ │ + "85": [182, 190, 193, 718, 760, 766, 768, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246], │ │ │ │ │ "850": [16, 17, 18, 19, 2199, 2235], │ │ │ │ │ "850083": 2207, │ │ │ │ │ "8501": 2222, │ │ │ │ │ "850229": 2235, │ │ │ │ │ "850287": 2207, │ │ │ │ │ "8504": 2202, │ │ │ │ │ "850458": 2207, │ │ │ │ │ @@ -35713,15 +35714,15 @@ │ │ │ │ │ "8592": 2223, │ │ │ │ │ "8594": 2265, │ │ │ │ │ "859511": 2207, │ │ │ │ │ "859588": [2220, 2228, 2230, 2231], │ │ │ │ │ "8596": 2232, │ │ │ │ │ "859691": 2191, │ │ │ │ │ "85a3": 2241, │ │ │ │ │ - "86": [16, 1433, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246], │ │ │ │ │ + "86": [16, 1433, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246], │ │ │ │ │ "860": [182, 760, 2199], │ │ │ │ │ "860059": 2204, │ │ │ │ │ "8601": [662, 923, 983, 2199, 2209, 2210, 2230, 2235, 2241, 2271, 2277, 2283, 2298], │ │ │ │ │ "8602": 2224, │ │ │ │ │ "860312": 2199, │ │ │ │ │ "8607": 2223, │ │ │ │ │ "860736": 15, │ │ │ │ │ @@ -35949,15 +35950,15 @@ │ │ │ │ │ "8890": [2224, 2225], │ │ │ │ │ "889157": 2235, │ │ │ │ │ "889273": 2235, │ │ │ │ │ "889493": 2186, │ │ │ │ │ "889659": 2186, │ │ │ │ │ "889987": 2205, │ │ │ │ │ "89": [207, 781, 1433, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2298], │ │ │ │ │ - "890": [24, 25, 32, 2197, 2199], │ │ │ │ │ + "890": [24, 25, 32, 2193, 2197, 2199], │ │ │ │ │ "8904": 2224, │ │ │ │ │ "890546": 2186, │ │ │ │ │ "890819": 2206, │ │ │ │ │ "8909": 2224, │ │ │ │ │ "891": [24, 25, 28, 32, 2197, 2199], │ │ │ │ │ "8910": [2243, 2246], │ │ │ │ │ "891236": 2193, │ │ │ │ │ @@ -36091,15 +36092,15 @@ │ │ │ │ │ "9093": 2271, │ │ │ │ │ "909316": 2230, │ │ │ │ │ "9094": 2225, │ │ │ │ │ "909500": 2195, │ │ │ │ │ "9096": 2225, │ │ │ │ │ "909872": 2185, │ │ │ │ │ "9099": 2225, │ │ │ │ │ - "91": [15, 182, 760, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2294, 2298], │ │ │ │ │ + "91": [15, 182, 760, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2294, 2298], │ │ │ │ │ "9100": 2225, │ │ │ │ │ "910199": 2199, │ │ │ │ │ "910400": 28, │ │ │ │ │ "911055": 2195, │ │ │ │ │ "911128": 2207, │ │ │ │ │ "911385": 2207, │ │ │ │ │ "9114": 2232, │ │ │ │ │ @@ -36178,27 +36179,25 @@ │ │ │ │ │ "9208": 2246, │ │ │ │ │ "920830": 2216, │ │ │ │ │ "9209": 2202, │ │ │ │ │ "9210": 2225, │ │ │ │ │ "921208": 2207, │ │ │ │ │ "921215": 2207, │ │ │ │ │ "921297": [102, 1158], │ │ │ │ │ - "921345": 2228, │ │ │ │ │ "921494": 15, │ │ │ │ │ "9217": 2235, │ │ │ │ │ "9218": 2228, │ │ │ │ │ "922": [2186, 2227], │ │ │ │ │ "9221": 2225, │ │ │ │ │ "922152": 2199, │ │ │ │ │ "9223372036854775808": [1499, 2294], │ │ │ │ │ "922818": 2184, │ │ │ │ │ "922883": 2210, │ │ │ │ │ "9229": [2202, 2225], │ │ │ │ │ "923061": [2185, 2197, 2199, 2202, 2204, 2215, 2257], │ │ │ │ │ - "923075": 2228, │ │ │ │ │ "9231": [2191, 2225], │ │ │ │ │ "923568": 2204, │ │ │ │ │ "924": 2263, │ │ │ │ │ "924296": 2195, │ │ │ │ │ "9243": 2246, │ │ │ │ │ "9244": 2230, │ │ │ │ │ "924556": 2205, │ │ │ │ │ @@ -36291,15 +36290,15 @@ │ │ │ │ │ "938819": 2204, │ │ │ │ │ "939": 2230, │ │ │ │ │ "939036": 2207, │ │ │ │ │ "939145": 2207, │ │ │ │ │ "939470": 2199, │ │ │ │ │ "939652": 2207, │ │ │ │ │ "9398": 2225, │ │ │ │ │ - "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], │ │ │ │ │ + "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], │ │ │ │ │ "9402": 2228, │ │ │ │ │ "941248": 2199, │ │ │ │ │ "9413": 2238, │ │ │ │ │ "941451": 2210, │ │ │ │ │ "9416": 2228, │ │ │ │ │ "9422": 2238, │ │ │ │ │ "942321": 2207, │ │ │ │ │ @@ -36592,15 +36591,14 @@ │ │ │ │ │ "984017": 2204, │ │ │ │ │ "984435": 2219, │ │ │ │ │ "9847": 2226, │ │ │ │ │ "984729": 2214, │ │ │ │ │ "9848": 2226, │ │ │ │ │ "984810": 2210, │ │ │ │ │ "984960": 2197, │ │ │ │ │ - "985": 2193, │ │ │ │ │ "9850": 2231, │ │ │ │ │ "9852": 2226, │ │ │ │ │ "9853": 2226, │ │ │ │ │ "9856": 2226, │ │ │ │ │ "985655": 2199, │ │ │ │ │ "9861": 2226, │ │ │ │ │ "986137": 2191, │ │ │ │ │ @@ -36623,15 +36621,15 @@ │ │ │ │ │ "988693": [155, 156, 730, 731], │ │ │ │ │ "9890": 2226, │ │ │ │ │ "9894": 2228, │ │ │ │ │ "9895": 2235, │ │ │ │ │ "989634": 2204, │ │ │ │ │ "989726": 2207, │ │ │ │ │ "989859": 2185, │ │ │ │ │ - "99": [15, 22, 145, 163, 284, 532, 741, 912, 1447, 1456, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2226, 2230, 2232, 2235, 2246, 2294, 2307], │ │ │ │ │ + "99": [15, 22, 145, 163, 284, 532, 741, 912, 1447, 1456, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2226, 2230, 2232, 2235, 2246, 2294, 2307], │ │ │ │ │ "990": [2199, 2230], │ │ │ │ │ "9900": 2199, │ │ │ │ │ "990000": 1894, │ │ │ │ │ "990317": 2199, │ │ │ │ │ "990340": 2207, │ │ │ │ │ "9905": 2226, │ │ │ │ │ "990582": [2184, 2195, 2214], │ │ │ │ │ @@ -37795,15 +37793,15 @@ │ │ │ │ │ "begin": [3, 5, 13, 16, 19, 121, 233, 234, 259, 267, 270, 425, 426, 427, 502, 513, 515, 533, 535, 541, 696, 807, 808, 866, 873, 890, 896, 898, 1044, 1345, 1391, 1403, 1404, 1433, 1469, 1476, 1483, 1486, 1488, 1490, 1498, 1499, 1699, 1930, 2127, 2186, 2199, 2202, 2208, 2210, 2212, 2220, 2221, 2225, 2228, 2229, 2271, 2277, 2289], │ │ │ │ │ "behav": [7, 63, 134, 205, 267, 341, 709, 778, 896, 1350, 1387, 2168, 2185, 2187, 2190, 2195, 2198, 2203, 2207, 2209, 2210, 2211, 2220, 2222, 2224, 2225, 2232, 2235, 2238, 2240, 2249, 2261, 2265, 2277, 2283, 2289, 2290, 2294, 2302, 2307], │ │ │ │ │ "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], │ │ │ │ │ "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], │ │ │ │ │ "behind": [2197, 2207, 2218, 2302, 2307], │ │ │ │ │ "behr": 32, │ │ │ │ │ "beij": [1145, 2207], │ │ │ │ │ - "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, 2185, 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], │ │ │ │ │ + "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], │ │ │ │ │ "belal01": 30, │ │ │ │ │ "belhb23": 30, │ │ │ │ │ "belld01": 30, │ │ │ │ │ "belld02": 30, │ │ │ │ │ "belong": [2, 150, 303, 445, 555, 655, 2195, 2210, 2211, 2217, 2222, 2228, 2232], │ │ │ │ │ "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], │ │ │ │ │ "belr833": 30, │ │ │ │ │ @@ -38095,15 +38093,15 @@ │ │ │ │ │ "c_parser_wrapp": [2199, 2203, 2298], │ │ │ │ │ "c_sum": [1148, 1149], │ │ │ │ │ "ca": [824, 2208], │ │ │ │ │ "cab": [2185, 2226], │ │ │ │ │ "caba": [824, 2184, 2186, 2208], │ │ │ │ │ "cabin": [24, 25, 28, 29, 32], │ │ │ │ │ "cac": [1185, 1246, 1288], │ │ │ │ │ - "cach": [10, 22, 1345, 1391, 1469, 1486, 1488, 1490, 1498, 2185, 2186, 2192, 2193, 2199, 2202, 2210, 2212, 2218, 2219, 2220, 2226, 2227, 2228, 2241, 2246, 2249, 2265, 2266, 2271, 2273, 2277, 2283, 2284, 2289, 2293, 2298, 2307], │ │ │ │ │ + "cach": [10, 22, 1345, 1391, 1469, 1486, 1488, 1490, 1498, 2186, 2192, 2193, 2199, 2202, 2210, 2212, 2218, 2219, 2220, 2226, 2227, 2228, 2241, 2246, 2249, 2265, 2266, 2271, 2273, 2277, 2283, 2284, 2289, 2293, 2298, 2307], │ │ │ │ │ "cache_arrai": 2210, │ │ │ │ │ "cache_d": [16, 17, 18, 19, 1469, 1486, 2199, 2203, 2232, 2235, 2249, 2298], │ │ │ │ │ "cache_readonli": 2255, │ │ │ │ │ "cacheableoffset": [2218, 2241], │ │ │ │ │ "cacher": 2197, │ │ │ │ │ "cacher_needs_upd": 2197, │ │ │ │ │ "caeen": 864, │ │ │ │ │ @@ -39809,15 +39807,15 @@ │ │ │ │ │ "farmer": 2199, │ │ │ │ │ "farthest": [91, 1458], │ │ │ │ │ "fashion": [34, 39, 46, 2221, 2246, 2283], │ │ │ │ │ "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], │ │ │ │ │ "fast_path": 2199, │ │ │ │ │ "fastavro": [1473, 2249], │ │ │ │ │ "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], │ │ │ │ │ - "fastest": [2185, 2186, 2193, 2197, 2199], │ │ │ │ │ + "fastest": [2186, 2193, 2197, 2199], │ │ │ │ │ "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], │ │ │ │ │ "fastparquetimpl": 2199, │ │ │ │ │ "fastpath": [39, 573, 2194, 2201, 2203, 2246, 2265, 2271, 2283, 2294, 2298, 2302, 2307], │ │ │ │ │ "fatal": 2229, │ │ │ │ │ "fault": [2228, 2235, 2239, 2246, 2249, 2271, 2275, 2289], │ │ │ │ │ "faulti": 2220, │ │ │ │ │ "favor": [34, 2220, 2222, 2225, 2226, 2228, 2230, 2231, 2232, 2235, 2238, 2239, 2241, 2246, 2249, 2265, 2266, 2283, 2289, 2294, 2298], │ │ │ │ │ @@ -40889,15 +40887,15 @@ │ │ │ │ │ "interchang": [66, 246, 916, 953, 2172, 2299, 2300, 2302, 2307, 2308], │ │ │ │ │ "interchange_object": [66, 1077], │ │ │ │ │ "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], │ │ │ │ │ "interest_r": 3, │ │ │ │ │ "interf": 2265, │ │ │ │ │ "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], │ │ │ │ │ "interleav": 2199, │ │ │ │ │ - "intermedi": [7, 2172, 2185, 2193, 2195, 2205, 2210, 2212, 2253, 2307], │ │ │ │ │ + "intermedi": [7, 2172, 2193, 2195, 2205, 2210, 2212, 2253, 2307], │ │ │ │ │ "intermix": 2186, │ │ │ │ │ "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], │ │ │ │ │ "internal_cach": 10, │ │ │ │ │ "internet": 2, │ │ │ │ │ "interoper": [2167, 2186, 2201, 2203, 2302], │ │ │ │ │ "interp1d": [146, 720, 1280], │ │ │ │ │ "interp_": 2201, │ │ │ │ │ @@ -41064,15 +41062,15 @@ │ │ │ │ │ "isanchor": [2265, 2298], │ │ │ │ │ "isdecim": [836, 837, 839, 840, 841, 842, 843, 844, 2208, 2225], │ │ │ │ │ "isdigit": [836, 837, 838, 840, 841, 842, 843, 844, 2208, 2225], │ │ │ │ │ "isetitem": [2294, 2298, 2302], │ │ │ │ │ "isfinit": 2289, │ │ │ │ │ "isin": [15, 25, 439, 2184, 2194, 2196, 2207, 2218, 2220, 2222, 2228, 2231, 2235, 2236, 2237, 2238, 2241, 2246, 2249, 2255, 2257, 2271, 2274, 2275, 2277, 2283, 2284, 2285, 2289, 2294, 2295, 2297, 2298, 2299, 2302, 2305, 2307], │ │ │ │ │ "isinf": 2289, │ │ │ │ │ - "isinst": [2, 392, 395, 1082, 1088, 1094, 1099, 1106, 1111, 1403, 1404, 2184, 2185, 2186, 2191, 2193, 2194, 2197, 2199, 2201, 2203, 2205, 2208, 2218, 2232, 2261, 2283, 2289, 2294, 2298, 2302, 2307], │ │ │ │ │ + "isinst": [2, 392, 395, 1082, 1088, 1094, 1099, 1106, 1111, 1403, 1404, 2184, 2185, 2186, 2191, 2194, 2197, 2199, 2201, 2203, 2205, 2208, 2218, 2232, 2261, 2283, 2289, 2294, 2298, 2302, 2307], │ │ │ │ │ "isleapyear": [2232, 2241], │ │ │ │ │ "islow": [836, 837, 838, 839, 841, 842, 843, 844, 2208, 2225], │ │ │ │ │ "ismethod": 2265, │ │ │ │ │ "isn": [5, 13, 17, 77, 133, 708, 1348, 2186, 2190, 2192, 2193, 2197, 2207, 2208, 2210, 2220, 2221, 2232, 2241, 2246, 2250, 2265, 2289], │ │ │ │ │ "isna": [10, 16, 18, 19, 101, 114, 149, 177, 178, 413, 636, 726, 755, 756, 1031, 1042, 1182, 1241, 1415, 1442, 1449, 1450, 2184, 2186, 2188, 2194, 2201, 2203, 2238, 2241, 2246, 2250, 2269, 2271, 2283, 2289, 2298, 2302], │ │ │ │ │ "isnan": [2221, 2289], │ │ │ │ │ "isnul": [148, 725, 2214, 2218, 2219, 2220, 2221, 2225, 2228, 2229, 2232, 2235, 2238, 2250, 2253, 2298], │ │ │ │ │ @@ -41506,15 +41504,15 @@ │ │ │ │ │ "logx": [186, 762, 1188, 1249, 2211, 2215, 2249], │ │ │ │ │ "lon": [10, 1069, 1071, 1072], │ │ │ │ │ "london": [26, 27, 29, 30, 31, 586, 2210, 2221, 2271], │ │ │ │ │ "london_mg_per_cub": 27, │ │ │ │ │ "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], │ │ │ │ │ "long_seri": 2186, │ │ │ │ │ "longdoubl": 2186, │ │ │ │ │ - "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, 2185, 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], │ │ │ │ │ + "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], │ │ │ │ │ "longest": [32, 923, 2217, 2272], │ │ │ │ │ "longitud": [10, 30, 197, 1069, 1071, 1072], │ │ │ │ │ "longlong": 2186, │ │ │ │ │ "longpanel": [2228, 2246, 2257], │ │ │ │ │ "longtabl": [259, 890, 1345, 1391, 1433, 1488, 1490, 2202, 2220, 2230, 2239, 2277, 2289, 2291, 2298], │ │ │ │ │ "longtablebuild": 2277, │ │ │ │ │ "longtim": 2228, │ │ │ │ │ @@ -41568,15 +41566,15 @@ │ │ │ │ │ "ly": 2210, │ │ │ │ │ "lz4": [256, 263, 888, 2199, 2236], │ │ │ │ │ "lz4hc": [256, 888, 2199, 2236], │ │ │ │ │ "lzip": 2218, │ │ │ │ │ "lzma": [251, 258, 265, 268, 272, 884, 889, 895, 1469, 1476, 1479, 1480, 1485, 1486, 1487, 2213, 2289, 2298, 2302], │ │ │ │ │ "lzmafil": [251, 258, 265, 268, 272, 884, 889, 895, 1469, 1476, 1479, 1480, 1485, 1486, 1487, 2302], │ │ │ │ │ "lzo": [256, 888, 2199], │ │ │ │ │ - "m": [1, 2, 5, 8, 13, 16, 17, 19, 22, 23, 24, 25, 27, 31, 32, 153, 163, 169, 241, 258, 264, 270, 273, 276, 284, 287, 298, 300, 301, 320, 322, 326, 423, 513, 515, 519, 522, 523, 525, 528, 532, 535, 537, 538, 541, 547, 548, 549, 551, 557, 558, 562, 563, 564, 566, 651, 677, 680, 741, 857, 889, 898, 900, 902, 912, 916, 917, 918, 923, 938, 939, 953, 954, 997, 999, 1000, 1008, 1017, 1051, 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, 1282, 1283, 1284, 1285, 1287, 1288, 1290, 1291, 1292, 1293, 1294, 1295, 1297, 1338, 1339, 1340, 1341, 1393, 1397, 1430, 1433, 1446, 1452, 1459, 1464, 1469, 1476, 1482, 1483, 1484, 1486, 1492, 1497, 1498, 1500, 1501, 1578, 1657, 1677, 1699, 1720, 1741, 2186, 2188, 2193, 2197, 2199, 2200, 2201, 2203, 2207, 2208, 2209, 2210, 2214, 2216, 2218, 2220, 2221, 2222, 2227, 2228, 2230, 2231, 2232, 2238, 2246, 2249, 2257, 2264, 2265, 2271, 2277, 2294, 2298, 2302], │ │ │ │ │ + "m": [1, 2, 5, 8, 13, 16, 17, 19, 22, 23, 24, 25, 27, 31, 32, 153, 163, 169, 241, 258, 264, 270, 273, 276, 284, 287, 298, 300, 301, 320, 322, 326, 423, 513, 515, 519, 522, 523, 525, 528, 532, 535, 537, 538, 541, 547, 548, 549, 551, 557, 558, 562, 563, 564, 566, 651, 677, 680, 741, 857, 889, 898, 900, 902, 912, 916, 917, 918, 923, 938, 939, 953, 954, 997, 999, 1000, 1008, 1017, 1051, 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, 1282, 1283, 1284, 1285, 1287, 1288, 1290, 1291, 1292, 1293, 1294, 1295, 1297, 1338, 1339, 1340, 1341, 1393, 1397, 1430, 1433, 1446, 1452, 1459, 1464, 1469, 1476, 1482, 1483, 1484, 1486, 1492, 1497, 1498, 1500, 1501, 1578, 1657, 1677, 1699, 1720, 1741, 2185, 2186, 2188, 2193, 2197, 2199, 2200, 2201, 2203, 2205, 2207, 2208, 2209, 2210, 2214, 2216, 2218, 2220, 2221, 2222, 2227, 2228, 2230, 2231, 2232, 2238, 2246, 2249, 2257, 2264, 2265, 2271, 2277, 2294, 2298, 2302], │ │ │ │ │ "m8": [46, 1114, 2210, 2216, 2228, 2230, 2298], │ │ │ │ │ "ma": [2211, 2283, 2298], │ │ │ │ │ "mac": [6, 22], │ │ │ │ │ "machin": [1, 2, 4, 11, 16, 19, 22, 1491, 2193, 2194, 2199, 2289], │ │ │ │ │ "maco": [5, 22, 250, 883, 2246, 2249, 2250, 2278], │ │ │ │ │ "macro": 2277, │ │ │ │ │ "mactch": 2200, │ │ │ │ │ @@ -43730,15 +43728,15 @@ │ │ │ │ │ "seri": [2, 3, 7, 8, 10, 12, 13, 14, 15, 18, 21, 24, 25, 26, 29, 32, 33, 34, 35, 41, 45, 46, 51, 52, 53, 56, 57, 61, 62, 63, 65, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 113, 114, 115, 116, 117, 118, 119, 121, 122, 123, 124, 125, 126, 127, 128, 129, 132, 134, 135, 137, 138, 139, 140, 141, 142, 143, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 170, 171, 172, 173, 174, 175, 177, 178, 180, 181, 182, 183, 186, 190, 191, 193, 194, 195, 196, 198, 199, 200, 201, 202, 204, 205, 206, 207, 208, 209, 210, 212, 213, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 233, 234, 240, 241, 242, 243, 244, 245, 249, 252, 256, 258, 261, 271, 273, 275, 276, 277, 278, 279, 280, 281, 283, 284, 285, 288, 289, 290, 291, 292, 293, 294, 295, 296, 299, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 