--- /srv/reproducible-results/rbuild-debian/r-b-build.Npg327ZI/b1/pandas_2.2.3+dfsg-8_i386.changes +++ /srv/reproducible-results/rbuild-debian/r-b-build.Npg327ZI/b2/pandas_2.2.3+dfsg-8_i386.changes ├── Files │ @@ -1,5 +1,5 @@ │ │ - 74f47cf544c0cfb39a296ccecd935b02 10793564 doc optional python-pandas-doc_2.2.3+dfsg-8_all.deb │ - c0cba754e40597583f61c8030500ead3 71243076 debug optional python3-pandas-lib-dbgsym_2.2.3+dfsg-8_i386.deb │ - b6d9034931efa6fbf490ec55af617ceb 6847900 python optional python3-pandas-lib_2.2.3+dfsg-8_i386.deb │ + 9caedf60c38df638f0d83e5935e51e43 10793344 doc optional python-pandas-doc_2.2.3+dfsg-8_all.deb │ + e59da80ebcd526603b17c18b486127fe 71246096 debug optional python3-pandas-lib-dbgsym_2.2.3+dfsg-8_i386.deb │ + f02a20ed7545972a0600300b3104fe16 6847780 python optional python3-pandas-lib_2.2.3+dfsg-8_i386.deb │ ad1d0d3815c32f9db583cfe0dd79d880 3096896 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 147404 2025-02-01 18:39:17.000000 control.tar.xz │ │ --rw-r--r-- 0 0 0 10645968 2025-02-01 18:39:17.000000 data.tar.xz │ │ +-rw-r--r-- 0 0 0 147356 2025-02-01 18:39:17.000000 control.tar.xz │ │ +-rw-r--r-- 0 0 0 10645796 2025-02-01 18:39:17.000000 data.tar.xz │ ├── control.tar.xz │ │ ├── control.tar │ │ │ ├── ./control │ │ │ │ @@ -1,13 +1,13 @@ │ │ │ │ Package: python-pandas-doc │ │ │ │ Source: pandas │ │ │ │ Version: 2.2.3+dfsg-8 │ │ │ │ Architecture: all │ │ │ │ Maintainer: Debian Science Team │ │ │ │ -Installed-Size: 209896 │ │ │ │ +Installed-Size: 209898 │ │ │ │ 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 │ │ │ │ @@ -6256,74 +6256,74 @@ │ │ │ │ -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) 2359159 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/searchindex.js │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 2359360 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 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./usr/share/doc/python-pandas-doc/html/user_guide/options.html │ │ │ │ --rw-r--r-- 0 root (0) root (0) 146148 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) 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) 115579 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) 65494 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/sparse.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 698240 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/style.html │ │ │ │ --rw-r--r-- 0 root (0) root (0) 87825 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/style.ipynb.gz │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 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) 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) 10566 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) 222541 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) 222536 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) 75128 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) 115292 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) 224090 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) 230436 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) 94984 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) 222566 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.18.0.html │ │ │ │ +-rw-r--r-- 0 root (0) root (0) 171419 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.18.1.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 349334 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.19.0.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 45179 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.19.1.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 48525 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.19.2.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 406081 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.20.0.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 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) 255124 2025-02-01 18:39:17.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.21.0.html │ │ │ ├── ./usr/share/doc/python-pandas-doc/html/searchindex.js │ │ │ │ ├── js-beautify {} │ │ │ │ │ @@ -21485,15 +21485,15 @@ │ │ │ │ │ "000830": 2214, │ │ │ │ │ "000895": 2195, │ │ │ │ │ "000951": 2186, │ │ │ │ │ "000k": 1489, │ │ │ │ │ "000m": 1489, │ │ │ │ │ "000n": 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"2030": 2265, │ │ │ │ │ "20303": 2265, │ │ │ │ │ "20306": 2302, │ │ │ │ │ "203098": 2186, │ │ │ │ │ "20342": 2246, │ │ │ │ │ "2035": 2199, │ │ │ │ │ "20353": 2246, │ │ │ │ │ @@ -26354,15 +26353,15 @@ │ │ │ │ │ "22556": 2246, │ │ │ │ │ "22557": 2246, │ │ │ │ │ "22578": 2246, │ │ │ │ │ "22579": 2246, │ │ │ │ │ "22580": 2246, │ │ │ │ │ "22591": 2246, │ │ │ │ │ "225944": 2207, │ │ │ │ │ - "226": [2185, 2186, 2188, 2195, 2197, 2199, 2207, 2210], │ │ │ │ │ + "226": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2207, 2210], │ │ │ │ │ "226001": 2207, │ │ │ │ │ "22610": 2271, │ │ │ │ │ "226127": 28, │ │ │ │ │ "226169": [2185, 2197, 2199, 2202, 2204, 2215, 2257], │ │ │ │ │ "2262": [985, 2210, 2220, 2250], │ │ │ │ │ "22628": 2246, │ │ │ │ │ "22631": 2246, │ │ │ │ │ @@ -26433,15 +26432,15 @@ │ │ │ │ │ "22981": 2246, │ │ │ │ │ "22984": 2246, │ │ │ │ │ "229864": 2207, │ │ │ │ │ "22988": 2246, │ │ │ │ │ "229938": 2207, │ │ │ │ │ "22994": 2246, │ │ │ │ │ "23": [15, 17, 18, 19, 24, 25, 26, 27, 29, 30, 31, 32, 213, 230, 259, 276, 277, 341, 345, 363, 511, 514, 516, 519, 522, 531, 532, 549, 561, 651, 676, 788, 804, 823, 836, 837, 838, 839, 840, 841, 842, 843, 844, 890, 902, 903, 924, 985, 1192, 1253, 1657, 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, 2222, 2225, 2226, 2227, 2228, 2230, 2231, 2232, 2235, 2238, 2246, 2247, 2249, 2264, 2265, 2271, 2277, 2283, 2289, 2294, 2298, 2302, 2307], │ │ │ │ │ - "230": [2185, 2186, 2188, 2195, 2197, 2199, 2207, 2210, 2218, 2254], │ │ │ │ │ + "230": [2185, 2186, 2188, 2195, 2197, 2199, 2207, 2210, 2254], │ │ │ │ │ "23000": [2199, 2246], │ │ │ │ │ "230012": 23, │ │ │ │ │ "230066": 2191, │ │ │ │ │ "23009": 2246, │ │ │ │ │ "2301": 2217, │ │ │ │ │ "23011": 2249, │ │ │ │ │ "23013": 2249, │ │ │ │ │ @@ -26949,15 +26948,15 @@ │ │ │ │ │ "253128": 2191, │ │ │ │ │ "25317": 2248, │ │ │ │ │ "25318": 2248, │ │ │ │ │ "253355": 2210, │ │ │ │ │ "25338": 2248, │ │ │ │ │ "253495": 2207, │ │ │ │ │ "253881": 2229, │ │ │ │ │ - "254": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2210, 2220], │ │ │ │ │ + "254": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2220], │ │ │ │ │ "254000": [2185, 2220], │ │ │ │ │ "25403": 2248, │ │ │ │ │ "25405": 2249, │ │ │ │ │ "25409": 2248, │ │ │ │ │ "254161": [2184, 2195, 2214], │ │ │ │ │ "25433": 2249, │ │ │ │ │ "25435": 2249, │ │ │ │ │ @@ -27436,14 +27435,15 @@ │ │ │ │ │ "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, │ │ │ │ │ @@ -27480,15 +27480,15 @@ │ │ │ │ │ "27840": 2250, │ │ │ │ │ "27841": 2250, │ │ │ │ │ "278445": 2184, │ │ │ │ │ "2786": 2217, │ │ │ │ │ "27865": 2250, │ │ │ │ │ "27874": 2265, │ │ │ │ │ "27892": 2271, │ │ │ │ │ - "279": [15, 2186, 2195, 2197, 2199, 2210], │ │ │ │ │ + "279": [15, 2186, 2193, 2195, 2197, 2199, 2210], │ │ │ │ │ "27900": 2251, │ │ │ │ │ "279321": 2186, │ │ │ │ │ "279344": 2186, │ │ │ │ │ "27943": 2250, │ │ │ │ │ "27951": 2265, │ │ │ │ │ "27952": 2265, │ │ │ │ │ "27953": 2271, │ │ │ │ │ @@ -27619,15 +27619,15 @@ │ │ │ │ │ "2854": 2185, │ │ │ │ │ "28556": 2283, │ │ │ │ │ "285569": 2207, │ │ │ │ │ "28557": 2265, │ │ │ │ │ "285737": 2207, │ │ │ │ │ "285805": 2207, │ │ │ │ │ "28584": 2271, │ │ │ │ │ - "286": [16, 17, 18, 19, 27, 2186, 2193, 2197, 2199, 2210, 2235, 2255], │ │ │ │ │ + "286": [16, 17, 18, 19, 27, 2186, 2197, 2199, 2210, 2235, 2255], │ │ │ │ │ "286094": 2207, │ │ │ │ │ "28619": 2265, │ │ │ │ │ "28621": [2265, 2298], │ │ │ │ │ "28631": 2251, │ │ │ │ │ "28652": 2265, │ │ │ │ │ "286539": 2210, │ │ │ │ │ "28663": 2265, │ │ │ │ │ @@ -27646,15 +27646,15 @@ │ │ │ │ │ "28766": 2265, │ │ │ │ │ "28769": 2265, │ │ │ │ │ "287725": 2185, │ │ │ │ │ "28779": 2265, │ │ │ │ │ "28787": 2265, │ │ │ │ │ "28791": 2265, │ │ │ │ │ "28795": 2265, │ │ │ │ │ - "288": [2186, 2193, 2197, 2199, 2210, 2257], │ │ │ │ │ + "288": [2186, 2197, 2199, 2210, 2257], │ │ │ │ │ "28805": 2265, │ │ │ │ │ "288098": 2207, │ │ │ │ │ "2881": 2238, │ │ │ │ │ "288112": 2186, │ │ │ │ │ "28814": 2265, │ │ │ │ │ "288256": 2207, │ │ │ │ │ "288374": 2207, │ │ │ │ │ @@ -28065,27 +28065,27 @@ │ │ │ │ │ "30am": [84, 595], │ │ │ │ │ "30d": [2210, 2271], │ │ │ │ │ "30min": [1272, 1275, 2209], │ │ │ │ │ "30t": 2222, │ │ │ │ │ "30th": 2199, │ │ │ │ │ "30x": 2225, │ │ │ │ │ "31": [2, 15, 17, 18, 19, 25, 28, 31, 107, 133, 207, 208, 213, 228, 264, 270, 276, 282, 288, 292, 294, 296, 303, 306, 307, 308, 309, 313, 314, 318, 326, 332, 333, 341, 345, 362, 513, 514, 515, 516, 517, 518, 519, 532, 535, 542, 547, 548, 549, 560, 629, 637, 639, 644, 646, 649, 650, 651, 652, 655, 658, 659, 660, 661, 666, 667, 673, 680, 688, 689, 708, 781, 782, 788, 898, 902, 940, 943, 948, 957, 967, 968, 970, 976, 978, 980, 997, 1164, 1192, 1221, 1253, 1271, 1323, 1344, 1487, 1524, 1560, 1699, 1720, 1741, 1793, 1815, 1857, 1947, 2000, 2054, 2145, 2184, 2185, 2186, 2188, 2190, 2191, 2192, 2193, 2194, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2213, 2216, 2217, 2218, 2219, 2221, 2222, 2225, 2226, 2228, 2230, 2231, 2232, 2235, 2238, 2241, 2246, 2249, 2264, 2265, 2271, 2283, 2289, 2294, 2298, 2302, 2307], │ │ │ │ │ - "310": [2185, 2186, 2197, 2199, 2201, 2210, 2231], │ │ │ │ │ + "310": [2186, 2197, 2199, 2201, 2210, 2218, 