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\"011736\": 2186,\n         \"011829\": 2207,\n         \"01183\": 2229,\n         \"011860\": [182, 760],\n         \"011975\": 2207,\n-        \"012\": 2193,\n         \"012108\": 2207,\n         \"012299\": 2207,\n         \"0123456789123456\": [2164, 2165],\n         \"012549\": 2207,\n         \"012694\": 2199,\n         \"012922\": 2219,\n-        \"013\": 2193,\n         \"013086\": 15,\n         \"0133\": 2202,\n         \"013448\": 2207,\n         \"013605\": 2207,\n         \"013684\": [182, 760],\n         \"013692\": [102, 1158],\n         \"013747\": 2199,\n         \"013768\": 2230,\n         \"013810\": [182, 760],\n         \"013863\": 2199,\n         \"013960\": [2185, 2197, 2199, 2202, 2204, 2215, 2257],\n-        \"014\": [2191, 2193],\n+        \"014\": 2191,\n         \"014061\": 2207,\n         \"014073\": 2204,\n         \"014103\": 2207,\n         \"014138\": 2191,\n         \"014144\": [102, 1158],\n         \"014648\": 2186,\n         \"014752\": 2235,\n@@ -21640,15 +21643,15 @@\n         \"017106\": 2207,\n         \"017118\": 2199,\n         \"017152\": 2186,\n         \"017263\": 2207,\n         \"017276\": 2191,\n         \"017587\": [2184, 2195, 2214],\n         \"017796\": 2207,\n-        \"018\": [2193, 2199],\n+        \"018\": 2199,\n         \"018007\": 2207,\n         \"018117\": 2191,\n         \"018193\": 2207,\n         \"018409\": 2207,\n         \"018601\": [2184, 2214],\n         \"018808\": 2207,\n         \"018904\": 2207,\n@@ -21671,15 +21674,14 @@\n         \"020208\": 2195,\n         \"020376\": 2207,\n         \"020399\": 2195,\n         \"020485\": 2207,\n         \"020544\": 2186,\n         \"020762\": 2220,\n         \"020940\": 2230,\n-        \"021\": 2193,\n         \"021244\": 2207,\n         \"021255\": 2230,\n         \"021292\": 2186,\n         \"021377\": 2207,\n         \"021382\": 2184,\n         \"021499\": 2186,\n         \"02155\": 30,\n@@ -21704,15 +21706,15 @@\n         \"024580\": [2184, 2195, 2214],\n         \"024738\": [102, 1158],\n         \"024786\": 2207,\n         \"024810\": 2207,\n         \"0249\": [267, 896],\n         \"024925\": 2195,\n         \"024967\": 2207,\n-        \"025\": [2186, 2222, 2227],\n+        \"025\": [2186, 2193, 2222, 2227],\n         \"025054\": 2184,\n         \"025270\": 2186,\n         \"025363\": 2186,\n         \"025367\": 2207,\n         \"025747\": [2191, 2197, 2207],\n         \"026036\": 2207,\n         \"026158\": 2210,\n@@ -21774,15 +21776,15 @@\n         \"033350\": 2207,\n         \"033387\": 2186,\n         \"033606\": 2186,\n         \"0336061024141463\": 2186,\n         \"033695\": 2186,\n         \"033718\": 2204,\n         \"033823\": 2210,\n-        \"034\": [1433, 2193],\n+        \"034\": 1433,\n         \"034069\": 2195,\n         \"034326\": [2184, 2257],\n         \"034374\": 2210,\n         \"034446\": 2207,\n         \"034512\": 2207,\n         \"034523\": 2210,\n         \"034571\": 2197,\n@@ -21833,14 +21835,15 @@\n         \"041\": [1447, 2200, 2232],\n         \"041290\": 2197,\n         \"041575\": 2219,\n         \"041665\": 2205,\n         \"041898\": 2207,\n         \"041927\": 2199,\n         \"041933\": 2184,\n+        \"042\": 2193,\n         \"042041\": 2207,\n         \"042275\": [283, 910],\n         \"042322\": 2207,\n         \"042379\": [2184, 2195, 2214],\n         \"0424\": 2257,\n         \"042856\": 2218,\n         \"042935\": 2207,\n@@ -21964,15 +21967,15 @@\n         \"059481\": 2207,\n         \"059552\": 2207,\n         \"059761\": 2207,\n         \"059869e\": 2191,\n         \"059881\": 2210,\n         \"059904\": 2214,\n         \"05t00\": 2261,\n-        \"06\": [26, 27, 29, 30, 31, 207, 213, 218, 230, 273, 292, 294, 332, 363, 526, 534, 536, 637, 644, 646, 688, 781, 788, 793, 804, 900, 969, 993, 1075, 1344, 1441, 1442, 1449, 1450, 1452, 1489, 1497, 1500, 1506, 1524, 1598, 1677, 2184, 2186, 2195, 2197, 2199, 2201, 2204, 2205, 2207, 2209, 2210, 2212, 2214, 2215, 2216, 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 \"066126\": 2207,\n         \"066510\": 2210,\n         \"066533\": 2210,\n         \"066786\": 2207,\n         \"067091\": 2199,\n         \"067137\": 2197,\n         \"067503\": 2207,\n@@ -22098,41 +22102,40 @@\n         \"079587\": 2230,\n         \"079631\": 2207,\n         \"0797\": 2202,\n         \"079769\": 2207,\n         \"079915\": 2193,\n         \"07t00\": 2261,\n         \"08\": [29, 30, 107, 207, 213, 230, 264, 273, 277, 292, 294, 316, 326, 330, 332, 629, 644, 646, 670, 680, 685, 688, 781, 788, 804, 900, 903, 1075, 1145, 1164, 1221, 1274, 1289, 1344, 1441, 1442, 1449, 1450, 1452, 1495, 1497, 1506, 1598, 1657, 1677, 1699, 1720, 1741, 2184, 2185, 2186, 2191, 2195, 2197, 2199, 2201, 2204, 2205, 2207, 2209, 2210, 2212, 2214, 2215, 2218, 2220, 2222, 2226, 2228, 2230, 2231, 2232, 2235, 2246, 2249, 2261, 2271, 2294, 2307],\n+        \"080\": 2193,\n         \"0800\": [953, 2210],\n         \"080174\": 2207,\n         \"080372\": 2199,\n         \"080952\": [2184, 2214],\n         \"081009\": 2195,\n         \"081161\": 2216,\n         \"081249\": 2207,\n         \"081304\": 2207,\n         \"081447\": 2210,\n         \"081666\": 2211,\n         \"081748\": 2210,\n         \"081842\": 2207,\n-        \"082\": 2193,\n         \"082240\": [2185, 2191, 2197, 2199],\n         \"082423\": [2191, 2197],\n         \"082523\": 2207,\n         \"082764\": 2197,\n         \"082900\": 2214,\n         \"082901\": 2212,\n         \"082960\": 2207,\n         \"083010\": 2207,\n         \"083333\": 2222,\n         \"083352\": 2191,\n         \"08335394550\": 1371,\n         \"083515\": 15,\n         \"083675\": 2207,\n-        \"084\": 2193,\n         \"084601\": 2191,\n         \"084844\": [2185, 2191, 2197, 2202, 2204],\n         \"084917\": 2195,\n         \"084n\": 2202,\n         \"084u\": 2202,\n         \"085070\": 2207,\n         \"085193\": 2207,\n@@ -22253,20 +22256,20 @@\n         \"0n\": [1489, 2298],\n         \"0px\": 2207,\n         \"0rc0\": 13,\n         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2197],\n         \"11915\": [2230, 2235],\n         \"11916\": 2199,\n         \"1192\": [2184, 2186],\n         \"11920\": 