321, 323, 324, 325, 328, 329, 331, 332, 333, 342, 343, 344, 345, 346, 351, 355, 356, 357, 359, 360, 362, 369, 373, 376, 377, 378, 385, 392, 401, 402, 403, 405, 406, 408, 411, 412, 414, 416, 417, 419, 420, 423, 424, 427, 428, 431, 432, 433, 435, 436, 439, 441, 442, 443, 444, 465, 484, 489, 503, 519, 540, 547, 548, 549, 568, 914, 931, 940, 942, 943, 945, 946, 947, 948, 949, 950, 952, 1027, 1028, 1029, 1030, 1031, 1034, 1035, 1040, 1052, 1060, 1064, 1069, 1071, 1072, 1078, 1081, 1084, 1088, 1093, 1097, 1101, 1104, 1110, 1111, 1112, 1113, 1115, 1117, 1118, 1120, 1122, 1141, 1143, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1199, 1200, 1201, 1202, 1204, 1205, 1206, 1207, 1208, 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1458, 1460, 1463, 1466, 1467, 1476, 1479, 1488, 1490, 1493, 1494, 1496, 1498, 1499, 1500, 2163, 2165, 2167, 2171, 2172, 2173, 2174, 2179, 2183, 2186, 2187, 2190, 2192, 2193, 2194, 2196, 2197, 2198, 2199, 2201, 2202, 2203, 2204, 2205, 2206, 2207, 2209, 2211, 2212, 2213, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2221, 2223, 2224, 2225, 2226, 2227, 2229, 2230, 2231, 2233, 2234, 2236, 2237, 2239, 2240, 2242, 2243, 2245, 2247, 2248, 2250, 2251, 2253, 2254, 2255, 2256, 2258, 2259, 2260, 2262, 2263, 2264, 2266, 2267, 2269, 2272, 2273, 2274, 2275, 2276, 2277, 2278, 2279, 2280, 2282, 2284, 2285, 2286, 2287, 2288, 2290, 2291, 2293, 2295, 2296, 2297, 2299, 2300, 2301, 2303, 2304, 2306, 2308, 2309], │ │ │ │ │ "serial": [9, 10, 16, 253, 265, 341, 352, 886, 895, 1431, 1474, 1478, 1479, 2172, 2199, 2202, 2215, 2218, 2226, 2228, 2230, 2231, 2235, 2238, 2239, 2261, 2271, 2285, 2289, 2298, 2302], │ │ │ │ │ "serialis": [258, 889, 2225, 2231], │ │ │ │ │ "serializ": 2199, │ │ │ │ │ "series1": 2185, │ │ │ │ │ "series2": [2185, 2211], │ │ │ │ │ "series_gen": 2194, │ │ │ │ │ - "series_gener": 2194, │ │ │ │ │ + "series_gener": [2193, 2194], │ │ │ │ │ "series_minut": 2210, │ │ │ │ │ "series_monthli": 2210, │ │ │ │ │ "series_second": 2210, │ │ │ │ │ "seriesformatt": [1345, 1391, 1488, 1490, 2202], │ │ │ │ │ "seriesgroupbi": [186, 205, 223, 709, 762, 778, 798, 1147, 1150, 1151, 1157, 1160, 1161, 1162, 1163, 1165, 1166, 1170, 1171, 1176, 1178, 1180, 1181, 1185, 1186, 1188, 1189, 1195, 1196, 1197, 1199, 1200, 1204, 1205, 1268, 1273, 1277, 1278, 1279, 1284, 1287, 1288, 1292, 1293, 2172, 2195, 2220, 2221, 2228, 2232, 2238, 2241, 2246, 2249, 2265, 2266, 2267, 2269, 2271, 2275, 2276, 2277, 2278, 2284, 2286, 2287, 2288, 2289, 2297, 2299, 2302, 2304, 2307, 2308], │ │ │ │ │ "serif": 2207, │ │ │ │ │ "seriou": 2, │ │ │ │ │ @@ -43955,15 +43953,15 @@ │ │ │ │ │ "slight": [3, 2195], │ │ │ │ │ "slightli": [3, 13, 203, 862, 866, 1387, 2185, 2197, 2199, 2217, 2228, 2277, 2294], │ │ │ │ │ "slinear": [146, 720, 1280, 2218], │ │ │ │ │ "sln": 2191, │ │ │ │ │ "sloper": 25, │ │ │ │ │ "slow": [2, 22, 1345, 1391, 1488, 1490, 1492, 1498, 2186, 2193, 2199, 2202, 2217, 2222, 2232, 2238, 2241, 2253, 2307], │ │ │ │ │ "slower": [1152, 1211, 2193, 2197, 2199, 2202, 2210, 2218, 2228], │ │ │ │ │ - "slowest": [2185, 2193], │ │ │ │ │ + "slowest": 2193, │ │ │ │ │ "slshape": 1433, │ │ │ │ │ "sm": [1275, 2186, 2210, 2227, 2232, 2307], │ │ │ │ │ "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], │ │ │ │ │ "smaller": [0, 94, 144, 268, 745, 1345, 1391, 1488, 1490, 1499, 2186, 2188, 2193, 2202, 2207, 2208, 2210, 2211, 2243, 2249], │ │ │ │ │ "smallest": [176, 179, 360, 588, 754, 757, 1191, 1194, 1242, 1243, 1252, 1255, 1499, 2199, 2205, 2235, 2246, 2264, 2294], │ │ │ │ │ "smallint": [2199, 2307], │ │ │ │ │ "smart": [22, 2186, 2277], │ │ │ │ │ @@ -44793,15 +44791,15 @@ │ │ │ │ │ "tolist": [15, 432, 891, 2199, 2222, 2238, 2246, 2289, 2298, 2302], │ │ │ │ │ "tolong": 2241, │ │ │ │ │ "tom": [13, 35, 2199, 2247, 2248, 2294], │ │ │ │ │ "tomaugsburg": 2231, │ │ │ │ │ "tomaugspurg": [13, 35], │ │ │ │ │ "toml": [2, 22, 2238, 2265], │ │ │ │ │ "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], │ │ │ │ │ - "took": [2185, 2193, 2199, 2223, 2241], │ │ │ │ │ + "took": [2193, 2199, 2223, 2241], │ │ │ │ │ "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], │ │ │ │ │ "tooltip": [1402, 1423, 2196, 2283], │ │ │ │ │ "toordin": 2302, │ │ │ │ │ "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], │ │ │ │ │ "topic": [0, 4, 13, 35, 2185, 2196], │ │ │ │ │ "topmost": 2204, │ │ │ │ │ "toprul": [259, 890, 1433, 2277], │ │ │ │ │ @@ -44960,15 +44958,15 @@ │ │ │ │ │ "tzfile": [286, 329, 330, 331, 684, 685, 686, 953, 956, 972, 1013, 1014, 2210, 2221], │ │ │ │ │ "tzinfo": [277, 278, 286, 324, 329, 330, 331, 334, 575, 679, 684, 685, 686, 903, 904, 953, 983, 995, 1001, 1004, 1012, 1344, 2210, 2221, 2222, 2238, 2239, 2241, 2283, 2294, 2303], │ │ │ │ │ "tzlocal": [2232, 2246, 2298], │ │ │ │ │ "tzname": 2294, │ │ │ │ │ "tzoffset": 2222, │ │ │ │ │ "tzser": 575, │ │ │ │ │ "tzutc": [2210, 2246], │ │ │ │ │ - "u": [1, 3, 4, 5, 7, 13, 17, 18, 31, 203, 258, 287, 311, 330, 331, 532, 663, 664, 685, 686, 889, 905, 909, 916, 917, 918, 920, 921, 927, 930, 938, 939, 941, 946, 953, 954, 957, 995, 1017, 1085, 1087, 1088, 1204, 1476, 1482, 1483, 1484, 1498, 1500, 2163, 2184, 2185, 2186, 2193, 2194, 2195, 2199, 2203, 2205, 2207, 2208, 2209, 2210, 2218, 2222, 2226, 2228, 2230, 2235, 2238, 2241, 2246, 2249, 2294, 2298, 2302, 2307], │ │ │ │ │ + "u": [1, 3, 4, 5, 7, 13, 17, 18, 31, 203, 258, 287, 311, 330, 331, 532, 663, 664, 685, 686, 889, 905, 909, 916, 917, 918, 920, 921, 927, 930, 938, 939, 941, 946, 953, 954, 957, 995, 1017, 1085, 1087, 1088, 1204, 1476, 1482, 1483, 1484, 1498, 1500, 2163, 2184, 2185, 2186, 2193, 2194, 2195, 2199, 2203, 2207, 2208, 2209, 2210, 2222, 2226, 2228, 2230, 2235, 2238, 2241, 2246, 2249, 2294, 2298, 2302, 2307], │ │ │ │ │ "u1": [131, 1118, 2185, 2186, 2199], │ │ │ │ │ "u4": 2197, │ │ │ │ │ "u5": 2197, │ │ │ │ │ "u8": 2186, │ │ │ │ │ "ubuntu": 5, │ │ │ │ │ "udf": [72, 73, 77, 273, 581, 582, 586, 900, 1148, 1149, 1152, 1168, 1203, 1207, 1208, 1211, 1225, 1264, 1269, 1270, 1304, 1321, 2195, 2196, 2294], │ │ │ │ │ "ufunc": [10, 586, 808, 1031, 2185, 2186, 2191, 2206, 2213, 2219, 2221, 2232, 2246, 2265, 2277, 2281, 2289, 2293, 2294, 2298, 2307], │ │ │ ├── ./usr/share/doc/python-pandas-doc/html/user_guide/advanced.html │ │ │ │ @@ -1847,26 +1847,25 @@ │ │ │ │ In [141]: indexer = np.arange(10000) │ │ │ │ │ │ │ │ In [142]: random.shuffle(indexer) │ │ │ │ │ │ │ │ In [143]: %timeit arr[indexer] │ │ │ │ .....: %timeit arr.take(indexer, axis=0) │ │ │ │ .....: │ │ │ │ -The slowest run took 5.75 times longer than the fastest. This could mean that an intermediate result is being cached. │ │ │ │ -440 us +- 381 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ -95.1 us +- 5.13 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ +3.07 ms +- 323 us per loop (mean +- std. dev. of 7 runs, 100 loops each) │ │ │ │ +634 us +- 212 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ │ │ │ │ │ │ │ │ 
In [144]: ser = pd.Series(arr[:, 0])
│ │ │ │  
│ │ │ │  In [145]: %timeit ser.iloc[indexer]
│ │ │ │     .....: %timeit ser.take(indexer)
│ │ │ │     .....: 
│ │ │ │ -160 us +- 8.46 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)
│ │ │ │ -143 us +- 1.9 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)
│ │ │ │ +3.37 ms +- 505 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +3.76 ms +- 678 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │
│ │ │ │
│ │ │ │ │ │ │ │
│ │ │ │