2231], │ │ │ │ │ "31016": 2271, │ │ │ │ │ "31025": [2265, 2298], │ │ │ │ │ "310274": 2191, │ │ │ │ │ "31043": 2271, │ │ │ │ │ "31048": 2277, │ │ │ │ │ "310530": 2207, │ │ │ │ │ "31064": 2271, │ │ │ │ │ "310823": 2207, │ │ │ │ │ "3109": [2184, 2199, 2205], │ │ │ │ │ "310957": 2230, │ │ │ │ │ "31096": 2271, │ │ │ │ │ - "311": [176, 179, 2186, 2197, 2199, 2201, 2210, 2231], │ │ │ │ │ + "311": [176, 179, 2186, 2197, 2199, 2201, 2210, 2218, 2231], │ │ │ │ │ "3110": [2184, 2199, 2205], │ │ │ │ │ "3111": [2184, 2199, 2205], │ │ │ │ │ "311128": 2191, │ │ │ │ │ "31126": 2271, │ │ │ │ │ "3113": [2184, 2199, 2205], │ │ │ │ │ "31131": 2265, │ │ │ │ │ "311389": 2207, │ │ │ │ │ @@ -28095,15 +28095,15 @@ │ │ │ │ │ "3116": [2184, 2199, 2205], │ │ │ │ │ "3117": [2184, 2199, 2205], │ │ │ │ │ "31172": 2271, │ │ │ │ │ "3118": [2184, 2199, 2205], │ │ │ │ │ "31183": 2266, │ │ │ │ │ "311877": 2199, │ │ │ │ │ "3119": [2184, 2199, 2205], │ │ │ │ │ - "312": [2186, 2193, 2197, 2199, 2210, 2219, 2255], │ │ │ │ │ + "312": [2186, 2197, 2199, 2210, 2219, 2255], │ │ │ │ │ "3120": [2184, 2199, 2205], │ │ │ │ │ "31200": 2271, │ │ │ │ │ "31204": 2271, │ │ │ │ │ "31205": 2266, │ │ │ │ │ "3121": [2184, 2199, 2205], │ │ │ │ │ "3122": [2184, 2199, 2205], │ │ │ │ │ "312403": 2191, │ │ │ │ │ @@ -28401,15 +28401,15 @@ │ │ │ │ │ "32668": 2271, │ │ │ │ │ "326687": 15, │ │ │ │ │ "32669": 2271, │ │ │ │ │ "32670": 2271, │ │ │ │ │ "32682": 2271, │ │ │ │ │ "32684": 2271, │ │ │ │ │ "32685": 2268, │ │ │ │ │ - "327": [29, 2184, 2186, 2197, 2199, 2205, 2210, 2246, 2255], │ │ │ │ │ + "327": [29, 2184, 2186, 2193, 2197, 2199, 2205, 2210, 2246, 2255], │ │ │ │ │ "32727": 2294, │ │ │ │ │ "327364": 2230, │ │ │ │ │ "32747": 2271, │ │ │ │ │ "32749": 2283, │ │ │ │ │ "3275": 2216, │ │ │ │ │ "32755": 2271, │ │ │ │ │ "32761": 2277, │ │ │ │ │ @@ -29405,15 +29405,14 @@ │ │ │ │ │ "376": [2186, 2197, 2199, 2210, 2255], │ │ │ │ │ "37601": [2277, 2298], │ │ │ │ │ "37605": 2289, │ │ │ │ │ "37609": 2277, │ │ │ │ │ "37610": 2277, │ │ │ │ │ "37615": 2283, │ │ │ │ │ "37621": 2277, │ │ │ │ │ - "3762104704": 2246, │ │ │ │ │ "37626": 2277, │ │ │ │ │ "37631": 2276, │ │ │ │ │ "37635": 2277, │ │ │ │ │ "37641": 2276, │ │ │ │ │ "37643": [2277, 2283, 2294], │ │ │ │ │ "3765": 2218, │ │ │ │ │ "37667": 2277, │ │ │ │ │ @@ -29430,17 +29429,16 @@ │ │ │ │ │ "37733": 2277, │ │ │ │ │ "37748": 2277, │ │ │ │ │ "37750": 2289, │ │ │ │ │ "377535": 2186, │ │ │ │ │ "37755": 2276, │ │ │ │ │ "37758": 2277, │ │ │ │ │ "377642": 2210, │ │ │ │ │ - "3776588400": 2246, │ │ │ │ │ "37768": 2277, │ │ │ │ │ - "3777": [2193, 2218], │ │ │ │ │ + "3777": 2218, │ │ │ │ │ "37782": 2302, │ │ │ │ │ "377887": 2207, │ │ │ │ │ "37799": 2277, │ │ │ │ │ "378": [2186, 2197, 2199, 2207, 2210, 2231], │ │ │ │ │ "3780": 2222, │ │ │ │ │ "37804": 2283, │ │ │ │ │ "378163": 2207, │ │ │ │ │ @@ -29537,15 +29535,15 @@ │ │ │ │ │ "38268": 2277, │ │ │ │ │ "38271": 2277, │ │ │ │ │ "38274": 2277, │ │ │ │ │ "38278": 2283, │ │ │ │ │ "38282": 2277, │ │ │ │ │ "38286": 2276, │ │ │ │ │ "38292": 2283, │ │ │ │ │ - "383": [16, 17, 18, 19, 2186, 2197, 2199, 2210, 2235], │ │ │ │ │ + "383": [16, 17, 18, 19, 2186, 2193, 2197, 2199, 2210, 2235], │ │ │ │ │ "3830": 2218, │ │ │ │ │ "38303": 2283, │ │ │ │ │ "38312": 2298, │ │ │ │ │ "383309": [2191, 2199], │ │ │ │ │ "38335": 2283, │ │ │ │ │ "3834": 2217, │ │ │ │ │ "38340": 2283, │ │ │ │ │ @@ -29557,26 +29555,28 @@ │ │ │ │ │ "383696": 2207, │ │ │ │ │ "38372": 2283, │ │ │ │ │ "383784": 2222, │ │ │ │ │ "38380": 2283, │ │ │ │ │ "38386": 2277, │ │ │ │ │ "383981": 2184, │ │ │ │ │ "384": [16, 17, 18, 19, 2186, 2197, 2199, 2210, 2235, 2246], │ │ │ │ │ + "3840222320": 2246, │ │ │ │ │ "38415": 2283, │ │ │ │ │ "384329": 2207, │ │ │ │ │ "38433": [2283, 2298], │ │ │ │ │ "38439": 2283, │ │ │ │ │ "38453": 2289, │ │ │ │ │ "38454": 2289, │ │ │ │ │ "384724": 2197, │ │ │ │ │ "3849": 2218, │ │ │ │ │ "384941": 2207, │ │ │ │ │ "385": [16, 17, 18, 19, 2186, 2197, 2199, 2210, 2235], │ │ │ │ │ "38502": 2283, │ │ │ │ │ "385062": 2207, │ │ │ │ │ + "3850856400": 2246, │ │ │ │ │ "38516": 2283, │ │ │ │ │ "38521": 2283, │ │ │ │ │ "38522": 30, │ │ │ │ │ "38523": 2283, │ │ │ │ │ "38525": 2277, │ │ │ │ │ "38527": 2283, │ │ │ │ │ "385327": 2214, │ │ │ │ │ @@ -31122,15 +31122,15 @@ │ │ │ │ │ "45162": 2294, │ │ │ │ │ "45170": 2289, │ │ │ │ │ "45174": 2289, │ │ │ │ │ "45180": 2289, │ │ │ │ │ "451849": 2199, │ │ │ │ │ "4519": 2218, │ │ │ │ │ "451921": 2207, │ │ │ │ │ - "452": [2199, 2207, 2210, 2249], │ │ │ │ │ + "452": [2185, 2193, 2199, 2207, 2210, 2249], │ │ │ │ │ "4520": [176, 179, 2218], │ │ │ │ │ "452012": 2207, │ │ │ │ │ "45218": 2294, │ │ │ │ │ "452214": 2199, │ │ │ │ │ "45224": 2294, │ │ │ │ │ "45227": 2289, │ │ │ │ │ "45236": 2294, │ │ │ │ │ @@ -31167,15 +31167,15 @@ │ │ │ │ │ "45361": 2294, │ │ │ │ │ "45362": 2294, │ │ │ │ │ "453684": 2207, │ │ │ │ │ "453749": [2184, 2214], │ │ │ │ │ "45384": 2289, │ │ │ │ │ "453846": 2201, │ │ │ │ │ "45387": 2294, │ │ │ │ │ - "454": [28, 2199, 2207, 2210, 2249], │ │ │ │ │ + "454": [28, 2193, 2199, 2207, 2210, 2249], │ │ │ │ │ "454020": 2207, │ │ │ │ │ "45404": 2294, │ │ │ │ │ "454118": 2207, │ │ │ │ │ "454131": 2197, │ │ │ │ │ "45414": 2294, │ │ │ │ │ "4542": 28, │ │ │ │ │ "454200": 28, │ │ │ │ │ @@ -31199,15 +31199,15 @@ │ │ │ │ │ "4548": 2218, │ │ │ │ │ "45481": 2302, │ │ │ │ │ "454811": 15, │ │ │ │ │ "45484": 2294, │ │ │ │ │ "454870": 2186, │ │ │ │ │ "45494": 2294, │ │ │ │ │ "454980": 2207, │ │ │ │ │ - "455": [2193, 2199, 2210, 2249], │ │ │ │ │ + "455": [2199, 2210, 2249], │ │ │ │ │ "4550": 2218, │ │ │ │ │ "45506": 2294, │ │ │ │ │ "4551": 2220, │ │ │ │ │ "455109": 2207, │ │ │ │ │ "455173": 2207, │ │ │ │ │ "45523": 2298, │ │ │ │ │ "455299": [2205, 2210], │ │ │ │ │ @@ -31247,15 +31247,15 @@ │ │ │ │ │ "45661": 2290, │ │ │ │ │ "456620": 2207, │ │ │ │ │ "456789": 2228, │ │ │ │ │ "45681": 2294, │ │ │ │ │ "45684": 2290, │ │ │ │ │ "45691": 2294, │ │ │ │ │ "45694": 2294, │ │ │ │ │ - "457": [2199, 2210], │ │ │ │ │ + "457": [2185, 2199, 2210], │ │ │ │ │ "457071": 2199, │ │ │ │ │ "45708": 2294, │ │ │ │ │ "45715": 2294, │ │ │ │ │ "45722": 2294, │ │ │ │ │ "45725": 2296, │ │ │ │ │ "457395": 2207, │ │ │ │ │ "45740": 2302, │ │ │ │ │ @@ -32360,15 +32360,15 @@ │ │ │ │ │ "5125": 2218, │ │ │ │ │ "51254": 2302, │ │ │ │ │ "51258": 2298, │ │ │ │ │ "512743": 2193, │ │ │ │ │ "51276": 2302, │ │ │ │ │ "5129": 2220, │ │ │ │ │ "51299": 2298, │ │ │ │ │ - "513": 2199, │ │ │ │ │ + "513": [2193, 2199], │ │ │ │ │ "51302": 2298, │ │ │ │ │ "51316": 2298, │ │ │ │ │ "51349": 2298, │ │ │ │ │ "513520": 2207, │ │ │ │ │ "51353": 2302, │ │ │ │ │ "5136": [2192, 2197], │ │ │ │ │ "513600": 2207, │ │ │ │ │ @@ -33639,15 +33639,15 @@ │ │ │ │ │ "5940742896293756": [16, 19], │ │ │ │ │ "5944": 2219, │ │ │ │ │ "59444": 2310, │ │ │ │ │ "594454": 2207, │ │ │ │ │ "5945": 2220, │ │ │ │ │ "5947": 2219, │ │ │ │ │ "594943": 2207, │ │ │ │ │ - "595": [2199, 2205, 2257], │ │ │ │ │ + "595": [2199, 2257], │ │ │ │ │ "5950": [2220, 2232], │ │ │ │ │ "595013": 2199, │ │ │ │ │ "5952": 2219, │ │ │ │ │ "595307": 2197, │ │ │ │ │ "595334": 2204, │ │ │ │ │ "595393": 2210, │ │ │ │ │ "595447": [2184, 2214], │ │ │ │ │ @@ -33708,15 +33708,15 @@ │ │ │ │ │ "600337": 2186, │ │ │ │ │ "6004": 2199, │ │ │ │ │ "6007": 2219, │ │ │ │ │ "600705": 2197, │ │ │ │ │ "600794": 2184, │ │ │ │ │ "6008": 2219, │ │ │ │ │ "600874": 2215, │ │ │ │ │ - "601": [2199, 2205, 2298], │ │ │ │ │ + "601": [2199, 2298], │ │ │ │ │ "6013": 2219, │ │ │ │ │ "6014": 2220, │ │ │ │ │ "601544": 2185, │ │ │ │ │ "601618": 2207, │ │ │ │ │ "6018": 2219, │ │ │ │ │ "601965": 15, │ │ │ │ │ "602": 2199, │ │ │ │ │ @@ -33939,19 +33939,21 @@ │ │ │ │ │ "6289": 2220, │ │ │ │ │ "628992": 2257, │ │ │ │ │ "629": 2199, │ │ │ │ │ "6290": 2220, │ │ │ │ │ "629003": 2207, │ │ │ │ │ "629165": 2230, │ │ │ │ │ "6292": [2220, 2230], │ │ │ │ │ + "6295": 2203, │ │ │ │ │ "629546": 2219, │ │ │ │ │ - "6296": 2220, │ │ │ │ │ + "6296": [2203, 2220], │ │ │ │ │ "629675": 2185, │ │ │ │ │ - "6297": 2220, │ │ │ │ │ - "6299": 2220, │ │ │ │ │ + "6297": [2203, 2220], │ │ │ │ │ + "6298": 2203, │ │ │ │ │ + "6299": [2203, 2220], │ │ │ │ │ "63": [15, 17, 19, 213, 788, 2184, 2185, 2186, 2188, 2190, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2218, 2220, 2222, 2226, 2227, 2228, 2230, 2232, 2235, 2241, 2246, 2271], │ │ │ │ │ "630": 2199, │ │ │ │ │ "630110": 15, │ │ │ │ │ "630256": 2207, │ │ │ │ │ "630482": 2207, │ │ │ │ │ "631": 2199, │ │ │ │ │ "631095": 2195, │ │ │ │ │ @@ -33964,15 +33966,15 @@ │ │ │ │ │ "632038": 2207, │ │ │ │ │ "6322": 2235, │ │ │ │ │ "6326": 2246, │ │ │ │ │ "632633": 2217, │ │ │ │ │ "6327": 2220, │ │ │ │ │ "632779": 2186, │ │ │ │ │ "6329": 2220, │ │ │ │ │ - "633": 2199, │ │ │ │ │ + "633": [2185, 2199], │ │ │ │ │ "633165": 2230, │ │ │ │ │ "6332": 2220, │ │ │ │ │ "633372": 2215, │ │ │ │ │ "6335": 2220, │ │ │ │ │ "633678": 2185, │ │ │ │ │ "6337": 2220, │ │ │ │ │ "634": 2199, │ │ │ │ │ @@ -34101,14 +34103,15 @@ │ │ │ │ │ "6496": [2221, 2222], │ │ │ │ │ "649646": 2207, │ │ │ │ │ "649682": 28, │ │ │ │ │ "649711": 2212, │ │ │ │ │ "649727": 2191, │ │ │ │ │ "649748": 2186, │ │ │ │ │ "64bit": 2298, │ │ │ │ │ + "64ec62289cb4": 2203, │ │ │ │ │ "65": [17, 19, 259, 890, 2184, 2185, 2186, 2188, 2190, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2205, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2218, 2220, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2255, 2271], │ │ │ │ │ "650": [2199, 2298], │ │ │ │ │ "65000000": [176, 179, 754, 757, 1242, 1243], │ │ │ │ │ "6504": 2220, │ │ │ │ │ "650762": 2199, │ │ │ │ │ "650776": 2202, │ │ │ │ │ "650794": [121, 696], │ │ │ │ │ @@ -34899,14 +34902,15 @@ │ │ │ │ │ "742682": 2219, │ │ │ │ │ "742802": [195, 770], │ │ │ │ │ "7429": 2221, │ │ │ │ │ "743089": 2207, │ │ │ │ │ "7431": 2221, │ │ │ │ │ "7431609117": 2199, │ │ │ │ │ "743161": [2185, 2191, 2197, 2199, 2202], │ │ │ │ │ + "743480": 2228, │ │ │ │ │ "743875": 2191, │ │ │ │ │ "743894": 2191, │ │ │ │ │ "7439": 2222, │ │ │ │ │ "744095": 2207, │ │ │ │ │ "7441": [2202, 2222], │ │ │ │ │ "744154": 2204, │ │ │ │ │ "744376": 2207, │ │ │ │ │ @@ -34963,14 +34967,15 @@ │ │ │ │ │ "752239": 2207, │ │ │ │ │ "7523": 2221, │ │ │ │ │ "752332": 2186, │ │ │ │ │ "752441": 2207, │ │ │ │ │ "7528": 2222, │ │ │ │ │ "752861": 2195, │ │ │ │ │ "7529": 2221, │ │ │ │ │ + "753279": 2228, │ │ │ │ │ "7534": 2221, │ │ │ │ │ "753444": 2207, │ │ │ │ │ "753606": 2199, │ │ │ │ │ "753611": 2207, │ │ │ │ │ "753623": 2191, │ │ │ │ │ "753747": 2207, │ │ │ │ │ "7539": 2221, │ │ │ │ │ @@ -35047,14 +35052,15 @@ │ │ │ │ │ "764": 2207, │ │ │ │ │ "7640": 2235, │ │ │ │ │ "764052": 2207, │ │ │ │ │ "764443e": 2204, │ │ │ │ │ "764851": 2186, │ │ │ │ │ "7655": 2222, │ │ │ │ │ "7656": 2221, │ │ │ │ │ + "766": 2193, │ │ │ │ │ "7660": [2202, 2222], │ │ │ │ │ "7661": 2222, │ │ │ │ │ "766822": 2207, │ │ │ │ │ "767": [268, 2265], │ │ │ │ │ "767101": 2185, │ │ │ │ │ "767252": 2184, │ │ │ │ │ "767440": 2186, │ │ │ │ │ @@ -35064,15 +35070,15 @@ │ │ │ │ │ "7683": 2222, │ │ │ │ │ "768681": 2207, │ │ │ │ │ "7687": [2246, 2271], │ │ │ │ │ "7692": 2228, │ │ │ │ │ "769691": 2207, │ │ │ │ │ "7697": 2222, │ │ │ │ │ "769804": [2185, 2191, 2197, 2199, 2202, 2204], │ │ │ │ │ - "77": [15, 81, 1447, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2205, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271], │ │ │ │ │ + "77": [15, 81, 1447, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2205, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271], │ │ │ │ │ "770": [2193, 2207], │ │ │ │ │ "7701": 2221, │ │ │ │ │ "770309": 2207, │ │ │ │ │ "7704": 2222, │ │ │ │ │ "770555": 2204, │ │ │ │ │ "770743": 2207, │ │ │ │ │ "7708": 2222, │ │ │ │ │ @@ -35649,15 +35655,15 @@ │ │ │ │ │ "848896": 2193, │ │ │ │ │ "848974": 2197, │ │ │ │ │ "849": [16, 17, 18, 19, 2199, 2235], │ │ │ │ │ "8494": 2223, │ │ │ │ │ "8496": 2241, │ │ │ │ │ "84960": 2210, │ │ │ │ │ "849980": 2195, │ │ │ │ │ - "85": [182, 190, 193, 718, 760, 766, 768, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246], │ │ │ │ │ + "85": [182, 190, 193, 718, 760, 766, 768, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246], │ │ │ │ │ "850": [16, 17, 18, 19, 2199, 2235], │ │ │ │ │ "850083": 2207, │ │ │ │ │ "8501": 2222, │ │ │ │ │ "850229": 2235, │ │ │ │ │ "850287": 2207, │ │ │ │ │ "8504": 2202, │ │ │ │ │ "850458": 2207, │ │ │ │ │ @@ -35751,15 +35757,14 @@ │ │ │ │ │ "861549": 2214, │ │ │ │ │ "8616": [2243, 2246], │ │ │ │ │ "861651": 2207, │ │ │ │ │ "861755": 2229, │ │ │ │ │ "8618": [2184, 2186], │ │ │ │ │ "861816": 2216, │ │ │ │ │ "861849": [15, 2184, 2185, 2186, 2191, 2195, 2197, 2199, 2202, 2206, 2210, 2214, 2215, 2220, 2225, 2231, 2235, 2241, 2257, 2260], │ │ │ │ │ - "862": 2185, │ │ │ │ │ "862071": 1340, │ │ │ │ │ "862093": 2207, │ │ │ │ │ "8621": 2224, │ │ │ │ │ "862288": 2207, │ │ │ │ │ "8623": 2223, │ │ │ │ │ "8624": 2223, │ │ │ │ │ "862495": [2184, 2195, 2214], │ │ │ │ │ @@ -35961,15 +35966,15 @@ │ │ │ │ │ "889": [24, 25, 32, 2199], │ │ │ │ │ "8890": [2224, 2225], │ │ │ │ │ "889157": 2235, │ │ │ │ │ "889273": 2235, │ │ │ │ │ "889493": 2186, │ │ │ │ │ "889659": 2186, │ │ │ │ │ "889987": 2205, │ │ │ │ │ - "89": [207, 781, 1433, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2298], │ │ │ │ │ + "89": [207, 781, 1433, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2298], │ │ │ │ │ "890": [24, 25, 32, 2197, 2199], │ │ │ │ │ "8904": 2224, │ │ │ │ │ "890546": 2186, │ │ │ │ │ "890819": 2206, │ │ │ │ │ "8909": 2224, │ │ │ │ │ "891": [24, 25, 28, 32, 2197, 2199], │ │ │ │ │ "8910": [2243, 2246], │ │ │ │ │ @@ -36301,15 +36306,15 @@ │ │ │ │ │ "938819": 2204, │ │ │ │ │ "939": 2230, │ │ │ │ │ "939036": 2207, │ │ │ │ │ "939145": 2207, │ │ │ │ │ "939470": 2199, │ │ │ │ │ "939652": 2207, │ │ │ │ │ "9398": 2225, │ │ │ │ │ - "94": [15, 282, 2184, 2185, 2186, 2188, 2191, 2192, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2226, 2230, 2232, 2235, 2246], │ │ │ │ │ + "94": [15, 282, 2184, 2185, 2186, 2188, 2191, 2192, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2226, 2230, 2232, 2235, 2246], │ │ │ │ │ "9402": 2228, │ │ │ │ │ "941248": 2199, │ │ │ │ │ "9413": 2238, │ │ │ │ │ "941451": 2210, │ │ │ │ │ "9416": 2228, │ │ │ │ │ "9422": 2238, │ │ │ │ │ "942321": 2207, │ │ │ │ │ @@ -36498,15 +36503,15 @@ │ │ │ │ │ "969883": 1010, │ │ │ │ │ "969917": 2207, │ │ │ │ │ "96hr": 234, │ │ │ │ │ "97": [31, 196, 771, 1447, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2226, 2228, 2230, 2232, 2235, 2246], │ │ │ │ │ "970": 2197, │ │ │ │ │ "9700": 2226, │ │ │ │ │ "970121": 28, │ │ │ │ │ - "971": [2197, 2294], │ │ │ │ │ + "971": [2197, 2205, 2294], │ │ │ │ │ "9710": 2226, │ │ │ │ │ "971205": 15, │ │ │ │ │ "9713": 2226, │ │ │ │ │ "9714": 2230, │ │ │ │ │ "971495": 2230, │ │ │ │ │ "971944": 2207, │ │ │ │ │ "972": 2193, │ │ │ │ │ @@ -36632,15 +36637,15 @@ │ │ │ │ │ "988693": [155, 156, 730, 731], │ │ │ │ │ "9890": 2226, │ │ │ │ │ "9894": 2228, │ │ │ │ │ "9895": 2235, │ │ │ │ │ "989634": 2204, │ │ │ │ │ "989726": 2207, │ │ │ │ │ "989859": 2185, │ │ │ │ │ - "99": [15, 22, 145, 163, 284, 532, 741, 912, 1447, 1456, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2226, 2230, 2232, 2235, 2246, 2294, 2307], │ │ │ │ │ + "99": [15, 22, 145, 163, 284, 532, 741, 912, 1447, 1456, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2226, 2230, 2232, 2235, 2246, 2294, 2307], │ │ │ │ │ "990": [2199, 2230], │ │ │ │ │ "9900": 2199, │ │ │ │ │ "990000": 1894, │ │ │ │ │ "990317": 2199, │ │ │ │ │ "990340": 2207, │ │ │ │ │ "9905": 2226, │ │ │ │ │ "990582": [2184, 2195, 2214], │ │ │ │ │ @@ -36800,15 +36805,15 @@ │ │ │ │ │ "__eq__": [1031, 1068, 2186, 2246, 2289, 2307], │ │ │ │ │ "__finalize__": [2192, 2194, 2197, 2199, 2218, 2220, 2298], │ │ │ │ │ "__floordiv__": [2241, 2307], │ │ │ │ │ "__from_arrow__": [10, 1068, 2299, 2302], │ │ │ │ │ "__fspath__": 2238, │ │ │ │ │ "__func__": 2202, │ │ │ │ │ "__getattr__": [15, 2199, 2218], │ │ │ │ │ - "__getattribute__": [10, 2294], │ │ │ │ │ + "__getattribute__": [10, 2203, 2294], │ │ │ │ │ "__getitem__": [2, 203, 1031, 1064, 1387, 2185, 2191, 2193, 2194, 2197, 2217, 2225, 2226, 2246, 2249, 2254, 2257, 2265, 2271, 2274, 2277, 2283, 2286, 2289, 2294, 2295, 2297, 2298, 2300, 2301, 2302, 2306, 2307, 2308], │ │ │ │ │ "__getstate__": 2218, │ │ │ │ │ "__git_version__": 2246, │ │ │ │ │ "__globally__": 2190, │ │ │ │ │ "__gt__": 2188, │ │ │ │ │ "__hash__": [1068, 2246, 2302], │ │ │ │ │ "__index_level_": 9, │ │ │ │ │ @@ -36842,14 +36847,15 @@ │ │ │ │ │ "__str__": 2217, │ │ │ │ │ "__sub__": 2241, │ │ │ │ │ "__subclasses__": 2186, │ │ │ │ │ "__truediv__": 2307, │ │ │ │ │ "__unicode__": [2217, 2220, 2249], │ │ │ │ │ "__version__": [5, 2199], │ │ │ │ │ "__xor__": 2298, │ │ │ │ │ + "_accessor": 2203, │ │ │ │ │ "_accumul": [1031, 2298], │ │ │ │ │ "_add_arithmetic_op": 10, │ │ │ │ │ "_add_comparison_op": 10, │ │ │ │ │ "_add_offset": 2210, │ │ │ │ │ "_add_timedeltalike_scalar": 2210, │ │ │ │ │ "_allows_duplicate_label": 2192, │ │ │ │ │ "_array_strptime_with_fallback": 2210, │ │ │ │ │ @@ -36863,14 +36869,15 @@ │ │ │ │ │ "_bootstrap": [2199, 2203, 2212, 2298], │ │ │ │ │ "_buffer": [16, 17, 18, 19, 2199, 2235], │ │ │ │ │ "_built_with_meson": 5, │ │ │ │ │ "_cacheabl": 2246, │ │ │ │ │ "_call_chain": [16, 17, 18, 19, 2199, 2235], │ │ │ │ │ "_call_with_frames_remov": 2199, │ │ │ │ │ "_caller": 153, │ │ │ │ │ + "_can_hold_identifiers_and_holds_nam": 2203, │ │ │ │ │ "_check_deprecated_callable_usag": [2185, 2197], │ │ │ │ │ "_check_for_loc": 2193, │ │ │ │ │ "_check_indexing_error": [2185, 2191, 2194], │ │ │ │ │ "_check_is_chained_assignment_poss": 2197, │ │ │ │ │ "_check_setitem_copi": 2197, │ │ │ │ │ "_check_tokenize_statu": 2199, │ │ │ │ │ "_cmp_method": 2186, │ │ │ │ │ @@ -36956,14 +36963,15 @@ │ │ │ │ │ "_hash": 2235, │ │ │ │ │ "_hash_pandas_object": 1043, │ │ │ │ │ "_ilocindex": 2197, │ │ │ │ │ "_import_class": 2199, │ │ │ │ │ "_indexed_sam": [2186, 2218], │ │ │ │ │ "_indexslic": 440, │ │ │ │ │ "_inferred_dtyp": [2208, 2249], │ │ │ │ │ + "_info_axi": 2203, │ │ │ │ │ "_internal_nam": 10, │ │ │ │ │ "_internal_names_set": 10, │ │ │ │ │ "_is_boolean": [1056, 1068, 1081], │ │ │ │ │ "_is_copi": 2197, │ │ │ │ │ "_is_mixed_typ": 2197, │ │ │ │ │ "_is_numer": [1068, 2246, 2298], │ │ │ │ │ "_is_scalar_access": [2185, 2197], │ │ │ │ │ @@ -37604,15 +37612,15 @@ │ │ │ │ │ "attende": 0, │ │ │ │ │ "attent": [3, 10, 2197, 2205, 2207, 2214, 2216], │ │ │ │ │ "attr": [15, 227, 705, 802, 1394, 1423, 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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": [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_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], │ │ │ │ │ @@ -38267,15 +38275,15 @@ │ │ │ │ │ "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": 2197, │ │ │ │ │ + "check_dict_or_set_index": [2193, 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], │ │ │ │ │ @@ -40265,15 +40273,15 @@ │ │ │ │ │ "get_indexer_non_uniqu": [379, 2192, 2197, 2238, 2243, 2246, 2249, 2265, 2277, 2289], │ │ │ │ │ "get_indexer_nonuniqu": 2302, │ │ │ │ │ "get_ipython": 2193, │ │ │ │ │ "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, 