--- /srv/rebuilderd/tmp/rebuilderdsWJ7cU/inputs/python-pandas-doc_2.2.3+dfsg-9_all.deb
+++ /srv/rebuilderd/tmp/rebuilderdsWJ7cU/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   147380 2025-03-29 13:01:52.000000 control.tar.xz
│ +-rw-r--r--   0        0        0 10647380 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 <debian-science-maintainers@lists.alioth.debian.org>
│ │ │ -Installed-Size: 209900
│ │ │ +Installed-Size: 209907
│ │ │  Depends: libjs-sphinxdoc (>= 8.1), libjs-mathjax
│ │ │  Suggests: python3-pandas
│ │ │  Section: doc
│ │ │  Priority: optional
│ │ │  Multi-Arch: foreign
│ │ │  Homepage: https://pandas.pydata.org/
│ │ │  Description: data structures for "relational" or "labeled" data - documentation
│ │ ├── ./md5sums
│ │ │ ├── ./md5sums
│ │ │ │┄ Files differ
├── data.tar.xz
│ ├── data.tar
│ │ ├── file list
│ │ │ @@ -6256,84 +6256,84 @@
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│ │ │  -rw-r--r--   0 root         (0) root         (0)    35785 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.23.4.html
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│ │ │ @@ -6367,15 +6367,15 @@
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│ │ │  -rw-r--r--   0 root         (0) root         (0)    44860 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v1.3.4.html
│ │ │  -rw-r--r--   0 root         (0) root         (0)    38080 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v1.3.5.html
│ │ │  -rw-r--r--   0 root         (0) root         (0)   360923 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v1.4.0.html
│ │ │  -rw-r--r--   0 root         (0) root         (0)    44033 2025-03-29 13:01:52.000000 ./usr/share/doc/python-pandas-doc/html/whatsnew/v1.4.1.html
│ │ ├── ./usr/share/doc/python-pandas-doc/html/getting_started/intro_tutorials/07_reshape_table_layout.html
│ │ │┄ Ordering differences only
│ │ │ @@ -636,16 +636,16 @@
│ │ │  <ul class="task-bullet">
│ │ │      <li><p>I want to sort the Titanic data according to the age of the passengers.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [6]: </span><span class="n">titanic</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;Age&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
│ │ │  <span class="gh">Out[6]: </span>
│ │ │  <span class="go">     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked</span>
│ │ │  <span class="go">803          804         1       3  ...   8.5167   NaN         C</span>
│ │ │  <span class="go">755          756         1       2  ...  14.5000   NaN         S</span>
│ │ │ -<span class="go">644          645         1       3  ...  19.2583   NaN         C</span>
│ │ │  <span class="go">469          470         1       3  ...  19.2583   NaN         C</span>
│ │ │ +<span class="go">644          645         1       3  ...  19.2583   NaN         C</span>
│ │ │  <span class="go">78            79         1       2  ...  29.0000   NaN         S</span>
│ │ │  
│ │ │  <span class="go">[5 rows x 12 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │      </li>
│ │ │  </ul><ul class="task-bullet">
│ │ │ ├── html2text {}
│ │ │ │ @@ -116,16 +116,16 @@
│ │ │ │  ********** SSoorrtt ttaabbllee rroowwss_## **********
│ │ │ │      * I want to sort the Titanic data according to the age of the passengers.
│ │ │ │        In [6]: titanic.sort_values(by="Age").head()
│ │ │ │        Out[6]:
│ │ │ │             PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked
│ │ │ │        803          804         1       3  ...   8.5167   NaN         C
│ │ │ │        755          756         1       2  ...  14.5000   NaN         S
│ │ │ │ -      644          645         1       3  ...  19.2583   NaN         C
│ │ │ │        469          470         1       3  ...  19.2583   NaN         C
│ │ │ │ +      644          645         1       3  ...  19.2583   NaN         C
│ │ │ │        78            79         1       2  ...  29.0000   NaN         S
│ │ │ │  
│ │ │ │        [5 rows x 12 columns]
│ │ │ │      * I want to sort the Titanic data according to the cabin class and age in
│ │ │ │        descending order.
│ │ │ │        In [7]: titanic.sort_values(by=['Pclass', 'Age'], ascending=False).head()
│ │ │ │        Out[7]:
│ │ ├── ./usr/share/doc/python-pandas-doc/html/searchindex.js
│ │ │ ├── js-beautify {}
│ │ │ │ @@ -21500,25 +21500,25 @@
│ │ │ │          "00180": 2294,
│ │ │ │          "002": [2193, 2264],
│ │ │ │          "002000": 2232,
│ │ │ │          "002040": 2235,
│ │ │ │          "002118": [2230, 2231],
│ │ │ │          "002653": 2207,
│ │ │ │          "002846": 2229,
│ │ │ │ -        "003": [2185, 2193, 2235],
│ │ │ │ +        "003": [2185, 2235],
│ │ │ │          "003144": 2210,
│ │ │ │          "003337": 2207,
│ │ │ │          "003494": 15,
│ │ │ │          "003507": [2209, 2218],
│ │ │ │          "003556": 2207,
│ │ │ │          "00360": 2294,
│ │ │ │          "003733": 2207,
│ │ │ │          "003932": 2216,
│ │ │ │          "003945": 2210,
│ │ │ │ -        "004": [2186, 2193, 2227],
│ │ │ │ +        "004": [2186, 2227],
│ │ │ │          "004000": 2232,
│ │ │ │          "004005006": [287, 939],
│ │ │ │          "004054": 2229,
│ │ │ │          "004091": [2204, 2257],
│ │ │ │          "004127": 2207,
│ │ │ │          "004194": 2186,
│ │ │ │          "004201": 2186,
│ │ │ │ @@ -21542,15 +21542,14 @@
│ │ │ │          "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 +21568,15 @@
│ │ │ │          "009673": 2195,
│ │ │ │          "009783": 2207,
│ │ │ │          "009797": 2186,
│ │ │ │          "009826": [102, 1158, 2205],
│ │ │ │          "009920": [2184, 2195, 2214],
│ │ │ │          "00am": 2230,
│ │ │ │          "00index": 2218,
│ │ │ │ -        "01": [3, 15, 16, 17, 19, 29, 30, 31, 79, 80, 82, 88, 107, 121, 182, 187, 207, 213, 218, 219, 230, 242, 261, 270, 271, 276, 277, 278, 283, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 298, 299, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 326, 329, 330, 331, 332, 333, 345, 362, 363, 423, 445, 510, 511, 513, 514, 515, 516, 517, 519, 521, 523, 525, 529, 531, 532, 533, 534, 535, 536, 537, 541, 542, 543, 544, 545, 546, 547, 548, 549, 551, 554, 556, 557, 558, 560, 561, 562, 563, 564, 565, 566, 575, 591, 592, 593, 600, 629, 637, 639, 640, 641, 642, 643, 644, 645, 646, 647, 649, 650, 651, 652, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 665, 666, 667, 668, 670, 671, 673, 674, 675, 676, 677, 678, 679, 680, 684, 685, 686, 688, 689, 696, 760, 763, 781, 788, 793, 804, 817, 874, 893, 898, 899, 902, 903, 904, 905, 909, 910, 917, 919, 922, 929, 934, 939, 940, 943, 944, 945, 948, 949, 953, 954, 957, 959, 960, 969, 972, 982, 984, 997, 1000, 1001, 1003, 1004, 1005, 1011, 1014, 1016, 1017, 1020, 1021, 1024, 1051, 1075, 1078, 1106, 1118, 1122, 1141, 1144, 1145, 1147, 1157, 1164, 1170, 1171, 1176, 1180, 1185, 1192, 1195, 1197, 1206, 1214, 1221, 1227, 1228, 1233, 1239, 1245, 1246, 1253, 1256, 1258, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1277, 1278, 1279, 1280, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1344, 1345, 1367, 1391, 1392, 1393, 1436, 1447, 1452, 1475, 1488, 1490, 1498, 1500, 1501, 1506, 1524, 1542, 1560, 1620, 1699, 1720, 1741, 1793, 1815, 1857, 1930, 1947, 1982, 2036, 2054, 2090, 2108, 2127, 2163, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2198, 2199, 2200, 2201, 2202, 2204, 2205, 2206, 2207, 2209, 2210, 2212, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2225, 2226, 2228, 2229, 2230, 2231, 2232, 2235, 2238, 2240, 2241, 2246, 2249, 2261, 2264, 2265, 2271, 2283, 2289, 2294, 2298, 2302, 2307],
│ │ │ │ +        "01": [3, 15, 16, 17, 19, 29, 30, 31, 79, 80, 82, 88, 107, 121, 182, 187, 207, 213, 218, 219, 230, 242, 261, 270, 271, 276, 277, 278, 283, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 298, 299, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 326, 329, 330, 331, 332, 333, 345, 362, 363, 423, 445, 510, 511, 513, 514, 515, 516, 517, 519, 521, 523, 525, 529, 531, 532, 533, 534, 535, 536, 537, 541, 542, 543, 544, 545, 546, 547, 548, 549, 551, 554, 556, 557, 558, 560, 561, 562, 563, 564, 565, 566, 575, 591, 592, 593, 600, 629, 637, 639, 640, 641, 642, 643, 644, 645, 646, 647, 649, 650, 651, 652, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 665, 666, 667, 668, 670, 671, 673, 674, 675, 676, 677, 678, 679, 680, 684, 685, 686, 688, 689, 696, 760, 763, 781, 788, 793, 804, 817, 874, 893, 898, 899, 902, 903, 904, 905, 909, 910, 917, 919, 922, 929, 934, 939, 940, 943, 944, 945, 948, 949, 953, 954, 957, 959, 960, 969, 972, 982, 984, 997, 1000, 1001, 1003, 1004, 1005, 1011, 1014, 1016, 1017, 1020, 1021, 1024, 1051, 1075, 1078, 1106, 1118, 1122, 1141, 1144, 1145, 1147, 1157, 1164, 1170, 1171, 1176, 1180, 1185, 1192, 1195, 1197, 1206, 1214, 1221, 1227, 1228, 1233, 1239, 1245, 1246, 1253, 1256, 1258, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1277, 1278, 1279, 1280, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1344, 1345, 1367, 1391, 1392, 1393, 1436, 1447, 1452, 1475, 1488, 1490, 1498, 1500, 1501, 1506, 1524, 1542, 1560, 1620, 1699, 1720, 1741, 1793, 1815, 1857, 1930, 1947, 1982, 2036, 2054, 2090, 2108, 2127, 2163, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2198, 2199, 2200, 2201, 2202, 2204, 2205, 2206, 2207, 2209, 2210, 2212, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2225, 2226, 2228, 2229, 2230, 2231, 2232, 2235, 2238, 2240, 2241, 2246, 2249, 2261, 2264, 2265, 2271, 2283, 2289, 2294, 2298, 2302, 2307],
│ │ │ │          "0100": [575, 893, 957, 970, 997, 1004, 1014, 1016, 1020, 1021, 1498, 2186, 2199, 2210, 2246, 2271],
│ │ │ │          "010000": [954, 1894],
│ │ │ │          "010010012": [923, 2209],
│ │ │ │          "010026": 2191,
│ │ │ │          "010081": 15,
│ │ │ │          "010165": 2199,
│ │ │ │          "010589": 2193,
│ │ │ │ @@ -21590,14 +21589,15 @@
│ │ │ │          "011374": 2195,
│ │ │ │          "011470": 2207,
│ │ │ │          "011736": 2186,
│ │ │ │          "011829": 2207,
│ │ │ │          "01183": 2229,
│ │ │ │          "011860": [182, 760],
│ │ │ │          "011975": 2207,
│ │ │ │ +        "012": 2193,
│ │ │ │          "012108": 2207,
│ │ │ │          "012299": 2207,
│ │ │ │          "0123456789123456": [2164, 2165],
│ │ │ │          "012549": 2207,
│ │ │ │          "012694": 2199,
│ │ │ │          "012922": 2219,
│ │ │ │          "013086": 15,
│ │ │ │ @@ -21632,22 +21632,23 @@
│ │ │ │          "015988": 2186,
│ │ │ │          "016009": 15,
│ │ │ │          "016287": 2210,
│ │ │ │          "016331": 2210,
│ │ │ │          "016424": [16, 19],
│ │ │ │          "016692": [2184, 2195, 2214],
│ │ │ │          "01685762652715874": [624, 1215],
│ │ │ │ +        "017": 2193,
│ │ │ │          "017106": 2207,
│ │ │ │          "017118": 2199,
│ │ │ │          "017152": 2186,
│ │ │ │          "017263": 2207,
│ │ │ │          "017276": 2191,
│ │ │ │          "017587": [2184, 2195, 2214],
│ │ │ │          "017796": 2207,
│ │ │ │ -        "018": 2199,
│ │ │ │ +        "018": [2193, 2199],
│ │ │ │          "018007": 2207,
│ │ │ │          "018117": 2191,
│ │ │ │          "018193": 2207,
│ │ │ │          "018409": 2207,
│ │ │ │          "018601": [2184, 2214],
│ │ │ │          "018808": 2207,
│ │ │ │          "018904": 2207,
│ │ │ │ @@ -21677,14 +21678,15 @@
│ │ │ │          "021244": 2207,
│ │ │ │          "021255": 2230,
│ │ │ │          "021292": 2186,
│ │ │ │          "021377": 2207,
│ │ │ │          "021382": 2184,
│ │ │ │          "021499": 2186,
│ │ │ │          "02155": 30,
│ │ │ │ +        "022": 2193,
│ │ │ │          "022070": 2184,
│ │ │ │          "022196": 2207,
│ │ │ │          "022777": 2207,
│ │ │ │          "023": [1447, 2193, 2200, 2232],
│ │ │ │          "023100": 2195,
│ │ │ │          "023167": 15,
│ │ │ │          "023202": 2199,
│ │ │ │ @@ -21716,14 +21718,15 @@
│ │ │ │          "026158": 2210,
│ │ │ │          "026220": 2191,
│ │ │ │          "026437": 2197,
│ │ │ │          "026458": 2216,
│ │ │ │          "0266708": 2202,
│ │ │ │          "026692": 2207,
│ │ │ │          "0267": 2202,
│ │ │ │ +        "027": 2193,
│ │ │ │          "027496": 2207,
│ │ │ │          "027778": [69, 109, 129, 171, 173, 199, 204, 206, 215, 216, 217, 220, 221, 222, 244, 275],
│ │ │ │          "028096": 2210,
│ │ │ │          "028152": 2207,
│ │ │ │          "028166": 15,
│ │ │ │          "028182": 2207,
│ │ │ │          "028578": 2207,
│ │ │ │ @@ -21737,15 +21740,15 @@
│ │ │ │          "029587": 2193,
│ │ │ │          "029630": 2195,
│ │ │ │          "029766": 2197,
│ │ │ │          "02d": 2205,
│ │ │ │          "02t00": [2199, 2210, 2235, 2261],
│ │ │ │          "02t02": 2235,
│ │ │ │          "02t05": [909, 2210],
│ │ │ │ -        "03": [26, 27, 29, 31, 79, 80, 82, 121, 182, 207, 213, 218, 219, 230, 264, 278, 286, 287, 290, 291, 292, 294, 296, 298, 301, 302, 304, 305, 306, 307, 310, 313, 314, 318, 321, 322, 326, 330, 331, 332, 362, 420, 423, 512, 517, 518, 519, 522, 524, 530, 534, 536, 543, 544, 545, 546, 547, 548, 549, 551, 557, 558, 562, 563, 564, 565, 566, 591, 592, 593, 637, 640, 642, 643, 644, 646, 651, 652, 656, 657, 658, 659, 666, 667, 673, 675, 677, 680, 681, 685, 686, 688, 696, 760, 781, 788, 793, 799, 804, 904, 939, 941, 943, 944, 945, 948, 949, 953, 955, 956, 957, 958, 962, 970, 973, 983, 990, 992, 995, 997, 999, 1002, 1006, 1007, 1008, 1009, 1013, 1014, 1018, 1051, 1075, 1145, 1169, 1192, 1226, 1253, 1269, 1270, 1276, 1280, 1289, 1344, 1393, 1447, 1452, 1489, 1498, 1500, 1506, 1542, 1699, 1741, 1793, 1815, 1982, 2000, 2108, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2204, 2205, 2207, 2209, 2210, 2212, 2214, 2215, 2216, 2218, 2219, 2220, 2222, 2225, 2226, 2228, 2229, 2230, 2231, 2232, 2235, 2238, 2240, 2241, 2246, 2261, 2264, 2271, 2283, 2298, 2302],
│ │ │ │ +        "03": [26, 27, 29, 31, 79, 80, 82, 121, 182, 207, 213, 218, 219, 230, 264, 278, 286, 287, 290, 291, 292, 294, 296, 298, 301, 302, 304, 305, 306, 307, 310, 313, 314, 318, 321, 322, 326, 330, 331, 332, 362, 420, 423, 512, 517, 518, 519, 522, 524, 530, 534, 536, 543, 544, 545, 546, 547, 548, 549, 551, 557, 558, 562, 563, 564, 565, 566, 591, 592, 593, 637, 640, 642, 643, 644, 646, 651, 652, 656, 657, 658, 659, 666, 667, 673, 675, 677, 680, 681, 685, 686, 688, 696, 760, 781, 788, 793, 799, 804, 904, 939, 941, 943, 944, 945, 948, 949, 953, 955, 956, 957, 958, 962, 970, 973, 983, 990, 992, 995, 997, 999, 1002, 1006, 1007, 1008, 1009, 1013, 1014, 1018, 1051, 1075, 1145, 1169, 1192, 1226, 1253, 1269, 1270, 1276, 1280, 1289, 1344, 1393, 1447, 1452, 1489, 1498, 1500, 1506, 1542, 1699, 1741, 1793, 1815, 1982, 2000, 2108, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2204, 2205, 2207, 2209, 2210, 2212, 2214, 2215, 2216, 2218, 2219, 2220, 2222, 2225, 2226, 2228, 2229, 2230, 2231, 2232, 2235, 2238, 2240, 2241, 2246, 2261, 2264, 2271, 2283, 2298, 2302],
│ │ │ │          "030": [1447, 2200, 2232],
│ │ │ │          "0300": 2271,
│ │ │ │          "030000": 18,
│ │ │ │          "030015": 2207,
│ │ │ │          "030045": 2186,
│ │ │ │          "030178": 2207,
│ │ │ │          "030388": 2207,
│ │ │ │ @@ -21755,14 +21758,15 @@
│ │ │ │          "030874": 2219,
│ │ │ │          "030876": 2186,
│ │ │ │          "031296": 2207,
│ │ │ │          "031393": 2228,
│ │ │ │          "03162": 2257,
│ │ │ │          "031833": 2207,
│ │ │ │          "031903": 2207,
│ │ │ │ +        "032": 2193,
│ │ │ │          "032059": 2191,
│ │ │ │          "032084": 2191,
│ │ │ │          "032131": 2207,
│ │ │ │          "032332": 2207,
│ │ │ │          "032628": 2207,
│ │ │ │          "032652": 2186,
│ │ │ │          "032685": 2207,
│ │ │ │ @@ -21863,15 +21867,14 @@
│ │ │ │          "046": 2207,
│ │ │ │          "046044": 2199,
│ │ │ │          "046582": 2207,
│ │ │ │          "046611": 2210,
│ │ │ │          "046731": 2207,
│ │ │ │          "046805": 2207,
│ │ │ │          "046882": 2199,
│ │ │ │ -        "047": 2193,
│ │ │ │          "047046": 2210,
│ │ │ │          "047107": 2207,
│ │ │ │          "047208": 2199,
│ │ │ │          "047551": 2191,
│ │ │ │          "047578": 2186,
│ │ │ │          "047606": 2186,
│ │ │ │          "047609": 2184,
│ │ │ │ @@ -21914,14 +21917,15 @@
│ │ │ │          "052021": 2210,
│ │ │ │          "052127": 2207,
│ │ │ │          "052580": 2195,
│ │ │ │          "052589": 2193,
│ │ │ │          "052599": 2186,
│ │ │ │          "052721": 2219,
│ │ │ │          "052849": 2212,
│ │ │ │ +        "053": 2193,
│ │ │ │          "0530": [1498, 2246],
│ │ │ │          "053136": 2191,
│ │ │ │          "053365": [182, 760],
│ │ │ │          "053667": 2207,
│ │ │ │          "053768": 2199,
│ │ │ │          "053785": 2219,
│ │ │ │          "054325": 2191,
│ │ │ │ @@ -22138,37 +22142,39 @@
│ │ │ │          "085631": [2166, 2218, 2229],
│ │ │ │          "0856630": 2202,
│ │ │ │          "0857": 2202,
│ │ │ │          "085701": 2207,
│ │ │ │          "085827": 2207,
│ │ │ │          "085908": 2207,
│ │ │ │          "085954": 2210,
│ │ │ │ +        "086": 2193,
│ │ │ │          "086037": 2207,
│ │ │ │          "086843": 2184,
│ │ │ │          "087183": 2199,
│ │ │ │          "087302": 2184,
│ │ │ │          "0874": [2184, 2186, 2191],
│ │ │ │          "087401": [2184, 2185, 2186, 2191, 2197, 2199, 2202, 2204, 2210, 2214, 2215, 2216, 2218, 2225, 2226, 2231, 2241, 2264],
│ │ │ │          "0875": 2202,
│ │ │ │          "088060": 2210,
│ │ │ │          "088224": 2199,
│ │ │ │          "088259": 2186,
│ │ │ │          "088417": 15,
│ │ │ │          "088563": 2199,
│ │ │ │          "088787": 2215,
│ │ │ │ +        "089": 2193,
│ │ │ │          "089069": 2222,
│ │ │ │          "089075": 2197,
│ │ │ │          "089227": 2207,
│ │ │ │          "089329": [2184, 2195, 2214],
│ │ │ │          "089354": 2235,
│ │ │ │          "089589": 2207,
│ │ │ │          "089641": 2207,
│ │ │ │          "089759": 2186,
│ │ │ │          "08t00": 2261,
│ │ │ │ -        "09": [29, 80, 84, 88, 107, 127, 157, 213, 218, 276, 277, 278, 322, 330, 331, 345, 562, 592, 595, 600, 629, 637, 677, 685, 686, 703, 732, 788, 793, 902, 903, 904, 987, 1008, 1075, 1164, 1221, 1344, 1452, 1489, 1501, 1506, 1524, 1578, 1598, 1657, 1677, 1699, 1720, 1741, 1758, 1839, 1876, 1894, 1912, 1964, 2018, 2184, 2185, 2186, 2191, 2195, 2197, 2199, 2201, 2204, 2205, 2207, 2209, 2210, 2212, 2213, 2218, 2220, 2221, 2222, 2226, 2228, 2230, 2231, 2232, 2233, 2235, 2238, 2249, 2250, 2261, 2271],
│ │ │ │ +        "09": [29, 80, 84, 88, 107, 127, 157, 213, 218, 276, 277, 278, 322, 330, 331, 345, 562, 592, 595, 600, 629, 637, 677, 685, 686, 703, 732, 788, 793, 902, 903, 904, 987, 1008, 1075, 1164, 1221, 1344, 1452, 1489, 1501, 1506, 1524, 1578, 1598, 1657, 1677, 1699, 1720, 1741, 1758, 1839, 1876, 1894, 1912, 1964, 2018, 2184, 2185, 2186, 2191, 2193, 2195, 2197, 2199, 2201, 2204, 2205, 2207, 2209, 2210, 2212, 2213, 2218, 2220, 2221, 2222, 2226, 2228, 2230, 2231, 2232, 2233, 2235, 2238, 2249, 2250, 2261, 2271],
│ │ │ │          "0900": [956, 1013],
│ │ │ │          "090118": 2219,
│ │ │ │          "090255": 2197,
│ │ │ │          "090310": 2207,
│ │ │ │          "090711": 2207,
│ │ │ │          "091": [2186, 2227],
│ │ │ │          "091000": 2207,
│ │ │ │ @@ -22250,20 +22256,20 @@
│ │ │ │          "0n": [1489, 2298],
│ │ │ │          "0px": 2207,
│ │ │ │          "0rc0": 13,
│ │ │ │          "0th": [26, 249, 882, 1202, 2185, 2197, 2199, 2235],
│ │ │ │          "0x00": 2294,
│ │ │ │          "0x40": 2294,
│ │ │ │          "0x7efd0c0b0690": 3,
│ │ │ │ -        "0x7f26874ba6d0": 2230,
│ │ │ │ -        "0x7f268af58220": 2210,
│ │ │ │ -        "0x7f26a60a7930": 2199,
│ │ │ │ -        "0x7f26af3dba30": 2197,
│ │ │ │ -        "0x7f26bd415d10": 2246,
│ │ │ │ -        "0x7f26df0501d0": 2195,
│ │ │ │ +        "0x7fdbe6e3e030": 2199,
│ │ │ │ +        "0x7fdbf6a465f0": 2230,
│ │ │ │ +        "0x7fdc00217250": 2197,
│ │ │ │ +        "0x7fdc018c3b90": 2195,
│ │ │ │ +        "0x7fdc1fc906e0": 2246,
│ │ │ │ +        "0x7fdc24e62de0": 2210,
│ │ │ │          "1": [1, 2, 4, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 39, 42, 44, 46, 49, 54, 56, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 134, 135, 136, 137, 138, 139, 140, 141, 143, 144, 145, 146, 148, 149, 151, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 177, 178, 180, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 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1974, 1975, 1976, 1982, 1983, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 2000, 2001, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2018, 2019, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2036, 2037, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2054, 2055, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2073, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2090, 2091, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100, 2101, 2108, 2109, 2111, 2112, 2113, 2114, 2115, 2116, 2117, 2118, 2119, 2127, 2128, 2130, 2131, 2132, 2133, 2134, 2135, 2136, 2137, 2138, 2145, 2146, 2148, 2149, 2150, 2151, 2152, 2153, 2154, 2155, 2156, 2163, 2164, 2165, 2166, 2184, 2185, 2186, 2187, 2188, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2198, 2199, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2208, 2209, 2210, 2211, 2212, 2214, 2216, 2217, 2218, 2220, 2222, 2224, 2225, 2227, 2228, 2230, 2232, 2238, 2240, 2241, 2243, 2245, 2246, 2249, 2257, 2259, 2260, 2263, 2298, 2307, 2309, 2310],
│ │ │ │          "10": [2, 3, 5, 6, 9, 10, 15, 16, 17, 18, 19, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 68, 69, 74, 80, 83, 84, 85, 88, 91, 94, 97, 98, 102, 105, 109, 111, 113, 119, 120, 121, 129, 133, 137, 138, 139, 140, 142, 144, 160, 163, 171, 173, 187, 188, 189, 190, 192, 193, 199, 202, 203, 204, 206, 207, 212, 213, 215, 216, 217, 220, 221, 222, 223, 228, 230, 234, 244, 258, 265, 268, 275, 276, 278, 284, 286, 288, 289, 293, 295, 296, 298, 300, 302, 316, 317, 318, 322, 323, 324, 329, 330, 331, 345, 395, 423, 427, 440, 445, 509, 514, 516, 534, 536, 544, 546, 551, 554, 556, 560, 562, 568, 569, 570, 571, 572, 577, 583, 592, 594, 595, 596, 600, 620, 621, 627, 635, 639, 641, 645, 647, 648, 649, 650, 652, 670, 671, 673, 677, 678, 679, 681, 684, 685, 686, 695, 696, 708, 713, 714, 738, 741, 763, 764, 765, 766, 768, 781, 787, 788, 798, 804, 808, 836, 837, 838, 839, 840, 841, 842, 843, 844, 849, 852, 863, 868, 874, 889, 895, 902, 904, 912, 923, 940, 942, 943, 944, 948, 957, 959, 960, 970, 982, 984, 995, 997, 1001, 1003, 1004, 1005, 1011, 1016, 1020, 1021, 1069, 1071, 1072, 1075, 1109, 1154, 1158, 1162, 1163, 1173, 1174, 1175, 1180, 1185, 1189, 1195, 1200, 1205, 1219, 1220, 1230, 1239, 1246, 1250, 1256, 1261, 1264, 1267, 1284, 1288, 1291, 1292, 1294, 1297, 1298, 1299, 1306, 1308, 1319, 1324, 1343, 1344, 1345, 1350, 1367, 1387, 1391, 1403, 1411, 1416, 1418, 1420, 1421, 1440, 1447, 1451, 1452, 1458, 1462, 1467, 1473, 1478, 1479, 1482, 1485, 1488, 1490, 1491, 1498, 1598, 1657, 1677, 1699, 1720, 1741, 1758, 1894, 1912, 2018, 2185, 2186, 2188, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2198, 2199, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2224, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2234, 2235, 2238, 2240, 2241, 2246, 2249, 2254, 2257, 2260, 2261, 2264, 2265, 2271, 2277, 2283, 2289, 2290, 2294, 2298, 2302, 2307, 2308],
│ │ │ │          "100": [3, 15, 17, 22, 30, 68, 97, 98, 111, 118, 132, 135, 141, 142, 145, 159, 161, 175, 182, 192, 202, 207, 212, 213, 233, 273, 303, 345, 359, 360, 427, 577, 587, 588, 620, 621, 655, 709, 717, 760, 781, 787, 788, 900, 1345, 1391, 1398, 1447, 1457, 1472, 1473, 1488, 1490, 2184, 2185, 2186, 2188, 2190, 2191, 2193, 2194, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2223, 2225, 2226, 2230, 2231, 2232, 2235, 2241, 2242, 2246, 2249, 2302, 2307],
│ │ │ │          "1000": [9, 10, 15, 24, 25, 28, 29, 32, 102, 141, 183, 191, 193, 194, 427, 717, 761, 767, 768, 769, 874, 1154, 1158, 1456, 1465, 1467, 1876, 1964, 2184, 2185, 2186, 2188, 2193, 2195, 2199, 2205, 2206, 2207, 2210, 2211, 2220, 2223, 2229, 2230, 2235, 2238, 2246, 2249, 2261, 2294],
│ │ │ │          "10000": [192, 1485, 2185, 2201, 2206, 2210, 2220, 2228, 2266],
│ │ │ │          "100000": [1354, 1372, 2199, 2201, 2210],
│ │ │ │          "1000000": [144, 2199, 2228],
│ │ │ │ @@ -22665,15 +22671,15 @@
│ │ │ │          "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,
│ │ │ │ @@ -22816,15 +22822,15 @@
│ │ │ │          "11370": 2230,
│ │ │ │          "11372": 2229,
│ │ │ │          "11374": 2229,
│ │ │ │          "11376": 2229,
│ │ │ │          "1138": 2192,
│ │ │ │          "11386": [2230, 2241],
│ │ │ │          "1139": 2192,
│ │ │ │ -        "114": [268, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2204, 2207, 2208, 2209, 2210, 2211, 2220, 2230, 2232, 2256],
│ │ │ │ +        "114": [268, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2204, 2205, 2207, 2208, 2209, 2210, 2211, 2220, 2230, 2232, 2256],
│ │ │ │          "1140": 2192,
│ │ │ │          "11402": 2229,
│ │ │ │          "11405": 2229,
│ │ │ │          "11406": 2230,
│ │ │ │          "114060": 2207,
│ │ │ │          "11408": 2229,
│ │ │ │          "11411": 2229,
│ │ │ │ @@ -23133,15 +23139,15 @@
│ │ │ │          "12180": 2230,
│ │ │ │          "12182": 2230,
│ │ │ │          "12185": 2232,
│ │ │ │          "12190": 2238,
│ │ │ │          "121950": 2207,
│ │ │ │          "12198": 2230,
│ │ │ │          "121991": 2207,
│ │ │ │ -        "122": [2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2204, 2207, 2208, 2209, 2210, 2211, 2220, 2232],
│ │ │ │ +        "122": [2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2204, 2207, 2208, 2209, 2210, 2211, 2220, 2232],
│ │ │ │          "1220": [2193, 2298],
│ │ │ │          "12202": 2230,
│ │ │ │          "12203": 2231,
│ │ │ │          "1221": 2298,
│ │ │ │          "12211": 2231,
│ │ │ │          "12213": 2265,
│ │ │ │          "12216": 2232,
│ │ │ │ @@ -23200,15 +23206,15 @@
│ │ │ │          "12386": 2230,
│ │ │ │          "12388": 2232,
│ │ │ │          "1239": [2185, 2191, 2194],
│ │ │ │          "12392": 2238,
│ │ │ │          "12396": 2241,
│ │ │ │          "12397": 2235,
│ │ │ │          "12399": 2232,
│ │ │ │ -        "124": [2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2204, 2208, 2210, 2211, 2220, 2232],
│ │ │ │ +        "124": [2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2204, 2208, 2210, 2211, 2220, 2232],
│ │ │ │          "1240": [2185, 2191, 2194],
│ │ │ │          "12401": 2238,
│ │ │ │          "12405": 2236,
│ │ │ │          "12409": 2230,
│ │ │ │          "12411": 2231,
│ │ │ │          "124124": 2207,
│ │ │ │          "12424": 2232,
│ │ │ │ @@ -23820,16 +23826,14 @@
│ │ │ │          "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 +23850,15 @@
│ │ │ │          "14015": 2235,
│ │ │ │          "14021": 2232,
│ │ │ │          "140249": 2207,
│ │ │ │          "14039": 2232,
│ │ │ │          "14041": 2232,
│ │ │ │          "140528": 2207,
│ │ │ │          "14058": 2232,
│ │ │ │ +        "140583402907440": 2246,
│ │ │ │          "14065": 2232,
│ │ │ │          "14066": 2232,
│ │ │ │          "14068": [2232, 2233],
│ │ │ │          "1408": [2197, 2231],
│ │ │ │          "14086": 2232,
│ │ │ │          "14088": 2232,
│ │ │ │          "1409": [2185, 2197],
│ │ │ │ @@ -24041,15 +24046,15 @@
│ │ │ │          "14684": 2234,
│ │ │ │          "14685": 2234,
│ │ │ │          "14686": 2246,
│ │ │ │          "14687": 2234,
│ │ │ │          "14689": 2234,
│ │ │ │          "14696": 2238,
│ │ │ │          "14699": 2235,
│ │ │ │ -        "147": [2185, 2186, 2188, 2195, 2197, 2199, 2200, 2201, 2210, 2211, 2232],
│ │ │ │ +        "147": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2201, 2210, 2211, 2232],
│ │ │ │          "1470": [16, 17, 18, 19, 2199, 2235],
│ │ │ │          "14704": 2289,
│ │ │ │          "14711": 2238,
│ │ │ │          "14712": 2234,
│ │ │ │          "14714": 2235,
│ │ │ │          "1472": [16, 17, 18, 19, 2199, 2235],
│ │ │ │          "14721": 2235,
│ │ │ │ @@ -24145,15 +24150,15 @@
│ │ │ │          "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, 2193, 2195, 2197, 2199, 2200, 2201, 2204, 2210, 2211],
│ │ │ │          "1500": [2212, 2241, 2246],
│ │ │ │          "15000": [2185, 2220],
│ │ │ │          "15001": 2238,
│ │ │ │          "150025": 2207,
│ │ │ │          "150031": 2207,
│ │ │ │          "150036": [2220, 2230],
│ │ │ │          "15005": 2235,
│ │ │ │ @@ -24404,15 +24409,15 @@
│ │ │ │          "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,
│ │ │ │ @@ -24844,15 +24849,15 @@
│ │ │ │          "17280": 2249,
│ │ │ │          "172800": [683, 2209, 2298],
│ │ │ │          "17284": 2241,
│ │ │ │          "172860": 2228,
│ │ │ │          "17294": 2265,
│ │ │ │          "17295": [2238, 2265],
│ │ │ │          "17296": 2238,
│ │ │ │ -        "173": [15, 2185, 2186, 2188, 2195, 2197, 2199, 2200, 2210, 2211, 2283],
│ │ │ │ +        "173": [15, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2210, 2211, 2283],
│ │ │ │          "173006": 2207,
│ │ │ │          "17301": 2241,
│ │ │ │          "173016": 2207,
│ │ │ │          "17302": 2238,
│ │ │ │          "17304": 2265,
│ │ │ │          "17312": 2241,
│ │ │ │          "173166": 2207,
│ │ │ │ @@ -25036,15 +25041,15 @@
│ │ │ │          "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,
│ │ │ │ @@ -25305,15 +25310,15 @@
│ │ │ │          "1888": [16, 17, 18, 19, 2199, 2235],
│ │ │ │          "188821": 2186,
│ │ │ │          "18885": 2246,
│ │ │ │          "1889": [16, 17, 18, 19, 2199, 2235],
│ │ │ │          "188908": 28,
│ │ │ │          "188928": 2185,
│ │ │ │          "18898": 2241,
│ │ │ │ -        "189": [16, 17, 18, 19, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2210, 2211, 2235, 2256],
│ │ │ │ +        "189": [16, 17, 18, 19, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2210, 2211, 2235, 2256],
│ │ │ │          "1890": [16, 17, 18, 19, 2235],
│ │ │ │          "18901": 2241,
│ │ │ │          "18902": 2265,
│ │ │ │          "189042": 2193,
│ │ │ │          "1891": [16, 17, 18, 19, 2235],
│ │ │ │          "18912": 2241,
│ │ │ │          "18914": 2241,
│ │ │ │ @@ -25337,15 +25342,15 @@
│ │ │ │          "1898": [2199, 2203, 2298],
│ │ │ │          "18980": 2241,
│ │ │ │          "1899": [2199, 2203, 2298],
│ │ │ │          "189952": 2199,
│ │ │ │          "189964": 2207,
│ │ │ │          "18khz": 2199,
│ │ │ │          "19": [15, 17, 18, 19, 22, 25, 26, 27, 28, 29, 30, 31, 32, 139, 140, 213, 345, 788, 953, 1174, 1175, 1274, 1433, 1598, 1657, 1677, 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, 2223, 2225, 2226, 2228, 2230, 2231, 2235, 2238, 2241, 2246, 2249, 2264, 2265, 2271, 2277, 2283, 2289, 2298, 2302],
│ │ │ │ -        "190": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211, 2249],
│ │ │ │ +        "190": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211, 2218, 2249],
│ │ │ │          "1900": [195, 770, 2199, 2203, 2298],
│ │ │ │          "19003": 2241,
│ │ │ │          "19012": 2241,
│ │ │ │          "19016": 2241,
│ │ │ │          "19017": 2241,
│ │ │ │          "19027": 2241,
│ │ │ │          "19029": 2243,
│ │ │ │ @@ -25746,19 +25751,20 @@
│ │ │ │          "2021": [288, 296, 318, 639, 652, 673, 940, 943, 948, 957, 970, 997, 1542, 2201, 2207, 2213, 2277, 2289, 2294],
│ │ │ │          "2022": [5, 22, 523, 525, 528, 537, 982, 1185, 1246, 1288, 1491, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1542, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1560, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1578, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1598, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1620, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1637, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1657, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1677, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1699, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1720, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1758, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1776, 1780, 1781, 1782, 1783, 1784, 1785, 1786, 1793, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1815, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1839, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1857, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1876, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1894, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1912, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1930, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1947, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1964, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1982, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 2000, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2018, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2036, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2054, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2094, 2095, 2096, 2097, 2098, 2099, 2100, 2108, 2112, 2113, 2114, 2115, 2116, 2117, 2118, 2127, 2131, 2132, 2133, 2134, 2135, 2136, 2137, 2145, 2149, 2150, 2151, 2152, 2153, 2154, 2155, 2186, 2203, 2213, 2227, 2298, 2302, 2307],
│ │ │ │          "2022a": 2294,
│ │ │ │          "2023": [34, 270, 298, 301, 320, 363, 511, 519, 526, 533, 543, 544, 545, 546, 547, 548, 549, 551, 554, 555, 556, 557, 558, 560, 563, 564, 565, 566, 567, 651, 894, 898, 954, 959, 960, 982, 984, 1000, 1001, 1003, 1004, 1005, 1011, 1016, 1020, 1021, 1024, 1122, 1141, 1147, 1157, 1170, 1171, 1176, 1180, 1185, 1195, 1197, 1206, 1214, 1227, 1228, 1233, 1239, 1245, 1246, 1256, 1258, 1268, 1271, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1283, 1284, 1285, 1287, 1288, 1290, 1291, 1292, 1293, 1294, 1295, 1297, 1501, 1620, 1930, 2090, 2127, 2145, 2213],
│ │ │ │          "202380": 2207,
│ │ │ │          "20239": [2241, 2265],
│ │ │ │          "2024": [270, 544, 546, 555, 567, 894, 898, 2127, 2213],
│ │ │ │ -        "2025": [36, 544, 546, 555, 567, 894, 898, 2228],
│ │ │ │ +        "2025": [36, 544, 546, 555, 567, 894, 898],
│ │ │ │          "20251": 2307,
│ │ │ │          "2026": 2228,
│ │ │ │          "202602": 2205,
│ │ │ │          "202646": 2230,
│ │ │ │ +        "2027": 2228,
│ │ │ │          "20271": 2241,
│ │ │ │          "202872": [2184, 2214],
│ │ │ │          "202946": 2207,
│ │ │ │          "203": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211, 2231, 2253],
│ │ │ │          "2030": 2265,
│ │ │ │          "20303": 2265,
│ │ │ │          "20306": 2302,
│ │ │ │ @@ -26347,15 +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, 2218],
│ │ │ │ +        "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,
│ │ │ │ @@ -26672,15 +26678,15 @@
│ │ │ │          "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,
│ │ │ │ @@ -27421,14 +27427,15 @@
│ │ │ │          "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,
│ │ │ │ @@ -27926,15 +27933,15 @@
│ │ │ │          "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,
│ │ │ │ @@ -28303,14 +28310,15 @@
│ │ │ │          "322262": 2186,
│ │ │ │          "32240": 2271,
│ │ │ │          "32255": 2271,
│ │ │ │          "32259": 2283,
│ │ │ │          "32262": 2283,
│ │ │ │          "32264": 2294,
│ │ │ │          "32265": 2277,
│ │ │ │ +        "322673": 2228,
│ │ │ │          "32276": 2271,
│ │ │ │          "32287": 2267,
│ │ │ │          "32289": 2271,
│ │ │ │          "323": [2186, 2197, 2199, 2210],
│ │ │ │          "3230": 2217,
│ │ │ │          "3232": 2249,
│ │ │ │          "3232235777": 2241,
│ │ │ │ @@ -28323,14 +28331,15 @@
│ │ │ │          "32385": 2267,
│ │ │ │          "32392": 2277,
│ │ │ │          "32394": 2271,
│ │ │ │          "32395": [2269, 2271],
│ │ │ │          "324": [2186, 2197, 2199, 2210, 2227, 2235, 2255],
│ │ │ │          "32402": 2271,
│ │ │ │          "324071": 2199,
│ │ │ │ +        "324076": 2228,
│ │ │ │          "32408": 2271,
│ │ │ │          "32409": 2269,
│ │ │ │          "32420": 2271,
│ │ │ │          "32423": 2271,
│ │ │ │          "32426": 2271,
│ │ │ │          "32430": 2298,
│ │ │ │          "32432": 2277,
│ │ │ │ @@ -28950,15 +28959,15 @@
│ │ │ │          "35462": 2272,
│ │ │ │          "35471": 2273,
│ │ │ │          "35486": 2272,
│ │ │ │          "35488": 2272,
│ │ │ │          "35490": 2272,
│ │ │ │          "35493": 2272,
│ │ │ │          "35499": 2272,
│ │ │ │ -        "355": [2186, 2197, 2199, 2210, 2255, 2298],
│ │ │ │ +        "355": [2186, 2193, 2197, 2199, 2210, 2255, 2298],
│ │ │ │          "35509": 2275,
│ │ │ │          "355203": 2207,
│ │ │ │          "35521": 2273,
│ │ │ │          "35529": 2277,
│ │ │ │          "35534": 2274,
│ │ │ │          "355392": 2186,
│ │ │ │          "35542": 2277,
│ │ │ │ @@ -29177,15 +29186,15 @@
│ │ │ │          "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 +29285,15 @@
│ │ │ │          "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,
│ │ │ │ @@ -29414,15 +29423,15 @@
│ │ │ │          "37748": 2277,
│ │ │ │          "37750": 2289,
│ │ │ │          "377535": 2186,
│ │ │ │          "37755": 2276,
│ │ │ │          "37758": 2277,
│ │ │ │          "377642": 2210,
│ │ │ │          "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,
│ │ │ │ @@ -30133,15 +30142,15 @@
│ │ │ │          "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,
│ │ │ │ @@ -30918,15 +30927,15 @@
│ │ │ │          "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 +31023,15 @@
│ │ │ │          "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,
│ │ │ │ @@ -31470,15 +31479,15 @@
│ │ │ │          "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],
│ │ │ │ @@ -31801,15 +31810,15 @@
│ │ │ │          "4867681": 2202,
│ │ │ │          "48678": 2295,
│ │ │ │          "4868": 2202,
│ │ │ │          "48681": 2298,
│ │ │ │          "48683": 2295,
│ │ │ │          "486899": 2207,
│ │ │ │          "48692": 2295,
│ │ │ │ -        "487": [2199, 2203, 2210],
│ │ │ │ +        "487": [2199, 2203, 2205, 2210],
│ │ │ │          "48701": 2295,
│ │ │ │          "48706": 2298,
│ │ │ │          "48723": 2298,
│ │ │ │          "48726": 2295,
│ │ │ │          "48733": 2295,
│ │ │ │          "48738": 2295,
│ │ │ │          "48749": 2298,
│ │ │ │ @@ -31885,15 +31894,15 @@
│ │ │ │          "490482": 2199,
│ │ │ │          "49054": 2295,
│ │ │ │          "490671": 2185,
│ │ │ │          "49068": 2298,
│ │ │ │          "490732": 2229,
│ │ │ │          "49075": 2298,
│ │ │ │          "490865": 2206,
│ │ │ │ -        "491": [16, 17, 18, 19, 28, 2199, 2210, 2235, 2249, 2298],
│ │ │ │ +        "491": [16, 17, 18, 19, 28, 2193, 2199, 2210, 2235, 2249, 2298],
│ │ │ │          "49106": 2298,
│ │ │ │          "49108": 2298,
│ │ │ │          "49109": 2298,
│ │ │ │          "49111": 2298,
│ │ │ │          "49121": 2298,
│ │ │ │          "49128": 2298,
│ │ │ │          "49139": 2229,
│ │ │ │ @@ -32536,15 +32545,15 @@
│ │ │ │          "523852": 2207,
│ │ │ │          "523873": 2207,
│ │ │ │          "52388": 2302,
│ │ │ │          "523889": [121, 696, 2212],
│ │ │ │          "523893": 28,
│ │ │ │          "5239": 2239,
│ │ │ │          "52391": 2299,
│ │ │ │ -        "524": 2199,
│ │ │ │ +        "524": [2193, 2199],
│ │ │ │          "52401": 2299,
│ │ │ │          "52404": 2299,
│ │ │ │          "524058": 2207,
│ │ │ │          "52406": 2299,
│ │ │ │          "524182": 2207,
│ │ │ │          "524183": 2207,
│ │ │ │          "52419": 2307,
│ │ │ │ @@ -32627,15 +32636,14 @@
│ │ │ │          "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 +32656,14 @@
│ │ │ │          "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,
│ │ │ │ @@ -33272,15 +33279,15 @@
│ │ │ │          "561757": 2184,
│ │ │ │          "56179": 2306,
│ │ │ │          "56183": 2306,
│ │ │ │          "56189": 2307,
│ │ │ │          "56195": 2307,
│ │ │ │          "561967": 2199,
│ │ │ │          "561982": 2207,
│ │ │ │ -        "562": [1193, 1254, 2199],
│ │ │ │ +        "562": [1193, 1254, 2193, 2199],
│ │ │ │          "56204": 2307,
│ │ │ │          "56205": [68, 577],
│ │ │ │          "56208": 2307,
│ │ │ │          "562167": 2199,
│ │ │ │          "56227": 2307,
│ │ │ │          "56236": 2307,
│ │ │ │          "56243": 2307,
│ │ │ │ @@ -33692,15 +33699,15 @@
│ │ │ │          "600337": 2186,
│ │ │ │          "6004": 2199,
│ │ │ │          "6007": 2219,
│ │ │ │          "600705": 2197,
│ │ │ │          "600794": 2184,
│ │ │ │          "6008": 2219,
│ │ │ │          "600874": 2215,
│ │ │ │ -        "601": [2199, 2298],
│ │ │ │ +        "601": [2199, 2205, 2298],
│ │ │ │          "6013": 2219,
│ │ │ │          "6014": 2220,
│ │ │ │          "601544": 2185,
│ │ │ │          "601618": 2207,
│ │ │ │          "6018": 2219,
│ │ │ │          "601965": 15,
│ │ │ │          "602": 2199,
│ │ │ │ @@ -33832,24 +33839,24 @@
│ │ │ │          "618553": 2207,
│ │ │ │          "6186": 2220,
│ │ │ │          "618673": 2197,
│ │ │ │          "618697": 2199,
│ │ │ │          "618757": 2207,
│ │ │ │          "618896": 2186,
│ │ │ │          "618906": 2195,
│ │ │ │ -        "619": [16, 17, 18, 19, 2199, 2203, 2232, 2235, 2298],
│ │ │ │ +        "619": [16, 17, 18, 19, 2199, 2203, 2205, 2232, 2235, 2298],
│ │ │ │          "619048": 1320,
│ │ │ │          "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,
│ │ │ │ @@ -34156,27 +34163,27 @@
│ │ │ │          "656823": 2207,
│ │ │ │          "657": [2184, 2199, 2205],
│ │ │ │          "6573": 2220,
│ │ │ │          "6574": 2220,
│ │ │ │          "657564": 2207,
│ │ │ │          "657575": 2199,
│ │ │ │          "657803": 2197,
│ │ │ │ -        "658": 2199,
│ │ │ │ +        "658": [2193, 2199],
│ │ │ │          "6581": [2225, 2232, 2246],
│ │ │ │          "658179": 2199,
│ │ │ │          "6583": 2220,
│ │ │ │          "6584": 2220,
│ │ │ │          "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 +34393,27 @@
│ │ │ │          "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,
│ │ │ │ @@ -34850,15 +34857,15 @@
│ │ │ │          "739013": 2207,
│ │ │ │          "7391": 2221,
│ │ │ │          "739185": 2207,
│ │ │ │          "7394": 2221,
│ │ │ │          "739537": 2195,
│ │ │ │          "7398": 2202,
│ │ │ │          "7399": 2221,
│ │ │ │ -        "74": [31, 182, 190, 193, 760, 766, 768, 964, 966, 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],
│ │ │ │ +        "74": [31, 182, 190, 193, 760, 766, 768, 964, 966, 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],
│ │ │ │          "740": 2199,
│ │ │ │          "7400": 2221,
│ │ │ │          "740050": 29,
│ │ │ │          "7401": 2225,
│ │ │ │          "740139": 2186,
│ │ │ │          "7402": 22,
│ │ │ │          "7403": 2225,
│ │ │ │ @@ -34957,15 +34964,14 @@
│ │ │ │          "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,
│ │ │ │ @@ -35295,15 +35301,15 @@
│ │ │ │          "7999": 2222,
│ │ │ │          "7d": 2246,
│ │ │ │          "7min": [213, 345, 788, 2210, 2271],
│ │ │ │          "7th": 2298,
│ │ │ │          "7x": 2226,
│ │ │ │          "8": [3, 4, 9, 10, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 63, 72, 73, 74, 92, 108, 111, 113, 120, 133, 136, 144, 146, 147, 154, 160, 163, 164, 169, 170, 172, 185, 188, 189, 190, 193, 196, 203, 205, 208, 212, 213, 223, 234, 245, 248, 251, 255, 256, 257, 259, 265, 268, 269, 272, 273, 274, 280, 284, 346, 353, 440, 445, 568, 583, 599, 603, 616, 630, 632, 635, 708, 712, 720, 738, 741, 742, 746, 748, 764, 765, 766, 768, 771, 778, 782, 787, 788, 798, 807, 822, 847, 860, 879, 881, 884, 888, 890, 895, 900, 907, 912, 1039, 1075, 1147, 1150, 1154, 1157, 1160, 1162, 1163, 1165, 1168, 1170, 1171, 1176, 1178, 1180, 1181, 1185, 1186, 1189, 1191, 1195, 1196, 1197, 1199, 1200, 1203, 1205, 1206, 1209, 1214, 1217, 1219, 1220, 1222, 1225, 1227, 1228, 1233, 1237, 1239, 1240, 1246, 1247, 1250, 1252, 1256, 1257, 1258, 1260, 1261, 1265, 1267, 1268, 1273, 1277, 1278, 1279, 1280, 1284, 1288, 1291, 1292, 1293, 1294, 1297, 1302, 1304, 1321, 1323, 1325, 1338, 1339, 1340, 1341, 1343, 1344, 1345, 1347, 1374, 1391, 1396, 1406, 1407, 1411, 1414, 1416, 1432, 1433, 1434, 1440, 1446, 1448, 1454, 1463, 1469, 1473, 1476, 1478, 1479, 1486, 1487, 1488, 1490, 1491, 1493, 1494, 1495, 1496, 1512, 1530, 1548, 1566, 1586, 1598, 1607, 1626, 1643, 1657, 1665, 1677, 1686, 1699, 1707, 1720, 1728, 1741, 1747, 1765, 1782, 1801, 1823, 1846, 1863, 1883, 1901, 1919, 1936, 1953, 1971, 1988, 2006, 2025, 2043, 2061, 2079, 2096, 2114, 2133, 2151, 2166, 2184, 2185, 2186, 2187, 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, 2221, 2222, 2223, 2224, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2235, 2238, 2240, 2241, 2242, 2246, 2249, 2251, 2257, 2260, 2263, 2264, 2265, 2270, 2271, 2277, 2278, 2283, 2289, 2294, 2295, 2298, 2302, 2307],
│ │ │ │          "80": [28, 74, 183, 198, 214, 249, 268, 271, 282, 583, 761, 882, 899, 1198, 1202, 1263, 1345, 1391, 1433, 1488, 1490, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2218, 2220, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2249, 2271],
│ │ │ │ -        "800": [65, 74, 583, 1280, 1302],
│ │ │ │ +        "800": [65, 74, 583, 1280, 1302, 2193],
│ │ │ │          "8000": [183, 761, 2185, 2220, 2223, 2230],
│ │ │ │          "80000": [150, 168, 2194, 2201, 2222],
│ │ │ │          "800000": [1320, 2184, 2201],
│ │ │ │          "8002": 2222,
│ │ │ │          "800523": 2207,
│ │ │ │          "8006": 2222,
│ │ │ │          "800959": 2191,
│ │ │ │ @@ -35455,15 +35461,15 @@
│ │ │ │          "819": [2186, 2227],
│ │ │ │          "8190": 2222,
│ │ │ │          "819059": 2207,
│ │ │ │          "8193": 2271,
│ │ │ │          "819476": 2207,
│ │ │ │          "819492": 2207,
│ │ │ │          "8199": 2222,
│ │ │ │ -        "82": [2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271],
│ │ │ │ +        "82": [2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271],
│ │ │ │          "820": 2199,
│ │ │ │          "820223": 2191,
│ │ │ │          "820408": 2215,
│ │ │ │          "820750": 2199,
│ │ │ │          "8208": 2222,
│ │ │ │          "820801": 2230,
│ │ │ │          "8209": 2222,
│ │ │ │ @@ -35522,15 +35528,15 @@
│ │ │ │          "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,
│ │ │ │ @@ -36048,15 +36054,15 @@
│ │ │ │          "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,
│ │ │ │ @@ -36092,15 +36098,15 @@
│ │ │ │          "9093": 2271,
│ │ │ │          "909316": 2230,
│ │ │ │          "9094": 2225,
│ │ │ │          "909500": 2195,
│ │ │ │          "9096": 2225,
│ │ │ │          "909872": 2185,
│ │ │ │          "9099": 2225,
│ │ │ │ -        "91": [15, 182, 760, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2294, 2298],
│ │ │ │ +        "91": [15, 182, 760, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2294, 2298],
│ │ │ │          "9100": 2225,
│ │ │ │          "910199": 2199,
│ │ │ │          "910400": 28,
│ │ │ │          "911055": 2195,
│ │ │ │          "911128": 2207,
│ │ │ │          "911385": 2207,
│ │ │ │          "9114": 2232,
│ │ │ │ @@ -36157,14 +36163,15 @@
│ │ │ │          "918201": 2195,
│ │ │ │          "918376": 2199,
│ │ │ │          "9184": 2225,
│ │ │ │          "9185": 2225,
│ │ │ │          "918553": 2207,
│ │ │ │          "9186": 2257,
│ │ │ │          "918953": 2207,
│ │ │ │ +        "919": 2193,
│ │ │ │          "9190": 2225,
│ │ │ │          "919069": [2184, 2214],
│ │ │ │          "9192": 2225,
│ │ │ │          "919339": 2219,
│ │ │ │          "919540": 2214,
│ │ │ │          "919624": 2191,
│ │ │ │          "9197": 2225,
│ │ │ │ @@ -36234,15 +36241,15 @@
│ │ │ │          "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,
│ │ │ │ @@ -36358,15 +36365,15 @@
│ │ │ │          "949264": 2210,
│ │ │ │          "9493": [2225, 2265],
│ │ │ │          "9494": 2230,
│ │ │ │          "9497": 2225,
│ │ │ │          "949707": 2207,
│ │ │ │          "949747": [1269, 1270],
│ │ │ │          "949965": 2217,
│ │ │ │ -        "95": [15, 133, 208, 586, 708, 782, 1447, 1456, 2184, 2185, 2186, 2188, 2191, 2192, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2226, 2230, 2232, 2235, 2246, 2298],
│ │ │ │ +        "95": [15, 133, 208, 586, 708, 782, 1447, 1456, 2184, 2185, 2186, 2188, 2191, 2192, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2220, 2222, 2226, 2230, 2232, 2235, 2246, 2298],
│ │ │ │          "950": [2194, 2207],
│ │ │ │          "9500": [183, 761],
│ │ │ │          "950057": 15,
│ │ │ │          "950088": 2207,
│ │ │ │          "950146": 2210,
│ │ │ │          "950460": 2199,
│ │ │ │          "950775": 2207,
│ │ │ │ @@ -37723,15 +37730,15 @@
│ │ │ │          "barboursvil": 2199,
│ │ │ │          "bare": [2, 2199, 2222, 2241, 2277],
│ │ │ │          "barf": 2217,
│ │ │ │          "barh": [26, 186, 188, 762, 764, 1188, 1249, 2211, 2220, 2221, 2228, 2260, 2294],
│ │ │ │          "bark": 1365,
│ │ │ │          "barplot": 2222,
│ │ │ │          "barycentr": [146, 720, 1280, 2201, 2218],
│ │ │ │ -        "base": [1, 3, 4, 5, 10, 11, 13, 16, 17, 18, 19, 20, 21, 22, 23, 25, 31, 32, 34, 49, 65, 83, 84, 88, 107, 111, 112, 121, 127, 136, 137, 138, 141, 142, 144, 147, 157, 160, 184, 187, 212, 213, 218, 224, 240, 248, 253, 276, 278, 279, 285, 286, 288, 296, 318, 328, 331, 345, 352, 415, 433, 445, 459, 478, 540, 568, 573, 594, 595, 600, 629, 633, 639, 652, 673, 686, 696, 703, 712, 714, 717, 718, 732, 738, 754, 757, 763, 787, 788, 793, 816, 823, 836, 837, 838, 839, 840, 841, 842, 843, 844, 881, 886, 902, 904, 905, 913, 938, 940, 943, 948, 952, 1031, 1040, 1052, 1068, 1073, 1075, 1119, 1125, 1141, 1148, 1149, 1164, 1173, 1193, 1207, 1208, 1221, 1242, 1243, 1254, 1265, 1269, 1270, 1286, 1342, 1343, 1398, 1423, 1431, 1444, 1453, 1467, 1470, 1474, 1475, 1498, 1519, 1537, 1556, 1574, 1593, 1614, 1633, 1650, 1672, 1693, 1715, 1736, 1754, 1772, 1789, 1808, 1830, 1853, 1870, 1890, 1908, 1926, 1943, 1960, 1978, 1995, 2013, 2032, 2050, 2068, 2086, 2103, 2121, 2141, 2159, 2163, 2166, 2183, 2184, 2185, 2187, 2188, 2191, 2192, 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],
│ │ │ │ @@ -38256,15 +38263,15 @@
│ │ │ │          "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],
│ │ │ │ @@ -40250,15 +40257,15 @@
│ │ │ │          "get_indexer_for": [2283, 2289],
│ │ │ │          "get_indexer_non_uniqu": [379, 2192, 2197, 2238, 2243, 2246, 2249, 2265, 2277, 2289],
│ │ │ │          "get_indexer_nonuniqu": 2302,
│ │ │ │          "get_item": [2191, 2194],
│ │ │ │          "get_jit_argu": 2212,
│ │ │ │          "get_letter_typ": 2195,
│ │ │ │          "get_level_valu": [1416, 2185, 2218, 2220, 2228, 2232, 2241, 2246, 2253, 2256],
│ │ │ │ -        "get_loc": [2, 362, 383, 426, 492, 2185, 2191, 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_method": [16, 17, 18, 19, 2199, 2235],
│ │ │ │          "get_near_stock_pric": [2216, 2223],
│ │ │ │          "get_offset": [2265, 2298],
│ │ │ │          "get_offset_nam": [2230, 2238],
│ │ │ │          "get_op_result_nam": 2186,
│ │ │ │ @@ -42040,15 +42047,15 @@
│ │ │ │          "name_9": 2205,
│ │ │ │          "name_funct": 2199,
│ │ │ │          "name_of_my_env": 22,
│ │ │ │          "name_or_index": 877,
│ │ │ │          "namebank": 2199,
│ │ │ │          "namedagg": [1148, 1149, 2195, 2249, 2271],
│ │ │ │          "namedtupl": [83, 91, 150, 151, 152, 509, 594, 920, 1102, 1148, 1149, 1458, 2186, 2195, 2211, 2220, 2229, 2235, 2249, 2265, 2293],
│ │ │ │ -        "nameerror": [15, 16, 17, 18, 19, 2194, 2199, 2201, 2203, 2218, 2221, 2231, 2294, 2298, 2302, 2307],
│ │ │ │ +        "nameerror": [15, 16, 17, 18, 19, 2194, 2199, 2201, 2203, 2218, 2221, 2231, 2283, 2294, 2298, 2302, 2307],
│ │ │ │          "nameless": [2195, 2222, 2298],
│ │ │ │          "nameresolutionerror": 2220,
│ │ │ │          "namespac": [10, 203, 272, 683, 1387, 1487, 2169, 2172, 2180, 2191, 2199, 2202, 2206, 2214, 2218, 2230, 2235, 2254, 2258, 2265],
│ │ │ │          "nan": [3, 15, 16, 17, 18, 19, 22, 24, 25, 26, 27, 28, 29, 31, 32, 34, 39, 44, 46, 49, 56, 58, 62, 63, 65, 69, 72, 73, 74, 75, 76, 78, 79, 80, 87, 89, 92, 94, 95, 96, 97, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 114, 117, 118, 119, 121, 122, 123, 124, 125, 128, 129, 132, 134, 135, 145, 146, 148, 149, 153, 158, 159, 161, 162, 163, 169, 171, 172, 173, 175, 177, 178, 180, 181, 182, 183, 184, 185, 198, 199, 200, 201, 204, 205, 206, 207, 208, 212, 213, 214, 215, 216, 217, 218, 220, 221, 222, 230, 233, 234, 241, 244, 245, 249, 257, 258, 259, 267, 269, 271, 272, 275, 279, 280, 281, 282, 284, 346, 356, 357, 362, 371, 376, 377, 379, 385, 393, 398, 402, 413, 414, 427, 436, 441, 442, 443, 444, 449, 467, 476, 490, 502, 573, 578, 583, 584, 585, 589, 591, 592, 597, 598, 599, 601, 604, 611, 615, 616, 617, 618, 619, 620, 623, 625, 626, 627, 628, 629, 630, 631, 636, 693, 694, 695, 696, 697, 698, 699, 700, 701, 704, 706, 707, 709, 710, 711, 715, 716, 720, 725, 726, 733, 734, 739, 740, 741, 747, 748, 749, 753, 755, 756, 758, 759, 760, 761, 773, 774, 775, 777, 778, 780, 781, 782, 787, 788, 790, 791, 792, 793, 795, 796, 797, 804, 807, 808, 812, 815, 821, 823, 824, 827, 828, 829, 832, 833, 834, 845, 846, 849, 850, 857, 862, 863, 866, 867, 868, 874, 878, 879, 882, 889, 890, 896, 897, 899, 901, 906, 907, 908, 912, 914, 962, 990, 1027, 1028, 1029, 1030, 1040, 1041, 1043, 1044, 1047, 1050, 1051, 1052, 1054, 1056, 1068, 1074, 1078, 1118, 1123, 1131, 1139, 1142, 1143, 1146, 1150, 1151, 1153, 1155, 1156, 1157, 1158, 1164, 1165, 1166, 1167, 1168, 1169, 1179, 1182, 1183, 1184, 1186, 1193, 1196, 1198, 1202, 1209, 1210, 1212, 1214, 1221, 1222, 1223, 1224, 1225, 1226, 1231, 1232, 1238, 1241, 1244, 1247, 1254, 1257, 1259, 1263, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1280, 1286, 1296, 1298, 1299, 1301, 1303, 1306, 1307, 1308, 1309, 1314, 1316, 1317, 1318, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1345, 1359, 1388, 1390, 1391, 1399, 1403, 1404, 1410, 1422, 1423, 1440, 1441, 1442, 1443, 1444, 1446, 1447, 1448, 1449, 1450, 1453, 1454, 1469, 1470, 1475, 1485, 1486, 1487, 1488, 1490, 1498, 1499, 1500, 2167, 2184, 2185, 2186, 2187, 2188, 2191, 2195, 2197, 2198, 2199, 2200, 2201, 2202, 2203, 2204, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2235, 2236, 2237, 2239, 2240, 2245, 2248, 2249, 2250, 2255, 2260, 2263, 2264, 2267, 2269, 2271, 2273, 2274, 2275, 2276, 2277, 2278, 2279, 2282, 2283, 2286, 2287, 2292, 2293, 2294, 2295, 2297, 2298, 2302, 2307, 2308],
│ │ │ │          "nan_as_nul": 66,
│ │ │ │          "nan_inf_to_error": 341,
│ │ │ │          "nan_rep": [256, 346, 351, 888, 2199, 2215],
│ │ ├── ./usr/share/doc/python-pandas-doc/html/user_guide/advanced.html
│ │ │ @@ -1847,25 +1847,25 @@
│ │ │  <span class="gp">In [141]: </span><span class="n">indexer</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10000</span><span class="p">)</span>
│ │ │  
│ │ │  <span class="gp">In [142]: </span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">indexer</span><span class="p">)</span>
│ │ │  
│ │ │  <span class="gp">In [143]: </span><span class="o">%</span><span class="k">timeit</span> arr[indexer]
│ │ │  <span class="gp">   .....: </span><span class="o">%</span><span class="k">timeit</span> arr.take(indexer, axis=0)
│ │ │  <span class="gp">   .....: </span>
│ │ │ -<span class="go">191 us +- 30.1 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)</span>
│ │ │ -<span class="go">48 us +- 2.32 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)</span>
│ │ │ +<span class="go">163 us +- 3.55 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)</span>
│ │ │ +<span class="go">43.1 us +- 2.83 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [144]: </span><span class="n">ser</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">arr</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span>
│ │ │  
│ │ │  <span class="gp">In [145]: </span><span class="o">%</span><span class="k">timeit</span> ser.iloc[indexer]
│ │ │  <span class="gp">   .....: </span><span class="o">%</span><span class="k">timeit</span> ser.take(indexer)
│ │ │  <span class="gp">   .....: </span>
│ │ │ -<span class="go">103 us +- 20.5 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)</span>
│ │ │ -<span class="go">82.9 us +- 1.46 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)</span>
│ │ │ +<span class="go">126 us +- 3.87 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)</span>
│ │ │ +<span class="go">112 us +- 2.91 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="index-types">
│ │ │  <span id="advanced-index-types"></span><h2>Index types<a class="headerlink" href="#index-types" title="Link to this heading">#</a></h2>
│ │ │  <p>We have discussed <code class="docutils literal notranslate"><span class="pre">MultiIndex</span></code> in the previous sections pretty extensively.
│ │ │  Documentation about <code class="docutils literal notranslate"><span class="pre">DatetimeIndex</span></code> and <code class="docutils literal notranslate"><span class="pre">PeriodIndex</span></code> are shown <a class="reference internal" href="timeseries.html#timeseries-overview"><span class="std std-ref">here</span></a>,
│ │ │ ├── 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)
│ │ │ │ +163 us +- 3.55 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)
│ │ │ │ +43.1 us +- 2.83 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)
│ │ │ │ +126 us +- 3.87 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)
│ │ │ │ +112 us +- 2.91 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 @@
│ │ │  <span class="gp">   ...: </span>        <span class="n">s</span> <span class="o">+=</span> <span class="n">f</span><span class="p">(</span><span class="n">a</span> <span class="o">+</span> <span class="n">i</span> <span class="o">*</span> <span class="n">dx</span><span class="p">)</span>
│ │ │  <span class="gp">   ...: </span>    <span class="k">return</span> <span class="n">s</span> <span class="o">*</span> <span class="n">dx</span>
│ │ │  <span class="gp">   ...: </span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>We achieve our result by using <a class="reference internal" href="../reference/api/pandas.DataFrame.apply.html#pandas.DataFrame.apply" title="pandas.DataFrame.apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DataFrame.apply()</span></code></a> (row-wise):</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [5]: </span><span class="o">%</span><span class="k">timeit</span> df.apply(lambda x: integrate_f(x[&quot;a&quot;], x[&quot;b&quot;], x[&quot;N&quot;]), axis=1)
│ │ │ -<span class="go">56.6 ms +- 411 us per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>
│ │ │ +<span class="go">65.6 ms +- 919 us per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Let’s take a look and see where the time is spent during this operation
│ │ │  using the <a class="reference external" href="https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-prun">prun ipython magic function</a>:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="go"># most time consuming 4 calls</span>
│ │ │  <span class="gp">In [6]: </span><span class="o">%</span><span class="k">prun</span> -l 4 df.apply(lambda x: integrate_f(x[&quot;a&quot;], x[&quot;b&quot;], x[&quot;N&quot;]), axis=1)  # noqa E999
│ │ │ -<span class="go">         605946 function calls (605928 primitive calls) in 0.180 seconds</span>
│ │ │ +<span class="go">         605946 function calls (605928 primitive calls) in 1.173 seconds</span>
│ │ │  
│ │ │  <span class="go">   Ordered by: internal time</span>
│ │ │  <span class="go">   List reduced from 159 to 4 due to restriction &lt;4&gt;</span>
│ │ │  
│ │ │  <span class="go">   ncalls  tottime  percall  cumtime  percall filename:lineno(function)</span>
│ │ │ -<span class="go">     1000    0.110    0.000    0.158    0.000 &lt;ipython-input-4-c2a74e076cf0&gt;:1(integrate_f)</span>
│ │ │ -<span class="go">   552423    0.047    0.000    0.047    0.000 &lt;ipython-input-3-c138bdd570e3&gt;:1(f)</span>
│ │ │ -<span class="go">     3000    0.004    0.000    0.015    0.000 series.py:1095(__getitem__)</span>
│ │ │ -<span class="go">     3000    0.003    0.000    0.007    0.000 series.py:1220(_get_value)</span>
│ │ │ +<span class="go">     1000    0.562    0.001    1.053    0.001 &lt;ipython-input-4-c2a74e076cf0&gt;:1(integrate_f)</span>
│ │ │ +<span class="go">   552423    0.491    0.000    0.491    0.000 &lt;ipython-input-3-c138bdd570e3&gt;:1(f)</span>
│ │ │ +<span class="go">     3000    0.017    0.000    0.086    0.000 series.py:1095(__getitem__)</span>
│ │ │ +<span class="go">    16098    0.017    0.000    0.022    0.000 {built-in method builtins.isinstance}</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>By far the majority of time is spend inside either <code class="docutils literal notranslate"><span class="pre">integrate_f</span></code> or <code class="docutils literal notranslate"><span class="pre">f</span></code>,
│ │ │  hence we’ll concentrate our efforts cythonizing these two functions.</p>
│ │ │  </section>
│ │ │  <section id="plain-cython">
│ │ │  <span id="enhancingperf-plain"></span><h3>Plain Cython<a class="headerlink" href="#plain-cython" title="Link to this heading">#</a></h3>
│ │ │ @@ -634,15 +634,15 @@
│ │ │  <span class="gp">   ...: </span>    for i in range(N):
│ │ │  <span class="gp">   ...: </span>        s += f_plain(a + i * dx)
│ │ │  <span class="gp">   ...: </span>    return s * dx
│ │ │  <span class="gp">   ...: </span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [9]: </span><span class="o">%</span><span class="k">timeit</span> df.apply(lambda x: integrate_f_plain(x[&quot;a&quot;], x[&quot;b&quot;], x[&quot;N&quot;]), axis=1)
│ │ │ -<span class="go">67.8 ms +- 13.3 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>
│ │ │ +<span class="go">74.1 ms +- 1.4 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>This has improved the performance compared to the pure Python approach by one-third.</p>
│ │ │  </section>
│ │ │  <section id="declaring-c-types">
│ │ │  <span id="enhancingperf-type"></span><h3>Declaring C types<a class="headerlink" href="#declaring-c-types" title="Link to this heading">#</a></h3>
│ │ │  <p>We can annotate the function variables and return types as well as use <code class="docutils literal notranslate"><span class="pre">cdef</span></code>
│ │ │ @@ -658,36 +658,36 @@
│ │ │  <span class="gp">   ....: </span>    for i in range(N):
│ │ │  <span class="gp">   ....: </span>        s += f_typed(a + i * dx)
│ │ │  <span class="gp">   ....: </span>    return s * dx
│ │ │  <span class="gp">   ....: </span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [11]: </span><span class="o">%</span><span class="k">timeit</span> df.apply(lambda x: integrate_f_typed(x[&quot;a&quot;], x[&quot;b&quot;], x[&quot;N&quot;]), axis=1)
│ │ │ -<span class="go">7.61 ms +- 469 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │ +<span class="go">8.82 ms +- 147 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Annotating the functions with C types yields an over ten times performance improvement compared to
│ │ │  the original Python implementation.</p>
│ │ │  </section>
│ │ │  <section id="using-ndarray">
│ │ │  <span id="enhancingperf-ndarray"></span><h3>Using ndarray<a class="headerlink" href="#using-ndarray" title="Link to this heading">#</a></h3>
│ │ │  <p>When re-profiling, time is spent creating a <a class="reference internal" href="../reference/api/pandas.Series.html#pandas.Series" title="pandas.Series"><code class="xref py py-class docutils literal notranslate"><span class="pre">Series</span></code></a> from each row, and calling <code class="docutils literal notranslate"><span class="pre">__getitem__</span></code> from both
│ │ │  the index and the series (three times for each row). These Python function calls are expensive and
│ │ │  can be improved by passing an <code class="docutils literal notranslate"><span class="pre">np.ndarray</span></code>.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [12]: </span><span class="o">%</span><span class="k">prun</span> -l 4 df.apply(lambda x: integrate_f_typed(x[&quot;a&quot;], x[&quot;b&quot;], x[&quot;N&quot;]), axis=1)
│ │ │ -<span class="go">         52523 function calls (52505 primitive calls) in 0.023 seconds</span>
│ │ │ +<span class="go">         52523 function calls (52505 primitive calls) in 0.122 seconds</span>
│ │ │  
│ │ │  <span class="go">   Ordered by: internal time</span>
│ │ │  <span class="go">   List reduced from 157 to 4 due to restriction &lt;4&gt;</span>
│ │ │  
│ │ │  <span class="go">   ncalls  tottime  percall  cumtime  percall filename:lineno(function)</span>
│ │ │ -<span class="go">     3000    0.004    0.000    0.015    0.000 series.py:1095(__getitem__)</span>
│ │ │ -<span class="go">     3000    0.003    0.000    0.007    0.000 series.py:1220(_get_value)</span>
│ │ │ -<span class="go">     3000    0.002    0.000    0.003    0.000 base.py:3777(get_loc)</span>
│ │ │ -<span class="go">    16098    0.002    0.000    0.003    0.000 {built-in method builtins.isinstance}</span>
│ │ │ +<span class="go">     3000    0.018    0.000    0.089    0.000 series.py:1095(__getitem__)</span>
│ │ │ +<span class="go">    16098    0.017    0.000    0.023    0.000 {built-in method builtins.isinstance}</span>
│ │ │ +<span class="go">     3000    0.015    0.000    0.027    0.000 indexing.py:2765(check_dict_or_set_indexers)</span>
│ │ │ +<span class="go">     3000    0.012    0.000    0.032    0.000 series.py:1220(_get_value)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [13]: </span><span class="o">%%</span><span class="k">cython</span>
│ │ │  <span class="gp">   ....: </span>cimport numpy as np
│ │ │  <span class="gp">   ....: </span>import numpy as np
│ │ │  <span class="gp">   ....: </span>cdef double f_typed(double x) except? -2:
│ │ │  <span class="gp">   ....: </span>    return x * (x - 1)
│ │ │ @@ -722,33 +722,33 @@
│ │ │  </div>
│ │ │  <p>This implementation creates an array of zeros and inserts the result
│ │ │  of <code class="docutils literal notranslate"><span class="pre">integrate_f_typed</span></code> applied over each row. Looping over an <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> is faster
│ │ │  in Cython than looping over a <a class="reference internal" href="../reference/api/pandas.Series.html#pandas.Series" title="pandas.Series"><code class="xref py py-class docutils literal notranslate"><span class="pre">Series</span></code></a> object.</p>
│ │ │  <p>Since <code class="docutils literal notranslate"><span class="pre">apply_integrate_f</span></code> is typed to accept an <code class="docutils literal notranslate"><span class="pre">np.ndarray</span></code>, <a class="reference internal" href="../reference/api/pandas.Series.to_numpy.html#pandas.Series.to_numpy" title="pandas.Series.to_numpy"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Series.to_numpy()</span></code></a>
│ │ │  calls are needed to utilize this function.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [14]: </span><span class="o">%</span><span class="k">timeit</span> apply_integrate_f(df[&quot;a&quot;].to_numpy(), df[&quot;b&quot;].to_numpy(), df[&quot;N&quot;].to_numpy())
│ │ │ -<span class="go">930 us +- 30.9 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>
│ │ │ +<span class="go">1.12 ms +- 33.3 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Performance has improved from the prior implementation by almost ten times.</p>
│ │ │  </section>
│ │ │  <section id="disabling-compiler-directives">
│ │ │  <span id="enhancingperf-boundswrap"></span><h3>Disabling compiler directives<a class="headerlink" href="#disabling-compiler-directives" title="Link to this heading">#</a></h3>
│ │ │  <p>The majority of the time is now spent in <code class="docutils literal notranslate"><span class="pre">apply_integrate_f</span></code>. Disabling Cython’s <code class="docutils literal notranslate"><span class="pre">boundscheck</span></code>
│ │ │  and <code class="docutils literal notranslate"><span class="pre">wraparound</span></code> checks can yield more performance.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [15]: </span><span class="o">%</span><span class="k">prun</span> -l 4 apply_integrate_f(df[&quot;a&quot;].to_numpy(), df[&quot;b&quot;].to_numpy(), df[&quot;N&quot;].to_numpy())
│ │ │ -<span class="go">         78 function calls in 0.001 seconds</span>
│ │ │ +<span class="go">         78 function calls in 0.002 seconds</span>
│ │ │  
│ │ │  <span class="go">   Ordered by: internal time</span>
│ │ │  <span class="go">   List reduced from 21 to 4 due to restriction &lt;4&gt;</span>
│ │ │  
│ │ │  <span class="go">   ncalls  tottime  percall  cumtime  percall filename:lineno(function)</span>
│ │ │ -<span class="go">        1    0.001    0.001    0.001    0.001 &lt;string&gt;:1(&lt;module&gt;)</span>
│ │ │ +<span class="go">        1    0.001    0.001    0.002    0.002 &lt;string&gt;:1(&lt;module&gt;)</span>
│ │ │  <span class="go">        1    0.000    0.000    0.000    0.000 {method &#39;disable&#39; of &#39;_lsprof.Profiler&#39; objects}</span>
│ │ │ -<span class="go">        1    0.000    0.000    0.001    0.001 {built-in method builtins.exec}</span>
│ │ │ +<span class="go">        1    0.000    0.000    0.002    0.002 {built-in method builtins.exec}</span>
│ │ │  <span class="go">        3    0.000    0.000    0.000    0.000 frame.py:4062(__getitem__)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [16]: </span><span class="o">%%</span><span class="k">cython</span>
│ │ │  <span class="gp">   ....: </span>cimport cython
│ │ │  <span class="gp">   ....: </span>cimport numpy as np
│ │ │  <span class="gp">   ....: </span>import numpy as np
│ │ │ @@ -782,15 +782,15 @@
│ │ │  <span class="go">                 from /build/reproducible-path/pandas-2.2.3+dfsg/buildtmp/.cache/ipython/cython/_cython_magic_883da8958ecc60be73b28b7124368f9c7cc2d174.c:1251:</span>
│ │ │  <span class="go">/usr/lib/x86_64-linux-gnu/python3-numpy/numpy/_core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning &quot;Using deprecated NumPy API, disable it with &quot; &quot;#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION&quot; [-Wcpp]</span>
│ │ │  <span class="go">   17 | #warning &quot;Using deprecated NumPy API, disable it with &quot; \</span>
│ │ │  <span class="go">      |  ^~~~~~~</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [17]: </span><span class="o">%</span><span class="k">timeit</span> apply_integrate_f_wrap(df[&quot;a&quot;].to_numpy(), df[&quot;b&quot;].to_numpy(), df[&quot;N&quot;].to_numpy())
│ │ │ -<span class="go">659 us +- 167 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>
│ │ │ +<span class="go">800 us +- 19.6 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>However, a loop indexer <code class="docutils literal notranslate"><span class="pre">i</span></code> accessing an invalid location in an array would cause a segfault because memory access isn’t checked.
│ │ │  For more about <code class="docutils literal notranslate"><span class="pre">boundscheck</span></code> and <code class="docutils literal notranslate"><span class="pre">wraparound</span></code>, see the Cython docs on
│ │ │  <a class="reference external" href="https://cython.readthedocs.io/en/latest/src/userguide/source_files_and_compilation.html#compiler-directives">compiler directives</a>.</p>
│ │ │  </section>
│ │ │  </section>
│ │ │ @@ -1148,19 +1148,19 @@
│ │ │  compared to standard Python syntax for large <a class="reference internal" href="../reference/api/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a>. This engine requires the
│ │ │  optional dependency <code class="docutils literal notranslate"><span class="pre">numexpr</span></code> to be installed.</p>
│ │ │  <p>The <code class="docutils literal notranslate"><span class="pre">'python'</span></code> engine is generally <em>not</em> useful except for testing
│ │ │  other evaluation engines against it. You will achieve <strong>no</strong> performance
│ │ │  benefits using <a class="reference internal" href="../reference/api/pandas.eval.html#pandas.eval" title="pandas.eval"><code class="xref py py-func docutils literal notranslate"><span class="pre">eval()</span></code></a> with <code class="docutils literal notranslate"><span class="pre">engine='python'</span></code> and may
│ │ │  incur a performance hit.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [40]: </span><span class="o">%</span><span class="k">timeit</span> df1 + df2 + df3 + df4
│ │ │ -<span class="go">9.01 ms +- 448 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │ +<span class="go">9.61 ms +- 150 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [41]: </span><span class="o">%</span><span class="k">timeit</span> pd.eval(&quot;df1 + df2 + df3 + df4&quot;, engine=&quot;python&quot;)
│ │ │ -<span class="go">12.3 ms +- 754 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │ +<span class="go">11.7 ms +- 355 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="the-dataframe-eval-method">
│ │ │  <h3>The <a class="reference internal" href="../reference/api/pandas.DataFrame.eval.html#pandas.DataFrame.eval" title="pandas.DataFrame.eval"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DataFrame.eval()</span></code></a> method<a class="headerlink" href="#the-dataframe-eval-method" title="Link to this heading">#</a></h3>
│ │ │  <p>In addition to the top level <a class="reference internal" href="../reference/api/pandas.eval.html#pandas.eval" title="pandas.eval"><code class="xref py py-func docutils literal notranslate"><span class="pre">pandas.eval()</span></code></a> function you can also