2232,\n         \"11920871129693428\": 2210,\n@@ -23424,15 +23427,15 @@\n         \"12988\": 2231,\n         \"12995\": 2232,\n         \"12997\": 2294,\n         \"12h\": [84, 595, 2210, 2231, 2239, 2240],\n         \"12pt\": 2207,\n         \"12th\": 2199,\n         \"13\": [9, 15, 16, 17, 18, 19, 24, 25, 26, 28, 29, 30, 31, 32, 77, 108, 127, 133, 134, 157, 182, 187, 208, 213, 230, 268, 288, 341, 420, 522, 524, 530, 564, 566, 639, 703, 708, 732, 760, 763, 782, 788, 799, 804, 940, 1169, 1226, 1276, 1298, 1299, 1306, 1308, 1397, 1430, 1447, 1498, 1501, 1598, 1657, 1677, 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, 2220, 2221, 2222, 2223, 2225, 2226, 2228, 2229, 2231, 2232, 2238, 2240, 2241, 2246, 2249, 2257, 2261, 2264, 2265, 2271, 2277, 2283, 2289, 2294, 2298, 2302, 2307],\n-        \"130\": [15, 1443, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2203, 2204, 2208, 2210, 2211, 2225, 2232, 2283],\n+        \"130\": [15, 1443, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2203, 2204, 2208, 2210, 2211, 2225, 2232, 2283],\n         \"13000\": [2185, 2220],\n         \"13000101\": 1498,\n         \"13001\": 2232,\n         \"13005\": 2231,\n         \"13006\": 2232,\n         \"13008\": 2231,\n         \"13012\": 2241,\n@@ -24073,15 +24076,15 @@\n         \"14781\": 2241,\n         \"147824074\": 1006,\n         \"14784\": 2235,\n         \"147855\": 2235,\n         \"14792\": 2235,\n         \"147970\": 2207,\n         \"14798\": 2235,\n-        \"148\": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2210, 2211, 2232],\n+        \"148\": [2185, 2186, 2188, 2195, 2197, 2199, 2200, 2201, 2202, 2210, 2211, 2232],\n         \"14800\": 2235,\n         \"148032\": 2199,\n         \"148084\": 15,\n         \"148098\": 2210,\n         \"14811\": 2277,\n         \"14816\": 2235,\n         \"1482\": 2202,\n@@ -24150,15 +24153,15 @@\n         \"14982\": 2235,\n         \"14983\": 2235,\n         \"1499\": 2212,\n         \"14992\": 2235,\n         \"14998\": 2235,\n         \"14t15\": [955, 956, 957, 962, 970, 983, 990, 995, 997, 999, 1002, 1006, 1007, 1008, 1009, 1013, 1014],\n         \"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, 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\"150036\": [2220, 2230],\n         \"15005\": 2235,\n@@ -24319,15 +24322,15 @@\n         \"15495\": 2238,\n         \"1549507744\": 2199,\n         \"1549507744249032\": 2197,\n         \"154951\": [15, 2185, 2197, 2199, 2202],\n         \"154971\": 22,\n         \"154975\": 22,\n         \"15498\": 2235,\n-        \"155\": [1447, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2201, 2207, 2210, 2211, 2232],\n+        \"155\": [1447, 2185, 2186, 2188, 2195, 2197, 2199, 2200, 2201, 2207, 2210, 2211, 2232],\n         \"15501\": 2246,\n         \"15503\": 2235,\n         \"15504\": 2235,\n         \"15506\": 2246,\n         \"15507\": 2238,\n         \"15516\": 2235,\n         \"15520\": 2235,\n@@ -24520,15 +24523,15 @@\n         \"161657\": 2195,\n         \"1617\": [16, 17, 18, 19, 2199, 2203, 2235, 2298],\n         \"16179\": 2236,\n         \"16180\": 2236,\n         \"16189\": 2246,\n         \"1619\": [16, 17, 18, 19, 2199, 2203, 2235, 2298],\n         \"16199\": 2237,\n-        \"162\": [2185, 2186, 2188, 2191, 2195, 2197, 2199, 2201, 2205, 2210, 2211, 2231, 2235],\n+        \"162\": [2185, 2186, 2188, 2191, 2195, 2197, 2199, 2201, 2210, 2211, 2231, 2235],\n         \"1620\": [16, 17, 18, 19, 2199, 2203, 2235, 2298],\n         \"16209\": 2236,\n         \"1621\": [2194, 2201, 2203, 2283, 2294, 2307],\n         \"16211\": 2238,\n         \"162114\": 2207,\n         \"16212\": 2238,\n         \"16223\": [2235, 2241],\n@@ -24550,15 +24553,15 @@\n         \"162754\": 2191,\n         \"16282\": 2236,\n         \"16284\": 2241,\n         \"16285\": 2236,\n         \"16288\": 2236,\n         \"16291\": 2236,\n         \"162969\": 2185,\n-        \"163\": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2201, 2210, 2211],\n+        \"163\": [2185, 2186, 2188, 2195, 2197, 2199, 2201, 2210, 2211],\n         \"1630\": 2263,\n         \"163008\": 2186,\n         \"16301\": 2238,\n         \"16302\": 2236,\n         \"16306\": 2236,\n         \"16316\": 2249,\n         \"16319\": 2236,\n@@ -24959,15 +24962,15 @@\n         \"17656\": 2265,\n         \"1766\": 2199,\n         \"176896\": 2207,\n         \"17690\": 2241,\n         \"17691\": 2249,\n         \"17697\": 2246,\n         \"1769950\": [182, 760],\n-        \"177\": [259, 890, 2185, 2186, 2188, 2195, 2197, 2199, 2200, 2203, 2210, 2211, 2283, 2298],\n+        \"177\": [259, 890, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2203, 2210, 2211, 2283, 2298],\n         \"17704\": 2238,\n         \"177045\": 2186,\n         \"17710\": 2238,\n         \"17717\": 2241,\n         \"17722\": 2241,\n         \"177310\": 2207,\n         \"17738\": 2238,\n@@ -24980,15 +24983,15 @@\n         \"17758\": 2241,\n         \"1776\": [195, 770, 2263],\n         \"17776\": 2239,\n         \"17778\": [2241, 2242],\n         \"17780\": 2238,\n         \"17791\": 2239,\n         \"17798\": 2238,\n-        \"178\": [69, 109, 129, 171, 173, 199, 204, 206, 215, 216, 217, 220, 221, 222, 244, 275, 2185, 2186, 2188, 2195, 2197, 2199, 2200, 2203, 2210, 2211, 2212, 2283, 2298],\n+        \"178\": [69, 109, 129, 171, 173, 199, 204, 206, 215, 216, 217, 220, 221, 222, 244, 275, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2203, 2210, 2211, 2212, 2283, 2298],\n         \"178035\": 2207,\n         \"17812\": 2249,\n         \"17813\": 2241,\n         \"17820\": 2249,\n         \"1783\": 2263,\n         \"17830\": 2241,\n         \"17832\": 2246,\n@@ -25081,15 +25084,15 @@\n         \"18178\": 2239,\n         \"1818\": 2217,\n         \"18184\": 2241,\n         \"18186\": 2239,\n         \"18187\": 2239,\n         \"181873\": 2207,\n         \"18198\": 2294,\n-        \"182\": [176, 179, 2185, 2186, 2188, 2195, 2197, 2199, 2200, 2210, 2211, 2298],\n+        \"182\": [176, 179, 2185, 2186, 2188, 2193, 2195, 2197, 2199, 2200, 2210, 2211, 2298],\n         \"18203\": 2239,\n         \"18213\": 2241,\n         \"18216\": 2239,\n         \"18217\": [2241, 2265],\n         \"18218\": 2241,\n         \"18221\": 2241,\n         \"18222\": 