2193, 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, 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_local_scop": 2193, │ │ │ │ │ "get_method": [16, 17, 18, 19, 2199, 2235], │ │ │ │ │ "get_near_stock_pric": [2216, 2223], │ │ │ │ │ "get_offset": [2265, 2298], │ │ │ │ │ "get_offset_nam": [2230, 2238], │ │ │ │ │ @@ -40833,15 +40841,15 @@ │ │ │ │ │ "inject": [120, 1387], │ │ │ │ │ "inkwarg": 2199, │ │ │ │ │ "inlin": [3, 2196, 2199, 2207, 2218, 2229, 2246], │ │ │ │ │ "inner": [16, 17, 19, 25, 30, 74, 96, 110, 153, 169, 241, 279, 404, 583, 619, 821, 1146, 1446, 1448, 2186, 2193, 2200, 2204, 2208, 2220, 2246, 2254, 2283, 2289, 2307], │ │ │ │ │ "inner_join": [16, 17, 19], │ │ │ │ │ "innermost": [247, 880, 1478, 2231], │ │ │ │ │ "inplac": [16, 17, 18, 19, 87, 89, 92, 111, 112, 114, 120, 124, 125, 146, 163, 181, 203, 209, 210, 212, 214, 228, 233, 234, 284, 370, 418, 421, 483, 500, 598, 601, 616, 633, 634, 636, 700, 701, 720, 741, 759, 783, 784, 787, 789, 807, 808, 912, 1166, 1167, 1223, 1224, 1280, 1387, 2190, 2192, 2214, 2215, 2218, 2220, 2221, 2222, 2228, 2229, 2230, 2231, 2235, 2238, 2241, 2246, 2265, 2271, 2273, 2275, 2276, 2277, 2278, 2289, 2290, 2291, 2292, 2293, 2295, 2297, 2298, 2302, 2307], │ │ │ │ │ - "input": [2, 3, 10, 13, 20, 24, 30, 31, 34, 49, 56, 63, 68, 69, 76, 78, 81, 85, 91, 92, 94, 97, 99, 100, 107, 108, 109, 120, 126, 129, 131, 134, 141, 143, 160, 162, 163, 171, 173, 183, 197, 199, 204, 206, 211, 212, 213, 215, 216, 217, 218, 219, 220, 221, 222, 227, 230, 233, 234, 244, 246, 256, 259, 264, 270, 273, 275, 278, 281, 284, 286, 346, 351, 354, 378, 380, 405, 415, 425, 426, 459, 465, 489, 499, 540, 573, 577, 578, 585, 596, 603, 616, 617, 620, 622, 629, 630, 631, 694, 702, 706, 707, 709, 710, 713, 717, 719, 734, 738, 739, 740, 741, 747, 749, 750, 753, 761, 773, 777, 780, 785, 787, 788, 790, 791, 792, 793, 795, 796, 797, 802, 804, 856, 877, 878, 888, 890, 893, 900, 901, 904, 912, 916, 927, 930, 938, 953, 1031, 1076, 1078, 1090, 1116, 1117, 1118, 1121, 1123, 1124, 1125, 1152, 1154, 1155, 1156, 1164, 1202, 1203, 1204, 1211, 1213, 1221, 1230, 1264, 1298, 1299, 1305, 1306, 1308, 1322, 1323, 1325, 1342, 1343, 1354, 1389, 1390, 1392, 1393, 1395, 1396, 1397, 1398, 1403, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1413, 1414, 1417, 1418, 1430, 1433, 1441, 1442, 1449, 1450, 1458, 1467, 1469, 1470, 1475, 1482, 1486, 1487, 1498, 1499, 1500, 2163, 2172, 2184, 2185, 2186, 2187, 2188, 2191, 2193, 2194, 2195, 2196, 2197, 2199, 2200, 2201, 2204, 2208, 2209, 2210, 2211, 2212, 2214, 2215, 2216, 2218, 2219, 2220, 2221, 2222, 2223, 2225, 2226, 2227, 2228, 2230, 2231, 2232, 2233, 2234, 2235, 2236, 2238, 2241, 2242, 2246, 2249, 2250, 2257, 2263, 2264, 2265, 2267, 2269, 2271, 2272, 2273, 2274, 2275, 2277, 2278, 2283, 2284, 2287, 2289, 2291, 2292, 2293, 2294, 2298, 2299, 2302, 2306, 2307, 2308, 2309], │ │ │ │ │ + "input": [2, 3, 10, 13, 20, 24, 30, 31, 34, 49, 56, 63, 68, 69, 76, 78, 81, 85, 91, 92, 94, 97, 99, 100, 107, 108, 109, 120, 126, 129, 131, 134, 141, 143, 160, 162, 163, 171, 173, 183, 197, 199, 204, 206, 211, 212, 213, 215, 216, 217, 218, 219, 220, 221, 222, 227, 230, 233, 234, 244, 246, 256, 259, 264, 270, 273, 275, 278, 281, 284, 286, 346, 351, 354, 378, 380, 405, 415, 425, 426, 459, 465, 489, 499, 540, 573, 577, 578, 585, 596, 603, 616, 617, 620, 622, 629, 630, 631, 694, 702, 706, 707, 709, 710, 713, 717, 719, 734, 738, 739, 740, 741, 747, 749, 750, 753, 761, 773, 777, 780, 785, 787, 788, 790, 791, 792, 793, 795, 796, 797, 802, 804, 856, 877, 878, 888, 890, 893, 900, 901, 904, 912, 916, 927, 930, 938, 953, 1031, 1076, 1078, 1090, 1116, 1117, 1118, 1121, 1123, 1124, 1125, 1152, 1154, 1155, 1156, 1164, 1202, 1203, 1204, 1211, 1213, 1221, 1230, 1264, 1298, 1299, 1305, 1306, 1308, 1322, 1323, 1325, 1342, 1343, 1354, 1389, 1390, 1392, 1393, 1395, 1396, 1397, 1398, 1403, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1413, 1414, 1417, 1418, 1430, 1433, 1441, 1442, 1449, 1450, 1458, 1467, 1469, 1470, 1475, 1482, 1486, 1487, 1498, 1499, 1500, 2163, 2172, 2184, 2185, 2186, 2187, 2188, 2191, 2193, 2194, 2195, 2196, 2197, 2199, 2200, 2201, 2203, 2204, 2208, 2209, 2210, 2211, 2212, 2214, 2215, 2216, 2218, 2219, 2220, 2221, 2222, 2223, 2225, 2226, 2227, 2228, 2230, 2231, 2232, 2233, 2234, 2235, 2236, 2238, 2241, 2242, 2246, 2249, 2250, 2257, 2263, 2264, 2265, 2267, 2269, 2271, 2272, 2273, 2274, 2275, 2277, 2278, 2283, 2284, 2287, 2289, 2291, 2292, 2293, 2294, 2298, 2299, 2302, 2306, 2307, 2308, 2309], │ │ │ │ │ "input_arrai": 2199, │ │ │ │ │ "insec": 873, │ │ │ │ │ "insensit": [533, 857, 1469, 1486, 2202, 2221, 2277], │ │ │ │ │ "insert": [2, 34, 63, 214, 255, 258, 267, 420, 789, 799, 821, 889, 896, 1061, 1345, 1391, 1416, 1488, 1490, 2185, 2186, 2191, 2193, 2195, 2196, 2202, 2207, 2217, 2218, 2219, 2220, 2221, 2222, 2225, 2226, 2228, 2229, 2233, 2238, 2242, 2246, 2249, 2265, 2271, 2277, 2283, 2289, 2293, 2294, 2298, 2302, 2304, 2306, 2307], │ │ │ │ │ "insert_on_conflict_noth": [267, 896], │ │ │ │ │ "insert_on_conflict_upd": [267, 896], │ │ │ │ │ "insid": [2, 8, 13, 22, 25, 77, 89, 124, 146, 203, 251, 259, 375, 466, 601, 700, 720, 884, 890, 1031, 1054, 1118, 1280, 1469, 1486, 1498, 2186, 2193, 2194, 2196, 2197, 2199, 2201, 2227, 2241, 2246, 2249, 2261, 2263, 2264, 2265, 2271, 2307], │ │ │ │ │ @@ -40967,15 +40975,15 @@ │ │ │ │ │ "ip": [10, 2241], │ │ │ │ │ "ipaddress": 10, │ │ │ │ │ "iparrai": 2241, │ │ │ │ │ "ipc": 2199, │ │ │ │ │ "ipi": 2202, │ │ │ │ │ "ipv4address": 10, │ │ │ │ │ "ipv6": [10, 1031], │ │ │ │ │ - "ipython": [4, 26, 257, 1069, 1071, 1072, 1345, 1391, 1488, 1490, 2184, 2186, 2193, 2194, 2196, 2197, 2199, 2207, 2219, 2222, 2227, 2230, 2232, 2235, 2236, 2242, 2246, 2247, 2251, 2257, 2258, 2265], │ │ │ │ │ + "ipython": [4, 26, 257, 1069, 1071, 1072, 1345, 1391, 1488, 1490, 2184, 2186, 2193, 2194, 2196, 2197, 2199, 2203, 2207, 2219, 2222, 2227, 2230, 2232, 2235, 2236, 2242, 2246, 2247, 2251, 2257, 2258, 2265], │ │ │ │ │ "ipythondir": 2202, │ │ │ │ │ "ipywidget": 2207, │ │ │ │ │ "iqr": [91, 190, 766, 1458], │ │ │ │ │ "iri": [1455, 1461, 2191, 2211, 2225], │ │ │ │ │ "irow": [2216, 2228, 2235, 2257], │ │ │ │ │ "irregular": [15, 2210, 2234, 2235, 2261, 2275, 2277], │ │ │ │ │ "irrelev": [0, 2298], │ │ │ │ │ @@ -44981,15 +44989,15 @@ │ │ │ │ │ "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 +1847,25 @@ │ │ │ │ In [141]: indexer = np.arange(10000) │ │ │ │ │ │ │ │ In [142]: random.shuffle(indexer) │ │ │ │ │ │ │ │ In [143]: %timeit arr[indexer] │ │ │ │ .....: %timeit arr.take(indexer, axis=0) │ │ │ │ .....: │ │ │ │ -310 us +- 4.77 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ -126 us +- 862 ns per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ +633 us +- 127 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ +223 us +- 7.74 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ │ │ │ │ │ │ │ │ 
In [144]: ser = pd.Series(arr[:, 0])
│ │ │ │  
│ │ │ │  In [145]: %timeit ser.iloc[indexer]
│ │ │ │     .....: %timeit ser.take(indexer)
│ │ │ │     .....: 
│ │ │ │ -147 us +- 8.29 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)
│ │ │ │ -126 us +- 3.59 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)
│ │ │ │ +452 us +- 89 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │ +457 us +- 167 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 +1245,23 @@ │ │ │ │ │ In [141]: indexer = np.arange(10000) │ │ │ │ │ │ │ │ │ │ In [142]: random.shuffle(indexer) │ │ │ │ │ │ │ │ │ │ In [143]: %timeit arr[indexer] │ │ │ │ │ .....: %timeit arr.take(indexer, axis=0) │ │ │ │ │ .....: │ │ │ │ │ -310 us +- 4.77 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ │ -126 us +- 862 ns per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ │ +633 us +- 127 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ │ +223 us +- 7.74 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ │ In [144]: ser = pd.Series(arr[:, 0]) │ │ │ │ │ │ │ │ │ │ In [145]: %timeit ser.iloc[indexer] │ │ │ │ │ .....: %timeit ser.take(indexer) │ │ │ │ │ .....: │ │ │ │ │ -147 us +- 8.29 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ │ -126 us +- 3.59 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ │ +452 us +- 89 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ │ +457 us +- 167 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)
│ │ │ │ -85.1 ms +- 78.9 us per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +184 ms +- 7.08 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 0.288 seconds
│ │ │ │ +         605946 function calls (605928 primitive calls) in 1.454 seconds
│ │ │ │  
│ │ │ │     Ordered by: internal time
│ │ │ │     List reduced from 159 to 4 due to restriction <4>
│ │ │ │  
│ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ -     1000    0.176    0.000    0.254    0.000 <ipython-input-4-c2a74e076cf0>:1(integrate_f)
│ │ │ │ -   552423    0.078    0.000    0.078    0.000 <ipython-input-3-c138bdd570e3>:1(f)
│ │ │ │ -     3000    0.006    0.000    0.023    0.000 series.py:1095(__getitem__)
│ │ │ │ -     3000    0.004    0.000    0.010    0.000 series.py:1220(_get_value)
│ │ │ │ +     1000    0.766    0.001    1.279    0.001 <ipython-input-4-c2a74e076cf0>:1(integrate_f)
│ │ │ │ +   552423    0.513    0.000    0.513    0.000 <ipython-input-3-c138bdd570e3>:1(f)
│ │ │ │ +     3000    0.027    0.000    0.119    0.000 series.py:1095(__getitem__)
│ │ │ │ +    16098    0.020    0.000    0.027    0.000 {built-in method builtins.isinstance}
│ │ │ │
│ │ │ │
│ │ │ │

