--- /srv/reproducible-results/rbuild-debian/r-b-build.hN8cf35f/b1/pandas_2.2.3+dfsg-8_amd64.changes +++ /srv/reproducible-results/rbuild-debian/r-b-build.hN8cf35f/b2/pandas_2.2.3+dfsg-8_amd64.changes ├── Files │ @@ -1,5 +1,5 @@ │ │ - 119053e4dbd2ebf8845e73abee59689f 10794968 doc optional python-pandas-doc_2.2.3+dfsg-8_all.deb │ + 44c692f6152084b83cd9468612ef29a6 10794604 doc optional python-pandas-doc_2.2.3+dfsg-8_all.deb │ f1f9f2170310b8b59540f1504ae4f513 35864748 debug optional python3-pandas-lib-dbgsym_2.2.3+dfsg-8_amd64.deb │ 551836e9c52e65c9632807c82058672b 4515236 python optional python3-pandas-lib_2.2.3+dfsg-8_amd64.deb │ 3aeb8fc374254d23864c68b1017c67e2 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 147388 2025-02-01 18:39:17.000000 control.tar.xz │ │ --rw-r--r-- 0 0 0 10647388 2025-02-01 18:39:17.000000 data.tar.xz │ │ +-rw-r--r-- 0 0 0 147404 2025-02-01 18:39:17.000000 control.tar.xz │ │ +-rw-r--r-- 0 0 0 10647008 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: 209900 │ │ │ │ +Installed-Size: 209896 │ │ │ │ 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) 2358676 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/searchindex.js │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 2358806 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) 283834 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) 283975 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 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/dsintro.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 81376 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/duplicates.html │ │ │ │ --rw-r--r-- 0 root (0) root (0) 115483 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/enhancingperf.html │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 115461 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/enhancingperf.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 107882 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/gotchas.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 300850 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/groupby.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 59715 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/index.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 395484 2025-02-01 18:39:17.000000 ./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) 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) 87862 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) 87822 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) 222517 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) 222660 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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 {} │ │ │ │ │ @@ -21500,15 +21500,15 @@ │ │ │ │ │ "00180": 2294, │ │ │ │ │ "002": [2193, 2264], │ │ │ │ │ "002000": 2232, │ │ │ │ │ "002040": 2235, │ │ │ │ │ "002118": [2230, 2231], │ │ │ │ │ "002653": 2207, │ │ │ │ │ "002846": 2229, │ │ │ │ │ - "003": [2185, 2193, 2235], │ │ │ │ │ + "003": [2185, 2235], │ │ │ │ │ "003144": 2210, │ │ │ │ │ "003337": 2207, │ │ │ │ │ "003494": 15, │ │ │ │ │ "003507": [2209, 2218], │ │ │ │ │ "003556": 2207, │ │ │ │ │ "00360": 2294, │ │ │ │ │ "003733": 2207, │ │ │ │ │ @@ -21523,15 +21523,15 @@ │ │ │ │ │ "004194": 2186, │ │ │ │ │ "004201": 2186, │ │ │ │ │ "004229": 2186, │ │ │ │ │ "004474": 2184, │ │ │ │ │ "004580": 2210, │ │ │ │ │ "00486": 30, │ │ │ │ │ "004956": 2207, │ │ │ │ │ - "005": 2209, │ │ │ │ │ + "005": [2193, 2209], │ │ │ │ │ "005000": 2218, │ │ │ │ │ "005361": 2207, │ │ │ │ │ "005383": 2220, │ │ │ │ │ "005446": 2219, │ │ │ │ │ "005462": 2191, │ │ │ │ │ "005977": 2199, │ │ │ │ │ "005979": 2186, │ │ │ │ │ @@ -21542,41 +21542,44 @@ │ │ │ │ │ "006438": 2215, │ │ │ │ │ "006549": [182, 760], │ │ │ │ │ "006695": 2186, │ │ │ │ │ "006747": [2185, 2197, 2199, 2202, 2204, 2215], │ │ │ │ │ "006871": 2212, │ │ │ │ │ "006888": 2220, │ │ │ │ │ "006938": 2207, │ │ │ │ │ + "007": 2193, │ │ │ │ │ "007200": 2184, │ │ │ │ │ "007207": [2184, 2214], │ │ │ │ │ "007717": 2199, │ │ │ │ │ "007824": 15, │ │ │ │ │ "007952": 2207, │ │ │ │ │ "007996": 2186, │ │ │ │ │ "007f": 203, │ │ │ │ │ + "008": 2193, │ │ │ │ │ "008182": 2204, │ │ │ │ │ "008298": 2186, │ │ │ │ │ "008344": 2207, │ │ │ │ │ "008358": 2207, │ │ │ │ │ "008500": 15, │ │ │ │ │ "008543": [102, 1158], │ │ │ │ │ "008943": [102, 1158], │ │ │ │ │ + "009": 2193, │ │ │ │ │ "009059": 2191, │ │ │ │ │ "009207": 2207, │ │ │ │ │ "009420": 2195, │ │ │ │ │ "009424": 2207, │ │ │ │ │ "009572": 2207, │ │ │ │ │ "009673": 2195, │ │ │ │ │ "009783": 2207, │ │ │ │ │ "009797": 2186, │ │ │ │ │ "009826": [102, 1158, 2205], │ │ │ │ │ "009920": [2184, 2195, 2214], │ │ │ │ │ "00am": 2230, │ │ │ │ │ "00index": 2218, │ │ │ │ │ - "01": [3, 15, 16, 17, 19, 29, 30, 31, 36, 79, 80, 82, 88, 107, 121, 182, 187, 207, 213, 218, 219, 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│ │ "010000": [954, 1894], │ │ │ │ │ "010010012": [923, 2209], │ │ │ │ │ "010026": 2191, │ │ │ │ │ "010081": 15, │ │ │ │ │ "010165": 2199, │ │ │ │ │ "010589": 2193, │ │ │ │ │ @@ -21638,25 +21641,25 @@ │ │ │ │ │ "017106": 2207, │ │ │ │ │ "017118": 2199, │ │ │ │ │ "017152": 2186, │ │ │ │ │ "017263": 2207, │ │ │ │ │ "017276": 2191, │ │ │ │ │ "017587": [2184, 2195, 2214], │ │ │ │ │ "017796": 2207, │ │ │ │ │ - "018": [2193, 2199], │ │ │ │ │ + "018": 2199, │ │ │ │ │ "018007": 2207, │ │ │ │ │ "018117": 2191, │ │ │ │ │ "018193": 2207, │ │ │ │ │ "018409": 2207, │ │ │ │ │ "018601": [2184, 2214], │ │ │ │ │ "018808": 2207, │ │ │ │ │ "018904": 2207, │ │ │ │ │ "018941": 2207, │ │ │ │ │ "018993": 2214, │ │ │ │ │ - "019": 2207, │ │ │ │ │ + "019": [2193, 2207], │ │ │ │ │ "019449": 2207, │ │ │ │ │ "019794": 2197, │ │ │ │ │ "01t00": [2163, 2199, 2210, 2235, 2246, 2261], │ │ │ │ │ "01t01": 2210, │ │ │ │ │ "01t03": 2210, │ │ │ │ │ "01t05": [909, 2210, 2235], │ │ │ │ │ "01t07": 1280, │ │ │ │ │ @@ -21669,50 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2195, 2214], │ │ │ │ │ "024738": [102, 1158], │ │ │ │ │ "024786": 2207, │ │ │ │ │ "024810": 2207, │ │ │ │ │ "0249": [267, 896], │ │ │ │ │ "024925": 2195, │ │ │ │ │ "024967": 2207, │ │ │ │ │ - "025": [2186, 2193, 2222, 2227], │ │ │ │ │ + "025": [2186, 2222, 2227], │ │ │ │ │ "025054": 2184, │ │ │ │ │ "025270": 2186, │ │ │ │ │ "025363": 2186, │ │ │ │ │ "025367": 2207, │ │ │ │ │ "025747": [2191, 2197, 2207], │ │ │ │ │ "026036": 2207, │ │ │ │ │ "026158": 2210, │ │ │ │ │ @@ -21728,26 +21728,26 @@ │ │ │ │ │ "028152": 2207, │ │ │ │ │ "028166": 15, │ │ │ │ │ "028182": 2207, │ │ │ │ │ "028578": 2207, │ │ │ │ │ "028603": 2195, │ │ │ │ │ "028662": 28, │ │ │ │ │ "028665": 15, │ │ │ │ │ - "029": [2186, 2227], │ │ │ │ │ + "029": [2186, 2193, 2227], │ │ │ │ │ "029302": 2191, │ │ │ │ │ "029399": 2184, │ │ │ │ │ "029582": 2207, │ │ │ │ │ "029587": 2193, │ │ │ │ │ "029630": 2195, │ │ │ │ │ "029766": 2197, │ │ │ │ │ "02d": 2205, │ │ │ │ │ "02t00": [2199, 2210, 2235, 2261], │ │ │ │ │ "02t02": 2235, │ │ │ │ │ "02t05": [909, 2210], │ │ │ │ │ - "03": [26, 27, 29, 31, 79, 80, 82, 121, 182, 207, 213, 218, 219, 230, 264, 278, 286, 287, 290, 291, 292, 294, 296, 298, 301, 302, 304, 305, 306, 307, 310, 313, 314, 318, 321, 322, 326, 330, 331, 332, 362, 420, 423, 512, 517, 518, 519, 522, 524, 530, 534, 536, 543, 544, 545, 546, 547, 548, 549, 551, 557, 558, 562, 563, 564, 565, 566, 591, 592, 593, 637, 640, 642, 643, 644, 646, 651, 652, 656, 657, 658, 659, 666, 667, 673, 675, 677, 680, 681, 685, 686, 688, 696, 760, 781, 788, 793, 799, 804, 904, 939, 941, 943, 944, 945, 948, 949, 953, 955, 956, 957, 958, 962, 970, 973, 983, 990, 992, 995, 997, 999, 1002, 1006, 1007, 1008, 1009, 1013, 1014, 1018, 1051, 1075, 1145, 1169, 1192, 1226, 1253, 1269, 1270, 1276, 1280, 1289, 1344, 1393, 1447, 1452, 1489, 1498, 1500, 1506, 1542, 1699, 1741, 1793, 1815, 1982, 2000, 2108, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2204, 2205, 2207, 2209, 2210, 2212, 2214, 2215, 2216, 2218, 2219, 2220, 2222, 2225, 2226, 2228, 2229, 2230, 2231, 2232, 2235, 2238, 2240, 2241, 2246, 2261, 2264, 