Index types#

│ │ │ │

We have discussed MultiIndex in the previous sections pretty extensively. │ │ │ │ Documentation about DatetimeIndex and PeriodIndex are shown here, │ │ │ │ ├── html2text {} │ │ │ │ │ @@ -1245,25 +1245,23 @@ │ │ │ │ │ In [141]: indexer = np.arange(10000) │ │ │ │ │ │ │ │ │ │ In [142]: random.shuffle(indexer) │ │ │ │ │ │ │ │ │ │ In [143]: %timeit arr[indexer] │ │ │ │ │ .....: %timeit arr.take(indexer, axis=0) │ │ │ │ │ .....: │ │ │ │ │ -The slowest run took 5.75 times longer than the fastest. This could mean that │ │ │ │ │ -an intermediate result is being cached. │ │ │ │ │ -440 us +- 381 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ │ -95.1 us +- 5.13 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ │ +3.07 ms +- 323 us per loop (mean +- std. dev. of 7 runs, 100 loops each) │ │ │ │ │ +634 us +- 212 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ │ In [144]: ser = pd.Series(arr[:, 0]) │ │ │ │ │ │ │ │ │ │ In [145]: %timeit ser.iloc[indexer] │ │ │ │ │ .....: %timeit ser.take(indexer) │ │ │ │ │ .....: │ │ │ │ │ -160 us +- 8.46 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ │ -143 us +- 1.9 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ │ +3.37 ms +- 505 us per loop (mean +- std. dev. of 7 runs, 100 loops each) │ │ │ │ │ +3.76 ms +- 678 us per loop (mean +- std. dev. of 7 runs, 100 loops each) │ │ │ │ │ ********** IInnddeexx ttyyppeess_## ********** │ │ │ │ │ We have discussed MultiIndex in the previous sections pretty extensively. │ │ │ │ │ Documentation about DatetimeIndex and PeriodIndex are shown _h_e_r_e, and │ │ │ │ │ documentation about TimedeltaIndex is found _h_e_r_e. │ │ │ │ │ In the following sub-sections we will highlight some other index types. │ │ │ │ │ ******** CCaatteeggoorriiccaallIInnddeexx_## ******** │ │ │ │ │ _C_a_t_e_g_o_r_i_c_a_l_I_n_d_e_x is a type of index that is useful for supporting indexing with │ │ │ ├── ./usr/share/doc/python-pandas-doc/html/user_guide/enhancingperf.html │ │ │ │ @@ -592,31 +592,31 @@ │ │ │ │ ...: s += f(a + i * dx) │ │ │ │ ...: return s * dx │ │ │ │ ...: │ │ │ │ │ │ │ │ │ │ │ │

We achieve our result by using DataFrame.apply() (row-wise):

│ │ │ │ 
In [5]: %timeit df.apply(lambda x: integrate_f(x["a"], x["b"], x["N"]), axis=1)
│ │ │ │ -73.9 ms +- 3.3 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +1.06 s +- 161 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │
│ │ │ │
│ │ │ │

Let’s take a look and see where the time is spent during this operation │ │ │ │ using the prun ipython magic function:

│ │ │ │ 
# most time consuming 4 calls
│ │ │ │  In [6]: %prun -l 4 df.apply(lambda x: integrate_f(x["a"], x["b"], x["N"]), axis=1)  # noqa E999
│ │ │ │ -         605946 function calls (605928 primitive calls) in 0.240 seconds
│ │ │ │ +         605946 function calls (605928 primitive calls) in 3.519 seconds
│ │ │ │  
│ │ │ │     Ordered by: internal time
│ │ │ │     List reduced from 159 to 4 due to restriction <4>
│ │ │ │  
│ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ -     1000    0.147    0.000    0.209    0.000 <ipython-input-4-c2a74e076cf0>:1(integrate_f)
│ │ │ │ -   552423    0.063    0.000    0.063    0.000 <ipython-input-3-c138bdd570e3>:1(f)
│ │ │ │ -     3000    0.005    0.000    0.020    0.000 series.py:1095(__getitem__)
│ │ │ │ -     3000    0.003    0.000    0.009    0.000 series.py:1220(_get_value)
│ │ │ │ +     1000    1.890    0.002    2.710    0.003 <ipython-input-4-c2a74e076cf0>:1(integrate_f)
│ │ │ │ +   552423    0.820    0.000    0.820    0.000 <ipython-input-3-c138bdd570e3>:1(f)
│ │ │ │ +     3000    0.140    0.000    0.141    0.000 base.py:3777(get_loc)
│ │ │ │ +     3000    0.134    0.000    0.321    0.000 series.py:1220(_get_value)
│ │ │ │
│ │ │ │
│ │ │ │

By far the majority of time is spend inside either integrate_f or f, │ │ │ │ hence we’ll concentrate our efforts cythonizing these two functions.