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)
│ │ │ │ -77.9 ms +- 1.18 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +145 ms +- 12.1 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)
│ │ │ │ -10.2 ms +- 17.2 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +28.3 ms +- 3.28 ms 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 0.034 seconds
│ │ │ │ +         52523 function calls (52505 primitive calls) in 0.119 seconds
│ │ │ │  
│ │ │ │     Ordered by: internal time
│ │ │ │     List reduced from 157 to 4 due to restriction <4>
│ │ │ │  
│ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ -     3000    0.006    0.000    0.022    0.000 series.py:1095(__getitem__)
│ │ │ │ -     3000    0.004    0.000    0.009    0.000 series.py:1220(_get_value)
│ │ │ │ -    16098    0.003    0.000    0.004    0.000 {built-in method builtins.isinstance}
│ │ │ │ -     3000    0.003    0.000    0.003    0.000 base.py:3777(get_loc)
│ │ │ │ +     3000    0.019    0.000    0.084    0.000 series.py:1095(__getitem__)
│ │ │ │ +    16098    0.015    0.000    0.019    0.000 {built-in method builtins.isinstance}
│ │ │ │ +     3000    0.013    0.000    0.034    0.000 series.py:1220(_get_value)
│ │ │ │ +     3000    0.012    0.000    0.021    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)
│ │ │ │ @@ -722,33 +722,33 @@
│ │ │ │
│ │ │ │