2271, 2283, 2298, 2302], │ │ │ │ │ + "03": [26, 27, 29, 31, 79, 80, 82, 121, 182, 207, 213, 218, 219, 230, 264, 278, 286, 287, 290, 291, 292, 294, 296, 298, 301, 302, 304, 305, 306, 307, 310, 313, 314, 318, 321, 322, 326, 330, 331, 332, 362, 420, 423, 512, 517, 518, 519, 522, 524, 530, 534, 536, 543, 544, 545, 546, 547, 548, 549, 551, 557, 558, 562, 563, 564, 565, 566, 591, 592, 593, 637, 640, 642, 643, 644, 646, 651, 652, 656, 657, 658, 659, 666, 667, 673, 675, 677, 680, 681, 685, 686, 688, 696, 760, 781, 788, 793, 799, 804, 904, 939, 941, 943, 944, 945, 948, 949, 953, 955, 956, 957, 958, 962, 970, 973, 983, 990, 992, 995, 997, 999, 1002, 1006, 1007, 1008, 1009, 1013, 1014, 1018, 1051, 1075, 1145, 1169, 1192, 1226, 1253, 1269, 1270, 1276, 1280, 1289, 1344, 1393, 1447, 1452, 1489, 1498, 1500, 1506, 1542, 1699, 1741, 1793, 1815, 1982, 2000, 2108, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2204, 2205, 2207, 2209, 2210, 2212, 2214, 2215, 2216, 2218, 2219, 2220, 2222, 2225, 2226, 2228, 2229, 2230, 2231, 2232, 2235, 2238, 2240, 2241, 2246, 2261, 2264, 2271, 2283, 2298, 2302], │ │ │ │ │ "030": [1447, 2200, 2232], │ │ │ │ │ "0300": 2271, │ │ │ │ │ "030000": 18, │ │ │ │ │ "030015": 2207, │ │ │ │ │ "030045": 2186, │ │ │ │ │ "030178": 2207, │ │ │ │ │ "030388": 2207, │ │ │ │ │ @@ -21803,18 +21803,19 @@ │ │ │ │ │ "036660": 2199, │ │ │ │ │ "036854": 2199, │ │ │ │ │ "037181": 2191, │ │ │ │ │ "037528": 2235, │ │ │ │ │ "037651": 2207, │ │ │ │ │ "037772": 2214, │ │ │ │ │ "037882": [2184, 2214], │ │ │ │ │ - "038": [1447, 2193, 2200, 2232], │ │ │ │ │ + "038": [1447, 2200, 2232], │ │ │ │ │ "038031": 2207, │ │ │ │ │ "038402": 2197, │ │ │ │ │ "038981": 2207, │ │ │ │ │ + "039": 2193, │ │ │ │ │ "039061": 2207, │ │ │ │ │ "039147": 2207, │ │ │ │ │ "039266": 2215, │ │ │ │ │ "039268": [15, 2185, 2186, 2191, 2197, 2199, 2202, 2204, 2215, 2216, 2218, 2219, 2235, 2241, 2264], │ │ │ │ │ "0393": [2186, 2191], │ │ │ │ │ "039575": [15, 2184, 2185, 2186, 2191, 2195, 2197, 2199, 2202, 2210, 2214, 2215, 2218, 2225, 2226, 2241, 2260], │ │ │ │ │ "0396": [2184, 2186], │ │ │ │ │ @@ -21895,15 +21896,15 @@ │ │ │ │ │ "049695": 2199, │ │ │ │ │ "049748": 2204, │ │ │ │ │ "049783": 2207, │ │ │ │ │ "049798": 2199, │ │ │ │ │ "049851": 2195, │ │ │ │ │ "04d": 2188, │ │ │ │ │ "04t00": 2261, │ │ │ │ │ - "05": [13, 26, 27, 29, 30, 31, 80, 148, 149, 177, 178, 183, 207, 213, 218, 230, 264, 273, 276, 292, 294, 298, 302, 316, 326, 330, 331, 332, 345, 363, 423, 551, 592, 597, 644, 646, 670, 680, 685, 686, 688, 725, 726, 755, 756, 761, 781, 788, 793, 804, 900, 902, 905, 944, 1075, 1145, 1274, 1289, 1344, 1441, 1442, 1447, 1449, 1450, 1452, 1465, 1495, 1498, 1500, 1506, 1524, 1542, 1560, 1677, 1699, 1758, 2163, 2184, 2185, 2186, 2188, 2195, 2197, 2199, 2200, 2201, 2204, 2205, 2207, 2209, 2210, 2212, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2222, 2223, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2235, 2241, 2246, 2249, 2261, 2264, 2271, 2283, 2298, 2302, 2307], │ │ │ │ │ + "05": [13, 26, 27, 29, 30, 31, 80, 148, 149, 177, 178, 183, 207, 213, 218, 230, 264, 273, 276, 292, 294, 298, 302, 316, 326, 330, 331, 332, 345, 363, 423, 551, 592, 597, 644, 646, 670, 680, 685, 686, 688, 725, 726, 755, 756, 761, 781, 788, 793, 804, 900, 902, 905, 944, 1075, 1145, 1274, 1289, 1344, 1441, 1442, 1447, 1449, 1450, 1452, 1465, 1495, 1498, 1500, 1506, 1524, 1542, 1560, 1677, 1699, 1758, 2163, 2184, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2201, 2204, 2205, 2207, 2209, 2210, 2212, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2222, 2223, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2235, 2241, 2246, 2249, 2261, 2264, 2271, 2283, 2298, 2302, 2307], │ │ │ │ │ "0500": [24, 25, 28, 29, 32, 1498, 2210, 2235], │ │ │ │ │ "050000": [522, 524, 530], │ │ │ │ │ "050038": 2207, │ │ │ │ │ "050046": 2210, │ │ │ │ │ "050390": 2186, │ │ │ │ │ "050498": 2207, │ │ │ │ │ "051514": 2186, │ │ │ │ │ @@ -21978,14 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│ │ │ │ "10697": 2228, │ │ │ │ │ "10698": 2228, │ │ │ │ │ "10699": 2228, │ │ │ │ │ "107": [2184, 2185, 2186, 2188, 2191, 2192, 2195, 2197, 2199, 2200, 2201, 2204, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2230, 2232, 2235], │ │ │ │ │ + "1070": 2193, │ │ │ │ │ "10704": 2228, │ │ │ │ │ "10709": 2229, │ │ │ │ │ "10711": 2235, │ │ │ │ │ "10713": 2228, │ │ │ │ │ "10713616": 2238, │ │ │ │ │ "10713648": 2238, │ │ │ │ │ "10713680": 2238, │ │ │ │ │ @@ -22709,15 +22711,15 @@ │ │ │ │ │ "110877": 2191, │ │ │ │ │ "110891": 2215, │ │ │ │ │ "110895": 2207, │ │ │ │ │ "1109": 30, │ │ │ │ │ "11094": 2228, │ │ │ │ │ "110968": 2185, │ │ │ │ │ "11097": 2228, │ │ │ │ │ - "111": [16, 17, 18, 19, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2230, 2232, 2235, 2246], │ │ │ │ │ + "111": [16, 17, 18, 19, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2230, 2232, 2235, 2246], │ │ │ │ │ "1110": 30, │ │ │ │ │ "11102": 2228, │ │ │ │ │ "111032": 2204, │ │ │ │ │ "1111": [2197, 2218], │ │ │ │ │ "111107": 2207, │ │ │ │ │ "11111": 2228, │ │ │ │ │ "111110": 2186, │ │ │ │ │ @@ -23458,15 +23460,15 @@ │ │ │ │ │ "13078": 2232, │ │ │ │ │ "13082": 2232, │ │ │ │ │ "13083": 2238, │ │ │ │ │ "130932": 2207, │ │ │ │ │ "13097": 2235, │ │ │ │ │ "13098": 2232, │ │ │ │ │ "130980": 2195, │ │ │ │ │ - "131": [2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2203, 2208, 2210, 2211, 2232, 2249, 2283], │ │ │ │ │ + "131": [2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2203, 2208, 2210, 2211, 2232, 2249, 2283], │ │ │ │ │ "1310": 2199, │ │ │ │ │ "13101": 2239, │ │ │ │ │ "13104": 2232, │ │ │ │ │ "13107": 2232, │ │ │ │ │ "13109": 2232, │ │ │ │ │ "13110": 2232, │ │ │ │ │ "13119": 2232, │ │ │ │ │ @@ -23593,15 +23595,15 @@ │ │ │ │ │ "13382": 2232, │ │ │ │ │ "13383": 2232, │ │ │ │ │ "13386": 2241, │ │ │ │ │ "13389": 2232, │ │ │ │ │ "13393": 2239, │ │ │ │ │ "13395": 2232, │ │ │ │ │ "13398": 2232, │ │ │ │ │ - "134": [2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2208, 2210, 2211, 2232, 2235, 2249, 2259, 2283], │ │ │ │ │ + "134": [2184, 2185, 2186, 2188, 2191, 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, │ │ │ │ │ @@ -23841,17 +23843,18 @@ │ │ │ │ │ "14001": 2238, │ │ │ │ │ "140069": 2229, │ │ │ │ │ "14007": 2241, │ │ │ │ │ "14012": 2232, │ │ │ │ │ "14013": 2241, │ │ │ │ │ "14015": 2235, │ │ │ │ │ "14021": 2232, │ │ │ │ │ - "140212096832912": 2246, │ │ │ │ │ "140249": 2207, │ │ │ │ │ "14039": 2232, │ │ │ │ │ + "140400324417488": 2246, │ │ │ │ │ + "140400324419792": 2246, │ │ │ │ │ "14041": 2232, │ │ │ │ │ "140528": 2207, │ │ │ │ │ "14058": 2232, │ │ │ │ │ "14065": 2232, │ │ │ │ │ "14066": 2232, │ │ │ │ │ "14068": [2232, 2233], │ │ │ │ │ "1408": [2197, 2231], │ │ │ │ │ @@ -24146,15 +24149,15 @@ │ │ │ │ │ "14982": 2235, │ │ │ │ │ "14983": 2235, │ │ │ │ │ "1499": 2212, │ │ │ │ │ "14992": 2235, │ │ │ │ │ "14998": 2235, │ │ │ │ │ "14t15": [955, 956, 957, 962, 970, 983, 990, 995, 997, 999, 1002, 1006, 1007, 1008, 1009, 1013, 1014], │ │ │ │ │ "15": [4, 15, 16, 17, 18, 19, 22, 25, 26, 29, 30, 31, 72, 73, 81, 88, 91, 108, 112, 116, 121, 127, 133, 137, 157, 186, 208, 213, 230, 258, 268, 271, 277, 278, 345, 586, 600, 696, 703, 708, 732, 762, 782, 788, 804, 889, 899, 903, 904, 953, 955, 956, 957, 958, 970, 973, 992, 995, 997, 999, 1005, 1008, 1009, 1013, 1014, 1018, 1103, 1147, 1157, 1170, 1171, 1173, 1176, 1180, 1185, 1188, 1195, 1197, 1198, 1202, 1206, 1214, 1227, 1228, 1233, 1239, 1245, 1246, 1249, 1256, 1258, 1263, 1265, 1268, 1272, 1273, 1274, 1275, 1277, 1278, 1279, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1290, 1291, 1292, 1293, 1294, 1295, 1297, 1321, 1334, 1458, 1485, 1498, 1500, 1506, 1524, 1542, 1560, 1578, 1598, 1657, 1677, 1758, 1839, 1876, 1894, 1912, 1964, 2018, 2036, 2054, 2090, 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, 2220, 2225, 2226, 2227, 2228, 2230, 2231, 2232, 2235, 2238, 2240, 2243, 2246, 2249, 2257, 2261, 2264, 2265, 2271, 2277, 2283, 2289, 2294, 2298, 2302, 2307], │ │ │ │ │ - "150": [15, 111, 118, 132, 135, 159, 161, 175, 213, 