--- /srv/rebuilderd/tmp/rebuilderdWwj7IT/inputs/python-pandas-doc_2.2.3+dfsg-9_all.deb +++ /srv/rebuilderd/tmp/rebuilderdWwj7IT/out/python-pandas-doc_2.2.3+dfsg-9_all.deb ├── file list │ @@ -1,3 +1,3 @@ │ -rw-r--r-- 0 0 0 4 2025-03-29 13:01:52.000000 debian-binary │ --rw-r--r-- 0 0 0 147368 2025-03-29 13:01:52.000000 control.tar.xz │ --rw-r--r-- 0 0 0 10646756 2025-03-29 13:01:52.000000 data.tar.xz │ +-rw-r--r-- 0 0 0 147376 2025-03-29 13:01:52.000000 control.tar.xz │ +-rw-r--r-- 0 0 0 10646416 2025-03-29 13:01:52.000000 data.tar.xz ├── control.tar.xz │ ├── control.tar │ │ ├── ./control │ │ │ @@ -1,13 +1,13 @@ │ │ │ Package: python-pandas-doc │ │ │ Source: pandas │ │ │ Version: 2.2.3+dfsg-9 │ │ │ Architecture: all │ │ │ Maintainer: Debian Science Team │ │ │ -Installed-Size: 209900 │ │ │ +Installed-Size: 209903 │ │ │ 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 │ │ │ @@ -6256,47 +6256,47 @@ │ │ │ -rw-r--r-- 0 root (0) root (0) 210184 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/reference/series.html │ │ │ -rw-r--r-- 0 root (0) root (0) 48665 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/reference/style.html │ │ │ -rw-r--r-- 0 root (0) root (0) 48657 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/reference/testing.html │ │ │ -rw-r--r-- 0 root (0) root (0) 53295 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/reference/window.html │ │ │ -rw-r--r-- 0 root (0) root (0) 244 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/release.html │ │ │ -rw-r--r-- 0 root (0) root (0) 269 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/reshaping.html │ │ │ -rw-r--r-- 0 root (0) root (0) 17010 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/search.html │ │ │ --rw-r--r-- 0 root (0) root (0) 2358635 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/searchindex.js │ │ │ +-rw-r--r-- 0 root (0) root (0) 2358581 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/searchindex.js │ │ │ -rw-r--r-- 0 root (0) root (0) 259 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/sparse.html │ │ │ -rw-r--r-- 0 root (0) root (0) 244 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/style.html │ │ │ -rw-r--r-- 0 root (0) root (0) 255 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/text.html │ │ │ -rw-r--r-- 0 root (0) root (0) 256 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/timedeltas.html │ │ │ -rw-r--r-- 0 root (0) root (0) 277 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/timeseries.html │ │ │ -rw-r--r-- 0 root (0) root (0) 272 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/tutorials.html │ │ │ drwxr-xr-x 0 root (0) root (0) 0 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/ │ │ │ -rw-r--r-- 0 root (0) root (0) 171380 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/10min.html │ │ │ --rw-r--r-- 0 root (0) root (0) 283837 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/advanced.html │ │ │ +-rw-r--r-- 0 root (0) root (0) 283839 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/advanced.html │ │ │ -rw-r--r-- 0 root (0) root (0) 436075 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/basics.html │ │ │ -rw-r--r-- 0 root (0) root (0) 36646 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/boolean.html │ │ │ -rw-r--r-- 0 root (0) root (0) 217515 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/categorical.html │ │ │ -rw-r--r-- 0 root (0) root (0) 18313 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/cookbook.html │ │ │ -rw-r--r-- 0 root (0) root (0) 66125 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/copy_on_write.html │ │ │ -rw-r--r-- 0 root (0) root (0) 160414 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/dsintro.html │ │ │ -rw-r--r-- 0 root (0) root (0) 81376 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/duplicates.html │ │ │ --rw-r--r-- 0 root (0) root (0) 115451 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/enhancingperf.html │ │ │ +-rw-r--r-- 0 root (0) root (0) 115454 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/enhancingperf.html │ │ │ -rw-r--r-- 0 root (0) root (0) 107882 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/gotchas.html │ │ │ -rw-r--r-- 0 root (0) root (0) 300850 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/groupby.html │ │ │ -rw-r--r-- 0 root (0) root (0) 59715 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/index.html │ │ │ -rw-r--r-- 0 root (0) root (0) 395484 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/indexing.html │ │ │ -rw-r--r-- 0 root (0) root (0) 41778 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/integer_na.html │ │ │ -rw-r--r-- 0 root (0) root (0) 1145820 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/io.html │ │ │ -rw-r--r-- 0 root (0) root (0) 208885 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/merging.html │ │ │ -rw-r--r-- 0 root (0) root (0) 178690 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/missing_data.html │ │ │ -rw-r--r-- 0 root (0) root (0) 112153 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/options.html │ │ │ -rw-r--r-- 0 root (0) root (0) 147524 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/pyarrow.html │ │ │ -rw-r--r-- 0 root (0) root (0) 162660 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/reshaping.html │ │ │ --rw-r--r-- 0 root (0) root (0) 115580 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/scale.html │ │ │ +-rw-r--r-- 0 root (0) root (0) 115581 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/scale.html │ │ │ -rw-r--r-- 0 root (0) root (0) 65863 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/sparse.html │ │ │ -rw-r--r-- 0 root (0) root (0) 698240 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/style.html │ │ │ --rw-r--r-- 0 root (0) root (0) 87860 