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

│ │ │ │

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

│ │ │ │ 
In [14]: %timeit apply_integrate_f(df["a"].to_numpy(), df["b"].to_numpy(), df["N"].to_numpy())
│ │ │ │ -1.89 ms +- 455 ns per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │ +2.43 ms +- 55.3 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 0.002 seconds
│ │ │ │ +         78 function calls in 0.003 seconds
│ │ │ │  
│ │ │ │     Ordered by: internal time
│ │ │ │     List reduced from 21 to 4 due to restriction <4>
│ │ │ │  
│ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ -        1    0.002    0.002    0.002    0.002 <string>:1(<module>)
│ │ │ │ +        1    0.002    0.002    0.003    0.003 <string>:1(<module>)
│ │ │ │          1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}
│ │ │ │ -        1    0.000    0.000    0.002    0.002 {built-in method builtins.exec}
│ │ │ │ +        1    0.000    0.000    0.003    0.003 {built-in method builtins.exec}
│ │ │ │          3    0.000    0.000    0.000    0.000 frame.py:4062(__getitem__)
│ │ │ │
│ │ │ │
│ │ │ │ 
In [16]: %%cython
│ │ │ │     ....: cimport cython
│ │ │ │     ....: cimport numpy as np
│ │ │ │     ....: import numpy as np
│ │ │ │ @@ -1180,19 +1180,19 @@
│ │ │ │  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
│ │ │ │ -14.8 ms +- 154 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +1.23 s +- 154 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │  

│ │ │ │  

│ │ │ │  
In [41]: %timeit pd.eval("df1 + df2 + df3 + df4", engine="python")
│ │ │ │ -15.3 ms +- 286 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +1.28 s +- 203 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │  

│ │ │ │  

│ │ │ │
│ │ │ │
│ │ │ │

The DataFrame.eval() method#

│ │ │ │

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

│ │ │ │ @@ -1307,39 +1307,39 @@ │ │ │ │ 
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
│ │ │ │ -15.3 ms +- 312 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +1.27 s +- 226 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │
│ │ │ │
│ │ │ │ 
In [61]: %timeit pd.eval("df1 + df2 + df3 + df4")
│ │ │ │ -7.99 ms +- 165 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +327 ms +- 44.1 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │
│ │ │ │
│ │ │ │

DataFrame comparison:

│ │ │ │ 
In [62]: %timeit (df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)
│ │ │ │ -16.1 ms +- 94.9 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +58.2 ms +- 4.26 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │
│ │ │ │
│ │ │ │ 
In [63]: %timeit pd.eval("(df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)")
│ │ │ │ -5.07 ms +- 20.3 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +18.5 ms +- 1.08 ms 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
│ │ │ │ -36.2 ms +- 200 us per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +2.02 s +- 452 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │
│ │ │ │
│ │ │ │ 
In [66]: %timeit pd.eval("df1 + df2 + df3 + df4 + s")
│ │ │ │ -16.2 ms +- 125 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +383 ms +- 132 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │
│ │ │ │
│ │ │ │
│ │ │ │