2265,\n@@ -25750,19 +25753,20 @@\n         \"2021\": [288, 296, 318, 639, 652, 673, 940, 943, 948, 957, 970, 997, 1542, 2201, 2207, 2213, 2277, 2289, 2294],\n         \"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, 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   \"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],\n         \"202380\": 2207,\n         \"20239\": [2241, 2265],\n         \"2024\": [270, 544, 546, 555, 567, 894, 898, 2127, 2213],\n-        \"2025\": [36, 544, 546, 555, 567, 894, 898, 2228],\n+        \"2025\": [36, 544, 546, 555, 567, 894, 898],\n         \"20251\": 2307,\n         \"2026\": 2228,\n         \"202602\": 2205,\n         \"202646\": 2230,\n+        \"2027\": 2228,\n         \"20271\": 2241,\n         \"202872\": [2184, 2214],\n         \"202946\": 2207,\n         \"203\": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211, 2231, 2253],\n         \"2030\": 2265,\n         \"20303\": 2265,\n         \"20306\": 2302,\n@@ -26195,15 +26199,15 @@\n         \"21867\": 2246,\n         \"218745\": 2207,\n         \"21877\": 2246,\n         \"218792\": 2230,\n         \"21891\": 2246,\n         \"21892\": 2289,\n         \"218983\": 2217,\n-        \"219\": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211],\n+        \"219\": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2211, 2218],\n         \"219049\": 2207,\n         \"219115\": 2184,\n         \"219182\": 2205,\n         \"219217\": [2185, 2197, 2199, 2202, 2204],\n         \"21925\": 2246,\n         \"219296\": 2207,\n         \"2193\": 2246,\n@@ -26411,15 +26415,15 @@\n         \"22818\": [2283, 2298],\n         \"22835\": 2246,\n         \"22858\": 2246,\n         \"22860\": 2246,\n         \"22862\": 2246,\n         \"22880\": 2246,\n         \"22887\": 2246,\n-        \"229\": [2185, 2186, 2188, 2195, 2197, 2199, 2210],\n+        \"229\": [2185, 2186, 2188, 2193, 2195, 2197, 2199, 2210],\n         \"22903\": 2246,\n         \"22905\": 2246,\n         \"22912\": 2246,\n         \"22922\": 2246,\n         \"229349\": 2207,\n         \"22938\": 2246,\n         \"229453\": 2197,\n@@ -26573,15 +26577,15 @@\n         \"23675\": 2246,\n         \"23677\": 2246,\n         \"23679\": 2249,\n         \"23682\": 2246,\n         \"23683\": 2249,\n         \"23687\": 2246,\n         \"23697\": 2289,\n-        \"237\": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2218, 2220, 2298],\n+        \"237\": [2185, 2186, 2188, 2195, 2197, 2199, 2210, 2220, 2298],\n         \"237000\": [2185, 2220],\n         \"23705\": 2249,\n         \"23711\": 2246,\n         \"237124\": 2207,\n         \"237159\": 2199,\n         \"23719\": 2265,\n         \"237242\": [2191, 2207],\n@@ -27051,15 +27055,15 @@\n         \"25851\": 2249,\n         \"25860\": 2249,\n         \"258648\": 2210,\n         \"25871\": 2249,\n         \"25880\": 2298,\n         \"25893\": 2249,\n         \"258993\": 2197,\n-        \"259\": [2185, 2186, 2188, 2195, 2197, 2199, 2210],\n+        \"259\": [2186, 2188, 2193, 2195, 2197, 2199, 2205, 2210],\n         \"25905\": 2249,\n         \"25913\": 2249,\n         \"259200\": [683, 2298],\n         \"259200000000000\": [931, 933, 937],\n         \"25922\": 2249,\n         \"259260\": 2228,\n         \"25928\": 2249,\n@@ -27269,15 +27273,15 @@\n         \"268413\": 2207,\n         \"2685\": 2221,\n         \"268520\": [2184, 2195, 2214],\n         \"2686\": 2215,\n         \"2687\": 2215,\n         \"2689\": 2215,\n         \"268968\": 2207,\n-        \"269\": [2185, 2186, 2188, 2195, 2197, 2199, 2210],\n+        \"269\": [2186, 2188, 2195, 2197, 2199, 2210],\n         \"2690\": 2215,\n         \"26916\": 2249,\n         \"26919\": 2283,\n         \"2692\": 2215,\n         \"269219\": [242, 817],\n         \"26934\": 2249,\n         \"26939\": 2265,\n@@ -27311,15 +27315,15 @@\n         \"2707\": 2199,\n         \"27080\": 2250,\n         \"27081\": 2271,\n         \"27082\": 2249,\n         \"27083\": 2249,\n         \"27084\": 2249,\n         \"27088\": 2249,\n-        \"271\": [2186, 2188, 2195, 2197, 2199, 2210],\n+        \"271\": [2186, 2188, 2195, 2197, 2199, 2205, 2210],\n         \"2710\": [2202, 2216],\n         \"27101\": 2277,\n         \"2710197\": 2202,\n         \"27103\": 2265,\n         \"27104\": 2277,\n         \"27106\": 2265,\n         \"27110\": 2249,\n@@ -27433,15 +27437,14 @@\n         \"276183\": 2257,\n         \"2762\": [2184, 2186, 2191],\n         \"276232\": [15, 2184, 2185, 2186, 2191, 2197, 2199, 2202, 2210, 2214, 2215, 2216, 2218, 2225, 2231, 2241, 2264],\n         \"27636\": 2250,\n         \"276386\": 2207,\n         \"27642\": 2250,\n         \"276464\": 2230,\n-        \"2765\": 2193,\n         \"27656\": [2294, 2298],\n         \"27660\": 2265,\n         \"2766617129497566\": 2257,\n         \"276662\": [2185, 2197, 2199, 2202, 2215, 2257],\n         \"27668\": 2265,\n         \"2767\": 2191,\n         \"27676\": 2265,\n@@ -27797,15 +27800,15 @@\n         \"29564\": 2289,\n         \"29565\": 2265,\n         \"29570\": 2277,\n         \"295722\": 2235,\n         \"29578\": 2265,\n         \"295968\": 2207,\n         \"295989\": 2197,\n-        \"296\": [514, 516, 2186, 2197, 2199, 2210, 2255],\n+        \"296\": [514, 516, 2186, 2193, 2197, 2199, 2210, 2255],\n         \"2960\": 2221,\n         \"29608\": 2265,\n         \"29618\": 2298,\n         \"29623\": 2283,\n         \"29624\": 2265,\n         \"296326\": 2207,\n         \"29641\": 2265,\n@@ -27974,15 +27977,15 @@\n         \"304611\": 2197,\n         \"30463\": 2265,\n         \"304662\": 2199,\n         \"304762\": 2207,\n         \"30482\": 2298,\n         \"30484\": 2271,\n         \"30489\": 2298,\n-        \"305\": [2186, 2197, 2199, 2210],\n+        \"305\": [2185, 2186, 2197, 2199, 2210],\n         \"30511\": 2271,\n         \"305288\": 2207,\n         \"305384\": 2197,\n         \"30543\": 2271,\n         \"30546\": 2298,\n         \"30562\": 2298,\n         \"305657\": 2207,\n@@ -29061,15 +29064,15 @@\n         \"35869\": 2277,\n         \"35873\": 2283,\n         \"35876\": 2273,\n         \"35878\": 2273,\n         \"35882\": 2273,\n         \"35889\": 2277,\n         \"35897\": 2274,\n-        \"359\": [69, 109, 129, 171, 173, 199, 204, 206, 