│ │ │  evaluate an expression in the “context” of a <a class="reference internal" href="../reference/api/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a>.</p>
│ │ │ @@ -1275,39 +1275,39 @@
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [58]: </span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span> <span class="o">=</span> <span class="mi">20000</span><span class="p">,</span> <span class="mi">100</span>
│ │ │  
│ │ │  <span class="gp">In [59]: </span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">df3</span><span class="p">,</span> <span class="n">df4</span> <span class="o">=</span> <span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">))</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p><a class="reference internal" href="../reference/api/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a> arithmetic:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [60]: </span><span class="o">%</span><span class="k">timeit</span> df1 + df2 + df3 + df4
│ │ │ -<span class="go">8.83 ms +- 443 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │ +<span class="go">9.95 ms +- 524 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [61]: </span><span class="o">%</span><span class="k">timeit</span> pd.eval(&quot;df1 + df2 + df3 + df4&quot;)
│ │ │ -<span class="go">5.63 ms +- 684 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │ +<span class="go">4.03 ms +- 124 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p><a class="reference internal" href="../reference/api/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a> comparison:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [62]: </span><span class="o">%</span><span class="k">timeit</span> (df1 &gt; 0) &amp; (df2 &gt; 0) &amp; (df3 &gt; 0) &amp; (df4 &gt; 0)
│ │ │ -<span class="go">10.7 ms +- 903 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │ +<span class="go">7.58 ms +- 189 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [63]: </span><span class="o">%</span><span class="k">timeit</span> pd.eval(&quot;(df1 &gt; 0) &amp; (df2 &gt; 0) &amp; (df3 &gt; 0) &amp; (df4 &gt; 0)&quot;)
│ │ │ -<span class="go">10.5 ms +- 682 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │ +<span class="go">5.91 ms +- 82.9 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p><a class="reference internal" href="../reference/api/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a> arithmetic with unaligned axes.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [64]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">50</span><span class="p">))</span>
│ │ │  
│ │ │  <span class="gp">In [65]: </span><span class="o">%</span><span class="k">timeit</span> df1 + df2 + df3 + df4 + s
│ │ │ -<span class="go">22.8 ms +- 443 us per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>
│ │ │ +<span class="go">20.1 ms +- 658 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [66]: </span><span class="o">%</span><span class="k">timeit</span> pd.eval(&quot;df1 + df2 + df3 + df4 + s&quot;)
│ │ │ -<span class="go">8.5 ms +- 241 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │ +<span class="go">5.09 ms +- 226 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="admonition note">
│ │ │  <p class="admonition-title">Note</p>
│ │ │  <p>Operations such as</p>
│ │ │  <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="mi">1</span> <span class="ow">and</span> <span class="mi">2</span>  <span class="c1"># would parse to 1 &amp; 2, but should evaluate to 2</span>
│ │ │  <span class="mi">3</span> <span class="ow">or</span> <span class="mi">4</span>  <span class="c1"># would parse to 3 | 4, but should evaluate to 3</span>
│ │ │ ├── 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)
│ │ │ │ -56.6 ms +- 411 us per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +65.6 ms +- 919 us 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 1.173 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
│ │ │ │ +     1000    0.562    0.001    1.053    0.001 <ipython-input-4-c2a74e076cf0>:1
│ │ │ │  (integrate_f)
│ │ │ │ -   552423    0.047    0.000    0.047    0.000 <ipython-input-3-c138bdd570e3>:1
│ │ │ │ +   552423    0.491    0.000    0.491    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)
│ │ │ │ +     3000    0.017    0.000    0.086    0.000 series.py:1095(__getitem__)
│ │ │ │ +    16098    0.017    0.000    0.022    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)
│ │ │ │ -67.8 ms +- 13.3 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)
│ │ │ │ +74.1 ms +- 1.4 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)
│ │ │ │ -7.61 ms +- 469 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +8.82 ms +- 147 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 0.122 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)
│ │ │ │ -    16098    0.002    0.000    0.003    0.000 {built-in method
│ │ │ │ +     3000    0.018    0.000    0.089    0.000 series.py:1095(__getitem__)
│ │ │ │ +    16098    0.017    0.000    0.023    0.000 {built-in method
│ │ │ │  builtins.isinstance}
│ │ │ │ +     3000    0.015    0.000    0.027    0.000 indexing.py:2765
│ │ │ │ +(check_dict_or_set_indexers)
│ │ │ │ +     3000    0.012    0.000    0.032    0.000 series.py:1220(_get_value)
│ │ │ │  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())
│ │ │ │ -930 us +- 30.9 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)
│ │ │ │ +1.12 ms +- 33.3 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
│ │ │ │ +         78 function calls in 0.002 seconds
│ │ │ │  
│ │ │ │     Ordered by: internal time
│ │ │ │     List reduced from 21 to 4 due to restriction <4>
│ │ │ │  
│ │ │ │     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
│ │ │ │ -        1    0.001    0.001    0.001    0.001 <string>:1(<module>)
│ │ │ │ +        1    0.001    0.001    0.002    0.002 <string>:1(<module>)
│ │ │ │          1    0.000    0.000    0.000    0.000 {method 'disable' of
│ │ │ │  '_lsprof.Profiler' objects}
│ │ │ │ -        1    0.000    0.000    0.001    0.001 {built-in method builtins.exec}
│ │ │ │ +        1    0.000    0.000    0.002    0.002 {built-in method builtins.exec}
│ │ │ │          3    0.000    0.000    0.000    0.000 frame.py:4062(__getitem__)
│ │ │ │  In [16]: %%cython
│ │ │ │     ....: cimport cython
│ │ │ │     ....: cimport numpy as np
│ │ │ │     ....: import numpy as np
│ │ │ │     ....: cdef np.float64_t f_typed(np.float64_t x) except? -2:
│ │ │ │     ....:     return x * (x - 1)
│ │ │ │ @@ -298,15 +300,15 @@
│ │ │ │  /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)
│ │ │ │ +800 us +- 19.6 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 +611,17 @@
│ │ │ │  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)
│ │ │ │ +9.61 ms +- 150 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)
│ │ │ │ +11.7 ms +- 355 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 +718,29 @@
│ │ │ │  _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)
│ │ │ │ +9.95 ms +- 524 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)
│ │ │ │ +4.03 ms +- 124 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)
│ │ │ │ +7.58 ms +- 189 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)
│ │ │ │ +5.91 ms +- 82.9 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)
│ │ │ │ +20.1 ms +- 658 us 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)
│ │ │ │ +5.09 ms +- 226 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 @@
│ │ │  <span class="gp">   ....: </span><span class="n">files</span> <span class="o">=</span> <span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span><span class="p">(</span><span class="s2">&quot;data/timeseries/&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s2">&quot;ts*.parquet&quot;</span><span class="p">)</span>
│ │ │  <span class="gp">   ....: </span><span class="n">counts</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
│ │ │  <span class="gp">   ....: </span><span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">files</span><span class="p">:</span>
│ │ │  <span class="gp">   ....: </span>    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
│ │ │  <span class="gp">   ....: </span>    <span class="n">counts</span> <span class="o">=</span> <span class="n">counts</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(),</span> <span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
│ │ │  <span class="gp">   ....: </span><span class="n">counts</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
│ │ │  <span class="gp">   ....: </span>
│ │ │ -<span class="go">CPU times: user 303 us, sys: 62 us, total: 365 us</span>
│ │ │ -<span class="go">Wall time: 370 us</span>
│ │ │ +<span class="go">CPU times: user 487 us, sys: 114 us, total: 601 us</span>
│ │ │ +<span class="go">Wall time: 619 us</span>
│ │ │  <span class="gh">Out[32]: </span><span class="go">Series([], dtype: int64)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Some readers, like <a class="reference internal" href="../reference/api/pandas.read_csv.html#pandas.read_csv" title="pandas.read_csv"><code class="xref py py-meth docutils literal notranslate"><span class="pre">pandas.read_csv()</span></code></a>, offer parameters to control the
│ │ │  <code class="docutils literal notranslate"><span class="pre">chunksize</span></code> when reading a single file.</p>
│ │ │  <p>Manually chunking is an OK option for workflows that don’t
│ │ │  require too sophisticated of operations. Some operations, like <a class="reference internal" href="../reference/api/pandas.DataFrame.groupby.html#pandas.DataFrame.groupby" title="pandas.DataFrame.groupby"><code class="xref py py-meth docutils literal notranslate"><span class="pre">pandas.DataFrame.groupby()</span></code></a>, are
│ │ │ ├── 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 487 us, sys: 114 us, total: 601 us
│ │ │ │ +Wall time: 619 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
│ │ │ │ │ @@ -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": "2026-01-17T15:27:05.855424Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:05.854923Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.251945Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.251350Z"
│ │ │ │ │                  },
│ │ │ │ │                  "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": "2026-01-17T15:27:06.255359Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.254813Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.644449Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.643609Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "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": "2026-01-17T15:27:06.652474Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.652059Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.708833Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.708029Z"
│ │ │ │ │                  },
│ │ │ │ │                  "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": "2026-01-17T15:27:06.712429Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.711988Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.725653Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.725041Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -193,18 +193,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 5,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:20.919383Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:20.918486Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:20.933466Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:20.931745Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.760915Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.760605Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.773179Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.772667Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<div>\n",
│ │ │ │ │ @@ -327,18 +327,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 6,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:20.937340Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:20.936406Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:20.953466Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:20.952431Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.775347Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.775102Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.786481Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.785897Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -448,18 +448,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 7,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:20.957131Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:20.956230Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:20.966993Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:20.965994Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.788731Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.788404Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.796420Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.795788Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -510,18 +510,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 8,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:20.970634Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:20.969932Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:20.981477Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:20.980557Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.799010Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.798594Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.806853Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.806291Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -585,18 +585,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 9,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:20.984960Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:20.984247Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.001454Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.000517Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.809007Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.808619Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.820900Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.820351Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -705,18 +705,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 10,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.004860Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.004138Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.017123Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.016274Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.823254Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.822948Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.832896Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.832355Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -787,18 +787,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 11,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.020534Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.019673Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.032952Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.031941Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.835204Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.834928Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.846057Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.845440Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [],
│ │ │ │ │              "source": [
│ │ │ │ │                  "# Hidden cell to just create the below example: code is covered throughout the guide.\n",
│ │ │ │ │                  "s = df.style\\\n",
│ │ │ │ │ @@ -861,18 +861,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 12,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.036263Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.035460Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.045644Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.044811Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.848257Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.847851Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.856597Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.856013Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -1012,18 +1012,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 13,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.048486Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.048209Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.059764Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.058920Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.858735Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.858402Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.868159Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.867577Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -1083,18 +1083,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 14,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.063075Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.062203Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.072067Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.070972Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.870280Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.869881Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.876582Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.876046Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │ @@ -1202,18 +1202,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 15,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.076042Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.075176Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.086811Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.085757Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.878853Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.878544Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.885927Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.885339Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -1296,18 +1296,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 16,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.090288Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.089470Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.099064Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.098046Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.888412Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.887978Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.894496Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.893989Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │ @@ -1387,18 +1387,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 17,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.102301Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.101574Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.111805Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.110836Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.896718Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.896319Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.902899Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.902402Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -1483,18 +1483,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 18,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.114943Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.114217Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.123870Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.122890Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.905235Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.904616Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.911131Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.910624Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │ @@ -1586,18 +1586,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 19,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.127132Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.126790Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.138473Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.137487Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.913441Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.912849Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.921046Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.920554Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -1687,18 +1687,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 20,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.141549Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.141215Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.150727Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.149697Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.923394Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.922799Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.929255Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.928712Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │ @@ -1802,18 +1802,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 21,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.153771Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.153434Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.162511Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.161465Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.931499Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.931182Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.937808Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.937148Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "name": "stdout",
│ │ │ │ │                      "output_type": "stream",
│ │ │ │ │                      "text": [
│ │ │ │ │ @@ -1844,18 +1844,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 22,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.165763Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.165426Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.179391Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.178326Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.940263Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.939780Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.952297Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.951673Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -1956,18 +1956,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 23,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.182383Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.182046Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.191448Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.190408Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.955121Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.954534Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.962827Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.962375Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │ @@ -2081,18 +2081,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 24,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.194475Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.194137Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.203643Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.202621Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.965496Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.965243Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.972008Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.971539Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -2204,18 +2204,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 25,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.206501Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.206217Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.217314Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.216363Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.974988Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.974474Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.985350Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.984664Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -2332,18 +2332,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 26,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.220164Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.219867Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.229802Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.228857Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.988202Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.987591Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:06.995457Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:06.994894Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │ @@ -2465,18 +2465,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 27,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.232844Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.232561Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.244690Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.243739Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:06.997511Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:06.997310Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.007220Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.006418Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -2595,18 +2595,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 28,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.247619Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.247336Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.258636Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.257706Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.009985Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.009470Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.018534Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.018029Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │ @@ -2732,18 +2732,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 29,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.261533Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.261250Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.276659Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.275713Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.020741Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.020312Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.031936Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.031422Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -2884,18 +2884,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 30,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.279465Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.279183Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.294327Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.293384Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.034260Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.033801Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.045783Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.045143Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │ @@ -3069,18 +3069,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 31,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.297281Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.296997Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.315274Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.314295Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.048223Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.047772Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.061433Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.060843Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -3244,18 +3244,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 32,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.318292Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.318005Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.327138Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.326150Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.063805Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.063340Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.070500Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.069793Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -3357,18 +3357,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 33,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.330016Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.329732Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.338371Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.337433Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.072942Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.072492Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.079314Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.078725Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │ @@ -3477,18 +3477,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 34,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.341340Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.341055Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.353114Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.352172Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.081318Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.080968Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.091417Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.090596Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -3615,18 +3615,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 35,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.355980Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.355698Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.366428Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.365481Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.094029Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.093382Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.101855Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.101251Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │ @@ -3757,18 +3757,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 36,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.369414Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.369131Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.383427Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.382449Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.104455Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.103771Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.115622Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.114840Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -3904,18 +3904,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 37,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.386283Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.386000Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.396753Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.395781Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.118091Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.117404Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.126598Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.125781Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │ @@ -4062,18 +4062,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 38,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.399787Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.399495Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.412573Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.411605Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.128953Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.128735Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.140335Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.139358Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<div>\n",
│ │ │ │ │ @@ -4164,18 +4164,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 39,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.415466Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.415180Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.427334Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.426339Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.143290Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.142593Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.153819Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.152932Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -4255,18 +4255,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 40,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.430254Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.429970Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.443995Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.443035Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.156945Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.156238Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.169231Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.168389Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -4347,18 +4347,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 41,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.447021Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.446733Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.458906Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.457927Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.171767Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.171289Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.182241Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.181353Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -4440,18 +4440,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 42,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.461857Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.461568Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.475309Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.474341Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.184973Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.184346Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.196945Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.195944Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -4571,18 +4571,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 43,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.478515Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.478229Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.482816Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.481827Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.199617Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.199366Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.204002Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.203242Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [],
│ │ │ │ │              "source": [
│ │ │ │ │                  "df4 = pd.DataFrame([[1,2],[3,4]])\n",
│ │ │ │ │                  "s4 = df4.style"
│ │ │ │ │              ]
│ │ │ │ │ @@ -4599,18 +4599,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 44,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.485743Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.485457Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.489900Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.488945Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.206808Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.206185Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.210281Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.209679Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [],
│ │ │ │ │              "source": [
│ │ │ │ │                  "from pandas.io.formats.style import Styler\n",
│ │ │ │ │                  "s4 = Styler(df4, uuid_len=0, cell_ids=False)"
│ │ │ │ │              ]
│ │ │ │ │ @@ -4636,18 +4636,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 45,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.492901Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.492622Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.501105Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.500148Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.212811Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.212315Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.220457Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.219947Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -4705,18 +4705,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 46,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.504064Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.503766Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.511022Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.510065Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.222485Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.222276Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.227340Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.226841Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -4782,18 +4782,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 47,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.513976Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.513697Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.527728Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.526774Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.229501Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.229143Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.239867Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.239330Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -4931,18 +4931,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 48,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.530779Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.530460Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.545715Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.544770Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.242136Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.241853Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.253280Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.252781Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -5083,18 +5083,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 49,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.548807Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.548523Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.555824Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.554823Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.255960Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.255688Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.260776Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.260258Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "name": "stdout",
│ │ │ │ │                      "output_type": "stream",
│ │ │ │ │                      "text": [
│ │ │ │ │ @@ -5158,18 +5158,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 50,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.558819Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.558506Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.565414Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.564460Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.262621Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.262417Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.267016Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.266548Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -5260,18 +5260,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 51,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.568496Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.568212Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.577224Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.576268Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.269276Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.269070Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.275661Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.275167Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -5351,18 +5351,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 52,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.580191Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.579892Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.592195Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.591226Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.281200Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.280994Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.291058Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.290440Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -5443,18 +5443,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 53,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.595206Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.594892Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.610610Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.609668Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.293289Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.292896Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.305420Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.304769Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -5535,18 +5535,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 54,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.613532Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.613247Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:21.624411Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:21.623439Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.307963Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.307609Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.316652Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.316037Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -5631,18 +5631,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 55,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:21.627392Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:21.627108Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.261539Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.260389Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.319144Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.318779Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.867489Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.866670Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -5880,18 +5880,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 56,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.265119Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.264714Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.278407Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.277307Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.870294Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.869614Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.882354Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.881810Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -6112,18 +6112,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 57,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.281815Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.281520Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.291402Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.290391Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.884784Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.884379Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.891636Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.890995Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -6212,18 +6212,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 58,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.294372Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.294075Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.305032Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.304060Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.893900Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.893500Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.902244Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.901707Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -6415,18 +6415,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 59,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.307952Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.307660Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.330816Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.329828Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.904387Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.904162Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.922056Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.921548Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -6810,18 +6810,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 60,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.333749Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.333457Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.402708Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.401708Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.924386Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.923872Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.982579Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.981939Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [],
│ │ │ │ │              "source": [
│ │ │ │ │                  "# Hide the construction of the display chart from the user\n",
│ │ │ │ │                  "from IPython.display import HTML\n",
│ │ │ │ │ @@ -6864,18 +6864,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 61,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.405617Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.405323Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.411825Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.410886Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.985202Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.984855Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:07.989340Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:07.988801Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "\n",
│ │ │ │ │ @@ -7847,18 +7847,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 62,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.414876Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.414558Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.425698Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.424740Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:07.991573Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:07.991277Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.001580Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.001025Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -7968,18 +7968,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 63,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.428625Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.428336Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.438745Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.437761Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.003948Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.003406Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.011224Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.010651Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -8084,18 +8084,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 64,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.441805Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.441515Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.730043Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.728891Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.013763Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.013218Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.218635Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.217791Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "ename": "ModuleNotFoundError",
│ │ │ │ │                      "evalue": "No module named 'ipywidgets'",
│ │ │ │ │                      "output_type": "error",
│ │ │ │ │ @@ -8125,18 +8125,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 65,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.733376Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.733070Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.738818Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.737697Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.221343Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.220899Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.224802Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.224256Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [],
│ │ │ │ │              "source": [
│ │ │ │ │                  "def magnify():\n",
│ │ │ │ │                  "    return [dict(selector=\"th\",\n",
│ │ │ │ │                  "                 props=[(\"font-size\", \"4pt\")]),\n",
│ │ │ │ │ @@ -8151,18 +8151,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 66,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.741773Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.741487Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.805955Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.805059Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.226920Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.226440Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.272087Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.271460Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -10009,18 +10009,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 67,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.808997Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.808714Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.851771Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.850888Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.274602Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.274143Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.305043Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.304299Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -11817,18 +11817,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 68,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.854960Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.854644Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.906180Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.905209Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.308797Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.308347Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.349765Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.349160Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -13655,18 +13655,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 69,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.909403Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.909108Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.916845Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.915903Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.353450Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.352877Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.359021Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.358437Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -13705,18 +13705,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 70,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.919719Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.919435Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.926697Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.925791Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.361168Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.360775Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.365912Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.365414Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -13754,18 +13754,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 71,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.929576Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.929296Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:22.936649Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:22.935686Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.368367Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.367975Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.372999Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.372509Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -13836,18 +13836,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 72,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:22.939593Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:22.939309Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.077580Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.076370Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.375336Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.374833Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.485340Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.484247Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [],
│ │ │ │ │              "source": [
│ │ │ │ │                  "df2.style.\\\n",
│ │ │ │ │                  "    map(style_negative, props='color:red;').\\\n",
│ │ │ │ │                  "    highlight_max(axis=0).\\\n",
│ │ │ │ │ @@ -13915,18 +13915,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 73,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.081375Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.080983Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.088931Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.087935Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.488629Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.488047Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.494738Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.493932Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "name": "stdout",
│ │ │ │ │                      "output_type": "stream",
│ │ │ │ │                      "text": [
│ │ │ │ │ @@ -13971,18 +13971,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 74,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.091943Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.091655Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.100488Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.099527Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.497178Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.496882Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.505210Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.504458Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -14022,18 +14022,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 75,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.103353Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.103063Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.111192Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.110193Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.507602Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.507200Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.513225Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.512638Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -14088,18 +14088,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 76,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.114156Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.113869Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.121517Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.120569Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.515513Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.515071Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.520823Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.520236Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -14148,18 +14148,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 77,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.124485Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.124201Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.132699Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.131724Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.523147Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.522622Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.528846Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.528297Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -14213,18 +14213,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 78,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.135616Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.135332Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.144013Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.143054Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.530946Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.530569Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.537286Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.536229Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -14284,18 +14284,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 79,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.146945Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.146629Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.155297Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.154338Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.540553Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.539192Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.547956Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.547337Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -14373,36 +14373,36 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 80,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.158285Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.158003Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.162391Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.161490Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.550406Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.549897Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.553183Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.552540Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [],
│ │ │ │ │              "source": [
│ │ │ │ │                  "from jinja2 import Environment, ChoiceLoader, FileSystemLoader\n",
│ │ │ │ │                  "from IPython.display import HTML\n",
│ │ │ │ │                  "from pandas.io.formats.style import Styler"
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 81,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.165250Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.164969Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.169100Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.168158Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.556034Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.555514Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.558438Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.557848Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [],
│ │ │ │ │              "source": [
│ │ │ │ │                  "# For reproducibility\n",
│ │ │ │ │                  "Styler = StylerReproducible\n"
│ │ │ │ │ @@ -14416,18 +14416,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 82,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.172090Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.171793Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.177122Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.176174Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.560895Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.560556Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.564689Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.564062Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "name": "stdout",
│ │ │ │ │                      "output_type": "stream",
│ │ │ │ │                      "text": [
│ │ │ │ │ @@ -14454,18 +14454,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 83,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.180084Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.179787Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.187656Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.186707Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.566983Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.566611Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.574700Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.573920Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [],
│ │ │ │ │              "source": [
│ │ │ │ │                  "class MyStyler(Styler):\n",
│ │ │ │ │                  "    env = Environment(\n",
│ │ │ │ │                  "        loader=ChoiceLoader([\n",
│ │ │ │ │ @@ -14488,18 +14488,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 84,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.190620Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.190336Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.217267Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.216333Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.577780Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.576636Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.597268Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.596659Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -14661,18 +14661,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 85,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.220290Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.220005Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.228280Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.227360Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.599703Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.599350Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.605633Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.605074Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -14834,18 +14834,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 86,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.231275Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.230991Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.258962Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.257991Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.608321Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.608019Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.629772Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.629141Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<style type=\"text/css\">\n",
│ │ │ │ │ @@ -15017,36 +15017,36 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 87,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.261985Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.261695Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.266291Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.265349Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.632131Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.631795Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.634935Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.634436Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [],
│ │ │ │ │              "source": [
│ │ │ │ │                  "with open(\"templates/html_style_structure.html\") as f:\n",
│ │ │ │ │                  "    style_structure = f.read()"
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 88,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.269139Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.268858Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.274678Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.273727Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.637290Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.636969Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.640853Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.640387Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<!--\n",
│ │ │ │ │ @@ -15106,36 +15106,36 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 89,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.277617Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.277336Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.281948Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.281029Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.642922Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.642426Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.645346Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.644856Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [],
│ │ │ │ │              "source": [
│ │ │ │ │                  "with open(\"templates/html_table_structure.html\") as f:\n",
│ │ │ │ │                  "    table_structure = f.read()"
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 90,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.284817Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.284537Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.290467Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.289534Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.647439Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.646974Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.650711Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.650226Z"
│ │ │ │ │                  }
│ │ │ │ │              },
│ │ │ │ │              "outputs": [
│ │ │ │ │                  {
│ │ │ │ │                      "data": {
│ │ │ │ │                          "text/html": [
│ │ │ │ │                              "<!--\n",
│ │ │ │ │ @@ -15208,18 +15208,18 @@
│ │ │ │ │              ]
│ │ │ │ │          },
│ │ │ │ │          {
│ │ │ │ │              "cell_type": "code",
│ │ │ │ │              "execution_count": 91,
│ │ │ │ │              "metadata": {
│ │ │ │ │                  "execution": {
│ │ │ │ │ -                    "iopub.execute_input": "2025-03-29T16:35:23.293293Z",
│ │ │ │ │ -                    "iopub.status.busy": "2025-03-29T16:35:23.293013Z",
│ │ │ │ │ -                    "iopub.status.idle": "2025-03-29T16:35:23.297099Z",
│ │ │ │ │ -                    "shell.execute_reply": "2025-03-29T16:35:23.296162Z"
│ │ │ │ │ +                    "iopub.execute_input": "2026-01-17T15:27:08.652755Z",
│ │ │ │ │ +                    "iopub.status.busy": "2026-01-17T15:27:08.652312Z",
│ │ │ │ │ +                    "iopub.status.idle": "2026-01-17T15:27:08.654842Z",
│ │ │ │ │ +                    "shell.execute_reply": "2026-01-17T15:27:08.654374Z"
│ │ │ │ │                  },
│ │ │ │ │                  "nbsphinx": "hidden"
│ │ │ │ │              },
│ │ │ │ │              "outputs": [],
│ │ │ │ │              "source": [
│ │ │ │ │                  "# # Hack to get the same style in the notebook as the\n",
│ │ │ │ │                  "# # main site. This is hidden in the docs.\n",
│ │ ├── ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.13.0.html
│ │ │ @@ -1613,20 +1613,20 @@
│ │ │  <span class="gp">In [94]: </span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">df3</span><span class="p">,</span> <span class="n">df4</span> <span class="o">=</span> <span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">))</span>
│ │ │  <span class="gp">   ....: </span>                      <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)]</span>
│ │ │  <span class="gp">   ....: </span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="go"># eval with NumExpr backend</span>
│ │ │  <span class="gp">In [95]: </span><span class="o">%</span><span class="k">timeit</span> pd.eval(&#39;df1 + df2 + df3 + df4&#39;)
│ │ │ -<span class="go">7.23 ms +- 150 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │ +<span class="go">4 ms +- 190 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="go"># pure Python evaluation</span>
│ │ │  <span class="gp">In [96]: </span><span class="o">%</span><span class="k">timeit</span> df1 + df2 + df3 + df4
│ │ │ -<span class="go">6.58 ms +- 226 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │ +<span class="go">3.15 ms +- 38.9 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>For more details, see the <a class="reference internal" href="../user_guide/enhancingperf.html#enhancingperf-eval"><span class="std std-ref">the docs</span></a></p>
│ │ │  </li>
│ │ │  <li><p>Similar to <code class="docutils literal notranslate"><span class="pre">pandas.eval</span></code>, <a class="reference internal" href="../reference/api/pandas.DataFrame.html#pandas.DataFrame" title="pandas.DataFrame"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a> has a new
│ │ │  <code class="docutils literal notranslate"><span class="pre">DataFrame.eval</span></code> method that evaluates an expression in the context of
│ │ │  the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. For example,</p>
│ │ │ ├── html2text {}
│ │ │ │ @@ -965,18 +965,18 @@
│ │ │ │  
│ │ │ │        In [94]: df1, df2, df3, df4 = [pd.DataFrame(np.random.randn(nrows,
│ │ │ │        ncols))
│ │ │ │           ....:                       for _ in range(4)]
│ │ │ │           ....:
│ │ │ │        # eval with NumExpr backend
│ │ │ │        In [95]: %timeit pd.eval('df1 + df2 + df3 + df4')
│ │ │ │ -      7.23 ms +- 150 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +      4 ms +- 190 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │        # pure Python evaluation
│ │ │ │        In [96]: %timeit df1 + df2 + df3 + df4
│ │ │ │ -      6.58 ms +- 226 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │ +      3.15 ms +- 38.9 us per loop (mean +- std. dev. of 7 runs, 100 loops each)
│ │ │ │        For more details, see the _t_h_e_ _d_o_c_s
│ │ │ │      * Similar to pandas.eval, _D_a_t_a_F_r_a_m_e has a new DataFrame.eval method that
│ │ │ │        evaluates an expression in the context of the DataFrame. For example,
│ │ │ │        In [97]: df = pd.DataFrame(np.random.randn(10, 2), columns=['a', 'b'])
│ │ │ │  
│ │ │ │        In [98]: df.eval('a + b')
│ │ │ │        Out[98]:
│ │ ├── ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.17.0.html
│ │ │ @@ -1458,18 +1458,18 @@
│ │ │  </div>
│ │ │  <div class="admonition note">
│ │ │  <p class="admonition-title">Note</p>
│ │ │  <p>If you want to perform calculations based on today’s date, use <code class="docutils literal notranslate"><span class="pre">Timestamp.now()</span></code> and <code class="docutils literal notranslate"><span class="pre">pandas.tseries.offsets</span></code>.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [65]: </span><span class="kn">import</span> <span class="nn">pandas.tseries.offsets</span> <span class="k">as</span> <span class="nn">offsets</span>
│ │ │  
│ │ │  <span class="gp">In [66]: </span><span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="o">.</span><span class="n">now</span><span class="p">()</span>
│ │ │ -<span class="gh">Out[66]: </span><span class="go">Timestamp(&#39;2025-03-29 16:36:01.527625&#39;)</span>
│ │ │ +<span class="gh">Out[66]: </span><span class="go">Timestamp(&#39;2026-01-17 15:27:49.322673&#39;)</span>
│ │ │  
│ │ │  <span class="gp">In [67]: </span><span class="n">pd</span><span class="o">.</span><span class="n">Timestamp</span><span class="o">.</span><span class="n">now</span><span class="p">()</span> <span class="o">+</span> <span class="n">offsets</span><span class="o">.</span><span class="n">DateOffset</span><span class="p">(</span><span class="n">years</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
│ │ │ -<span class="gh">Out[67]: </span><span class="go">Timestamp(&#39;2026-03-29 16:36:01.528798&#39;)</span>
│ │ │ +<span class="gh">Out[67]: </span><span class="go">Timestamp(&#39;2027-01-17 15:27:49.324076&#39;)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </div>
│ │ │  </section>
│ │ │  </section>
│ │ │  <section id="changes-to-index-comparisons">
│ │ │  <h3>Changes to Index comparisons<a class="headerlink" href="#changes-to-index-comparisons" title="Link to this heading">#</a></h3>
│ │ │ ├── html2text {}
│ │ │ │ @@ -814,18 +814,18 @@
│ │ │ │  freq=None)
│ │ │ │  Note
│ │ │ │  If you want to perform calculations based on today’s date, use Timestamp.now()
│ │ │ │  and pandas.tseries.offsets.