│ │ │ │
│ │ │ │
│ │ │ │

Plain Cython#

│ │ │ │ @@ -634,15 +634,15 @@ │ │ │ │ ...: for i in range(N): │ │ │ │ ...: s += f_plain(a + i * dx) │ │ │ │ ...: return s * dx │ │ │ │ ...: │ │ │ │ │ │ │ │ │ │ │ │ 
In [9]: %timeit df.apply(lambda x: integrate_f_plain(x["a"], x["b"], x["N"]), axis=1)
│ │ │ │ -69.3 ms +- 225 us per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +783 ms +- 99.7 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │
│ │ │ │
│ │ │ │

This has improved the performance compared to the pure Python approach by one-third.

│ │ │ │
│ │ │ │
│ │ │ │

Declaring C types#

│ │ │ │

We can annotate the function variables and return types as well as use cdef │ │ │ │ @@ -658,36 +658,36 @@ │ │ │ │ ....: for i in range(N): │ │ │ │ ....: s += f_typed(a + i * dx) │ │ │ │ ....: return s * dx │ │ │ │ ....: │ │ │ │ │ │ │ │ │ │ │ │ 

In [11]: %timeit df.apply(lambda x: integrate_f_typed(x["a"], x["b"], x["N"]), axis=1)
│ │ │ │ -9.29 ms +- 30.4 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +185 ms +- 11.1 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │
│ │ │ │
│ │ │ │

Annotating the functions with C types yields an over ten times performance improvement compared to │ │ │ │ the original Python implementation.

│ │ │ │
│ │ │ │
│ │ │ │

Using ndarray#

│ │ │ │

When re-profiling, time is spent creating a Series from each row, and calling __getitem__ from both │ │ │ │ the index and the series (three times for each row). These Python function calls are expensive and │ │ │ │ can be improved by passing an np.ndarray.

│ │ │ │ 
In [12]: %prun -l 4 df.apply(lambda x: integrate_f_typed(x["a"], x["b"], x["N"]), axis=1)
│ │ │ │ -         52523 function calls (52505 primitive calls) in 0.029 seconds
│ │ │ │ +         52523 function calls (52505 primitive calls) in 0.472 seconds
│ │ │ │  
│ │ │ │     Ordered by: internal time
│ │ │ │     List reduced from 157 to 4 due to restriction <4>
│ │ │ │  
│ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ -     3000    0.005    0.000    0.019    0.000 series.py:1095(__getitem__)
│ │ │ │ -     3000    0.003    0.000    0.008    0.000 series.py:1220(_get_value)
│ │ │ │ -    16098    0.003    0.000    0.004    0.000 {built-in method builtins.isinstance}
│ │ │ │ -     3000    0.003    0.000    0.003    0.000 base.py:3777(get_loc)
│ │ │ │ +     3000    0.065    0.000    0.065    0.000 base.py:3777(get_loc)
│ │ │ │ +     1001    0.059    0.000    0.142    0.000 apply.py:1247(series_generator)
│ │ │ │ +     3000    0.055    0.000    0.126    0.000 series.py:1220(_get_value)
│ │ │ │ +     3000    0.052    0.000    0.234    0.000 series.py:1095(__getitem__)
│ │ │ │
│ │ │ │
│ │ │ │ 
In [13]: %%cython
│ │ │ │     ....: cimport numpy as np
│ │ │ │     ....: import numpy as np
│ │ │ │     ....: cdef double f_typed(double x) except? -2:
│ │ │ │     ....:     return x * (x - 1)
│ │ │ │ @@ -722,33 +722,33 @@
│ │ │ │
│ │ │ │

This implementation creates an array of zeros and inserts the result │ │ │ │ of integrate_f_typed applied over each row. Looping over an ndarray is faster │ │ │ │ in Cython than looping over a Series object.

│ │ │ │

Since apply_integrate_f is typed to accept an np.ndarray, Series.to_numpy() │ │ │ │ calls are needed to utilize this function.

│ │ │ │ 
In [14]: %timeit apply_integrate_f(df["a"].to_numpy(), df["b"].to_numpy(), df["N"].to_numpy())
│ │ │ │ -1.3 ms +- 12.8 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │ +12.4 ms +- 2.77 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │
│ │ │ │
│ │ │ │

Performance has improved from the prior implementation by almost ten times.

│ │ │ │
│ │ │ │
│ │ │ │

Disabling compiler directives#

│ │ │ │

The majority of the time is now spent in apply_integrate_f. Disabling Cython’s boundscheck │ │ │ │ and wraparound checks can yield more performance.

│ │ │ │ 
In [15]: %prun -l 4 apply_integrate_f(df["a"].to_numpy(), df["b"].to_numpy(), df["N"].to_numpy())
│ │ │ │ -         78 function calls in 0.002 seconds
│ │ │ │ +         78 function calls in 0.003 seconds
│ │ │ │  
│ │ │ │     Ordered by: internal time
│ │ │ │     List reduced from 21 to 4 due to restriction <4>
│ │ │ │  
│ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ -        1    0.002    0.002    0.002    0.002 <string>:1(<module>)
│ │ │ │ +        1    0.003    0.003    0.003    0.003 <string>:1(<module>)
│ │ │ │          1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}
│ │ │ │ -        1    0.000    0.000    0.002    0.002 {built-in method builtins.exec}
│ │ │ │ +        1    0.000    0.000    0.003    0.003 {built-in method builtins.exec}
│ │ │ │          3    0.000    0.000    0.000    0.000 frame.py:4062(__getitem__)
│ │ │ │
│ │ │ │
│ │ │ │ 
In [16]: %%cython
│ │ │ │     ....: cimport cython
│ │ │ │     ....: cimport numpy as np
│ │ │ │     ....: import numpy as np
│ │ │ │ @@ -782,15 +782,15 @@
│ │ │ │                   from /build/reproducible-path/pandas-2.2.3+dfsg/buildtmp/.cache/ipython/cython/_cython_magic_883da8958ecc60be73b28b7124368f9c7cc2d174.c:1251:
│ │ │ │  /usr/lib/x86_64-linux-gnu/python3-numpy/numpy/_core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning "Using deprecated NumPy API, disable it with " "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
│ │ │ │     17 | #warning "Using deprecated NumPy API, disable it with " \
│ │ │ │        |  ^~~~~~~
│ │ │ │
│ │ │ │
│ │ │ │ 
In [17]: %timeit apply_integrate_f_wrap(df["a"].to_numpy(), df["b"].to_numpy(), df["N"].to_numpy())
│ │ │ │ -985 us +- 4.28 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │ +8.09 ms +- 588 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │
│ │ │ │
│ │ │ │

However, a loop indexer i accessing an invalid location in an array would cause a segfault because memory access isn’t checked. │ │ │ │ For more about boundscheck and wraparound, see the Cython docs on │ │ │ │ compiler directives.

│ │ │ │
│ │ │ │ │ │ │ │ @@ -1148,19 +1148,19 @@ │ │ │ │ compared to standard Python syntax for large DataFrame. This engine requires the │ │ │ │ optional dependency numexpr to be installed.

│ │ │ │

The 'python' engine is generally not useful except for testing │ │ │ │ other evaluation engines against it. You will achieve no performance │ │ │ │ benefits using eval() with engine='python' and may │ │ │ │ incur a performance hit.

│ │ │ │ 
In [40]: %timeit df1 + df2 + df3 + df4
│ │ │ │ -11.2 ms +- 1.32 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +229 ms +- 37.6 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │
│ │ │ │
│ │ │ │ 
In [41]: %timeit pd.eval("df1 + df2 + df3 + df4", engine="python")
│ │ │ │ -11.3 ms +- 197 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +211 ms +- 27.5 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │
│ │ │ │
│ │ │ │ │ │ │ │
│ │ │ │

The DataFrame.eval() method#

│ │ │ │

In addition to the top level pandas.eval() function you can also │ │ │ │ evaluate an expression in the “context” of a DataFrame.