215, 216, 217, 220, 221, 222, 244, 275, 2186, 2193, 2197, 2199, 2207, 2210, 2255, 2298],\n+        \"359\": [69, 109, 129, 171, 173, 199, 204, 206, 215, 216, 217, 220, 221, 222, 244, 275, 2186, 2197, 2199, 2207, 2210, 2255, 2298],\n         \"3590\": 2217,\n         \"35923\": 2283,\n         \"359235\": 2207,\n         \"359261\": 2191,\n         \"359284\": 2195,\n         \"359299\": 2191,\n         \"35931\": 2273,\n@@ -29258,15 +29261,15 @@\n         \"36870\": 2277,\n         \"368714\": 2184,\n         \"368824\": 2230,\n         \"36888\": 2277,\n         \"36889\": 2275,\n         \"36893\": 2283,\n         \"36895\": 2277,\n-        \"369\": [2186, 2197, 2199, 2210],\n+        \"369\": [2185, 2186, 2197, 2199, 2210],\n         \"36900\": 2298,\n         \"36904\": 2275,\n         \"36907\": 2277,\n         \"36908\": 2277,\n         \"369081\": 2207,\n         \"36909\": 2283,\n         \"3691\": 2217,\n@@ -29421,23 +29424,22 @@\n         \"37705\": 2277,\n         \"37711\": 2276,\n         \"37722\": 2277,\n         \"377245\": 15,\n         \"37725\": 2277,\n         \"377263\": 2207,\n         \"37733\": 2277,\n-        \"3773406912\": 2246,\n         \"37748\": 2277,\n         \"37750\": 2289,\n         \"377535\": 2186,\n         \"37755\": 2276,\n         \"37758\": 2277,\n         \"377642\": 2210,\n         \"37768\": 2277,\n-        \"3777\": 2218,\n+        \"3777\": [2193, 2218],\n         \"37782\": 2302,\n         \"377887\": 2207,\n         \"37799\": 2277,\n         \"378\": [2186, 2197, 2199, 2207, 2210, 2231],\n         \"3780\": 2222,\n         \"37804\": 2283,\n         \"378163\": 2207,\n@@ -29445,18 +29447,20 @@\n         \"37821\": 2277,\n         \"378261\": 2218,\n         \"37827\": 2277,\n         \"378298\": 2207,\n         \"378430\": 2207,\n         \"378528\": 2197,\n         \"37867\": 2277,\n+        \"3786966512\": 2246,\n         \"3787\": 2228,\n         \"37877\": [2277, 2298],\n         \"378782\": 993,\n         \"378849\": 2191,\n+        \"3788646784\": 2246,\n         \"37899\": 2289,\n         \"379\": [2186, 2197, 2199, 2210, 2231],\n         \"37901\": 2277,\n         \"37909\": 2277,\n         \"379098\": 2207,\n         \"37910\": 2276,\n         \"37918\": 2298,\n@@ -29640,15 +29644,14 @@\n         \"3877\": 2217,\n         \"38774\": 2278,\n         \"38778\": 2283,\n         \"38780\": 2283,\n         \"38782\": 2283,\n         \"38787\": 2283,\n         \"38788\": 2278,\n-        \"3879182560\": 2246,\n         \"38792\": 2283,\n         \"387949\": 2207,\n         \"38798\": 2298,\n         \"388\": [2186, 2197, 2199, 2210],\n         \"38801\": 2278,\n         \"3881\": [2202, 2220],\n         \"388138\": 2210,\n@@ -30595,15 +30598,15 @@\n         \"430489\": 2199,\n         \"4305\": 2218,\n         \"430505\": 2186,\n         \"43059\": 2289,\n         \"43075\": 2286,\n         \"43080\": 2289,\n         \"430860\": [2207, 2212],\n-        \"431\": [28, 2186, 2193, 2199, 2210, 2298],\n+        \"431\": [28, 2186, 2199, 2210, 2298],\n         \"43101\": 2289,\n         \"43102\": 2289,\n         \"43108\": 2286,\n         \"431125\": 2184,\n         \"43115\": 2289,\n         \"431186\": 2199,\n         \"4312\": 2218,\n@@ -31309,15 +31312,15 @@\n         \"45967\": 2294,\n         \"45981\": 2298,\n         \"459855\": 2207,\n         \"45986\": [2294, 2298],\n         \"45991\": 2294,\n         \"45999\": 2294,\n         \"46\": [17, 19, 23, 29, 278, 331, 686, 904, 1323, 2184, 2185, 2186, 2188, 2190, 2191, 2193, 2194, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2238, 2241, 2246, 2249, 2265, 2271, 2277, 2283, 2297, 2302],\n-        \"460\": [2199, 2205, 2210],\n+        \"460\": [2199, 2210],\n         \"46001\": 2294,\n         \"4601\": 2218,\n         \"46015\": 2294,\n         \"46018\": 2291,\n         \"46026\": 2294,\n         \"46037\": 2291,\n         \"4604\": 2218,\n@@ -32060,15 +32063,15 @@\n         \"4987\": 2225,\n         \"4988\": 2238,\n         \"498861\": 2191,\n         \"49888\": 2300,\n         \"49889\": 2299,\n         \"49890\": 2298,\n         \"49897\": 2298,\n-        \"499\": [2184, 2185, 2199, 2205, 2210, 2249],\n+        \"499\": [2184, 2199, 2205, 2210, 2249],\n         \"49907\": 2297,\n         \"499148\": 2207,\n         \"49921\": 2298,\n         \"49922\": 2298,\n         \"49929\": 2298,\n         \"4993\": 2218,\n         \"49944\": 2302,\n@@ -32358,15 +32361,15 @@\n         \"5125\": 2218,\n         \"51254\": 2302,\n         \"51258\": 2298,\n         \"512743\": 2193,\n         \"51276\": 2302,\n         \"5129\": 2220,\n         \"51299\": 2298,\n-        \"513\": [2193, 2199],\n+        \"513\": 2199,\n         \"51302\": 2298,\n         \"51316\": 2298,\n         \"51349\": 2298,\n         \"513520\": 2207,\n         \"51353\": 2302,\n         \"5136\": [2192, 2197],\n         \"513600\": 2207,\n@@ -32684,15 +32687,15 @@\n         \"52979\": 2302,\n         \"52981\": 2302,\n         \"52986\": 2302,\n         \"529895\": 2219,\n         \"52998\": 2302,\n         \"52w\": 1433,\n         \"53\": [15, 17, 19, 24, 25, 28, 29, 32, 532, 662, 957, 1377, 1793, 1815, 2184, 2185, 2186, 2188, 2190, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2238, 2241, 2246, 2249, 2271, 2283, 2294],\n-        \"530\": 2199,\n+        \"530\": [2199, 2205],\n         \"53001\": 2300,\n         \"53009\": 2302,\n         \"530113\": 2207,\n         \"53013\": 2302,\n         \"53014\": 2302,\n         \"53039\": 2302,\n         \"53045\": 2302,\n@@ -32702,15 +32705,15 @@\n         \"530570\": 2260,\n         \"53058\": 2302,\n         \"530606\": 2207,\n         \"53077\": 2300,\n         \"53088\": 2302,\n         \"530905\": 2204,\n         \"53093\": 2300,\n-        \"531\": [2193, 2199],\n+        \"531\": 2199,\n         \"53103\": 2302,\n         \"5310949\": 2202,\n         \"531096\": 2230,\n         \"5311\": 2202,\n         \"53111\": 2307,\n         \"53117\": 2300,\n         \"5312\": 2218,\n@@ -32854,15 +32857,15 @@\n         \"53746\": 2302,\n         \"53747\": 2302,\n         \"53767\": 2302,\n         \"5377\": 2271,\n         \"53786\": 2302,\n         \"537874\": 2207,\n         \"53792\": 2302,\n-        \"538\": [2191, 2199],\n+        \"538\": [2191, 2199, 2205],\n         \"53806\": 2302,\n         \"53811\": 2302,\n         \"53831\": 2302,\n         \"53832\": 2302,\n         \"53846\": 2304,\n         \"538468\": 2210,\n         \"53854\": 2302,\n@@ -32884,15 +32887,15 @@\n         \"539708\": 2195,\n         \"53979\": 2302,\n         \"53983\": 2302,\n         \"539890\": 2257,\n         \"539990\": 2210,\n         \"53h\": 2209,\n         \"54\": [15, 17, 19, 29, 213, 345, 788, 2184, 2185, 2186, 2188, 2190, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2203, 2204, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2218, 2220, 2222, 2225, 2226, 2228, 2230, 2231, 2232, 2235, 2238, 2241, 2246, 2249, 2271, 2283],\n-        \"540\": [69, 109, 129, 171, 173, 199, 204, 206, 215, 216, 217, 220, 221, 222, 244, 275, 2193, 2199],\n+        \"540\": [69, 109, 129, 171, 173, 199, 204, 206, 215, 216, 217, 220, 221, 222, 244, 275, 2199],\n         \"5401\": 2277,\n         \"54011\": 2302,\n         \"540132\": 2207,\n         \"5402\": 2219,\n         \"540338\": 2235,\n         \"54037\": 2302,\n         \"54063\": 2302,\n@@ -32963,15 +32966,15 @@\n         \"54466\": 2308,\n         \"54467\": 2307,\n         \"54477\": 2302,\n         \"544785\": 2207,\n         \"54480\": 2307,\n         \"544883\": 2166,\n         \"54495\": 2302,\n-        \"545\": [1444, 2193, 2199, 2203, 2257],\n+        \"545\": [1444, 2199, 2203, 2257],\n         \"545034\": 2186,\n         \"54508\": 2302,\n         \"545163\": 2207,\n         \"5453\": 2220,\n         \"545371\": 2207,\n         \"54550\": 2307,\n         \"54564\": 2307,\n@@ -33772,15 +33775,15 @@\n         \"6098\": 2219,\n         \"609836\": 2186,\n         \"61\": [15, 17, 19, 26, 27, 31, 242, 283, 532, 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, 2223, 2226, 2228, 2230, 2231, 2232, 2235, 2241, 2246, 2271],\n         \"610\": 2199,\n         \"6105\": 2219,\n         \"610691\": 2199,\n         \"610871\": 2207,\n-        \"611\": 2199,\n+        \"611\": [2193, 2199],\n         \"6111\": 2186,\n         \"611107\": 2207,\n         \"611112\": 2218,\n         \"611185\": 2197,\n         \"6113\": [2186, 2228],\n         \"6114\": 2186,\n         \"611510\": 2184,\n@@ -33871,15 +33874,15 @@\n         \"6209\": 2219,\n         \"621\": 2199,\n         \"621034\": 2186,\n         \"6212\": 2219,\n         \"6214\": 2218,\n         \"621452\": 2207,\n         \"621592\": 2207,\n-        \"622\": [16, 17, 18, 19, 2197, 2199, 2202, 2203, 2205, 2231, 2235, 2298],\n+        \"622\": [16, 17, 18, 19, 2193, 2197, 2199, 2202, 2203, 2231, 2235, 2298],\n         \"622109\": 2230,\n         \"6223\": 2220,\n         \"622526\": 2186,\n         \"622638\": 2207,\n         \"622727\": 2195,\n         \"622970\": 2207,\n         \"623\": [16, 17, 18, 19, 2199, 2203, 2235, 2298],\n@@ -33937,23 +33940,21 @@\n         \"6289\": 2220,\n         \"628992\": 2257,\n         \"629\": 2199,\n         \"6290\": 2220,\n         \"629003\": 2207,\n         \"629165\": 2230,\n         \"6292\": [2220, 2230],\n-        \"6295\": 2203,\n         \"629546\": 2219,\n-        \"6296\": [2203, 2220],\n+        \"6296\": 2220,\n         \"629675\": 2185,\n-        \"6297\": [2203, 2220],\n-        \"6298\": 2203,\n-        \"6299\": [2203, 2220],\n+        \"6297\": 2220,\n+        \"6299\": 2220,\n         \"63\": [15, 17, 19, 213, 788, 2184, 2185, 2186, 2188, 2190, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2218, 2220, 2222, 2226, 2227, 2228, 2230, 2232, 2235, 2241, 2246, 2271],\n-        \"630\": [2199, 2205, 2218],\n+        \"630\": 2199,\n         \"630110\": 15,\n         \"630256\": 2207,\n         \"630482\": 2207,\n         \"631\": 2199,\n         \"631095\": 2195,\n         \"6313\": 2220,\n         \"631502\": 2207,\n@@ -34101,15 +34102,14 @@\n         \"6496\": [2221, 2222],\n         \"649646\": 2207,\n         \"649682\": 28,\n         \"649711\": 2212,\n         \"649727\": 2191,\n         \"649748\": 2186,\n         \"64bit\": 2298,\n-        \"64ec62289cb4\": 2203,\n         \"65\": [17, 19, 259, 890, 2184, 2185, 2186, 2188, 2190, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2205, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2218, 2220, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2255, 2271],\n         \"650\": [2199, 2298],\n         \"65000000\": [176, 179, 754, 757, 1242, 1243],\n         \"6504\": 2220,\n         \"650762\": 2199,\n         \"650776\": 2202,\n         \"650794\": [121, 696],\n@@ -34466,14 +34466,15 @@\n         \"689\": 2199,\n         \"6893\": 2220,\n         \"6894\": 2220,\n         \"689569\": 2207,\n         \"6897\": 2221,\n         \"6899\": 2220,\n         \"69\": [17, 19, 188, 189, 764, 765, 1433, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2205, 2207, 2208, 2209, 2210, 2211, 2212, 2214, 2218, 2220, 2222, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2271],\n+        \"690\": 2218,\n         \"6900\": 2220,\n         \"6902\": 2199,\n         \"690288\": 2191,\n         \"6903\": 2199,\n         \"6904\": [2199, 2220],\n         \"690438\": 2191,\n         \"690539\": 2235,\n@@ -34492,28 +34493,26 @@\n         \"691592\": 2207,\n         \"691944\": 2207,\n         \"692\": 2199,\n         \"6923\": 2220,\n         \"692424\": 2186,\n         \"692498\": 2207,\n         \"6926\": 2220,\n-        \"692651\": 2228,\n         \"6927\": 2220,\n         \"6929\": 2277,\n         \"693\": [1433, 2199],\n         \"6930\": 2230,\n         \"693043\": 2210,\n         \"6932\": 2222,\n         \"693205\": [2184, 2214],\n         \"693429\": 28,\n         \"6937\": 2221,\n         \"693884\": 2210,\n         \"6939\": 2220,\n         \"694\": 2199,\n-        \"694263\": 2228,\n         \"694268\": 28,\n         \"6945\": 2241,\n         \"694592\": 2207,\n         \"695\": 2199,\n         \"6951\": 2220,\n         \"695148\": 2186,\n         \"6952\": 2220,\n@@ -35401,15 +35400,15 @@\n         \"809185\": 2219,\n         \"8092\": 2222,\n         \"809797\": 2207,\n         \"809829\": 2207,\n         \"809926\": 2207,\n         \"80px\": 2207,\n         \"81\": [15, 187, 763, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246, 2249, 2271],\n-        \"810\": [182, 760, 2193, 