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/style.ipynb.gz │ │ │ +-rw-r--r-- 0 root (0) root (0) 87785 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/style.ipynb.gz │ │ │ -rw-r--r-- 0 root (0) root (0) 165302 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/text.html │ │ │ -rw-r--r-- 0 root (0) root (0) 100947 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/timedeltas.html │ │ │ -rw-r--r-- 0 root (0) root (0) 486621 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/timeseries.html │ │ │ -rw-r--r-- 0 root (0) root (0) 204461 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/visualization.html │ │ │ -rw-r--r-- 0 root (0) root (0) 141947 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/user_guide/window.html │ │ │ -rw-r--r-- 0 root (0) root (0) 270 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/visualization.html │ │ │ drwxr-xr-x 0 root (0) root (0) 0 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/ │ │ │ @@ -6316,18 +6316,18 @@ │ │ │ -rw-r--r-- 0 root (0) root (0) 145199 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.16.0.html │ │ │ -rw-r--r-- 0 root (0) root (0) 115518 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.16.1.html │ │ │ -rw-r--r-- 0 root (0) root (0) 64656 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.16.2.html │ │ │ -rw-r--r-- 0 root (0) root (0) 231394 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.17.0.html │ │ │ -rw-r--r-- 0 root (0) root (0) 95028 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.17.1.html │ │ │ -rw-r--r-- 0 root (0) root (0) 224091 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.18.0.html │ │ │ -rw-r--r-- 0 root (0) root (0) 171888 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.18.1.html │ │ │ --rw-r--r-- 0 root (0) root (0) 349360 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.19.0.html │ │ │ +-rw-r--r-- 0 root (0) root (0) 350916 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.19.0.html │ │ │ -rw-r--r-- 0 root (0) root (0) 45179 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.19.1.html │ │ │ -rw-r--r-- 0 root (0) root (0) 48525 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.19.2.html │ │ │ --rw-r--r-- 0 root (0) root (0) 406224 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.20.0.html │ │ │ +-rw-r--r-- 0 root (0) root (0) 407739 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.20.0.html │ │ │ -rw-r--r-- 0 root (0) root (0) 52898 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.20.2.html │ │ │ -rw-r--r-- 0 root (0) root (0) 43404 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.20.3.html │ │ │ -rw-r--r-- 0 root (0) root (0) 255116 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.21.0.html │ │ │ -rw-r--r-- 0 root (0) root (0) 61789 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.21.1.html │ │ │ -rw-r--r-- 0 root (0) root (0) 59841 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.22.0.html │ │ │ -rw-r--r-- 0 root (0) root (0) 401704 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.23.0.html │ │ │ -rw-r--r-- 0 root (0) root (0) 59871 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.23.1.html │ │ ├── ./usr/share/doc/python-pandas-doc/html/searchindex.js │ │ │ ├── js-beautify {} │ │ │ │ @@ -21531,26 +21531,26 @@ │ │ │ │ "005000": 2218, │ │ │ │ "005361": 2207, │ │ │ │ "005383": 2220, │ │ │ │ "005446": 2219, │ │ │ │ "005462": 2191, │ │ │ │ "005977": 2199, │ │ │ │ "005979": 2186, │ │ │ │ + "006": 2193, │ │ │ │ "006123": 2207, │ │ │ │ "006154": [2185, 2197, 2199, 2202, 2204, 2215, 2257], │ │ │ │ "0062": 2191, │ │ │ │ "006349": 2195, │ │ │ │ "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, │ │ │ │ @@ -21569,15 +21569,15 @@ │ │ │ │ "009673": 2195, │ │ │ │ "009783": 2207, │ │ │ │ "009797": 2186, │ │ │ │ "009826": [102, 1158, 2205], │ │ │ │ "009920": 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│ │ "10000": [192, 1485, 2185, 2201, 2206, 2210, 2220, 2228, 2266], │ │ │ │ "100000": [1354, 1372, 2199, 2201, 2210], │ │ │ │ "1000000": [144, 2199, 2228], │ │ │ │ @@ -22305,15 +22305,15 @@ │ │ │ │ "10083": 2226, │ │ │ │ "100845": 2207, │ │ │ │ "10087": 2227, │ │ │ │ "10089": 2229, │ │ │ │ "10092": 2226, │ │ │ │ "10096": 2227, │ │ │ │ "100y": 1344, │ │ │ │ - "101": [207, 781, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2222, 2230, 2231, 2232, 2235, 2246], │ │ │ │ + "101": [207, 781, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2222, 2230, 2231, 2232, 2235, 2246], │ │ │ │ "10103": 2231, │ │ │ │ "10110": 2228, │ │ │ │ "10114": 2228, │ │ │ │ "10115": 2228, │ │ │ │ "101154": 2207, │ │ │ │ "10117": 2246, │ │ │ │ "10119": 2227, │ │ │ │ @@ -22465,15 +22465,15 @@ │ │ │ │ "10477": 2228, │ │ │ │ "104803": 2195, │ │ │ │ "10482": 2228, │ │ │ │ "10483": 2228, │ │ │ │ "10485": 2228, │ │ │ │ "10486": 2230, │ │ │ │ "104971": 2207, │ │ │ │ - "105": [2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2204, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2230, 2232, 2235, 2246, 2271], │ │ │ │ + "105": [2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2204, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2230, 2232, 2235, 2246, 2271], │ │ │ │ "10503": 2230, │ │ │ │ "10505": 2228, │ │ │ │ "10508": 2228, │ │ │ │ "10510": 2229, │ │ │ │ "10511": 2249, │ │ │ │ "1051201": 2205, │ │ │ │ "1051471": 2205, │ │ │ │ @@ -22665,15 +22665,15 @@ │ │ │ │ "10h": [2210, 2235], │ │ │ │ "10m": [16, 1447, 2200], │ │ │ │ "10min": 2230, │ │ │ │ "10t00": 2261, │ │ │ │ "10th": [2205, 2241], │ │ │ │ "10x": [1469, 1486, 1498, 2216, 2219, 2225, 2257], │ │ │ │ "11": [2, 10, 15, 17, 18, 19, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 88, 108, 111, 113, 120, 127, 139, 140, 157, 162, 196, 213, 286, 288, 289, 293, 295, 296, 300, 316, 317, 318, 323, 324, 329, 330, 420, 423, 440, 