Note

│ │ │ │

Operations such as

│ │ │ │ 
1 and 2  # would parse to 1 & 2, but should evaluate to 2
│ │ │ │  3 or 4  # would parse to 3 | 4, but should evaluate to 3
│ │ │ │ ├── html2text {}
│ │ │ │ │ @@ -110,32 +110,33 @@
│ │ │ │ │     ...:     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)
│ │ │ │ │ -85.1 ms +- 78.9 us per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │ +184 ms +- 7.08 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 0.288 seconds
│ │ │ │ │ +         605946 function calls (605928 primitive calls) in 1.454 seconds
│ │ │ │ │  
│ │ │ │ │     Ordered by: internal time
│ │ │ │ │     List reduced from 159 to 4 due to restriction <4>
│ │ │ │ │  
│ │ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ │ -     1000    0.176    0.000    0.254    0.000 :1
│ │ │ │ │ +     1000    0.766    0.001    1.279    0.001 :1
│ │ │ │ │  (integrate_f)
│ │ │ │ │ -   552423    0.078    0.000    0.078    0.000 :1
│ │ │ │ │ +   552423    0.513    0.000    0.513    0.000 :1
│ │ │ │ │  (f)
│ │ │ │ │ -     3000    0.006    0.000    0.023    0.000 series.py:1095(__getitem__)
│ │ │ │ │ -     3000    0.004    0.000    0.010    0.000 series.py:1220(_get_value)
│ │ │ │ │ +     3000    0.027    0.000    0.119    0.000 series.py:1095(__getitem__)
│ │ │ │ │ +    16098    0.020    0.000    0.027    0.000 {built-in method
│ │ │ │ │ +builtins.isinstance}
│ │ │ │ │  By far the majority of time is spend inside either integrate_f or f, hence
│ │ │ │ │  we’ll concentrate our efforts cythonizing these two functions.
│ │ │ │ │  ******** PPllaaiinn CCyytthhoonn_## ********
│ │ │ │ │  First we’re going to need to import the Cython magic function to IPython:
│ │ │ │ │  In [7]: %load_ext Cython
│ │ │ │ │  Now, let’s simply copy our functions over to Cython:
│ │ │ │ │  In [8]: %%cython
│ │ │ │ │ @@ -146,15 +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)
│ │ │ │ │ -77.9 ms +- 1.18 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │ +145 ms +- 12.1 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:
│ │ │ │ │ @@ -166,35 +167,36 @@
│ │ │ │ │     ....:     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)
│ │ │ │ │ -10.2 ms +- 17.2 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +28.3 ms +- 3.28 ms 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 0.034 seconds
│ │ │ │ │ +         52523 function calls (52505 primitive calls) in 0.119 seconds
│ │ │ │ │  
│ │ │ │ │     Ordered by: internal time
│ │ │ │ │     List reduced from 157 to 4 due to restriction <4>
│ │ │ │ │  
│ │ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ │ -     3000    0.006    0.000    0.022    0.000 series.py:1095(__getitem__)
│ │ │ │ │ -     3000    0.004    0.000    0.009    0.000 series.py:1220(_get_value)
│ │ │ │ │ -    16098    0.003    0.000    0.004    0.000 {built-in method
│ │ │ │ │ +     3000    0.019    0.000    0.084    0.000 series.py:1095(__getitem__)
│ │ │ │ │ +    16098    0.015    0.000    0.019    0.000 {built-in method
│ │ │ │ │  builtins.isinstance}
│ │ │ │ │ -     3000    0.003    0.000    0.003    0.000 base.py:3777(get_loc)
│ │ │ │ │ +     3000    0.013    0.000    0.034    0.000 series.py:1220(_get_value)
│ │ │ │ │ +     3000    0.012    0.000    0.021    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
│ │ │ │ │ @@ -235,31 +237,31 @@
│ │ │ │ │  This implementation creates an array of zeros and inserts the result of
│ │ │ │ │  integrate_f_typed applied over each row. Looping over an ndarray is faster in
│ │ │ │ │  Cython than looping over a _S_e_r_i_e_s object.
│ │ │ │ │  Since apply_integrate_f is typed to accept an np.ndarray, _S_e_r_i_e_s_._t_o___n_u_m_p_y_(_)
│ │ │ │ │  calls are needed to utilize this function.
│ │ │ │ │  In [14]: %timeit apply_integrate_f(df["a"].to_numpy(), df["b"].to_numpy(), df
│ │ │ │ │  ["N"].to_numpy())
│ │ │ │ │ -1.89 ms +- 455 ns per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │ │ +2.43 ms +- 55.3 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 0.002 seconds
│ │ │ │ │ +         78 function calls in 0.003 seconds
│ │ │ │ │  
│ │ │ │ │     Ordered by: internal time
│ │ │ │ │     List reduced from 21 to 4 due to restriction <4>
│ │ │ │ │  
│ │ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ │ -        1    0.002    0.002    0.002    0.002 :1()
│ │ │ │ │ +        1    0.002    0.002    0.003    0.003 :1()
│ │ │ │ │          1    0.000    0.000    0.000    0.000 {method 'disable' of
│ │ │ │ │  '_lsprof.Profiler' objects}
│ │ │ │ │ -        1    0.000    0.000    0.002    0.002 {built-in method builtins.exec}
│ │ │ │ │ +        1    0.000    0.000    0.003    0.003 {built-in method builtins.exec}
│ │ │ │ │          3    0.000    0.000    0.000    0.000 frame.py:4062(__getitem__)
│ │ │ │ │  In [16]: %%cython
│ │ │ │ │     ....: cimport cython
│ │ │ │ │     ....: cimport numpy as np
│ │ │ │ │     ....: import numpy as np
│ │ │ │ │     ....: cdef np.float64_t f_typed(np.float64_t x) except? -2:
│ │ │ │ │     ....:     return x * (x - 1)
│ │ │ │ │ @@ -646,17 +648,17 @@
│ │ │ │ │  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
│ │ │ │ │ -14.8 ms +- 154 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +1.23 s +- 154 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │  In [41]: %timeit pd.eval("df1 + df2 + df3 + df4", engine="python")
│ │ │ │ │ -15.3 ms +- 286 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +1.28 s +- 203 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │  ******** TThhee _DD_aa_tt_aa_FF_rr_aa_mm_ee_.._ee_vv_aa_ll_((_)) mmeetthhoodd_## ********
│ │ │ │ │  In addition to the top level _p_a_n_d_a_s_._e_v_a_l_(_) function you can also evaluate an
│ │ │ │ │  expression in the “context” of a _D_a_t_a_F_r_a_m_e.
│ │ │ │ │  In [42]: df = pd.DataFrame(np.random.randn(5, 2), columns=["a", "b"])
│ │ │ │ │  
│ │ │ │ │  In [43]: df.eval("a + b")
│ │ │ │ │  Out[43]:
│ │ │ │ │ @@ -753,29 +755,29 @@
│ │ │ │ │  _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
│ │ │ │ │ -15.3 ms +- 312 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +1.27 s +- 226 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │  In [61]: %timeit pd.eval("df1 + df2 + df3 + df4")
│ │ │ │ │ -7.99 ms +- 165 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +327 ms +- 44.1 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │  _D_a_t_a_F_r_a_m_e comparison:
│ │ │ │ │  In [62]: %timeit (df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)
│ │ │ │ │ -16.1 ms +- 94.9 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +58.2 ms +- 4.26 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │  In [63]: %timeit pd.eval("(df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)")
│ │ │ │ │ -5.07 ms +- 20.3 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +18.5 ms +- 1.08 ms 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
│ │ │ │ │ -36.2 ms +- 200 us per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ │ +2.02 s +- 452 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ │  In [66]: %timeit pd.eval("df1 + df2 + df3 + df4 + s")
│ │ │ │ │ -16.2 ms +- 125 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ │ +383 ms +- 132 ms per loop (mean +- std. dev. of 7 runs, 1 loop 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/pyarrow.html
│ │ │ │ @@ -986,19 +986,26 @@
│ │ │ │  Cell In[33], line 1
│ │ │ │  ----> 1 table = pa.table([pa.array([1, 2, 3], type=pa.int64())], names=["a"])
│ │ │ │  
│ │ │ │  NameError: name 'pa' is not defined
│ │ │ │  
│ │ │ │  In [34]: df = table.to_pandas(types_mapper=pd.ArrowDtype)
│ │ │ │  ---------------------------------------------------------------------------
│ │ │ │ -NameError                                 Traceback (most recent call last)
│ │ │ │ -Cell In[34], line 1
│ │ │ │ +AttributeError                            Traceback (most recent call last)
│ │ │ │ +<ipython-input-34-64ec62289cb4> in ?()
│ │ │ │  ----> 1 df = table.to_pandas(types_mapper=pd.ArrowDtype)
│ │ │ │  
│ │ │ │ -NameError: name 'table' is not defined
│ │ │ │ +/usr/lib/python3/dist-packages/pandas/core/generic.py in ?(self, name)
│ │ │ │ +   6295             and name not in self._accessors
│ │ │ │ +   6296             and self._info_axis._can_hold_identifiers_and_holds_name(name)
│ │ │ │ +   6297         ):
│ │ │ │ +   6298             return self[name]
│ │ │ │ +-> 6299         return object.__getattribute__(self, name)
│ │ │ │ +
│ │ │ │ +AttributeError: 'DataFrame' object has no attribute 'to_pandas'
│ │ │ │  
│ │ │ │  In [35]: df
│ │ │ │  Out[35]: 
│ │ │ │       a    b
│ │ │ │  0  xxx  yyy
│ │ │ │  1   ¡¡   ¡¡
│ │ │ │ ├── html2text {}
│ │ │ │ │ @@ -526,19 +526,27 @@
│ │ │ │ │  Cell In[33], line 1
│ │ │ │ │  ----> 1 table = pa.table([pa.array([1, 2, 3], type=pa.int64())], names=["a"])
│ │ │ │ │  
│ │ │ │ │  NameError: name 'pa' is not defined
│ │ │ │ │  
│ │ │ │ │  In [34]: df = table.to_pandas(types_mapper=pd.ArrowDtype)
│ │ │ │ │  ---------------------------------------------------------------------------
│ │ │ │ │ -NameError                                 Traceback (most recent call last)
│ │ │ │ │ -Cell In[34], line 1
│ │ │ │ │ +AttributeError                            Traceback (most recent call last)
│ │ │ │ │ + in ?()
│ │ │ │ │  ----> 1 df = table.to_pandas(types_mapper=pd.ArrowDtype)
│ │ │ │ │  
│ │ │ │ │ -NameError: name 'table' is not defined
│ │ │ │ │ +/usr/lib/python3/dist-packages/pandas/core/generic.py in ?(self, name)
│ │ │ │ │ +   6295             and name not in self._accessors
│ │ │ │ │ +   6296             and self._info_axis._can_hold_identifiers_and_holds_name
│ │ │ │ │ +(name)
│ │ │ │ │ +   6297         ):
│ │ │ │ │ +   6298             return self[name]
│ │ │ │ │ +-> 6299         return object.__getattribute__(self, name)
│ │ │ │ │ +
│ │ │ │ │ +AttributeError: 'DataFrame' object has no attribute 'to_pandas'
│ │ │ │ │  
│ │ │ │ │  In [35]: df
│ │ │ │ │  Out[35]:
│ │ │ │ │       a    b
│ │ │ │ │  0  xxx  yyy
│ │ │ │ │  1   ¡¡   ¡¡
│ │ │ ├── ./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 595 us, sys: 0 ns, total: 595 us
│ │ │ │ -Wall time: 601 us
│ │ │ │ +CPU times: user 960 us, sys: 0 ns, total: 960 us
│ │ │ │ +Wall time: 971 us
│ │ │ │  Out[32]: Series([], dtype: int32)
│ │ │ │
│ │ │ │
│ │ │ │