│ │ │ │  In [65]: import pandas.tseries.offsets as offsets
│ │ │ │  
│ │ │ │  In [66]: pd.Timestamp.now()
│ │ │ │ -Out[66]: Timestamp('2025-03-29 16:36:01.527625')
│ │ │ │ +Out[66]: Timestamp('2026-01-17 15:27:49.322673')
│ │ │ │  
│ │ │ │  In [67]: pd.Timestamp.now() + offsets.DateOffset(years=1)
│ │ │ │ -Out[67]: Timestamp('2026-03-29 16:36:01.528798')
│ │ │ │ +Out[67]: Timestamp('2027-01-17 15:27:49.324076')
│ │ │ │  ******** CChhaannggeess ttoo IInnddeexx ccoommppaarriissoonnss_## ********
│ │ │ │  Operator equal on Index should behavior similarly to Series (_G_H_ _9_9_4_7, _G_H_ _1_0_6_3_7)
│ │ │ │  Starting in v0.17.0, comparing Index objects of different lengths will raise a
│ │ │ │  ValueError. This is to be consistent with the behavior of Series.
│ │ │ │  Previous behavior:
│ │ │ │  In [2]: pd.Index([1, 2, 3]) == pd.Index([1, 4, 5])
│ │ │ │  Out[2]: array([ True, False, False], dtype=bool)
│ │ ├── ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.19.0.html
│ │ │ @@ -756,41 +756,49 @@
│ │ │  <span class="gp">In [3]: </span><span class="n">left</span>
│ │ │  <span class="gh">Out[3]: </span>
│ │ │  <span class="go">    a left_val</span>
│ │ │  <span class="go">0   1        a</span>
│ │ │  <span class="go">1   5        b</span>
│ │ │  <span class="go">2  10        c</span>
│ │ │  
│ │ │ +<span class="go">[3 rows x 2 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [4]: </span><span class="n">right</span>
│ │ │  <span class="gh">Out[4]: </span>
│ │ │  <span class="go">   a  right_val</span>
│ │ │  <span class="go">0  1          1</span>
│ │ │  <span class="go">1  2          2</span>
│ │ │  <span class="go">2  3          3</span>
│ │ │  <span class="go">3  6          6</span>
│ │ │  <span class="go">4  7          7</span>
│ │ │ +
│ │ │ +<span class="go">[5 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>We typically want to match exactly when possible, and use the most
│ │ │  recent value otherwise.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [5]: </span><span class="n">pd</span><span class="o">.</span><span class="n">merge_asof</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;a&quot;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[5]: </span>
│ │ │  <span class="go">    a left_val  right_val</span>
│ │ │  <span class="go">0   1        a          1</span>
│ │ │  <span class="go">1   5        b          3</span>
│ │ │  <span class="go">2  10        c          7</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>We can also match rows ONLY with prior data, and not an exact match.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [6]: </span><span class="n">pd</span><span class="o">.</span><span class="n">merge_asof</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="n">allow_exact_matches</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
│ │ │  <span class="gh">Out[6]: </span>
│ │ │  <span class="go">    a left_val  right_val</span>
│ │ │  <span class="go">0   1        a        NaN</span>
│ │ │  <span class="go">1   5        b        3.0</span>
│ │ │  <span class="go">2  10        c        7.0</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>In a typical time-series example, we have <code class="docutils literal notranslate"><span class="pre">trades</span></code> and <code class="docutils literal notranslate"><span class="pre">quotes</span></code> and we want to <code class="docutils literal notranslate"><span class="pre">asof-join</span></code> them.
│ │ │  This also illustrates using the <code class="docutils literal notranslate"><span class="pre">by</span></code> parameter to group data before merging.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [7]: </span><span class="n">trades</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
│ │ │  <span class="gp">   ...: </span>    <span class="p">{</span>
│ │ │  <span class="gp">   ...: </span>        <span class="s2">&quot;time&quot;</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span>
│ │ │ @@ -838,38 +846,44 @@
│ │ │  <span class="go">                     time ticker   price  quantity</span>
│ │ │  <span class="go">0 2016-05-25 13:30:00.023   MSFT   51.95        75</span>
│ │ │  <span class="go">1 2016-05-25 13:30:00.038   MSFT   51.95       155</span>
│ │ │  <span class="go">2 2016-05-25 13:30:00.048   GOOG  720.77       100</span>
│ │ │  <span class="go">3 2016-05-25 13:30:00.048   GOOG  720.92       100</span>
│ │ │  <span class="go">4 2016-05-25 13:30:00.048   AAPL   98.00       100</span>
│ │ │  
│ │ │ +<span class="go">[5 rows x 4 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [10]: </span><span class="n">quotes</span>
│ │ │  <span class="gh">Out[10]: </span>
│ │ │  <span class="go">                     time ticker     bid     ask</span>
│ │ │  <span class="go">0 2016-05-25 13:30:00.023   GOOG  720.50  720.93</span>
│ │ │  <span class="go">1 2016-05-25 13:30:00.023   MSFT   51.95   51.96</span>
│ │ │  <span class="go">2 2016-05-25 13:30:00.030   MSFT   51.97   51.98</span>
│ │ │  <span class="go">3 2016-05-25 13:30:00.041   MSFT   51.99   52.00</span>
│ │ │  <span class="go">4 2016-05-25 13:30:00.048   GOOG  720.50  720.93</span>
│ │ │  <span class="go">5 2016-05-25 13:30:00.049   AAPL   97.99   98.01</span>
│ │ │  <span class="go">6 2016-05-25 13:30:00.072   GOOG  720.50  720.88</span>
│ │ │  <span class="go">7 2016-05-25 13:30:00.075   MSFT   52.01   52.03</span>
│ │ │ +
│ │ │ +<span class="go">[8 rows x 4 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>An asof merge joins on the <code class="docutils literal notranslate"><span class="pre">on</span></code>, typically a datetimelike field, which is ordered, and
│ │ │  in this case we are using a grouper in the <code class="docutils literal notranslate"><span class="pre">by</span></code> field. This is like a left-outer join, except
│ │ │  that forward filling happens automatically taking the most recent non-NaN value.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [11]: </span><span class="n">pd</span><span class="o">.</span><span class="n">merge_asof</span><span class="p">(</span><span class="n">trades</span><span class="p">,</span> <span class="n">quotes</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s2">&quot;ticker&quot;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[11]: </span>
│ │ │  <span class="go">                     time ticker   price  quantity     bid     ask</span>
│ │ │  <span class="go">0 2016-05-25 13:30:00.023   MSFT   51.95        75   51.95   51.96</span>
│ │ │  <span class="go">1 2016-05-25 13:30:00.038   MSFT   51.95       155   51.97   51.98</span>
│ │ │  <span class="go">2 2016-05-25 13:30:00.048   GOOG  720.77       100  720.50  720.93</span>
│ │ │  <span class="go">3 2016-05-25 13:30:00.048   GOOG  720.92       100  720.50  720.93</span>
│ │ │  <span class="go">4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN</span>
│ │ │ +
│ │ │ +<span class="go">[5 rows x 6 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>This returns a merged DataFrame with the entries in the same order as the original left
│ │ │  passed DataFrame (<code class="docutils literal notranslate"><span class="pre">trades</span></code> in this case), with the fields of the <code class="docutils literal notranslate"><span class="pre">quotes</span></code> merged.</p>
│ │ │  </section>
│ │ │  <section id="method-rolling-is-now-time-series-aware">
│ │ │  <span id="whatsnew-0190-enhancements-rolling-ts"></span><h3>Method <code class="docutils literal notranslate"><span class="pre">.rolling()</span></code> is now time-series aware<a class="headerlink" href="#method-rolling-is-now-time-series-aware" title="Link to this heading">#</a></h3>
│ │ │ @@ -885,45 +899,53 @@
│ │ │  <span class="gh">Out[13]: </span>
│ │ │  <span class="go">                       B</span>
│ │ │  <span class="go">2013-01-01 09:00:00  0.0</span>
│ │ │  <span class="go">2013-01-01 09:00:01  1.0</span>
│ │ │  <span class="go">2013-01-01 09:00:02  2.0</span>
│ │ │  <span class="go">2013-01-01 09:00:03  NaN</span>
│ │ │  <span class="go">2013-01-01 09:00:04  4.0</span>
│ │ │ +
│ │ │ +<span class="go">[5 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>This is a regular frequency index. Using an integer window parameter works to roll along the window frequency.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [14]: </span><span class="n">dft</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
│ │ │  <span class="gh">Out[14]: </span>
│ │ │  <span class="go">                       B</span>
│ │ │  <span class="go">2013-01-01 09:00:00  NaN</span>
│ │ │  <span class="go">2013-01-01 09:00:01  1.0</span>
│ │ │  <span class="go">2013-01-01 09:00:02  3.0</span>
│ │ │  <span class="go">2013-01-01 09:00:03  NaN</span>
│ │ │  <span class="go">2013-01-01 09:00:04  NaN</span>
│ │ │  
│ │ │ +<span class="go">[5 rows x 1 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [15]: </span><span class="n">dft</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">min_periods</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
│ │ │  <span class="gh">Out[15]: </span>
│ │ │  <span class="go">                       B</span>
│ │ │  <span class="go">2013-01-01 09:00:00  0.0</span>
│ │ │  <span class="go">2013-01-01 09:00:01  1.0</span>
│ │ │  <span class="go">2013-01-01 09:00:02  3.0</span>
│ │ │  <span class="go">2013-01-01 09:00:03  2.0</span>
│ │ │  <span class="go">2013-01-01 09:00:04  4.0</span>
│ │ │ +
│ │ │ +<span class="go">[5 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Specifying an offset allows a more intuitive specification of the rolling frequency.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [16]: </span><span class="n">dft</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="s2">&quot;2s&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
│ │ │  <span class="gh">Out[16]: </span>
│ │ │  <span class="go">                       B</span>
│ │ │  <span class="go">2013-01-01 09:00:00  0.0</span>
│ │ │  <span class="go">2013-01-01 09:00:01  1.0</span>
│ │ │  <span class="go">2013-01-01 09:00:02  3.0</span>
│ │ │  <span class="go">2013-01-01 09:00:03  2.0</span>
│ │ │  <span class="go">2013-01-01 09:00:04  4.0</span>
│ │ │ +
│ │ │ +<span class="go">[5 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Using a non-regular, but still monotonic index, rolling with an integer window does not impart any special calculation.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [17]: </span><span class="n">dft</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
│ │ │  <span class="gp">   ....: </span>    <span class="p">{</span><span class="s2">&quot;B&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="mi">4</span><span class="p">]},</span>
│ │ │  <span class="gp">   ....: </span>    <span class="n">index</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="p">(</span>
│ │ │  <span class="gp">   ....: </span>        <span class="p">[</span>
│ │ │ @@ -944,35 +966,41 @@
│ │ │  <span class="go">foo                     </span>
│ │ │  <span class="go">2013-01-01 09:00:00  0.0</span>
│ │ │  <span class="go">2013-01-01 09:00:02  1.0</span>
│ │ │  <span class="go">2013-01-01 09:00:03  2.0</span>
│ │ │  <span class="go">2013-01-01 09:00:05  NaN</span>
│ │ │  <span class="go">2013-01-01 09:00:06  4.0</span>
│ │ │  
│ │ │ +<span class="go">[5 rows x 1 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [19]: </span><span class="n">dft</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
│ │ │  <span class="gh">Out[19]: </span>
│ │ │  <span class="go">                       B</span>
│ │ │  <span class="go">foo                     </span>
│ │ │  <span class="go">2013-01-01 09:00:00  NaN</span>
│ │ │  <span class="go">2013-01-01 09:00:02  1.0</span>
│ │ │  <span class="go">2013-01-01 09:00:03  3.0</span>
│ │ │  <span class="go">2013-01-01 09:00:05  NaN</span>
│ │ │  <span class="go">2013-01-01 09:00:06  NaN</span>
│ │ │ +
│ │ │ +<span class="go">[5 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Using the time-specification generates variable windows for this sparse data.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [20]: </span><span class="n">dft</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="s2">&quot;2s&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
│ │ │  <span class="gh">Out[20]: </span>
│ │ │  <span class="go">                       B</span>
│ │ │  <span class="go">foo                     </span>
│ │ │  <span class="go">2013-01-01 09:00:00  0.0</span>
│ │ │  <span class="go">2013-01-01 09:00:02  1.0</span>
│ │ │  <span class="go">2013-01-01 09:00:03  3.0</span>
│ │ │  <span class="go">2013-01-01 09:00:05  NaN</span>
│ │ │  <span class="go">2013-01-01 09:00:06  4.0</span>
│ │ │ +
│ │ │ +<span class="go">[5 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Furthermore, we now allow an optional <code class="docutils literal notranslate"><span class="pre">on</span></code> parameter to specify a column (rather than the
│ │ │  default of the index) in a DataFrame.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [21]: </span><span class="n">dft</span> <span class="o">=</span> <span class="n">dft</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
│ │ │  
│ │ │  <span class="gp">In [22]: </span><span class="n">dft</span>
│ │ │ @@ -980,22 +1008,26 @@
│ │ │  <span class="go">                  foo    B</span>
│ │ │  <span class="go">0 2013-01-01 09:00:00  0.0</span>
│ │ │  <span class="go">1 2013-01-01 09:00:02  1.0</span>
│ │ │  <span class="go">2 2013-01-01 09:00:03  2.0</span>
│ │ │  <span class="go">3 2013-01-01 09:00:05  NaN</span>
│ │ │  <span class="go">4 2013-01-01 09:00:06  4.0</span>
│ │ │  
│ │ │ +<span class="go">[5 rows x 2 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [23]: </span><span class="n">dft</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="s2">&quot;2s&quot;</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;foo&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
│ │ │  <span class="gh">Out[23]: </span>
│ │ │  <span class="go">                  foo    B</span>
│ │ │  <span class="go">0 2013-01-01 09:00:00  0.0</span>
│ │ │  <span class="go">1 2013-01-01 09:00:02  1.0</span>
│ │ │  <span class="go">2 2013-01-01 09:00:03  3.0</span>
│ │ │  <span class="go">3 2013-01-01 09:00:05  NaN</span>
│ │ │  <span class="go">4 2013-01-01 09:00:06  4.0</span>
│ │ │ +
│ │ │ +<span class="go">[5 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="method-read-csv-has-improved-support-for-duplicate-column-names">
│ │ │  <span id="whatsnew-0190-enhancements-read-csv-dupe-col-names-support"></span><h3>Method <code class="docutils literal notranslate"><span class="pre">read_csv</span></code> has improved support for duplicate column names<a class="headerlink" href="#method-read-csv-has-improved-support-for-duplicate-column-names" title="Link to this heading">#</a></h3>
│ │ │  <p><a class="reference internal" href="../user_guide/io.html#io-dupe-names"><span class="std std-ref">Duplicate column names</span></a> are now supported in <a class="reference internal" href="../reference/api/pandas.read_csv.html#pandas.read_csv" title="pandas.read_csv"><code class="xref py py-func docutils literal notranslate"><span class="pre">read_csv()</span></code></a> whether
│ │ │  they are in the file or passed in as the <code class="docutils literal notranslate"><span class="pre">names</span></code> parameter (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/7160">GH 7160</a>, <a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/9424">GH 9424</a>)</p>
│ │ │ @@ -1068,36 +1100,38 @@
│ │ │  <span class="gp">In [28]: </span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">StringIO</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
│ │ │  <span class="gh">Out[28]: </span>
│ │ │  <span class="go">  col1 col2  col3</span>
│ │ │  <span class="go">0    a    b     1</span>
│ │ │  <span class="go">1    a    b     2</span>
│ │ │  <span class="go">2    c    d     3</span>
│ │ │  
│ │ │ +<span class="go">[3 rows x 3 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [29]: </span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">StringIO</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[29]: </span>
│ │ │  <span class="go">col1    object</span>
│ │ │  <span class="go">col2    object</span>
│ │ │  <span class="go">col3     int64</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 3, dtype: object</span>
│ │ │  
│ │ │  <span class="gp">In [30]: </span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">StringIO</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;category&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[30]: </span>
│ │ │  <span class="go">col1    category</span>
│ │ │  <span class="go">col2    category</span>
│ │ │  <span class="go">col3    category</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 3, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Individual columns can be parsed as a <code class="docutils literal notranslate"><span class="pre">Categorical</span></code> using a dict specification</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [31]: </span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">StringIO</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;col1&quot;</span><span class="p">:</span> <span class="s2">&quot;category&quot;</span><span class="p">})</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[31]: </span>
│ │ │  <span class="go">col1    category</span>
│ │ │  <span class="go">col2      object</span>
│ │ │  <span class="go">col3       int64</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 3, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="admonition note">
│ │ │  <p class="admonition-title">Note</p>
│ │ │  <p>The resulting categories will always be parsed as strings (object dtype).
│ │ │  If the categories are numeric they can be converted using the
│ │ │  <a class="reference internal" href="../reference/api/pandas.to_numeric.html#pandas.to_numeric" title="pandas.to_numeric"><code class="xref py py-func docutils literal notranslate"><span class="pre">to_numeric()</span></code></a> function, or as appropriate, another converter
│ │ │ @@ -1105,34 +1139,34 @@
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [32]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">StringIO</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;category&quot;</span><span class="p">)</span>
│ │ │  
│ │ │  <span class="gp">In [33]: </span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[33]: </span>
│ │ │  <span class="go">col1    category</span>
│ │ │  <span class="go">col2    category</span>
│ │ │  <span class="go">col3    category</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 3, dtype: object</span>
│ │ │  
│ │ │  <span class="gp">In [34]: </span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;col3&quot;</span><span class="p">]</span>
│ │ │  <span class="gh">Out[34]: </span>
│ │ │  <span class="go">0    1</span>
│ │ │  <span class="go">1    2</span>
│ │ │  <span class="go">2    3</span>
│ │ │ -<span class="go">Name: col3, dtype: category</span>
│ │ │ +<span class="go">Name: col3, Length: 3, dtype: category</span>
│ │ │  <span class="go">Categories (3, object): [&#39;1&#39;, &#39;2&#39;, &#39;3&#39;]</span>
│ │ │  
│ │ │  <span class="gp">In [35]: </span><span class="n">new_categories</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;col3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">categories</span><span class="p">)</span>
│ │ │  
│ │ │  <span class="gp">In [36]: </span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;col3&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;col3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">cat</span><span class="o">.</span><span class="n">rename_categories</span><span class="p">(</span><span class="n">new_categories</span><span class="p">)</span>
│ │ │  
│ │ │  <span class="gp">In [37]: </span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;col3&quot;</span><span class="p">]</span>
│ │ │  <span class="gh">Out[37]: </span>
│ │ │  <span class="go">0    1</span>
│ │ │  <span class="go">1    2</span>
│ │ │  <span class="go">2    3</span>
│ │ │ -<span class="go">Name: col3, dtype: category</span>
│ │ │ +<span class="go">Name: col3, Length: 3, dtype: category</span>
│ │ │  <span class="go">Categories (3, int64): [1, 2, 3]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="categorical-concatenation">
│ │ │  <span id="whatsnew-0190-enhancements-union-categoricals"></span><h3>Categorical concatenation<a class="headerlink" href="#categorical-concatenation" title="Link to this heading">#</a></h3>
│ │ │ @@ -1167,15 +1201,15 @@
│ │ │  <p><strong>New behavior</strong>:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [44]: </span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">])</span>
│ │ │  <span class="gh">Out[44]: </span>
│ │ │  <span class="go">0    a</span>
│ │ │  <span class="go">1    b</span>
│ │ │  <span class="go">0    b</span>
│ │ │  <span class="go">1    c</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 4, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="semi-month-offsets">
│ │ │  <span id="whatsnew-0190-enhancements-semi-month-offsets"></span><h3>Semi-month offsets<a class="headerlink" href="#semi-month-offsets" title="Link to this heading">#</a></h3>
│ │ │  <p>pandas has gained new frequency offsets, <code class="docutils literal notranslate"><span class="pre">SemiMonthEnd</span></code> (‘SM’) and <code class="docutils literal notranslate"><span class="pre">SemiMonthBegin</span></code> (‘SMS’).
│ │ │  These provide date offsets anchored (by default) to the 15th and end of month, and 15th and 1st of month respectively.
│ │ │ @@ -1257,14 +1291,16 @@
│ │ │  <span class="gp">In [57]: </span><span class="n">idx</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;[ab](?P&lt;digit&gt;\d)&quot;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[57]: </span>
│ │ │  <span class="go">        digit</span>
│ │ │  <span class="go">  match      </span>
│ │ │  <span class="go">0 0         1</span>
│ │ │  <span class="go">  1         2</span>
│ │ │  <span class="go">1 0         1</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p><code class="docutils literal notranslate"><span class="pre">Index.astype()</span></code> now accepts an optional boolean argument <code class="docutils literal notranslate"><span class="pre">copy</span></code>, which allows optional copying if the requirements on dtype are satisfied (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/13209">GH 13209</a>)</p>
│ │ │  </section>
│ │ │  <section id="google-bigquery-enhancements">
│ │ │  <span id="whatsnew-0190-gbq"></span><h3>Google BigQuery enhancements<a class="headerlink" href="#google-bigquery-enhancements" title="Link to this heading">#</a></h3>
│ │ │  <ul class="simple">
│ │ │ @@ -1292,15 +1328,15 @@
│ │ │  </div>
│ │ │  <p><strong>New behavior</strong>:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [58]: </span><span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">([</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[58]: </span>
│ │ │  <span class="go">a    bool</span>
│ │ │  <span class="go">b    bool</span>
│ │ │  <span class="go">c    bool</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 3, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="downcast-values-to-smallest-possible-dtype-in-to-numeric">
│ │ │  <span id="whatsnew-0190-enhancements-to-numeric-downcast"></span><h3>Downcast values to smallest possible dtype in <code class="docutils literal notranslate"><span class="pre">to_numeric</span></code><a class="headerlink" href="#downcast-values-to-smallest-possible-dtype-in-to-numeric" title="Link to this heading">#</a></h3>
│ │ │  <p><code class="docutils literal notranslate"><span class="pre">pd.to_numeric()</span></code> now accepts a <code class="docutils literal notranslate"><span class="pre">downcast</span></code> parameter, which will downcast the data if possible to smallest specified numerical dtype (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/13352">GH 13352</a>)</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [59]: </span><span class="n">s</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;1&quot;</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
│ │ │ @@ -1407,14 +1443,16 @@
│ │ │  <span class="go">                   date  a</span>
│ │ │  <span class="go">v d                       </span>
│ │ │  <span class="go">1 2015-01-04 2015-01-04  0</span>
│ │ │  <span class="go">2 2015-01-11 2015-01-11  1</span>
│ │ │  <span class="go">3 2015-01-18 2015-01-18  2</span>
│ │ │  <span class="go">4 2015-01-25 2015-01-25  3</span>
│ │ │  <span class="go">5 2015-02-01 2015-02-01  4</span>
│ │ │ +
│ │ │ +<span class="go">[5 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [74]: </span><span class="n">df</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="s2">&quot;M&quot;</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;date&quot;</span><span class="p">)[[</span><span class="s2">&quot;a&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
│ │ │  <span class="gh">Out[74]:</span>
│ │ │  <span class="go">            a</span>
│ │ │  <span class="go">date</span>
│ │ │  <span class="go">2015-01-31  6</span>
│ │ │ @@ -1458,19 +1496,23 @@
│ │ │  
│ │ │  <span class="gp">In [71]: </span><span class="n">df</span>
│ │ │  <span class="gh">Out[71]: </span>
│ │ │  <span class="go">      A  B  C</span>
│ │ │  <span class="go">row1  2  3  4</span>
│ │ │  <span class="go">row2  7  5  8</span>
│ │ │  
│ │ │ +<span class="go">[2 rows x 3 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [72]: </span><span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;row2&quot;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
│ │ │  <span class="gh">Out[72]: </span>
│ │ │  <span class="go">      B  A  C</span>
│ │ │  <span class="go">row1  3  2  4</span>
│ │ │  <span class="go">row2  5  7  8</span>
│ │ │ +
│ │ │ +<span class="go">[2 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </li>
│ │ │  <li><p>Added documentation to <a class="reference internal" href="../user_guide/io.html#io-dtypes"><span class="std std-ref">I/O</span></a> regarding the perils of reading in columns with mixed dtypes and how to handle it (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/13746">GH 13746</a>)</p></li>
│ │ │  <li><p><a class="reference internal" href="../reference/api/pandas.DataFrame.to_html.html#pandas.DataFrame.to_html" title="pandas.DataFrame.to_html"><code class="xref py py-meth docutils literal notranslate"><span class="pre">to_html()</span></code></a> now has a <code class="docutils literal notranslate"><span class="pre">border</span></code> argument to control the value in the opening <code class="docutils literal notranslate"><span class="pre">&lt;table&gt;</span></code> tag. The default is the value of the <code class="docutils literal notranslate"><span class="pre">html.border</span></code> option, which defaults to 1. This also affects the notebook HTML repr, but since Jupyter’s CSS includes a border-width attribute, the visual effect is the same. (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/11563">GH 11563</a>).</p></li>
│ │ │  <li><p>Raise <code class="docutils literal notranslate"><span class="pre">ImportError</span></code> in the sql functions when <code class="docutils literal notranslate"><span class="pre">sqlalchemy</span></code> is not installed and a connection string is used (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/11920">GH 11920</a>).</p></li>
│ │ │  <li><p>Compatibility with matplotlib 2.0. Older versions of pandas should also work with matplotlib 2.0 (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/13333">GH 13333</a>)</p></li>
│ │ │ @@ -1531,27 +1573,29 @@
│ │ │  
│ │ │  <span class="gp">In [77]: </span><span class="n">s1</span> <span class="o">+</span> <span class="n">s2</span>
│ │ │  <span class="gh">Out[77]: </span>
│ │ │  <span class="go">A    3.0</span>
│ │ │  <span class="go">B    4.0</span>
│ │ │  <span class="go">C    NaN</span>
│ │ │  <span class="go">D    NaN</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 4, dtype: float64</span>
│ │ │  
│ │ │  <span class="gp">In [78]: </span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s2">&quot;ABC&quot;</span><span class="p">))</span>
│ │ │  
│ │ │  <span class="gp">In [79]: </span><span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s2">&quot;ABD&quot;</span><span class="p">))</span>
│ │ │  
│ │ │  <span class="gp">In [80]: </span><span class="n">df1</span> <span class="o">+</span> <span class="n">df2</span>
│ │ │  <span class="gh">Out[80]: </span>
│ │ │  <span class="go">     0</span>
│ │ │  <span class="go">A  3.0</span>
│ │ │  <span class="go">B  4.0</span>
│ │ │  <span class="go">C  NaN</span>
│ │ │  <span class="go">D  NaN</span>
│ │ │ +
│ │ │ +<span class="go">[4 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="comparison-operators">
│ │ │  <h4>Comparison operators<a class="headerlink" href="#comparison-operators" title="Link to this heading">#</a></h4>
│ │ │  <p>Comparison operators raise <code class="docutils literal notranslate"><span class="pre">ValueError</span></code> when <code class="docutils literal notranslate"><span class="pre">.index</span></code> are different.</p>
│ │ │  <p><strong>Previous behavior</strong> (<code class="docutils literal notranslate"><span class="pre">Series</span></code>):</p>
│ │ │ @@ -1580,15 +1624,15 @@
│ │ │  <p>If you want to compare <code class="docutils literal notranslate"><span class="pre">Series</span></code> aligning its <code class="docutils literal notranslate"><span class="pre">.index</span></code>, see flexible comparison methods section below:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [82]: </span><span class="n">s1</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">s2</span><span class="p">)</span>
│ │ │  <span class="gh">Out[82]: </span>
│ │ │  <span class="go">A    False</span>
│ │ │  <span class="go">B     True</span>
│ │ │  <span class="go">C    False</span>
│ │ │  <span class="go">D    False</span>
│ │ │ -<span class="go">dtype: bool</span>
│ │ │ +<span class="go">Length: 4, dtype: bool</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </div>
│ │ │  <p><strong>Current behavior</strong> (<code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, no change):</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [3]: </span><span class="n">df1</span> <span class="o">==</span> <span class="n">df2</span>
│ │ │  <span class="gh">Out[3]:</span>
│ │ │  <span class="go">ValueError: Can only compare identically-labeled DataFrame objects</span>
│ │ │ @@ -1616,30 +1660,30 @@
│ │ │  
│ │ │  <span class="gp">In [85]: </span><span class="n">s1</span> <span class="o">&amp;</span> <span class="n">s2</span>
│ │ │  <span class="gh">Out[85]: </span>
│ │ │  <span class="go">A     True</span>
│ │ │  <span class="go">B    False</span>
│ │ │  <span class="go">C    False</span>
│ │ │  <span class="go">D    False</span>
│ │ │ -<span class="go">dtype: bool</span>
│ │ │ +<span class="go">Length: 4, dtype: bool</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="admonition note">
│ │ │  <p class="admonition-title">Note</p>
│ │ │  <p><code class="docutils literal notranslate"><span class="pre">Series</span></code> logical operators fill a <code class="docutils literal notranslate"><span class="pre">NaN</span></code> result with <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
│ │ │  </div>
│ │ │  <div class="admonition note">
│ │ │  <p class="admonition-title">Note</p>
│ │ │  <p>To achieve the same result as previous versions (compare values based on only left hand side index), you can use <code class="docutils literal notranslate"><span class="pre">reindex_like</span></code>:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [86]: </span><span class="n">s1</span> <span class="o">&amp;</span> <span class="n">s2</span><span class="o">.</span><span class="n">reindex_like</span><span class="p">(</span><span class="n">s1</span><span class="p">)</span>
│ │ │  <span class="gh">Out[86]: </span>
│ │ │  <span class="go">A     True</span>
│ │ │  <span class="go">B    False</span>
│ │ │  <span class="go">C    False</span>
│ │ │ -<span class="go">dtype: bool</span>
│ │ │ +<span class="go">Length: 3, dtype: bool</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </div>
│ │ │  <p><strong>Current behavior</strong> (<code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, no change):</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [87]: </span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s2">&quot;ABC&quot;</span><span class="p">))</span>
│ │ │  
│ │ │  <span class="gp">In [88]: </span><span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="s2">&quot;ABD&quot;</span><span class="p">))</span>
│ │ │ @@ -1647,14 +1691,16 @@
│ │ │  <span class="gp">In [89]: </span><span class="n">df1</span> <span class="o">&amp;</span> <span class="n">df2</span>
│ │ │  <span class="gh">Out[89]: </span>
│ │ │  <span class="go">       0</span>
│ │ │  <span class="go">A   True</span>
│ │ │  <span class="go">B  False</span>
│ │ │  <span class="go">C  False</span>
│ │ │  <span class="go">D  False</span>
│ │ │ +
│ │ │ +<span class="go">[4 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="flexible-comparison-methods">
│ │ │  <h4>Flexible comparison methods<a class="headerlink" href="#flexible-comparison-methods" title="Link to this heading">#</a></h4>
│ │ │  <p><code class="docutils literal notranslate"><span class="pre">Series</span></code> flexible comparison methods like <code class="docutils literal notranslate"><span class="pre">eq</span></code>, <code class="docutils literal notranslate"><span class="pre">ne</span></code>, <code class="docutils literal notranslate"><span class="pre">le</span></code>, <code class="docutils literal notranslate"><span class="pre">lt</span></code>, <code class="docutils literal notranslate"><span class="pre">ge</span></code> and <code class="docutils literal notranslate"><span class="pre">gt</span></code> now align both <code class="docutils literal notranslate"><span class="pre">index</span></code>. Use these operators if you want to compare two <code class="docutils literal notranslate"><span class="pre">Series</span></code>
│ │ │  which has the different <code class="docutils literal notranslate"><span class="pre">index</span></code>.</p>
│ │ │ @@ -1664,23 +1710,23 @@
│ │ │  
│ │ │  <span class="gp">In [92]: </span><span class="n">s1</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">s2</span><span class="p">)</span>
│ │ │  <span class="gh">Out[92]: </span>
│ │ │  <span class="go">a    False</span>
│ │ │  <span class="go">b     True</span>
│ │ │  <span class="go">c    False</span>
│ │ │  <span class="go">d    False</span>
│ │ │ -<span class="go">dtype: bool</span>
│ │ │ +<span class="go">Length: 4, dtype: bool</span>
│ │ │  
│ │ │  <span class="gp">In [93]: </span><span class="n">s1</span><span class="o">.</span><span class="n">ge</span><span class="p">(</span><span class="n">s2</span><span class="p">)</span>
│ │ │  <span class="gh">Out[93]: </span>
│ │ │  <span class="go">a    False</span>
│ │ │  <span class="go">b     True</span>
│ │ │  <span class="go">c     True</span>
│ │ │  <span class="go">d    False</span>
│ │ │ -<span class="go">dtype: bool</span>
│ │ │ +<span class="go">Length: 4, dtype: bool</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Previously, this worked the same as comparison operators (see above).</p>
│ │ │  </section>
│ │ │  </section>
│ │ │  <section id="series-type-promotion-on-assignment">
│ │ │  <span id="whatsnew-0190-api-promote"></span><h3><code class="docutils literal notranslate"><span class="pre">Series</span></code> type promotion on assignment<a class="headerlink" href="#series-type-promotion-on-assignment" title="Link to this heading">#</a></h3>
│ │ │ @@ -1700,15 +1746,15 @@
│ │ │  
│ │ │  <span class="gp">In [96]: </span><span class="n">s</span><span class="p">[</span><span class="s2">&quot;b&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">3.0</span>
│ │ │  
│ │ │  <span class="gp">In [97]: </span><span class="n">s</span>
│ │ │  <span class="gh">Out[97]: </span>
│ │ │  <span class="go">a    2016-01-01 00:00:00</span>
│ │ │  <span class="go">b                    3.0</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 2, dtype: object</span>
│ │ │  
│ │ │  <span class="gp">In [98]: </span><span class="n">s</span><span class="o">.</span><span class="n">dtype</span>
│ │ │  <span class="gh">Out[98]: </span><span class="go">dtype(&#39;O&#39;)</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="function-to-datetime-changes">
│ │ │ @@ -1740,21 +1786,25 @@
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [100]: </span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;key&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;v1&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">]})</span>
│ │ │  
│ │ │  <span class="gp">In [101]: </span><span class="n">df1</span>
│ │ │  <span class="gh">Out[101]: </span>
│ │ │  <span class="go">   key  v1</span>
│ │ │  <span class="go">0    1  10</span>
│ │ │  
│ │ │ +<span class="go">[1 rows x 2 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [102]: </span><span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;key&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="s2">&quot;v1&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">]})</span>
│ │ │  
│ │ │  <span class="gp">In [103]: </span><span class="n">df2</span>
│ │ │  <span class="gh">Out[103]: </span>
│ │ │  <span class="go">   key  v1</span>
│ │ │  <span class="go">0    1  20</span>
│ │ │  <span class="go">1    2  30</span>
│ │ │ +
│ │ │ +<span class="go">[2 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p><strong>Previous behavior</strong>:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [5]: </span><span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;outer&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[5]:</span>
│ │ │  <span class="go">   key    v1</span>
│ │ │  <span class="go">0  1.0  10.0</span>
│ │ │ @@ -1773,35 +1823,39 @@
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [104]: </span><span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">&quot;outer&quot;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[104]: </span>
│ │ │  <span class="go">   key  v1</span>
│ │ │  <span class="go">0    1  10</span>
│ │ │  <span class="go">1    1  20</span>
│ │ │  <span class="go">2    2  30</span>
│ │ │  
│ │ │ +<span class="go">[3 rows x 2 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [105]: </span><span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">&quot;outer&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[105]: </span>
│ │ │  <span class="go">key    int64</span>
│ │ │  <span class="go">v1     int64</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 2, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Of course if you have missing values that are introduced, then the
│ │ │  resulting dtype will be upcast, which is unchanged from previous.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [106]: </span><span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">&quot;outer&quot;</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;key&quot;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[106]: </span>
│ │ │  <span class="go">   key  v1_x  v1_y</span>
│ │ │  <span class="go">0    1  10.0    20</span>
│ │ │  <span class="go">1    2   NaN    30</span>
│ │ │  
│ │ │ +<span class="go">[2 rows x 3 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [107]: </span><span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">&quot;outer&quot;</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;key&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[107]: </span>
│ │ │  <span class="go">key       int64</span>
│ │ │  <span class="go">v1_x    float64</span>
│ │ │  <span class="go">v1_y      int64</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 3, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="method-describe-changes">
│ │ │  <span id="whatsnew-0190-api-describe"></span><h3>Method <code class="docutils literal notranslate"><span class="pre">.describe()</span></code> changes<a class="headerlink" href="#method-describe-changes" title="Link to this heading">#</a></h3>
│ │ │  <p>Percentile identifiers in the index of a <code class="docutils literal notranslate"><span class="pre">.describe()</span></code> output will now be rounded to the least precision that keeps them distinct (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/13104">GH 13104</a>)</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [108]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
│ │ │ @@ -1844,15 +1898,15 @@
│ │ │  <span class="go">0.05%     0.002000</span>
│ │ │  <span class="go">0.1%      0.004000</span>
│ │ │  <span class="go">50%       2.000000</span>
│ │ │  <span class="go">99.9%     3.996000</span>
│ │ │  <span class="go">99.95%    3.998000</span>
│ │ │  <span class="go">99.99%    3.999600</span>
│ │ │  <span class="go">max       4.000000</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 12, dtype: float64</span>
│ │ │  
│ │ │  <span class="gp">In [111]: </span><span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">percentiles</span><span class="o">=</span><span class="p">[</span><span class="mf">0.0001</span><span class="p">,</span> <span class="mf">0.0005</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">,</span> <span class="mf">0.9995</span><span class="p">,</span> <span class="mf">0.9999</span><span class="p">])</span>
│ │ │  <span class="gh">Out[111]: </span>
│ │ │  <span class="go">               0</span>
│ │ │  <span class="go">count   5.000000</span>
│ │ │  <span class="go">mean    2.000000</span>
│ │ │  <span class="go">std     1.581139</span>
│ │ │ @@ -1861,14 +1915,16 @@
│ │ │  <span class="go">0.05%   0.002000</span>
│ │ │  <span class="go">0.1%    0.004000</span>
│ │ │  <span class="go">50%     2.000000</span>
│ │ │  <span class="go">99.9%   3.996000</span>
│ │ │  <span class="go">99.95%  3.998000</span>
│ │ │  <span class="go">99.99%  3.999600</span>
│ │ │  <span class="go">max     4.000000</span>
│ │ │ +
│ │ │ +<span class="go">[12 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Furthermore:</p>
│ │ │  <ul class="simple">
│ │ │  <li><p>Passing duplicated <code class="docutils literal notranslate"><span class="pre">percentiles</span></code> will now raise a <code class="docutils literal notranslate"><span class="pre">ValueError</span></code>.</p></li>
│ │ │  <li><p>Bug in <code class="docutils literal notranslate"><span class="pre">.describe()</span></code> on a DataFrame with a mixed-dtype column index, which would previously raise a <code class="docutils literal notranslate"><span class="pre">TypeError</span></code> (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/13288">GH 13288</a>)</p></li>
│ │ │  </ul>
│ │ │ @@ -2137,25 +2193,25 @@
│ │ │  <span class="gh">Out[142]: </span><span class="go">CategoricalIndex([&#39;b&#39;, &#39;a&#39;, &#39;c&#39;], categories=[&#39;b&#39;, &#39;a&#39;, &#39;c&#39;], ordered=False, dtype=&#39;category&#39;, name=&#39;C&#39;)</span>
│ │ │  
│ │ │  <span class="gp">In [143]: </span><span class="n">df_grouped</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[143]: </span>
│ │ │  <span class="go">A       int64</span>
│ │ │  <span class="go">C    category</span>
│ │ │  <span class="go">B     float64</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 3, dtype: object</span>
│ │ │  
│ │ │  <span class="gp">In [144]: </span><span class="n">df_set_idx</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">levels</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
│ │ │  <span class="gh">Out[144]: </span><span class="go">CategoricalIndex([&#39;b&#39;, &#39;a&#39;, &#39;c&#39;], categories=[&#39;b&#39;, &#39;a&#39;, &#39;c&#39;], ordered=False, dtype=&#39;category&#39;, name=&#39;C&#39;)</span>
│ │ │  
│ │ │  <span class="gp">In [145]: </span><span class="n">df_set_idx</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[145]: </span>
│ │ │  <span class="go">A       int64</span>
│ │ │  <span class="go">C    category</span>
│ │ │  <span class="go">B       int64</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 3, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="function-read-csv-will-progressively-enumerate-chunks">
│ │ │  <span id="whatsnew-0190-api-autogenerated-chunksize-index"></span><h3>Function <code class="docutils literal notranslate"><span class="pre">read_csv</span></code> will progressively enumerate chunks<a class="headerlink" href="#function-read-csv-will-progressively-enumerate-chunks" title="Link to this heading">#</a></h3>
│ │ │  <p>When <a class="reference internal" href="../reference/api/pandas.read_csv.html#pandas.read_csv" title="pandas.read_csv"><code class="xref py py-func docutils literal notranslate"><span class="pre">read_csv()</span></code></a> is called with <code class="docutils literal notranslate"><span class="pre">chunksize=n</span></code> and without specifying an index,
│ │ │  each chunk used to have an independently generated index from <code class="docutils literal notranslate"><span class="pre">0</span></code> to <code class="docutils literal notranslate"><span class="pre">n-1</span></code>.