509, 512, 518, 522, 524, 526, 530, 534, 536, 554, 556, 600, 635, 639, 641, 645, 647, 649, 650, 652, 670, 671, 673, 678, 679, 681, 684, 685, 703, 732, 771, 788, 799, 940, 943, 948, 985, 993, 1010, 1019, 1023, 1025, 1169, 1174, 1175, 1195, 1200, 1226, 1256, 1261, 1276, 1292, 1298, 1299, 1306, 1308, 1321, 1433, 1452, 1482, 1498, 1542, 1560, 1598, 1620, 1637, 1677, 1699, 1720, 1741, 1839, 1930, 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, 2217, 2218, 2219, 2220, 2222, 2223, 2224, 2225, 2228, 2229, 2230, 2231, 2232, 2235, 2238, 2240, 2241, 2246, 2249, 2250, 2257, 2261, 2264, 2265, 2271, 2277, 2278, 2283, 2289, 2294, 2297, 2298, 2302, 2307], │ │ │ │ - "110": [213, 359, 360, 587, 588, 788, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2223, 2230, 2232, 2235, 2246], │ │ │ │ + "110": [213, 359, 360, 587, 588, 788, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2223, 2230, 2232, 2235, 2246], │ │ │ │ "1100": [2195, 2210], │ │ │ │ "11000": [2185, 2220], │ │ │ │ "11002": 2228, │ │ │ │ "11007": 2229, │ │ │ │ "1101": 2210, │ │ │ │ "11010": 2228, │ │ │ │ "11014": 2228, │ │ │ │ @@ -23456,15 +23456,15 @@ │ │ │ │ "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, │ │ │ │ @@ -23820,16 +23820,14 @@ │ │ │ │ "13965": 2235, │ │ │ │ "13966": 2235, │ │ │ │ "139683": 2235, │ │ │ │ "13971": 2238, │ │ │ │ "13972": 2232, │ │ │ │ "13977": 2232, │ │ │ │ "13980": 2232, │ │ │ │ - "139804400157008": 2246, │ │ │ │ - "139804622088560": 2246, │ │ │ │ "13981": 2232, │ │ │ │ "13985": 2232, │ │ │ │ "139853": 2207, │ │ │ │ "13988": 2232, │ │ │ │ "13990": 2232, │ │ │ │ "13994": 2232, │ │ │ │ "139976": 2186, │ │ │ │ @@ -23846,14 +23844,15 @@ │ │ │ │ "14015": 2235, │ │ │ │ "14021": 2232, │ │ │ │ "140249": 2207, │ │ │ │ "14039": 2232, │ │ │ │ "14041": 2232, │ │ │ │ "140528": 2207, │ │ │ │ "14058": 2232, │ │ │ │ + "140603243132560": 2246, │ │ │ │ "14065": 2232, │ │ │ │ "14066": 2232, │ │ │ │ "14068": [2232, 2233], │ │ │ │ "1408": [2197, 2231], │ │ │ │ "14086": 2232, │ │ │ │ "14088": 2232, │ │ │ │ "1409": [2185, 2197], │ │ │ │ @@ -24145,15 +24144,15 @@ │ │ │ │ "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, 2218], │ │ │ │ + "150": [15, 111, 118, 132, 135, 159, 161, 175, 213, 233, 788, 2185, 2186, 2188, 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, │ │ │ │ @@ -24247,15 +24246,15 @@ │ │ │ │ "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, │ │ │ │ @@ -24404,15 +24403,15 @@ │ │ │ │ "15785": 2241, │ │ │ │ "15787": 2235, │ │ │ │ "157892": [15, 2185, 2186, 2191, 2197, 2199, 2202, 2215, 2216, 2218, 2219, 2235, 2241, 2264], │ │ │ │ "157898": 2207, │ │ │ │ "1579": [2184, 2186, 2191, 2194], │ │ │ │ "15793": 2238, │ │ │ │ "15797": 2235, │ │ │ │ - "158": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2201, 2210, 2211, 2256], │ │ │ │ + "158": [2185, 2186, 2188, 2195, 2197, 2199, 2201, 2210, 2211, 2256], │ │ │ │ "1580": [2184, 2186, 2194], │ │ │ │ "15800": 2241, │ │ │ │ "158091": 15, │ │ │ │ "158131": 2186, │ │ │ │ "15819": 2236, │ │ │ │ "15822": 2235, │ │ │ │ "15828": 2235, │ │ │ │ @@ -24726,15 +24725,15 @@ │ │ │ │ "16875": 2238, │ │ │ │ "16877": 2238, │ │ │ │ "1688": [2185, 2265], │ │ │ │ "16889": 2238, │ │ │ │ "1689": [2185, 2197, 2265], │ │ │ │ "168904": 2197, │ │ │ │ "16896": 2238, │ │ │ │ - "169": [2185, 2186, 2188, 2195, 2197, 2199, 2200, 2201, 2210, 2211, 2283], │ │ │ │ + "169": [2185, 2186, 2188, 2195, 2197, 2199, 2200, 2201, 2205, 2210, 2211, 2283], │ │ │ │ "1690": [2185, 2197, 2265], │ │ │ │ "16900": 2238, │ │ │ │ "16905": 2238, │ │ │ │ "169060": 2207, │ │ │ │ "1690785": [1345, 1391, 1488, 1490, 2202], │ │ │ │ "16909": 2238, │ │ │ │ "1691": [2185, 2197, 2263, 2265], │ │ │ │ @@ -24905,15 +24904,15 @@ │ │ │ │ "1747": 2197, │ │ │ │ "17471": 2238, │ │ │ │ "17474": 2238, │ │ │ │ "1748": 2263, │ │ │ │ "17493": 2249, │ │ │ │ "174950": 15, │ │ │ │ "17496": 2238, │ │ │ │ - "175": [118, 132, 135, 159, 161, 175, 2185, 2186, 2188, 2195, 2197, 2199, 2200, 2203, 2210, 2211, 2283, 2298], │ │ │ │ + "175": [118, 132, 135, 159, 161, 175, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2203, 2210, 2211, 2283, 2298], │ │ │ │ "1750": 2246, │ │ │ │ "17504": 2238, │ │ │ │ "17508": 2238, │ │ │ │ "17509": 2238, │ │ │ │ "17513": 2238, │ │ │ │ "17525": 2238, │ │ │ │ "17527": 2238, │ │ │ │ @@ -25036,15 +25035,15 @@ │ │ │ │ "1798606054": 2199, │ │ │ │ "179861": [2185, 2197, 2199, 2202], │ │ │ │ "17990": 2239, │ │ │ │ "17994": 2246, │ │ │ │ "179963": 2191, │ │ │ │ "17min": [213, 345, 788, 2210, 2271], │ │ │ │ "18": [0, 13, 15, 17, 18, 19, 22, 23, 25, 26, 29, 30, 31, 69, 72, 73, 109, 129, 171, 173, 195, 199, 204, 206, 213, 215, 216, 217, 219, 220, 221, 222, 230, 242, 244, 268, 271, 275, 345, 770, 788, 804, 817, 899, 1019, 1341, 1432, 1434, 1485, 1497, 1498, 1677, 1699, 1720, 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, 2223, 2225, 2226, 2228, 2232, 2235, 2238, 2240, 2241, 2246, 2257, 2261, 2264, 2265, 2271, 2277, 2283, 2289, 2294, 2298, 2302, 2307], │ │ │ │ - "180": [69, 109, 129, 171, 173, 199, 204, 206, 215, 216, 217, 220, 221, 222, 244, 272, 275, 1487, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2210, 2211, 2212, 2235, 2283], │ │ │ │ + "180": [69, 109, 129, 171, 173, 199, 204, 206, 215, 216, 217, 220, 221, 222, 244, 272, 275, 1487, 2185, 2186, 2188, 2195, 2197, 2199, 2200, 2210, 2211, 2212, 2235, 2283], │ │ │ │ "180000": 168, │ │ │ │ "18000000000000": [1773, 1854, 1891, 1909, 1927, 1979, 2033, 2087], │ │ │ │ "18002": 2241, │ │ │ │ "18004": 2239, │ │ │ │ "18020": 2239, │ │ │ │ "18021": 2302, │ │ │ │ "18034": 2241, │ │ │ │ @@ -26347,15 +26346,15 @@ │ │ │ │ "22556": 2246, │ │ │ │ "22557": 2246, │ │ │ │ "22578": 2246, │ │ │ │ "22579": 2246, │ │ │ │ "22580": 2246, │ │ │ │ "22591": 2246, │ │ │ │ "225944": 2207, │ │ │ │ - "226": [2185, 2186, 2188, 2195, 2197, 