│ │ │ │ @@ -1275,40 +1275,41 @@ │ │ │ │ 
In [58]: nrows, ncols = 20000, 100
│ │ │ │  
│ │ │ │  In [59]: df1, df2, df3, df4 = [pd.DataFrame(np.random.randn(nrows, ncols)) for _ in range(4)]
│ │ │ │
│ │ │ │
│ │ │ │

DataFrame arithmetic:

│ │ │ │ 
In [60]: %timeit df1 + df2 + df3 + df4
│ │ │ │ -11 ms +- 2.27 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +The slowest run took 7.23 times longer than the fastest. This could mean that an intermediate result is being cached.
│ │ │ │ +334 ms +- 340 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │
│ │ │ │
│ │ │ │ 
In [61]: %timeit pd.eval("df1 + df2 + df3 + df4")
│ │ │ │ -4.66 ms +- 171 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +85.7 ms +- 9.65 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │
│ │ │ │
│ │ │ │

DataFrame comparison:

│ │ │ │ 
In [62]: %timeit (df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)
│ │ │ │ -7.91 ms +- 1.22 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +182 ms +- 32.1 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │
│ │ │ │
│ │ │ │ 
In [63]: %timeit pd.eval("(df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)")
│ │ │ │ -7.3 ms +- 821 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +90.4 ms +- 12.8 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │
│ │ │ │
│ │ │ │

DataFrame arithmetic with unaligned axes.

│ │ │ │ 
In [64]: s = pd.Series(np.random.randn(50))
│ │ │ │  
│ │ │ │  In [65]: %timeit df1 + df2 + df3 + df4 + s
│ │ │ │ -The slowest run took 12.94 times longer than the fastest. This could mean that an intermediate result is being cached.
│ │ │ │ -103 ms +- 90.7 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +The slowest run took 15.90 times longer than the fastest. This could mean that an intermediate result is being cached.
│ │ │ │ +2.76 s +- 1.83 s per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │
│ │ │ │
│ │ │ │ 
In [66]: %timeit pd.eval("df1 + df2 + df3 + df4 + s")
│ │ │ │ -8.6 ms +- 3.44 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +72.7 ms +- 7.86 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │
│ │ │ │
│ │ │ │
│ │ │ │