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 595 us, sys: 0 ns, total: 595 us │ │ │ │ │ -Wall time: 601 us │ │ │ │ │ +CPU times: user 960 us, sys: 0 ns, total: 960 us │ │ │ │ │ +Wall time: 971 us │ │ │ │ │ Out[32]: Series([], dtype: int32) │ │ │ │ │ Some readers, like _p_a_n_d_a_s_._r_e_a_d___c_s_v_(_), offer parameters to control the chunksize │ │ │ │ │ when reading a single file. │ │ │ │ │ Manually chunking is an OK option for workflows that don’t require too │ │ │ │ │ sophisticated of operations. Some operations, like _p_a_n_d_a_s_._D_a_t_a_F_r_a_m_e_._g_r_o_u_p_b_y_(_), │ │ │ │ │ are much harder to do chunkwise. In these cases, you may be better switching to │ │ │ │ │ a different library that implements these out-of-core algorithms for you. │ │ │ ├── ./usr/share/doc/python-pandas-doc/html/user_guide/style.ipynb.gz │ │ │ │ ├── style.ipynb │ │ │ │ │ ├── Pretty-printed │ │ │ │ │ │┄ Similarity: 0.9985610875706213% │ │ │ │ │ │┄ Differences: {"'cells'": "{1: {'metadata': {'execution': {'iopub.execute_input': '2025-02-07T20:54:16.552859Z', " │ │ │ │ │ │┄ "'iopub.status.busy': '2025-02-07T20:54:16.552451Z', 'iopub.status.idle': " │ │ │ │ │ │┄ "'2025-02-07T20:54:18.442922Z', 'shell.execute_reply': " │ │ │ │ │ │┄ "'2025-02-07T20:54:18.442092Z'}}}, 3: {'metadata': {'execution': " │ │ │ │ │ │┄ "{'iopub.execute_input': '2025-02-07T20:54:18.446880Z', 'iopub.status.busy': " │ │ │ │ │ │┄ "'2025-02-07T20:54:18.446470Z', 'iopub.status.idle': '2025-02-07T20:54:2 […] │ │ │ │ │ │ @@ -39,18 +39,18 @@ │ │ │ │ │ │ ] │ │ │ │ │ │ }, │ │ │ │ │ │ { │ │ │ │ │ │ "cell_type": "code", │ │ │ │ │ │ "execution_count": 1, │ │ │ │ │ │ "metadata": { │ │ │ │ │ │ "execution": { │ │ │ │ │ │ - "iopub.execute_input": "2026-03-13T06:59:48.318246Z", │ │ │ │ │ │ - "iopub.status.busy": "2026-03-13T06:59:48.318010Z", │ │ │ │ │ │ - "iopub.status.idle": "2026-03-13T06:59:48.751525Z", │ │ │ │ │ │ - "shell.execute_reply": "2026-03-13T06:59:48.750812Z" │ │ │ │ │ │ + "iopub.execute_input": "2025-02-07T20:54:16.552859Z", │ │ │ │ │ │ + "iopub.status.busy": "2025-02-07T20:54:16.552451Z", │ │ │ │ │ │ + "iopub.status.idle": "2025-02-07T20:54:18.442922Z", │ │ │ │ │ │ + "shell.execute_reply": "2025-02-07T20:54:18.442092Z" │ │ │ │ │ │ }, │ │ │ │ │ │ "nbsphinx": "hidden" │ │ │ │ │ │ }, │ │ │ │ │ │ "outputs": [], │ │ │ │ │ │ "source": [ │ │ │ │ │ │ "import matplotlib.pyplot\n", │ │ │ │ │ │ "# We have this here to trigger matplotlib's font cache stuff.\n", │ │ │ │ │ │ @@ -77,36 +77,36 @@ │ │ │ │ │ │ ] │ │ │ │ │ │ }, │ │ │ │ │ │ { │ │ │ │ │ │ "cell_type": "code", │ │ │ │ │ │ "execution_count": 2, │ │ │ │ │ │ "metadata": { │ │ │ │ │ │ "execution": { │ │ │ │ │ │ - "iopub.execute_input": "2026-03-13T06:59:48.754634Z", │ │ │ │ │ │ - "iopub.status.busy": "2026-03-13T06:59:48.754297Z", │ │ │ │ │ │ - "iopub.status.idle": "2026-03-13T06:59:49.018604Z", │ │ │ │ │ │ - "shell.execute_reply": "2026-03-13T06:59:49.018024Z" │ │ │ │ │ │ + "iopub.execute_input": "2025-02-07T20:54:18.446880Z", │ │ │ │ │ │ + "iopub.status.busy": "2025-02-07T20:54:18.446470Z", │ │ │ │ │ │ + "iopub.status.idle": "2025-02-07T20:54:20.856915Z", │ │ │ │ │ │ + "shell.execute_reply": "2025-02-07T20:54:20.855684Z" │ │ │ │ │ │ } │ │ │ │ │ │ }, │ │ │ │ │ │ "outputs": [], │ │ │ │ │ │ "source": [ │ │ │ │ │ │ "import pandas as pd\n", │ │ │ │ │ │ "import numpy as np\n", │ │ │ │ │ │ "import matplotlib as mpl\n" │ │ │ │ │ │ ] │ │ │ │ │ │ }, │ │ │ │ │ │ { │ │ │ │ │ │ "cell_type": "code", │ │ │ │ │ │ "execution_count": 3, │ │ │ │ │ │ "metadata": { │ │ │ │ │ │ "execution": { │ │ │ │ │ │ - "iopub.execute_input": "2026-03-13T06:59:49.021503Z", │ │ │ │ │ │ - "iopub.status.busy": "2026-03-13T06:59:49.021180Z", │ │ │ │ │ │ - "iopub.status.idle": "2026-03-13T06:59:49.133038Z", │ │ │ │ │ │ - "shell.execute_reply": "2026-03-13T06:59:49.132438Z" │ │ │ │ │ │ + "iopub.execute_input": "2025-02-07T20:54:20.860745Z", │ │ │ │ │ │ + "iopub.status.busy": "2025-02-07T20:54:20.860182Z", │ │ │ │ │ │ + "iopub.status.idle": "2025-02-07T20:54:21.196932Z", │ │ │ │ │ │ + "shell.execute_reply": "2025-02-07T20:54:21.195753Z" │ │ │ │ │ │ }, │ │ │ │ │ │ "nbsphinx": "hidden" │ │ │ │ │ │ }, │ │ │ │ │ │ "outputs": [], │ │ │ │ │ │ "source": [ │ │ │ │ │ │ "# For reproducibility - this doesn't respect uuid_len or positionally-passed uuid but the places here that use that coincidentally bypass this anyway\n", │ │ │ │ │ │ "from pandas.io.formats.style import Styler\n", │ │ │ │ │ │ @@ -123,18 +123,18 @@ │ │ │ │ │ │ ] │ │ │ │ │ │ }, │ │ │ │ │ │ { │ │ │ │ │ │ "cell_type": "code", │ │ │ │ │ │ "execution_count": 4, │ │ │ │ │ │ "metadata": { │ │ │ │ │ │ "execution": { │ │ │ │ │ │ - "iopub.execute_input": "2026-03-13T06:59:49.135729Z", │ │ │ │ │ │ - "iopub.status.busy": "2026-03-13T06:59:49.135435Z", │ │ │ │ │ │ - "iopub.status.idle": "2026-03-13T06:59:49.145087Z", │ │ │ │ │ │ - "shell.execute_reply": "2026-03-13T06:59:49.144549Z" │ │ │ │ │ │ + "iopub.execute_input": "2025-02-07T20:54:21.201717Z", │ │ │ │ │ │ + "iopub.status.busy": "2025-02-07T20:54:21.201184Z", │ │ │ │ │ │ + "iopub.status.idle": "2025-02-07T20:54:21.221163Z", │ │ │ │ │ │ + "shell.execute_reply": "2025-02-07T20:54:21.219979Z" │ │ │ │ │ │ } │ │ │ │ │ │ }, │ │ │ │ │ │ "outputs": [ │ │ │ │ │ │ { │ │ │ │ │ │ "data": { │ │ │ │ │ │ "text/html": [ │ │ │ │ │ │ "