2199, 2207, 2210, 2218], │ │ │ │ + "226": [2185, 2186, 2188, 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, │ │ │ │ @@ -26672,15 +26671,15 @@ │ │ │ │ "24076": 2249, │ │ │ │ "24077": 2246, │ │ │ │ "240774": 2191, │ │ │ │ "24084": 2246, │ │ │ │ "240848": 2207, │ │ │ │ "240893": 2207, │ │ │ │ "240991": 2186, │ │ │ │ - "241": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2207, 2210, 2220, 2246, 2254, 2298], │ │ │ │ + "241": [2185, 2186, 2188, 2195, 2197, 2199, 2207, 2210, 2220, 2246, 2254, 2298], │ │ │ │ "24103": 2302, │ │ │ │ "241039": 2230, │ │ │ │ "2411": 2214, │ │ │ │ "24110": 2246, │ │ │ │ "24112": 2265, │ │ │ │ "24113": 2246, │ │ │ │ "24114": 2265, │ │ │ │ @@ -27149,15 +27148,15 @@ │ │ │ │ "26381": 2249, │ │ │ │ "26385": 2250, │ │ │ │ "26388": [2269, 2270], │ │ │ │ "26390": 2249, │ │ │ │ "263914": 2207, │ │ │ │ "263952": [2184, 2214], │ │ │ │ "26397": 2249, │ │ │ │ - "264": [283, 910, 2186, 2188, 2195, 2197, 2199, 2210], │ │ │ │ + "264": [283, 910, 2186, 2188, 2195, 2197, 2199, 2210, 2218], │ │ │ │ "2640": 2215, │ │ │ │ "264043": 2207, │ │ │ │ "26405": [2249, 2265], │ │ │ │ "2641": 2216, │ │ │ │ "26411": 2283, │ │ │ │ "26421": 2249, │ │ │ │ "264223": 2197, │ │ │ │ @@ -27241,15 +27240,15 @@ │ │ │ │ "26773": 2249, │ │ │ │ "267767": [1148, 1149], │ │ │ │ "267827": 2207, │ │ │ │ "267828": 2207, │ │ │ │ "2678400": 2218, │ │ │ │ "2678703": 2218, │ │ │ │ "2679": 2246, │ │ │ │ - "268": [2186, 2188, 2195, 2197, 2199, 2210], │ │ │ │ + "268": [2186, 2188, 2193, 2195, 2197, 2199, 2210], │ │ │ │ "2680": 2215, │ │ │ │ "268100": 2207, │ │ │ │ "268106": 2207, │ │ │ │ "268121": 2199, │ │ │ │ "26825": 2249, │ │ │ │ "2683": 2215, │ │ │ │ "26835": 2249, │ │ │ │ @@ -27342,15 +27341,15 @@ │ │ │ │ "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, 2195, 2197, 2199, 2202, 2205, 2210, 2257], │ │ │ │ "2730": 2199, │ │ │ │ "27309": 2249, │ │ │ │ "27311": 2265, │ │ │ │ "27315": 2277, │ │ │ │ "27321": 2249, │ │ │ │ "273290": 2207, │ │ │ │ "273322": 2207, │ │ │ │ @@ -27386,15 +27385,15 @@ │ │ │ │ "27470": 2250, │ │ │ │ "27481": 2250, │ │ │ │ "27482": 2250, │ │ │ │ "27484": 2250, │ │ │ │ "27489": 2265, │ │ │ │ "27495": 2265, │ │ │ │ "27496": 2250, │ │ │ │ - "275": [2186, 2188, 2195, 2197, 2199, 2210, 2220], │ │ │ │ + "275": [2186, 2188, 2193, 2195, 2197, 2199, 2210, 2220], │ │ │ │ "2750": 25, │ │ │ │ "27500": 2250, │ │ │ │ "27516": 2250, │ │ │ │ "27519": 2265, │ │ │ │ "27526": 2250, │ │ │ │ "27529": 2265, │ │ │ │ "27538": 2265, │ │ │ │ @@ -27909,15 +27908,15 @@ │ │ │ │ "30163": 2265, │ │ │ │ "30164": 2271, │ │ │ │ "30171": 2265, │ │ │ │ "30173": 2283, │ │ │ │ "30175": 2265, │ │ │ │ "301911": 2199, │ │ │ │ "30193": 2298, │ │ │ │ - "302": [2186, 2197, 2199, 2207, 2210, 2256], │ │ │ │ + "302": [2186, 2193, 2197, 2199, 2207, 2210, 2256], │ │ │ │ "30200": [2265, 2277], │ │ │ │ "30208": 2265, │ │ │ │ "302092": 2195, │ │ │ │ "30219": 2298, │ │ │ │ "30225": 2265, │ │ │ │ "30227": 2265, │ │ │ │ "30228": 2294, │ │ │ │ @@ -27926,15 +27925,15 @@ │ │ │ │ "30253": 2265, │ │ │ │ "30270": 2265, │ │ │ │ "30280": 2265, │ │ │ │ "302827": 2197, │ │ │ │ "30288": 2265, │ │ │ │ "30289": 2265, │ │ │ │ "30296": [2265, 2298], │ │ │ │ - "303": [2184, 2186, 2197, 2199, 2202, 2205, 2209, 2210, 2222, 2255], │ │ │ │ + "303": [2184, 2186, 2197, 2199, 2202, 2209, 2210, 2222, 2255], │ │ │ │ "30308": 2265, │ │ │ │ "303099": 2186, │ │ │ │ "3032": [2184, 2199, 2205], │ │ │ │ "30320": 2271, │ │ │ │ "303202": 2219, │ │ │ │ "303218": 2207, │ │ │ │ "30322": 2265, │ │ │ │ @@ -28025,15 +28024,15 @@ │ │ │ │ "3087": 2257, │ │ │ │ "30871": 2271, │ │ │ │ "30876": 2271, │ │ │ │ "30880": 2271, │ │ │ │ "308847": [16, 19], │ │ │ │ "30889": 2265, │ │ │ │ "308975": 2210, │ │ │ │ - "309": [2186, 2197, 2199, 2201, 2210, 2255], │ │ │ │ + "309": [2186, 2197, 2199, 2201, 2210, 2218, 2255], │ │ │ │ "309040": 2197, │ │ │ │ "309059": 2197, │ │ │ │ "30918": 2271, │ │ │ │ "309210": 2207, │ │ │ │ "30924": 2271, │ │ │ │ "30929": 2265, │ │ │ │ "30936": 2265, │ │ │ │ @@ -28126,15 +28125,15 @@ │ │ │ │ "3136390": 2220, │ │ │ │ "31364": 2277, │ │ │ │ "31368": 2273, │ │ │ │ "313805": 2207, │ │ │ │ "31389": 2266, │ │ │ │ "313939": 2199, │ │ │ │ "31396": 2271, │ │ │ │ - "314": [2186, 2197, 2199, 2201, 2210], │ │ │ │ + "314": [2185, 2186, 2197, 2199, 2201, 2210], │ │ │ │ "31401": 2271, │ │ │ │ "3141": [1109, 2217, 2246], │ │ │ │ "31413": 2271, │ │ │ │ "314148": 2191, │ │ │ │ "31415": 2271, │ │ │ │ "314159265": 2, │ │ │ │ "31422": 2277, │ │ │ │ @@ -29177,15 +29176,15 @@ │ │ │ │ "36419": 2289, │ │ │ │ "36430": 2274, │ │ │ │ "36432": 2277, │ │ │ │ "36451": 2274, │ │ │ │ "364628": 2207, │ │ │ │ "364886": 2230, │ │ │ │ "36495": 2276, │ │ │ │ - "365": [303, 445, 514, 516, 544, 546, 655, 923, 2186, 2191, 2195, 2197, 2199, 2205, 2209, 2210, 2249], │ │ │ │ + "365": [303, 445, 514, 516, 544, 546, 655, 923, 2186, 2191, 2195, 2197, 2199, 2209, 2210, 2249], │ │ │ │ "3650": 2218, │ │ │ │ "36525": 2277, │ │ │ │ "36526": 2274, │ │ │ │ "36527": 2274, │ │ │ │ "36530": 2277, │ │ │ │ "36541": 2277, │ │ │ │ "3656": 2217, │ │ │ │ @@ -29276,15 +29275,15 @@ │ │ │ │ "36980": 2277, │ │ │ │ "36984": 2289, │ │ │ │ "369849": [2185, 2197, 2199, 2202], │ │ │ │ "3698493577": 2199, │ │ │ │ "369894": 2206, │ │ │ │ "36991": 2283, │ │ │ │ "37": [15, 17, 18, 19, 139, 140, 213, 230, 345, 788, 804, 1010, 1174, 1175, 2184, 2185, 2186, 2188, 2190, 2191, 2193, 2194, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2217, 2218, 2219, 2220, 2222, 2225, 2226, 2228, 2230, 2231, 2232, 2235, 2238, 2241, 2246, 2249, 2265, 2271, 2283, 2298], │ │ │ │ - "370": [3, 134, 709, 1264, 2186, 2197, 2199, 2205, 2210, 2241, 2255], │ │ │ │ + "370": [3, 134, 709, 1264, 2186, 2197, 2199, 2210, 2241, 2255], │ │ │ │ "37001": 2277, │ │ │ │ "370076": 2230, │ │ │ │ "370079": 2210, │ │ │ │ "3702": 