2200, 2298],\n+        \"810\": [182, 760, 2200, 2298],\n         \"8100\": 2199,\n         \"8103\": 2222,\n         \"810332\": 2207,\n         \"810340\": 2186,\n         \"810847\": 2195,\n         \"811\": [2200, 2298],\n         \"8110935116651191\": 2186,\n@@ -35652,15 +35651,15 @@\n         \"848896\": 2193,\n         \"848974\": 2197,\n         \"849\": [16, 17, 18, 19, 2199, 2235],\n         \"8494\": 2223,\n         \"8496\": 2241,\n         \"84960\": 2210,\n         \"849980\": 2195,\n-        \"85\": [182, 190, 193, 718, 760, 766, 768, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246],\n+        \"85\": [182, 190, 193, 718, 760, 766, 768, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2218, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246],\n         \"850\": [16, 17, 18, 19, 2199, 2235],\n         \"850083\": 2207,\n         \"8501\": 2222,\n         \"850229\": 2235,\n         \"850287\": 2207,\n         \"8504\": 2202,\n         \"850458\": 2207,\n@@ -35729,15 +35728,15 @@\n         \"8592\": 2223,\n         \"8594\": 2265,\n         \"859511\": 2207,\n         \"859588\": [2220, 2228, 2230, 2231],\n         \"8596\": 2232,\n         \"859691\": 2191,\n         \"85a3\": 2241,\n-        \"86\": [16, 1433, 2184, 2185, 2186, 2188, 2191, 2193, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246],\n+        \"86\": [16, 1433, 2184, 2185, 2186, 2188, 2191, 2195, 2197, 2199, 2200, 2201, 2202, 2204, 2207, 2208, 2209, 2210, 2211, 2212, 2218, 2222, 2223, 2226, 2228, 2230, 2232, 2235, 2241, 2246],\n         \"860\": [182, 760, 2199],\n         \"860059\": 2204,\n         \"8601\": [662, 923, 983, 2199, 2209, 2210, 2230, 2235, 2241, 2271, 2277, 2283, 2298],\n         \"8602\": 2224,\n         \"860312\": 2199,\n         \"8607\": 2223,\n         \"860736\": 15,\n@@ -36064,23 +36063,25 @@\n         \"901\": 2199,\n         \"9011\": 2224,\n         \"9012\": 2224,\n         \"9016\": 2225,\n         \"902\": 2199,\n         \"903\": 2199,\n         \"9031\": 2246,\n+        \"903170\": 2228,\n         \"903246\": 2207,\n         \"903450\": 1340,\n         \"9037\": 2225,\n         \"903794\": 2186,\n         \"904\": 2199,\n         \"9046\": 2277,\n+        \"904676\": 2228,\n         \"904807\": 2191,\n         \"9049\": 2225,\n-        \"905\": [2193, 2199],\n+        \"905\": 2199,\n         \"905029\": 2207,\n         \"905122\": 2199,\n         \"9052\": 2230,\n         \"9054\": 2226,\n         \"9057\": 2227,\n         \"905793e\": 2204,\n         \"905836\": 2215,\n@@ -36802,15 +36803,15 @@\n         \"__eq__\": [1031, 1068, 2186, 2246, 2289, 2307],\n         \"__finalize__\": [2192, 2194, 2197, 2199, 2218, 2220, 2298],\n         \"__floordiv__\": [2241, 2307],\n         \"__from_arrow__\": [10, 1068, 2299, 2302],\n         \"__fspath__\": 2238,\n         \"__func__\": 2202,\n         \"__getattr__\": [15, 2199, 2218],\n-        \"__getattribute__\": [10, 2203, 2294],\n+        \"__getattribute__\": [10, 2294],\n         \"__getitem__\": [2, 203, 1031, 1064, 1387, 2185, 2191, 2193, 2194, 2197, 2217, 2225, 2226, 2246, 2249, 2254, 2257, 2265, 2271, 2274, 2277, 2283, 2286, 2289, 2294, 2295, 2297, 2298, 2300, 2301, 2302, 2306, 2307, 2308],\n         \"__getstate__\": 2218,\n         \"__git_version__\": 2246,\n         \"__globally__\": 2190,\n         \"__gt__\": 2188,\n         \"__hash__\": [1068, 2246, 2302],\n         \"__index_level_\": 9,\n@@ -36844,15 +36845,14 @@\n         \"__str__\": 2217,\n         \"__sub__\": 2241,\n         \"__subclasses__\": 2186,\n         \"__truediv__\": 2307,\n         \"__unicode__\": [2217, 2220, 2249],\n         \"__version__\": [5, 2199],\n         \"__xor__\": 2298,\n-        \"_accessor\": 2203,\n         \"_accumul\": [1031, 2298],\n         \"_add_arithmetic_op\": 10,\n         \"_add_comparison_op\": 10,\n         \"_add_offset\": 2210,\n         \"_add_timedeltalike_scalar\": 2210,\n         \"_allows_duplicate_label\": 2192,\n         \"_array_strptime_with_fallback\": 2210,\n@@ -36866,15 +36866,14 @@\n         \"_bootstrap\": [2199, 2203, 2212, 2298],\n         \"_buffer\": [16, 17, 18, 19, 2199, 2235],\n         \"_built_with_meson\": 5,\n         \"_cacheabl\": 2246,\n         \"_call_chain\": [16, 17, 18, 19, 2199, 2235],\n         \"_call_with_frames_remov\": 2199,\n         \"_caller\": 153,\n-        \"_can_hold_identifiers_and_holds_nam\": 2203,\n         \"_check_deprecated_callable_usag\": [2185, 2197],\n         \"_check_for_loc\": 2193,\n         \"_check_indexing_error\": [2185, 2191, 2194],\n         \"_check_is_chained_assignment_poss\": 2197,\n         \"_check_setitem_copi\": 2197,\n         \"_check_tokenize_statu\": 2199,\n         \"_cmp_method\": 2186,\n@@ -36960,15 +36959,14 @@\n         \"_hash\": 2235,\n         \"_hash_pandas_object\": 1043,\n         \"_ilocindex\": 2197,\n         \"_import_class\": 2199,\n         \"_indexed_sam\": [2186, 2218],\n         \"_indexslic\": 440,\n         \"_inferred_dtyp\": [2208, 2249],\n-        \"_info_axi\": 2203,\n         \"_internal_nam\": 10,\n         \"_internal_names_set\": 10,\n         \"_is_boolean\": [1056, 1068, 1081],\n         \"_is_copi\": 2197,\n         \"_is_mixed_typ\": 2197,\n         \"_is_numer\": [1068, 2246, 2298],\n         \"_is_scalar_access\": [2185, 2197],\n@@ -37609,15 +37607,15 @@\n         \"attende\": 0,\n         \"attent\": [3, 10, 2197, 2205, 2207, 2214, 2216],\n         \"attr\": [15, 227, 705, 802, 1394, 1423, 1475, 1487, 2169, 2180, 2192, 2199, 2203, 2241, 2265, 2277, 2289, 2298, 2302, 2307],\n         \"attr_col\": [272, 2199],\n         \"attribut\": [4, 9, 10, 15, 24, 25, 31, 37, 38, 39, 46, 49, 63, 85, 107, 142, 153, 203, 210, 230, 249, 257, 266, 267, 272, 280, 286, 334, 337, 341, 342, 343, 344, 354, 386, 423, 441, 442, 443, 444, 445, 457, 459, 478, 487, 494, 509, 510, 514, 516, 532, 538, 540, 568, 573, 596, 629, 783, 784, 804, 882, 896, 914, 915, 916, 927, 930, 938, 953, 1027, 1028, 1029, 1030, 1031, 1068, 1069, 1071, 1072, 1078, 1081, 1090, 1091, 1117, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1140, 1141, 1142, 1143, 1144, 1164, 1168, 1202, 1203, 1221, 1263, 1264, 1342, 1345, 1347, 1374, 1387, 1391, 1394, 1395, 1396, 1402, 1403, 1404, 1405, 1413, 1414, 1420, 1421, 