│ │ │ @@ -2180,14 +2236,16 @@
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [147]: </span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">StringIO</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">chunksize</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
│ │ │  <span class="gh">Out[147]: </span>
│ │ │  <span class="go">   A  B</span>
│ │ │  <span class="go">0  0  1</span>
│ │ │  <span class="go">1  2  3</span>
│ │ │  <span class="go">2  4  5</span>
│ │ │  <span class="go">3  6  7</span>
│ │ │ +
│ │ │ +<span class="go">[4 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="sparse-changes">
│ │ │  <span id="whatsnew-0190-sparse"></span><h3>Sparse changes<a class="headerlink" href="#sparse-changes" title="Link to this heading">#</a></h3>
│ │ │  <p>These changes allow pandas to handle sparse data with more dtypes, and for work to make a smoother experience with data handling.</p>
│ │ │  <section id="types-int64-and-bool-support-enhancements">
│ │ │ ├── html2text {}
│ │ │ │ @@ -207,37 +207,45 @@
│ │ │ │  In [3]: left
│ │ │ │  Out[3]:
│ │ │ │      a left_val
│ │ │ │  0   1        a
│ │ │ │  1   5        b
│ │ │ │  2  10        c
│ │ │ │  
│ │ │ │ +[3 rows x 2 columns]
│ │ │ │ +
│ │ │ │  In [4]: right
│ │ │ │  Out[4]:
│ │ │ │     a  right_val
│ │ │ │  0  1          1
│ │ │ │  1  2          2
│ │ │ │  2  3          3
│ │ │ │  3  6          6
│ │ │ │  4  7          7
│ │ │ │ +
│ │ │ │ +[5 rows x 2 columns]
│ │ │ │  We typically want to match exactly when possible, and use the most recent value
│ │ │ │  otherwise.
│ │ │ │  In [5]: pd.merge_asof(left, right, on="a")
│ │ │ │  Out[5]:
│ │ │ │      a left_val  right_val
│ │ │ │  0   1        a          1
│ │ │ │  1   5        b          3
│ │ │ │  2  10        c          7
│ │ │ │ +
│ │ │ │ +[3 rows x 3 columns]
│ │ │ │  We can also match rows ONLY with prior data, and not an exact match.
│ │ │ │  In [6]: pd.merge_asof(left, right, on="a", allow_exact_matches=False)
│ │ │ │  Out[6]:
│ │ │ │      a left_val  right_val
│ │ │ │  0   1        a        NaN
│ │ │ │  1   5        b        3.0
│ │ │ │  2  10        c        7.0
│ │ │ │ +
│ │ │ │ +[3 rows x 3 columns]
│ │ │ │  In a typical time-series example, we have trades and quotes and we want to
│ │ │ │  asof-join them. This also illustrates using the by parameter to group data
│ │ │ │  before merging.
│ │ │ │  In [7]: trades = pd.DataFrame(
│ │ │ │     ...:     {
│ │ │ │     ...:         "time": pd.to_datetime(
│ │ │ │     ...:             [
│ │ │ │ @@ -285,37 +293,43 @@
│ │ │ │                       time ticker   price  quantity
│ │ │ │  0 2016-05-25 13:30:00.023   MSFT   51.95        75
│ │ │ │  1 2016-05-25 13:30:00.038   MSFT   51.95       155
│ │ │ │  2 2016-05-25 13:30:00.048   GOOG  720.77       100
│ │ │ │  3 2016-05-25 13:30:00.048   GOOG  720.92       100
│ │ │ │  4 2016-05-25 13:30:00.048   AAPL   98.00       100
│ │ │ │  
│ │ │ │ +[5 rows x 4 columns]
│ │ │ │ +
│ │ │ │  In [10]: quotes
│ │ │ │  Out[10]:
│ │ │ │                       time ticker     bid     ask
│ │ │ │  0 2016-05-25 13:30:00.023   GOOG  720.50  720.93
│ │ │ │  1 2016-05-25 13:30:00.023   MSFT   51.95   51.96
│ │ │ │  2 2016-05-25 13:30:00.030   MSFT   51.97   51.98
│ │ │ │  3 2016-05-25 13:30:00.041   MSFT   51.99   52.00
│ │ │ │  4 2016-05-25 13:30:00.048   GOOG  720.50  720.93
│ │ │ │  5 2016-05-25 13:30:00.049   AAPL   97.99   98.01
│ │ │ │  6 2016-05-25 13:30:00.072   GOOG  720.50  720.88
│ │ │ │  7 2016-05-25 13:30:00.075   MSFT   52.01   52.03
│ │ │ │ +
│ │ │ │ +[8 rows x 4 columns]
│ │ │ │  An asof merge joins on the on, typically a datetimelike field, which is
│ │ │ │  ordered, and in this case we are using a grouper in the by field. This is like
│ │ │ │  a left-outer join, except that forward filling happens automatically taking the
│ │ │ │  most recent non-NaN value.
│ │ │ │  In [11]: pd.merge_asof(trades, quotes, on="time", by="ticker")
│ │ │ │  Out[11]:
│ │ │ │                       time ticker   price  quantity     bid     ask
│ │ │ │  0 2016-05-25 13:30:00.023   MSFT   51.95        75   51.95   51.96
│ │ │ │  1 2016-05-25 13:30:00.038   MSFT   51.95       155   51.97   51.98
│ │ │ │  2 2016-05-25 13:30:00.048   GOOG  720.77       100  720.50  720.93
│ │ │ │  3 2016-05-25 13:30:00.048   GOOG  720.92       100  720.50  720.93
│ │ │ │  4 2016-05-25 13:30:00.048   AAPL   98.00       100     NaN     NaN
│ │ │ │ +
│ │ │ │ +[5 rows x 6 columns]
│ │ │ │  This returns a merged DataFrame with the entries in the same order as the
│ │ │ │  original left passed DataFrame (trades in this case), with the fields of the
│ │ │ │  quotes merged.
│ │ │ │  ******** MMeetthhoodd ..rroolllliinngg(()) iiss nnooww ttiimmee--sseerriieess aawwaarree_## ********
│ │ │ │  .rolling() objects are now time-series aware and can accept a time-series
│ │ │ │  offset (or convertible) for the window argument (_G_H_ _1_3_3_2_7, _G_H_ _1_2_9_9_5). See the
│ │ │ │  full documentation _h_e_r_e.
│ │ │ │ @@ -329,43 +343,51 @@
│ │ │ │  Out[13]:
│ │ │ │                         B
│ │ │ │  2013-01-01 09:00:00  0.0
│ │ │ │  2013-01-01 09:00:01  1.0
│ │ │ │  2013-01-01 09:00:02  2.0
│ │ │ │  2013-01-01 09:00:03  NaN
│ │ │ │  2013-01-01 09:00:04  4.0
│ │ │ │ +
│ │ │ │ +[5 rows x 1 columns]
│ │ │ │  This is a regular frequency index. Using an integer window parameter works to
│ │ │ │  roll along the window frequency.
│ │ │ │  In [14]: dft.rolling(2).sum()
│ │ │ │  Out[14]:
│ │ │ │                         B
│ │ │ │  2013-01-01 09:00:00  NaN
│ │ │ │  2013-01-01 09:00:01  1.0
│ │ │ │  2013-01-01 09:00:02  3.0
│ │ │ │  2013-01-01 09:00:03  NaN
│ │ │ │  2013-01-01 09:00:04  NaN
│ │ │ │  
│ │ │ │ +[5 rows x 1 columns]
│ │ │ │ +
│ │ │ │  In [15]: dft.rolling(2, min_periods=1).sum()
│ │ │ │  Out[15]:
│ │ │ │                         B
│ │ │ │  2013-01-01 09:00:00  0.0
│ │ │ │  2013-01-01 09:00:01  1.0
│ │ │ │  2013-01-01 09:00:02  3.0
│ │ │ │  2013-01-01 09:00:03  2.0
│ │ │ │  2013-01-01 09:00:04  4.0
│ │ │ │ +
│ │ │ │ +[5 rows x 1 columns]
│ │ │ │  Specifying an offset allows a more intuitive specification of the rolling
│ │ │ │  frequency.
│ │ │ │  In [16]: dft.rolling("2s").sum()
│ │ │ │  Out[16]:
│ │ │ │                         B
│ │ │ │  2013-01-01 09:00:00  0.0
│ │ │ │  2013-01-01 09:00:01  1.0
│ │ │ │  2013-01-01 09:00:02  3.0
│ │ │ │  2013-01-01 09:00:03  2.0
│ │ │ │  2013-01-01 09:00:04  4.0
│ │ │ │ +
│ │ │ │ +[5 rows x 1 columns]
│ │ │ │  Using a non-regular, but still monotonic index, rolling with an integer window
│ │ │ │  does not impart any special calculation.
│ │ │ │  In [17]: dft = pd.DataFrame(
│ │ │ │     ....:     {"B": [0, 1, 2, np.nan, 4]},
│ │ │ │     ....:     index=pd.Index(
│ │ │ │     ....:         [
│ │ │ │     ....:             pd.Timestamp("20130101 09:00:00"),
│ │ │ │ @@ -385,54 +407,64 @@
│ │ │ │  foo
│ │ │ │  2013-01-01 09:00:00  0.0
│ │ │ │  2013-01-01 09:00:02  1.0
│ │ │ │  2013-01-01 09:00:03  2.0
│ │ │ │  2013-01-01 09:00:05  NaN
│ │ │ │  2013-01-01 09:00:06  4.0
│ │ │ │  
│ │ │ │ +[5 rows x 1 columns]
│ │ │ │ +
│ │ │ │  In [19]: dft.rolling(2).sum()
│ │ │ │  Out[19]:
│ │ │ │                         B
│ │ │ │  foo
│ │ │ │  2013-01-01 09:00:00  NaN
│ │ │ │  2013-01-01 09:00:02  1.0
│ │ │ │  2013-01-01 09:00:03  3.0
│ │ │ │  2013-01-01 09:00:05  NaN
│ │ │ │  2013-01-01 09:00:06  NaN
│ │ │ │ +
│ │ │ │ +[5 rows x 1 columns]
│ │ │ │  Using the time-specification generates variable windows for this sparse data.
│ │ │ │  In [20]: dft.rolling("2s").sum()
│ │ │ │  Out[20]:
│ │ │ │                         B
│ │ │ │  foo
│ │ │ │  2013-01-01 09:00:00  0.0
│ │ │ │  2013-01-01 09:00:02  1.0
│ │ │ │  2013-01-01 09:00:03  3.0
│ │ │ │  2013-01-01 09:00:05  NaN
│ │ │ │  2013-01-01 09:00:06  4.0
│ │ │ │ +
│ │ │ │ +[5 rows x 1 columns]
│ │ │ │  Furthermore, we now allow an optional on parameter to specify a column (rather
│ │ │ │  than the default of the index) in a DataFrame.
│ │ │ │  In [21]: dft = dft.reset_index()
│ │ │ │  
│ │ │ │  In [22]: dft
│ │ │ │  Out[22]:
│ │ │ │                    foo    B
│ │ │ │  0 2013-01-01 09:00:00  0.0
│ │ │ │  1 2013-01-01 09:00:02  1.0
│ │ │ │  2 2013-01-01 09:00:03  2.0
│ │ │ │  3 2013-01-01 09:00:05  NaN
│ │ │ │  4 2013-01-01 09:00:06  4.0
│ │ │ │  
│ │ │ │ +[5 rows x 2 columns]
│ │ │ │ +
│ │ │ │  In [23]: dft.rolling("2s", on="foo").sum()
│ │ │ │  Out[23]:
│ │ │ │                    foo    B
│ │ │ │  0 2013-01-01 09:00:00  0.0
│ │ │ │  1 2013-01-01 09:00:02  1.0
│ │ │ │  2 2013-01-01 09:00:03  3.0
│ │ │ │  3 2013-01-01 09:00:05  NaN
│ │ │ │  4 2013-01-01 09:00:06  4.0
│ │ │ │ +
│ │ │ │ +[5 rows x 2 columns]
│ │ │ │  ******** MMeetthhoodd rreeaadd__ccssvv hhaass iimmpprroovveedd ssuuppppoorrtt ffoorr dduupplliiccaattee ccoolluummnn nnaammeess_## ********
│ │ │ │  _D_u_p_l_i_c_a_t_e_ _c_o_l_u_m_n_ _n_a_m_e_s are now supported in _r_e_a_d___c_s_v_(_) whether they are in the
│ │ │ │  file or passed in as the names parameter (_G_H_ _7_1_6_0, _G_H_ _9_4_2_4)
│ │ │ │  In [24]: data = "0,1,2\n3,4,5"
│ │ │ │  
│ │ │ │  In [25]: names = ["a", "b", "a"]
│ │ │ │  PPrreevviioouuss bbeehhaavviioorr:
│ │ │ │ @@ -505,65 +537,67 @@
│ │ │ │  In [28]: pd.read_csv(StringIO(data))
│ │ │ │  Out[28]:
│ │ │ │    col1 col2  col3
│ │ │ │  0    a    b     1
│ │ │ │  1    a    b     2
│ │ │ │  2    c    d     3
│ │ │ │  
│ │ │ │ +[3 rows x 3 columns]
│ │ │ │ +
│ │ │ │  In [29]: pd.read_csv(StringIO(data)).dtypes
│ │ │ │  Out[29]:
│ │ │ │  col1    object
│ │ │ │  col2    object
│ │ │ │  col3     int64
│ │ │ │ -dtype: object
│ │ │ │ +Length: 3, dtype: object
│ │ │ │  
│ │ │ │  In [30]: pd.read_csv(StringIO(data), dtype="category").dtypes
│ │ │ │  Out[30]:
│ │ │ │  col1    category
│ │ │ │  col2    category
│ │ │ │  col3    category
│ │ │ │ -dtype: object
│ │ │ │ +Length: 3, dtype: object
│ │ │ │  Individual columns can be parsed as a Categorical using a dict specification
│ │ │ │  In [31]: pd.read_csv(StringIO(data), dtype={"col1": "category"}).dtypes
│ │ │ │  Out[31]:
│ │ │ │  col1    category
│ │ │ │  col2      object
│ │ │ │  col3       int64
│ │ │ │ -dtype: object
│ │ │ │ +Length: 3, dtype: object
│ │ │ │  Note
│ │ │ │  The resulting categories will always be parsed as strings (object dtype). If
│ │ │ │  the categories are numeric they can be converted using the _t_o___n_u_m_e_r_i_c_(_)
│ │ │ │  function, or as appropriate, another converter such as _t_o___d_a_t_e_t_i_m_e_(_).
│ │ │ │  In [32]: df = pd.read_csv(StringIO(data), dtype="category")
│ │ │ │  
│ │ │ │  In [33]: df.dtypes
│ │ │ │  Out[33]:
│ │ │ │  col1    category
│ │ │ │  col2    category
│ │ │ │  col3    category
│ │ │ │ -dtype: object
│ │ │ │ +Length: 3, dtype: object
│ │ │ │  
│ │ │ │  In [34]: df["col3"]
│ │ │ │  Out[34]:
│ │ │ │  0    1
│ │ │ │  1    2
│ │ │ │  2    3
│ │ │ │ -Name: col3, dtype: category
│ │ │ │ +Name: col3, Length: 3, dtype: category
│ │ │ │  Categories (3, object): ['1', '2', '3']
│ │ │ │  
│ │ │ │  In [35]: new_categories = pd.to_numeric(df["col3"].cat.categories)
│ │ │ │  
│ │ │ │  In [36]: df["col3"] = df["col3"].cat.rename_categories(new_categories)
│ │ │ │  
│ │ │ │  In [37]: df["col3"]
│ │ │ │  Out[37]:
│ │ │ │  0    1
│ │ │ │  1    2
│ │ │ │  2    3
│ │ │ │ -Name: col3, dtype: category
│ │ │ │ +Name: col3, Length: 3, dtype: category
│ │ │ │  Categories (3, int64): [1, 2, 3]
│ │ │ │  ******** CCaatteeggoorriiccaall ccoonnccaatteennaattiioonn_## ********
│ │ │ │      * A function union_categoricals() has been added for combining
│ │ │ │        categoricals, see _U_n_i_o_n_i_n_g_ _C_a_t_e_g_o_r_i_c_a_l_s (_G_H_ _1_3_3_6_1, _G_H_ _1_3_7_6_3, _G_H_ _1_3_8_4_6, _G_H
│ │ │ │        _1_4_1_7_3)
│ │ │ │        In [38]: from pandas.api.types import union_categoricals
│ │ │ │  
│ │ │ │ @@ -586,15 +620,15 @@
│ │ │ │  NNeeww bbeehhaavviioorr:
│ │ │ │  In [44]: pd.concat([s1, s2])
│ │ │ │  Out[44]:
│ │ │ │  0    a
│ │ │ │  1    b
│ │ │ │  0    b
│ │ │ │  1    c
│ │ │ │ -dtype: object
│ │ │ │ +Length: 4, dtype: object
│ │ │ │  ******** SSeemmii--mmoonntthh ooffffsseettss_## ********
│ │ │ │  pandas has gained new frequency offsets, SemiMonthEnd (‘SM’) and SemiMonthBegin
│ │ │ │  (‘SMS’). These provide date offsets anchored (by default) to the 15th and end
│ │ │ │  of month, and 15th and 1st of month respectively. (_G_H_ _1_5_4_3)
│ │ │ │  In [45]: from pandas.tseries.offsets import SemiMonthEnd, SemiMonthBegin
│ │ │ │  SSeemmiiMMoonntthhEEnndd:
│ │ │ │  In [46]: pd.Timestamp("2016-01-01") + SemiMonthEnd()
│ │ │ │ @@ -664,14 +698,16 @@
│ │ │ │  In [57]: idx.str.extractall(r"[ab](?P<digit>\d)")
│ │ │ │  Out[57]:
│ │ │ │          digit
│ │ │ │    match
│ │ │ │  0 0         1
│ │ │ │    1         2
│ │ │ │  1 0         1
│ │ │ │ +
│ │ │ │ +[3 rows x 1 columns]
│ │ │ │  Index.astype() now accepts an optional boolean argument copy, which allows
│ │ │ │  optional copying if the requirements on dtype are satisfied (_G_H_ _1_3_2_0_9)
│ │ │ │  ******** GGooooggllee BBiiggQQuueerryy eennhhaanncceemmeennttss_## ********
│ │ │ │      * The _r_e_a_d___g_b_q_(_) method has gained the dialect argument to allow users to
│ │ │ │        specify whether to use BigQuery’s legacy SQL or BigQuery’s standard SQL.
│ │ │ │        See the _d_o_c_s for more details (_G_H_ _1_3_6_1_5).
│ │ │ │      * The _t_o___g_b_q_(_) method now allows the DataFrame column order to differ from
│ │ │ │ @@ -704,15 +740,15 @@
│ │ │ │  dtype: object
│ │ │ │  NNeeww bbeehhaavviioorr:
│ │ │ │  In [58]: pd.get_dummies(["a", "b", "a", "c"]).dtypes
│ │ │ │  Out[58]:
│ │ │ │  a    bool
│ │ │ │  b    bool
│ │ │ │  c    bool
│ │ │ │ -dtype: object
│ │ │ │ +Length: 3, dtype: object
│ │ │ │  ******** DDoowwnnccaasstt vvaalluueess ttoo ssmmaalllleesstt ppoossssiibbllee ddttyyppee iinn ttoo__nnuummeerriicc_## ********
│ │ │ │  pd.to_numeric() now accepts a downcast parameter, which will downcast the data
│ │ │ │  if possible to smallest specified numerical dtype (_G_H_ _1_3_3_5_2)
│ │ │ │  In [59]: s = ["1", 2, 3]
│ │ │ │  
│ │ │ │  In [60]: pd.to_numeric(s, downcast="unsigned")
│ │ │ │  Out[60]: array([1, 2, 3], dtype=uint8)
│ │ │ │ @@ -808,14 +844,16 @@
│ │ │ │                           date  a
│ │ │ │        v d
│ │ │ │        1 2015-01-04 2015-01-04  0
│ │ │ │        2 2015-01-11 2015-01-11  1
│ │ │ │        3 2015-01-18 2015-01-18  2
│ │ │ │        4 2015-01-25 2015-01-25  3
│ │ │ │        5 2015-02-01 2015-02-01  4
│ │ │ │ +
│ │ │ │ +      [5 rows x 2 columns]
│ │ │ │        In [74]: df.resample("M", on="date")[["a"]].sum()
│ │ │ │        Out[74]:
│ │ │ │                    a
│ │ │ │        date
│ │ │ │        2015-01-31  6
│ │ │ │        2015-02-28  4
│ │ │ │  
│ │ │ │ @@ -874,19 +912,23 @@
│ │ │ │  
│ │ │ │        In [71]: df
│ │ │ │        Out[71]:
│ │ │ │              A  B  C
│ │ │ │        row1  2  3  4
│ │ │ │        row2  7  5  8
│ │ │ │  
│ │ │ │ +      [2 rows x 3 columns]
│ │ │ │ +
│ │ │ │        In [72]: df.sort_values(by="row2", axis=1)
│ │ │ │        Out[72]:
│ │ │ │              B  A  C
│ │ │ │        row1  3  2  4
│ │ │ │        row2  5  7  8
│ │ │ │ +
│ │ │ │ +      [2 rows x 3 columns]
│ │ │ │      * Added documentation to _I_/_O regarding the perils of reading in columns
│ │ │ │        with mixed dtypes and how to handle it (_G_H_ _1_3_7_4_6)
│ │ │ │      * _t_o___h_t_m_l_(_) now has a border argument to control the value in the opening
│ │ │ │        <table> tag. The default is the value of the html.border option, which
│ │ │ │        defaults to 1. This also affects the notebook HTML repr, but since
│ │ │ │        Jupyter’s CSS includes a border-width attribute, the visual effect is the
│ │ │ │        same. (_G_H_ _1_1_5_6_3).
│ │ │ │ @@ -948,27 +990,29 @@
│ │ │ │  
│ │ │ │  In [77]: s1 + s2
│ │ │ │  Out[77]:
│ │ │ │  A    3.0
│ │ │ │  B    4.0
│ │ │ │  C    NaN
│ │ │ │  D    NaN
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 4, dtype: float64
│ │ │ │  
│ │ │ │  In [78]: df1 = pd.DataFrame([1, 2, 3], index=list("ABC"))
│ │ │ │  
│ │ │ │  In [79]: df2 = pd.DataFrame([2, 2, 2], index=list("ABD"))
│ │ │ │  
│ │ │ │  In [80]: df1 + df2
│ │ │ │  Out[80]:
│ │ │ │       0
│ │ │ │  A  3.0
│ │ │ │  B  4.0
│ │ │ │  C  NaN
│ │ │ │  D  NaN
│ │ │ │ +
│ │ │ │ +[4 rows x 1 columns]
│ │ │ │  ****** CCoommppaarriissoonn ooppeerraattoorrss_## ******
│ │ │ │  Comparison operators raise ValueError when .index are different.
│ │ │ │  PPrreevviioouuss bbeehhaavviioorr (Series):
│ │ │ │  Series compared values ignoring the .index as long as both had the same length:
│ │ │ │  In [1]: s1 == s2
│ │ │ │  Out[1]:
│ │ │ │  A    False
│ │ │ │ @@ -988,15 +1032,15 @@
│ │ │ │  methods section below:
│ │ │ │  In [82]: s1.eq(s2)
│ │ │ │  Out[82]:
│ │ │ │  A    False
│ │ │ │  B     True
│ │ │ │  C    False
│ │ │ │  D    False
│ │ │ │ -dtype: bool
│ │ │ │ +Length: 4, dtype: bool
│ │ │ │  CCuurrrreenntt bbeehhaavviioorr (DataFrame, no change):
│ │ │ │  In [3]: df1 == df2
│ │ │ │  Out[3]:
│ │ │ │  ValueError: Can only compare identically-labeled DataFrame objects
│ │ │ │  ****** LLooggiiccaall ooppeerraattoorrss_## ******
│ │ │ │  Logical operators align both .index of left and right hand side.
│ │ │ │  PPrreevviioouuss bbeehhaavviioorr (Series), only left hand side index was kept:
│ │ │ │ @@ -1015,61 +1059,63 @@
│ │ │ │  
│ │ │ │  In [85]: s1 & s2
│ │ │ │  Out[85]:
│ │ │ │  A     True
│ │ │ │  B    False
│ │ │ │  C    False
│ │ │ │  D    False
│ │ │ │ -dtype: bool
│ │ │ │ +Length: 4, dtype: bool
│ │ │ │  Note
│ │ │ │  Series logical operators fill a NaN result with False.
│ │ │ │  Note
│ │ │ │  To achieve the same result as previous versions (compare values based on only
│ │ │ │  left hand side index), you can use reindex_like:
│ │ │ │  In [86]: s1 & s2.reindex_like(s1)
│ │ │ │  Out[86]:
│ │ │ │  A     True
│ │ │ │  B    False
│ │ │ │  C    False
│ │ │ │ -dtype: bool
│ │ │ │ +Length: 3, dtype: bool
│ │ │ │  CCuurrrreenntt bbeehhaavviioorr (DataFrame, no change):
│ │ │ │  In [87]: df1 = pd.DataFrame([True, False, True], index=list("ABC"))
│ │ │ │  
│ │ │ │  In [88]: df2 = pd.DataFrame([True, True, True], index=list("ABD"))
│ │ │ │  
│ │ │ │  In [89]: df1 & df2
│ │ │ │  Out[89]:
│ │ │ │         0
│ │ │ │  A   True
│ │ │ │  B  False
│ │ │ │  C  False
│ │ │ │  D  False
│ │ │ │ +
│ │ │ │ +[4 rows x 1 columns]
│ │ │ │  ****** FFlleexxiibbllee ccoommppaarriissoonn mmeetthhooddss_## ******
│ │ │ │  Series flexible comparison methods like eq, ne, le, lt, ge and gt now align
│ │ │ │  both index. Use these operators if you want to compare two Series which has the
│ │ │ │  different index.
│ │ │ │  In [90]: s1 = pd.Series([1, 2, 3], index=["a", "b", "c"])
│ │ │ │  
│ │ │ │  In [91]: s2 = pd.Series([2, 2, 2], index=["b", "c", "d"])
│ │ │ │  
│ │ │ │  In [92]: s1.eq(s2)
│ │ │ │  Out[92]:
│ │ │ │  a    False
│ │ │ │  b     True
│ │ │ │  c    False
│ │ │ │  d    False
│ │ │ │ -dtype: bool
│ │ │ │ +Length: 4, dtype: bool
│ │ │ │  
│ │ │ │  In [93]: s1.ge(s2)
│ │ │ │  Out[93]:
│ │ │ │  a    False
│ │ │ │  b     True
│ │ │ │  c     True
│ │ │ │  d    False
│ │ │ │ -dtype: bool
│ │ │ │ +Length: 4, dtype: bool
│ │ │ │  Previously, this worked the same as comparison operators (see above).
│ │ │ │  ******** SSeerriieess ttyyppee pprroommoottiioonn oonn aassssiiggnnmmeenntt_## ********
│ │ │ │  A Series will now correctly promote its dtype for assignment with incompat
│ │ │ │  values to the current dtype (_G_H_ _1_3_2_3_4)
│ │ │ │  In [94]: s = pd.Series()
│ │ │ │  PPrreevviioouuss bbeehhaavviioorr:
│ │ │ │  In [2]: s["a"] = pd.Timestamp("2016-01-01")
│ │ │ │ @@ -1081,15 +1127,15 @@
│ │ │ │  
│ │ │ │  In [96]: s["b"] = 3.0
│ │ │ │  
│ │ │ │  In [97]: s
│ │ │ │  Out[97]:
│ │ │ │  a    2016-01-01 00:00:00
│ │ │ │  b                    3.0
│ │ │ │ -dtype: object
│ │ │ │ +Length: 2, dtype: object
│ │ │ │  
│ │ │ │  In [98]: s.dtype
│ │ │ │  Out[98]: dtype('O')
│ │ │ │  ******** FFuunnccttiioonn ..ttoo__ddaatteettiimmee(()) cchhaannggeess_## ********
│ │ │ │  Previously if .to_datetime() encountered mixed integers/floats and strings, but
│ │ │ │  no datetimes with errors='coerce' it would convert all to NaT.
│ │ │ │  PPrreevviioouuss bbeehhaavviioorr:
│ │ │ │ @@ -1116,21 +1162,25 @@
│ │ │ │  In [100]: df1 = pd.DataFrame({"key": [1], "v1": [10]})
│ │ │ │  
│ │ │ │  In [101]: df1
│ │ │ │  Out[101]:
│ │ │ │     key  v1
│ │ │ │  0    1  10
│ │ │ │  
│ │ │ │ +[1 rows x 2 columns]
│ │ │ │ +
│ │ │ │  In [102]: df2 = pd.DataFrame({"key": [1, 2], "v1": [20, 30]})
│ │ │ │  
│ │ │ │  In [103]: df2
│ │ │ │  Out[103]:
│ │ │ │     key  v1
│ │ │ │  0    1  20
│ │ │ │  1    2  30
│ │ │ │ +
│ │ │ │ +[2 rows x 2 columns]
│ │ │ │  PPrreevviioouuss bbeehhaavviioorr:
│ │ │ │  In [5]: pd.merge(df1, df2, how='outer')
│ │ │ │  Out[5]:
│ │ │ │     key    v1
│ │ │ │  0  1.0  10.0
│ │ │ │  1  1.0  20.0
│ │ │ │  2  2.0  30.0
│ │ │ │ @@ -1145,33 +1195,37 @@
│ │ │ │  In [104]: pd.merge(df1, df2, how="outer")
│ │ │ │  Out[104]:
│ │ │ │     key  v1
│ │ │ │  0    1  10
│ │ │ │  1    1  20
│ │ │ │  2    2  30
│ │ │ │  
│ │ │ │ +[3 rows x 2 columns]
│ │ │ │ +
│ │ │ │  In [105]: pd.merge(df1, df2, how="outer").dtypes
│ │ │ │  Out[105]:
│ │ │ │  key    int64
│ │ │ │  v1     int64
│ │ │ │ -dtype: object
│ │ │ │ +Length: 2, dtype: object
│ │ │ │  Of course if you have missing values that are introduced, then the resulting
│ │ │ │  dtype will be upcast, which is unchanged from previous.
│ │ │ │  In [106]: pd.merge(df1, df2, how="outer", on="key")
│ │ │ │  Out[106]:
│ │ │ │     key  v1_x  v1_y
│ │ │ │  0    1  10.0    20
│ │ │ │  1    2   NaN    30
│ │ │ │  
│ │ │ │ +[2 rows x 3 columns]
│ │ │ │ +
│ │ │ │  In [107]: pd.merge(df1, df2, how="outer", on="key").dtypes
│ │ │ │  Out[107]:
│ │ │ │  key       int64
│ │ │ │  v1_x    float64
│ │ │ │  v1_y      int64
│ │ │ │ -dtype: object
│ │ │ │ +Length: 3, dtype: object
│ │ │ │  ******** MMeetthhoodd ..ddeessccrriibbee(()) cchhaannggeess_## ********
│ │ │ │  Percentile identifiers in the index of a .describe() output will now be rounded
│ │ │ │  to the least precision that keeps them distinct (_G_H_ _1_3_1_0_4)
│ │ │ │  In [108]: s = pd.Series([0, 1, 2, 3, 4])
│ │ │ │  
│ │ │ │  In [109]: df = pd.DataFrame([0, 1, 2, 3, 4])
│ │ │ │  PPrreevviioouuss bbeehhaavviioorr:
│ │ │ │ @@ -1209,15 +1263,15 @@
│ │ │ │  0.05%     0.002000
│ │ │ │  0.1%      0.004000
│ │ │ │  50%       2.000000
│ │ │ │  99.9%     3.996000
│ │ │ │  99.95%    3.998000
│ │ │ │  99.99%    3.999600
│ │ │ │  max       4.000000
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 12, dtype: float64
│ │ │ │  
│ │ │ │  In [111]: df.describe(percentiles=[0.0001, 0.0005, 0.001, 0.999, 0.9995,
│ │ │ │  0.9999])
│ │ │ │  Out[111]:
│ │ │ │                 0
│ │ │ │  count   5.000000
│ │ │ │  mean    2.000000
│ │ │ │ @@ -1227,14 +1281,16 @@
│ │ │ │  0.05%   0.002000
│ │ │ │  0.1%    0.004000
│ │ │ │  50%     2.000000
│ │ │ │  99.9%   3.996000
│ │ │ │  99.95%  3.998000
│ │ │ │  99.99%  3.999600
│ │ │ │  max     4.000000
│ │ │ │ +
│ │ │ │ +[12 rows x 1 columns]
│ │ │ │  Furthermore:
│ │ │ │      * Passing duplicated percentiles will now raise a ValueError.
│ │ │ │      * Bug in .describe() on a DataFrame with a mixed-dtype column index, which
│ │ │ │        would previously raise a TypeError (_G_H_ _1_3_2_8_8)
│ │ │ │  ******** PPeerriioodd cchhaannggeess_## ********
│ │ │ │  ****** TThhee PPeerriiooddIInnddeexx nnooww hhaass ppeerriioodd ddttyyppee_## ******
│ │ │ │  PeriodIndex now has its own period dtype. The period dtype is a pandas
│ │ │ │ @@ -1448,26 +1504,26 @@
│ │ │ │  ordered=False, dtype='category', name='C')
│ │ │ │  
│ │ │ │  In [143]: df_grouped.reset_index().dtypes
│ │ │ │  Out[143]:
│ │ │ │  A       int64
│ │ │ │  C    category
│ │ │ │  B     float64
│ │ │ │ -dtype: object
│ │ │ │ +Length: 3, dtype: object
│ │ │ │  
│ │ │ │  In [144]: df_set_idx.index.levels[1]
│ │ │ │  Out[144]: CategoricalIndex(['b', 'a', 'c'], categories=['b', 'a', 'c'],
│ │ │ │  ordered=False, dtype='category', name='C')
│ │ │ │  
│ │ │ │  In [145]: df_set_idx.reset_index().dtypes
│ │ │ │  Out[145]:
│ │ │ │  A       int64
│ │ │ │  C    category
│ │ │ │  B       int64
│ │ │ │ -dtype: object
│ │ │ │ +Length: 3, dtype: object
│ │ │ │  ******** FFuunnccttiioonn rreeaadd__ccssvv wwiillll pprrooggrreessssiivveellyy eennuummeerraattee cchhuunnkkss_## ********
│ │ │ │  When _r_e_a_d___c_s_v_(_) is called with chunksize=n and without specifying an index,
│ │ │ │  each chunk used to have an independently generated index from 0 to n-1. They
│ │ │ │  are now given instead a progressive index, starting from 0 for the first chunk,
│ │ │ │  from n for the second, and so on, so that, when concatenated, they are
│ │ │ │  identical to the result of calling _r_e_a_d___c_s_v_(_) without the chunksize= argument
│ │ │ │  (_G_H_ _1_2_1_8_5).
│ │ │ │ @@ -1484,14 +1540,16 @@
│ │ │ │  In [147]: pd.concat(pd.read_csv(StringIO(data), chunksize=2))
│ │ │ │  Out[147]:
│ │ │ │     A  B
│ │ │ │  0  0  1
│ │ │ │  1  2  3
│ │ │ │  2  4  5
│ │ │ │  3  6  7
│ │ │ │ +
│ │ │ │ +[4 rows x 2 columns]
│ │ │ │  ******** SSppaarrssee cchhaannggeess_## ********
│ │ │ │  These changes allow pandas to handle sparse data with more dtypes, and for work
│ │ │ │  to make a smoother experience with data handling.
│ │ │ │  ****** TTyyppeess iinntt6644 aanndd bbooooll ssuuppppoorrtt eennhhaanncceemmeennttss_## ******
│ │ │ │  Sparse data structures now gained enhanced support of int64 and bool dtype (_G_H
│ │ │ │  _6_6_7, _G_H_ _1_3_8_4_9).
│ │ │ │  Previously, sparse data were float64 dtype by default, even if all inputs were
│ │ ├── ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.20.0.html
│ │ │ @@ -784,43 +784,49 @@
│ │ │  <span class="go">2000-01-04       NaN       NaN       NaN</span>
│ │ │  <span class="go">2000-01-05       NaN       NaN       NaN</span>
│ │ │  <span class="go">2000-01-06       NaN       NaN       NaN</span>
│ │ │  <span class="go">2000-01-07       NaN       NaN       NaN</span>
│ │ │  <span class="go">2000-01-08  0.113648 -1.478427  0.524988</span>
│ │ │  <span class="go">2000-01-09  0.404705  0.577046 -1.715002</span>
│ │ │  <span class="go">2000-01-10 -1.039268 -0.370647 -1.157892</span>
│ │ │ +
│ │ │ +<span class="go">[10 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>One can operate using string function names, callables, lists, or dictionaries of these.</p>
│ │ │  <p>Using a single function is equivalent to <code class="docutils literal notranslate"><span class="pre">.apply</span></code>.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [4]: </span><span class="n">df</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span><span class="s1">&#39;sum&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[4]: </span>
│ │ │  <span class="go">A   -1.068226</span>
│ │ │  <span class="go">B   -1.387015</span>
│ │ │  <span class="go">C   -4.892029</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 3, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Multiple aggregations with a list of functions.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [5]: </span><span class="n">df</span><span class="o">.</span><span class="n">agg</span><span class="p">([</span><span class="s1">&#39;sum&#39;</span><span class="p">,</span> <span class="s1">&#39;min&#39;</span><span class="p">])</span>
│ │ │  <span class="gh">Out[5]: </span>
│ │ │  <span class="go">            A         B         C</span>
│ │ │  <span class="go">sum -1.068226 -1.387015 -4.892029</span>
│ │ │  <span class="go">min -1.135632 -1.478427 -1.715002</span>
│ │ │ +
│ │ │ +<span class="go">[2 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Using a dict provides the ability to apply specific aggregations per column.