233, 788, 2185, 2186, 2188, 2195, 2197, 2199, 2200, 2201, 2204, 2210, 2211], │ │ │ │ │ + "150": [15, 111, 118, 132, 135, 159, 161, 175, 213, 233, 788, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2201, 2204, 2210, 2211], │ │ │ │ │ "1500": [2212, 2241, 2246], │ │ │ │ │ "15000": [2185, 2220], │ │ │ │ │ "15001": 2238, │ │ │ │ │ "150025": 2207, │ │ │ │ │ "150031": 2207, │ │ │ │ │ "150036": [2220, 2230], │ │ │ │ │ "15005": 2235, │ │ │ │ │ @@ -24248,15 +24251,15 @@ │ │ │ │ │ "15272": 2289, │ │ │ │ │ "15277": 2235, │ │ │ │ │ "15289": 2235, │ │ │ │ │ "15296": 2241, │ │ │ │ │ "152963": 2207, │ │ │ │ │ "15297": 2235, │ │ │ │ │ "152996": 2207, │ │ │ │ │ - "153": [2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2210, 2211, 2231], │ │ │ │ │ + "153": [2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2210, 2211, 2231], │ │ │ │ │ "15300": 2235, │ │ │ │ │ "153009": 2207, │ │ │ │ │ "15305": 2238, │ │ │ │ │ "15306": 2249, │ │ │ │ │ "15314": 2246, │ │ │ │ │ "153266": 2207, │ │ │ │ │ "15328": 2235, │ │ │ │ │ @@ -25590,15 +25593,14 @@ │ │ │ │ │ "19900315": 2230, │ │ │ │ │ "19909": 2241, │ │ │ │ │ "1990q1": 2210, │ │ │ │ │ "1991": [2210, 2249], │ │ │ │ │ "19910905": 2249, │ │ │ │ │ "19917": 2271, │ │ │ │ │ "19920": 2241, │ │ │ │ │ - "1993": 2193, │ │ │ │ │ "19935": 2241, │ │ │ │ │ "199379": 2207, │ │ │ │ │ "19939": 2241, │ │ │ │ │ "1994": 2246, │ │ │ │ │ "19944": 2246, │ │ │ │ │ "19954": 2246, │ │ │ │ │ "19956": 2277, │ │ │ │ │ @@ -25748,19 +25750,20 @@ │ │ │ │ │ "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, 2228], │ │ │ │ │ + "2025": [36, 544, 546, 555, 567, 894, 898], │ │ │ │ │ "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, │ │ │ │ │ @@ -25867,15 +25870,15 @@ │ │ │ │ │ "20675": 2241, │ │ │ │ │ "20678": 2241, │ │ │ │ │ "2068": [30, 31], │ │ │ │ │ "20690": 2241, │ │ │ │ │ "206900": 2201, │ │ │ │ │ "20698": 2241, │ │ │ │ │ "20699": 2246, │ │ │ │ │ - "207": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211], │ │ │ │ │ + "207": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2210, 2211], │ │ │ │ │ "20700": 2246, │ │ │ │ │ "2071": 2210, │ │ │ │ │ "207103": 2191, │ │ │ │ │ "20712": 2307, │ │ │ │ │ "20716": 2241, │ │ │ │ │ "2072": 2210, │ │ │ │ │ "20721": 2265, │ │ │ │ │ @@ -25922,14 +25925,15 @@ │ │ │ │ │ "20859": 2241, │ │ │ │ │ "20868": 2294, │ │ │ │ │ "20869": 2246, │ │ │ │ │ "208707": 2199, │ │ │ │ │ "208843": [2184, 2214], │ │ │ │ │ "209": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211, 2212, 2253], │ │ │ │ │ "209013": 15, │ │ │ │ │ + "209014": 2228, │ │ │ │ │ "20902": 2241, │ │ │ │ │ "209097": 2207, │ │ │ │ │ "20911": 2246, │ │ │ │ │ "209138": 2185, │ │ │ │ │ "20920": 2241, │ │ │ │ │ "20921": 2241, │ │ │ │ │ "20925": 2242, │ │ │ │ │ @@ -25970,20 +25974,21 @@ │ │ │ │ │ "210427": 2199, │ │ │ │ │ "21052": 2242, │ │ │ │ │ "210526": 2222, │ │ │ │ │ "21055": 2298, │ │ │ │ │ "21063": 2242, │ │ │ │ │ "21071": 2242, │ │ │ │ │ "21078": 2242, │ │ │ │ │ + "210783": 2228, │ │ │ │ │ "21083": 2242, │ │ │ │ │ "2109": 2264, │ │ │ │ │ "21090": 2271, │ │ │ │ │ "210945": 2195, │ │ │ │ │ "21097": 2242, │ │ │ │ │ - "211": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2203, 2210, 2211, 2212, 2254], │ │ │ │ │ + "211": [2185, 2186, 2188, 2195, 2197, 2199, 2203, 2210, 2211, 2212, 2254], │ │ │ │ │ "2110": 2264, │ │ │ │ │ "21101": 2242, │ │ │ │ │ "21103": 2242, │ │ │ │ │ "21104": 2243, │ │ │ │ │ "211056": 2197, │ │ │ │ │ "21106": 2242, │ │ │ │ │ "21107": 2242, │ │ │ │ │ @@ -26547,15 +26552,15 @@ │ │ │ │ │ "23574": 2265, │ │ │ │ │ "23575": 2246, │ │ │ │ │ "23579": 2246, │ │ │ │ │ "235796": 2204, │ │ │ │ │ "235806": 2207, │ │ │ │ │ "23585": [2246, 2265], │ │ │ │ │ "23598": 2298, │ │ │ │ │ - "236": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2203, 2210, 2220, 2298], │ │ │ │ │ + "236": [2185, 2186, 2188, 2195, 2197, 2199, 2203, 2210, 2220, 2298], │ │ │ │ │ "236000": [2185, 2220], │ │ │ │ │ "23601": [2246, 2265], │ │ │ │ │ "23614": 2246, │ │ │ │ │ "236170": 2207, │ │ │ │ │ "23619": 30, │ │ │ │ │ "23621": 2265, │ │ │ │ │ "23623": 2246, │ │ │ │ │ @@ -26745,15 +26750,15 @@ │ │ │ │ │ "243678": 2210, │ │ │ │ │ "2437": 2216, │ │ │ │ │ "24371": 2246, │ │ │ │ │ "24372": 2246, │ │ │ │ │ "24382": 2271, │ │ │ │ │ "2439": [196, 771], │ │ │ │ │ "24398": 2246, │ │ │ │ │ - "244": [268, 745, 2185, 2186, 2188, 2195, 2197, 2199, 2203, 2205, 2210, 2220, 2222, 2224, 2246, 2254, 2298], │ │ │ │ │ + "244": [268, 745, 2185, 2186, 2188, 2195, 2197, 2199, 2203, 2210, 2220, 2222, 2224, 2246, 2254, 2298], │ │ │ │ │ "24405": 2246, │ │ │ │ │ "24408": 2246, │ │ │ │ │ "244140625": 2298, │ │ │ │ │ "24415": 2246, │ │ │ │ │ "24416": 2249, │ │ │ │ │ "24435": [2283, 2298], │ │ │ │ │ "244413": 2199, │ │ │ │ │ @@ -27200,15 +27205,15 @@ │ │ │ │ │ "2658": 2257, │ │ │ │ │ "26581": [2249, 2265], │ │ │ │ │ "265879e": 2191, │ │ │ │ │ "265936": 2229, │ │ │ │ │ "26597": [2249, 2265], │ │ │ │ │ "26598": 2257, │ │ │ │ │ "26599": [2271, 2283], │ │ │ │ │ - "266": [2185, 2186, 2188, 2195, 2197, 2199, 2210], │ │ │ │ │ + "266": [2186, 2188, 2195, 2197, 2199, 2210], │ │ │ │ │ "266046": [2184, 2214], │ │ │ │ │ "2661": 2204, │ │ │ │ │ "26610": 2249, │ │ │ │ │ "266143": [2185, 2197], │ │ │ │ │ "26615": 2289, │ │ │ │ │ "266152": 2207, │ │ │ │ │ "266154": 31, │ │ │ │ │ @@ -27344,15 +27349,15 @@ │ │ │ │ │ "27250": 2249, │ │ │ │ │ "272593": 2230, │ │ │ │ │ "27261": 2251, │ │ │ │ │ "272673": 2207, │ │ │ │ │ "27283": 2265, │ │ │ │ │ "27292": 2265, │ │ │ │ │ "272968": 2195, │ │ │ │ │ - "273": [2186, 2188, 2195, 2197, 2199, 2202, 2210, 2257], │ │ │ │ │ + "273": [2186, 2188, 2193, 2195, 2197, 2199, 2202, 2210, 2257], │ │ │ │ │ "2730": 2199, │ │ │ │ │ "27309": 2249, │ │ │ │ │ "27311": 2265, │ │ │ │ │ "27315": 2277, │ │ │ │ │ "27321": 2249, │ │ │ │ │ "273290": 2207, │ │ │ │ │ "273322": 2207, │ │ │ │ │ @@ -27423,15 +27428,14 @@ │ │ │ │ │ "276183": 2257, │ │ │ │ │ "2762": [2184, 2186, 2191], │ │ │ │ │ "276232": [15, 2184, 2185, 2186, 2191, 2197, 2199, 2202, 2210, 2214, 2215, 2216, 2218, 2225, 2231, 2241, 2264], │ │ │ │ │ "27636": 2250, │ │ │ │ │ "276386": 2207, │ │ │ │ │ "27642": 2250, │ │ │ │ │ "276464": 2230, │ │ │ │ │ - "2765": 2193, │ │ │ │ │ "27656": [2294, 2298], │ │ │ │ │ "27660": 2265, │ │ │ │ │ "2766617129497566": 2257, │ │ │ │ │ "276662": [2185, 2197, 2199, 2202, 2215, 2257], │ │ │ │ │ "27668": 2265, │ │ │ │ │ "2767": 2191, │ │ │ │ │ "27676": 2265, │ │ │ │ │ @@ -27571,15 +27575,15 @@ │ │ │ │ │ "283627": 2229, │ │ │ │ │ "28368": 2265, │ │ │ │ │ "2837": 2216, │ │ │ │ │ "28375": 2271, │ │ │ │ │ "28383": 2265, │ │ │ │ │ "28385": 2298, │ │ │ │ │ "28394": 2277, │ │ │ │ │ - "284": [16, 17, 18, 19, 2185, 2186, 2197, 2199, 2210, 2235], │ │ │ │ │ + "284": [16, 17, 18, 19, 2186, 2197, 2199, 2210, 2235], │ │ │ │ │ "28406": 2265, │ │ │ │ │ "28410": 2265, │ │ │ │ │ "28425": 2265, │ │ │ │ │ "28426": 2265, │ │ │ │ │ "28427": 2265, │ │ │ │ │ "284319": 2202, │ │ │ │ │ "2846": 2185, │ │ │ │ │ @@ -27622,15 +27626,15 @@ │ │ │ │ │ "28664": 2265, │ │ │ │ │ "28668": 2265, │ │ │ │ │ "28669": 2265, │ │ │ │ │ "28678": 2251, │ │ │ │ │ "286879": 2218, │ │ │ │ │ "28690": 2283, │ │ │ │ │ "28699": 2265, │ │ │ │ │ - "287": [16, 17, 18, 19, 2186, 2197, 2199, 2210, 2235], │ │ │ │ │ + "287": [16, 17, 18, 19, 2186, 2197, 2199, 2205, 2210, 2235], │ │ │ │ │ "28735": 2265, │ │ │ │ │ "28741": 2265, │ │ │ │ │ "287456": 2207, │ │ │ │ │ "28759": 2277, │ │ │ │ │ "28766": 2265, │ │ │ │ │ "28769": 2265, │ │ │ │ │ "287725": 2185, │ │ │ │ │ @@ -27800,15 +27804,15 @@ │ │ │ │ │ "29624": 2265, │ │ │ │ │ "296326": 2207, │ │ │ │ │ "29641": 2265, │ │ │ │ │ "29650": 2265, │ │ │ │ │ "29664": 2265, │ │ │ │ │ "29684": 2271, │ │ │ │ │ "29688": 2294, │ │ │ │ │ - "297": [2186, 2197, 2199, 2210, 2255], │ │ │ │ │ + "297": [2186, 2193, 2197, 2199, 2210, 2255], │ │ │ │ │ "297019e": 2191, │ │ │ │ │ "29718": 2265, │ │ │ │ │ "29723": [2265, 2271], │ │ │ │ │ "29731": 2298, │ │ │ │ │ "29733": 2265, │ │ │ │ │ "29742": 2265, │ │ │ │ │ "297424": 2207, │ │ │ │ │ @@ -28876,15 +28880,15 @@ │ │ │ │ │ "349825": 2207, │ │ │ │ │ "34986": 2298, │ │ │ │ │ "349893": 2185, │ │ │ │ │ "3499": 2217, │ │ │ │ │ "34994": 2271, │ │ │ │ │ "34998": 2298, │ │ │ │ │ "35": [15, 17, 18, 19, 23, 25, 27, 133, 142, 160, 190, 193, 208, 213, 345, 708, 738, 766, 768, 782, 788, 823, 953, 957, 997, 1323, 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], │ │ │ │ │ - "350": [134, 268, 271, 709, 899, 1259, 1264, 1485, 2186, 2197, 2199, 2210], │ │ │ │ │ + "350": [134, 268, 271, 709, 899, 1259, 1264, 1485, 2185, 2186, 2197, 2199, 2210], │ │ │ │ │ "35003": 2298, │ │ │ │ │ "35014": 2277, │ │ │ │ │ "35028": 2277, │ │ │ │ │ "35038": 2271, │ │ │ │ │ "35046": 2277, │ │ │ │ │ "35058": 2277, │ │ │ │ │ "350621": 2207, │ │ │ │ │ @@ -28934,15 +28938,15 @@ │ │ │ │ │ "353713": 2191, │ │ │ │ │ "35374": 2271, │ │ │ │ │ "35376": 2274, │ │ │ │ │ "353795": 2207, │ │ │ │ │ "35382": 2277, │ │ │ │ │ "35392": 2289, │ │ │ │ │ "353925": 2191, │ │ │ │ │ - "354": [2186, 2197, 2199, 2210, 2298], │ │ │ │ │ + "354": [2186, 2193, 2197, 2199, 2210, 2298], │ │ │ │ │ "35407": [2289, 2298], │ │ │ │ │ "35416": 2283, │ │ │ │ │ "3542": [2186, 2227], │ │ │ │ │ "354342": 2207, │ │ │ │ │ "354360": 2207, │ │ │ │ │ "35439": 2272, │ │ │ │ │ "35446": 2272, │ │ │ │ │ @@ -29119,15 +29123,15 @@ │ │ │ │ │ "3616": 2217, │ │ │ │ │ "361719": 2197, │ │ │ │ │ "361733": 2207, │ │ │ │ │ "36176": 2277, │ │ │ │ │ "36179": [2277, 2298], │ │ │ │ │ "36189": 2274, │ │ │ │ │ "36197": 2273, │ │ │ │ │ - "362": [1193, 1254, 2186, 2197, 2199, 2210, 2255, 2298], │ │ │ │ │ + "362": [1193, 1254, 2186, 2193, 2197, 2199, 2210, 2255, 2298], │ │ │ │ │ "36204": 2277, │ │ │ │ │ "36210": 2277, │ │ │ │ │ "36212": 2277, │ │ │ │ │ "362228": 2210, │ │ │ │ │ "36226": 30, │ │ │ │ │ "36240": 2277, │ │ │ │ │ "36241": 2274, │ │ │ │ │ @@ -29417,15 +29421,15 @@ │ │ │ │ │ "37748": 2277, │ │ │ │ │ "37750": 2289, │ │ │ │ │ "377535": 2186, │ │ │ │ │ "37755": 2276, │ │ │ │ │ "37758": 2277, │ │ │ │ │ "377642": 2210, │ │ │ │ │ "37768": 2277, │ │ │ │ │ - "3777": 2218, │ │ │ │ │ + "3777": [2193, 2218], │ │ │ │ │ "37782": 2302, │ │ │ │ │ "377887": 2207, │ │ │ │ │ "37799": 2277, │ │ │ │ │ "378": [2186, 2197, 2199, 2207, 2210, 2231], │ │ │ │ │ "3780": 2222, │ │ │ │ │ "37804": 2283, │ │ │ │ │ "378163": 2207, │ │ │ │ │ @@ -29849,15 +29853,15 @@ │ │ │ │ │ "39650": 2283, │ │ │ │ │ "39660": 2283, │ │ │ │ │ "39664": 2283, │ │ │ │ │ "396774": 2218, │ │ │ │ │ "396780": [2185, 2197, 2199, 2202], │ │ │ │ │ "396823": [2184, 2214], │ │ │ │ │ "39695": 2280, │ │ │ │ │ - "397": [2186, 2193, 2197, 2199, 2210, 2218], │ │ │ │ │ + "397": [2186, 2197, 2199, 2210], │ │ │ │ │ "39701": 2283, │ │ │ │ │ "39710": 2283, │ │ │ │ │ "39716": 2283, │ │ │ │ │ "397191": 15, │ │ │ │ │ "39720": 2283, │ │ │ │ │ "397203": 2230, │ │ │ │ │ "39725": 2283, │ │ │ │ │ @@ -30035,15 +30039,15 @@ │ │ │ │ │ "40585": 2283, │ │ │ │ │ "40589": 2294, │ │ │ │ │ "405906": 2207, │ │ │ │ │ "405919": 2195, │ │ │ │ │ "406": [2186, 2199, 2210], │ │ │ │ │ "4060": 2222, │ │ │ │ │ "40606": 2283, │ │ │ │ │ - "4062": 2217, │ │ │ │ │ + "4062": [2193, 2217], │ │ │ │ │ "40628": [2283, 2298], │ │ │ │ │ "4063": 2217, │ │ │ │ │ "406345": 2207, │ │ │ │ │ "40638": 2298, │ │ │ │ │ "4065": 2218, │ │ │ │ │ "40660": 2283, │ │ │ │ │ "40662": 2281, │ │ │ │ │ @@ -30705,15 +30709,15 @@ │ │ │ │ │ "43464": 2289, │ │ │ │ │ "43469": 2289, │ │ │ │ │ "43476": 2289, │ │ │ │ │ "43480": 2289, │ │ │ │ │ "434813": 2207, │ │ │ │ │ "43485": 2302, │ │ │ │ │ "43495": 2289, │ │ │ │ │ - "435": [2186, 2199, 2210, 2256, 2298], │ │ │ │ │ + "435": [2185, 2186, 2199, 2210, 2256, 2298], │ │ │ │ │ "43500": 2289, │ │ │ │ │ "43505": 2289, │ │ │ │ │ "43507": 2289, │ │ │ │ │ "4351": 2218, │ │ │ │ │ "43515": 2289, │ │ │ │ │ "435223": 2207, │ │ │ │ │ "43523": 2298, │ │ │ │ │ @@ -31059,15 +31063,15 @@ │ │ │ │ │ "44965": 2294, │ │ │ │ │ "449695": 2214, │ │ │ │ │ "44977": 2294, │ │ │ │ │ "44978": 2289, │ │ │ │ │ "449784": 2207, │ │ │ │ │ "4498": 2218, │ │ │ │ │ "45": [17, 18, 19, 26, 27, 31, 88, 91, 111, 213, 230, 259, 345, 600, 633, 788, 804, 890, 1154, 1272, 1275, 1286, 1433, 1458, 1498, 2184, 2185, 2186, 2188, 2190, 2191, 2193, 2194, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2238, 2241, 2246, 2249, 2265, 2271, 2283], │ │ │ │ │ - "450": [2199, 2210, 2249], │ │ │ │ │ + "450": [2193, 2199, 2210, 2249], │ │ │ │ │ "4500": 24, │ │ │ │ │ "450000": 2212, │ │ │ │ │ "45018": 2298, │ │ │ │ │ "450185": 2207, │ │ │ │ │ "45032": 2289, │ │ │ │ │ "45033": 2298, │ │ │ │ │ "45034": 2294, │ │ │ │ │ @@ -31636,15 +31640,15 @@ │ │ │ │ │ "47753": 2294, │ │ │ │ │ "47761": 2298, │ │ │ │ │ "47762": 2293, │ │ │ │ │ "47772": 2307, │ │ │ │ │ "477769": 2197, │ │ │ │ │ "47787": 2294, │ │ │ │ │ "477996": 2207, │ │ │ │ │ - "478": [2184, 2193, 2199, 2205, 2210], │ │ │ │ │ + "478": [2184, 2199, 2205, 2210], │ │ │ │ │ "47809": 2294, │ │ │ │ │ "47812": 2294, │ │ │ │ │ "478155": 2207, │ │ │ │ │ "47819": 2298, │ │ │ │ │ "478240": 2207, │ │ │ │ │ "47834": 2298, │ │ │ │ │ "47836": 2294, │ │ │ │ │ @@ -31897,15 +31901,14 @@ │ │ │ │ │ "49108": 2298, │ │ │ │ │ "49109": 2298, │ │ │ │ │ "49111": 2298, │ │ │ │ │ "49121": 2298, │ │ │ │ │ "49128": 2298, │ │ │ │ │ "49139": 2229, │ │ │ │ │ "49148": 2298, │ │ │ │ │ - "491572": 2228, │ │ │ │ │ "49159": 2298, │ │ │ │ │ "49162": 2295, │ │ │ │ │ "491708": 29, │ │ │ │ │ "49172": 2298, │ │ │ │ │ "49176": 2298, │ │ │ │ │ "49177": 2298, │ │ │ │ │ "49178": 2298, │ │ │ │ │ @@ -31978,15 +31981,14 @@ │ │ │ │ │ "49519": 2302, │ │ │ │ │ "49521": 2298, │ │ │ │ │ "49523": 2298, │ │ │ │ │ "49525": 2298, │ │ │ │ │ "495291": 15, │ │ │ │ │ "4953": 2202, │ │ │ │ │ "4953086": 2202, │ │ │ │ │ - "495556": 2228, │ │ │ │ │ "49558": 2298, │ │ │ │ │ "4956": 2218, │ │ │ │ │ "495614": 2199, │ │ │ │ │ "49565": 2298, │ │ │ │ │ "49572": 2298, │ │ │ │ │ "495763": 2201, │ │ │ │ │ "495767": [2184, 2195, 2214], │ │ │ │ │ @@ -32048,15 +32050,15 @@ │ │ │ │ │ "4987": 2225, │ │ │ │ │ "4988": 2238, │ │ │ │ │ "498861": 2191, │ │ │ │ │ "49888": 2300, │ │ │ │ │ "49889": 2299, │ │ │ │ │ "49890": 2298, │ │ │ │ │ "49897": 2298, │ │ │ │ │ - "499": [2184, 2199, 2205, 2210, 2249], │ │ │ │ │ + "499": [2184, 2185, 2199, 2205, 2210, 2249], │ │ │ │ │ "49907": 2297, │ │ │ │ │ "499148": 2207, │ │ │ │ │ "49921": 2298, │ │ │ │ │ "49922": 2298, │ │ │ │ │ "49929": 2298, │ │ │ │ │ "4993": 2218, │ │ │ │ │ "49944": 2302, │ │ │ │ │ @@ -32326,15 +32328,15 @@ │ │ │ │ │ "511763": [2184, 2195, 2214], │ │ │ │ │ "511806": 2207, │ │ │ │ │ "51182": 2298, │ │ │ │ │ "51183": 2298, │ │ │ │ │ "51186": 2298, │ │ │ │ │ "511885": [1199, 1260], │ │ │ │ │ "51197": 2298, │ │ │ │ │ - "512": [28, 2184, 2199, 2205], │ │ │ │ │ + "512": [28, 2184, 2193, 2199, 2205], │ │ │ │ │ "51203": 2302, │ │ │ │ │ "512043": 2207, │ │ │ │ │ "51205": 2298, │ │ │ │ │ "51206": 2298, │ │ │ │ │ "5121": 2218, │ │ │ │ │ "51223": 2298, │ │ │ │ │ "51227": 2298, │ │ │ │ │ @@ -32937,15 +32939,15 @@ │ │ │ │ │ "54341": 2302, │ │ │ │ │ "54346": 2302, │ │ │ │ │ "5436": 2224, │ │ │ │ │ "5437": 2218, │ │ │ │ │ "54371": 2302, │ │ │ │ │ "54379": 2302, │ │ │ │ │ "54383": 2302, │ │ │ │ │ - "544": [2185, 2199], │ │ │ │ │ + "544": 2199, │ │ │ │ │ "5441": 2218, │ │ │ │ │ "5443": 2219, │ │ │ │ │ "54430": 2302, │ │ │ │ │ "54443": 2302, │ │ │ │ │ "54459": 2307, │ │ │ │ │ "54466": 2308, │ │ │ │ │ "54467": 2307, │ │ │ │ │ @@ -33798,15 +33800,15 @@ │ │ │ │ │ "614264": 2207, │ │ │ │ │ "614266": 2199, │ │ │ │ │ "614523": 2191, │ │ │ │ │ "614533": 2197, │ │ │ │ │ "614581": [2184, 2195], │ │ │ │ │ "6148": 2219, │ │ │ │ │ "6149": 2220, │ │ │ │ │ - "615": 2199, │ │ │ │ │ + "615": [2193, 2199], │ │ │ │ │ "6150": 2219, │ │ │ │ │ "6152": 2219, │ │ │ │ │ "615303": 2191, │ │ │ │ │ "615385": [121, 696, 2212], │ │ │ │ │ "615396": 2230, │ │ │ │ │ "6155": 2219, │ │ │ │ │ "615556": 27, │ │ │ │ │ @@ -33824,15 +33826,15 @@ │ │ │ │ │ "6169": 2219, │ │ │ │ │ "617": [16, 17, 18, 19, 2199, 2203, 2232, 2235, 2298], │ │ │ │ │ "6171": 2219, │ │ │ │ │ "6175": 2220, │ │ │ │ │ "617509": 2199, │ │ │ │ │ "6177": 2220, │ │ │ │ │ "6178": 2220, │ │ │ │ │ - "618": 2199, │ │ │ │ │ + "618": [2193, 2199, 2205], │ │ │ │ │ "618153": 15, │ │ │ │ │ "618321": 2199, │ │ │ │ │ "618372": 2207, │ │ │ │ │ "618553": 2207, │ │ │ │ │ "6186": 2220, │ │ │ │ │ "618673": 2197, │ │ │ │ │ "618697": 2199, │ │ │ │ │ @@ -33881,15 +33883,15 @@ │ │ │ │ │ "6240": 2220, │ │ │ │ │ "624607": 15, │ │ │ │ │ "624615": 2207, │ │ │ │ │ "624699e": 2191, │ │ │ │ │ "624747": 2199, │ │ │ │ │ "624938": 2191, │ │ │ │ │ "624988": 2230, │ │ │ │ │ - "625": [205, 778, 2199, 2203, 2205, 2298], │ │ │ │ │ + "625": [205, 778, 2199, 2203, 2298], │ │ │ │ │ "6252": 2220, │ │ │ │ │ "625210": 2207, │ │ │ │ │ "6254": 2220, │ │ │ │ │ "625415": 2207, │ │ │ │ │ "6255": 2192, │ │ │ │ │ "6256": [2192, 2202], │ │ │ │ │ "6257": 2192, │ │ │ │ │ @@ -33905,15 +33907,15 @@ │ │ │ │ │ "626300": 1323, │ │ │ │ │ "6263001": 1323, │ │ │ │ │ "6264": 2192, │ │ │ │ │ "626404": 2235, │ │ │ │ │ "626444": 15, │ │ │ │ │ "6265": 2220, │ │ │ │ │ "626968": 2217, │ │ │ │ │ - "627": 2199, │ │ │ │ │ + "627": [2199, 2205], │ │ │ │ │ "627068": 2207, │ │ │ │ │ "627081": [2184, 2195, 2214], │ │ │ │ │ "6273": 2220, │ │ │ │ │ "6274": 2220, │ │ │ │ │ "627712": 2197, │ │ │ │ │ "627796": 2235, │ │ │ │ │ "6279": 2271, │ │ │ │ │ @@ -33960,15 +33962,15 @@ │ │ │ │ │ "633": 2199, │ │ │ │ │ "633165": 2230, │ │ │ │ │ "6332": 2220, │ │ │ │ │ "633372": 2215, │ │ │ │ │ "6335": 2220, │ │ │ │ │ "633678": 2185, │ │ │ │ │ "6337": 2220, │ │ │ │ │ - "634": 2199, │ │ │ │ │ + "634": [2193, 2199], │ │ │ │ │ "6341": 2220, │ │ │ │ │ "6342": 2220, │ │ │ │ │ "634248": 2199, │ │ │ │ │ "6344": 2220, │ │ │ │ │ "6345": 2220, │ │ │ │ │ "634509": 2191, │ │ │ │ │ "634686": 2207, │ │ │ │ │ @@ -34768,15 +34770,15 @@ │ │ │ │ │ "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, │ │ │ │ │ @@ -34984,15 +34986,15 @@ │ │ │ │ │ "7575": 2222, │ │ │ │ │ "757508": 2205, │ │ │ │ │ "757555": 2193, │ │ │ │ │ "7576": 2294, │ │ │ │ │ "757698": 2195, │ │ │ │ │ "757745": 2207, │ │ │ │ │ "757772": 2207, │ │ │ │ │ - "758": [27, 2185, 2193, 2298], │ │ │ │ │ + "758": [27, 2185, 2298], │ │ │ │ │ "758070": 2207, │ │ │ │ │ "758294": 2191, │ │ │ │ │ "7586": 2221, │ │ │ │ │ "758602": 2207, │ │ │ │ │ "7588": 2231, │ │ │ │ │ "759": 32, │ │ │ │ │ "759104": 2185, │ │ │ │ │ @@ -35524,15 +35526,15 @@ │ │ │ │ │ "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, │ │ │ │ │ @@ -35640,15 +35642,15 @@ │ │ │ │ │ "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], │ │ │ │ │ - "850": [16, 17, 18, 19, 2199, 2235], │ │ │ │ │ + "850": [16, 17, 18, 19, 2193, 2199, 2235], │ │ │ │ │ "850083": 2207, │ │ │ │ │ "8501": 2222, │ │ │ │ │ "850229": 2235, │ │ │ │ │ "850287": 2207, │ │ │ │ │ "8504": 2202, │ │ │ │ │ "850458": 2207, │ │ │ │ │ "8505": 2228, │ │ │ │ │ @@ -35804,15 +35806,14 @@ │ │ │ │ │ "8685": 2228, │ │ │ │ │ "868579": 2207, │ │ │ │ │ "868584": 2197, │ │ │ │ │ "8687": 2223, │ │ │ │ │ "8688": 2225, │ │ │ │ │ "8689": 2223, │ │ │ │ │ "868951": 2207, │ │ │ │ │ - "869": 2205, │ │ │ │ │ "869081": 2199, │ │ │ │ │ "869127": 2230, │ │ │ │ │ "869226": 2186, │ │ │ │ │ "869339": 2207, │ │ │ │ │ "869551": 2191, │ │ │ │ │ "8697": 2224, │ │ │ │ │ "87": [15, 18, 133, 196, 208, 242, 283, 586, 708, 771, 782, 817, 910, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2218, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2298], │ │ │ │ │ @@ -35873,15 +35874,14 @@ │ │ │ │ │ "877353": 2207, │ │ │ │ │ "877384e": 2204, │ │ │ │ │ "877657": 2199, │ │ │ │ │ "8778": 2224, │ │ │ │ │ "8781": 2224, │ │ │ │ │ "8783": 2224, │ │ │ │ │ "878575": 2207, │ │ │ │ │ - "879": 2205, │ │ │ │ │ "8790": 2228, │ │ │ │ │ "8791": 2224, │ │ │ │ │ "879103": 2207, │ │ │ │ │ "8794": 2225, │ │ │ │ │ "8795": 2224, │ │ │ │ │ "879536": 2229, │ │ │ │ │ "879758": 2216, │ │ │ │ │ @@ -36024,15 +36024,15 @@ │ │ │ │ │ "8983": 2224, │ │ │ │ │ "8984": 2202, │ │ │ │ │ "8984347": 2202, │ │ │ │ │ "8986": 2225, │ │ │ │ │ "898725": 2197, │ │ │ │ │ "898872": 2214, │ │ │ │ │ "8989": 2224, │ │ │ │ │ - "899": [2193, 2199], │ │ │ │ │ + "899": 2199, │ │ │ │ │ "8991": 2289, │ │ │ │ │ "899173761": 2199, │ │ │ │ │ "899260": 15, │ │ │ │ │ "8994": 2224, │ │ │ │ │ "899734": 15, │ │ │ │ │ "8999": 2229, │ │ │ │ │ "8a2e": 2241, │ │ │ │ │ @@ -36393,15 +36393,14 @@ │ │ │ │ │ "9542": 2226, │ │ │ │ │ "9542078401": 2199, │ │ │ │ │ "954208": [2185, 2197, 2199, 2202], │ │ │ │ │ "9543": 2227, │ │ │ │ │ "954504": 2230, │ │ │ │ │ "954680": 2184, │ │ │ │ │ "954773": 2207, │ │ │ │ │ - "955": 2218, │ │ │ │ │ "9552": 2226, │ │ │ │ │ "955398": 2191, │ │ │ │ │ "9556635297215477": 2206, │ │ │ │ │ "955697": 2214, │ │ │ │ │ "9557": 2206, │ │ │ │ │ "955755": 2207, │ │ │ │ │ "9558": 2228, │ │ │ │ │ @@ -36568,15 +36567,15 @@ │ │ │ │ │ "9792": 2227, │ │ │ │ │ "9794": 2226, │ │ │ │ │ "9795": 2226, │ │ │ │ │ "979542": 2185, │ │ │ │ │ "979573": 2207, │ │ │ │ │ "979600": 2186, │ │ │ │ │ "9798": 2226, │ │ │ │ │ - "98": [15, 1447, 2184, 2185, 2186, 2188, 2191, 2192, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2226, 2230, 2232, 2235, 2238, 2246, 2294], │ │ │ │ │ + "98": [15, 1447, 2184, 2185, 2186, 2188, 2191, 2192, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2226, 2230, 2232, 2235, 2238, 2246, 2294], │ │ │ │ │ "980": 2199, │ │ │ │ │ "9804": 2226, │ │ │ │ │ "9805": 2226, │ │ │ │ │ "9807": 2226, │ │ │ │ │ "980796": 2207, │ │ │ │ │ "980950": 2195, │ │ │ │ │ "981": [2199, 2207], │ │ │ │ │ @@ -37448,15 +37447,15 @@ │ │ │ │ │ "apply_func": 2212, │ │ │ │ │ "apply_if_cal": [2185, 2197], │ │ │ │ │ "apply_index": [1395, 1400, 1413, 1414, 1415, 2207, 2271, 2289, 2298], │ │ │ │ │ "apply_integrate_f": 2193, │ │ │ │ │ "apply_integrate_f_numba": 2193, │ │ │ │ │ "apply_integrate_f_wrap": 2193, │ │ │ │ │ "apply_raw": 2194, │ │ │ │ │ - "apply_series_gener": 2194, │ │ │ │ │ + "apply_series_gener": [2193, 2194], │ │ │ │ │ "apply_series_numba": 2194, │ │ │ │ │ "apply_standard": 2194, │ │ │ │ │ "applymap": [162, 1348, 2184, 2217, 2235, 2238, 2246, 2277, 2283, 2302], │ │ │ │ │ "applymap_index": [1396, 1414, 2289, 2302], │ │ │ │ │ "applymark": 2, │ │ │ │ │ "applytypeerror": 2221, │ │ │ │ │ "approach": [2, 4, 10, 15, 16, 17, 19, 21, 22, 28, 31, 1077, 1433, 2186, 2188, 2193, 2196, 2199, 2223, 2246, 2249, 2260], │ │ │ │ │ @@ -37728,15 +37727,15 @@ │ │ │ │ │ "barboursvil": 2199, │ │ │ │ │ "bare": [2, 2199, 2222, 2241, 2277], │ │ │ │ │ "barf": 2217, │ │ │ │ │ "barh": [26, 186, 188, 762, 764, 1188, 1249, 2211, 2220, 2221, 2228, 2260, 2294], │ │ │ │ │ "bark": 1365, │ │ │ │ │ "barplot": 2222, │ │ │ │ │ "barycentr": [146, 720, 1280, 2201, 2218], │ │ │ │ │ - "base": [1, 3, 4, 5, 10, 11, 13, 16, 17, 18, 19, 20, 21, 22, 23, 25, 31, 32, 34, 49, 65, 83, 84, 88, 107, 111, 112, 121, 127, 136, 137, 138, 141, 142, 144, 147, 157, 160, 184, 187, 212, 213, 218, 224, 240, 248, 253, 276, 278, 279, 285, 286, 288, 296, 318, 328, 331, 345, 352, 415, 433, 445, 459, 478, 540, 568, 573, 594, 595, 600, 629, 633, 639, 652, 673, 686, 696, 703, 712, 714, 717, 718, 732, 738, 754, 757, 763, 787, 788, 793, 816, 823, 836, 837, 838, 839, 840, 841, 842, 843, 844, 881, 886, 902, 904, 905, 913, 938, 940, 943, 948, 952, 1031, 1040, 1052, 1068, 1073, 1075, 1119, 1125, 1141, 1148, 1149, 1164, 1173, 1193, 1207, 1208, 1221, 1242, 1243, 1254, 1265, 1269, 1270, 1286, 1342, 1343, 1398, 1423, 1431, 1444, 1453, 1467, 1470, 1474, 1475, 1498, 1519, 1537, 1556, 1574, 1593, 1614, 1633, 1650, 1672, 1693, 1715, 1736, 1754, 1772, 1789, 1808, 1830, 1853, 1870, 1890, 1908, 1926, 1943, 1960, 1978, 1995, 2013, 2032, 2050, 2068, 2086, 2103, 2121, 2141, 2159, 2163, 2166, 2183, 2184, 2185, 2187, 2188, 2191, 2192, 2194, 2195, 2196, 2199, 2200, 2201, 2203, 2207, 2208, 2210, 2211, 2212, 2213, 2214, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2224, 2226, 2228, 2229, 2230, 2231, 2232, 2235, 2236, 2238, 2240, 2241, 2246, 2249, 2253, 2255, 2261, 2264, 2265, 2274, 2277, 2283, 2291, 2298, 2302], │ │ │ │ │ + "base": [1, 3, 4, 5, 10, 11, 13, 16, 17, 18, 19, 20, 21, 22, 23, 25, 31, 32, 34, 49, 65, 83, 84, 88, 107, 111, 112, 121, 127, 136, 137, 138, 141, 142, 144, 147, 157, 160, 184, 187, 212, 213, 218, 224, 240, 248, 253, 276, 278, 279, 285, 286, 288, 296, 318, 328, 331, 345, 352, 415, 433, 445, 459, 478, 540, 568, 573, 594, 595, 600, 629, 633, 639, 652, 673, 686, 696, 703, 712, 714, 717, 718, 732, 