1422, 1424, 1432, 1433, 1435, 1436, 1475, 1487, 1488, 1490, 1494, 1495, 1496, 1506, 1524, 1542, 1560, 1578, 1598, 1620, 1637, 1657, 1677, 1699, 1720, 1741, 1758, 1776, 1793, 1815, 1839, 1857, 1876, 1894, 1912, 1930, 1947, 1964, 1982, 2000, 2018, 2036, 2054, 2072, 2090, 2108, 2127, 2145, 2167, 2172, 2184, 2185, 2192, 2193, 2196, 2199, 2202, 2203, 2204, 2206, 2208, 2210, 2211, 2214, 2216, 2217, 2218, 2220, 2221, 2222, 2223, 2224, 2225, 2226, 2228, 2229, 2230, 2231, 2232, 2235, 2236, 2238, 2239, 2241, 2242, 2246, 2249, 2251, 2253, 2254, 2257, 2259, 2263, 2265, 2271, 2273, 2277, 2278, 2280, 2283, 2289, 2292, 2293, 2295, 2297, 2298, 2302, 2307],\n         \"attribute2\": [1395, 1396, 1413, 1414],\n         \"attributeconflictwarn\": [2217, 2294],\n-        \"attributeerror\": [10, 15, 845, 1069, 1071, 1072, 2203, 2220, 2221, 2222, 2225, 2226, 2227, 2228, 2230, 2231, 2232, 2233, 2235, 2238, 2239, 2241, 2246, 2247, 2249, 2250, 2265, 2266, 2269, 2271, 2274, 2275, 2276, 2278, 2279, 2281, 2283, 2286, 2289, 2290, 2294, 2295, 2298, 2301, 2302, 2307, 2308],\n+        \"attributeerror\": [10, 15, 845, 1069, 1071, 1072, 2220, 2221, 2222, 2225, 2226, 2227, 2228, 2230, 2231, 2232, 2233, 2235, 2238, 2239, 2241, 2246, 2247, 2249, 2250, 2265, 2266, 2269, 2271, 2274, 2275, 2276, 2278, 2279, 2281, 2283, 2286, 2289, 2290, 2294, 2295, 2298, 2301, 2302, 2307, 2308],\n         \"attrs_onli\": [1487, 2199],\n         \"audienc\": 2207,\n         \"audit\": [16, 17, 18, 19, 2199, 2222, 2235],\n         \"aug\": [1699, 1720, 2210, 2213],\n         \"augment\": [2225, 2231, 2277],\n         \"augspurg\": [35, 2247, 2248],\n         \"august\": [586, 2210, 2213],\n@@ -37738,15 +37736,15 @@\n         \"barboursvil\": 2199,\n         \"bare\": [2, 2199, 2222, 2241, 2277],\n         \"barf\": 2217,\n         \"barh\": [26, 186, 188, 762, 764, 1188, 1249, 2211, 2220, 2221, 2228, 2260, 2294],\n         \"bark\": 1365,\n         \"barplot\": 2222,\n         \"barycentr\": [146, 720, 1280, 2201, 2218],\n-        \"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],\n+        \"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],\n         \"base_dtyp\": 2199,\n         \"base_pars\": 2199,\n         \"base_typ\": [2194, 2201, 2203, 2294, 2302, 2307],\n         \"basebal\": [15, 2186, 2191, 2197, 2227, 2231],\n         \"baseblockmanag\": [2197, 2199, 2298],\n         \"basebooleanreducetest\": 2307,\n         \"basebuff\": [16, 17, 18, 19, 2199, 2235],\n@@ -38272,15 +38270,15 @@\n         \"cheat\": [21, 2234],\n         \"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],\n         \"check_array_index\": 2172,\n         \"check_categor\": [1494, 1495, 1496, 2242],\n         \"check_category_ord\": 1496,\n         \"check_column_typ\": 1494,\n         \"check_datetimelike_compat\": [1494, 1496],\n-        \"check_dict_or_set_index\": [2193, 2197],\n+        \"check_dict_or_set_index\": 2197,\n         \"check_dtyp\": [1493, 1494, 1496, 2271, 2272, 2299],\n         \"check_dtype_backend\": 2199,\n         \"check_exact\": [1493, 1494, 1495, 1496, 2272, 2277, 2307, 2308],\n         \"check_extens\": 2294,\n         \"check_flag\": [1494, 1496, 2290],\n         \"check_frame_typ\": 1494,\n         \"check_freq\": [1494, 1496, 2278],\n@@ -40270,15 +40268,15 @@\n         \"get_indexer_non_uniqu\": [379, 2192, 2197, 2238, 2243, 2246, 2249, 2265, 2277, 2289],\n         \"get_indexer_nonuniqu\": 2302,\n         \"get_ipython\": 2193,\n         \"get_item\": [2191, 2194],\n         \"get_jit_argu\": 2212,\n         \"get_letter_typ\": 2195,\n         \"get_level_valu\": [1416, 2185, 2218, 2220, 2228, 2232, 2241, 2246, 2253, 2256],\n-        \"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],\n+        \"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],\n         \"get_loc_level\": 2246,\n         \"get_local\": 2265,\n         \"get_local_scop\": 2193,\n         \"get_method\": [16, 17, 18, 19, 2199, 2235],\n         \"get_near_stock_pric\": [2216, 2223],\n         \"get_offset\": [2265, 2298],\n         \"get_offset_nam\": [2230, 2238],\n@@ -40838,15 +40836,15 @@\n         \"inject\": [120, 1387],\n         \"inkwarg\": 2199,\n         \"inlin\": [3, 2196, 2199, 2207, 2218, 2229, 2246],\n         \"inner\": [16, 17, 19, 25, 30, 74, 96, 110, 153, 169, 241, 279, 404, 583, 619, 821, 1146, 1446, 1448, 2186, 2193, 2200, 2204, 2208, 2220, 2246, 2254, 2283, 2289, 2307],\n         \"inner_join\": [16, 17, 19],\n         \"innermost\": [247, 880, 1478, 2231],\n         \"inplac\": [16, 17, 18, 19, 87, 89, 92, 111, 112, 114, 120, 124, 125, 146, 163, 181, 203, 209, 210, 212, 214, 228, 233, 234, 284, 370, 418, 421, 483, 500, 598, 601, 616, 633, 634, 636, 700, 701, 720, 741, 759, 783, 784, 787, 789, 807, 808, 912, 1166, 1167, 1223, 1224, 1280, 1387, 2190, 2192, 2214, 2215, 2218, 2220, 2221, 2222, 2228, 2229, 2230, 2231, 2235, 2238, 2241, 2246, 2265, 2271, 2273, 2275, 2276, 2277, 2278, 2289, 2290, 2291, 2292, 2293, 2295, 2297, 2298, 2302, 2307],\n-        \"input\": [2, 3, 10, 13, 20, 24, 30, 31, 34, 49, 56, 63, 68, 69, 76, 78, 81, 85, 91, 92, 94, 97, 99, 100, 107, 108, 109, 120, 126, 129, 131, 134, 141, 143, 160, 162, 163, 171, 173, 183, 197, 199, 204, 206, 211, 212, 213, 215, 216, 217, 218, 219, 220, 221, 222, 227, 230, 233, 234, 244, 246, 256, 259, 264, 270, 273, 275, 278, 281, 284, 286, 346, 351, 354, 378, 380, 405, 415, 425, 426, 459, 465, 489, 499, 540, 573, 577, 578, 585, 596, 603, 616, 617, 620, 622, 629, 630, 631, 694, 702, 706, 707, 709, 710, 713, 717, 719, 734, 738, 739, 740, 741, 747, 749, 750, 753, 761, 773, 777, 780, 785, 787, 788, 790, 791, 792, 793, 795, 796, 797, 802, 804, 856, 877, 878, 888, 890, 893, 900, 901, 904, 912, 916, 927, 930, 938, 953, 1031, 1076, 1078, 1090, 1116, 1117, 1118, 1121, 1123, 1124, 1125, 1152, 1154, 1155, 1156, 1164, 1202, 1203, 1204, 1211, 1213, 1221, 1230, 1264, 1298, 1299, 1305, 1306, 1308, 1322, 1323, 1325, 1342, 1343, 1354, 1389, 1390, 1392, 1393, 1395, 1396, 1397, 1398, 1403, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1413, 1414, 1417, 1418, 1430, 1433, 1441, 1442, 1449, 1450, 1458, 1467, 1469, 1470, 1475, 1482, 1486, 1487, 1498, 1499, 1500, 2163, 2172, 2184, 2185, 2186, 2187, 2188, 2191, 2193, 2194, 2195, 2196, 2197, 2199, 2200, 2201, 