2217, │ │ │ │ "370251": 2230, │ │ │ │ "370255": 2197, │ │ │ │ "37037": 2277, │ │ │ │ @@ -30133,15 +30132,15 @@ │ │ │ │ "410599": 2207, │ │ │ │ "41062": 2294, │ │ │ │ "41069": 2283, │ │ │ │ "41074": 2282, │ │ │ │ "410835": [2185, 2197, 2199, 2202, 2215, 2257], │ │ │ │ "41090": 2302, │ │ │ │ "41098": 2283, │ │ │ │ - "411": [15, 2186, 2193, 2199, 2207, 2210], │ │ │ │ + "411": [15, 2186, 2199, 2207, 2210], │ │ │ │ "4110": 2219, │ │ │ │ "411013": 2191, │ │ │ │ "41108": 2283, │ │ │ │ "41111": 2283, │ │ │ │ "41118": 2298, │ │ │ │ "4112": 2218, │ │ │ │ "411202": 2199, │ │ │ │ @@ -30871,15 +30870,15 @@ │ │ │ │ "44084": 2289, │ │ │ │ "44085": 2289, │ │ │ │ "440865": 2207, │ │ │ │ "4409": 2218, │ │ │ │ "44091": 2289, │ │ │ │ "44096": 2289, │ │ │ │ "44098": 2289, │ │ │ │ - "441": [586, 2186, 2199, 2210], │ │ │ │ + "441": [586, 2186, 2193, 2199, 2210], │ │ │ │ "44100": 2289, │ │ │ │ "44105": 2289, │ │ │ │ "44106": 2288, │ │ │ │ "441075": 2207, │ │ │ │ "44109": 2289, │ │ │ │ "441091": 2207, │ │ │ │ "441153": 2191, │ │ │ │ @@ -30891,15 +30890,15 @@ │ │ │ │ "44176": 2289, │ │ │ │ "44178": 2289, │ │ │ │ "44179": 2289, │ │ │ │ "44181": 2289, │ │ │ │ "44182": 2289, │ │ │ │ "44190": 2288, │ │ │ │ "44199": 2294, │ │ │ │ - "442": [2186, 2199, 2210], │ │ │ │ + "442": [2186, 2199, 2205, 2210], │ │ │ │ "44201": 2289, │ │ │ │ "4421": 2218, │ │ │ │ "442118": 2207, │ │ │ │ "44212": [2223, 2289], │ │ │ │ "442141": 2230, │ │ │ │ "442166": 2207, │ │ │ │ "442353": 2186, │ │ │ │ @@ -30918,15 +30917,15 @@ │ │ │ │ "44265": 2289, │ │ │ │ "4427": [2192, 2197], │ │ │ │ "44270": 2289, │ │ │ │ "44276": 2289, │ │ │ │ "44279": 2302, │ │ │ │ "4428": [2192, 2197], │ │ │ │ "4429": [2192, 2197], │ │ │ │ - "443": [2193, 2199, 2210], │ │ │ │ + "443": [2199, 2210], │ │ │ │ "4430": [2192, 2197], │ │ │ │ "44304": 2289, │ │ │ │ "4431": [2192, 2197], │ │ │ │ "443110": 2185, │ │ │ │ "44312": 2289, │ │ │ │ "44316": 2289, │ │ │ │ "44322": 2289, │ │ │ │ @@ -31014,15 +31013,15 @@ │ │ │ │ "44752": 2289, │ │ │ │ "44764": 2294, │ │ │ │ "44766": 2289, │ │ │ │ "44772": 2289, │ │ │ │ "44779": 2257, │ │ │ │ "44787": 2294, │ │ │ │ "44793": 2289, │ │ │ │ - "448": [2193, 2199, 2210, 2249], │ │ │ │ + "448": [2199, 2210, 2249], │ │ │ │ "4480": 2218, │ │ │ │ "44802": 2294, │ │ │ │ "44818": 2289, │ │ │ │ "4482": 2218, │ │ │ │ "44821": 2289, │ │ │ │ "44826": 2289, │ │ │ │ "4486": 2218, │ │ │ │ @@ -31030,15 +31029,15 @@ │ │ │ │ "44864": 2289, │ │ │ │ "44873": 2289, │ │ │ │ "4488": 2218, │ │ │ │ "448851": 2195, │ │ │ │ "44891": 2289, │ │ │ │ "44896": 2298, │ │ │ │ "448992": 2207, │ │ │ │ - "449": [2199, 2210, 2249], │ │ │ │ + "449": [2199, 2205, 2210, 2249], │ │ │ │ "44900": 2289, │ │ │ │ "449092": 2191, │ │ │ │ "4491": 2219, │ │ │ │ "44914": 2294, │ │ │ │ "44916": 2289, │ │ │ │ "44917": 2298, │ │ │ │ "44923": 2298, │ │ │ │ @@ -31470,15 +31469,15 @@ │ │ │ │ "4686": 2218, │ │ │ │ "46867": 2292, │ │ │ │ "46870": 2293, │ │ │ │ "46877": 2302, │ │ │ │ "4688": 2221, │ │ │ │ "46891": 2293, │ │ │ │ "46896": 2294, │ │ │ │ - "469": [29, 2193, 2199, 2207, 2210, 2220], │ │ │ │ + "469": [29, 2199, 2207, 2210, 2220], │ │ │ │ "4690": 2221, │ │ │ │ "46900": 2292, │ │ │ │ "46903": 2294, │ │ │ │ "46904": 2292, │ │ │ │ "46906": 2298, │ │ │ │ "469070": 2207, │ │ │ │ "4691": [2184, 2186, 2191], │ │ │ │ @@ -32627,15 +32626,14 @@ │ │ │ │ "5271": 2218, │ │ │ │ "527146": 2210, │ │ │ │ "527206": 2207, │ │ │ │ "52728": 2302, │ │ │ │ "527525": [1195, 1256, 1292], │ │ │ │ "52760": 2299, │ │ │ │ "52761": 2299, │ │ │ │ - "527625": 2228, │ │ │ │ "52764": 2302, │ │ │ │ "527645": 2207, │ │ │ │ "52777": 2302, │ │ │ │ "52781": 2302, │ │ │ │ "52788": 2302, │ │ │ │ "52795": 2299, │ │ │ │ "528": 2199, │ │ │ │ @@ -32648,15 +32646,14 @@ │ │ │ │ "52836": 2302, │ │ │ │ "52840": 2302, │ │ │ │ "52843": 2299, │ │ │ │ "52849": 2302, │ │ │ │ "528496": 2199, │ │ │ │ "52859": 2302, │ │ │ │ "52872": 2300, │ │ │ │ - "528798": 2228, │ │ │ │ "528895": 2166, │ │ │ │ "52897": 2302, │ │ │ │ "52898": 2302, │ │ │ │ "529": 2199, │ │ │ │ "52904": 2302, │ │ │ │ "52909": 2300, │ │ │ │ "52927": 2307, │ │ │ │ @@ -33841,15 +33838,15 @@ │ │ │ │ "619104": 2207, │ │ │ │ "619366": 2207, │ │ │ │ "6194": 2232, │ │ │ │ "61941": 2257, │ │ │ │ "6199759194": 2199, │ │ │ │ "619976": [2185, 2191, 2197, 2199, 2202, 2204], │ │ │ │ "619993": 2235, │ │ │ │ - "62": [15, 17, 18, 19, 81, 187, 213, 242, 283, 763, 788, 817, 910, 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, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271], │ │ │ │ + "62": [15, 17, 18, 19, 81, 187, 213, 242, 283, 763, 788, 817, 910, 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, 2228, 2230, 2232, 2235, 2241, 2246, 2271], │ │ │ │ "620": [16, 17, 18, 19, 2199, 2203, 2232, 2235, 2262, 2298], │ │ │ │ "620145": 2191, │ │ │ │ "62036035": [624, 1215], │ │ │ │ "620399": 2199, │ │ │ │ "620498": 2207, │ │ │ │ "6205": 2220, │ │ │ │ "620544": 2191, │ │ │ │ @@ -34089,15 +34086,15 @@ │ │ │ │ "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], │ │ │ │ + "650": [2193, 2199, 2298], │ │ │ │ "65000000": [176, 179, 754, 757, 1242, 1243], │ │ │ │ "6504": 2220, │ │ │ │ "650762": 2199, │ │ │ │ "650776": 2202, │ │ │ │ "650794": [121, 696], │ │ │ │ "6508": 2249, │ │ │ │ "650845e": 2191, │ │ │ │ @@ -34168,15 +34165,15 @@ │ │ │ │ "658444": [2184, 2257], │ │ │ │ "658537": 2184, │ │ │ │ "658598": 2207, │ │ │ │ "6587": 2220, │ │ │ │ "6588664275960427": 2206, │ │ │ │ "658899": 2207, │ │ │ │ "6589": 2206, │ │ │ │ - "659": [2193, 2199], │ │ │ │ + "659": 2199, │ │ │ │ "659221": 2207, │ │ │ │ "659369": 2207, │ │ │ │ "659584": 2207, │ │ │ │ "659955": 2207, │ │ │ │ "66": [17, 19, 139, 140, 219, 273, 900, 1174, 1175, 1433, 2184, 2185, 2186, 2188, 2190, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271], │ │ │ │ "660": [2199, 2201], │ │ │ │ "6600": 