738, 754, 757, 763, 787, 788, 793, 816, 823, 836, 837, 838, 839, 840, 841, 842, 843, 844, 881, 886, 902, 904, 905, 913, 938, 940, 943, 948, 952, 1031, 1040, 1052, 1068, 1073, 1075, 1119, 1125, 1141, 1148, 1149, 1164, 1173, 1193, 1207, 1208, 1221, 1242, 1243, 1254, 1265, 1269, 1270, 1286, 1342, 1343, 1398, 1423, 1431, 1444, 1453, 1467, 1470, 1474, 1475, 1498, 1519, 1537, 1556, 1574, 1593, 1614, 1633, 1650, 1672, 1693, 1715, 1736, 1754, 1772, 1789, 1808, 1830, 1853, 1870, 1890, 1908, 1926, 1943, 1960, 1978, 1995, 2013, 2032, 2050, 2068, 2086, 2103, 2121, 2141, 2159, 2163, 2166, 2183, 2184, 2185, 2187, 2188, 2191, 2192, 2193, 2194, 2195, 2196, 2199, 2200, 2201, 2203, 2207, 2208, 2210, 2211, 2212, 2213, 2214, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2224, 2226, 2228, 2229, 2230, 2231, 2232, 2235, 2236, 2238, 2240, 2241, 2246, 2249, 2253, 2255, 2261, 2264, 2265, 2274, 2277, 2283, 2291, 2298, 2302], │ │ │ │ │ "base_dtyp": 2199, │ │ │ │ │ "base_pars": 2199, │ │ │ │ │ "base_typ": [2194, 2201, 2203, 2294, 2302, 2307], │ │ │ │ │ "basebal": [15, 2186, 2191, 2197, 2227, 2231], │ │ │ │ │ "baseblockmanag": [2197, 2199, 2298], │ │ │ │ │ "basebooleanreducetest": 2307, │ │ │ │ │ "basebuff": [16, 17, 18, 19, 2199, 2235], │ │ │ │ │ @@ -37798,15 +37797,15 @@ │ │ │ │ │ "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, 2186, 2188, 2191, 2194, 2195, 2197, 2199, 2201, 2204, 2206, 2210, 2211, 2212, 2214, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2225, 2226, 2228, 2229, 2230, 2231, 2232, 2233, 2234, 2235, 2237, 2238, 2239, 2241, 2242, 2246, 2249, 2250, 2261, 2265, 2266, 2267, 2271, 2275, 2277, 2278, 2283, 2286, 2287, 2289, 2294, 2296, 2298, 2302, 2304, 2307, 2308], │ │ │ │ │ + "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, 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, │ │ │ │ │ @@ -38098,15 +38097,15 @@ │ │ │ │ │ "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, 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, 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], │ │ │ │ │ "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, │ │ │ │ │ @@ -38261,15 +38260,15 @@ │ │ │ │ │ "cheat": [21, 2234], │ │ │ │ │ "check": [1, 2, 4, 5, 6, 8, 12, 13, 18, 21, 22, 23, 24, 25, 26, 27, 30, 32, 36, 62, 75, 80, 81, 147, 153, 163, 169, 228, 256, 284, 346, 384, 386, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 420, 445, 447, 448, 453, 454, 455, 461, 469, 473, 478, 500, 501, 584, 592, 603, 615, 741, 799, 836, 837, 838, 839, 840, 841, 842, 843, 844, 888, 912, 976, 977, 978, 979, 1076, 1079, 1081, 1082, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1093, 1095, 1097, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1110, 1111, 1112, 1113, 1114, 1115, 1127, 1136, 1141, 1146, 1184, 1345, 1354, 1370, 1391, 1441, 1442, 1446, 1449, 1450, 1475, 1482, 1483, 1488, 1490, 1493, 1494, 1495, 1496, 1499, 1512, 1530, 1548, 1566, 1586, 1607, 1626, 1643, 1665, 1686, 1707, 1728, 1747, 1765, 1782, 1801, 1823, 1846, 1863, 1883, 1901, 1919, 1936, 1953, 1971, 1988, 2006, 2025, 2043, 2061, 2079, 2096, 2114, 2133, 2151, 2168, 2185, 2186, 2188, 2190, 2191, 2192, 2193, 2194, 2195, 2197, 2199, 2200, 2201, 2202, 2208, 2211, 2217, 2218, 2220, 2222, 2224, 2225, 2227, 2228, 2229, 2230, 2231, 2232, 2234, 2235, 2238, 2240, 2241, 2246, 2253, 2255, 2261, 2265, 2271, 2277, 2279, 2283, 2289, 2294, 2298, 2302, 2307, 2308], │ │ │ │ │ "check_array_index": 2172, │ │ │ │ │ "check_categor": [1494, 1495, 1496, 2242], │ │ │ │ │ "check_category_ord": 1496, │ │ │ │ │ "check_column_typ": 1494, │ │ │ │ │ "check_datetimelike_compat": [1494, 1496], │ │ │ │ │ - "check_dict_or_set_index": [2193, 2197], │ │ │ │ │ + "check_dict_or_set_index": 2197, │ │ │ │ │ "check_dtyp": [1493, 1494, 1496, 2271, 2272, 2299], │ │ │ │ │ "check_dtype_backend": 2199, │ │ │ │ │ "check_exact": [1493, 1494, 1495, 1496, 2272, 2277, 2307, 2308], │ │ │ │ │ "check_extens": 2294, │ │ │ │ │ "check_flag": [1494, 1496, 2290], │ │ │ │ │ "check_frame_typ": 1494, │ │ │ │ │ "check_freq": [1494, 1496, 2278], │ │ │ │ │ @@ -39812,15 +39811,15 @@ │ │ │ │ │ "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": [2186, 2197, 2199], │ │ │ │ │ + "fastest": [2185, 2186, 2197, 2199, 2218], │ │ │ │ │ "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], │ │ │ │ │ @@ -40255,15 +40254,15 @@ │ │ │ │ │ "get_indexer_for": [2283, 2289], │ │ │ │ │ "get_indexer_non_uniqu": [379, 2192, 2197, 2238, 2243, 2246, 2249, 2265, 2277, 2289], │ │ │ │ │ "get_indexer_nonuniqu": 2302, │ │ │ │ │ "get_item": [2191, 2194], │ │ │ │ │ "get_jit_argu": 2212, │ │ │ │ │ "get_letter_typ": 2195, │ │ │ │ │ "get_level_valu": [1416, 2185, 2218, 2220, 2228, 2232, 2241, 2246, 2253, 2256], │ │ │ │ │ - "get_loc": [2, 362, 383, 426, 492, 2185, 2191, 2194, 2197, 2225, 2228, 2231, 2235, 2238, 2241, 2246, 2249, 2265, 2271, 2273, 2277, 2283, 2289, 2298, 2299], │ │ │ │ │ + "get_loc": [2, 362, 383, 426, 492, 2185, 2191, 2193, 2194, 2197, 2225, 2228, 2231, 2235, 2238, 2241, 2246, 2249, 2265, 2271, 2273, 2277, 2283, 2289, 2298, 2299], │ │ │ │ │ "get_loc_level": 2246, │ │ │ │ │ "get_local": 2265, │ │ │ │ │ "get_method": [16, 17, 18, 19, 2199, 2235], │ │ │ │ │ "get_near_stock_pric": [2216, 2223], │ │ │ │ │ "get_offset": [2265, 2298], │ │ │ │ │ "get_offset_nam": [2230, 2238], │ │ │ │ │ "get_op_result_nam": 2186, │ │ │ │ │ @@ -40892,15 +40891,15 @@ │ │ │ │ │ "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, 2195, 2205, 2210, 2212, 2253, 2307], │ │ │ │ │ + "intermedi": [7, 2172, 2185, 2195, 2205, 2210, 2212, 2218, 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, │ │ │ │ │ @@ -41509,15 +41508,15 @@ │ │ │ │ │ "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, 2191, 2197, 2199, 2202, 2210, 2214, 2215, 2217, 2218, 2219, 2220, 2221, 2222, 2224, 2225, 2226, 2228, 2230, 2231, 2233, 2235, 2238, 2242, 2243, 2246, 2247, 2257, 2261, 2263, 2264, 2265, 2266, 2271, 2275, 2277, 2278, 2292, 2294, 2295, 2298, 2302], │ │ │ │ │ + "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, 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, │ │ │ │ │ @@ -41612,15 +41611,15 @@ │ │ │ │ │ "maldiv": [176, 179, 754, 757, 1242, 1243], │ │ │ │ │ "male": [18, 23, 25, 28, 32, 1204, 2195, 2220], │ │ │ │ │ "malform": [1469, 1486, 2199, 2225, 2246, 2265, 2283, 2289], │ │ │ │ │ "malfunct": 2238, │ │ │ │ │ "malta": [176, 179, 754, 757, 1242, 1243, 2199], │ │ │ │ │ "mamba": [1, 13], │ │ │ │ │ "mammal": [172, 198, 210, 211, 214, 249, 271, 285, 494, 784, 882, 899, 913, 1198, 1202, 1263, 2195], │ │ │ │ │ - "manag": [2, 5, 22, 34, 341, 1345, 1391, 1451, 1488, 1490, 1793, 1815, 2186, 2193, 2197, 2199, 2202, 2218, 2222, 2224, 2232, 2238, 2246, 2277, 2298], │ │ │ │ │ + "manag": [2, 5, 22, 34, 341, 1345, 1391, 1451, 1488, 1490, 1793, 1815, 2186, 2197, 2199, 2202, 2218, 2222, 2224, 2232, 2238, 2246, 2277, 2298], │ │ │ │ │ "manchest": 2199, │ │ │ │ │ "mangl": [2195, 2241, 2246, 2289], │ │ │ │ │ "mangle_dupe_col": [2283, 2294, 2298], │ │ │ │ │ "mango": [394, 399], │ │ │ │ │ "mani": [1, 2, 3, 5, 7, 8, 10, 13, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 31, 34, 35, 85, 102, 114, 168, 342, 596, 754, 757, 1031, 1064, 1153, 1158, 1166, 1212, 1223, 1242, 1243, 1272, 1274, 1275, 1286, 1358, 1387, 1390, 1469, 1486, 1498, 2166, 2167, 2173, 2185, 2186, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2199, 2200, 2202, 2205, 2206, 2207, 2210, 2212, 2214, 2216, 2217, 2218, 2219, 2221, 2223, 2225, 2228, 2231, 2232, 2235, 2238, 2241, 2246, 2254, 2255, 2256, 2260, 2261, 2271, 2277, 2283, 2289, 2298, 2302, 2307, 2308], │ │ │ │ │ "manifest": [2223, 2224, 2241, 2273], │ │ │ │ │ "manipul": [10, 15, 21, 23, 33, 34, 35, 1423, 2172, 2185, 2186, 2195, 2204, 2207, 2210, 2218, 2222, 2257], │ │ │ │ │ @@ -43958,14 +43957,15 @@ │ │ │ │ │ "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, 2218], │ │ │ │ │ "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], │ │ │ │ │ @@ -44795,15 +44795,15 @@ │ │ │ │ │ "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": [2199, 2223, 2241], │ │ │ │ │ + "took": [2185, 2199, 2218, 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], │ │ │ │ │ @@ -44962,15 +44962,15 @@ │ │ │ │ │ "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, 2205, 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 │ │ │ │ @@ -1847,25 +1847,26 @@ │ │ │ │ In [141]: indexer = np.arange(10000) │ │ │ │ │ │ │ │ In [142]: random.shuffle(indexer) │ │ │ │ │ │ │ │ In [143]: %timeit arr[indexer] │ │ │ │ .....: %timeit arr.take(indexer, axis=0) │ │ │ │ .....: │ │ │ │ -544 us +- 49.3 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ -122 us +- 6.36 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ +350 us +- 169 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ +106 us +- 19.1 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ │ │ │ │ │ │ │ │ 
In [144]: ser = pd.Series(arr[:, 0])
│ │ │ │  
│ │ │ │  In [145]: %timeit ser.iloc[indexer]
│ │ │ │     .....: %timeit ser.take(indexer)
│ │ │ │     .....: 
│ │ │ │ -284 us +- 22.1 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │ -266 us +- 16.8 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │ +251 us +- 69.4 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)
│ │ │ │ +The slowest run took 7.62 times longer than the fastest. This could mean that an intermediate result is being cached.
│ │ │ │ +499 us +- 435 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │
│ │ │ │
│ │ │ │ │ │ │ │
│ │ │ │

Index types#

│ │ │ │

We have discussed MultiIndex in the previous sections pretty extensively. │ │ │ │ Documentation about DatetimeIndex and PeriodIndex are shown here, │ │ │ │ ├── html2text {} │ │ │ │ │ @@ -1245,23 +1245,25 @@ │ │ │ │ │ In [141]: indexer = np.arange(10000) │ │ │ │ │ │ │ │ │ │ In [142]: random.shuffle(indexer) │ │ │ │ │ │ │ │ │ │ In [143]: %timeit arr[indexer] │ │ │ │ │ .....: %timeit arr.take(indexer, axis=0) │ │ │ │ │ .....: │ │ │ │ │ -544 us +- 49.3 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ │ -122 us +- 6.36 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ │ +350 us +- 169 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ │ +106 us +- 19.1 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ │ In [144]: ser = pd.Series(arr[:, 0]) │ │ │ │ │ │ │ │ │ │ In [145]: %timeit ser.iloc[indexer] │ │ │ │ │ .....: %timeit ser.take(indexer) │ │ │ │ │ .....: │ │ │ │ │ -284 us +- 22.1 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ │ -266 us +- 16.8 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ │ +251 us +- 69.4 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ │ +The slowest run took 7.62 times longer than the fastest. This could mean that │ │ │ │ │ +an intermediate result is being cached. │ │ │ │ │ +499 us +- 435 us per loop (mean +- std. dev. of 7 runs, 1,000 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)
│ │ │ │ -211 ms +- 18.6 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +159 ms +- 68.7 ms per loop (mean +- std. dev. of 7 runs, 10 loops 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 1.397 seconds
│ │ │ │ +         605946 function calls (605928 primitive calls) in 0.618 seconds
│ │ │ │  
│ │ │ │     Ordered by: internal time
│ │ │ │     List reduced from 159 to 4 due to restriction <4>
│ │ │ │  
│ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ -     1000    0.758    0.001    1.236    0.001 <ipython-input-4-c2a74e076cf0>:1(integrate_f)
│ │ │ │ -   552423    0.478    0.000    0.478    0.000 <ipython-input-3-c138bdd570e3>:1(f)
│ │ │ │ -     3000    0.025    0.000    0.111    0.000 series.py:1095(__getitem__)
│ │ │ │ -    16098    0.018    0.000    0.024    0.000 {built-in method builtins.isinstance}
│ │ │ │ +     1000    0.362    0.000    0.512    0.001 <ipython-input-4-c2a74e076cf0>:1(integrate_f)
│ │ │ │ +   552423    0.150    0.000    0.150    0.000 <ipython-input-3-c138bdd570e3>:1(f)
│ │ │ │ +        1    0.015    0.015    0.615    0.615 apply.py:1070(apply_series_generator)
│ │ │ │ +     3000    0.014    0.000    0.029    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)
│ │ │ │ -135 ms +- 12.6 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +131 ms +- 42.2 ms per loop (mean +- std. dev. of 7 runs, 10 loops 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)
│ │ │ │ -25.7 ms +- 899 us per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +18.6 ms +- 634 us per loop (mean +- std. dev. of 7 runs, 10 loops 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 0.134 seconds
│ │ │ │ +         52523 function calls (52505 primitive calls) in 0.062 seconds
│ │ │ │  
│ │ │ │     Ordered by: internal time
│ │ │ │     List reduced from 157 to 4 due to restriction <4>
│ │ │ │  
│ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ -     3000    0.022    0.000    0.094    0.000 series.py:1095(__getitem__)
│ │ │ │ -    16098    0.015    0.000    0.021    0.000 {built-in method builtins.isinstance}
│ │ │ │ -     3000    0.015    0.000    0.038    0.000 series.py:1220(_get_value)
│ │ │ │ -     3000    0.014    0.000    0.024    0.000 indexing.py:2765(check_dict_or_set_indexers)
│ │ │ │ +     3000    0.009    0.000    0.039    0.000 series.py:1095(__getitem__)
│ │ │ │ +     3000    0.008    0.000    0.009    0.000 base.py:3777(get_loc)
│ │ │ │ +     3000    0.007    0.000    0.019    0.000 series.py:1220(_get_value)
│ │ │ │ +    16098    0.005    0.000    0.007    0.000 {built-in method builtins.isinstance}
│ │ │ │
│ │ │ │
│ │ │ │ 
In [13]: %%cython
│ │ │ │     ....: cimport numpy as np
│ │ │ │     ....: import numpy as np
│ │ │ │     ....: cdef double f_typed(double x) except? -2:
│ │ │ │     ....:     return x * (x - 1)
│ │ │ │ @@ -722,34 +722,34 @@
│ │ │ │
│ │ │ │

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())
│ │ │ │ -2.14 ms +- 23.7 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +1.69 ms +- 153 us 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 0.003 seconds
│ │ │ │ +         78 function calls in 0.002 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    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}
│ │ │ │ -        1    0.000    0.000    0.003    0.003 {built-in method builtins.exec}
│ │ │ │ -        3    0.000    0.000    0.000    0.000 managers.py:1993(dtype)
│ │ │ │ +        1    0.000    0.000    0.002    0.002 {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:
│ │ │ │ @@ -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())
│ │ │ │ -1.51 ms +- 14.9 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │ +1.14 ms +- 135 us per loop (mean +- std. dev. of 7 runs, 1,000 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
│ │ │ │ -45.5 ms +- 3.47 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +10.2 ms +- 207 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │
│ │ │ │
│ │ │ │ 
In [41]: %timeit pd.eval("df1 + df2 + df3 + df4", engine="python")
│ │ │ │ -48.9 ms +- 1.67 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +12.9 ms +- 850 us per loop (mean +- std. dev. of 7 runs, 100 loops 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.

│ │ │ │ @@ -1275,39 +1275,39 @@ │ │ │ │ 
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
│ │ │ │ -46.3 ms +- 3.78 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +10.5 ms +- 450 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │
│ │ │ │
│ │ │ │ 
In [61]: %timeit pd.eval("df1 + df2 + df3 + df4")
│ │ │ │ -14.4 ms +- 2.58 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +4.44 ms +- 297 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │
│ │ │ │
│ │ │ │

DataFrame comparison:

│ │ │ │ 
In [62]: %timeit (df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)
│ │ │ │ -20.6 ms +- 1.6 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +6.98 ms +- 273 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │
│ │ │ │
│ │ │ │ 
In [63]: %timeit pd.eval("(df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)")
│ │ │ │ -24.9 ms +- 6.01 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +6.05 ms +- 354 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │
│ │ │ │
│ │ │ │

DataFrame arithmetic with unaligned axes.