Note

│ │ │ │

Operations such as

│ │ │ │ 
1 and 2  # would parse to 1 & 2, but should evaluate to 2
│ │ │ │  3 or 4  # would parse to 3 | 4, but should evaluate to 3
│ │ │ │ ├── html2text {}
│ │ │ │ │ @@ -110,32 +110,32 @@
│ │ │ │ │     ...:     dx = (b - a) / N
│ │ │ │ │     ...:     for i in range(N):
│ │ │ │ │     ...:         s += f(a + i * dx)
│ │ │ │ │     ...:     return s * dx
│ │ │ │ │     ...:
│ │ │ │ │  We achieve our result by using _D_a_t_a_F_r_a_m_e_._a_p_p_l_y_(_) (row-wise):
│ │ │ │ │  In [5]: %timeit df.apply(lambda x: integrate_f(x["a"], x["b"], x["N"]), axis=1)
│ │ │ │ │ -73.9 ms +- 3.3 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │ +1.06 s +- 161 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │  Let’s take a look and see where the time is spent during this operation using
│ │ │ │ │  the _p_r_u_n_ _i_p_y_t_h_o_n_ _m_a_g_i_c_ _f_u_n_c_t_i_o_n:
│ │ │ │ │  # most time consuming 4 calls
│ │ │ │ │  In [6]: %prun -l 4 df.apply(lambda x: integrate_f(x["a"], x["b"], x["N"]),
│ │ │ │ │  axis=1)  # noqa E999
│ │ │ │ │ -         605946 function calls (605928 primitive calls) in 0.240 seconds
│ │ │ │ │ +         605946 function calls (605928 primitive calls) in 3.519 seconds
│ │ │ │ │  
│ │ │ │ │     Ordered by: internal time
│ │ │ │ │     List reduced from 159 to 4 due to restriction <4>
│ │ │ │ │  
│ │ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ │ -     1000    0.147    0.000    0.209    0.000 :1
│ │ │ │ │ +     1000    1.890    0.002    2.710    0.003 :1
│ │ │ │ │  (integrate_f)
│ │ │ │ │ -   552423    0.063    0.000    0.063    0.000 :1
│ │ │ │ │ +   552423    0.820    0.000    0.820    0.000 :1
│ │ │ │ │  (f)
│ │ │ │ │ -     3000    0.005    0.000    0.020    0.000 series.py:1095(__getitem__)
│ │ │ │ │ -     3000    0.003    0.000    0.009    0.000 series.py:1220(_get_value)
│ │ │ │ │ +     3000    0.140    0.000    0.141    0.000 base.py:3777(get_loc)
│ │ │ │ │ +     3000    0.134    0.000    0.321    0.000 series.py:1220(_get_value)
│ │ │ │ │  By far the majority of time is spend inside either integrate_f or f, hence
│ │ │ │ │  we’ll concentrate our efforts cythonizing these two functions.
│ │ │ │ │  ******** PPllaaiinn CCyytthhoonn_## ********
│ │ │ │ │  First we’re going to need to import the Cython magic function to IPython:
│ │ │ │ │  In [7]: %load_ext Cython
│ │ │ │ │  Now, let’s simply copy our functions over to Cython:
│ │ │ │ │  In [8]: %%cython
│ │ │ │ │ @@ -146,15 +146,15 @@
│ │ │ │ │     ...:     dx = (b - a) / N
│ │ │ │ │     ...:     for i in range(N):
│ │ │ │ │     ...:         s += f_plain(a + i * dx)
│ │ │ │ │     ...:     return s * dx
│ │ │ │ │     ...:
│ │ │ │ │  In [9]: %timeit df.apply(lambda x: integrate_f_plain(x["a"], x["b"], x["N"]),
│ │ │ │ │  axis=1)
│ │ │ │ │ -69.3 ms +- 225 us per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │ +783 ms +- 99.7 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │  This has improved the performance compared to the pure Python approach by one-
│ │ │ │ │  third.
│ │ │ │ │  ******** DDeeccllaarriinngg CC ttyyppeess_## ********
│ │ │ │ │  We can annotate the function variables and return types as well as use cdef and
│ │ │ │ │  cpdef to improve performance:
│ │ │ │ │  In [10]: %%cython
│ │ │ │ │     ....: cdef double f_typed(double x) except? -2:
│ │ │ │ │ @@ -166,35 +166,34 @@
│ │ │ │ │     ....:     dx = (b - a) / N
│ │ │ │ │     ....:     for i in range(N):
│ │ │ │ │     ....:         s += f_typed(a + i * dx)
│ │ │ │ │     ....:     return s * dx
│ │ │ │ │     ....:
│ │ │ │ │  In [11]: %timeit df.apply(lambda x: integrate_f_typed(x["a"], x["b"], x["N"]),
│ │ │ │ │  axis=1)
│ │ │ │ │ -9.29 ms +- 30.4 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +185 ms +- 11.1 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │  Annotating the functions with C types yields an over ten times performance
│ │ │ │ │  improvement compared to the original Python implementation.
│ │ │ │ │  ******** UUssiinngg nnddaarrrraayy_## ********
│ │ │ │ │  When re-profiling, time is spent creating a _S_e_r_i_e_s from each row, and calling
│ │ │ │ │  __getitem__ from both the index and the series (three times for each row).
│ │ │ │ │  These Python function calls are expensive and can be improved by passing an
│ │ │ │ │  np.ndarray.
│ │ │ │ │  In [12]: %prun -l 4 df.apply(lambda x: integrate_f_typed(x["a"], x["b"], x
│ │ │ │ │  ["N"]), axis=1)
│ │ │ │ │ -         52523 function calls (52505 primitive calls) in 0.029 seconds
│ │ │ │ │ +         52523 function calls (52505 primitive calls) in 0.472 seconds
│ │ │ │ │  
│ │ │ │ │     Ordered by: internal time
│ │ │ │ │     List reduced from 157 to 4 due to restriction <4>
│ │ │ │ │  
│ │ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ │ -     3000    0.005    0.000    0.019    0.000 series.py:1095(__getitem__)
│ │ │ │ │ -     3000    0.003    0.000    0.008    0.000 series.py:1220(_get_value)
│ │ │ │ │ -    16098    0.003    0.000    0.004    0.000 {built-in method
│ │ │ │ │ -builtins.isinstance}
│ │ │ │ │ -     3000    0.003    0.000    0.003    0.000 base.py:3777(get_loc)
│ │ │ │ │ +     3000    0.065    0.000    0.065    0.000 base.py:3777(get_loc)
│ │ │ │ │ +     1001    0.059    0.000    0.142    0.000 apply.py:1247(series_generator)
│ │ │ │ │ +     3000    0.055    0.000    0.126    0.000 series.py:1220(_get_value)
│ │ │ │ │ +     3000    0.052    0.000    0.234    0.000 series.py:1095(__getitem__)
│ │ │ │ │  In [13]: %%cython
│ │ │ │ │     ....: cimport numpy as np
│ │ │ │ │     ....: import numpy as np
│ │ │ │ │     ....: cdef double f_typed(double x) except? -2:
│ │ │ │ │     ....:     return x * (x - 1)
│ │ │ │ │     ....: cpdef double integrate_f_typed(double a, double b, int N):
│ │ │ │ │     ....:     cdef int i
│ │ │ │ │ @@ -235,31 +234,31 @@
│ │ │ │ │  This implementation creates an array of zeros and inserts the result of
│ │ │ │ │  integrate_f_typed applied over each row. Looping over an ndarray is faster in
│ │ │ │ │  Cython than looping over a _S_e_r_i_e_s object.
│ │ │ │ │  Since apply_integrate_f is typed to accept an np.ndarray, _S_e_r_i_e_s_._t_o___n_u_m_p_y_(_)
│ │ │ │ │  calls are needed to utilize this function.
│ │ │ │ │  In [14]: %timeit apply_integrate_f(df["a"].to_numpy(), df["b"].to_numpy(), df
│ │ │ │ │  ["N"].to_numpy())
│ │ │ │ │ -1.3 ms +- 12.8 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │ │ +12.4 ms +- 2.77 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │  Performance has improved from the prior implementation by almost ten times.
│ │ │ │ │  ******** DDiissaabblliinngg ccoommppiilleerr ddiirreeccttiivveess_## ********
│ │ │ │ │  The majority of the time is now spent in apply_integrate_f. Disabling Cython’s
│ │ │ │ │  boundscheck and wraparound checks can yield more performance.
│ │ │ │ │  In [15]: %prun -l 4 apply_integrate_f(df["a"].to_numpy(), df["b"].to_numpy(),
│ │ │ │ │  df["N"].to_numpy())
│ │ │ │ │ -         78 function calls in 0.002 seconds
│ │ │ │ │ +         78 function calls in 0.003 seconds
│ │ │ │ │  
│ │ │ │ │     Ordered by: internal time
│ │ │ │ │     List reduced from 21 to 4 due to restriction <4>
│ │ │ │ │  
│ │ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ │ -        1    0.002    0.002    0.002    0.002 :1()
│ │ │ │ │ +        1    0.003    0.003    0.003    0.003 :1()
│ │ │ │ │          1    0.000    0.000    0.000    0.000 {method 'disable' of
│ │ │ │ │  '_lsprof.Profiler' objects}
│ │ │ │ │ -        1    0.000    0.000    0.002    0.002 {built-in method builtins.exec}
│ │ │ │ │ +        1    0.000    0.000    0.003    0.003 {built-in method builtins.exec}
│ │ │ │ │          3    0.000    0.000    0.000    0.000 frame.py:4062(__getitem__)
│ │ │ │ │  In [16]: %%cython
│ │ │ │ │     ....: cimport cython
│ │ │ │ │     ....: cimport numpy as np
│ │ │ │ │     ....: import numpy as np
│ │ │ │ │     ....: cdef np.float64_t f_typed(np.float64_t x) except? -2:
│ │ │ │ │     ....:     return x * (x - 1)
│ │ │ │ │ @@ -298,15 +297,15 @@
│ │ │ │ │  /usr/lib/x86_64-linux-gnu/python3-numpy/numpy/_core/include/numpy/
│ │ │ │ │  npy_1_7_deprecated_api.h:17:2: warning: #warning "Using deprecated NumPy API,
│ │ │ │ │  disable it with " "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
│ │ │ │ │     17 | #warning "Using deprecated NumPy API, disable it with " \
│ │ │ │ │        |  ^~~~~~~
│ │ │ │ │  In [17]: %timeit apply_integrate_f_wrap(df["a"].to_numpy(), df["b"].to_numpy(),
│ │ │ │ │  df["N"].to_numpy())
│ │ │ │ │ -985 us +- 4.28 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │ │ +8.09 ms +- 588 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │  However, a loop indexer i accessing an invalid location in an array would cause
│ │ │ │ │  a segfault because memory access isn’t checked. For more about boundscheck and
│ │ │ │ │  wraparound, see the Cython docs on _c_o_m_p_i_l_e_r_ _d_i_r_e_c_t_i_v_e_s.
│ │ │ │ │  ********** NNuummbbaa ((JJIITT ccoommppiillaattiioonn))_## **********
│ │ │ │ │  An alternative to statically compiling Cython code is to use a dynamic just-in-
│ │ │ │ │  time (JIT) compiler with _N_u_m_b_a.
│ │ │ │ │  Numba allows you to write a pure Python function which can be JIT compiled to
│ │ │ │ │ @@ -609,17 +608,17 @@
│ │ │ │ │  The 'numexpr' engine is the more performant engine that can yield performance
│ │ │ │ │  improvements compared to standard Python syntax for large _D_a_t_a_F_r_a_m_e. This
│ │ │ │ │  engine requires the optional dependency numexpr to be installed.
│ │ │ │ │  The 'python' engine is generally nnoott useful except for testing other evaluation
│ │ │ │ │  engines against it. You will achieve nnoo performance benefits using _e_v_a_l_(_) with
│ │ │ │ │  engine='python' and may incur a performance hit.
│ │ │ │ │  In [40]: %timeit df1 + df2 + df3 + df4
│ │ │ │ │ -11.2 ms +- 1.32 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +229 ms +- 37.6 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │  In [41]: %timeit pd.eval("df1 + df2 + df3 + df4", engine="python")
│ │ │ │ │ -11.3 ms +- 197 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +211 ms +- 27.5 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │  ******** TThhee _DD_aa_tt_aa_FF_rr_aa_mm_ee_.._ee_vv_aa_ll_((_)) mmeetthhoodd_## ********
│ │ │ │ │  In addition to the top level _p_a_n_d_a_s_._e_v_a_l_(_) function you can also evaluate an
│ │ │ │ │  expression in the “context” of a _D_a_t_a_F_r_a_m_e.
│ │ │ │ │  In [42]: df = pd.DataFrame(np.random.randn(5, 2), columns=["a", "b"])
│ │ │ │ │  
│ │ │ │ │  In [43]: df.eval("a + b")
│ │ │ │ │  Out[43]:
│ │ │ │ │ @@ -716,31 +715,33 @@
│ │ │ │ │  _p_a_n_d_a_s_._e_v_a_l_(_) works well with expressions containing large arrays.
│ │ │ │ │  In [58]: nrows, ncols = 20000, 100
│ │ │ │ │  
│ │ │ │ │  In [59]: df1, df2, df3, df4 = [pd.DataFrame(np.random.randn(nrows, ncols)) for
│ │ │ │ │  _ in range(4)]
│ │ │ │ │  _D_a_t_a_F_r_a_m_e arithmetic:
│ │ │ │ │  In [60]: %timeit df1 + df2 + df3 + df4
│ │ │ │ │ -11 ms +- 2.27 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +The slowest run took 7.23 times longer than the fastest. This could mean that
│ │ │ │ │ +an intermediate result is being cached.
│ │ │ │ │ +334 ms +- 340 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │  In [61]: %timeit pd.eval("df1 + df2 + df3 + df4")
│ │ │ │ │ -4.66 ms +- 171 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +85.7 ms +- 9.65 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │  _D_a_t_a_F_r_a_m_e comparison:
│ │ │ │ │  In [62]: %timeit (df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)
│ │ │ │ │ -7.91 ms +- 1.22 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +182 ms +- 32.1 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │  In [63]: %timeit pd.eval("(df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)")
│ │ │ │ │ -7.3 ms +- 821 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +90.4 ms +- 12.8 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │  _D_a_t_a_F_r_a_m_e arithmetic with unaligned axes.
│ │ │ │ │  In [64]: s = pd.Series(np.random.randn(50))
│ │ │ │ │  
│ │ │ │ │  In [65]: %timeit df1 + df2 + df3 + df4 + s
│ │ │ │ │ -The slowest run took 12.94 times longer than the fastest. This could mean that
│ │ │ │ │ +The slowest run took 15.90 times longer than the fastest. This could mean that
│ │ │ │ │  an intermediate result is being cached.
│ │ │ │ │ -103 ms +- 90.7 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │ +2.76 s +- 1.83 s per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │  In [66]: %timeit pd.eval("df1 + df2 + df3 + df4 + s")
│ │ │ │ │ -8.6 ms +- 3.44 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +72.7 ms +- 7.86 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │  Note
│ │ │ │ │  Operations such as
│ │ │ │ │  1 and 2  # would parse to 1 & 2, but should evaluate to 2
│ │ │ │ │  3 or 4  # would parse to 3 | 4, but should evaluate to 3
│ │ │ │ │  ~1  # this is okay, but slower when using eval
│ │ │ │ │  should be performed in Python. An exception will be raised if you try to
│ │ │ │ │  perform any boolean/bitwise operations with scalar operands that are not of
│ │ │ ├── ./usr/share/doc/python-pandas-doc/html/user_guide/scale.html
│ │ │ │ @@ -1086,16 +1086,16 @@
│ │ │ │     ....: files = pathlib.Path("data/timeseries/").glob("ts*.parquet")
│ │ │ │     ....: counts = pd.Series(dtype=int)
│ │ │ │     ....: for path in files:
│ │ │ │     ....:     df = pd.read_parquet(path)
│ │ │ │     ....:     counts = counts.add(df["name"].value_counts(), fill_value=0)
│ │ │ │     ....: counts.astype(int)
│ │ │ │     ....: 
│ │ │ │ -CPU times: user 417 us, sys: 277 us, total: 694 us
│ │ │ │ -Wall time: 704 us
│ │ │ │ +CPU times: user 0 ns, sys: 2 ms, total: 2 ms
│ │ │ │ +Wall time: 10 ms
│ │ │ │  Out[32]: Series([], dtype: int64)
│ │ │ │
│ │ │ │
│ │ │ │

Some readers, like pandas.read_csv(), offer parameters to control the │ │ │ │ chunksize when reading a single file.