2220, │ │ │ │ @@ -34386,27 +34383,27 @@ │ │ │ │ "6814": 2185, │ │ │ │ "681456": [15, 2219], │ │ │ │ "6815": 2185, │ │ │ │ "6816": 2220, │ │ │ │ "6817": [2185, 2220], │ │ │ │ "6818": [2185, 2220], │ │ │ │ "681973": 2207, │ │ │ │ - "682": [2193, 2197], │ │ │ │ + "682": 2197, │ │ │ │ "682402": 2199, │ │ │ │ "682596e": 2191, │ │ │ │ "682781": 2207, │ │ │ │ "683276": 2207, │ │ │ │ "683333": 2222, │ │ │ │ "6834": 2220, │ │ │ │ "683463": 2230, │ │ │ │ "683536": 2197, │ │ │ │ "683763": 2186, │ │ │ │ "683774": 2207, │ │ │ │ "683847": 2195, │ │ │ │ - "684": [2193, 2197], │ │ │ │ + "684": 2197, │ │ │ │ "684267": 2207, │ │ │ │ "684360": 2195, │ │ │ │ "684640": 2186, │ │ │ │ "6847": 2220, │ │ │ │ "684718": 2197, │ │ │ │ "685": [2186, 2197, 2227], │ │ │ │ "685094": 2207, │ │ │ │ @@ -34489,15 +34486,15 @@ │ │ │ │ "693043": 2210, │ │ │ │ "6932": 2222, │ │ │ │ "693205": [2184, 2214], │ │ │ │ "693429": 28, │ │ │ │ "6937": 2221, │ │ │ │ "693884": 2210, │ │ │ │ "6939": 2220, │ │ │ │ - "694": 2199, │ │ │ │ + "694": [2193, 2199], │ │ │ │ "694268": 28, │ │ │ │ "6945": 2241, │ │ │ │ "694592": 2207, │ │ │ │ "695": 2199, │ │ │ │ "6951": 2220, │ │ │ │ "695148": 2186, │ │ │ │ "6952": 2220, │ │ │ │ @@ -34957,15 +34954,14 @@ │ │ │ │ "7534": 2221, │ │ │ │ "753444": 2207, │ │ │ │ "753606": 2199, │ │ │ │ "753611": 2207, │ │ │ │ "753623": 2191, │ │ │ │ "753747": 2207, │ │ │ │ "7539": 2221, │ │ │ │ - "754": 2193, │ │ │ │ "7540": 2222, │ │ │ │ "754005": 2210, │ │ │ │ "7542": 2222, │ │ │ │ "7543": 2222, │ │ │ │ "754659": 2191, │ │ │ │ "755": [29, 2298], │ │ │ │ "7551": 2221, │ │ │ │ @@ -35109,25 +35105,27 @@ │ │ │ │ "775558e": 2222, │ │ │ │ "775602": 2207, │ │ │ │ "7757": 2238, │ │ │ │ "7758": 2222, │ │ │ │ "775880": 2186, │ │ │ │ "7760": 2222, │ │ │ │ "7761": 2222, │ │ │ │ + "776196": 2228, │ │ │ │ "7762": 2222, │ │ │ │ "7763": 2222, │ │ │ │ "7766": 2222, │ │ │ │ "776734": 2207, │ │ │ │ "7768": [2222, 2241], │ │ │ │ "7769": 2191, │ │ │ │ "776904": [2185, 2197, 2199, 2215, 2218, 2219, 2257], │ │ │ │ "777037": 2207, │ │ │ │ "7771": 2225, │ │ │ │ "7772": 2222, │ │ │ │ "7774": 2222, │ │ │ │ + "777455": 2228, │ │ │ │ "777482e": 2191, │ │ │ │ "777490": 2207, │ │ │ │ "7775": 2249, │ │ │ │ "777643": 2207, │ │ │ │ "7777": 2222, │ │ │ │ "7778": 2222, │ │ │ │ "7780": 2222, │ │ │ │ @@ -35522,15 +35520,15 @@ │ │ │ │ "8285": 2225, │ │ │ │ "8287": 2232, │ │ │ │ "828904": 2191, │ │ │ │ "8292": 2232, │ │ │ │ "829645": 2207, │ │ │ │ "829678": 2191, │ │ │ │ "829721": 2212, │ │ │ │ - "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], │ │ │ │ + "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], │ │ │ │ "8302": 2224, │ │ │ │ "8303": 2222, │ │ │ │ "830429": 2207, │ │ │ │ "8305": 2222, │ │ │ │ "830545": 2199, │ │ │ │ "8306": [2243, 2246], │ │ │ │ "830957": 2207, │ │ │ │ @@ -36020,15 +36018,15 @@ │ │ │ │ "8983": 2224, │ │ │ │ "8984": 2202, │ │ │ │ "8984347": 2202, │ │ │ │ "8986": 2225, │ │ │ │ "898725": 2197, │ │ │ │ "898872": 2214, │ │ │ │ "8989": 2224, │ │ │ │ - "899": 2199, │ │ │ │ + "899": [2193, 2199], │ │ │ │ "8991": 2289, │ │ │ │ "899173761": 2199, │ │ │ │ "899260": 15, │ │ │ │ "8994": 2224, │ │ │ │ "899734": 15, │ │ │ │ "8999": 2229, │ │ │ │ "8a2e": 2241, │ │ │ │ @@ -36048,15 +36046,15 @@ │ │ │ │ "9009": 2225, │ │ │ │ "900906": 2207, │ │ │ │ "901": 2199, │ │ │ │ "9011": 2224, │ │ │ │ "9012": 2224, │ │ │ │ "9016": 2225, │ │ │ │ "902": 2199, │ │ │ │ - "903": [2193, 2199], │ │ │ │ + "903": 2199, │ │ │ │ "9031": 2246, │ │ │ │ "903246": 2207, │ │ │ │ "903450": 1340, │ │ │ │ "9037": 2225, │ │ │ │ "903794": 2186, │ │ │ │ "904": 2199, │ │ │ │ "9046": 2277, │ │ │ │ @@ -36234,15 +36232,15 @@ │ │ │ │ "929553": 2199, │ │ │ │ "929556": 2207, │ │ │ │ "929561": 2207, │ │ │ │ "929563": 2191, │ │ │ │ "929567": 2191, │ │ │ │ "9297": 2230, │ │ │ │ "93": [15, 718, 1447, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2207, 2208, 2209, 2210, 2211, 2214, 2218, 2220, 2222, 2226, 2230, 2232, 2235, 2246, 2257, 2298], │ │ │ │ - "930": [2193, 2289], │ │ │ │ + "930": 2289, │ │ │ │ "930201": 2191, │ │ │ │ "9304": 2283, │ │ │ │ "930687": 2217, │ │ │ │ "930806": 2210, │ │ │ │ "9309": 2271, │ │ │ │ "9311": [2225, 2228], │ │ │ │ "931203": 2204, │ │ ├── ./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) │ │ │ .....: │ │ │ -191 us +- 30.1 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ -48 us +- 2.32 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ +314 us +- 57.1 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ +53.6 us +- 5.83 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)
│ │ │     .....: 
│ │ │ -103 us +- 20.5 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)
│ │ │ -82.9 us +- 1.46 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)
│ │ │ +96.1 us +- 1.56 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)
│ │ │ +99.2 us +- 21.9 us per loop (mean +- std. dev. of 7 runs, 10,000 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) │ │ │ │ .....: │ │ │ │ -191 us +- 30.1 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ -48 us +- 2.32 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ +314 us +- 57.1 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each) │ │ │ │ +53.6 us +- 5.83 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) │ │ │ │ .....: │ │ │ │ -103 us +- 20.5 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ -82.9 us +- 1.46 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ +96.1 us +- 1.56 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each) │ │ │ │ +99.2 us +- 21.9 us per loop (mean +- std. dev. of 7 runs, 10,000 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)
│ │ │ -56.6 ms +- 411 us per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ +49.3 ms +- 2.23 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 0.180 seconds