│ │ │ │ 
In [64]: s = pd.Series(np.random.randn(50))
│ │ │ │  
│ │ │ │  In [65]: %timeit df1 + df2 + df3 + df4 + s
│ │ │ │ -73.8 ms +- 2.03 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +18.8 ms +- 1.5 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │
│ │ │ │
│ │ │ │ 
In [66]: %timeit pd.eval("df1 + df2 + df3 + df4 + s")
│ │ │ │ -15 ms +- 1.27 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +7.09 ms +- 2.83 ms per loop (mean +- std. dev. of 7 runs, 100 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 {}
│ │ │ │ │ @@ -110,33 +110,33 @@
│ │ │ │ │     ...:     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)
│ │ │ │ │ -211 ms +- 18.6 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │ +159 ms +- 68.7 ms per loop (mean +- std. dev. of 7 runs, 10 loops 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 1.397 seconds
│ │ │ │ │ +         605946 function calls (605928 primitive calls) in 0.618 seconds
│ │ │ │ │  
│ │ │ │ │     Ordered by: internal time
│ │ │ │ │     List reduced from 159 to 4 due to restriction <4>
│ │ │ │ │  
│ │ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ │ -     1000    0.758    0.001    1.236    0.001 :1
│ │ │ │ │ +     1000    0.362    0.000    0.512    0.001 :1
│ │ │ │ │  (integrate_f)
│ │ │ │ │ -   552423    0.478    0.000    0.478    0.000 :1
│ │ │ │ │ +   552423    0.150    0.000    0.150    0.000 :1
│ │ │ │ │  (f)
│ │ │ │ │ -     3000    0.025    0.000    0.111    0.000 series.py:1095(__getitem__)
│ │ │ │ │ -    16098    0.018    0.000    0.024    0.000 {built-in method
│ │ │ │ │ -builtins.isinstance}
│ │ │ │ │ +        1    0.015    0.015    0.615    0.615 apply.py:1070
│ │ │ │ │ +(apply_series_generator)
│ │ │ │ │ +     3000    0.014    0.000    0.029    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
│ │ │ │ │ @@ -147,15 +147,15 @@
│ │ │ │ │     ...:     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)
│ │ │ │ │ -135 ms +- 12.6 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │ +131 ms +- 42.2 ms per loop (mean +- std. dev. of 7 runs, 10 loops 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:
│ │ │ │ │ @@ -167,36 +167,35 @@
│ │ │ │ │     ....:     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)
│ │ │ │ │ -25.7 ms +- 899 us per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │ +18.6 ms +- 634 us per loop (mean +- std. dev. of 7 runs, 10 loops 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 0.134 seconds
│ │ │ │ │ +         52523 function calls (52505 primitive calls) in 0.062 seconds
│ │ │ │ │  
│ │ │ │ │     Ordered by: internal time
│ │ │ │ │     List reduced from 157 to 4 due to restriction <4>
│ │ │ │ │  
│ │ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ │ -     3000    0.022    0.000    0.094    0.000 series.py:1095(__getitem__)
│ │ │ │ │ -    16098    0.015    0.000    0.021    0.000 {built-in method
│ │ │ │ │ +     3000    0.009    0.000    0.039    0.000 series.py:1095(__getitem__)
│ │ │ │ │ +     3000    0.008    0.000    0.009    0.000 base.py:3777(get_loc)
│ │ │ │ │ +     3000    0.007    0.000    0.019    0.000 series.py:1220(_get_value)
│ │ │ │ │ +    16098    0.005    0.000    0.007    0.000 {built-in method
│ │ │ │ │  builtins.isinstance}
│ │ │ │ │ -     3000    0.015    0.000    0.038    0.000 series.py:1220(_get_value)
│ │ │ │ │ -     3000    0.014    0.000    0.024    0.000 indexing.py:2765
│ │ │ │ │ -(check_dict_or_set_indexers)
│ │ │ │ │  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
│ │ │ │ │ @@ -237,32 +236,32 @@
│ │ │ │ │  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())
│ │ │ │ │ -2.14 ms +- 23.7 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +1.69 ms +- 153 us 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 0.003 seconds
│ │ │ │ │ +         78 function calls in 0.002 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    0.000    0.000    0.000    0.000 {method 'disable' of
│ │ │ │ │  '_lsprof.Profiler' objects}
│ │ │ │ │ -        1    0.000    0.000    0.003    0.003 {built-in method builtins.exec}
│ │ │ │ │ -        3    0.000    0.000    0.000    0.000 managers.py:1993(dtype)
│ │ │ │ │ +        1    0.000    0.000    0.002    0.002 {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)
│ │ │ │ │     ....: cpdef np.float64_t integrate_f_typed(np.float64_t a, np.float64_t b,
│ │ │ │ │ @@ -300,15 +299,15 @@
│ │ │ │ │  /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())
│ │ │ │ │ -1.51 ms +- 14.9 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │ │ +1.14 ms +- 135 us per loop (mean +- std. dev. of 7 runs, 1,000 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
│ │ │ │ │ @@ -611,17 +610,17 @@
│ │ │ │ │  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
│ │ │ │ │ -45.5 ms +- 3.47 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │ +10.2 ms +- 207 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │  In [41]: %timeit pd.eval("df1 + df2 + df3 + df4", engine="python")
│ │ │ │ │ -48.9 ms +- 1.67 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │ +12.9 ms +- 850 us per loop (mean +- std. dev. of 7 runs, 100 loops 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]:
│ │ │ │ │ @@ -718,29 +717,29 @@
│ │ │ │ │  _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
│ │ │ │ │ -46.3 ms +- 3.78 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │ +10.5 ms +- 450 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │  In [61]: %timeit pd.eval("df1 + df2 + df3 + df4")
│ │ │ │ │ -14.4 ms +- 2.58 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +4.44 ms +- 297 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │  _D_a_t_a_F_r_a_m_e comparison:
│ │ │ │ │  In [62]: %timeit (df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)
│ │ │ │ │ -20.6 ms +- 1.6 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │ +6.98 ms +- 273 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │  In [63]: %timeit pd.eval("(df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)")
│ │ │ │ │ -24.9 ms +- 6.01 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +6.05 ms +- 354 us per loop (mean +- std. dev. of 7 runs, 100 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
│ │ │ │ │ -73.8 ms +- 2.03 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │ +18.8 ms +- 1.5 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │  In [66]: %timeit pd.eval("df1 + df2 + df3 + df4 + s")
│ │ │ │ │ -15 ms +- 1.27 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +7.09 ms +- 2.83 ms per loop (mean +- std. dev. of 7 runs, 100 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 625 us, sys: 244 us, total: 869 us
│ │ │ │ -Wall time: 879 us
│ │ │ │ +CPU times: user 331 us, sys: 287 us, total: 618 us
│ │ │ │ +Wall time: 627 us
│ │ │ │  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 625 us, sys: 244 us, total: 869 us │ │ │ │ │ -Wall time: 879 us │ │ │ │ │ +CPU times: user 331 us, sys: 287 us, total: 618 us │ │ │ │ │ +Wall time: 627 us │ │ │ │ │ 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': '2026-04-12T08:06:59.315130Z', " │ │ │ │ │ │┄ "'iopub.status.busy': '2026-04-12T08:06:59.314874Z', 'iopub.status.idle': " │ │ │ │ │ │┄ "'2026-04-12T08:06:59.674203Z', 'shell.execute_reply': " │ │ │ │ │ │┄ "'2026-04-12T08:06:59.673472Z'}}}, 3: {'metadata': {'execution': " │ │ │ │ │ │┄ "{'iopub.execute_input': '2026-04-12T08:06:59.677437Z', 'iopub.status.busy': " │ │ │ │ │ │┄ "'2026-04-12T08:06:59.676672Z', 'iopub.status.idle': '2026-04-12T08:06:5 […] │ │ │ │ │ │ @@ -39,18 +39,18 @@ │ │ │ │ │ │ ] │ │ │ │ │ │ }, │ │ │ │ │ │ { │ │ │ │ │ │ "cell_type": "code", │ │ │ │ │ │ "execution_count": 1, │ │ │ │ │ │ "metadata": { │ │ │ │ │ │ "execution": { │ │ │ │ │ │ - "iopub.execute_input": "2025-03-10T00:14:26.077877Z", │ │ │ │ │ │ - "iopub.status.busy": "2025-03-10T00:14:26.077533Z", │ │ │ │ │ │ - "iopub.status.idle": "2025-03-10T00:14:26.722736Z", │ │ │ │ │ │ - "shell.execute_reply": "2025-03-10T00:14:26.721547Z" │ │ │ │ │ │ + "iopub.execute_input": "2026-04-12T08:06:59.315130Z", │ │ │ │ │ │ + "iopub.status.busy": "2026-04-12T08:06:59.314874Z", │ │ │ │ │ │ + "iopub.status.idle": "2026-04-12T08:06:59.674203Z", │ │ │ │ │ │ + "shell.execute_reply": "2026-04-12T08:06:59.673472Z" │ │ │ │ │ │ }, │ │ │ │ │ │ "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": "2025-03-10T00:14:26.726930Z", │ │ │ │ │ │ - "iopub.status.busy": "2025-03-10T00:14:26.726471Z", │ │ │ │ │ │ - "iopub.status.idle": "2025-03-10T00:14:27.107788Z", │ │ │ │ │ │ - "shell.execute_reply": "2025-03-10T00:14:27.106646Z" │ │ │ │ │ │ + "iopub.execute_input": "2026-04-12T08:06:59.677437Z", │ │ │ │ │ │ + "iopub.status.busy": "2026-04-12T08:06:59.676672Z", │ │ │ │ │ │ + "iopub.status.idle": "2026-04-12T08:06:59.910277Z", │ │ │ │ │ │ + "shell.execute_reply": "2026-04-12T08:06:59.909550Z" │ │ │ │ │ │ } │ │ │ │ │ │ }, │ │ │ │ │ │ "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": "2025-03-10T00:14:27.113754Z", │ │ │ │ │ │ - "iopub.status.busy": "2025-03-10T00:14:27.113258Z", │ │ │ │ │ │ - "iopub.status.idle": "2025-03-10T00:14:27.308722Z", │ │ │ │ │ │ - "shell.execute_reply": "2025-03-10T00:14:27.307617Z" │ │ │ │ │ │ + "iopub.execute_input": "2026-04-12T08:06:59.912896Z", │ │ │ │ │ │ + "iopub.status.busy": "2026-04-12T08:06:59.912501Z", │ │ │ │ │ │ + "iopub.status.idle": "2026-04-12T08:07:00.031423Z", │ │ │ │ │ │ + "shell.execute_reply": "2026-04-12T08:07:00.030723Z" │ │ │ │ │ │ }, │ │ │ │ │ │ "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": "2025-03-10T00:14:27.312900Z", │ │ │ │ │ │ - "iopub.status.busy": "2025-03-10T00:14:27.312389Z", │ │ │ │ │ │ - "iopub.status.idle": "2025-03-10T00:14:27.328547Z", │ │ │ │ │ │ - "shell.execute_reply": "2025-03-10T00:14:27.327436Z" │ │ │ │ │ │ + "iopub.execute_input": "2026-04-12T08:07:00.033871Z", │ │ │ │ │ │ + "iopub.status.busy": "2026-04-12T08:07:00.033551Z", │ │ │ │ │ │ + "iopub.status.idle": "2026-04-12T08:07:00.043357Z", │ │ │ │ │ │ + "shell.execute_reply": "2026-04-12T08:07:00.042793Z" │ │ │ │ │ │ } │ │ │ │ │ │ }, │ │ │ │ │ │ "outputs": [ │ │ │ │ │ │ { │ │ │ │ │ │ "data": { │ │ │ │ │ │ "text/html": [ │ │ │ │ │ │ "