│ │ │  You will get a matrix-like output of all of the aggregators. The output has one column
│ │ │  per unique function. Those functions applied to a particular column will be <code class="docutils literal notranslate"><span class="pre">NaN</span></code>:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [6]: </span><span class="n">df</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span><span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;sum&#39;</span><span class="p">,</span> <span class="s1">&#39;min&#39;</span><span class="p">],</span> <span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;min&#39;</span><span class="p">,</span> <span class="s1">&#39;max&#39;</span><span class="p">]})</span>
│ │ │  <span class="gh">Out[6]: </span>
│ │ │  <span class="go">            A         B</span>
│ │ │  <span class="go">sum -1.068226       NaN</span>
│ │ │  <span class="go">min -1.135632 -1.478427</span>
│ │ │  <span class="go">max       NaN  1.212112</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>The API also supports a <code class="docutils literal notranslate"><span class="pre">.transform()</span></code> function for broadcasting results.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [7]: </span><span class="n">df</span><span class="o">.</span><span class="n">transform</span><span class="p">([</span><span class="s1">&#39;abs&#39;</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">min</span><span class="p">()])</span>
│ │ │  <span class="gh">Out[7]: </span>
│ │ │  <span class="go">                   A                   B                   C          </span>
│ │ │  <span class="go">                 abs  &lt;lambda&gt;       abs  &lt;lambda&gt;       abs  &lt;lambda&gt;</span>
│ │ │ @@ -830,14 +836,16 @@
│ │ │  <span class="go">2000-01-04       NaN       NaN       NaN       NaN       NaN       NaN</span>
│ │ │  <span class="go">2000-01-05       NaN       NaN       NaN       NaN       NaN       NaN</span>
│ │ │  <span class="go">2000-01-06       NaN       NaN       NaN       NaN       NaN       NaN</span>
│ │ │  <span class="go">2000-01-07       NaN       NaN       NaN       NaN       NaN       NaN</span>
│ │ │  <span class="go">2000-01-08  0.113648  1.249281  1.478427  0.000000  0.524988  2.239990</span>
│ │ │  <span class="go">2000-01-09  0.404705  1.540338  0.577046  2.055473  1.715002  0.000000</span>
│ │ │  <span class="go">2000-01-10  1.039268  0.096364  0.370647  1.107780  1.157892  0.557110</span>
│ │ │ +
│ │ │ +<span class="go">[10 rows x 6 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>When presented with mixed dtypes that cannot be aggregated, <code class="docutils literal notranslate"><span class="pre">.agg()</span></code> will only take the valid
│ │ │  aggregations. This is similar to how groupby <code class="docutils literal notranslate"><span class="pre">.agg()</span></code> works. (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/15015">GH 15015</a>)</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [8]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;A&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
│ │ │  <span class="gp">   ...: </span>                   <span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">],</span>
│ │ │  <span class="gp">   ...: </span>                   <span class="s1">&#39;C&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;foo&#39;</span><span class="p">,</span> <span class="s1">&#39;bar&#39;</span><span class="p">,</span> <span class="s1">&#39;baz&#39;</span><span class="p">],</span>
│ │ │ @@ -846,15 +854,15 @@
│ │ │  
│ │ │  <span class="gp">In [9]: </span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[9]: </span>
│ │ │  <span class="go">A             int64</span>
│ │ │  <span class="go">B           float64</span>
│ │ │  <span class="go">C            object</span>
│ │ │  <span class="go">D    datetime64[ns]</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 4, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">10</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">agg</span><span class="p">([</span><span class="s1">&#39;min&#39;</span><span class="p">,</span> <span class="s1">&#39;sum&#39;</span><span class="p">])</span>
│ │ │  <span class="n">Out</span><span class="p">[</span><span class="mi">10</span><span class="p">]:</span>
│ │ │       <span class="n">A</span>    <span class="n">B</span>          <span class="n">C</span>          <span class="n">D</span>
│ │ │  <span class="nb">min</span>  <span class="mi">1</span>  <span class="mf">1.0</span>        <span class="n">bar</span> <span class="mi">2013</span><span class="o">-</span><span class="mi">01</span><span class="o">-</span><span class="mi">01</span>
│ │ │  <span class="nb">sum</span>  <span class="mi">6</span>  <span class="mf">6.0</span>  <span class="n">foobarbaz</span>        <span class="n">NaT</span>
│ │ │ @@ -867,21 +875,21 @@
│ │ │  fixed-width text files and <a class="reference internal" href="../reference/api/pandas.read_excel.html#pandas.read_excel" title="pandas.read_excel"><code class="xref py py-func docutils literal notranslate"><span class="pre">read_excel()</span></code></a> for parsing Excel files, now accept the <code class="docutils literal notranslate"><span class="pre">dtype</span></code> keyword argument for specifying the types of specific columns (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/14295">GH 14295</a>). See the <a class="reference internal" href="../user_guide/io.html#io-dtypes"><span class="std std-ref">io docs</span></a> for more information.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [10]: </span><span class="n">data</span> <span class="o">=</span> <span class="s2">&quot;a  b</span><span class="se">\n</span><span class="s2">1  2</span><span class="se">\n</span><span class="s2">3  4&quot;</span>
│ │ │  
│ │ │  <span class="gp">In [11]: </span><span class="n">pd</span><span class="o">.</span><span class="n">read_fwf</span><span class="p">(</span><span class="n">StringIO</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[11]: </span>
│ │ │  <span class="go">a    int64</span>
│ │ │  <span class="go">b    int64</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 2, dtype: object</span>
│ │ │  
│ │ │  <span class="gp">In [12]: </span><span class="n">pd</span><span class="o">.</span><span class="n">read_fwf</span><span class="p">(</span><span class="n">StringIO</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="s1">&#39;float64&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="s1">&#39;object&#39;</span><span class="p">})</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[12]: </span>
│ │ │  <span class="go">a    float64</span>
│ │ │  <span class="go">b     object</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 2, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="method-to-datetime-has-gained-an-origin-parameter">
│ │ │  <span id="whatsnew-0120-enhancements-datetime-origin"></span><h3>Method <code class="docutils literal notranslate"><span class="pre">.to_datetime()</span></code> has gained an <code class="docutils literal notranslate"><span class="pre">origin</span></code> parameter<a class="headerlink" href="#method-to-datetime-has-gained-an-origin-parameter" title="Link to this heading">#</a></h3>
│ │ │  <p><a class="reference internal" href="../reference/api/pandas.to_datetime.html#pandas.to_datetime" title="pandas.to_datetime"><code class="xref py py-func docutils literal notranslate"><span class="pre">to_datetime()</span></code></a> has gained a new parameter, <code class="docutils literal notranslate"><span class="pre">origin</span></code>, to define a reference date
│ │ │  from where to compute the resulting timestamps when parsing numerical values with a specific <code class="docutils literal notranslate"><span class="pre">unit</span></code> specified. (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/11276">GH 11276</a>, <a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/11745">GH 11745</a>)</p>
│ │ │ @@ -920,24 +928,28 @@
│ │ │  <span class="go">baz   one     1  2</span>
│ │ │  <span class="go">      two     1  3</span>
│ │ │  <span class="go">foo   one     2  4</span>
│ │ │  <span class="go">      two     2  5</span>
│ │ │  <span class="go">qux   one     3  6</span>
│ │ │  <span class="go">      two     3  7</span>
│ │ │  
│ │ │ +<span class="go">[8 rows x 2 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [19]: </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;second&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
│ │ │  <span class="gh">Out[19]: </span>
│ │ │  <span class="go">          B</span>
│ │ │  <span class="go">second A   </span>
│ │ │  <span class="go">one    1  2</span>
│ │ │  <span class="go">       2  4</span>
│ │ │  <span class="go">       3  6</span>
│ │ │  <span class="go">two    1  4</span>
│ │ │  <span class="go">       2  5</span>
│ │ │  <span class="go">       3  7</span>
│ │ │ +
│ │ │ +<span class="go">[6 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="better-support-for-compressed-urls-in-read-csv">
│ │ │  <span id="whatsnew-0200-enhancements-compressed-urls"></span><h3>Better support for compressed URLs in <code class="docutils literal notranslate"><span class="pre">read_csv</span></code><a class="headerlink" href="#better-support-for-compressed-urls-in-read-csv" title="Link to this heading">#</a></h3>
│ │ │  <p>The compression code was refactored (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/12688">GH 12688</a>). As a result, reading
│ │ │  dataframes from URLs in <a class="reference internal" href="../reference/api/pandas.read_csv.html#pandas.read_csv" title="pandas.read_csv"><code class="xref py py-func docutils literal notranslate"><span class="pre">read_csv()</span></code></a> or <a class="reference internal" href="../reference/api/pandas.read_table.html#pandas.read_table" title="pandas.read_table"><code class="xref py py-func docutils literal notranslate"><span class="pre">read_table()</span></code></a> now supports
│ │ │ @@ -1297,14 +1309,16 @@
│ │ │  
│ │ │  <span class="gp">In [23]: </span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
│ │ │  <span class="gh">Out[23]: </span>
│ │ │  <span class="go">              A  B</span>
│ │ │  <span class="go">first second      </span>
│ │ │  <span class="go">bar   one     1  0</span>
│ │ │  <span class="go">      two     1  1</span>
│ │ │ +
│ │ │ +<span class="go">[2 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="pickle-file-io-now-supports-compression">
│ │ │  <span id="whatsnew-0200-enhancements-pickle-compression"></span><h3>Pickle file IO now supports compression<a class="headerlink" href="#pickle-file-io-now-supports-compression" title="Link to this heading">#</a></h3>
│ │ │  <p><a class="reference internal" href="../reference/api/pandas.read_pickle.html#pandas.read_pickle" title="pandas.read_pickle"><code class="xref py py-func docutils literal notranslate"><span class="pre">read_pickle()</span></code></a>, <a class="reference internal" href="../reference/api/pandas.DataFrame.to_pickle.html#pandas.DataFrame.to_pickle" title="pandas.DataFrame.to_pickle"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DataFrame.to_pickle()</span></code></a> and <a class="reference internal" href="../reference/api/pandas.Series.to_pickle.html#pandas.Series.to_pickle" title="pandas.Series.to_pickle"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Series.to_pickle()</span></code></a>
│ │ │  can now read from and write to compressed pickle files. Compression methods
│ │ │ @@ -1325,14 +1339,16 @@
│ │ │  <span class="gh">Out[27]: </span>
│ │ │  <span class="go">          A    B                   C</span>
│ │ │  <span class="go">0 -1.344312  foo 2013-01-01 00:00:00</span>
│ │ │  <span class="go">1  0.844885  foo 2013-01-01 00:00:01</span>
│ │ │  <span class="go">2  1.075770  foo 2013-01-01 00:00:02</span>
│ │ │  <span class="go">3 -0.109050  foo 2013-01-01 00:00:03</span>
│ │ │  <span class="go">4  1.643563  foo 2013-01-01 00:00:04</span>
│ │ │ +
│ │ │ +<span class="go">[5 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>The default is to infer the compression type from the extension (<code class="docutils literal notranslate"><span class="pre">compression='infer'</span></code>):</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [28]: </span><span class="n">df</span><span class="o">.</span><span class="n">to_pickle</span><span class="p">(</span><span class="s2">&quot;data.pkl.gz&quot;</span><span class="p">)</span>
│ │ │  
│ │ │  <span class="gp">In [29]: </span><span class="n">rt</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_pickle</span><span class="p">(</span><span class="s2">&quot;data.pkl.gz&quot;</span><span class="p">)</span>
│ │ │  
│ │ │ @@ -1341,26 +1357,28 @@
│ │ │  <span class="go">          A    B                   C</span>
│ │ │  <span class="go">0 -1.344312  foo 2013-01-01 00:00:00</span>
│ │ │  <span class="go">1  0.844885  foo 2013-01-01 00:00:01</span>
│ │ │  <span class="go">2  1.075770  foo 2013-01-01 00:00:02</span>
│ │ │  <span class="go">3 -0.109050  foo 2013-01-01 00:00:03</span>
│ │ │  <span class="go">4  1.643563  foo 2013-01-01 00:00:04</span>
│ │ │  
│ │ │ +<span class="go">[5 rows x 3 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [31]: </span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;A&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to_pickle</span><span class="p">(</span><span class="s2">&quot;s1.pkl.bz2&quot;</span><span class="p">)</span>
│ │ │  
│ │ │  <span class="gp">In [32]: </span><span class="n">rt</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_pickle</span><span class="p">(</span><span class="s2">&quot;s1.pkl.bz2&quot;</span><span class="p">)</span>
│ │ │  
│ │ │  <span class="gp">In [33]: </span><span class="n">rt</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
│ │ │  <span class="gh">Out[33]: </span>
│ │ │  <span class="go">0   -1.344312</span>
│ │ │  <span class="go">1    0.844885</span>
│ │ │  <span class="go">2    1.075770</span>
│ │ │  <span class="go">3   -0.109050</span>
│ │ │  <span class="go">4    1.643563</span>
│ │ │ -<span class="go">Name: A, dtype: float64</span>
│ │ │ +<span class="go">Name: A, Length: 5, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="uint64-support-improved">
│ │ │  <span id="whatsnew-0200-enhancements-uint64-support"></span><h3>UInt64 support improved<a class="headerlink" href="#uint64-support-improved" title="Link to this heading">#</a></h3>
│ │ │  <p>pandas has significantly improved support for operations involving unsigned,
│ │ │  or purely non-negative, integers. Previously, handling these integers would
│ │ │ @@ -1456,14 +1474,16 @@
│ │ │  <span class="gh">Out[39]: </span>
│ │ │  <span class="go">     A  B          C</span>
│ │ │  <span class="go">idx                 </span>
│ │ │  <span class="go">0    1  a 2016-01-01</span>
│ │ │  <span class="go">1    2  b 2016-01-02</span>
│ │ │  <span class="go">2    3  c 2016-01-03</span>
│ │ │  
│ │ │ +<span class="go">[3 rows x 3 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [40]: </span><span class="n">df</span><span class="o">.</span><span class="n">to_json</span><span class="p">(</span><span class="n">orient</span><span class="o">=</span><span class="s1">&#39;table&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[40]: </span><span class="go">&#39;{&quot;schema&quot;:{&quot;fields&quot;:[{&quot;name&quot;:&quot;idx&quot;,&quot;type&quot;:&quot;integer&quot;},{&quot;name&quot;:&quot;A&quot;,&quot;type&quot;:&quot;integer&quot;},{&quot;name&quot;:&quot;B&quot;,&quot;type&quot;:&quot;string&quot;},{&quot;name&quot;:&quot;C&quot;,&quot;type&quot;:&quot;datetime&quot;}],&quot;primaryKey&quot;:[&quot;idx&quot;],&quot;pandas_version&quot;:&quot;1.4.0&quot;},&quot;data&quot;:[{&quot;idx&quot;:0,&quot;A&quot;:1,&quot;B&quot;:&quot;a&quot;,&quot;C&quot;:&quot;2016-01-01T00:00:00.000&quot;},{&quot;idx&quot;:1,&quot;A&quot;:2,&quot;B&quot;:&quot;b&quot;,&quot;C&quot;:&quot;2016-01-02T00:00:00.000&quot;},{&quot;idx&quot;:2,&quot;A&quot;:3,&quot;B&quot;:&quot;c&quot;,&quot;C&quot;:&quot;2016-01-03T00:00:00.000&quot;}]}&#39;</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>See <a class="reference internal" href="../user_guide/io.html#io-table-schema"><span class="std std-ref">IO: Table Schema for more information</span></a>.</p>
│ │ │  <p>Additionally, the repr for <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> and <code class="docutils literal notranslate"><span class="pre">Series</span></code> can now publish
│ │ │  this JSON Table schema representation of the Series or DataFrame if you are
│ │ │ @@ -1518,14 +1538,16 @@
│ │ │  <span class="go">4   5.0  1.453425  1.057737  0.165562  0.515018</span>
│ │ │  <span class="go">5   6.0 -1.336936  0.562861  1.392855 -0.063328</span>
│ │ │  <span class="go">6   7.0  0.121668  1.207603 -0.002040  1.627796</span>
│ │ │  <span class="go">7   8.0  0.354493  1.037528 -0.385684  0.519818</span>
│ │ │  <span class="go">8   9.0  1.686583 -1.325963  1.428984 -2.089354</span>
│ │ │  <span class="go">9  10.0 -0.129820  0.631523 -0.586538  0.290720</span>
│ │ │  
│ │ │ +<span class="go">[10 rows x 5 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [46]: </span><span class="n">styled</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">style</span>
│ │ │  <span class="gp">   ....: </span>          <span class="o">.</span><span class="n">applymap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">val</span><span class="p">:</span> <span class="s1">&#39;color:red;&#39;</span> <span class="k">if</span> <span class="n">val</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s1">&#39;color:black;&#39;</span><span class="p">)</span>
│ │ │  <span class="gp">   ....: </span>          <span class="o">.</span><span class="n">highlight_max</span><span class="p">())</span>
│ │ │  <span class="gp">   ....: </span>
│ │ │  
│ │ │  <span class="gp">In [47]: </span><span class="n">styled</span><span class="o">.</span><span class="n">to_excel</span><span class="p">(</span><span class="s1">&#39;styled.xlsx&#39;</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;openpyxl&#39;</span><span class="p">)</span>
│ │ │  </pre></div>
│ │ │ @@ -1585,31 +1607,35 @@
│ │ │  <span class="gh">Out[53]: </span>
│ │ │  <span class="go">               A</span>
│ │ │  <span class="go">B               </span>
│ │ │  <span class="go">(-0.003, 1.5]  0</span>
│ │ │  <span class="go">(1.5, 3.0]     1</span>
│ │ │  <span class="go">(-0.003, 1.5]  2</span>
│ │ │  <span class="go">(-0.003, 1.5]  3</span>
│ │ │ +
│ │ │ +<span class="go">[4 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Selecting via a specific interval:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [54]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">Interval</span><span class="p">(</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">)]</span>
│ │ │  <span class="gh">Out[54]: </span>
│ │ │  <span class="go">A    1</span>
│ │ │ -<span class="go">Name: (1.5, 3.0], dtype: int64</span>
│ │ │ +<span class="go">Name: (1.5, 3.0], Length: 1, dtype: int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Selecting via a scalar value that is contained <em>in</em> the intervals.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [55]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
│ │ │  <span class="gh">Out[55]: </span>
│ │ │  <span class="go">               A</span>
│ │ │  <span class="go">B               </span>
│ │ │  <span class="go">(-0.003, 1.5]  0</span>
│ │ │  <span class="go">(-0.003, 1.5]  2</span>
│ │ │  <span class="go">(-0.003, 1.5]  3</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="other-enhancements">
│ │ │  <span id="whatsnew-0200-enhancements-other"></span><h3>Other enhancements<a class="headerlink" href="#other-enhancements" title="Link to this heading">#</a></h3>
│ │ │  <ul class="simple">
│ │ │  <li><p><code class="docutils literal notranslate"><span class="pre">DataFrame.rolling()</span></code> now accepts the parameter <code class="docutils literal notranslate"><span class="pre">closed='right'|'left'|'both'|'neither'</span></code> to choose the rolling window-endpoint closedness. See the <a class="reference internal" href="../user_guide/window.html#window-endpoints"><span class="std std-ref">documentation</span></a> (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/13965">GH 13965</a>)</p></li>
│ │ │ @@ -1941,36 +1967,36 @@
│ │ │  <p>Previously, <code class="docutils literal notranslate"><span class="pre">concat</span></code> of multiple objects with different <code class="docutils literal notranslate"><span class="pre">float</span></code> dtypes would automatically upcast results to a dtype of <code class="docutils literal notranslate"><span class="pre">float64</span></code>.
│ │ │  Now the smallest acceptable dtype will be used (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/13247">GH 13247</a>)</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [71]: </span><span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">ndmin</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
│ │ │  
│ │ │  <span class="gp">In [72]: </span><span class="n">df1</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[72]: </span>
│ │ │  <span class="go">0    float32</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 1, dtype: object</span>
│ │ │  
│ │ │  <span class="gp">In [73]: </span><span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">ndmin</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
│ │ │  
│ │ │  <span class="gp">In [74]: </span><span class="n">df2</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[74]: </span>
│ │ │  <span class="go">0    float32</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 1, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Previous behavior:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [7]: </span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">])</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[7]:</span>
│ │ │  <span class="go">0    float64</span>
│ │ │  <span class="go">dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>New behavior:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [75]: </span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">])</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[75]: </span>
│ │ │  <span class="go">0    float32</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 1, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="pandas-google-bigquery-support-has-moved">
│ │ │  <span id="whatsnew-0200-api-breaking-gbq"></span><h3>pandas Google BigQuery support has moved<a class="headerlink" href="#pandas-google-bigquery-support-has-moved" title="Link to this heading">#</a></h3>
│ │ │  <p>pandas has split off Google BigQuery support into a separate package <code class="docutils literal notranslate"><span class="pre">pandas-gbq</span></code>. You can <code class="docutils literal notranslate"><span class="pre">conda</span> <span class="pre">install</span> <span class="pre">pandas-gbq</span> <span class="pre">-c</span> <span class="pre">conda-forge</span></code> or
│ │ │  <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">pandas-gbq</span></code> to get it. The functionality of <a class="reference internal" href="../reference/api/pandas.read_gbq.html#pandas.read_gbq" title="pandas.read_gbq"><code class="xref py py-func docutils literal notranslate"><span class="pre">read_gbq()</span></code></a> and <a class="reference internal" href="../reference/api/pandas.DataFrame.to_gbq.html#pandas.DataFrame.to_gbq" title="pandas.DataFrame.to_gbq"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DataFrame.to_gbq()</span></code></a> remain the same with the
│ │ │ @@ -2038,14 +2064,16 @@
│ │ │  <p>Sorting works as expected</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [76]: </span><span class="n">df</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span>
│ │ │  <span class="gh">Out[76]: </span>
│ │ │  <span class="go">                     a</span>
│ │ │  <span class="go">2011-12-31 23:59:59  1</span>
│ │ │  <span class="go">2012-01-01 00:00:00  2</span>
│ │ │  <span class="go">2012-01-01 00:00:01  3</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">90</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">is_lexsorted</span><span class="p">()</span>
│ │ │  <span class="n">Out</span><span class="p">[</span><span class="mi">90</span><span class="p">]:</span> <span class="kc">True</span>
│ │ │  
│ │ │  <span class="n">In</span> <span class="p">[</span><span class="mi">91</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">is_monotonic</span>
│ │ │  <span class="n">Out</span><span class="p">[</span><span class="mi">91</span><span class="p">]:</span> <span class="kc">True</span>
│ │ │ @@ -2061,14 +2089,16 @@
│ │ │  <span class="gp">In [78]: </span><span class="n">df</span>
│ │ │  <span class="gh">Out[78]: </span>
│ │ │  <span class="go">      value</span>
│ │ │  <span class="go">a bb      1</span>
│ │ │  <span class="go">  aa      2</span>
│ │ │  <span class="go">b bb      3</span>
│ │ │  <span class="go">  aa      4</span>
│ │ │ +
│ │ │ +<span class="go">[4 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Previous behavior:</p>
│ │ │  <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">11</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span>
│ │ │  <span class="n">Out</span><span class="p">[</span><span class="mi">11</span><span class="p">]:</span>
│ │ │        <span class="n">value</span>
│ │ │  <span class="n">a</span> <span class="n">bb</span>      <span class="mi">1</span>
│ │ │ @@ -2146,21 +2176,25 @@
│ │ │  <span class="gh">Out[80]: </span>
│ │ │  <span class="go">      B                                          </span>
│ │ │  <span class="go">  count mean       std  min   25%  50%   75%  max</span>
│ │ │  <span class="go">A                                                </span>
│ │ │  <span class="go">1   2.0  1.5  0.707107  1.0  1.25  1.5  1.75  2.0</span>
│ │ │  <span class="go">2   2.0  3.5  0.707107  3.0  3.25  3.5  3.75  4.0</span>
│ │ │  
│ │ │ +<span class="go">[2 rows x 8 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [81]: </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">([</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="s2">&quot;std&quot;</span><span class="p">,</span> <span class="s2">&quot;min&quot;</span><span class="p">,</span> <span class="s2">&quot;max&quot;</span><span class="p">])</span>
│ │ │  <span class="gh">Out[81]: </span>
│ │ │  <span class="go">     B                  </span>
│ │ │  <span class="go">  mean       std min max</span>
│ │ │  <span class="go">A                       </span>
│ │ │  <span class="go">1  1.5  0.707107   1   2</span>
│ │ │  <span class="go">2  3.5  0.707107   3   4</span>
│ │ │ +
│ │ │ +<span class="go">[2 rows x 4 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="window-binary-corr-cov-operations-return-a-multiindex-dataframe">
│ │ │  <span id="whatsnew-0200-api-breaking-rolling-pairwise"></span><h3>Window binary corr/cov operations return a MultiIndex DataFrame<a class="headerlink" href="#window-binary-corr-cov-operations-return-a-multiindex-dataframe" title="Link to this heading">#</a></h3>
│ │ │  <p>A binary window operation, like <code class="docutils literal notranslate"><span class="pre">.corr()</span></code> or <code class="docutils literal notranslate"><span class="pre">.cov()</span></code>, when operating on a <code class="docutils literal notranslate"><span class="pre">.rolling(..)</span></code>, <code class="docutils literal notranslate"><span class="pre">.expanding(..)</span></code>, or <code class="docutils literal notranslate"><span class="pre">.ewm(..)</span></code> object,
│ │ │  will now return a 2-level <code class="docutils literal notranslate"><span class="pre">MultiIndexed</span> <span class="pre">DataFrame</span></code> rather than a <code class="docutils literal notranslate"><span class="pre">Panel</span></code>, as <code class="docutils literal notranslate"><span class="pre">Panel</span></code> is now deprecated,
│ │ │ @@ -2180,14 +2214,16 @@
│ │ │  <span class="go">bar                A         B</span>
│ │ │  <span class="go">foo                           </span>
│ │ │  <span class="go">2016-04-05  0.640880  0.126205</span>
│ │ │  <span class="go">2016-04-06  0.171465  0.737086</span>
│ │ │  <span class="go">2016-04-07  0.127029  0.369650</span>
│ │ │  <span class="go">2016-04-08  0.604334  0.103104</span>
│ │ │  <span class="go">2016-04-09  0.802374  0.945553</span>
│ │ │ +
│ │ │ +<span class="go">[5 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Previous behavior:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [2]: </span><span class="n">df</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="mi">12</span><span class="p">)</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
│ │ │  <span class="gh">Out[2]:</span>
│ │ │  <span class="go">&lt;class &#39;pandas.core.panel.Panel&#39;&gt;</span>
│ │ │  <span class="go">Dimensions: 100 (items) x 2 (major_axis) x 2 (minor_axis)</span>
│ │ │ @@ -2204,23 +2240,27 @@
│ │ │  <span class="go">bar                    A         B</span>
│ │ │  <span class="go">foo        bar                    </span>
│ │ │  <span class="go">2016-04-07 B   -0.132090  1.000000</span>
│ │ │  <span class="go">2016-04-08 A    1.000000 -0.145775</span>
│ │ │  <span class="go">           B   -0.145775  1.000000</span>
│ │ │  <span class="go">2016-04-09 A    1.000000  0.119645</span>
│ │ │  <span class="go">           B    0.119645  1.000000</span>
│ │ │ +
│ │ │ +<span class="go">[5 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Retrieving a correlation matrix for a cross-section</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [87]: </span><span class="n">df</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="mi">12</span><span class="p">)</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;2016-04-07&#39;</span><span class="p">]</span>
│ │ │  <span class="gh">Out[87]: </span>
│ │ │  <span class="go">bar        A        B</span>
│ │ │  <span class="go">bar                  </span>
│ │ │  <span class="go">A    1.00000 -0.13209</span>
│ │ │  <span class="go">B   -0.13209  1.00000</span>
│ │ │ +
│ │ │ +<span class="go">[2 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="hdfstore-where-string-comparison">
│ │ │  <span id="whatsnew-0200-api-breaking-hdfstore-where"></span><h3>HDFStore where string comparison<a class="headerlink" href="#hdfstore-where-string-comparison" title="Link to this heading">#</a></h3>
│ │ │  <p>In previous versions most types could be compared to string column in a <code class="docutils literal notranslate"><span class="pre">HDFStore</span></code>
│ │ │  usually resulting in an invalid comparison, returning an empty result frame. These comparisons will now raise a
│ │ │ @@ -2228,15 +2268,15 @@
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [88]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;unparsed_date&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;2014-01-01&#39;</span><span class="p">,</span> <span class="s1">&#39;2014-01-01&#39;</span><span class="p">]})</span>
│ │ │  
│ │ │  <span class="gp">In [89]: </span><span class="n">df</span><span class="o">.</span><span class="n">to_hdf</span><span class="p">(</span><span class="s1">&#39;store.h5&#39;</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="s1">&#39;key&#39;</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;table&#39;</span><span class="p">,</span> <span class="n">data_columns</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
│ │ │  
│ │ │  <span class="gp">In [90]: </span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[90]: </span>
│ │ │  <span class="go">unparsed_date    object</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 1, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Previous behavior:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [4]: </span><span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="s1">&#39;store.h5&#39;</span><span class="p">,</span> <span class="s1">&#39;key&#39;</span><span class="p">,</span> <span class="n">where</span><span class="o">=</span><span class="s1">&#39;unparsed_date &gt; ts&#39;</span><span class="p">)</span>
│ │ │  <span class="go">File &quot;&lt;string&gt;&quot;, line 1</span>
│ │ │  <span class="go">  (unparsed_date &gt; 1970-01-01 00:00:01.388552400)</span>
│ │ │  <span class="go">                        ^</span>
│ │ │ @@ -2287,22 +2327,26 @@
│ │ │  <span class="gp">In [97]: </span><span class="n">left</span>
│ │ │  <span class="gh">Out[97]: </span>
│ │ │  <span class="go">    a</span>
│ │ │  <span class="go">2  20</span>
│ │ │  <span class="go">1  10</span>
│ │ │  <span class="go">0   0</span>
│ │ │  
│ │ │ +<span class="go">[3 rows x 1 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [98]: </span><span class="n">right</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">300</span><span class="p">]},</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
│ │ │  
│ │ │  <span class="gp">In [99]: </span><span class="n">right</span>
│ │ │  <span class="gh">Out[99]: </span>
│ │ │  <span class="go">     b</span>
│ │ │  <span class="go">1  100</span>
│ │ │  <span class="go">2  200</span>
│ │ │  <span class="go">3  300</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Previous behavior:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [4]: </span><span class="n">left</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;inner&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[4]:</span>
│ │ │  <span class="go">   a    b</span>
│ │ │  <span class="go">1  10  100</span>
│ │ │ @@ -2311,14 +2355,16 @@
│ │ │  </div>
│ │ │  <p>New behavior:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [100]: </span><span class="n">left</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;inner&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[100]: </span>
│ │ │  <span class="go">    a    b</span>
│ │ │  <span class="go">2  20  200</span>
│ │ │  <span class="go">1  10  100</span>
│ │ │ +
│ │ │ +<span class="go">[2 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </li>
│ │ │  </ul>
│ │ │  </section>
│ │ │  <section id="pivot-table-always-returns-a-dataframe">
│ │ │  <span id="whatsnew-0200-api-breaking-pivot-table"></span><h3>Pivot table always returns a DataFrame<a class="headerlink" href="#pivot-table-always-returns-a-dataframe" title="Link to this heading">#</a></h3>
│ │ │ @@ -2331,14 +2377,16 @@
│ │ │  
│ │ │  <span class="gp">In [102]: </span><span class="n">df</span>
│ │ │  <span class="gh">Out[102]: </span>
│ │ │  <span class="go">   col1 col2  col3</span>
│ │ │  <span class="go">0     3    C     1</span>
│ │ │  <span class="go">1     4    D     3</span>
│ │ │  <span class="go">2     5    E     9</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Previous behavior:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [2]: </span><span class="n">df</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="s1">&#39;col1&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;col3&#39;</span><span class="p">,</span> <span class="s1">&#39;col2&#39;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="s2">&quot;sum&quot;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[2]:</span>
│ │ │  <span class="go">col3  col2</span>
│ │ │  <span class="go">1     C       3</span>
│ │ │ @@ -2351,14 +2399,16 @@
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [103]: </span><span class="n">df</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="s1">&#39;col1&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;col3&#39;</span><span class="p">,</span> <span class="s1">&#39;col2&#39;</span><span class="p">],</span> <span class="n">aggfunc</span><span class="o">=</span><span class="s2">&quot;sum&quot;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[103]: </span>
│ │ │  <span class="go">           col1</span>
│ │ │  <span class="go">col3 col2      </span>
│ │ │  <span class="go">1    C        3</span>
│ │ │  <span class="go">3    D        4</span>
│ │ │  <span class="go">9    E        5</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="other-api-changes">
│ │ │  <span id="whatsnew-0200-api"></span><h3>Other API changes<a class="headerlink" href="#other-api-changes" title="Link to this heading">#</a></h3>
│ │ │  <ul class="simple">
│ │ │  <li><p><code class="docutils literal notranslate"><span class="pre">numexpr</span></code> version is now required to be &gt;= 2.4.6 and it will not be used at all if this requisite is not fulfilled (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/15213">GH 15213</a>).</p></li>
│ │ │ @@ -2571,14 +2621,16 @@
│ │ │  
│ │ │  <span class="gp">In [105]: </span><span class="n">df</span>
│ │ │  <span class="gh">Out[105]: </span>
│ │ │  <span class="go">   A  B</span>
│ │ │  <span class="go">a  1  4</span>
│ │ │  <span class="go">b  2  5</span>
│ │ │  <span class="go">c  3  6</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Previous behavior, where you wish to get the 0th and the 2nd elements from the index in the ‘A’ column.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [3]: </span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="s1">&#39;A&#39;</span><span class="p">]</span>
│ │ │  <span class="gh">Out[3]:</span>
│ │ │  <span class="go">a    1</span>
│ │ │  <span class="go">c    3</span>
│ │ │ @@ -2586,23 +2638,23 @@
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Using <code class="docutils literal notranslate"><span class="pre">.loc</span></code>. Here we will select the appropriate indexes from the index, then use <em>label</em> indexing.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [106]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]],</span> <span class="s1">&#39;A&#39;</span><span class="p">]</span>
│ │ │  <span class="gh">Out[106]: </span>
│ │ │  <span class="go">a    1</span>
│ │ │  <span class="go">c    3</span>
│ │ │ -<span class="go">Name: A, dtype: int64</span>
│ │ │ +<span class="go">Name: A, Length: 2, dtype: int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Using <code class="docutils literal notranslate"><span class="pre">.iloc</span></code>. Here we will get the location of the ‘A’ column, then use <em>positional</em> indexing to select things.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [107]: </span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">)]</span>
│ │ │  <span class="gh">Out[107]: </span>
│ │ │  <span class="go">a    1</span>
│ │ │  <span class="go">c    3</span>
│ │ │ -<span class="go">Name: A, dtype: int64</span>
│ │ │ +<span class="go">Name: A, Length: 2, dtype: int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="deprecate-panel">
│ │ │  <span id="whatsnew-0200-api-breaking-deprecate-panel"></span><h3>Deprecate Panel<a class="headerlink" href="#deprecate-panel" title="Link to this heading">#</a></h3>
│ │ │  <p><code class="docutils literal notranslate"><span class="pre">Panel</span></code> is deprecated and will be removed in a future version. The recommended way to represent 3-D data are
│ │ │  with a <code class="docutils literal notranslate"><span class="pre">MultiIndex</span></code> on a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> via the <code class="xref py py-meth docutils literal notranslate"><span class="pre">to_frame()</span></code> or with the <a class="reference external" href="http://xarray.pydata.org/en/stable/">xarray package</a>. pandas
│ │ │ @@ -2686,25 +2738,29 @@
│ │ │  <span class="gh">Out[109]: </span>
│ │ │  <span class="go">   A  B  C</span>
│ │ │  <span class="go">0  1  0  0</span>
│ │ │  <span class="go">1  1  1  1</span>
│ │ │  <span class="go">2  1  2  2</span>
│ │ │  <span class="go">3  2  3  3</span>
│ │ │  <span class="go">4  2  4  4</span>
│ │ │ +
│ │ │ +<span class="go">[5 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Here is a typical useful syntax for computing different aggregations for different columns. This
│ │ │  is a natural, and useful syntax. We aggregate from the dict-to-list by taking the specified
│ │ │  columns and applying the list of functions. This returns a <code class="docutils literal notranslate"><span class="pre">MultiIndex</span></code> for the columns (this is <em>not</em> deprecated).</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [110]: </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span><span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="s1">&#39;sum&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">:</span> <span class="s1">&#39;min&#39;</span><span class="p">})</span>
│ │ │  <span class="gh">Out[110]: </span>
│ │ │  <span class="go">   B  C</span>
│ │ │  <span class="go">A      </span>
│ │ │  <span class="go">1  3  0</span>
│ │ │  <span class="go">2  7  3</span>
│ │ │ +
│ │ │ +<span class="go">[2 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Here’s an example of the first deprecation, passing a dict to a grouped <code class="docutils literal notranslate"><span class="pre">Series</span></code>. This
│ │ │  is a combination aggregation &amp; renaming:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [6]: </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">B</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span><span class="s1">&#39;foo&#39;</span><span class="p">:</span> <span class="s1">&#39;count&#39;</span><span class="p">})</span>
│ │ │  <span class="go">FutureWarning: using a dict on a Series for aggregation</span>
│ │ │  <span class="go">is deprecated and will be removed in a future version</span>
│ │ │ @@ -2719,14 +2775,16 @@
│ │ │  <p>You can accomplish the same operation, more idiomatically by:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [111]: </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">B</span><span class="o">.</span><span class="n">agg</span><span class="p">([</span><span class="s1">&#39;count&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;count&#39;</span><span class="p">:</span> <span class="s1">&#39;foo&#39;</span><span class="p">})</span>
│ │ │  <span class="gh">Out[111]: </span>
│ │ │  <span class="go">   foo</span>
│ │ │  <span class="go">A     </span>
│ │ │  <span class="go">1    3</span>
│ │ │  <span class="go">2    2</span>
│ │ │ +
│ │ │ +<span class="go">[2 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Here’s an example of the second deprecation, passing a dict-of-dict to a grouped <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>:</p>
│ │ │  <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">23</span><span class="p">]:</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">)</span>
│ │ │      <span class="o">...</span><span class="p">:</span>    <span class="o">.</span><span class="n">agg</span><span class="p">({</span><span class="s1">&#39;B&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;foo&#39;</span><span class="p">:</span> <span class="s1">&#39;sum&#39;</span><span class="p">},</span> <span class="s1">&#39;C&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;bar&#39;</span><span class="p">:</span> <span class="s1">&#39;min&#39;</span><span class="p">}})</span>
│ │ │      <span class="o">...</span><span class="p">:</span>  <span class="p">)</span>
│ │ │  <span class="ne">FutureWarning</span><span class="p">:</span> <span class="n">using</span> <span class="n">a</span> <span class="nb">dict</span> <span class="k">with</span> <span class="n">renaming</span> <span class="ow">is</span> <span class="n">deprecated</span> <span class="ow">and</span>
│ │ │ @@ -2747,14 +2805,16 @@
│ │ │  <span class="gp">   .....: </span> <span class="p">)</span>
│ │ │  <span class="gp">   .....: </span>
│ │ │  <span class="gh">Out[112]: </span>
│ │ │  <span class="go">   foo  bar</span>
│ │ │  <span class="go">A          </span>
│ │ │  <span class="go">1    3    0</span>
│ │ │  <span class="go">2    7    3</span>
│ │ │ +
│ │ │ +<span class="go">[2 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="deprecate-plotting">
│ │ │  <span id="whatsnew-0200-privacy-deprecate-plotting"></span><h3>Deprecate .plotting<a class="headerlink" href="#deprecate-plotting" title="Link to this heading">#</a></h3>
│ │ │  <p>The <code class="docutils literal notranslate"><span class="pre">pandas.tools.plotting</span></code> module has been deprecated,  in favor of the top level <code class="docutils literal notranslate"><span class="pre">pandas.plotting</span></code> module. All the public plotting functions are now available
│ │ │  from <code class="docutils literal notranslate"><span class="pre">pandas.plotting</span></code> (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/12548">GH 12548</a>).</p>
│ │ │ ├── html2text {}
│ │ │ │ @@ -235,39 +235,45 @@
│ │ │ │  2000-01-04       NaN       NaN       NaN
│ │ │ │  2000-01-05       NaN       NaN       NaN
│ │ │ │  2000-01-06       NaN       NaN       NaN
│ │ │ │  2000-01-07       NaN       NaN       NaN
│ │ │ │  2000-01-08  0.113648 -1.478427  0.524988
│ │ │ │  2000-01-09  0.404705  0.577046 -1.715002
│ │ │ │  2000-01-10 -1.039268 -0.370647 -1.157892
│ │ │ │ +
│ │ │ │ +[10 rows x 3 columns]
│ │ │ │  One can operate using string function names, callables, lists, or dictionaries
│ │ │ │  of these.
│ │ │ │  Using a single function is equivalent to .apply.
│ │ │ │  In [4]: df.agg('sum')
│ │ │ │  Out[4]:
│ │ │ │  A   -1.068226
│ │ │ │  B   -1.387015
│ │ │ │  C   -4.892029
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 3, dtype: float64
│ │ │ │  Multiple aggregations with a list of functions.
│ │ │ │  In [5]: df.agg(['sum', 'min'])
│ │ │ │  Out[5]:
│ │ │ │              A         B         C
│ │ │ │  sum -1.068226 -1.387015 -4.892029
│ │ │ │  min -1.135632 -1.478427 -1.715002
│ │ │ │ +
│ │ │ │ +[2 rows x 3 columns]
│ │ │ │  Using a dict provides the ability to apply specific aggregations per column.
│ │ │ │  You will get a matrix-like output of all of the aggregators. The output has one
│ │ │ │  column per unique function. Those functions applied to a particular column will
│ │ │ │  be NaN:
│ │ │ │  In [6]: df.agg({'A': ['sum', 'min'], 'B': ['min', 'max']})
│ │ │ │  Out[6]:
│ │ │ │              A         B
│ │ │ │  sum -1.068226       NaN
│ │ │ │  min -1.135632 -1.478427
│ │ │ │  max       NaN  1.212112
│ │ │ │ +
│ │ │ │ +[3 rows x 2 columns]
│ │ │ │  The API also supports a .transform() function for broadcasting results.
│ │ │ │  In [7]: df.transform(['abs', lambda x: x - x.min()])
│ │ │ │  Out[7]:
│ │ │ │                     A                   B                   C
│ │ │ │                   abs  <lambda>       abs  <lambda>       abs  <lambda>
│ │ │ │  2000-01-01  0.469112  1.604745  0.282863  1.195563  1.509059  0.205944
│ │ │ │  2000-01-02  1.135632  0.000000  1.212112  2.690539  0.173215  1.541787
│ │ │ │ @@ -275,14 +281,16 @@
│ │ │ │  2000-01-04       NaN       NaN       NaN       NaN       NaN       NaN
│ │ │ │  2000-01-05       NaN       NaN       NaN       NaN       NaN       NaN
│ │ │ │  2000-01-06       NaN       NaN       NaN       NaN       NaN       NaN
│ │ │ │  2000-01-07       NaN       NaN       NaN       NaN       NaN       NaN
│ │ │ │  2000-01-08  0.113648  1.249281  1.478427  0.000000  0.524988  2.239990
│ │ │ │  2000-01-09  0.404705  1.540338  0.577046  2.055473  1.715002  0.000000
│ │ │ │  2000-01-10  1.039268  0.096364  0.370647  1.107780  1.157892  0.557110
│ │ │ │ +
│ │ │ │ +[10 rows x 6 columns]
│ │ │ │  When presented with mixed dtypes that cannot be aggregated, .agg() will only
│ │ │ │  take the valid aggregations. This is similar to how groupby .agg() works. (_G_H
│ │ │ │  _1_5_0_1_5)
│ │ │ │  In [8]: df = pd.DataFrame({'A': [1, 2, 3],
│ │ │ │     ...:                    'B': [1., 2., 3.],
│ │ │ │     ...:                    'C': ['foo', 'bar', 'baz'],
│ │ │ │     ...:                    'D': pd.date_range('20130101', periods=3)})
│ │ │ │ @@ -290,15 +298,15 @@
│ │ │ │  
│ │ │ │  In [9]: df.dtypes
│ │ │ │  Out[9]:
│ │ │ │  A             int64
│ │ │ │  B           float64
│ │ │ │  C            object
│ │ │ │  D    datetime64[ns]
│ │ │ │ -dtype: object
│ │ │ │ +Length: 4, dtype: object
│ │ │ │  In [10]: df.agg(['min', 'sum'])
│ │ │ │  Out[10]:
│ │ │ │       A    B          C          D
│ │ │ │  min  1  1.0        bar 2013-01-01
│ │ │ │  sum  6  6.0  foobarbaz        NaT
│ │ │ │  ******** KKeeyywwoorrdd aarrgguummeenntt ddttyyppee ffoorr ddaattaa IIOO_## ********
│ │ │ │  The 'python' engine for _r_e_a_d___c_s_v_(_), as well as the _r_e_a_d___f_w_f_(_) function for
│ │ │ │ @@ -307,22 +315,22 @@
│ │ │ │  (_G_H_ _1_4_2_9_5). See the _i_o_ _d_o_c_s for more information.
│ │ │ │  In [10]: data = "a  b\n1  2\n3  4"
│ │ │ │  
│ │ │ │  In [11]: pd.read_fwf(StringIO(data)).dtypes
│ │ │ │  Out[11]:
│ │ │ │  a    int64
│ │ │ │  b    int64
│ │ │ │ -dtype: object
│ │ │ │ +Length: 2, dtype: object
│ │ │ │  
│ │ │ │  In [12]: pd.read_fwf(StringIO(data), dtype={'a': 'float64', 'b':
│ │ │ │  'object'}).dtypes
│ │ │ │  Out[12]:
│ │ │ │  a    float64
│ │ │ │  b     object
│ │ │ │ -dtype: object
│ │ │ │ +Length: 2, dtype: object
│ │ │ │  ******** MMeetthhoodd ..ttoo__ddaatteettiimmee(()) hhaass ggaaiinneedd aann oorriiggiinn ppaarraammeetteerr_## ********
│ │ │ │  _t_o___d_a_t_e_t_i_m_e_(_) has gained a new parameter, origin, to define a reference date
│ │ │ │  from where to compute the resulting timestamps when parsing numerical values
│ │ │ │  with a specific unit specified. (_G_H_ _1_1_2_7_6, _G_H_ _1_1_7_4_5)
│ │ │ │  For example, with 1960-01-01 as the starting date:
│ │ │ │  In [13]: pd.to_datetime([1, 2, 3], unit='D', origin=pd.Timestamp('1960-01-01'))
│ │ │ │  Out[13]: DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'],
│ │ │ │ @@ -358,24 +366,28 @@
│ │ │ │  baz   one     1  2
│ │ │ │        two     1  3
│ │ │ │  foo   one     2  4
│ │ │ │        two     2  5
│ │ │ │  qux   one     3  6
│ │ │ │        two     3  7
│ │ │ │  
│ │ │ │ +[8 rows x 2 columns]
│ │ │ │ +
│ │ │ │  In [19]: df.groupby(['second', 'A']).sum()
│ │ │ │  Out[19]:
│ │ │ │            B
│ │ │ │  second A
│ │ │ │  one    1  2
│ │ │ │         2  4
│ │ │ │         3  6
│ │ │ │  two    1  4
│ │ │ │         2  5
│ │ │ │         3  7
│ │ │ │ +
│ │ │ │ +[6 rows x 1 columns]
│ │ │ │  ******** BBeetttteerr ssuuppppoorrtt ffoorr ccoommpprreesssseedd UURRLLss iinn rreeaadd__ccssvv_## ********
│ │ │ │  The compression code was refactored (_G_H_ _1_2_6_8_8). As a result, reading dataframes
│ │ │ │  from URLs in _r_e_a_d___c_s_v_(_) or _r_e_a_d___t_a_b_l_e_(_) now supports additional compression
│ │ │ │  methods: xz, bz2, and zip (_G_H_ _1_4_5_7_0). Previously, only gzip compression was
│ │ │ │  supported. By default, compression of URLs and paths are now inferred using
│ │ │ │  their file extensions. Additionally, support for bz2 compression in the python
│ │ │ │  2 C-engine improved (_G_H_ _1_4_8_7_4).
│ │ │ │ @@ -797,14 +809,16 @@
│ │ │ │  
│ │ │ │  In [23]: df.head(2)
│ │ │ │  Out[23]:
│ │ │ │                A  B
│ │ │ │  first second
│ │ │ │  bar   one     1  0
│ │ │ │        two     1  1
│ │ │ │ +
│ │ │ │ +[2 rows x 2 columns]
│ │ │ │  ******** PPiicckkllee ffiillee IIOO nnooww ssuuppppoorrttss ccoommpprreessssiioonn_## ********
│ │ │ │  _r_e_a_d___p_i_c_k_l_e_(_), _D_a_t_a_F_r_a_m_e_._t_o___p_i_c_k_l_e_(_) and _S_e_r_i_e_s_._t_o___p_i_c_k_l_e_(_) can now read from
│ │ │ │  and write to compressed pickle files. Compression methods can be an explicit
│ │ │ │  parameter or be inferred from the file extension. See _t_h_e_ _d_o_c_s_ _h_e_r_e_.
│ │ │ │  In [24]: df = pd.DataFrame({'A': np.random.randn(1000),
│ │ │ │     ....:                    'B': 'foo',
│ │ │ │     ....:                    'C': pd.date_range('20130101', periods=1000,
│ │ │ │ @@ -819,14 +833,16 @@
│ │ │ │  Out[27]:
│ │ │ │            A    B                   C
│ │ │ │  0 -1.344312  foo 2013-01-01 00:00:00
│ │ │ │  1  0.844885  foo 2013-01-01 00:00:01
│ │ │ │  2  1.075770  foo 2013-01-01 00:00:02
│ │ │ │  3 -0.109050  foo 2013-01-01 00:00:03
│ │ │ │  4  1.643563  foo 2013-01-01 00:00:04
│ │ │ │ +
│ │ │ │ +[5 rows x 3 columns]
│ │ │ │  The default is to infer the compression type from the extension
│ │ │ │  (compression='infer'):
│ │ │ │  In [28]: df.to_pickle("data.pkl.gz")
│ │ │ │  
│ │ │ │  In [29]: rt = pd.read_pickle("data.pkl.gz")
│ │ │ │  
│ │ │ │  In [30]: rt.head()
│ │ │ │ @@ -834,26 +850,28 @@
│ │ │ │            A    B                   C
│ │ │ │  0 -1.344312  foo 2013-01-01 00:00:00
│ │ │ │  1  0.844885  foo 2013-01-01 00:00:01
│ │ │ │  2  1.075770  foo 2013-01-01 00:00:02
│ │ │ │  3 -0.109050  foo 2013-01-01 00:00:03
│ │ │ │  4  1.643563  foo 2013-01-01 00:00:04
│ │ │ │  
│ │ │ │ +[5 rows x 3 columns]
│ │ │ │ +
│ │ │ │  In [31]: df["A"].to_pickle("s1.pkl.bz2")
│ │ │ │  
│ │ │ │  In [32]: rt = pd.read_pickle("s1.pkl.bz2")
│ │ │ │  
│ │ │ │  In [33]: rt.head()
│ │ │ │  Out[33]:
│ │ │ │  0   -1.344312
│ │ │ │  1    0.844885
│ │ │ │  2    1.075770
│ │ │ │  3   -0.109050
│ │ │ │  4    1.643563
│ │ │ │ -Name: A, dtype: float64
│ │ │ │ +Name: A, Length: 5, dtype: float64
│ │ │ │  ******** UUIInntt6644 ssuuppppoorrtt iimmpprroovveedd_## ********
│ │ │ │  pandas has significantly improved support for operations involving unsigned, or
│ │ │ │  purely non-negative, integers. Previously, handling these integers would result
│ │ │ │  in improper rounding or data-type casting, leading to incorrect results.
│ │ │ │  Notably, a new numerical index, UInt64Index, has been created (_G_H_ _1_4_9_3_7)
│ │ │ │  In [1]: idx = pd.UInt64Index([1, 2, 3])
│ │ │ │  In [2]: df = pd.DataFrame({'A': ['a', 'b', 'c']}, index=idx)
│ │ │ │ @@ -940,14 +958,16 @@
│ │ │ │  Out[39]:
│ │ │ │       A  B          C
│ │ │ │  idx
│ │ │ │  0    1  a 2016-01-01
│ │ │ │  1    2  b 2016-01-02
│ │ │ │  2    3  c 2016-01-03
│ │ │ │  
│ │ │ │ +[3 rows x 3 columns]
│ │ │ │ +
│ │ │ │  In [40]: df.to_json(orient='table')
│ │ │ │  Out[40]: '{"schema":{"fields":[{"name":"idx","type":"integer"},{"name":
│ │ │ │  "A","type":"integer"},{"name":"B","type":"string"},{"name":"C","type":
│ │ │ │  "datetime"}],"primaryKey":["idx"],"pandas_version":"1.4.0"},"data":[{"idx":
│ │ │ │  0,"A":1,"B":"a","C":"2016-01-01T00:00:00.000"},{"idx":1,"A":2,"B":"b","C":
│ │ │ │  "2016-01-02T00:00:00.000"},{"idx":2,"A":3,"B":"c","C":"2016-01-03T00:00:
│ │ │ │  00.000"}]}'
│ │ │ │ @@ -1000,14 +1020,16 @@
│ │ │ │  4   5.0  1.453425  1.057737  0.165562  0.515018
│ │ │ │  5   6.0 -1.336936  0.562861  1.392855 -0.063328
│ │ │ │  6   7.0  0.121668  1.207603 -0.002040  1.627796
│ │ │ │  7   8.0  0.354493  1.037528 -0.385684  0.519818
│ │ │ │  8   9.0  1.686583 -1.325963  1.428984 -2.089354
│ │ │ │  9  10.0 -0.129820  0.631523 -0.586538  0.290720
│ │ │ │  
│ │ │ │ +[10 rows x 5 columns]
│ │ │ │ +
│ │ │ │  In [46]: styled = (df.style
│ │ │ │     ....:           .applymap(lambda val: 'color:red;' if val < 0 else 'color:
│ │ │ │  black;')
│ │ │ │     ....:           .highlight_max())
│ │ │ │     ....:
│ │ │ │  
│ │ │ │  In [47]: styled.to_excel('styled.xlsx', engine='openpyxl')
│ │ │ │ @@ -1059,27 +1081,31 @@
│ │ │ │  Out[53]:
│ │ │ │                 A
│ │ │ │  B
│ │ │ │  (-0.003, 1.5]  0
│ │ │ │  (1.5, 3.0]     1
│ │ │ │  (-0.003, 1.5]  2
│ │ │ │  (-0.003, 1.5]  3
│ │ │ │ +
│ │ │ │ +[4 rows x 1 columns]
│ │ │ │  Selecting via a specific interval:
│ │ │ │  In [54]: df.loc[pd.Interval(1.5, 3.0)]
│ │ │ │  Out[54]:
│ │ │ │  A    1
│ │ │ │ -Name: (1.5, 3.0], dtype: int64
│ │ │ │ +Name: (1.5, 3.0], Length: 1, dtype: int64
│ │ │ │  Selecting via a scalar value that is contained iinn the intervals.
│ │ │ │  In [55]: df.loc[0]
│ │ │ │  Out[55]:
│ │ │ │                 A
│ │ │ │  B
│ │ │ │  (-0.003, 1.5]  0
│ │ │ │  (-0.003, 1.5]  2
│ │ │ │  (-0.003, 1.5]  3
│ │ │ │ +
│ │ │ │ +[3 rows x 1 columns]
│ │ │ │  ******** OOtthheerr eennhhaanncceemmeennttss_## ********
│ │ │ │      * DataFrame.rolling() now accepts the parameter
│ │ │ │        closed='right'|'left'|'both'|'neither' to choose the rolling window-
│ │ │ │        endpoint closedness. See the _d_o_c_u_m_e_n_t_a_t_i_o_n (_G_H_ _1_3_9_6_5)
│ │ │ │      * Integration with the feather-format, including a new top-level
│ │ │ │        pd.read_feather() and DataFrame.to_feather() method, see _h_e_r_e.