│ │ │ +         605946 function calls (605928 primitive calls) in 0.175 seconds
│ │ │  
│ │ │     Ordered by: internal time
│ │ │     List reduced from 159 to 4 due to restriction <4>
│ │ │  
│ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ -     1000    0.110    0.000    0.158    0.000 <ipython-input-4-c2a74e076cf0>:1(integrate_f)
│ │ │ -   552423    0.047    0.000    0.047    0.000 <ipython-input-3-c138bdd570e3>:1(f)
│ │ │ -     3000    0.004    0.000    0.015    0.000 series.py:1095(__getitem__)
│ │ │ -     3000    0.003    0.000    0.007    0.000 series.py:1220(_get_value)
│ │ │ +     1000    0.101    0.000    0.153    0.000 <ipython-input-4-c2a74e076cf0>:1(integrate_f)
│ │ │ +   552423    0.052    0.000    0.052    0.000 <ipython-input-3-c138bdd570e3>:1(f)
│ │ │ +     3000    0.004    0.000    0.014    0.000 series.py:1095(__getitem__)
│ │ │ +     3000    0.003    0.000    0.006    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)
│ │ │ -67.8 ms +- 13.3 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ +58.5 ms +- 2.13 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)
│ │ │ -7.61 ms +- 469 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ +6.66 ms +- 131 us per loop (mean +- std. dev. of 7 runs, 100 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 0.023 seconds
│ │ │ +         52523 function calls (52505 primitive calls) in 0.021 seconds
│ │ │  
│ │ │     Ordered by: internal time
│ │ │     List reduced from 157 to 4 due to restriction <4>
│ │ │  
│ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ -     3000    0.004    0.000    0.015    0.000 series.py:1095(__getitem__)
│ │ │ -     3000    0.003    0.000    0.007    0.000 series.py:1220(_get_value)
│ │ │ -     3000    0.002    0.000    0.003    0.000 base.py:3777(get_loc)
│ │ │ +     3000    0.004    0.000    0.014    0.000 series.py:1095(__getitem__)
│ │ │ +     3000    0.002    0.000    0.006    0.000 series.py:1220(_get_value)
│ │ │      16098    0.002    0.000    0.003    0.000 {built-in method builtins.isinstance}
│ │ │ +     3000    0.002    0.000    0.002    0.000 base.py:3777(get_loc)
│ │ │
│ │ │
│ │ │ 
In [13]: %%cython
│ │ │     ....: cimport numpy as np
│ │ │     ....: import numpy as np
│ │ │     ....: cdef double f_typed(double x) except? -2:
│ │ │     ....:     return x * (x - 1)
│ │ │ @@ -722,15 +722,15 @@
│ │ │
│ │ │

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())
│ │ │ -930 us +- 30.9 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ +899 us +- 2.63 us per loop (mean +- std. dev. of 7 runs, 1,000 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 │ │ │ @@ -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())
│ │ │ -659 us +- 167 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ +650 us +- 2.36 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
│ │ │ -9.01 ms +- 448 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ +10.3 ms +- 694 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │
│ │ │
│ │ │ 
In [41]: %timeit pd.eval("df1 + df2 + df3 + df4", engine="python")
│ │ │ -12.3 ms +- 754 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ +10.1 ms +- 441 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
│ │ │ -8.83 ms +- 443 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ +9.27 ms +- 268 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │
│ │ │
│ │ │ 
In [61]: %timeit pd.eval("df1 + df2 + df3 + df4")
│ │ │ -5.63 ms +- 684 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ +3.64 ms +- 159 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)
│ │ │ -10.7 ms +- 903 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ +4.3 ms +- 275 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)")
│ │ │ -10.5 ms +- 682 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ +5.26 ms +- 302 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
│ │ │ -22.8 ms +- 443 us per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ +15.1 ms +- 1.4 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │
│ │ │
│ │ │ 
In [66]: %timeit pd.eval("df1 + df2 + df3 + df4 + s")
│ │ │ -8.5 ms +- 241 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ +3.53 ms +- 105 us 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 {}
│ │ │ │ @@ -110,32 +110,32 @@
│ │ │ │     ...:     dx = (b - a) / N
│ │ │ │     ...:     for i in range(N):
│ │ │ │     ...:         s += f(a + i * dx)
│ │ │ │     ...:     return s * dx
│ │ │ │     ...:
│ │ │ │  We achieve our result by using _D_a_t_a_F_r_a_m_e_._a_p_p_l_y_(_) (row-wise):
│ │ │ │  In [5]: %timeit df.apply(lambda x: integrate_f(x["a"], x["b"], x["N"]), axis=1)
│ │ │ │ -56.6 ms +- 411 us per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +49.3 ms +- 2.23 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 0.180 seconds
│ │ │ │ +         605946 function calls (605928 primitive calls) in 0.175 seconds
│ │ │ │  
│ │ │ │     Ordered by: internal time
│ │ │ │     List reduced from 159 to 4 due to restriction <4>
│ │ │ │  
│ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ -     1000    0.110    0.000    0.158    0.000 :1
│ │ │ │ +     1000    0.101    0.000    0.153    0.000 :1
│ │ │ │  (integrate_f)
│ │ │ │ -   552423    0.047    0.000    0.047    0.000 :1
│ │ │ │ +   552423    0.052    0.000    0.052    0.000 :1
│ │ │ │  (f)
│ │ │ │ -     3000    0.004    0.000    0.015    0.000 series.py:1095(__getitem__)
│ │ │ │ -     3000    0.003    0.000    0.007    0.000 series.py:1220(_get_value)
│ │ │ │ +     3000    0.004    0.000    0.014    0.000 series.py:1095(__getitem__)
│ │ │ │ +     3000    0.003    0.000    0.006    0.000 series.py:1220(_get_value)
│ │ │ │  By far the majority of time is spend inside either integrate_f or f, hence
│ │ │ │  we’ll concentrate our efforts cythonizing these two functions.