│ │ │ │

Manually chunking is an OK option for workflows that don’t │ │ │ │ require too sophisticated of operations. Some operations, like pandas.DataFrame.groupby(), are │ │ │ │ ├── html2text {} │ │ │ │ │ @@ -644,16 +644,16 @@ │ │ │ │ │ ....: files = pathlib.Path("data/timeseries/").glob("ts*.parquet") │ │ │ │ │ ....: counts = pd.Series(dtype=int) │ │ │ │ │ ....: for path in files: │ │ │ │ │ ....: df = pd.read_parquet(path) │ │ │ │ │ ....: counts = counts.add(df["name"].value_counts(), fill_value=0) │ │ │ │ │ ....: counts.astype(int) │ │ │ │ │ ....: │ │ │ │ │ -CPU times: user 417 us, sys: 277 us, total: 694 us │ │ │ │ │ -Wall time: 704 us │ │ │ │ │ +CPU times: user 0 ns, sys: 2 ms, total: 2 ms │ │ │ │ │ +Wall time: 10 ms │ │ │ │ │ Out[32]: Series([], dtype: int64) │ │ │ │ │ Some readers, like _p_a_n_d_a_s_._r_e_a_d___c_s_v_(_), offer parameters to control the chunksize │ │ │ │ │ when reading a single file. │ │ │ │ │ Manually chunking is an OK option for workflows that don’t require too │ │ │ │ │ sophisticated of operations. Some operations, like _p_a_n_d_a_s_._D_a_t_a_F_r_a_m_e_._g_r_o_u_p_b_y_(_), │ │ │ │ │ are much harder to do chunkwise. In these cases, you may be better switching to │ │ │ │ │ a different library that implements these out-of-core algorithms for you. │ │ │ ├── ./usr/share/doc/python-pandas-doc/html/user_guide/style.ipynb.gz │ │ │ │ ├── style.ipynb │ │ │ │ │ ├── Pretty-printed │ │ │ │ │ │┄ Similarity: 0.9985610875706213% │ │ │ │ │ │┄ Differences: {"'cells'": "{1: {'metadata': {'execution': {'iopub.execute_input': '2025-03-06T13:38:12.093316Z', " │ │ │ │ │ │┄ "'iopub.status.busy': '2025-03-06T13:38:12.092674Z', 'iopub.status.idle': " │ │ │ │ │ │┄ "'2025-03-06T13:38:16.843011Z', 'shell.execute_reply': " │ │ │ │ │ │┄ "'2025-03-06T13:38:16.827148Z'}}}, 3: {'metadata': {'execution': " │ │ │ │ │ │┄ "{'iopub.execute_input': '2025-03-06T13:38:16.873338Z', 'iopub.status.busy': " │ │ │ │ │ │┄ "'2025-03-06T13:38:16.872585Z', 'iopub.status.idle': '2025-03-06T13:38:2 […] │ │ │ │ │ │ @@ -39,18 +39,18 @@ │ │ │ │ │ │ ] │ │ │ │ │ │ }, │ │ │ │ │ │ { │ │ │ │ │ │ "cell_type": "code", │ │ │ │ │ │ "execution_count": 1, │ │ │ │ │ │ "metadata": { │ │ │ │ │ │ "execution": { │ │ │ │ │ │ - "iopub.execute_input": "2026-04-08T16:41:16.089910Z", │ │ │ │ │ │ - "iopub.status.busy": "2026-04-08T16:41:16.089546Z", │ │ │ │ │ │ - "iopub.status.idle": "2026-04-08T16:41:16.537613Z", │ │ │ │ │ │ - "shell.execute_reply": "2026-04-08T16:41:16.536878Z" │ │ │ │ │ │ + "iopub.execute_input": "2025-03-06T13:38:12.093316Z", │ │ │ │ │ │ + "iopub.status.busy": "2025-03-06T13:38:12.092674Z", │ │ │ │ │ │ + "iopub.status.idle": "2025-03-06T13:38:16.843011Z", │ │ │ │ │ │ + "shell.execute_reply": "2025-03-06T13:38:16.827148Z" │ │ │ │ │ │ }, │ │ │ │ │ │ "nbsphinx": "hidden" │ │ │ │ │ │ }, │ │ │ │ │ │ "outputs": [], │ │ │ │ │ │ "source": [ │ │ │ │ │ │ "import matplotlib.pyplot\n", │ │ │ │ │ │ "# We have this here to trigger matplotlib's font cache stuff.\n", │ │ │ │ │ │ @@ -77,36 +77,36 @@ │ │ │ │ │ │ ] │ │ │ │ │ │ }, │ │ │ │ │ │ { │ │ │ │ │ │ "cell_type": "code", │ │ │ │ │ │ "execution_count": 2, │ │ │ │ │ │ "metadata": { │ │ │ │ │ │ "execution": { │ │ │ │ │ │ - "iopub.execute_input": "2026-04-08T16:41:16.540623Z", │ │ │ │ │ │ - "iopub.status.busy": "2026-04-08T16:41:16.540275Z", │ │ │ │ │ │ - "iopub.status.idle": "2026-04-08T16:41:16.791038Z", │ │ │ │ │ │ - "shell.execute_reply": "2026-04-08T16:41:16.790054Z" │ │ │ │ │ │ + "iopub.execute_input": "2025-03-06T13:38:16.873338Z", │ │ │ │ │ │ + "iopub.status.busy": "2025-03-06T13:38:16.872585Z", │ │ │ │ │ │ + "iopub.status.idle": "2025-03-06T13:38:21.371109Z", │ │ │ │ │ │ + "shell.execute_reply": "2025-03-06T13:38:21.354911Z" │ │ │ │ │ │ } │ │ │ │ │ │ }, │ │ │ │ │ │ "outputs": [], │ │ │ │ │ │ "source": [ │ │ │ │ │ │ "import pandas as pd\n", │ │ │ │ │ │ "import numpy as np\n", │ │ │ │ │ │ "import matplotlib as mpl\n" │ │ │ │ │ │ ] │ │ │ │ │ │ }, │ │ │ │ │ │ { │ │ │ │ │ │ "cell_type": "code", │ │ │ │ │ │ "execution_count": 3, │ │ │ │ │ │ "metadata": { │ │ │ │ │ │ "execution": { │ │ │ │ │ │ - "iopub.execute_input": "2026-04-08T16:41:16.793964Z", │ │ │ │ │ │ - "iopub.status.busy": "2026-04-08T16:41:16.793607Z", │ │ │ │ │ │ - "iopub.status.idle": "2026-04-08T16:41:17.111208Z", │ │ │ │ │ │ - "shell.execute_reply": "2026-04-08T16:41:17.110279Z" │ │ │ │ │ │ + "iopub.execute_input": "2025-03-06T13:38:21.405209Z", │ │ │ │ │ │ + "iopub.status.busy": "2025-03-06T13:38:21.404404Z", │ │ │ │ │ │ + "iopub.status.idle": "2025-03-06T13:38:22.862919Z", │ │ │ │ │ │ + "shell.execute_reply": "2025-03-06T13:38:22.846794Z" │ │ │ │ │ │ }, │ │ │ │ │ │ "nbsphinx": "hidden" │ │ │ │ │ │ }, │ │ │ │ │ │ "outputs": [], │ │ │ │ │ │ "source": [ │ │ │ │ │ │ "# For reproducibility - this doesn't respect uuid_len or positionally-passed uuid but the places here that use that coincidentally bypass this anyway\n", │ │ │ │ │ │ "from pandas.io.formats.style import Styler\n", │ │ │ │ │ │ @@ -123,18 +123,18 @@ │ │ │ │ │ │ ] │ │ │ │ │ │ }, │ │ │ │ │ │ { │ │ │ │ │ │ "cell_type": "code", │ │ │ │ │ │ "execution_count": 4, │ │ │ │ │ │ "metadata": { │ │ │ │ │ │ "execution": { │ │ │ │ │ │ - "iopub.execute_input": "2026-04-08T16:41:17.114950Z", │ │ │ │ │ │ - "iopub.status.busy": "2026-04-08T16:41:17.114568Z", │ │ │ │ │ │ - "iopub.status.idle": "2026-04-08T16:41:17.126077Z", │ │ │ │ │ │ - "shell.execute_reply": "2026-04-08T16:41:17.125185Z" │ │ │ │ │ │ + "iopub.execute_input": "2025-03-06T13:38:22.889146Z", │ │ │ │ │ │ + "iopub.status.busy": "2025-03-06T13:38:22.888415Z", │ │ │ │ │ │ + "iopub.status.idle": "2025-03-06T13:38:22.986871Z", │ │ │ │ │ │ + "shell.execute_reply": "2025-03-06T13:38:22.974773Z" │ │ │ │ │ │ } │ │ │ │ │ │ }, │ │ │ │ │ │ "outputs": [ │ │ │ │ │ │ { │ │ │ │ │ │ "data": { │ │ │ │ │ │ "text/html": [ │ │ │ │ │ │ "