│ │ │ │      * Series.str.replace() now accepts a callable, as replacement, which is
│ │ │ │ @@ -1423,32 +1449,32 @@
│ │ │ │  automatically upcast results to a dtype of float64. Now the smallest acceptable
│ │ │ │  dtype will be used (_G_H_ _1_3_2_4_7)
│ │ │ │  In [71]: df1 = pd.DataFrame(np.array([1.0], dtype=np.float32, ndmin=2))
│ │ │ │  
│ │ │ │  In [72]: df1.dtypes
│ │ │ │  Out[72]:
│ │ │ │  0    float32
│ │ │ │ -dtype: object
│ │ │ │ +Length: 1, dtype: object
│ │ │ │  
│ │ │ │  In [73]: df2 = pd.DataFrame(np.array([np.nan], dtype=np.float32, ndmin=2))
│ │ │ │  
│ │ │ │  In [74]: df2.dtypes
│ │ │ │  Out[74]:
│ │ │ │  0    float32
│ │ │ │ -dtype: object
│ │ │ │ +Length: 1, dtype: object
│ │ │ │  Previous behavior:
│ │ │ │  In [7]: pd.concat([df1, df2]).dtypes
│ │ │ │  Out[7]:
│ │ │ │  0    float64
│ │ │ │  dtype: object
│ │ │ │  New behavior:
│ │ │ │  In [75]: pd.concat([df1, df2]).dtypes
│ │ │ │  Out[75]:
│ │ │ │  0    float32
│ │ │ │ -dtype: object
│ │ │ │ +Length: 1, dtype: object
│ │ │ │  ******** ppaannddaass GGooooggllee BBiiggQQuueerryy ssuuppppoorrtt hhaass mmoovveedd_## ********
│ │ │ │  pandas has split off Google BigQuery support into a separate package pandas-
│ │ │ │  gbq. You can conda install pandas-gbq -c conda-forge or pip install pandas-gbq
│ │ │ │  to get it. The functionality of _r_e_a_d___g_b_q_(_) and _D_a_t_a_F_r_a_m_e_._t_o___g_b_q_(_) remain the
│ │ │ │  same with the currently released version of pandas-gbq=0.1.4. Documentation is
│ │ │ │  now hosted _h_e_r_e (_G_H_ _1_5_3_4_7)
│ │ │ │  ******** MMeemmoorryy uussaaggee ffoorr IInnddeexx iiss mmoorree aaccccuurraattee_## ********
│ │ │ │ @@ -1508,14 +1534,16 @@
│ │ │ │  Sorting works as expected
│ │ │ │  In [76]: df.sort_index()
│ │ │ │  Out[76]:
│ │ │ │                       a
│ │ │ │  2011-12-31 23:59:59  1
│ │ │ │  2012-01-01 00:00:00  2
│ │ │ │  2012-01-01 00:00:01  3
│ │ │ │ +
│ │ │ │ +[3 rows x 1 columns]
│ │ │ │  In [90]: df.sort_index().index.is_lexsorted()
│ │ │ │  Out[90]: True
│ │ │ │  
│ │ │ │  In [91]: df.sort_index().index.is_monotonic
│ │ │ │  Out[91]: True
│ │ │ │  However, this example, which has a non-monotonic 2nd level, doesn’t behave as
│ │ │ │  desired.
│ │ │ │ @@ -1527,14 +1555,16 @@
│ │ │ │  In [78]: df
│ │ │ │  Out[78]:
│ │ │ │        value
│ │ │ │  a bb      1
│ │ │ │    aa      2
│ │ │ │  b bb      3
│ │ │ │    aa      4
│ │ │ │ +
│ │ │ │ +[4 rows x 1 columns]
│ │ │ │  Previous behavior:
│ │ │ │  In [11]: df.sort_index()
│ │ │ │  Out[11]:
│ │ │ │        value
│ │ │ │  a bb      1
│ │ │ │    aa      2
│ │ │ │  b bb      3
│ │ │ │ @@ -1603,21 +1633,25 @@
│ │ │ │  Out[80]:
│ │ │ │        B
│ │ │ │    count mean       std  min   25%  50%   75%  max
│ │ │ │  A
│ │ │ │  1   2.0  1.5  0.707107  1.0  1.25  1.5  1.75  2.0
│ │ │ │  2   2.0  3.5  0.707107  3.0  3.25  3.5  3.75  4.0
│ │ │ │  
│ │ │ │ +[2 rows x 8 columns]
│ │ │ │ +
│ │ │ │  In [81]: df.groupby('A').agg(["mean", "std", "min", "max"])
│ │ │ │  Out[81]:
│ │ │ │       B
│ │ │ │    mean       std min max
│ │ │ │  A
│ │ │ │  1  1.5  0.707107   1   2
│ │ │ │  2  3.5  0.707107   3   4
│ │ │ │ +
│ │ │ │ +[2 rows x 4 columns]
│ │ │ │  ******** WWiinnddooww bbiinnaarryy ccoorrrr//ccoovv ooppeerraattiioonnss rreettuurrnn aa MMuullttiiIInnddeexx DDaattaaFFrraammee_## ********
│ │ │ │  A binary window operation, like .corr() or .cov(), when operating on a .rolling
│ │ │ │  (..), .expanding(..), or .ewm(..) object, will now return a 2-level
│ │ │ │  MultiIndexed DataFrame rather than a Panel, as Panel is now deprecated, see
│ │ │ │  _h_e_r_e. These are equivalent in function, but a MultiIndexed DataFrame enjoys
│ │ │ │  more support in pandas. See the section on _W_i_n_d_o_w_e_d_ _B_i_n_a_r_y_ _O_p_e_r_a_t_i_o_n_s for more
│ │ │ │  information. (_G_H_ _1_5_6_7_7)
│ │ │ │ @@ -1635,14 +1669,16 @@
│ │ │ │  bar                A         B
│ │ │ │  foo
│ │ │ │  2016-04-05  0.640880  0.126205
│ │ │ │  2016-04-06  0.171465  0.737086
│ │ │ │  2016-04-07  0.127029  0.369650
│ │ │ │  2016-04-08  0.604334  0.103104
│ │ │ │  2016-04-09  0.802374  0.945553
│ │ │ │ +
│ │ │ │ +[5 rows x 2 columns]
│ │ │ │  Previous behavior:
│ │ │ │  In [2]: df.rolling(12).corr()
│ │ │ │  Out[2]:
│ │ │ │  <class 'pandas.core.panel.Panel'>
│ │ │ │  Dimensions: 100 (items) x 2 (major_axis) x 2 (minor_axis)
│ │ │ │  Items axis: 2016-01-01 00:00:00 to 2016-04-09 00:00:00
│ │ │ │  Major_axis axis: A to B
│ │ │ │ @@ -1655,33 +1691,37 @@
│ │ │ │  bar                    A         B
│ │ │ │  foo        bar
│ │ │ │  2016-04-07 B   -0.132090  1.000000
│ │ │ │  2016-04-08 A    1.000000 -0.145775
│ │ │ │             B   -0.145775  1.000000
│ │ │ │  2016-04-09 A    1.000000  0.119645
│ │ │ │             B    0.119645  1.000000
│ │ │ │ +
│ │ │ │ +[5 rows x 2 columns]
│ │ │ │  Retrieving a correlation matrix for a cross-section
│ │ │ │  In [87]: df.rolling(12).corr().loc['2016-04-07']
│ │ │ │  Out[87]:
│ │ │ │  bar        A        B
│ │ │ │  bar
│ │ │ │  A    1.00000 -0.13209
│ │ │ │  B   -0.13209  1.00000
│ │ │ │ +
│ │ │ │ +[2 rows x 2 columns]
│ │ │ │  ******** HHDDFFSSttoorree wwhheerree ssttrriinngg ccoommppaarriissoonn_## ********
│ │ │ │  In previous versions most types could be compared to string column in a
│ │ │ │  HDFStore usually resulting in an invalid comparison, returning an empty result
│ │ │ │  frame. These comparisons will now raise a TypeError (_G_H_ _1_5_4_9_2)
│ │ │ │  In [88]: df = pd.DataFrame({'unparsed_date': ['2014-01-01', '2014-01-01']})
│ │ │ │  
│ │ │ │  In [89]: df.to_hdf('store.h5', key='key', format='table', data_columns=True)
│ │ │ │  
│ │ │ │  In [90]: df.dtypes
│ │ │ │  Out[90]:
│ │ │ │  unparsed_date    object
│ │ │ │ -dtype: object
│ │ │ │ +Length: 1, dtype: object
│ │ │ │  Previous behavior:
│ │ │ │  In [4]: pd.read_hdf('store.h5', 'key', where='unparsed_date > ts')
│ │ │ │  File "<string>", line 1
│ │ │ │    (unparsed_date > 1970-01-01 00:00:01.388552400)
│ │ │ │                          ^
│ │ │ │  SyntaxError: invalid token
│ │ │ │  New behavior:
│ │ │ │ @@ -1717,34 +1757,40 @@
│ │ │ │        In [97]: left
│ │ │ │        Out[97]:
│ │ │ │            a
│ │ │ │        2  20
│ │ │ │        1  10
│ │ │ │        0   0
│ │ │ │  
│ │ │ │ +      [3 rows x 1 columns]
│ │ │ │ +
│ │ │ │        In [98]: right = pd.DataFrame({'b': [100, 200, 300]}, index=[1, 2, 3])
│ │ │ │  
│ │ │ │        In [99]: right
│ │ │ │        Out[99]:
│ │ │ │             b
│ │ │ │        1  100
│ │ │ │        2  200
│ │ │ │        3  300
│ │ │ │ +
│ │ │ │ +      [3 rows x 1 columns]
│ │ │ │        Previous behavior:
│ │ │ │        In [4]: left.join(right, how='inner')
│ │ │ │        Out[4]:
│ │ │ │           a    b
│ │ │ │        1  10  100
│ │ │ │        2  20  200
│ │ │ │        New behavior:
│ │ │ │        In [100]: left.join(right, how='inner')
│ │ │ │        Out[100]:
│ │ │ │            a    b
│ │ │ │        2  20  200
│ │ │ │        1  10  100
│ │ │ │ +
│ │ │ │ +      [2 rows x 2 columns]
│ │ │ │  ******** PPiivvoott ttaabbllee aallwwaayyss rreettuurrnnss aa DDaattaaFFrraammee_## ********
│ │ │ │  The documentation for _p_i_v_o_t___t_a_b_l_e_(_) states that a DataFrame is aallwwaayyss returned.
│ │ │ │  Here a bug is fixed that allowed this to return a Series under certain
│ │ │ │  circumstance. (_G_H_ _4_3_8_6)
│ │ │ │  In [101]: df = pd.DataFrame({'col1': [3, 4, 5],
│ │ │ │     .....:                    'col2': ['C', 'D', 'E'],
│ │ │ │     .....:                    'col3': [1, 3, 9]})
│ │ │ │ @@ -1752,14 +1798,16 @@
│ │ │ │  
│ │ │ │  In [102]: df
│ │ │ │  Out[102]:
│ │ │ │     col1 col2  col3
│ │ │ │  0     3    C     1
│ │ │ │  1     4    D     3
│ │ │ │  2     5    E     9
│ │ │ │ +
│ │ │ │ +[3 rows x 3 columns]
│ │ │ │  Previous behavior:
│ │ │ │  In [2]: df.pivot_table('col1', index=['col3', 'col2'], aggfunc="sum")
│ │ │ │  Out[2]:
│ │ │ │  col3  col2
│ │ │ │  1     C       3
│ │ │ │  3     D       4
│ │ │ │  9     E       5
│ │ │ │ @@ -1768,14 +1816,16 @@
│ │ │ │  In [103]: df.pivot_table('col1', index=['col3', 'col2'], aggfunc="sum")
│ │ │ │  Out[103]:
│ │ │ │             col1
│ │ │ │  col3 col2
│ │ │ │  1    C        3
│ │ │ │  3    D        4
│ │ │ │  9    E        5
│ │ │ │ +
│ │ │ │ +[3 rows x 1 columns]
│ │ │ │  ******** OOtthheerr AAPPII cchhaannggeess_## ********
│ │ │ │      * numexpr version is now required to be >= 2.4.6 and it will not be used at
│ │ │ │        all if this requisite is not fulfilled (_G_H_ _1_5_2_1_3).
│ │ │ │      * CParserError has been renamed to ParserError in pd.read_csv() and will be
│ │ │ │        removed in the future (_G_H_ _1_2_6_6_5)
│ │ │ │      * SparseArray.cumsum() and SparseSeries.cumsum() will now always return
│ │ │ │        SparseArray and SparseSeries respectively (_G_H_ _1_2_8_5_5)
│ │ │ │ @@ -1922,35 +1972,37 @@
│ │ │ │  
│ │ │ │  In [105]: df
│ │ │ │  Out[105]:
│ │ │ │     A  B
│ │ │ │  a  1  4
│ │ │ │  b  2  5
│ │ │ │  c  3  6
│ │ │ │ +
│ │ │ │ +[3 rows x 2 columns]
│ │ │ │  Previous behavior, where you wish to get the 0th and the 2nd elements from the
│ │ │ │  index in the ‘A’ column.
│ │ │ │  In [3]: df.ix[[0, 2], 'A']
│ │ │ │  Out[3]:
│ │ │ │  a    1
│ │ │ │  c    3
│ │ │ │  Name: A, dtype: int64
│ │ │ │  Using .loc. Here we will select the appropriate indexes from the index, then
│ │ │ │  use llaabbeell indexing.
│ │ │ │  In [106]: df.loc[df.index[[0, 2]], 'A']
│ │ │ │  Out[106]:
│ │ │ │  a    1
│ │ │ │  c    3
│ │ │ │ -Name: A, dtype: int64
│ │ │ │ +Name: A, Length: 2, dtype: int64
│ │ │ │  Using .iloc. Here we will get the location of the ‘A’ column, then use
│ │ │ │  ppoossiittiioonnaall indexing to select things.
│ │ │ │  In [107]: df.iloc[[0, 2], df.columns.get_loc('A')]
│ │ │ │  Out[107]:
│ │ │ │  a    1
│ │ │ │  c    3
│ │ │ │ -Name: A, dtype: int64
│ │ │ │ +Name: A, Length: 2, dtype: int64
│ │ │ │  ******** DDeepprreeccaattee PPaanneell_## ********
│ │ │ │  Panel is deprecated and will be removed in a future version. The recommended
│ │ │ │  way to represent 3-D data are with a MultiIndex on a DataFrame via the to_frame
│ │ │ │  () or with the _x_a_r_r_a_y_ _p_a_c_k_a_g_e. pandas provides a to_xarray() method to automate
│ │ │ │  this conversion (_G_H_ _1_3_5_6_3).
│ │ │ │  In [133]: import pandas._testing as tm
│ │ │ │  
│ │ │ │ @@ -2025,24 +2077,28 @@
│ │ │ │  Out[109]:
│ │ │ │     A  B  C
│ │ │ │  0  1  0  0
│ │ │ │  1  1  1  1
│ │ │ │  2  1  2  2
│ │ │ │  3  2  3  3
│ │ │ │  4  2  4  4
│ │ │ │ +
│ │ │ │ +[5 rows x 3 columns]
│ │ │ │  Here is a typical useful syntax for computing different aggregations for
│ │ │ │  different columns. This is a natural, and useful syntax. We aggregate from the
│ │ │ │  dict-to-list by taking the specified columns and applying the list of
│ │ │ │  functions. This returns a MultiIndex for the columns (this is nnoott deprecated).
│ │ │ │  In [110]: df.groupby('A').agg({'B': 'sum', 'C': 'min'})
│ │ │ │  Out[110]:
│ │ │ │     B  C
│ │ │ │  A
│ │ │ │  1  3  0
│ │ │ │  2  7  3
│ │ │ │ +
│ │ │ │ +[2 rows x 2 columns]
│ │ │ │  Here’s an example of the first deprecation, passing a dict to a grouped Series.
│ │ │ │  This is a combination aggregation & renaming:
│ │ │ │  In [6]: df.groupby('A').B.agg({'foo': 'count'})
│ │ │ │  FutureWarning: using a dict on a Series for aggregation
│ │ │ │  is deprecated and will be removed in a future version
│ │ │ │  
│ │ │ │  Out[6]:
│ │ │ │ @@ -2053,14 +2109,16 @@
│ │ │ │  You can accomplish the same operation, more idiomatically by:
│ │ │ │  In [111]: df.groupby('A').B.agg(['count']).rename(columns={'count': 'foo'})
│ │ │ │  Out[111]:
│ │ │ │     foo
│ │ │ │  A
│ │ │ │  1    3
│ │ │ │  2    2
│ │ │ │ +
│ │ │ │ +[2 rows x 1 columns]
│ │ │ │  Here’s an example of the second deprecation, passing a dict-of-dict to a
│ │ │ │  grouped DataFrame:
│ │ │ │  In [23]: (df.groupby('A')
│ │ │ │      ...:    .agg({'B': {'foo': 'sum'}, 'C': {'bar': 'min'}})
│ │ │ │      ...:  )
│ │ │ │  FutureWarning: using a dict with renaming is deprecated and
│ │ │ │  will be removed in a future version
│ │ │ │ @@ -2078,14 +2136,16 @@
│ │ │ │     .....:  )
│ │ │ │     .....:
│ │ │ │  Out[112]:
│ │ │ │     foo  bar
│ │ │ │  A
│ │ │ │  1    3    0
│ │ │ │  2    7    3
│ │ │ │ +
│ │ │ │ +[2 rows x 2 columns]
│ │ │ │  ******** DDeepprreeccaattee ..pplloottttiinngg_## ********
│ │ │ │  The pandas.tools.plotting module has been deprecated, in favor of the top level
│ │ │ │  pandas.plotting module. All the public plotting functions are now available
│ │ │ │  from pandas.plotting (_G_H_ _1_2_5_4_8).
│ │ │ │  Furthermore, the top-level pandas.scatter_matrix and pandas.plot_params are
│ │ │ │  deprecated. Users can import these from pandas.plotting as well.
│ │ │ │  Previous script:
│ │ ├── ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.21.0.html
│ │ │ @@ -757,37 +757,37 @@
│ │ │  <span class="gp">   ...: </span>
│ │ │  
│ │ │  <span class="gp">In [2]: </span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[2]: </span>
│ │ │  <span class="go">A     int64</span>
│ │ │  <span class="go">B    object</span>
│ │ │  <span class="go">C    object</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 3, dtype: object</span>
│ │ │  
│ │ │  <span class="gp">In [3]: </span><span class="n">df</span><span class="o">.</span><span class="n">infer_objects</span><span class="p">()</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[3]: </span>
│ │ │  <span class="go">A     int64</span>
│ │ │  <span class="go">B     int64</span>
│ │ │  <span class="go">C    object</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 3, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Note that column <code class="docutils literal notranslate"><span class="pre">'C'</span></code> was not converted - only scalar numeric types
│ │ │  will be converted to a new type.  Other types of conversion should be accomplished
│ │ │  using the <a class="reference internal" href="../reference/api/pandas.to_numeric.html#pandas.to_numeric" title="pandas.to_numeric"><code class="xref py py-func docutils literal notranslate"><span class="pre">to_numeric()</span></code></a> function (or <a class="reference internal" href="../reference/api/pandas.to_datetime.html#pandas.to_datetime" title="pandas.to_datetime"><code class="xref py py-func docutils literal notranslate"><span class="pre">to_datetime()</span></code></a>, <a class="reference internal" href="../reference/api/pandas.to_timedelta.html#pandas.to_timedelta" title="pandas.to_timedelta"><code class="xref py py-func docutils literal notranslate"><span class="pre">to_timedelta()</span></code></a>).</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [4]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">infer_objects</span><span class="p">()</span>
│ │ │  
│ │ │  <span class="gp">In [5]: </span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;C&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;C&#39;</span><span class="p">],</span> <span class="n">errors</span><span class="o">=</span><span class="s1">&#39;coerce&#39;</span><span class="p">)</span>
│ │ │  
│ │ │  <span class="gp">In [6]: </span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[6]: </span>
│ │ │  <span class="go">A    int64</span>
│ │ │  <span class="go">B    int64</span>
│ │ │  <span class="go">C    int64</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 3, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="improved-warnings-when-attempting-to-create-columns">
│ │ │  <span id="whatsnew-0210-enhancements-attribute-access"></span><h3>Improved warnings when attempting to create columns<a class="headerlink" href="#improved-warnings-when-attempting-to-create-columns" title="Link to this heading">#</a></h3>
│ │ │  <p>New users are often puzzled by the relationship between column operations and
│ │ │  attribute access on <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> instances (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/7175">GH 7175</a>). One specific
│ │ │ @@ -820,26 +820,32 @@
│ │ │  
│ │ │  <span class="gp">In [8]: </span><span class="n">df</span>
│ │ │  <span class="gh">Out[8]: </span>
│ │ │  <span class="go">   A  B  C  D</span>
│ │ │  <span class="go">0  0  1  2  3</span>
│ │ │  <span class="go">1  4  5  6  7</span>
│ │ │  
│ │ │ +<span class="go">[2 rows x 4 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [9]: </span><span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
│ │ │  <span class="gh">Out[9]: </span>
│ │ │  <span class="go">   A  D</span>
│ │ │  <span class="go">0  0  3</span>
│ │ │  <span class="go">1  4  7</span>
│ │ │  
│ │ │ +<span class="go">[2 rows x 2 columns]</span>
│ │ │ +
│ │ │  <span class="go"># the following is now equivalent</span>
│ │ │  <span class="gp">In [10]: </span><span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">])</span>
│ │ │  <span class="gh">Out[10]: </span>
│ │ │  <span class="go">   A  D</span>
│ │ │  <span class="go">0  0  3</span>
│ │ │  <span class="go">1  4  7</span>
│ │ │ +
│ │ │ +<span class="go">[2 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="methods-rename-reindex-now-also-accept-axis-keyword">
│ │ │  <span id="whatsnew-0210-enhancements-rename-reindex-axis"></span><h3>Methods <code class="docutils literal notranslate"><span class="pre">rename</span></code>, <code class="docutils literal notranslate"><span class="pre">reindex</span></code> now also accept axis keyword<a class="headerlink" href="#methods-rename-reindex-now-also-accept-axis-keyword" title="Link to this heading">#</a></h3>
│ │ │  <p>The <a class="reference internal" href="../reference/api/pandas.DataFrame.rename.html#pandas.DataFrame.rename" title="pandas.DataFrame.rename"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DataFrame.rename()</span></code></a> and <a class="reference internal" href="../reference/api/pandas.DataFrame.reindex.html#pandas.DataFrame.reindex" title="pandas.DataFrame.reindex"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DataFrame.reindex()</span></code></a> methods have gained
│ │ │  the <code class="docutils literal notranslate"><span class="pre">axis</span></code> keyword to specify the axis to target with the operation
│ │ │ @@ -850,52 +856,64 @@
│ │ │  <span class="gp">In [12]: </span><span class="n">df</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="nb">str</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="s1">&#39;columns&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[12]: </span>
│ │ │  <span class="go">   a  b</span>
│ │ │  <span class="go">0  1  4</span>
│ │ │  <span class="go">1  2  5</span>
│ │ │  <span class="go">2  3  6</span>
│ │ │  
│ │ │ +<span class="go">[3 rows x 2 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [13]: </span><span class="n">df</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="nb">id</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="s1">&#39;index&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[13]: </span>
│ │ │  <span class="go">          A  B</span>
│ │ │  <span class="go">10730032  1  4</span>
│ │ │  <span class="go">10730064  2  5</span>
│ │ │  <span class="go">10730096  3  6</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>And <code class="docutils literal notranslate"><span class="pre">reindex</span></code>:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [14]: </span><span class="n">df</span><span class="o">.</span><span class="n">reindex</span><span class="p">([</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="s1">&#39;columns&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[14]: </span>
│ │ │  <span class="go">   A  B   C</span>
│ │ │  <span class="go">0  1  4 NaN</span>
│ │ │  <span class="go">1  2  5 NaN</span>
│ │ │  <span class="go">2  3  6 NaN</span>
│ │ │  
│ │ │ +<span class="go">[3 rows x 3 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [15]: </span><span class="n">df</span><span class="o">.</span><span class="n">reindex</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="s1">&#39;index&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[15]: </span>
│ │ │  <span class="go">     A    B</span>
│ │ │  <span class="go">0  1.0  4.0</span>
│ │ │  <span class="go">1  2.0  5.0</span>
│ │ │  <span class="go">3  NaN  NaN</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>The “index, columns” style continues to work as before.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [16]: </span><span class="n">df</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="nb">id</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="nb">str</span><span class="o">.</span><span class="n">lower</span><span class="p">)</span>
│ │ │  <span class="gh">Out[16]: </span>
│ │ │  <span class="go">          a  b</span>
│ │ │  <span class="go">10730032  1  4</span>
│ │ │  <span class="go">10730064  2  5</span>
│ │ │  <span class="go">10730096  3  6</span>
│ │ │  
│ │ │ +<span class="go">[3 rows x 2 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [17]: </span><span class="n">df</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">])</span>
│ │ │  <span class="gh">Out[17]: </span>
│ │ │  <span class="go">     A    B   C</span>
│ │ │  <span class="go">0  1.0  4.0 NaN</span>
│ │ │  <span class="go">1  2.0  5.0 NaN</span>
│ │ │  <span class="go">3  NaN  NaN NaN</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>We <em>highly</em> encourage using named arguments to avoid confusion when using either
│ │ │  style.</p>
│ │ │  </section>
│ │ │  <section id="categoricaldtype-for-specifying-categoricals">
│ │ │  <span id="whatsnew-0210-enhancements-categorical-dtype"></span><h3><code class="docutils literal notranslate"><span class="pre">CategoricalDtype</span></code> for specifying categoricals<a class="headerlink" href="#categoricaldtype-for-specifying-categoricals" title="Link to this heading">#</a></h3>
│ │ │ @@ -913,15 +931,15 @@
│ │ │  
│ │ │  <span class="gp">In [21]: </span><span class="n">s</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
│ │ │  <span class="gh">Out[21]: </span>
│ │ │  <span class="go">0    a</span>
│ │ │  <span class="go">1    b</span>
│ │ │  <span class="go">2    c</span>
│ │ │  <span class="go">3    a</span>
│ │ │ -<span class="go">dtype: category</span>
│ │ │ +<span class="go">Length: 4, dtype: category</span>
│ │ │  <span class="go">Categories (4, object): [&#39;a&#39; &lt; &#39;b&#39; &lt; &#39;c&#39; &lt; &#39;d&#39;]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>One place that deserves special mention is in <a class="reference internal" href="../reference/api/pandas.read_csv.html#pandas.read_csv" title="pandas.read_csv"><code class="xref py py-meth docutils literal notranslate"><span class="pre">read_csv()</span></code></a>. Previously, with
│ │ │  <code class="docutils literal notranslate"><span class="pre">dtype={'col':</span> <span class="pre">'category'}</span></code>, the returned values and categories would always
│ │ │  be strings.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [22]: </span><span class="n">data</span> <span class="o">=</span> <span class="s1">&#39;A,B</span><span class="se">\n</span><span class="s1">a,1</span><span class="se">\n</span><span class="s1">b,2</span><span class="se">\n</span><span class="s1">c,3&#39;</span>
│ │ │ @@ -973,26 +991,30 @@
│ │ │  <span class="gp">   ....: </span>
│ │ │  
│ │ │  <span class="gp">In [29]: </span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
│ │ │  <span class="gh">Out[29]: </span>
│ │ │  <span class="go">     Store    Product  Revenue  Quantity</span>
│ │ │  <span class="go">0  Store_2  Product_2    32.09         7</span>
│ │ │  <span class="go">1  Store_1  Product_3    14.20         1</span>
│ │ │ +
│ │ │ +<span class="go">[2 rows x 4 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Now, to find prices per store/product, we can simply do:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [30]: </span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;Store&#39;</span><span class="p">,</span> <span class="s1">&#39;Product&#39;</span><span class="p">])</span>
│ │ │  <span class="gp">   ....: </span>   <span class="o">.</span><span class="n">pipe</span><span class="p">(</span><span class="k">lambda</span> <span class="n">grp</span><span class="p">:</span> <span class="n">grp</span><span class="o">.</span><span class="n">Revenue</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="n">grp</span><span class="o">.</span><span class="n">Quantity</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
│ │ │  <span class="gp">   ....: </span>   <span class="o">.</span><span class="n">unstack</span><span class="p">()</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
│ │ │  <span class="gp">   ....: </span>
│ │ │  <span class="gh">Out[30]: </span>
│ │ │  <span class="go">Product  Product_1  Product_2  Product_3</span>
│ │ │  <span class="go">Store                                   </span>
│ │ │  <span class="go">Store_1       6.73       6.72       7.14</span>
│ │ │  <span class="go">Store_2       7.59       6.98       7.23</span>
│ │ │ +
│ │ │ +<span class="go">[2 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>See the <a class="reference internal" href="../user_guide/groupby.html#groupby-pipe"><span class="std std-ref">documentation</span></a> for more.</p>
│ │ │  </section>
│ │ │  <section id="categorical-rename-categories-accepts-a-dict-like">
│ │ │  <span id="whatsnew-0210-enhancements-rename-categories"></span><h3><code class="docutils literal notranslate"><span class="pre">Categorical.rename_categories</span></code> accepts a dict-like<a class="headerlink" href="#categorical-rename-categories-accepts-a-dict-like" title="Link to this heading">#</a></h3>
│ │ │  <p><a class="reference internal" href="../reference/api/pandas.Series.cat.rename_categories.html#pandas.Series.cat.rename_categories" title="pandas.Series.cat.rename_categories"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rename_categories()</span></code></a> now accepts a dict-like argument for
│ │ │ @@ -1163,15 +1185,15 @@
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [35]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
│ │ │  
│ │ │  <span class="gp">In [36]: </span><span class="n">s</span>
│ │ │  <span class="gh">Out[36]: </span>
│ │ │  <span class="go">0    1</span>
│ │ │  <span class="go">1    2</span>
│ │ │  <span class="go">2    3</span>
│ │ │ -<span class="go">dtype: int64</span>
│ │ │ +<span class="go">Length: 3, dtype: int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Previous behavior</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [4]: </span><span class="n">s</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span>
│ │ │  <span class="gh">Out[4]:</span>
│ │ │  <span class="go">1    2.0</span>
│ │ │  <span class="go">2    3.0</span>
│ │ │ @@ -1196,23 +1218,23 @@
│ │ │  </div>
│ │ │  <p>The idiomatic way to achieve selecting potentially not-found elements is via <code class="docutils literal notranslate"><span class="pre">.reindex()</span></code></p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [37]: </span><span class="n">s</span><span class="o">.</span><span class="n">reindex</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
│ │ │  <span class="gh">Out[37]: </span>
│ │ │  <span class="go">1    2.0</span>
│ │ │  <span class="go">2    3.0</span>
│ │ │  <span class="go">3    NaN</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 3, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Selection with all keys found is unchanged.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [38]: </span><span class="n">s</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]</span>
│ │ │  <span class="gh">Out[38]: </span>
│ │ │  <span class="go">1    2</span>
│ │ │  <span class="go">2    3</span>
│ │ │ -<span class="go">dtype: int64</span>
│ │ │ +<span class="go">Length: 2, dtype: int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="na-naming-changes">
│ │ │  <span id="whatsnew-0210-api-na-changes"></span><h3>NA naming changes<a class="headerlink" href="#na-naming-changes" title="Link to this heading">#</a></h3>
│ │ │  <p>In order to promote more consistency among the pandas API, we have added additional top-level
│ │ │  functions <a class="reference internal" href="../reference/api/pandas.isna.html#pandas.isna" title="pandas.isna"><code class="xref py py-func docutils literal notranslate"><span class="pre">isna()</span></code></a> and <a class="reference internal" href="../reference/api/pandas.notna.html#pandas.notna" title="pandas.notna"><code class="xref py py-func docutils literal notranslate"><span class="pre">notna()</span></code></a> that are aliases for <a class="reference internal" href="../reference/api/pandas.isnull.html#pandas.isnull" title="pandas.isnull"><code class="xref py py-func docutils literal notranslate"><span class="pre">isnull()</span></code></a> and <a class="reference internal" href="../reference/api/pandas.notnull.html#pandas.notnull" title="pandas.notnull"><code class="xref py py-func docutils literal notranslate"><span class="pre">notnull()</span></code></a>.
│ │ │ @@ -1228,15 +1250,15 @@
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [39]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
│ │ │  
│ │ │  <span class="gp">In [40]: </span><span class="n">s</span>
│ │ │  <span class="gh">Out[40]: </span>
│ │ │  <span class="go">0    1</span>
│ │ │  <span class="go">1    2</span>
│ │ │  <span class="go">2    3</span>
│ │ │ -<span class="go">dtype: int64</span>
│ │ │ +<span class="go">Length: 3, dtype: int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Previously:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [2]: </span><span class="nb">type</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">s</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
│ │ │  <span class="gh">Out[2]: </span><span class="go">numpy.int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │ @@ -1272,15 +1294,15 @@
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [45]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">])</span>
│ │ │  
│ │ │  <span class="gp">In [46]: </span><span class="n">s</span>
│ │ │  <span class="gh">Out[46]: </span>
│ │ │  <span class="go">False    1</span>
│ │ │  <span class="go">True     2</span>
│ │ │  <span class="go">False    3</span>
│ │ │ -<span class="go">dtype: int64</span>
│ │ │ +<span class="go">Length: 3, dtype: int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [59]: </span><span class="n">s</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])]</span>
│ │ │  <span class="gh">Out[59]:</span>
│ │ │  <span class="go">True     2</span>
│ │ │  <span class="go">False    1</span>
│ │ │  <span class="go">False    3</span>
│ │ │ @@ -1289,40 +1311,40 @@
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Current behavior</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [47]: </span><span class="n">s</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])]</span>
│ │ │  <span class="gh">Out[47]: </span>
│ │ │  <span class="go">False    1</span>
│ │ │  <span class="go">False    3</span>
│ │ │ -<span class="go">dtype: int64</span>
│ │ │ +<span class="go">Length: 2, dtype: int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Furthermore, previously if you had an index that was non-numeric (e.g. strings), then a boolean Index would raise a <code class="docutils literal notranslate"><span class="pre">KeyError</span></code>.
│ │ │  This will now be treated as a boolean indexer.</p>
│ │ │  <p>Previously behavior:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [48]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">])</span>
│ │ │  
│ │ │  <span class="gp">In [49]: </span><span class="n">s</span>
│ │ │  <span class="gh">Out[49]: </span>
│ │ │  <span class="go">a    1</span>
│ │ │  <span class="go">b    2</span>
│ │ │  <span class="go">c    3</span>
│ │ │ -<span class="go">dtype: int64</span>
│ │ │ +<span class="go">Length: 3, dtype: int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [39]: </span><span class="n">s</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])]</span>
│ │ │  <span class="go">KeyError: &quot;None of [Index([True, False, True], dtype=&#39;object&#39;)] are in the [index]&quot;</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Current behavior</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [50]: </span><span class="n">s</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])]</span>
│ │ │  <span class="gh">Out[50]: </span>
│ │ │  <span class="go">a    1</span>
│ │ │  <span class="go">c    3</span>
│ │ │ -<span class="go">dtype: int64</span>
│ │ │ +<span class="go">Length: 2, dtype: int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="periodindex-resampling">
│ │ │  <span id="whatsnew-0210-api-breaking-period-index-resampling"></span><h3><code class="docutils literal notranslate"><span class="pre">PeriodIndex</span></code> resampling<a class="headerlink" href="#periodindex-resampling" title="Link to this heading">#</a></h3>
│ │ │  <p>In previous versions of pandas, resampling a <code class="docutils literal notranslate"><span class="pre">Series</span></code>/<code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> indexed by a <code class="docutils literal notranslate"><span class="pre">PeriodIndex</span></code> returned a <code class="docutils literal notranslate"><span class="pre">DatetimeIndex</span></code> in some cases (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/12884">GH 12884</a>). Resampling to a multiplied frequency now returns a <code class="docutils literal notranslate"><span class="pre">PeriodIndex</span></code> (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/15944">GH 15944</a>). As a minor enhancement, resampling a <code class="docutils literal notranslate"><span class="pre">PeriodIndex</span></code> can now handle <code class="docutils literal notranslate"><span class="pre">NaT</span></code> values (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/13224">GH 13224</a>)</p>
│ │ │  <p>Previous behavior:</p>
│ │ │ @@ -1538,15 +1560,15 @@
│ │ │  </div>
│ │ │  <p>New behavior</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [56]: </span><span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">utc</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
│ │ │  <span class="gh">Out[56]: </span>
│ │ │  <span class="go">0   2013-01-01 00:00:00+00:00</span>
│ │ │  <span class="go">1   2013-01-01 00:00:00+00:00</span>
│ │ │  <span class="go">2   2013-01-01 00:00:00+00:00</span>
│ │ │ -<span class="go">dtype: datetime64[ns, UTC]</span>
│ │ │ +<span class="go">Length: 3, dtype: datetime64[ns, UTC]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Additionally, DataFrames with datetime columns that were parsed by <a class="reference internal" href="../reference/api/pandas.read_sql_table.html#pandas.read_sql_table" title="pandas.read_sql_table"><code class="xref py py-func docutils literal notranslate"><span class="pre">read_sql_table()</span></code></a> and <a class="reference internal" href="../reference/api/pandas.read_sql_query.html#pandas.read_sql_query" title="pandas.read_sql_query"><code class="xref py py-func docutils literal notranslate"><span class="pre">read_sql_query()</span></code></a> will also be localized to UTC only if the original SQL columns were timezone aware datetime columns.</p>
│ │ │  </section>
│ │ │  <section id="consistency-of-range-functions">
│ │ │  <span id="whatsnew-0210-api-consistency-of-range-functions"></span><h3>Consistency of range functions<a class="headerlink" href="#consistency-of-range-functions" title="Link to this heading">#</a></h3>
│ │ │  <p>In previous versions, there were some inconsistencies between the various range functions: <a class="reference internal" href="../reference/api/pandas.date_range.html#pandas.date_range" title="pandas.date_range"><code class="xref py py-func docutils literal notranslate"><span class="pre">date_range()</span></code></a>, <a class="reference internal" href="../reference/api/pandas.bdate_range.html#pandas.bdate_range" title="pandas.bdate_range"><code class="xref py py-func docutils literal notranslate"><span class="pre">bdate_range()</span></code></a>, <a class="reference internal" href="../reference/api/pandas.period_range.html#pandas.period_range" title="pandas.period_range"><code class="xref py py-func docutils literal notranslate"><span class="pre">period_range()</span></code></a>, <a class="reference internal" href="../reference/api/pandas.timedelta_range.html#pandas.timedelta_range" title="pandas.timedelta_range"><code class="xref py py-func docutils literal notranslate"><span class="pre">timedelta_range()</span></code></a>, and <a class="reference internal" href="../reference/api/pandas.interval_range.html#pandas.interval_range" title="pandas.interval_range"><code class="xref py py-func docutils literal notranslate"><span class="pre">interval_range()</span></code></a>. (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/17471">GH 17471</a>).</p>
│ │ │ @@ -1662,14 +1684,16 @@
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [59]: </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;bar&#39;</span><span class="p">,</span> <span class="s1">&#39;baz&#39;</span><span class="p">])]</span>
│ │ │  <span class="gh">Out[59]: </span>
│ │ │  <span class="go">     A</span>
│ │ │  <span class="go">bar  2</span>
│ │ │  <span class="go">baz  3</span>
│ │ │ +
│ │ │ +<span class="go">[2 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="series-argmax-and-series-argmin">
│ │ │  <span id="whatsnew-0210-deprecations-argmin-min"></span><h3>Series.argmax and Series.argmin<a class="headerlink" href="#series-argmax-and-series-argmin" title="Link to this heading">#</a></h3>
│ │ │  <p>The behavior of <a class="reference internal" href="../reference/api/pandas.Series.argmax.html#pandas.Series.argmax" title="pandas.Series.argmax"><code class="xref py py-func docutils literal notranslate"><span class="pre">Series.argmax()</span></code></a> and <a class="reference internal" href="../reference/api/pandas.Series.argmin.html#pandas.Series.argmin" title="pandas.Series.argmin"><code class="xref py py-func docutils literal notranslate"><span class="pre">Series.argmin()</span></code></a> have been deprecated in favor of <a class="reference internal" href="../reference/api/pandas.Series.idxmax.html#pandas.Series.idxmax" title="pandas.Series.idxmax"><code class="xref py py-func docutils literal notranslate"><span class="pre">Series.idxmax()</span></code></a> and <a class="reference internal" href="../reference/api/pandas.Series.idxmin.html#pandas.Series.idxmin" title="pandas.Series.idxmin"><code class="xref py py-func docutils literal notranslate"><span class="pre">Series.idxmin()</span></code></a>, respectively (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/16830">GH 16830</a>).</p>
│ │ │  <p>For compatibility with NumPy arrays, <code class="docutils literal notranslate"><span class="pre">pd.Series</span></code> implements <code class="docutils literal notranslate"><span class="pre">argmax</span></code> and
│ │ │ ├── html2text {}
│ │ │ │ @@ -212,35 +212,35 @@
│ │ │ │     ...:
│ │ │ │  
│ │ │ │  In [2]: df.dtypes
│ │ │ │  Out[2]:
│ │ │ │  A     int64
│ │ │ │  B    object
│ │ │ │  C    object
│ │ │ │ -dtype: object
│ │ │ │ +Length: 3, dtype: object
│ │ │ │  
│ │ │ │  In [3]: df.infer_objects().dtypes
│ │ │ │  Out[3]:
│ │ │ │  A     int64
│ │ │ │  B     int64
│ │ │ │  C    object
│ │ │ │ -dtype: object
│ │ │ │ +Length: 3, dtype: object
│ │ │ │  Note that column 'C' was not converted - only scalar numeric types will be
│ │ │ │  converted to a new type. Other types of conversion should be accomplished using
│ │ │ │  the _t_o___n_u_m_e_r_i_c_(_) function (or _t_o___d_a_t_e_t_i_m_e_(_), _t_o___t_i_m_e_d_e_l_t_a_(_)).
│ │ │ │  In [4]: df = df.infer_objects()
│ │ │ │  
│ │ │ │  In [5]: df['C'] = pd.to_numeric(df['C'], errors='coerce')
│ │ │ │  
│ │ │ │  In [6]: df.dtypes
│ │ │ │  Out[6]:
│ │ │ │  A    int64
│ │ │ │  B    int64
│ │ │ │  C    int64
│ │ │ │ -dtype: object
│ │ │ │ +Length: 3, dtype: object
│ │ │ │  ******** IImmpprroovveedd wwaarrnniinnggss wwhheenn aatttteemmppttiinngg ttoo ccrreeaattee ccoolluummnnss_## ********
│ │ │ │  New users are often puzzled by the relationship between column operations and
│ │ │ │  attribute access on DataFrame instances (_G_H_ _7_1_7_5). One specific instance of
│ │ │ │  this confusion is attempting to create a new column by setting an attribute on
│ │ │ │  the DataFrame:
│ │ │ │  In [1]: df = pd.DataFrame({'one': [1., 2., 3.]})
│ │ │ │  In [2]: df.two = [4, 5, 6]
│ │ │ │ @@ -264,73 +264,91 @@
│ │ │ │  
│ │ │ │  In [8]: df
│ │ │ │  Out[8]:
│ │ │ │     A  B  C  D
│ │ │ │  0  0  1  2  3
│ │ │ │  1  4  5  6  7
│ │ │ │  
│ │ │ │ +[2 rows x 4 columns]
│ │ │ │ +
│ │ │ │  In [9]: df.drop(['B', 'C'], axis=1)
│ │ │ │  Out[9]:
│ │ │ │     A  D
│ │ │ │  0  0  3
│ │ │ │  1  4  7
│ │ │ │  
│ │ │ │ +[2 rows x 2 columns]
│ │ │ │ +
│ │ │ │  # the following is now equivalent
│ │ │ │  In [10]: df.drop(columns=['B', 'C'])
│ │ │ │  Out[10]:
│ │ │ │     A  D
│ │ │ │  0  0  3
│ │ │ │  1  4  7
│ │ │ │ +
│ │ │ │ +[2 rows x 2 columns]
│ │ │ │  ******** MMeetthhooddss rreennaammee,, rreeiinnddeexx nnooww aallssoo aacccceepptt aaxxiiss kkeeyywwoorrdd_## ********
│ │ │ │  The _D_a_t_a_F_r_a_m_e_._r_e_n_a_m_e_(_) and _D_a_t_a_F_r_a_m_e_._r_e_i_n_d_e_x_(_) methods have gained the axis
│ │ │ │  keyword to specify the axis to target with the operation (_G_H_ _1_2_3_9_2).
│ │ │ │  Here’s rename:
│ │ │ │  In [11]: df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
│ │ │ │  
│ │ │ │  In [12]: df.rename(str.lower, axis='columns')
│ │ │ │  Out[12]:
│ │ │ │     a  b
│ │ │ │  0  1  4
│ │ │ │  1  2  5
│ │ │ │  2  3  6
│ │ │ │  
│ │ │ │ +[3 rows x 2 columns]
│ │ │ │ +
│ │ │ │  In [13]: df.rename(id, axis='index')
│ │ │ │  Out[13]:
│ │ │ │            A  B
│ │ │ │  10730032  1  4
│ │ │ │  10730064  2  5
│ │ │ │  10730096  3  6
│ │ │ │ +
│ │ │ │ +[3 rows x 2 columns]
│ │ │ │  And reindex:
│ │ │ │  In [14]: df.reindex(['A', 'B', 'C'], axis='columns')
│ │ │ │  Out[14]:
│ │ │ │     A  B   C
│ │ │ │  0  1  4 NaN
│ │ │ │  1  2  5 NaN
│ │ │ │  2  3  6 NaN
│ │ │ │  
│ │ │ │ +[3 rows x 3 columns]
│ │ │ │ +
│ │ │ │  In [15]: df.reindex([0, 1, 3], axis='index')
│ │ │ │  Out[15]:
│ │ │ │       A    B
│ │ │ │  0  1.0  4.0
│ │ │ │  1  2.0  5.0
│ │ │ │  3  NaN  NaN
│ │ │ │ +
│ │ │ │ +[3 rows x 2 columns]
│ │ │ │  The “index, columns” style continues to work as before.
│ │ │ │  In [16]: df.rename(index=id, columns=str.lower)
│ │ │ │  Out[16]:
│ │ │ │            a  b
│ │ │ │  10730032  1  4
│ │ │ │  10730064  2  5
│ │ │ │  10730096  3  6
│ │ │ │  
│ │ │ │ +[3 rows x 2 columns]
│ │ │ │ +
│ │ │ │  In [17]: df.reindex(index=[0, 1, 3], columns=['A', 'B', 'C'])
│ │ │ │  Out[17]:
│ │ │ │       A    B   C
│ │ │ │  0  1.0  4.0 NaN
│ │ │ │  1  2.0  5.0 NaN
│ │ │ │  3  NaN  NaN NaN
│ │ │ │ +
│ │ │ │ +[3 rows x 3 columns]
│ │ │ │  We hhiigghhllyy encourage using named arguments to avoid confusion when using either
│ │ │ │  style.
│ │ │ │  ******** CCaatteeggoorriiccaallDDttyyppee ffoorr ssppeecciiffyyiinngg ccaatteeggoorriiccaallss_## ********
│ │ │ │  pandas.api.types.CategoricalDtype has been added to the public API and expanded
│ │ │ │  to include the categories and ordered attributes. A CategoricalDtype can be
│ │ │ │  used to specify the set of categories and orderedness of an array, independent
│ │ │ │  of the data. This can be useful for example, when converting string data to a
│ │ │ │ @@ -344,15 +362,15 @@
│ │ │ │  
│ │ │ │  In [21]: s.astype(dtype)
│ │ │ │  Out[21]:
│ │ │ │  0    a
│ │ │ │  1    b
│ │ │ │  2    c
│ │ │ │  3    a
│ │ │ │ -dtype: category
│ │ │ │ +Length: 4, dtype: category
│ │ │ │  Categories (4, object): ['a' < 'b' < 'c' < 'd']
│ │ │ │  One place that deserves special mention is in _r_e_a_d___c_s_v_(_). Previously, with
│ │ │ │  dtype={'col': 'category'}, the returned values and categories would always be
│ │ │ │  strings.
│ │ │ │  In [22]: data = 'A,B\na,1\nb,2\nc,3'
│ │ │ │  
│ │ │ │  In [23]: pd.read_csv(StringIO(data), dtype={'B': 'category'}).B.cat.categories
│ │ │ │ @@ -398,24 +416,28 @@
│ │ │ │     ....:
│ │ │ │  
│ │ │ │  In [29]: df.head(2)
│ │ │ │  Out[29]:
│ │ │ │       Store    Product  Revenue  Quantity
│ │ │ │  0  Store_2  Product_2    32.09         7
│ │ │ │  1  Store_1  Product_3    14.20         1
│ │ │ │ +
│ │ │ │ +[2 rows x 4 columns]
│ │ │ │  Now, to find prices per store/product, we can simply do:
│ │ │ │  In [30]: (df.groupby(['Store', 'Product'])
│ │ │ │     ....:    .pipe(lambda grp: grp.Revenue.sum() / grp.Quantity.sum())
│ │ │ │     ....:    .unstack().round(2))
│ │ │ │     ....:
│ │ │ │  Out[30]:
│ │ │ │  Product  Product_1  Product_2  Product_3
│ │ │ │  Store
│ │ │ │  Store_1       6.73       6.72       7.14
│ │ │ │  Store_2       7.59       6.98       7.23
│ │ │ │ +
│ │ │ │ +[2 rows x 3 columns]
│ │ │ │  See the _d_o_c_u_m_e_n_t_a_t_i_o_n for more.