│ │ │ │  ******** PPllaaiinn CCyytthhoonn_## ********
│ │ │ │  First we’re going to need to import the Cython magic function to IPython:
│ │ │ │  In [7]: %load_ext Cython
│ │ │ │  Now, let’s simply copy our functions over to Cython:
│ │ │ │  In [8]: %%cython
│ │ │ │ @@ -146,15 +146,15 @@
│ │ │ │     ...:     dx = (b - a) / N
│ │ │ │     ...:     for i in range(N):
│ │ │ │     ...:         s += f_plain(a + i * dx)
│ │ │ │     ...:     return s * dx
│ │ │ │     ...:
│ │ │ │  In [9]: %timeit df.apply(lambda x: integrate_f_plain(x["a"], x["b"], x["N"]),
│ │ │ │  axis=1)
│ │ │ │ -67.8 ms +- 13.3 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +58.5 ms +- 2.13 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 +166,35 @@
│ │ │ │     ....:     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)
│ │ │ │ -7.61 ms +- 469 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +6.66 ms +- 131 us per loop (mean +- std. dev. of 7 runs, 100 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 0.023 seconds
│ │ │ │ +         52523 function calls (52505 primitive calls) in 0.021 seconds
│ │ │ │  
│ │ │ │     Ordered by: internal time
│ │ │ │     List reduced from 157 to 4 due to restriction <4>
│ │ │ │  
│ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ -     3000    0.004    0.000    0.015    0.000 series.py:1095(__getitem__)
│ │ │ │ -     3000    0.003    0.000    0.007    0.000 series.py:1220(_get_value)
│ │ │ │ -     3000    0.002    0.000    0.003    0.000 base.py:3777(get_loc)
│ │ │ │ +     3000    0.004    0.000    0.014    0.000 series.py:1095(__getitem__)
│ │ │ │ +     3000    0.002    0.000    0.006    0.000 series.py:1220(_get_value)
│ │ │ │      16098    0.002    0.000    0.003    0.000 {built-in method
│ │ │ │  builtins.isinstance}
│ │ │ │ +     3000    0.002    0.000    0.002    0.000 base.py:3777(get_loc)
│ │ │ │  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
│ │ │ │ @@ -235,15 +235,15 @@
│ │ │ │  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())
│ │ │ │ -930 us +- 30.9 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │ +899 us +- 2.63 us per loop (mean +- std. dev. of 7 runs, 1,000 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 0.001 seconds
│ │ │ │ @@ -298,15 +298,15 @@
│ │ │ │  /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())
│ │ │ │ -659 us +- 167 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │ +650 us +- 2.36 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
│ │ │ │ @@ -609,17 +609,17 @@
│ │ │ │  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
│ │ │ │ -9.01 ms +- 448 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +10.3 ms +- 694 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │  In [41]: %timeit pd.eval("df1 + df2 + df3 + df4", engine="python")
│ │ │ │ -12.3 ms +- 754 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +10.1 ms +- 441 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]:
│ │ │ │ @@ -716,29 +716,29 @@
│ │ │ │  _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
│ │ │ │ -8.83 ms +- 443 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +9.27 ms +- 268 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │  In [61]: %timeit pd.eval("df1 + df2 + df3 + df4")
│ │ │ │ -5.63 ms +- 684 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +3.64 ms +- 159 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)
│ │ │ │ -10.7 ms +- 903 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +4.3 ms +- 275 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)")
│ │ │ │ -10.5 ms +- 682 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +5.26 ms +- 302 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
│ │ │ │ -22.8 ms +- 443 us per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +15.1 ms +- 1.4 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │  In [66]: %timeit pd.eval("df1 + df2 + df3 + df4 + s")
│ │ │ │ -8.5 ms +- 241 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +3.53 ms +- 105 us 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 303 us, sys: 62 us, total: 365 us
│ │ │ -Wall time: 370 us
│ │ │ +CPU times: user 273 us, sys: 169 us, total: 442 us
│ │ │ +Wall time: 449 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 303 us, sys: 62 us, total: 365 us │ │ │ │ -Wall time: 370 us │ │ │ │ +CPU times: user 273 us, sys: 169 us, total: 442 us │ │ │ │ +Wall time: 449 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': '2025-07-24T21:37:10.298065Z', " │ │ │ │ │┄ "'iopub.status.busy': '2025-07-24T21:37:10.297897Z', 'iopub.status.idle': " │ │ │ │ │┄ "'2025-07-24T21:37:10.581053Z', 'shell.execute_reply': " │ │ │ │ │┄ "'2025-07-24T21:37:10.580399Z'}}}, 3: {'metadata': {'execution': " │ │ │ │ │┄ "{'iopub.execute_input': '2025-07-24T21:37:10.583119Z', 'iopub.status.busy': " │ │ │ │ │┄ "'2025-07-24T21:37:10.582897Z', 'iopub.status.idle': '2025-07-24T21:37:1 […] │ │ │ │ │ @@ -39,18 +39,18 @@ │ │ │ │ │ ] │ │ │ │ │ }, │ │ │ │ │ { │ │ │ │ │ "cell_type": "code", │ │ │ │ │ "execution_count": 1, │ │ │ │ │ "metadata": { │ │ │ │ │ "execution": { │ │ │ │ │ - "iopub.execute_input": "2025-03-29T16:35:19.862775Z", │ │ │ │ │ - "iopub.status.busy": "2025-03-29T16:35:19.862389Z", │ │ │ │ │ - "iopub.status.idle": "2025-03-29T16:35:20.384606Z", │ │ │ │ │ - "shell.execute_reply": "2025-03-29T16:35:20.383343Z" │ │ │ │ │ + "iopub.execute_input": "2025-07-24T21:37:10.298065Z", │ │ │ │ │ + "iopub.status.busy": "2025-07-24T21:37:10.297897Z", │ │ │ │ │ + "iopub.status.idle": "2025-07-24T21:37:10.581053Z", │ │ │ │ │ + "shell.execute_reply": "2025-07-24T21:37:10.580399Z" │ │ │ │ │ }, │ │ │ │ │ "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": "2025-03-29T16:35:20.388545Z", │ │ │ │ │ - "iopub.status.busy": "2025-03-29T16:35:20.387589Z", │ │ │ │ │ - "iopub.status.idle": "2025-03-29T16:35:20.773571Z", │ │ │ │ │ - "shell.execute_reply": "2025-03-29T16:35:20.772501Z" │ │ │ │ │ + "iopub.execute_input": "2025-07-24T21:37:10.583119Z", │ │ │ │ │ + "iopub.status.busy": "2025-07-24T21:37:10.582897Z", │ │ │ │ │ + "iopub.status.idle": "2025-07-24T21:37:10.800115Z", │ │ │ │ │ + "shell.execute_reply": "2025-07-24T21:37:10.799498Z" │ │ │ │ │ } │ │ │ │ │ }, │ │ │ │ │ "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": "2025-03-29T16:35:20.778136Z", │ │ │ │ │ - "iopub.status.busy": "2025-03-29T16:35:20.776764Z", │ │ │ │ │ - "iopub.status.idle": "2025-03-29T16:35:20.850331Z", │ │ │ │ │ - "shell.execute_reply": "2025-03-29T16:35:20.849409Z" │ │ │ │ │ + "iopub.execute_input": "2025-07-24T21:37:10.802293Z", │ │ │ │ │ + "iopub.status.busy": "2025-07-24T21:37:10.802062Z", │ │ │ │ │ + "iopub.status.idle": "2025-07-24T21:37:10.843256Z", │ │ │ │ │ + "shell.execute_reply": "2025-07-24T21:37:10.842723Z" │ │ │ │ │ }, │ │ │ │ │ "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": "2025-03-29T16:35:20.854113Z", │ │ │ │ │ - "iopub.status.busy": "2025-03-29T16:35:20.853164Z", │ │ │ │ │ - "iopub.status.idle": "2025-03-29T16:35:20.865750Z", │ │ │ │ │ - "shell.execute_reply": "2025-03-29T16:35:20.864889Z" │ │ │ │ │ + "iopub.execute_input": "2025-07-24T21:37:10.845103Z", │ │ │ │ │ + "iopub.status.busy": "2025-07-24T21:37:10.844887Z", │ │ │ │ │ + "iopub.status.idle": "2025-07-24T21:37:10.852238Z", │ │ │ │ │ + "shell.execute_reply": "2025-07-24T21:37:10.851708Z" │ │ │ │ │ } │ │ │ │ │ }, │ │ │ │ │ "outputs": [ │ │ │ │ │ { │ │ │ │ │ "data": { │ │ │ │ │ "text/html": [ │ │ │ │ │ "