│ │ │ │  ******** CCaatteeggoorriiccaall..rreennaammee__ccaatteeggoorriieess aacccceeppttss aa ddiicctt--lliikkee_## ********
│ │ │ │  _r_e_n_a_m_e___c_a_t_e_g_o_r_i_e_s_(_) now accepts a dict-like argument for new_categories. The
│ │ │ │  previous categories are looked up in the dictionary’s keys and replaced if
│ │ │ │  found. The behavior of missing and extra keys is the same as in
│ │ │ │  _D_a_t_a_F_r_a_m_e_._r_e_n_a_m_e_(_).
│ │ │ │  In [31]: c = pd.Categorical(['a', 'a', 'b'])
│ │ │ │ @@ -556,15 +578,15 @@
│ │ │ │  In [35]: s = pd.Series([1, 2, 3])
│ │ │ │  
│ │ │ │  In [36]: s
│ │ │ │  Out[36]:
│ │ │ │  0    1
│ │ │ │  1    2
│ │ │ │  2    3
│ │ │ │ -dtype: int64
│ │ │ │ +Length: 3, dtype: int64
│ │ │ │  Previous behavior
│ │ │ │  In [4]: s.loc[[1, 2, 3]]
│ │ │ │  Out[4]:
│ │ │ │  1    2.0
│ │ │ │  2    3.0
│ │ │ │  3    NaN
│ │ │ │  dtype: float64
│ │ │ │ @@ -585,21 +607,21 @@
│ │ │ │  The idiomatic way to achieve selecting potentially not-found elements is via
│ │ │ │  .reindex()
│ │ │ │  In [37]: s.reindex([1, 2, 3])
│ │ │ │  Out[37]:
│ │ │ │  1    2.0
│ │ │ │  2    3.0
│ │ │ │  3    NaN
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 3, dtype: float64
│ │ │ │  Selection with all keys found is unchanged.
│ │ │ │  In [38]: s.loc[[1, 2]]
│ │ │ │  Out[38]:
│ │ │ │  1    2
│ │ │ │  2    3
│ │ │ │ -dtype: int64
│ │ │ │ +Length: 2, dtype: int64
│ │ │ │  ******** NNAA nnaammiinngg cchhaannggeess_## ********
│ │ │ │  In order to promote more consistency among the pandas API, we have added
│ │ │ │  additional top-level functions _i_s_n_a_(_) and _n_o_t_n_a_(_) that are aliases for _i_s_n_u_l_l_(_)
│ │ │ │  and _n_o_t_n_u_l_l_(_). The naming scheme is now more consistent with methods like
│ │ │ │  .dropna() and .fillna(). Furthermore in all cases where .isnull() and .notnull
│ │ │ │  () methods are defined, these have additional methods named .isna() and .notna
│ │ │ │  (), these are included for classes Categorical, Index, Series, and DataFrame.
│ │ │ │ @@ -616,15 +638,15 @@
│ │ │ │  In [39]: s = pd.Series([1, 2, 3])
│ │ │ │  
│ │ │ │  In [40]: s
│ │ │ │  Out[40]:
│ │ │ │  0    1
│ │ │ │  1    2
│ │ │ │  2    3
│ │ │ │ -dtype: int64
│ │ │ │ +Length: 3, dtype: int64
│ │ │ │  Previously:
│ │ │ │  In [2]: type(list(s)[0])
│ │ │ │  Out[2]: numpy.int64
│ │ │ │  New behavior:
│ │ │ │  In [41]: type(list(s)[0])
│ │ │ │  Out[41]: int
│ │ │ │  Furthermore this will now correctly box the results of iteration for
│ │ │ │ @@ -649,49 +671,49 @@
│ │ │ │  In [45]: s = pd.Series([1, 2, 3], index=[False, True, False])
│ │ │ │  
│ │ │ │  In [46]: s
│ │ │ │  Out[46]:
│ │ │ │  False    1
│ │ │ │  True     2
│ │ │ │  False    3
│ │ │ │ -dtype: int64
│ │ │ │ +Length: 3, dtype: int64
│ │ │ │  In [59]: s.loc[pd.Index([True, False, True])]
│ │ │ │  Out[59]:
│ │ │ │  True     2
│ │ │ │  False    1
│ │ │ │  False    3
│ │ │ │  True     2
│ │ │ │  dtype: int64
│ │ │ │  Current behavior
│ │ │ │  In [47]: s.loc[pd.Index([True, False, True])]
│ │ │ │  Out[47]:
│ │ │ │  False    1
│ │ │ │  False    3
│ │ │ │ -dtype: int64
│ │ │ │ +Length: 2, dtype: int64
│ │ │ │  Furthermore, previously if you had an index that was non-numeric (e.g.
│ │ │ │  strings), then a boolean Index would raise a KeyError. This will now be treated
│ │ │ │  as a boolean indexer.
│ │ │ │  Previously behavior:
│ │ │ │  In [48]: s = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
│ │ │ │  
│ │ │ │  In [49]: s
│ │ │ │  Out[49]:
│ │ │ │  a    1
│ │ │ │  b    2
│ │ │ │  c    3
│ │ │ │ -dtype: int64
│ │ │ │ +Length: 3, dtype: int64
│ │ │ │  In [39]: s.loc[pd.Index([True, False, True])]
│ │ │ │  KeyError: "None of [Index([True, False, True], dtype='object')] are in the
│ │ │ │  [index]"
│ │ │ │  Current behavior
│ │ │ │  In [50]: s.loc[pd.Index([True, False, True])]
│ │ │ │  Out[50]:
│ │ │ │  a    1
│ │ │ │  c    3
│ │ │ │ -dtype: int64
│ │ │ │ +Length: 2, dtype: int64
│ │ │ │  ******** PPeerriiooddIInnddeexx rreessaammpplliinngg_## ********
│ │ │ │  In previous versions of pandas, resampling a Series/DataFrame indexed by a
│ │ │ │  PeriodIndex returned a DatetimeIndex in some cases (_G_H_ _1_2_8_8_4). Resampling to a
│ │ │ │  multiplied frequency now returns a PeriodIndex (_G_H_ _1_5_9_4_4). As a minor
│ │ │ │  enhancement, resampling a PeriodIndex can now handle NaT values (_G_H_ _1_3_2_2_4)
│ │ │ │  Previous behavior:
│ │ │ │  In [1]: pi = pd.period_range('2017-01', periods=12, freq='M')
│ │ │ │ @@ -875,15 +897,15 @@
│ │ │ │  dtype: datetime64[ns]
│ │ │ │  New behavior
│ │ │ │  In [56]: pd.to_datetime(s, utc=True)
│ │ │ │  Out[56]:
│ │ │ │  0   2013-01-01 00:00:00+00:00
│ │ │ │  1   2013-01-01 00:00:00+00:00
│ │ │ │  2   2013-01-01 00:00:00+00:00
│ │ │ │ -dtype: datetime64[ns, UTC]
│ │ │ │ +Length: 3, dtype: datetime64[ns, UTC]
│ │ │ │  Additionally, DataFrames with datetime columns that were parsed by
│ │ │ │  _r_e_a_d___s_q_l___t_a_b_l_e_(_) and _r_e_a_d___s_q_l___q_u_e_r_y_(_) will also be localized to UTC only if the
│ │ │ │  original SQL columns were timezone aware datetime columns.
│ │ │ │  ******** CCoonnssiisstteennccyy ooff rraannggee ffuunnccttiioonnss_## ********
│ │ │ │  In previous versions, there were some inconsistencies between the various range
│ │ │ │  functions: _d_a_t_e___r_a_n_g_e_(_), _b_d_a_t_e___r_a_n_g_e_(_), _p_e_r_i_o_d___r_a_n_g_e_(_), _t_i_m_e_d_e_l_t_a___r_a_n_g_e_(_), and
│ │ │ │  _i_n_t_e_r_v_a_l___r_a_n_g_e_(_). (_G_H_ _1_7_4_7_1).
│ │ │ │ @@ -1030,14 +1052,16 @@
│ │ │ │  bar  2
│ │ │ │  baz  3
│ │ │ │  In [59]: df.loc[df.index.map(lambda x: x in ['bar', 'baz'])]
│ │ │ │  Out[59]:
│ │ │ │       A
│ │ │ │  bar  2
│ │ │ │  baz  3
│ │ │ │ +
│ │ │ │ +[2 rows x 1 columns]
│ │ │ │  ******** SSeerriieess..aarrggmmaaxx aanndd SSeerriieess..aarrggmmiinn_## ********
│ │ │ │  The behavior of _S_e_r_i_e_s_._a_r_g_m_a_x_(_) and _S_e_r_i_e_s_._a_r_g_m_i_n_(_) have been deprecated in
│ │ │ │  favor of _S_e_r_i_e_s_._i_d_x_m_a_x_(_) and _S_e_r_i_e_s_._i_d_x_m_i_n_(_), respectively (_G_H_ _1_6_8_3_0).
│ │ │ │  For compatibility with NumPy arrays, pd.Series implements argmax and argmin.
│ │ │ │  Since pandas 0.13.0, argmax has been an alias for _p_a_n_d_a_s_._S_e_r_i_e_s_._i_d_x_m_a_x_(_), and
│ │ │ │  argmin has been an alias for _p_a_n_d_a_s_._S_e_r_i_e_s_._i_d_x_m_i_n_(_). They return the llaabbeell of
│ │ │ │  the maximum or minimum, rather than the ppoossiittiioonn.
│ │ ├── ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.22.0.html
│ │ │ @@ -745,24 +745,24 @@
│ │ │  <p><em>pandas 0.22</em></p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [8]: </span><span class="n">grouper</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">],</span> <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">])</span>
│ │ │  
│ │ │  <span class="gp">In [9]: </span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">grouper</span><span class="p">,</span> <span class="n">observed</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
│ │ │  <span class="gh">Out[9]: </span>
│ │ │  <span class="go">a    3</span>
│ │ │  <span class="go">b    0</span>
│ │ │ -<span class="go">dtype: int64</span>
│ │ │ +<span class="go">Length: 2, dtype: int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>To restore the 0.21 behavior of returning <code class="docutils literal notranslate"><span class="pre">NaN</span></code> for unobserved groups,
│ │ │  use <code class="docutils literal notranslate"><span class="pre">min_count&gt;=1</span></code>.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [10]: </span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">grouper</span><span class="p">,</span> <span class="n">observed</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">min_count</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
│ │ │  <span class="gh">Out[10]: </span>
│ │ │  <span class="go">a    3.0</span>
│ │ │  <span class="go">b    NaN</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 2, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="resample">
│ │ │  <h3>Resample<a class="headerlink" href="#resample" title="Link to this heading">#</a></h3>
│ │ │  <p>The sum and product of all-<em>NA</em> bins has changed from <code class="docutils literal notranslate"><span class="pre">NaN</span></code> to <code class="docutils literal notranslate"><span class="pre">0</span></code> for
│ │ │  sum and <code class="docutils literal notranslate"><span class="pre">1</span></code> for product.</p>
│ │ │ @@ -787,23 +787,23 @@
│ │ │  <p><em>pandas 0.22.0</em></p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [11]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">date_range</span><span class="p">(</span><span class="s2">&quot;2017&quot;</span><span class="p">,</span> <span class="n">periods</span><span class="o">=</span><span class="mi">4</span><span class="p">))</span>
│ │ │  
│ │ │  <span class="gp">In [12]: </span><span class="n">s</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="s2">&quot;2d&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
│ │ │  <span class="gh">Out[12]: </span>
│ │ │  <span class="go">2017-01-01    2.0</span>
│ │ │  <span class="go">2017-01-03    0.0</span>
│ │ │ -<span class="go">Freq: 2D, dtype: float64</span>
│ │ │ +<span class="go">Freq: 2D, Length: 2, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>To restore the 0.21 behavior of returning <code class="docutils literal notranslate"><span class="pre">NaN</span></code>, use <code class="docutils literal notranslate"><span class="pre">min_count&gt;=1</span></code>.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [13]: </span><span class="n">s</span><span class="o">.</span><span class="n">resample</span><span class="p">(</span><span class="s2">&quot;2d&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">min_count</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
│ │ │  <span class="gh">Out[13]: </span>
│ │ │  <span class="go">2017-01-01    2.0</span>
│ │ │  <span class="go">2017-01-03    NaN</span>
│ │ │ -<span class="go">Freq: 2D, dtype: float64</span>
│ │ │ +<span class="go">Freq: 2D, Length: 2, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>In particular, upsampling and taking the sum or product is affected, as
│ │ │  upsampling introduces missing values even if the original series was
│ │ │  entirely valid.</p>
│ │ │  <p><em>pandas 0.21.x</em></p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [14]: </span><span class="n">idx</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DatetimeIndex</span><span class="p">([</span><span class="s1">&#39;2017-01-01&#39;</span><span class="p">,</span> <span class="s1">&#39;2017-01-02&#39;</span><span class="p">])</span>
│ │ │ @@ -856,15 +856,15 @@
│ │ │  <p><em>pandas 0.22.0</em></p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [14]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">])</span>
│ │ │  
│ │ │  <span class="gp">In [15]: </span><span class="n">s</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">min_periods</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
│ │ │  <span class="gh">Out[15]: </span>
│ │ │  <span class="go">0    0.0</span>
│ │ │  <span class="go">1    0.0</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 2, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>The default behavior of <code class="docutils literal notranslate"><span class="pre">min_periods=None</span></code>, implying that <code class="docutils literal notranslate"><span class="pre">min_periods</span></code>
│ │ │  equals the window size, is unchanged.</p>
│ │ │  </section>
│ │ │  </section>
│ │ │  <section id="compatibility">
│ │ │ ├── html2text {}
│ │ │ │ @@ -192,22 +192,22 @@
│ │ │ │  ppaannddaass 00..2222
│ │ │ │  In [8]: grouper = pd.Categorical(["a", "a"], categories=["a", "b"])
│ │ │ │  
│ │ │ │  In [9]: pd.Series([1, 2]).groupby(grouper, observed=False).sum()
│ │ │ │  Out[9]:
│ │ │ │  a    3
│ │ │ │  b    0
│ │ │ │ -dtype: int64
│ │ │ │ +Length: 2, dtype: int64
│ │ │ │  To restore the 0.21 behavior of returning NaN for unobserved groups, use
│ │ │ │  min_count>=1.
│ │ │ │  In [10]: pd.Series([1, 2]).groupby(grouper, observed=False).sum(min_count=1)
│ │ │ │  Out[10]:
│ │ │ │  a    3.0
│ │ │ │  b    NaN
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 2, dtype: float64
│ │ │ │  ******** RReessaammppllee_## ********
│ │ │ │  The sum and product of all-NNAA bins has changed from NaN to 0 for sum and 1 for
│ │ │ │  product.
│ │ │ │  ppaannddaass 00..2211..xx
│ │ │ │  In [11]: s = pd.Series([1, 1, np.nan, np.nan],
│ │ │ │     ....:               index=pd.date_range('2017', periods=4))
│ │ │ │     ....: s
│ │ │ │ @@ -227,21 +227,21 @@
│ │ │ │  In [11]: s = pd.Series([1, 1, np.nan, np.nan], index=pd.date_range("2017",
│ │ │ │  periods=4))
│ │ │ │  
│ │ │ │  In [12]: s.resample("2d").sum()
│ │ │ │  Out[12]:
│ │ │ │  2017-01-01    2.0
│ │ │ │  2017-01-03    0.0
│ │ │ │ -Freq: 2D, dtype: float64
│ │ │ │ +Freq: 2D, Length: 2, dtype: float64
│ │ │ │  To restore the 0.21 behavior of returning NaN, use min_count>=1.
│ │ │ │  In [13]: s.resample("2d").sum(min_count=1)
│ │ │ │  Out[13]:
│ │ │ │  2017-01-01    2.0
│ │ │ │  2017-01-03    NaN
│ │ │ │ -Freq: 2D, dtype: float64
│ │ │ │ +Freq: 2D, Length: 2, dtype: float64
│ │ │ │  In particular, upsampling and taking the sum or product is affected, as
│ │ │ │  upsampling introduces missing values even if the original series was entirely
│ │ │ │  valid.
│ │ │ │  ppaannddaass 00..2211..xx
│ │ │ │  In [14]: idx = pd.DatetimeIndex(['2017-01-01', '2017-01-02'])
│ │ │ │  
│ │ │ │  In [15]: pd.Series([1, 2], index=idx).resample('12H').sum()
│ │ │ │ @@ -281,15 +281,15 @@
│ │ │ │  ppaannddaass 00..2222..00
│ │ │ │  In [14]: s = pd.Series([np.nan, np.nan])
│ │ │ │  
│ │ │ │  In [15]: s.rolling(2, min_periods=0).sum()
│ │ │ │  Out[15]:
│ │ │ │  0    0.0
│ │ │ │  1    0.0
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 2, dtype: float64
│ │ │ │  The default behavior of min_periods=None, implying that min_periods equals the
│ │ │ │  window size, is unchanged.
│ │ │ │  ********** CCoommppaattiibbiilliittyy_## **********
│ │ │ │  If you maintain a library that should work across pandas versions, it may be
│ │ │ │  easiest to exclude pandas 0.21 from your requirements. Otherwise, all your sum
│ │ │ │  () calls would need to check if the Series is empty before summing.
│ │ │ │  With setuptools, in your setup.py use:
│ │ ├── ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.23.0.html
│ │ │ @@ -756,42 +756,46 @@
│ │ │  <span class="go">     foo bar        baz qux</span>
│ │ │  <span class="go">idx                        </span>
│ │ │  <span class="go">0      1   a 2018-01-01   a</span>
│ │ │  <span class="go">1      2   b 2018-01-02   b</span>
│ │ │  <span class="go">2      3   c 2018-01-03   c</span>
│ │ │  <span class="go">3      4   d 2018-01-04   c</span>
│ │ │  
│ │ │ +<span class="go">[4 rows x 4 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [3]: </span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[3]: </span>
│ │ │  <span class="go">foo             int64</span>
│ │ │  <span class="go">bar            object</span>
│ │ │  <span class="go">baz    datetime64[ns]</span>
│ │ │  <span class="go">qux          category</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 4, dtype: object</span>
│ │ │  
│ │ │  <span class="gp">In [4]: </span><span class="n">df</span><span class="o">.</span><span class="n">to_json</span><span class="p">(</span><span class="s1">&#39;test.json&#39;</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">&#39;table&#39;</span><span class="p">)</span>
│ │ │  
│ │ │  <span class="gp">In [5]: </span><span class="n">new_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="s1">&#39;test.json&#39;</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">&#39;table&#39;</span><span class="p">)</span>
│ │ │  
│ │ │  <span class="gp">In [6]: </span><span class="n">new_df</span>
│ │ │  <span class="gh">Out[6]: </span>
│ │ │  <span class="go">     foo bar        baz qux</span>
│ │ │  <span class="go">idx                        </span>
│ │ │  <span class="go">0      1   a 2018-01-01   a</span>
│ │ │  <span class="go">1      2   b 2018-01-02   b</span>
│ │ │  <span class="go">2      3   c 2018-01-03   c</span>
│ │ │  <span class="go">3      4   d 2018-01-04   c</span>
│ │ │  
│ │ │ +<span class="go">[4 rows x 4 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [7]: </span><span class="n">new_df</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[7]: </span>
│ │ │  <span class="go">foo             int64</span>
│ │ │  <span class="go">bar            object</span>
│ │ │  <span class="go">baz    datetime64[ns]</span>
│ │ │  <span class="go">qux          category</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 4, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Please note that the string <code class="docutils literal notranslate"><span class="pre">index</span></code> is not supported with the round trip format, as it is used by default in <code class="docutils literal notranslate"><span class="pre">write_json</span></code> to indicate a missing index name.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [8]: </span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;index&#39;</span>
│ │ │  
│ │ │  <span class="gp">In [9]: </span><span class="n">df</span><span class="o">.</span><span class="n">to_json</span><span class="p">(</span><span class="s1">&#39;test.json&#39;</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">&#39;table&#39;</span><span class="p">)</span>
│ │ │  
│ │ │ @@ -801,21 +805,23 @@
│ │ │  <span class="gh">Out[11]: </span>
│ │ │  <span class="go">   foo bar        baz qux</span>
│ │ │  <span class="go">0    1   a 2018-01-01   a</span>
│ │ │  <span class="go">1    2   b 2018-01-02   b</span>
│ │ │  <span class="go">2    3   c 2018-01-03   c</span>
│ │ │  <span class="go">3    4   d 2018-01-04   c</span>
│ │ │  
│ │ │ +<span class="go">[4 rows x 4 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [12]: </span><span class="n">new_df</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[12]: </span>
│ │ │  <span class="go">foo             int64</span>
│ │ │  <span class="go">bar            object</span>
│ │ │  <span class="go">baz    datetime64[ns]</span>
│ │ │  <span class="go">qux          category</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 4, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="method-assign-accepts-dependent-arguments">
│ │ │  <span id="whatsnew-0230-enhancements-assign-dependent"></span><h3>Method <code class="docutils literal notranslate"><span class="pre">.assign()</span></code> accepts dependent arguments<a class="headerlink" href="#method-assign-accepts-dependent-arguments" title="Link to this heading">#</a></h3>
│ │ │  <p>The <a class="reference internal" href="../reference/api/pandas.DataFrame.assign.html#pandas.DataFrame.assign" title="pandas.DataFrame.assign"><code class="xref py py-func docutils literal notranslate"><span class="pre">DataFrame.assign()</span></code></a> now accepts dependent keyword arguments for python version later than 3.6 (see also <a class="reference external" href="https://www.python.org/dev/peps/pep-0468/">PEP 468</a>). Later keyword arguments may now refer to earlier ones if the argument is a callable. See the
│ │ │  <a class="reference internal" href="../user_guide/dsintro.html#dsintro-chained-assignment"><span class="std std-ref">documentation here</span></a> (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/14207">GH 14207</a>)</p>
│ │ │ @@ -824,20 +830,24 @@
│ │ │  <span class="gp">In [14]: </span><span class="n">df</span>
│ │ │  <span class="gh">Out[14]: </span>
│ │ │  <span class="go">   A</span>
│ │ │  <span class="go">0  1</span>
│ │ │  <span class="go">1  2</span>
│ │ │  <span class="go">2  3</span>
│ │ │  
│ │ │ +<span class="go">[3 rows x 1 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [15]: </span><span class="n">df</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">B</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">A</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="p">[</span><span class="s1">&#39;B&#39;</span><span class="p">])</span>
│ │ │  <span class="gh">Out[15]: </span>
│ │ │  <span class="go">   A  B  C</span>
│ │ │  <span class="go">0  1  1  2</span>
│ │ │  <span class="go">1  2  2  4</span>
│ │ │  <span class="go">2  3  3  6</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="admonition warning">
│ │ │  <p class="admonition-title">Warning</p>
│ │ │  <p>This may subtly change the behavior of your code when you’re
│ │ │  using <code class="docutils literal notranslate"><span class="pre">.assign()</span></code> to update an existing column. Previously, callables
│ │ │  referring to other variables being updated would get the “old” values</p>
│ │ │ @@ -855,14 +865,16 @@
│ │ │  <p>New behavior:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [16]: </span><span class="n">df</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">A</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">A</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="k">lambda</span> <span class="n">df</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">A</span> <span class="o">*</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
│ │ │  <span class="gh">Out[16]: </span>
│ │ │  <span class="go">   A  C</span>
│ │ │  <span class="go">0  2 -2</span>
│ │ │  <span class="go">1  3 -3</span>
│ │ │  <span class="go">2  4 -4</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="merging-on-a-combination-of-columns-and-index-levels">
│ │ │  <span id="whatsnew-0230-enhancements-merge-on-columns-and-levels"></span><h3>Merging on a combination of columns and index levels<a class="headerlink" href="#merging-on-a-combination-of-columns-and-index-levels" title="Link to this heading">#</a></h3>
│ │ │  <p>Strings passed to <a class="reference internal" href="../reference/api/pandas.DataFrame.merge.html#pandas.DataFrame.merge" title="pandas.DataFrame.merge"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DataFrame.merge()</span></code></a> as the <code class="docutils literal notranslate"><span class="pre">on</span></code>, <code class="docutils literal notranslate"><span class="pre">left_on</span></code>, and <code class="docutils literal notranslate"><span class="pre">right_on</span></code>
│ │ │ @@ -890,14 +902,16 @@
│ │ │  <span class="gp">In [21]: </span><span class="n">left</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;key1&#39;</span><span class="p">,</span> <span class="s1">&#39;key2&#39;</span><span class="p">])</span>
│ │ │  <span class="gh">Out[21]: </span>
│ │ │  <span class="go">       A   B key2   C   D</span>
│ │ │  <span class="go">key1                     </span>
│ │ │  <span class="go">K0    A0  B0   K0  C0  D0</span>
│ │ │  <span class="go">K1    A2  B2   K0  C1  D1</span>
│ │ │  <span class="go">K2    A3  B3   K1  C3  D3</span>
│ │ │ +
│ │ │ +<span class="go">[3 rows x 5 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="sorting-by-a-combination-of-columns-and-index-levels">
│ │ │  <span id="whatsnew-0230-enhancements-sort-by-columns-and-levels"></span><h3>Sorting by a combination of columns and index levels<a class="headerlink" href="#sorting-by-a-combination-of-columns-and-index-levels" title="Link to this heading">#</a></h3>
│ │ │  <p>Strings passed to <a class="reference internal" href="../reference/api/pandas.DataFrame.sort_values.html#pandas.DataFrame.sort_values" title="pandas.DataFrame.sort_values"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DataFrame.sort_values()</span></code></a> as the <code class="docutils literal notranslate"><span class="pre">by</span></code> parameter may
│ │ │  now refer to either column names or index level names.  This enables sorting
│ │ │ @@ -923,25 +937,29 @@
│ │ │  <span class="go">a     1       6</span>
│ │ │  <span class="go">      2       5</span>
│ │ │  <span class="go">      2       4</span>
│ │ │  <span class="go">b     2       3</span>
│ │ │  <span class="go">      1       2</span>
│ │ │  <span class="go">      1       1</span>
│ │ │  
│ │ │ +<span class="go">[6 rows x 1 columns]</span>
│ │ │ +
│ │ │  <span class="go"># Sort by &#39;second&#39; (index) and &#39;A&#39; (column)</span>
│ │ │  <span class="gp">In [26]: </span><span class="n">df_multi</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;second&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">])</span>
│ │ │  <span class="gh">Out[26]: </span>
│ │ │  <span class="go">              A</span>
│ │ │  <span class="go">first second   </span>
│ │ │  <span class="go">b     1       1</span>
│ │ │  <span class="go">      1       2</span>
│ │ │  <span class="go">a     1       6</span>
│ │ │  <span class="go">b     2       3</span>
│ │ │  <span class="go">a     2       4</span>
│ │ │  <span class="go">      2       5</span>
│ │ │ +
│ │ │ +<span class="go">[6 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="extending-pandas-with-custom-types-experimental">
│ │ │  <span id="whatsnew-023-enhancements-extension"></span><h3>Extending pandas with custom types (experimental)<a class="headerlink" href="#extending-pandas-with-custom-types-experimental" title="Link to this heading">#</a></h3>
│ │ │  <p>pandas now supports storing array-like objects that aren’t necessarily 1-D NumPy
│ │ │  arrays as columns in a DataFrame or values in a Series. This allows third-party
│ │ │ @@ -1007,14 +1025,16 @@
│ │ │  <span class="gp">In [31]: </span><span class="n">df</span>
│ │ │  <span class="gh">Out[31]: </span>
│ │ │  <span class="go">   A  B  values    C</span>
│ │ │  <span class="go">0  a  c       1  foo</span>
│ │ │  <span class="go">1  a  d       2  bar</span>
│ │ │  <span class="go">2  b  c       3  foo</span>
│ │ │  <span class="go">3  b  d       4  bar</span>
│ │ │ +
│ │ │ +<span class="go">[4 rows x 4 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>To show all values, the previous behavior:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [32]: </span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">],</span> <span class="n">observed</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
│ │ │  <span class="gh">Out[32]: </span>
│ │ │  <span class="go">         values</span>
│ │ │  <span class="go">A B C          </span>
│ │ │ @@ -1038,14 +1058,16 @@
│ │ │  <span class="gh">Out[33]: </span>
│ │ │  <span class="go">         values</span>
│ │ │  <span class="go">A B C          </span>
│ │ │  <span class="go">a c foo       1</span>
│ │ │  <span class="go">  d bar       1</span>
│ │ │  <span class="go">b c foo       1</span>
│ │ │  <span class="go">  d bar       1</span>
│ │ │ +
│ │ │ +<span class="go">[4 rows x 1 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>For pivoting operations, this behavior is <em>already</em> controlled by the <code class="docutils literal notranslate"><span class="pre">dropna</span></code> keyword:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [34]: </span><span class="n">cat1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Categorical</span><span class="p">([</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">],</span>
│ │ │  <span class="gp">   ....: </span>                      <span class="n">categories</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;z&quot;</span><span class="p">],</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
│ │ │  <span class="gp">   ....: </span>
│ │ │  
│ │ │ @@ -1058,14 +1080,16 @@
│ │ │  <span class="gp">In [37]: </span><span class="n">df</span>
│ │ │  <span class="gh">Out[37]: </span>
│ │ │  <span class="go">   A  B  values</span>
│ │ │  <span class="go">0  a  c       1</span>
│ │ │  <span class="go">1  a  d       2</span>
│ │ │  <span class="go">2  b  c       3</span>
│ │ │  <span class="go">3  b  d       4</span>
│ │ │ +
│ │ │ +<span class="go">[4 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [1]: </span><span class="n">pd</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="s1">&#39;values&#39;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">],</span> <span class="n">dropna</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
│ │ │  
│ │ │  <span class="gh">Out[1]:</span>
│ │ │  <span class="go">     values</span>
│ │ │  <span class="go">A B</span>
│ │ │ @@ -1103,37 +1127,37 @@
│ │ │  <span class="gp">In [39]: </span><span class="n">s</span>
│ │ │  <span class="gh">Out[39]: </span>
│ │ │  <span class="go">1    0</span>
│ │ │  <span class="go">2    1</span>
│ │ │  <span class="go">3    2</span>
│ │ │  <span class="go">4    3</span>
│ │ │  <span class="go">5    4</span>
│ │ │ -<span class="go">dtype: int64</span>
│ │ │ +<span class="go">Length: 5, dtype: int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Pass a <code class="docutils literal notranslate"><span class="pre">Series</span></code>:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [40]: </span><span class="n">s</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">min_periods</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">raw</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
│ │ │  <span class="gh">Out[40]: </span>
│ │ │  <span class="go">1    0.0</span>
│ │ │  <span class="go">2    1.0</span>
│ │ │  <span class="go">3    2.0</span>
│ │ │  <span class="go">4    3.0</span>
│ │ │  <span class="go">5    4.0</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 5, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Mimic the original behavior of passing a ndarray:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [41]: </span><span class="n">s</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">min_periods</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">raw</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
│ │ │  <span class="gh">Out[41]: </span>
│ │ │  <span class="go">1    0.0</span>
│ │ │  <span class="go">2    1.0</span>
│ │ │  <span class="go">3    2.0</span>
│ │ │  <span class="go">4    3.0</span>
│ │ │  <span class="go">5    4.0</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 5, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="dataframe-interpolate-has-gained-the-limit-area-kwarg">
│ │ │  <span id="whatsnew-0210-enhancements-limit-area"></span><h3><code class="docutils literal notranslate"><span class="pre">DataFrame.interpolate</span></code> has gained the <code class="docutils literal notranslate"><span class="pre">limit_area</span></code> kwarg<a class="headerlink" href="#dataframe-interpolate-has-gained-the-limit-area-kwarg" title="Link to this heading">#</a></h3>
│ │ │  <p><a class="reference internal" href="../reference/api/pandas.DataFrame.interpolate.html#pandas.DataFrame.interpolate" title="pandas.DataFrame.interpolate"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DataFrame.interpolate()</span></code></a> has gained a <code class="docutils literal notranslate"><span class="pre">limit_area</span></code> parameter to allow further control of which <code class="docutils literal notranslate"><span class="pre">NaN</span></code> s are replaced.
│ │ │  Use <code class="docutils literal notranslate"><span class="pre">limit_area='inside'</span></code> to fill only NaNs surrounded by valid values or use <code class="docutils literal notranslate"><span class="pre">limit_area='outside'</span></code> to fill only <code class="docutils literal notranslate"><span class="pre">NaN</span></code> s
│ │ │ @@ -1149,83 +1173,83 @@
│ │ │  <span class="go">2     5.0</span>
│ │ │  <span class="go">3     NaN</span>
│ │ │  <span class="go">4     NaN</span>
│ │ │  <span class="go">5     NaN</span>
│ │ │  <span class="go">6    13.0</span>
│ │ │  <span class="go">7     NaN</span>
│ │ │  <span class="go">8     NaN</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 9, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Fill one consecutive inside value in both directions</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [44]: </span><span class="n">ser</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="n">limit_direction</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">limit_area</span><span class="o">=</span><span class="s1">&#39;inside&#39;</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
│ │ │  <span class="gh">Out[44]: </span>
│ │ │  <span class="go">0     NaN</span>
│ │ │  <span class="go">1     NaN</span>
│ │ │  <span class="go">2     5.0</span>
│ │ │  <span class="go">3     7.0</span>
│ │ │  <span class="go">4     NaN</span>
│ │ │  <span class="go">5    11.0</span>
│ │ │  <span class="go">6    13.0</span>
│ │ │  <span class="go">7     NaN</span>
│ │ │  <span class="go">8     NaN</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 9, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Fill all consecutive outside values backward</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [45]: </span><span class="n">ser</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="n">limit_direction</span><span class="o">=</span><span class="s1">&#39;backward&#39;</span><span class="p">,</span> <span class="n">limit_area</span><span class="o">=</span><span class="s1">&#39;outside&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[45]: </span>
│ │ │  <span class="go">0     5.0</span>
│ │ │  <span class="go">1     5.0</span>
│ │ │  <span class="go">2     5.0</span>
│ │ │  <span class="go">3     NaN</span>
│ │ │  <span class="go">4     NaN</span>
│ │ │  <span class="go">5     NaN</span>
│ │ │  <span class="go">6    13.0</span>
│ │ │  <span class="go">7     NaN</span>
│ │ │  <span class="go">8     NaN</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 9, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Fill all consecutive outside values in both directions</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [46]: </span><span class="n">ser</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span><span class="n">limit_direction</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">limit_area</span><span class="o">=</span><span class="s1">&#39;outside&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[46]: </span>
│ │ │  <span class="go">0     5.0</span>
│ │ │  <span class="go">1     5.0</span>
│ │ │  <span class="go">2     5.0</span>
│ │ │  <span class="go">3     NaN</span>
│ │ │  <span class="go">4     NaN</span>
│ │ │  <span class="go">5     NaN</span>
│ │ │  <span class="go">6    13.0</span>
│ │ │  <span class="go">7    13.0</span>
│ │ │  <span class="go">8    13.0</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 9, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="function-get-dummies-now-supports-dtype-argument">
│ │ │  <span id="whatsnew-0210-enhancements-get-dummies-dtype"></span><h3>Function <code class="docutils literal notranslate"><span class="pre">get_dummies</span></code> now supports <code class="docutils literal notranslate"><span class="pre">dtype</span></code> argument<a class="headerlink" href="#function-get-dummies-now-supports-dtype-argument" title="Link to this heading">#</a></h3>
│ │ │  <p>The <a class="reference internal" href="../reference/api/pandas.get_dummies.html#pandas.get_dummies" title="pandas.get_dummies"><code class="xref py py-func docutils literal notranslate"><span class="pre">get_dummies()</span></code></a> now accepts a <code class="docutils literal notranslate"><span class="pre">dtype</span></code> argument, which specifies a dtype for the new columns. The default remains uint8. (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/18330">GH 18330</a>)</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [47]: </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="s1">&#39;c&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]})</span>
│ │ │  
│ │ │  <span class="gp">In [48]: </span><span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;c&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[48]: </span>
│ │ │  <span class="go">a      int64</span>
│ │ │  <span class="go">b      int64</span>
│ │ │  <span class="go">c_5     bool</span>
│ │ │  <span class="go">c_6     bool</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 4, dtype: object</span>
│ │ │  
│ │ │  <span class="gp">In [49]: </span><span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;c&#39;</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │  <span class="gh">Out[49]: </span>
│ │ │  <span class="go">a      int64</span>
│ │ │  <span class="go">b      int64</span>
│ │ │  <span class="go">c_5     bool</span>
│ │ │  <span class="go">c_6     bool</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 4, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="timedelta-mod-method">
│ │ │  <span id="whatsnew-0230-enhancements-timedelta-mod"></span><h3>Timedelta mod method<a class="headerlink" href="#timedelta-mod-method" title="Link to this heading">#</a></h3>
│ │ │  <p><code class="docutils literal notranslate"><span class="pre">mod</span></code> (%) and <code class="docutils literal notranslate"><span class="pre">divmod</span></code> operations are now defined on <code class="docutils literal notranslate"><span class="pre">Timedelta</span></code> objects
│ │ │  when operating with either timedelta-like or with numeric arguments.
│ │ │ @@ -1245,15 +1269,15 @@
│ │ │  <span class="gp">In [53]: </span><span class="n">s</span>
│ │ │  <span class="gh">Out[53]: </span>
│ │ │  <span class="go">0   -inf</span>
│ │ │  <span class="go">1    0.0</span>
│ │ │  <span class="go">2    1.0</span>
│ │ │  <span class="go">3    NaN</span>
│ │ │  <span class="go">4    inf</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 5, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Previous behavior:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [11]: </span><span class="n">s</span><span class="o">.</span><span class="n">rank</span><span class="p">()</span>
│ │ │  <span class="gh">Out[11]:</span>
│ │ │  <span class="go">0    1.0</span>
│ │ │  <span class="go">1    2.0</span>
│ │ │ @@ -1267,27 +1291,27 @@
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [54]: </span><span class="n">s</span><span class="o">.</span><span class="n">rank</span><span class="p">()</span>
│ │ │  <span class="gh">Out[54]: </span>
│ │ │  <span class="go">0    1.0</span>
│ │ │  <span class="go">1    2.0</span>
│ │ │  <span class="go">2    3.0</span>
│ │ │  <span class="go">3    NaN</span>
│ │ │  <span class="go">4    4.0</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 5, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Furthermore, previously if you rank <code class="docutils literal notranslate"><span class="pre">inf</span></code> or <code class="docutils literal notranslate"><span class="pre">-inf</span></code> values together with <code class="docutils literal notranslate"><span class="pre">NaN</span></code> values, the calculation won’t distinguish <code class="docutils literal notranslate"><span class="pre">NaN</span></code> from infinity when using ‘top’ or ‘bottom’ argument.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [55]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">])</span>
│ │ │  
│ │ │  <span class="gp">In [56]: </span><span class="n">s</span>
│ │ │  <span class="gh">Out[56]: </span>
│ │ │  <span class="go">0    NaN</span>
│ │ │  <span class="go">1    NaN</span>
│ │ │  <span class="go">2   -inf</span>
│ │ │  <span class="go">3   -inf</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 4, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Previous behavior:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [15]: </span><span class="n">s</span><span class="o">.</span><span class="n">rank</span><span class="p">(</span><span class="n">na_option</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[15]:</span>
│ │ │  <span class="go">0    2.5</span>
│ │ │  <span class="go">1    2.5</span>
│ │ │ @@ -1299,15 +1323,15 @@
│ │ │  <p>Current behavior:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [57]: </span><span class="n">s</span><span class="o">.</span><span class="n">rank</span><span class="p">(</span><span class="n">na_option</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[57]: </span>
│ │ │  <span class="go">0    1.5</span>
│ │ │  <span class="go">1    1.5</span>
│ │ │  <span class="go">2    3.5</span>
│ │ │  <span class="go">3    3.5</span>
│ │ │ -<span class="go">dtype: float64</span>
│ │ │ +<span class="go">Length: 4, dtype: float64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>These bugs were squashed:</p>
│ │ │  <ul class="simple">
│ │ │  <li><p>Bug in <a class="reference internal" href="../reference/api/pandas.DataFrame.rank.html#pandas.DataFrame.rank" title="pandas.DataFrame.rank"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DataFrame.rank()</span></code></a> and <a class="reference internal" href="../reference/api/pandas.Series.rank.html#pandas.Series.rank" title="pandas.Series.rank"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Series.rank()</span></code></a> when <code class="docutils literal notranslate"><span class="pre">method='dense'</span></code> and <code class="docutils literal notranslate"><span class="pre">pct=True</span></code> in which percentile ranks were not being used with the number of distinct observations (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/15630">GH 15630</a>)</p></li>
│ │ │  <li><p>Bug in <a class="reference internal" href="../reference/api/pandas.Series.rank.html#pandas.Series.rank" title="pandas.Series.rank"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Series.rank()</span></code></a> and <a class="reference internal" href="../reference/api/pandas.DataFrame.rank.html#pandas.DataFrame.rank" title="pandas.DataFrame.rank"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DataFrame.rank()</span></code></a> when <code class="docutils literal notranslate"><span class="pre">ascending='False'</span></code> failed to return correct ranks for infinity if <code class="docutils literal notranslate"><span class="pre">NaN</span></code> were present (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/19538">GH 19538</a>)</p></li>
│ │ │  <li><p>Bug in <code class="xref py py-func docutils literal notranslate"><span class="pre">DataFrameGroupBy.rank()</span></code> where ranks were incorrect when both infinity and <code class="docutils literal notranslate"><span class="pre">NaN</span></code> were present (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/20561">GH 20561</a>)</p></li>
│ │ │ @@ -1324,23 +1348,23 @@
│ │ │  
│ │ │  <span class="gp">In [60]: </span><span class="n">s</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
│ │ │  <span class="gh">Out[60]: </span>
│ │ │  <span class="go">0    NaN</span>
│ │ │  <span class="go">1     bb</span>
│ │ │  <span class="go">2     cc</span>
│ │ │  <span class="go">3     dd</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 4, dtype: object</span>
│ │ │  
│ │ │  <span class="gp">In [61]: </span><span class="n">s</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">join</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">na_rep</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[61]: </span>
│ │ │  <span class="go">0    a-</span>
│ │ │  <span class="go">1    bb</span>
│ │ │  <span class="go">2    cc</span>
│ │ │  <span class="go">3    dd</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 4, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Furthermore, <a class="reference internal" href="../reference/api/pandas.Series.str.cat.html#pandas.Series.str.cat" title="pandas.Series.str.cat"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Series.str.cat()</span></code></a> now works for <code class="docutils literal notranslate"><span class="pre">CategoricalIndex</span></code> as well (previously raised a <code class="docutils literal notranslate"><span class="pre">ValueError</span></code>; see <a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/20842">GH 20842</a>).</p>
│ │ │  </section>
│ │ │  <section id="dataframe-astype-performs-column-wise-conversion-to-categorical">
│ │ │  <span id="whatsnew-0230-enhancements-astype-category"></span><h3><code class="docutils literal notranslate"><span class="pre">DataFrame.astype</span></code> performs column-wise conversion to <code class="docutils literal notranslate"><span class="pre">Categorical</span></code><a class="headerlink" href="#dataframe-astype-performs-column-wise-conversion-to-categorical" title="Link to this heading">#</a></h3>
│ │ │  <p><a class="reference internal" href="../reference/api/pandas.DataFrame.astype.html#pandas.DataFrame.astype" title="pandas.DataFrame.astype"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DataFrame.astype()</span></code></a> can now perform column-wise conversion to <code class="docutils literal notranslate"><span class="pre">Categorical</span></code> by supplying the string <code class="docutils literal notranslate"><span class="pre">'category'</span></code> or
│ │ │ @@ -1502,15 +1526,15 @@
│ │ │  <span class="gp">   ....: </span>           <span class="s1">&#39;Net result&#39;</span><span class="p">:</span> <span class="mi">300</span><span class="p">})</span>
│ │ │  <span class="gp">   ....: </span>
│ │ │  <span class="gh">Out[72]: </span>
│ │ │  <span class="go">Income        2000</span>
│ │ │  <span class="go">Expenses     -1500</span>
│ │ │  <span class="go">Taxes         -200</span>
│ │ │  <span class="go">Net result     300</span>
│ │ │ -<span class="go">dtype: int64</span>
│ │ │ +<span class="go">Length: 4, dtype: int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Notice that the Series is now ordered by insertion order. This new behavior is
│ │ │  used for all relevant pandas types (<code class="docutils literal notranslate"><span class="pre">Series</span></code>, <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, <code class="docutils literal notranslate"><span class="pre">SparseSeries</span></code>
│ │ │  and <code class="docutils literal notranslate"><span class="pre">SparseDataFrame</span></code>).</p>
│ │ │  <p>If you wish to retain the old behavior while using Python &gt;= 3.6, you can use
│ │ │  <code class="docutils literal notranslate"><span class="pre">.sort_index()</span></code>:</p>
│ │ │ @@ -1520,15 +1544,15 @@
│ │ │  <span class="gp">   ....: </span>           <span class="s1">&#39;Net result&#39;</span><span class="p">:</span> <span class="mi">300</span><span class="p">})</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span>
│ │ │  <span class="gp">   ....: </span>
│ │ │  <span class="gh">Out[73]: </span>
│ │ │  <span class="go">Expenses     -1500</span>
│ │ │  <span class="go">Income        2000</span>
│ │ │  <span class="go">Net result     300</span>
│ │ │  <span class="go">Taxes         -200</span>
│ │ │ -<span class="go">dtype: int64</span>
│ │ │ +<span class="go">Length: 4, dtype: int64</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="deprecate-panel">
│ │ │  <span id="whatsnew-0230-api-breaking-deprecate-panel"></span><h3>Deprecate Panel<a class="headerlink" href="#deprecate-panel" title="Link to this heading">#</a></h3>
│ │ │  <p><code class="docutils literal notranslate"><span class="pre">Panel</span></code> was deprecated in the 0.20.x release, showing as a <code class="docutils literal notranslate"><span class="pre">DeprecationWarning</span></code>. Using <code class="docutils literal notranslate"><span class="pre">Panel</span></code> will now show a <code class="docutils literal notranslate"><span class="pre">FutureWarning</span></code>. The recommended way to represent 3-D data are
│ │ │  with a <code class="docutils literal notranslate"><span class="pre">MultiIndex</span></code> on a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> via the <code class="xref py py-meth docutils literal notranslate"><span class="pre">to_frame()</span></code> or with the <a class="reference external" href="http://xarray.pydata.org/en/stable/">xarray package</a>. pandas
│ │ │ @@ -1615,14 +1639,16 @@
│ │ │  <span class="go">   A  B  C</span>
│ │ │  <span class="go">0  1  2  3</span>
│ │ │  <span class="go">1  1  2  3</span>
│ │ │  <span class="go">2  1  2  3</span>
│ │ │  <span class="go">3  1  2  3</span>
│ │ │  <span class="go">4  1  2  3</span>
│ │ │  <span class="go">5  1  2  3</span>
│ │ │ +
│ │ │ +<span class="go">[6 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Previous behavior: if the returned shape happened to match the length of original columns, this would return a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>.
│ │ │  If the return shape did not match, a <code class="docutils literal notranslate"><span class="pre">Series</span></code> with lists was returned.</p>
│ │ │  <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">3</span><span class="p">]:</span> <span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
│ │ │  <span class="n">Out</span><span class="p">[</span><span class="mi">3</span><span class="p">]:</span>
│ │ │     <span class="n">A</span>  <span class="n">B</span>  <span class="n">C</span>
│ │ │ @@ -1649,63 +1675,69 @@
│ │ │  <span class="gh">Out[76]: </span>
│ │ │  <span class="go">0    [1, 2, 3]</span>
│ │ │  <span class="go">1    [1, 2, 3]</span>
│ │ │  <span class="go">2    [1, 2, 3]</span>
│ │ │  <span class="go">3    [1, 2, 3]</span>
│ │ │  <span class="go">4    [1, 2, 3]</span>
│ │ │  <span class="go">5    [1, 2, 3]</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 6, dtype: object</span>
│ │ │  
│ │ │  <span class="gp">In [77]: </span><span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
│ │ │  <span class="gh">Out[77]: </span>
│ │ │  <span class="go">0    [1, 2]</span>
│ │ │  <span class="go">1    [1, 2]</span>
│ │ │  <span class="go">2    [1, 2]</span>
│ │ │  <span class="go">3    [1, 2]</span>
│ │ │  <span class="go">4    [1, 2]</span>
│ │ │  <span class="go">5    [1, 2]</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 6, dtype: object</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>To have expanded columns, you can use <code class="docutils literal notranslate"><span class="pre">result_type='expand'</span></code></p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [78]: </span><span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">result_type</span><span class="o">=</span><span class="s1">&#39;expand&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[78]: </span>
│ │ │  <span class="go">   0  1  2</span>
│ │ │  <span class="go">0  1  2  3</span>
│ │ │  <span class="go">1  1  2  3</span>
│ │ │  <span class="go">2  1  2  3</span>
│ │ │  <span class="go">3  1  2  3</span>
│ │ │  <span class="go">4  1  2  3</span>
│ │ │  <span class="go">5  1  2  3</span>
│ │ │ +
│ │ │ +<span class="go">[6 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>To broadcast the result across the original columns (the old behaviour for
│ │ │  list-likes of the correct length), you can use <code class="docutils literal notranslate"><span class="pre">result_type='broadcast'</span></code>.
│ │ │  The shape must match the original columns.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [79]: </span><span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">result_type</span><span class="o">=</span><span class="s1">&#39;broadcast&#39;</span><span class="p">)</span>
│ │ │  <span class="gh">Out[79]: </span>
│ │ │  <span class="go">   A  B  C</span>
│ │ │  <span class="go">0  1  2  3</span>
│ │ │  <span class="go">1  1  2  3</span>
│ │ │  <span class="go">2  1  2  3</span>
│ │ │  <span class="go">3  1  2  3</span>
│ │ │  <span class="go">4  1  2  3</span>
│ │ │  <span class="go">5  1  2  3</span>
│ │ │ +
│ │ │ +<span class="go">[6 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>Returning a <code class="docutils literal notranslate"><span class="pre">Series</span></code> allows one to control the exact return structure and column names:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [80]: </span><span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="s1">&#39;E&#39;</span><span class="p">,</span> <span class="s1">&#39;F&#39;</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
│ │ │  <span class="gh">Out[80]: </span>
│ │ │  <span class="go">   D  E  F</span>
│ │ │  <span class="go">0  1  2  3</span>
│ │ │  <span class="go">1  1  2  3</span>
│ │ │  <span class="go">2  1  2  3</span>
│ │ │  <span class="go">3  1  2  3</span>
│ │ │  <span class="go">4  1  2  3</span>
│ │ │  <span class="go">5  1  2  3</span>
│ │ │ +
│ │ │ +<span class="go">[6 rows x 3 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="concatenation-will-no-longer-sort">
│ │ │  <span id="whatsnew-0230-api-breaking-concat"></span><h3>Concatenation will no longer sort<a class="headerlink" href="#concatenation-will-no-longer-sort" title="Link to this heading">#</a></h3>
│ │ │  <p>In a future version of pandas <a class="reference internal" href="../reference/api/pandas.concat.html#pandas.concat" title="pandas.concat"><code class="xref py py-func docutils literal notranslate"><span class="pre">pandas.concat()</span></code></a> will no longer sort the non-concatenation axis when it is not already aligned.
│ │ │  The current behavior is the same as the previous (sorting), but now a warning is issued when <code class="docutils literal notranslate"><span class="pre">sort</span></code> is not specified and the non-concatenation axis is not aligned (<a class="extlink-issue reference external" href="https://github.com/pandas-dev/pandas/issues/4588">GH 4588</a>).</p>
│ │ │ @@ -1716,24 +1748,28 @@
│ │ │  <span class="gp">In [83]: </span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">])</span>
│ │ │  <span class="gh">Out[83]: </span>
│ │ │  <span class="go">     b  a</span>
│ │ │  <span class="go">0  1.0  1</span>
│ │ │  <span class="go">1  2.0  2</span>
│ │ │  <span class="go">0  NaN  4</span>
│ │ │  <span class="go">1  NaN  5</span>
│ │ │ +
│ │ │ +<span class="go">[4 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>To keep the previous behavior (sorting) and silence the warning, pass <code class="docutils literal notranslate"><span class="pre">sort=True</span></code></p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [84]: </span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">],</span> <span class="n">sort</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
│ │ │  <span class="gh">Out[84]: </span>
│ │ │  <span class="go">   a    b</span>
│ │ │  <span class="go">0  1  1.0</span>
│ │ │  <span class="go">1  2  2.0</span>
│ │ │  <span class="go">0  4  NaN</span>
│ │ │  <span class="go">1  5  NaN</span>
│ │ │ +
│ │ │ +<span class="go">[4 rows x 2 columns]</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>To accept the future behavior (no sorting), pass <code class="docutils literal notranslate"><span class="pre">sort=False</span></code></p>
│ │ │  <p>Note that this change also applies to <code class="xref py py-meth docutils literal notranslate"><span class="pre">DataFrame.append()</span></code>, which has also received a <code class="docutils literal notranslate"><span class="pre">sort</span></code> keyword for controlling this behavior.</p>
│ │ │  </section>
│ │ │  <section id="build-changes">
│ │ │  <span id="whatsnew-0230-api-breaking-build-changes"></span><h3>Build changes<a class="headerlink" href="#build-changes" title="Link to this heading">#</a></h3>
│ │ │ @@ -1812,28 +1848,30 @@
│ │ │  
│ │ │  <span class="gp">In [87]: </span><span class="n">extracted</span>
│ │ │  <span class="gh">Out[87]: </span>
│ │ │  <span class="go">    0</span>
│ │ │  <span class="go">0  10</span>
│ │ │  <span class="go">1  12</span>
│ │ │  
│ │ │ +<span class="go">[2 rows x 1 columns]</span>
│ │ │ +
│ │ │  <span class="gp">In [88]: </span><span class="nb">type</span><span class="p">(</span><span class="n">extracted</span><span class="p">)</span>
│ │ │  <span class="gh">Out[88]: </span><span class="go">pandas.core.frame.DataFrame</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>To restore previous behavior, simply set <code class="docutils literal notranslate"><span class="pre">expand</span></code> to <code class="docutils literal notranslate"><span class="pre">False</span></code>:</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [89]: </span><span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s1">&#39;number 10&#39;</span><span class="p">,</span> <span class="s1">&#39;12 eggs&#39;</span><span class="p">])</span>
│ │ │  
│ │ │  <span class="gp">In [90]: </span><span class="n">extracted</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;.*(\d\d).*&#39;</span><span class="p">,</span> <span class="n">expand</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
│ │ │  
│ │ │  <span class="gp">In [91]: </span><span class="n">extracted</span>
│ │ │  <span class="gh">Out[91]: </span>
│ │ │  <span class="go">0    10</span>
│ │ │  <span class="go">1    12</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="go">Length: 2, dtype: object</span>
│ │ │  
│ │ │  <span class="gp">In [92]: </span><span class="nb">type</span><span class="p">(</span><span class="n">extracted</span><span class="p">)</span>
│ │ │  <span class="gh">Out[92]: </span><span class="go">pandas.core.series.Series</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="default-value-for-the-ordered-parameter-of-categoricaldtype">
│ │ │ ├── html2text {}
│ │ │ │ @@ -210,42 +210,46 @@
│ │ │ │       foo bar        baz qux
│ │ │ │  idx
│ │ │ │  0      1   a 2018-01-01   a
│ │ │ │  1      2   b 2018-01-02   b
│ │ │ │  2      3   c 2018-01-03   c
│ │ │ │  3      4   d 2018-01-04   c
│ │ │ │  
│ │ │ │ +[4 rows x 4 columns]
│ │ │ │ +
│ │ │ │  In [3]: df.dtypes
│ │ │ │  Out[3]:
│ │ │ │  foo             int64
│ │ │ │  bar            object
│ │ │ │  baz    datetime64[ns]
│ │ │ │  qux          category
│ │ │ │ -dtype: object
│ │ │ │ +Length: 4, dtype: object
│ │ │ │  
│ │ │ │  In [4]: df.to_json('test.json', orient='table')
│ │ │ │  
│ │ │ │  In [5]: new_df = pd.read_json('test.json', orient='table')
│ │ │ │  
│ │ │ │  In [6]: new_df
│ │ │ │  Out[6]:
│ │ │ │       foo bar        baz qux
│ │ │ │  idx
│ │ │ │  0      1   a 2018-01-01   a
│ │ │ │  1      2   b 2018-01-02   b
│ │ │ │  2      3   c 2018-01-03   c
│ │ │ │  3      4   d 2018-01-04   c
│ │ │ │  
│ │ │ │ +[4 rows x 4 columns]
│ │ │ │ +
│ │ │ │  In [7]: new_df.dtypes
│ │ │ │  Out[7]:
│ │ │ │  foo             int64
│ │ │ │  bar            object
│ │ │ │  baz    datetime64[ns]
│ │ │ │  qux          category
│ │ │ │ -dtype: object
│ │ │ │ +Length: 4, dtype: object
│ │ │ │  Please note that the string index is not supported with the round trip format,
│ │ │ │  as it is used by default in write_json to indicate a missing index name.
│ │ │ │  In [8]: df.index.name = 'index'
│ │ │ │  
│ │ │ │  In [9]: df.to_json('test.json', orient='table')
│ │ │ │  
│ │ │ │  In [10]: new_df = pd.read_json('test.json', orient='table')
│ │ │ │ @@ -254,41 +258,47 @@
│ │ │ │  Out[11]:
│ │ │ │     foo bar        baz qux
│ │ │ │  0    1   a 2018-01-01   a
│ │ │ │  1    2   b 2018-01-02   b
│ │ │ │  2    3   c 2018-01-03   c
│ │ │ │  3    4   d 2018-01-04   c
│ │ │ │  
│ │ │ │ +[4 rows x 4 columns]
│ │ │ │ +
│ │ │ │  In [12]: new_df.dtypes
│ │ │ │  Out[12]:
│ │ │ │  foo             int64
│ │ │ │  bar            object
│ │ │ │  baz    datetime64[ns]
│ │ │ │  qux          category
│ │ │ │ -dtype: object
│ │ │ │ +Length: 4, dtype: object
│ │ │ │  ******** MMeetthhoodd ..aassssiiggnn(()) aacccceeppttss ddeeppeennddeenntt aarrgguummeennttss_## ********
│ │ │ │  The _D_a_t_a_F_r_a_m_e_._a_s_s_i_g_n_(_) now accepts dependent keyword arguments for python
│ │ │ │  version later than 3.6 (see also _P_E_P_ _4_6_8). Later keyword arguments may now
│ │ │ │  refer to earlier ones if the argument is a callable. See the _d_o_c_u_m_e_n_t_a_t_i_o_n_ _h_e_r_e
│ │ │ │  (_G_H_ _1_4_2_0_7)
│ │ │ │  In [13]: df = pd.DataFrame({'A': [1, 2, 3]})
│ │ │ │  
│ │ │ │  In [14]: df
│ │ │ │  Out[14]:
│ │ │ │     A
│ │ │ │  0  1
│ │ │ │  1  2
│ │ │ │  2  3
│ │ │ │  
│ │ │ │ +[3 rows x 1 columns]
│ │ │ │ +
│ │ │ │  In [15]: df.assign(B=df.A, C=lambda x: x['A'] + x['B'])
│ │ │ │  Out[15]:
│ │ │ │     A  B  C
│ │ │ │  0  1  1  2
│ │ │ │  1  2  2  4
│ │ │ │  2  3  3  6
│ │ │ │ +
│ │ │ │ +[3 rows x 3 columns]
│ │ │ │  Warning
│ │ │ │  This may subtly change the behavior of your code when you’re using .assign() to
│ │ │ │  update an existing column. Previously, callables referring to other variables
│ │ │ │  being updated would get the “old” values
│ │ │ │  Previous behavior:
│ │ │ │  In [2]: df = pd.DataFrame({"A": [1, 2, 3]})
│ │ │ │  
│ │ │ │ @@ -301,14 +311,16 @@
│ │ │ │  New behavior:
│ │ │ │  In [16]: df.assign(A=df.A + 1, C=lambda df: df.A * -1)
│ │ │ │  Out[16]:
│ │ │ │     A  C
│ │ │ │  0  2 -2
│ │ │ │  1  3 -3
│ │ │ │  2  4 -4
│ │ │ │ +
│ │ │ │ +[3 rows x 2 columns]
│ │ │ │  ******** MMeerrggiinngg oonn aa ccoommbbiinnaattiioonn ooff ccoolluummnnss aanndd iinnddeexx lleevveellss_## ********
│ │ │ │  Strings passed to _D_a_t_a_F_r_a_m_e_._m_e_r_g_e_(_) as the on, left_on, and right_on parameters
│ │ │ │  may now refer to either column names or index level names. This enables merging
│ │ │ │  DataFrame instances on a combination of index levels and columns without
│ │ │ │  resetting indexes. See the _M_e_r_g_e_ _o_n_ _c_o_l_u_m_n_s_ _a_n_d_ _l_e_v_e_l_s documentation section.
│ │ │ │  (_G_H_ _1_4_3_5_5)
│ │ │ │  In [17]: left_index = pd.Index(['K0', 'K0', 'K1', 'K2'], name='key1')
│ │ │ │ @@ -330,14 +342,16 @@
│ │ │ │  In [21]: left.merge(right, on=['key1', 'key2'])
│ │ │ │  Out[21]:
│ │ │ │         A   B key2   C   D
│ │ │ │  key1
│ │ │ │  K0    A0  B0   K0  C0  D0
│ │ │ │  K1    A2  B2   K0  C1  D1
│ │ │ │  K2    A3  B3   K1  C3  D3
│ │ │ │ +
│ │ │ │ +[3 rows x 5 columns]
│ │ │ │  ******** SSoorrttiinngg bbyy aa ccoommbbiinnaattiioonn ooff ccoolluummnnss aanndd iinnddeexx lleevveellss_## ********
│ │ │ │  Strings passed to _D_a_t_a_F_r_a_m_e_._s_o_r_t___v_a_l_u_e_s_(_) as the by parameter may now refer to
│ │ │ │  either column names or index level names. This enables sorting DataFrame
│ │ │ │  instances by a combination of index levels and columns without resetting
│ │ │ │  indexes. See the _S_o_r_t_i_n_g_ _b_y_ _I_n_d_e_x_e_s_ _a_n_d_ _V_a_l_u_e_s documentation section. (_G_H
│ │ │ │  _1_4_3_5_3)
│ │ │ │  # Build MultiIndex
│ │ │ │ @@ -359,25 +373,29 @@
│ │ │ │  a     1       6
│ │ │ │        2       5
│ │ │ │        2       4
│ │ │ │  b     2       3
│ │ │ │        1       2
│ │ │ │        1       1
│ │ │ │  
│ │ │ │ +[6 rows x 1 columns]
│ │ │ │ +
│ │ │ │  # Sort by 'second' (index) and 'A' (column)
│ │ │ │  In [26]: df_multi.sort_values(by=['second', 'A'])
│ │ │ │  Out[26]:
│ │ │ │                A
│ │ │ │  first second
│ │ │ │  b     1       1
│ │ │ │        1       2
│ │ │ │  a     1       6
│ │ │ │  b     2       3
│ │ │ │  a     2       4
│ │ │ │        2       5
│ │ │ │ +
│ │ │ │ +[6 rows x 1 columns]
│ │ │ │  ******** EExxtteennddiinngg ppaannddaass wwiitthh ccuussttoomm ttyyppeess ((eexxppeerriimmeennttaall))_## ********
│ │ │ │  pandas now supports storing array-like objects that aren’t necessarily 1-
│ │ │ │  D NumPy arrays as columns in a DataFrame or values in a Series. This allows
│ │ │ │  third-party libraries to implement extensions to NumPy’s types, similar to how
│ │ │ │  pandas implemented categoricals, datetimes with timezones, periods, and
│ │ │ │  intervals.
│ │ │ │  As a demonstration, we’ll use _c_y_b_e_r_p_a_n_d_a_s, which provides an IPArray type for
│ │ │ │ @@ -434,14 +452,16 @@
│ │ │ │  In [31]: df
│ │ │ │  Out[31]:
│ │ │ │     A  B  values    C
│ │ │ │  0  a  c       1  foo
│ │ │ │  1  a  d       2  bar
│ │ │ │  2  b  c       3  foo
│ │ │ │  3  b  d       4  bar
│ │ │ │ +
│ │ │ │ +[4 rows x 4 columns]
│ │ │ │  To show all values, the previous behavior:
│ │ │ │  In [32]: df.groupby(['A', 'B', 'C'], observed=False).count()
│ │ │ │  Out[32]:
│ │ │ │           values
│ │ │ │  A B C
│ │ │ │  a c bar       0
│ │ │ │      foo       1
│ │ │ │ @@ -461,14 +481,16 @@
│ │ │ │  Out[33]:
│ │ │ │           values
│ │ │ │  A B C
│ │ │ │  a c foo       1
│ │ │ │    d bar       1
│ │ │ │  b c foo       1
│ │ │ │    d bar       1
│ │ │ │ +
│ │ │ │ +[4 rows x 1 columns]
│ │ │ │  For pivoting operations, this behavior is aallrreeaaddyy controlled by the dropna
│ │ │ │  keyword:
│ │ │ │  In [34]: cat1 = pd.Categorical(["a", "a", "b", "b"],
│ │ │ │     ....:                       categories=["a", "b", "z"], ordered=True)
│ │ │ │     ....:
│ │ │ │  
│ │ │ │  In [35]: cat2 = pd.Categorical(["c", "d", "c", "d"],
│ │ │ │ @@ -480,14 +502,16 @@
│ │ │ │  In [37]: df
│ │ │ │  Out[37]:
│ │ │ │     A  B  values
│ │ │ │  0  a  c       1
│ │ │ │  1  a  d       2
│ │ │ │  2  b  c       3
│ │ │ │  3  b  d       4
│ │ │ │ +
│ │ │ │ +[4 rows x 3 columns]
│ │ │ │  In [1]: pd.pivot_table(df, values='values', index=['A', 'B'], dropna=True)
│ │ │ │  
│ │ │ │  Out[1]:
│ │ │ │       values
│ │ │ │  A B
│ │ │ │  a c     1.0
│ │ │ │    d     2.0
│ │ │ │ @@ -522,33 +546,33 @@
│ │ │ │  In [39]: s
│ │ │ │  Out[39]:
│ │ │ │  1    0
│ │ │ │  2    1
│ │ │ │  3    2
│ │ │ │  4    3
│ │ │ │  5    4
│ │ │ │ -dtype: int64
│ │ │ │ +Length: 5, dtype: int64
│ │ │ │  Pass a Series:
│ │ │ │  In [40]: s.rolling(2, min_periods=1).apply(lambda x: x.iloc[-1], raw=False)
│ │ │ │  Out[40]:
│ │ │ │  1    0.0
│ │ │ │  2    1.0
│ │ │ │  3    2.0
│ │ │ │  4    3.0
│ │ │ │  5    4.0
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 5, dtype: float64
│ │ │ │  Mimic the original behavior of passing a ndarray:
│ │ │ │  In [41]: s.rolling(2, min_periods=1).apply(lambda x: x[-1], raw=True)
│ │ │ │  Out[41]:
│ │ │ │  1    0.0
│ │ │ │  2    1.0
│ │ │ │  3    2.0
│ │ │ │  4    3.0
│ │ │ │  5    4.0
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 5, dtype: float64
│ │ │ │  ******** DDaattaaFFrraammee..iinntteerrppoollaattee hhaass ggaaiinneedd tthhee lliimmiitt__aarreeaa kkwwaarrgg_## ********
│ │ │ │  _D_a_t_a_F_r_a_m_e_._i_n_t_e_r_p_o_l_a_t_e_(_) has gained a limit_area parameter to allow further
│ │ │ │  control of which NaN s are replaced. Use limit_area='inside' to fill only NaNs
│ │ │ │  surrounded by valid values or use limit_area='outside' to fill only NaN s
│ │ │ │  outside the existing valid values while preserving those inside. (_G_H_ _1_6_2_8_4) See
│ │ │ │  the _f_u_l_l_ _d_o_c_u_m_e_n_t_a_t_i_o_n_ _h_e_r_e.
│ │ │ │  In [42]: ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan,
│ │ │ │ @@ -562,74 +586,74 @@
│ │ │ │  2     5.0
│ │ │ │  3     NaN
│ │ │ │  4     NaN
│ │ │ │  5     NaN
│ │ │ │  6    13.0
│ │ │ │  7     NaN
│ │ │ │  8     NaN
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 9, dtype: float64
│ │ │ │  Fill one consecutive inside value in both directions
│ │ │ │  In [44]: ser.interpolate(limit_direction='both', limit_area='inside', limit=1)
│ │ │ │  Out[44]:
│ │ │ │  0     NaN
│ │ │ │  1     NaN
│ │ │ │  2     5.0
│ │ │ │  3     7.0
│ │ │ │  4     NaN
│ │ │ │  5    11.0
│ │ │ │  6    13.0
│ │ │ │  7     NaN
│ │ │ │  8     NaN
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 9, dtype: float64
│ │ │ │  Fill all consecutive outside values backward
│ │ │ │  In [45]: ser.interpolate(limit_direction='backward', limit_area='outside')
│ │ │ │  Out[45]:
│ │ │ │  0     5.0
│ │ │ │  1     5.0
│ │ │ │  2     5.0
│ │ │ │  3     NaN
│ │ │ │  4     NaN
│ │ │ │  5     NaN
│ │ │ │  6    13.0
│ │ │ │  7     NaN
│ │ │ │  8     NaN
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 9, dtype: float64
│ │ │ │  Fill all consecutive outside values in both directions
│ │ │ │  In [46]: ser.interpolate(limit_direction='both', limit_area='outside')
│ │ │ │  Out[46]:
│ │ │ │  0     5.0
│ │ │ │  1     5.0
│ │ │ │  2     5.0
│ │ │ │  3     NaN
│ │ │ │  4     NaN
│ │ │ │  5     NaN
│ │ │ │  6    13.0
│ │ │ │  7    13.0
│ │ │ │  8    13.0
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 9, dtype: float64
│ │ │ │  ******** FFuunnccttiioonn ggeett__dduummmmiieess nnooww ssuuppppoorrttss ddttyyppee aarrgguummeenntt_## ********
│ │ │ │  The _g_e_t___d_u_m_m_i_e_s_(_) now accepts a dtype argument, which specifies a dtype for the
│ │ │ │  new columns. The default remains uint8. (_G_H_ _1_8_3_3_0)
│ │ │ │  In [47]: df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [5, 6]})
│ │ │ │  
│ │ │ │  In [48]: pd.get_dummies(df, columns=['c']).dtypes
│ │ │ │  Out[48]:
│ │ │ │  a      int64
│ │ │ │  b      int64
│ │ │ │  c_5     bool
│ │ │ │  c_6     bool
│ │ │ │ -dtype: object
│ │ │ │ +Length: 4, dtype: object
│ │ │ │  
│ │ │ │  In [49]: pd.get_dummies(df, columns=['c'], dtype=bool).dtypes
│ │ │ │  Out[49]:
│ │ │ │  a      int64
│ │ │ │  b      int64
│ │ │ │  c_5     bool
│ │ │ │  c_6     bool
│ │ │ │ -dtype: object
│ │ │ │ +Length: 4, dtype: object
│ │ │ │  ******** TTiimmeeddeellttaa mmoodd mmeetthhoodd_## ********
│ │ │ │  mod (%) and divmod operations are now defined on Timedelta objects when
│ │ │ │  operating with either timedelta-like or with numeric arguments. See the
│ │ │ │  _d_o_c_u_m_e_n_t_a_t_i_o_n_ _h_e_r_e. (_G_H_ _1_9_3_6_5)
│ │ │ │  In [50]: td = pd.Timedelta(hours=37)
│ │ │ │  
│ │ │ │  In [51]: td % pd.Timedelta(minutes=45)
│ │ │ │ @@ -642,15 +666,15 @@
│ │ │ │  In [53]: s
│ │ │ │  Out[53]:
│ │ │ │  0   -inf
│ │ │ │  1    0.0
│ │ │ │  2    1.0
│ │ │ │  3    NaN
│ │ │ │  4    inf
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 5, dtype: float64
│ │ │ │  Previous behavior:
│ │ │ │  In [11]: s.rank()
│ │ │ │  Out[11]:
│ │ │ │  0    1.0
│ │ │ │  1    2.0
│ │ │ │  2    3.0
│ │ │ │  3    NaN
│ │ │ │ @@ -660,27 +684,27 @@
│ │ │ │  In [54]: s.rank()
│ │ │ │  Out[54]:
│ │ │ │  0    1.0
│ │ │ │  1    2.0
│ │ │ │  2    3.0
│ │ │ │  3    NaN
│ │ │ │  4    4.0
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 5, dtype: float64
│ │ │ │  Furthermore, previously if you rank inf or -inf values together with NaN
│ │ │ │  values, the calculation won’t distinguish NaN from infinity when using ‘top’ or
│ │ │ │  ‘bottom’ argument.
│ │ │ │  In [55]: s = pd.Series([np.nan, np.nan, -np.inf, -np.inf])
│ │ │ │  
│ │ │ │  In [56]: s
│ │ │ │  Out[56]:
│ │ │ │  0    NaN
│ │ │ │  1    NaN
│ │ │ │  2   -inf
│ │ │ │  3   -inf
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 4, dtype: float64
│ │ │ │  Previous behavior:
│ │ │ │  In [15]: s.rank(na_option='top')
│ │ │ │  Out[15]:
│ │ │ │  0    2.5
│ │ │ │  1    2.5
│ │ │ │  2    2.5
│ │ │ │  3    2.5
│ │ │ │ @@ -688,15 +712,15 @@
│ │ │ │  Current behavior:
│ │ │ │  In [57]: s.rank(na_option='top')
│ │ │ │  Out[57]:
│ │ │ │  0    1.5
│ │ │ │  1    1.5
│ │ │ │  2    3.5
│ │ │ │  3    3.5
│ │ │ │ -dtype: float64
│ │ │ │ +Length: 4, dtype: float64
│ │ │ │  These bugs were squashed:
│ │ │ │      * Bug in _D_a_t_a_F_r_a_m_e_._r_a_n_k_(_) and _S_e_r_i_e_s_._r_a_n_k_(_) when method='dense' and
│ │ │ │        pct=True in which percentile ranks were not being used with the number of
│ │ │ │        distinct observations (_G_H_ _1_5_6_3_0)
│ │ │ │      * Bug in _S_e_r_i_e_s_._r_a_n_k_(_) and _D_a_t_a_F_r_a_m_e_._r_a_n_k_(_) when ascending='False' failed
│ │ │ │        to return correct ranks for infinity if NaN were present (_G_H_ _1_9_5_3_8)
│ │ │ │      * Bug in DataFrameGroupBy.rank() where ranks were incorrect when both
│ │ │ │ @@ -714,23 +738,23 @@
│ │ │ │  
│ │ │ │  In [60]: s.str.cat(t)
│ │ │ │  Out[60]:
│ │ │ │  0    NaN
│ │ │ │  1     bb
│ │ │ │  2     cc
│ │ │ │  3     dd
│ │ │ │ -dtype: object
│ │ │ │ +Length: 4, dtype: object
│ │ │ │  
│ │ │ │  In [61]: s.str.cat(t, join='left', na_rep='-')
│ │ │ │  Out[61]:
│ │ │ │  0    a-
│ │ │ │  1    bb
│ │ │ │  2    cc
│ │ │ │  3    dd
│ │ │ │ -dtype: object
│ │ │ │ +Length: 4, dtype: object
│ │ │ │  Furthermore, _S_e_r_i_e_s_._s_t_r_._c_a_t_(_) now works for CategoricalIndex as well
│ │ │ │  (previously raised a ValueError; see _G_H_ _2_0_8_4_2).
│ │ │ │  ******** DDaattaaFFrraammee..aassttyyppee ppeerrffoorrmmss ccoolluummnn--wwiissee ccoonnvveerrssiioonn ttoo CCaatteeggoorriiccaall_## ********
│ │ │ │  _D_a_t_a_F_r_a_m_e_._a_s_t_y_p_e_(_) can now perform column-wise conversion to Categorical by
│ │ │ │  supplying the string 'category' or a CategoricalDtype. Previously, attempting
│ │ │ │  this would raise a NotImplementedError. See the _O_b_j_e_c_t_ _c_r_e_a_t_i_o_n section of the
│ │ │ │  documentation for more details and examples. (_G_H_ _1_2_8_6_0, _G_H_ _1_8_0_9_9)
│ │ │ │ @@ -897,15 +921,15 @@
│ │ │ │     ....:            'Net result': 300})
│ │ │ │     ....:
│ │ │ │  Out[72]:
│ │ │ │  Income        2000
│ │ │ │  Expenses     -1500
│ │ │ │  Taxes         -200
│ │ │ │  Net result     300
│ │ │ │ -dtype: int64
│ │ │ │ +Length: 4, dtype: int64
│ │ │ │  Notice that the Series is now ordered by insertion order. This new behavior is
│ │ │ │  used for all relevant pandas types (Series, DataFrame, SparseSeries and
│ │ │ │  SparseDataFrame).
│ │ │ │  If you wish to retain the old behavior while using Python >= 3.6, you can use
│ │ │ │  .sort_index():
│ │ │ │  In [73]: pd.Series({'Income': 2000,
│ │ │ │     ....:            'Expenses': -1500,
│ │ │ │ @@ -913,15 +937,15 @@
│ │ │ │     ....:            'Net result': 300}).sort_index()
│ │ │ │     ....:
│ │ │ │  Out[73]:
│ │ │ │  Expenses     -1500
│ │ │ │  Income        2000
│ │ │ │  Net result     300
│ │ │ │  Taxes         -200
│ │ │ │ -dtype: int64
│ │ │ │ +Length: 4, dtype: int64
│ │ │ │  ******** DDeepprreeccaattee PPaanneell_## ********
│ │ │ │  Panel was deprecated in the 0.20.x release, showing as a DeprecationWarning.
│ │ │ │  Using Panel will now show a FutureWarning. The recommended way to represent 3-
│ │ │ │  D data are with a MultiIndex on a DataFrame via the to_frame() or with the
│ │ │ │  _x_a_r_r_a_y_ _p_a_c_k_a_g_e. pandas provides a to_xarray() method to automate this
│ │ │ │  conversion (_G_H_ _1_3_5_6_3, _G_H_ _1_8_3_2_4).
│ │ │ │  In [75]: import pandas._testing as tm
│ │ │ │ @@ -998,14 +1022,16 @@
│ │ │ │     A  B  C
│ │ │ │  0  1  2  3
│ │ │ │  1  1  2  3
│ │ │ │  2  1  2  3
│ │ │ │  3  1  2  3
│ │ │ │  4  1  2  3
│ │ │ │  5  1  2  3
│ │ │ │ +
│ │ │ │ +[6 rows x 3 columns]
│ │ │ │  Previous behavior: if the returned shape happened to match the length of
│ │ │ │  original columns, this would return a DataFrame. If the return shape did not
│ │ │ │  match, a Series with lists was returned.
│ │ │ │  In [3]: df.apply(lambda x: [1, 2, 3], axis=1)
│ │ │ │  Out[3]:
│ │ │ │     A  B  C
│ │ │ │  0  1  2  3
│ │ │ │ @@ -1030,59 +1056,65 @@
│ │ │ │  Out[76]:
│ │ │ │  0    [1, 2, 3]
│ │ │ │  1    [1, 2, 3]
│ │ │ │  2    [1, 2, 3]
│ │ │ │  3    [1, 2, 3]
│ │ │ │  4    [1, 2, 3]
│ │ │ │  5    [1, 2, 3]
│ │ │ │ -dtype: object
│ │ │ │ +Length: 6, dtype: object
│ │ │ │  
│ │ │ │  In [77]: df.apply(lambda x: [1, 2], axis=1)
│ │ │ │  Out[77]:
│ │ │ │  0    [1, 2]
│ │ │ │  1    [1, 2]
│ │ │ │  2    [1, 2]
│ │ │ │  3    [1, 2]
│ │ │ │  4    [1, 2]
│ │ │ │  5    [1, 2]
│ │ │ │ -dtype: object
│ │ │ │ +Length: 6, dtype: object
│ │ │ │  To have expanded columns, you can use result_type='expand'
│ │ │ │  In [78]: df.apply(lambda x: [1, 2, 3], axis=1, result_type='expand')
│ │ │ │  Out[78]:
│ │ │ │     0  1  2
│ │ │ │  0  1  2  3
│ │ │ │  1  1  2  3
│ │ │ │  2  1  2  3
│ │ │ │  3  1  2  3
│ │ │ │  4  1  2  3
│ │ │ │  5  1  2  3
│ │ │ │ +
│ │ │ │ +[6 rows x 3 columns]
│ │ │ │  To broadcast the result across the original columns (the old behaviour for
│ │ │ │  list-likes of the correct length), you can use result_type='broadcast'. The
│ │ │ │  shape must match the original columns.
│ │ │ │  In [79]: df.apply(lambda x: [1, 2, 3], axis=1, result_type='broadcast')
│ │ │ │  Out[79]:
│ │ │ │     A  B  C
│ │ │ │  0  1  2  3
│ │ │ │  1  1  2  3
│ │ │ │  2  1  2  3
│ │ │ │  3  1  2  3
│ │ │ │  4  1  2  3
│ │ │ │  5  1  2  3
│ │ │ │ +
│ │ │ │ +[6 rows x 3 columns]
│ │ │ │  Returning a Series allows one to control the exact return structure and column
│ │ │ │  names:
│ │ │ │  In [80]: df.apply(lambda x: pd.Series([1, 2, 3], index=['D', 'E', 'F']),
│ │ │ │  axis=1)
│ │ │ │  Out[80]:
│ │ │ │     D  E  F
│ │ │ │  0  1  2  3
│ │ │ │  1  1  2  3
│ │ │ │  2  1  2  3
│ │ │ │  3  1  2  3
│ │ │ │  4  1  2  3
│ │ │ │  5  1  2  3
│ │ │ │ +
│ │ │ │ +[6 rows x 3 columns]
│ │ │ │  ******** CCoonnccaatteennaattiioonn wwiillll nnoo lloonnggeerr ssoorrtt_## ********
│ │ │ │  In a future version of pandas _p_a_n_d_a_s_._c_o_n_c_a_t_(_) will no longer sort the non-
│ │ │ │  concatenation axis when it is not already aligned. The current behavior is the
│ │ │ │  same as the previous (sorting), but now a warning is issued when sort is not
│ │ │ │  specified and the non-concatenation axis is not aligned (_G_H_ _4_5_8_8).
│ │ │ │  In [81]: df1 = pd.DataFrame({"a": [1, 2], "b": [1, 2]}, columns=['b', 'a'])
│ │ │ │  
│ │ │ │ @@ -1091,22 +1123,26 @@
│ │ │ │  In [83]: pd.concat([df1, df2])
│ │ │ │  Out[83]:
│ │ │ │       b  a
│ │ │ │  0  1.0  1
│ │ │ │  1  2.0  2
│ │ │ │  0  NaN  4
│ │ │ │  1  NaN  5
│ │ │ │ +
│ │ │ │ +[4 rows x 2 columns]
│ │ │ │  To keep the previous behavior (sorting) and silence the warning, pass sort=True
│ │ │ │  In [84]: pd.concat([df1, df2], sort=True)
│ │ │ │  Out[84]:
│ │ │ │     a    b
│ │ │ │  0  1  1.0
│ │ │ │  1  2  2.0
│ │ │ │  0  4  NaN
│ │ │ │  1  5  NaN
│ │ │ │ +
│ │ │ │ +[4 rows x 2 columns]
│ │ │ │  To accept the future behavior (no sorting), pass sort=False
│ │ │ │  Note that this change also applies to DataFrame.append(), which has also
│ │ │ │  received a sort keyword for controlling this behavior.
│ │ │ │  ******** BBuuiilldd cchhaannggeess_## ********
│ │ │ │      * Building pandas for development now requires cython >= 0.24 (_G_H_ _1_8_6_1_3)
│ │ │ │      * Building from source now explicitly requires setuptools in setup.py (_G_H
│ │ │ │        _1_8_1_1_3)
│ │ │ │ @@ -1176,26 +1212,28 @@
│ │ │ │  
│ │ │ │  In [87]: extracted
│ │ │ │  Out[87]:
│ │ │ │      0
│ │ │ │  0  10
│ │ │ │  1  12
│ │ │ │  
│ │ │ │ +[2 rows x 1 columns]
│ │ │ │ +
│ │ │ │  In [88]: type(extracted)
│ │ │ │  Out[88]: pandas.core.frame.DataFrame
│ │ │ │  To restore previous behavior, simply set expand to False:
│ │ │ │  In [89]: s = pd.Series(['number 10', '12 eggs'])
│ │ │ │  
│ │ │ │  In [90]: extracted = s.str.extract(r'.*(\d\d).*', expand=False)
│ │ │ │  
│ │ │ │  In [91]: extracted
│ │ │ │  Out[91]:
│ │ │ │  0    10
│ │ │ │  1    12
│ │ │ │ -dtype: object
│ │ │ │ +Length: 2, dtype: object
│ │ │ │  
│ │ │ │  In [92]: type(extracted)
│ │ │ │  Out[92]: pandas.core.series.Series
│ │ │ │  ******** DDeeffaauulltt vvaalluuee ffoorr tthhee oorrddeerreedd ppaarraammeetteerr ooff CCaatteeggoorriiccaallDDttyyppee_## ********
│ │ │ │  The default value of the ordered parameter for CategoricalDtype has changed
│ │ │ │  from False to None to allow updating of categories without impacting ordered.
│ │ │ │  Behavior should remain consistent for downstream objects, such as _C_a_t_e_g_o_r_i_c_a_l
│ │ ├── ./usr/share/doc/python-pandas-doc/html/whatsnew/v0.24.0.html
│ │ │ @@ -820,18 +820,18 @@
│ │ │  a new ndarray of period objects each time.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [18]: </span><span class="n">idx</span><span class="o">.</span><span class="n">values</span>
│ │ │  <span class="gh">Out[18]: </span>
│ │ │  <span class="go">array([Period(&#39;2000-01-01&#39;, &#39;D&#39;), Period(&#39;2000-01-02&#39;, &#39;D&#39;),</span>
│ │ │  <span class="go">       Period(&#39;2000-01-03&#39;, &#39;D&#39;), Period(&#39;2000-01-04&#39;, &#39;D&#39;)], dtype=object)</span>
│ │ │  
│ │ │  <span class="gp">In [19]: </span><span class="nb">id</span><span class="p">(</span><span class="n">idx</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
│ │ │ -<span class="gh">Out[19]: </span><span class="go">139804622088560</span>
│ │ │ +<span class="gh">Out[19]: </span><span class="go">140583402907440</span>
│ │ │  
│ │ │  <span class="gp">In [20]: </span><span class="nb">id</span><span class="p">(</span><span class="n">idx</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
│ │ │ -<span class="gh">Out[20]: </span><span class="go">139804400157008</span>
│ │ │ +<span class="gh">Out[20]: </span><span class="go">140583402907440</span>
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>If you need an actual NumPy array, use <a class="reference internal" href="../reference/api/pandas.Series.to_numpy.html#pandas.Series.to_numpy" title="pandas.Series.to_numpy"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Series.to_numpy()</span></code></a> or <code class="xref py py-meth docutils literal notranslate"><span class="pre">Index.to_numpy()</span></code>.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [21]: </span><span class="n">idx</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
│ │ │  <span class="gh">Out[21]: </span>
│ │ │  <span class="go">array([Period(&#39;2000-01-01&#39;, &#39;D&#39;), Period(&#39;2000-01-02&#39;, &#39;D&#39;),</span>
│ │ │  <span class="go">       Period(&#39;2000-01-03&#39;, &#39;D&#39;), Period(&#39;2000-01-04&#39;, &#39;D&#39;)], dtype=object)</span>
│ │ │ ├── html2text {}
│ │ │ │ @@ -268,18 +268,18 @@
│ │ │ │  each time.
│ │ │ │  In [18]: idx.values
│ │ │ │  Out[18]:
│ │ │ │  array([Period('2000-01-01', 'D'), Period('2000-01-02', 'D'),
│ │ │ │         Period('2000-01-03', 'D'), Period('2000-01-04', 'D')], dtype=object)
│ │ │ │  
│ │ │ │  In [19]: id(idx.values)
│ │ │ │ -Out[19]: 139804622088560
│ │ │ │ +Out[19]: 140583402907440
│ │ │ │  
│ │ │ │  In [20]: id(idx.values)
│ │ │ │ -Out[20]: 139804400157008
│ │ │ │ +Out[20]: 140583402907440
│ │ │ │  If you need an actual NumPy array, use _S_e_r_i_e_s_._t_o___n_u_m_p_y_(_) or Index.to_numpy().
│ │ │ │  In [21]: idx.to_numpy()
│ │ │ │  Out[21]:
│ │ │ │  array([Period('2000-01-01', 'D'), Period('2000-01-02', 'D'),
│ │ │ │         Period('2000-01-03', 'D'), Period('2000-01-04', 'D')], dtype=object)
│ │ │ │  
│ │ │ │  In [22]: pd.Series(idx).to_numpy()
│ │ ├── ./usr/share/doc/python-pandas-doc/html/whatsnew/v1.3.0.html
│ │ │ @@ -883,19 +883,20 @@
│ │ │  <span class="g g-Whitespace">    </span><span class="mi">132</span>         <span class="s2">&quot;pyarrow&gt;=10.0.1 is required for PyArrow backed StringArray.&quot;</span>
│ │ │  <span class="g g-Whitespace">    </span><span class="mi">133</span>     <span class="p">)</span>
│ │ │  <span class="g g-Whitespace">    </span><span class="mi">134</span> <span class="bp">self</span><span class="o">.</span><span class="n">storage</span> <span class="o">=</span> <span class="n">storage</span>
│ │ │  
│ │ │  <span class="ne">ImportError</span>: pyarrow&gt;=10.0.1 is required for PyArrow backed StringArray.
│ │ │  
│ │ │  <span class="gp">In [8]: </span><span class="n">s</span>
│ │ │ -<span class="gh">Out[8]: </span>
│ │ │ -<span class="go">0    C</span>
│ │ │ -<span class="go">1    a</span>
│ │ │ -<span class="go">2    B</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="gt">---------------------------------------------------------------------------</span>
│ │ │ +<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
│ │ │ +<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
│ │ │ +<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">s</span>
│ │ │ +
│ │ │ +<span class="ne">NameError</span>: name &#39;s&#39; is not defined
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>You can also create a PyArrow backed string array using pandas options.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [9]: </span><span class="k">with</span> <span class="n">pd</span><span class="o">.</span><span class="n">option_context</span><span class="p">(</span><span class="s2">&quot;string_storage&quot;</span><span class="p">,</span> <span class="s2">&quot;pyarrow&quot;</span><span class="p">):</span>
│ │ │  <span class="gp">   ...: </span>    <span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="s1">&#39;abc&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;def&#39;</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;string&quot;</span><span class="p">)</span>
│ │ │  <span class="gp">   ...: </span>
│ │ │  <span class="gt">---------------------------------------------------------------------------</span>
│ │ │ @@ -952,43 +953,49 @@
│ │ │  <span class="g g-Whitespace">    </span><span class="mi">132</span>         <span class="s2">&quot;pyarrow&gt;=10.0.1 is required for PyArrow backed StringArray.&quot;</span>
│ │ │  <span class="g g-Whitespace">    </span><span class="mi">133</span>     <span class="p">)</span>
│ │ │  <span class="g g-Whitespace">    </span><span class="mi">134</span> <span class="bp">self</span><span class="o">.</span><span class="n">storage</span> <span class="o">=</span> <span class="n">storage</span>
│ │ │  
│ │ │  <span class="ne">ImportError</span>: pyarrow&gt;=10.0.1 is required for PyArrow backed StringArray.
│ │ │  
│ │ │  <span class="gp">In [10]: </span><span class="n">s</span>
│ │ │ -<span class="gh">Out[10]: </span>
│ │ │ -<span class="go">0    C</span>
│ │ │ -<span class="go">1    a</span>
│ │ │ -<span class="go">2    B</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="gt">---------------------------------------------------------------------------</span>
│ │ │ +<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
│ │ │ +<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
│ │ │ +<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">s</span>
│ │ │ +
│ │ │ +<span class="ne">NameError</span>: name &#39;s&#39; is not defined
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>The usual string accessor methods work. Where appropriate, the return type of the Series
│ │ │  or columns of a DataFrame will also have string dtype.</p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [11]: </span><span class="n">s</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span>
│ │ │ -<span class="gh">Out[11]: </span>
│ │ │ -<span class="go">0    C</span>
│ │ │ -<span class="go">1    A</span>
│ │ │ -<span class="go">2    B</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="gt">---------------------------------------------------------------------------</span>
│ │ │ +<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
│ │ │ +<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">11</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
│ │ │ +<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">s</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span>
│ │ │ +
│ │ │ +<span class="ne">NameError</span>: name &#39;s&#39; is not defined
│ │ │  
│ │ │  <span class="gp">In [12]: </span><span class="n">s</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">expand</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │ -<span class="gh">Out[12]: </span>
│ │ │ -<span class="go">0    object</span>
│ │ │ -<span class="go">dtype: object</span>
│ │ │ +<span class="gt">---------------------------------------------------------------------------</span>
│ │ │ +<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
│ │ │ +<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">12</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
│ │ │ +<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">s</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">expand</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">dtypes</span>
│ │ │ +
│ │ │ +<span class="ne">NameError</span>: name &#39;s&#39; is not defined
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  <p>String accessor methods returning integers will return a value with <a class="reference internal" href="../reference/api/pandas.Int64Dtype.html#pandas.Int64Dtype" title="pandas.Int64Dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">Int64Dtype</span></code></a></p>
│ │ │  <div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="gp">In [13]: </span><span class="n">s</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">)</span>
│ │ │ -<span class="gh">Out[13]: </span>
│ │ │ -<span class="go">0    0</span>
│ │ │ -<span class="go">1    1</span>
│ │ │ -<span class="go">2    0</span>
│ │ │ -<span class="go">dtype: int64</span>
│ │ │ +<span class="gt">---------------------------------------------------------------------------</span>
│ │ │ +<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
│ │ │ +<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">13</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
│ │ │ +<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">s</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">)</span>
│ │ │ +
│ │ │ +<span class="ne">NameError</span>: name &#39;s&#39; is not defined
│ │ │  </pre></div>
│ │ │  </div>
│ │ │  </section>
│ │ │  <section id="centered-datetime-like-rolling-windows">
│ │ │  <span id="whatsnew-130-enhancements-centered-datetimelike-rolling-window"></span><h3>Centered datetime-like rolling windows<a class="headerlink" href="#centered-datetime-like-rolling-windows" title="Link to this heading">#</a></h3>
│ │ │  <p>When performing rolling calculations on DataFrame and Series
│ │ │  objects with a datetime-like index, a centered datetime-like window can now be
│ │ │ ├── html2text {}
│ │ │ │ @@ -352,19 +352,20 @@
│ │ │ │      132         "pyarrow>=10.0.1 is required for PyArrow backed StringArray."
│ │ │ │      133     )
│ │ │ │      134 self.storage = storage
│ │ │ │  
│ │ │ │  ImportError: pyarrow>=10.0.1 is required for PyArrow backed StringArray.
│ │ │ │  
│ │ │ │  In [8]: s
│ │ │ │ -Out[8]:
│ │ │ │ -0    C
│ │ │ │ -1    a
│ │ │ │ -2    B
│ │ │ │ -dtype: object
│ │ │ │ +---------------------------------------------------------------------------
│ │ │ │ +NameError                                 Traceback (most recent call last)
│ │ │ │ +Cell In[8], line 1
│ │ │ │ +----> 1 s
│ │ │ │ +
│ │ │ │ +NameError: name 's' is not defined
│ │ │ │  You can also create a PyArrow backed string array using pandas options.
│ │ │ │  In [9]: with pd.option_context("string_storage", "pyarrow"):
│ │ │ │     ...:     s = pd.Series(['abc', None, 'def'], dtype="string")
│ │ │ │     ...:
│ │ │ │  ---------------------------------------------------------------------------
│ │ │ │  ImportError                               Traceback (most recent call last)
│ │ │ │  Cell In[9], line 2
│ │ │ │ @@ -425,39 +426,45 @@
│ │ │ │      132         "pyarrow>=10.0.1 is required for PyArrow backed StringArray."
│ │ │ │      133     )
│ │ │ │      134 self.storage = storage
│ │ │ │  
│ │ │ │  ImportError: pyarrow>=10.0.1 is required for PyArrow backed StringArray.
│ │ │ │  
│ │ │ │  In [10]: s
│ │ │ │ -Out[10]:
│ │ │ │ -0    C
│ │ │ │ -1    a
│ │ │ │ -2    B
│ │ │ │ -dtype: object
│ │ │ │ +---------------------------------------------------------------------------
│ │ │ │ +NameError                                 Traceback (most recent call last)
│ │ │ │ +Cell In[10], line 1
│ │ │ │ +----> 1 s
│ │ │ │ +
│ │ │ │ +NameError: name 's' is not defined
│ │ │ │  The usual string accessor methods work. Where appropriate, the return type of
│ │ │ │  the Series or columns of a DataFrame will also have string dtype.
│ │ │ │  In [11]: s.str.upper()
│ │ │ │ -Out[11]:
│ │ │ │ -0    C
│ │ │ │ -1    A
│ │ │ │ -2    B
│ │ │ │ -dtype: object
│ │ │ │ +---------------------------------------------------------------------------
│ │ │ │ +NameError                                 Traceback (most recent call last)
│ │ │ │ +Cell In[11], line 1
│ │ │ │ +----> 1 s.str.upper()
│ │ │ │ +
│ │ │ │ +NameError: name 's' is not defined
│ │ │ │  
│ │ │ │  In [12]: s.str.split('b', expand=True).dtypes
│ │ │ │ -Out[12]:
│ │ │ │ -0    object
│ │ │ │ -dtype: object
│ │ │ │ +---------------------------------------------------------------------------
│ │ │ │ +NameError                                 Traceback (most recent call last)
│ │ │ │ +Cell In[12], line 1
│ │ │ │ +----> 1 s.str.split('b', expand=True).dtypes
│ │ │ │ +
│ │ │ │ +NameError: name 's' is not defined
│ │ │ │  String accessor methods returning integers will return a value with _I_n_t_6_4_D_t_y_p_e
│ │ │ │  In [13]: s.str.count("a")
│ │ │ │ -Out[13]:
│ │ │ │ -0    0
│ │ │ │ -1    1
│ │ │ │ -2    0
│ │ │ │ -dtype: int64
│ │ │ │ +---------------------------------------------------------------------------
│ │ │ │ +NameError                                 Traceback (most recent call last)
│ │ │ │ +Cell In[13], line 1
│ │ │ │ +----> 1 s.str.count("a")
│ │ │ │ +
│ │ │ │ +NameError: name 's' is not defined
│ │ │ │  ******** CCeenntteerreedd ddaatteettiimmee--lliikkee rroolllliinngg wwiinnddoowwss_## ********
│ │ │ │  When performing rolling calculations on DataFrame and Series objects with a
│ │ │ │  datetime-like index, a centered datetime-like window can now be used (_G_H
│ │ │ │  _3_8_7_8_0). For example:
│ │ │ │  In [14]: df = pd.DataFrame(
│ │ │ │     ....:     {"A": [0, 1, 2, 3, 4]}, index=pd.date_range("2020", periods=5,
│ │ │ │  freq="1D")