2203, 2204, 2208, 2209, 2210, 2211, 2212, 2214, 2215, 2216, 2218, 2219, 2220, 2221, 2222, 2223, 2225, 2226, 2227, 2228, 2230, 2231, 2232, 2233, 2234, 2235, 2236, 2238, 2241, 2242, 2246, 2249, 2250, 2257, 2263, 2264, 2265, 2267, 2269, 2271, 2272, 2273, 2274, 2275, 2277, 2278, 2283, 2284, 2287, 2289, 2291, 2292, 2293, 2294, 2298, 2299, 2302, 2306, 2307, 2308, 2309],\n+        \"input\": [2, 3, 10, 13, 20, 24, 30, 31, 34, 49, 56, 63, 68, 69, 76, 78, 81, 85, 91, 92, 94, 97, 99, 100, 107, 108, 109, 120, 126, 129, 131, 134, 141, 143, 160, 162, 163, 171, 173, 183, 197, 199, 204, 206, 211, 212, 213, 215, 216, 217, 218, 219, 220, 221, 222, 227, 230, 233, 234, 244, 246, 256, 259, 264, 270, 273, 275, 278, 281, 284, 286, 346, 351, 354, 378, 380, 405, 415, 425, 426, 459, 465, 489, 499, 540, 573, 577, 578, 585, 596, 603, 616, 617, 620, 622, 629, 630, 631, 694, 702, 706, 707, 709, 710, 713, 717, 719, 734, 738, 739, 740, 741, 747, 749, 750, 753, 761, 773, 777, 780, 785, 787, 788, 790, 791, 792, 793, 795, 796, 797, 802, 804, 856, 877, 878, 888, 890, 893, 900, 901, 904, 912, 916, 927, 930, 938, 953, 1031, 1076, 1078, 1090, 1116, 1117, 1118, 1121, 1123, 1124, 1125, 1152, 1154, 1155, 1156, 1164, 1202, 1203, 1204, 1211, 1213, 1221, 1230, 1264, 1298, 1299, 1305, 1306, 1308, 1322, 1323, 1325, 1342, 1343, 1354, 1389, 1390, 1392, 1393, 1395, 1396, 1397, 1398, 1403, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1413, 1414, 1417, 1418, 1430, 1433, 1441, 1442, 1449, 1450, 1458, 1467, 1469, 1470, 1475, 1482, 1486, 1487, 1498, 1499, 1500, 2163, 2172, 2184, 2185, 2186, 2187, 2188, 2191, 2193, 2194, 2195, 2196, 2197, 2199, 2200, 2201, 2204, 2208, 2209, 2210, 2211, 2212, 2214, 2215, 2216, 2218, 2219, 2220, 2221, 2222, 2223, 2225, 2226, 2227, 2228, 2230, 2231, 2232, 2233, 2234, 2235, 2236, 2238, 2241, 2242, 2246, 2249, 2250, 2257, 2263, 2264, 2265, 2267, 2269, 2271, 2272, 2273, 2274, 2275, 2277, 2278, 2283, 2284, 2287, 2289, 2291, 2292, 2293, 2294, 2298, 2299, 2302, 2306, 2307, 2308, 2309],\n         \"input_arrai\": 2199,\n         \"insec\": 873,\n         \"insensit\": [533, 857, 1469, 1486, 2202, 2221, 2277],\n         \"insert\": [2, 34, 63, 214, 255, 258, 267, 420, 789, 799, 821, 889, 896, 1061, 1345, 1391, 1416, 1488, 1490, 2185, 2186, 2191, 2193, 2195, 2196, 2202, 2207, 2217, 2218, 2219, 2220, 2221, 2222, 2225, 2226, 2228, 2229, 2233, 2238, 2242, 2246, 2249, 2265, 2271, 2277, 2283, 2289, 2293, 2294, 2298, 2302, 2304, 2306, 2307],\n         \"insert_on_conflict_noth\": [267, 896],\n         \"insert_on_conflict_upd\": [267, 896],\n         \"insid\": [2, 8, 13, 22, 25, 77, 89, 124, 146, 203, 251, 259, 375, 466, 601, 700, 720, 884, 890, 1031, 1054, 1118, 1280, 1469, 1486, 1498, 2186, 2193, 2194, 2196, 2197, 2199, 2201, 2227, 2241, 2246, 2249, 2261, 2263, 2264, 2265, 2271, 2307],\n@@ -40972,15 +40970,15 @@\n         \"ip\": [10, 2241],\n         \"ipaddress\": 10,\n         \"iparrai\": 2241,\n         \"ipc\": 2199,\n         \"ipi\": 2202,\n         \"ipv4address\": 10,\n         \"ipv6\": [10, 1031],\n-        \"ipython\": [4, 26, 257, 1069, 1071, 1072, 1345, 1391, 1488, 1490, 2184, 2186, 2193, 2194, 2196, 2197, 2199, 2203, 2207, 2219, 2222, 2227, 2230, 2232, 2235, 2236, 2242, 2246, 2247, 2251, 2257, 2258, 2265],\n+        \"ipython\": [4, 26, 257, 1069, 1071, 1072, 1345, 1391, 1488, 1490, 2184, 2186, 2193, 2194, 2196, 2197, 2199, 2207, 2219, 2222, 2227, 2230, 2232, 2235, 2236, 2242, 2246, 2247, 2251, 2257, 2258, 2265],\n         \"ipythondir\": 2202,\n         \"ipywidget\": 2207,\n         \"iqr\": [91, 190, 766, 1458],\n         \"iri\": [1455, 1461, 2191, 2211, 2225],\n         \"irow\": [2216, 2228, 2235, 2257],\n         \"irregular\": [15, 2210, 2234, 2235, 2261, 2275, 2277],\n         \"irrelev\": [0, 2298],\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/user_guide/advanced.html", "source2": "./usr/share/doc/python-pandas-doc/html/user_guide/advanced.html", "unified_diff": "@@ -1847,25 +1847,25 @@\n <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>\n \n <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>\n \n <span class=\"gp\">In [143]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> arr[indexer]\n <span class=\"gp\">   .....: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> arr.take(indexer, axis=0)\n <span class=\"gp\">   .....: </span>\n-<span class=\"go\">499 us +- 2.13 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>\n-<span class=\"go\">217 us +- 7.17 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>\n+<span class=\"go\">305 us +- 59.8 ns per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>\n+<span class=\"go\">124 us +- 177 ns per loop (mean +- std. dev. of 7 runs, 10,000 loops each)</span>\n </pre></div>\n </div>\n <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>\n \n <span class=\"gp\">In [145]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> ser.iloc[indexer]\n <span class=\"gp\">   .....: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> ser.take(indexer)\n <span class=\"gp\">   .....: </span>\n-<span class=\"go\">269 us +- 1.29 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>\n-<span class=\"go\">259 us +- 2.03 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>\n+<span class=\"go\">199 us +- 52.1 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)</span>\n+<span class=\"go\">369 us +- 63.9 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)</span>\n </pre></div>\n </div>\n </section>\n <section id=\"index-types\">\n <span id=\"advanced-index-types\"></span><h2>Index types<a class=\"headerlink\" href=\"#index-types\" title=\"Link to this heading\">#</a></h2>\n <p>We have discussed <code class=\"docutils literal notranslate\"><span class=\"pre\">MultiIndex</span></code> in the previous sections pretty extensively.\n 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>,\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -1245,23 +1245,23 @@\n In [141]: indexer = np.arange(10000)\n \n In [142]: random.shuffle(indexer)\n \n In [143]: %timeit arr[indexer]\n    .....: %timeit arr.take(indexer, axis=0)\n    .....:\n-499 us +- 2.13 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)\n-217 us +- 7.17 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)\n+305 us +- 59.8 ns per loop (mean +- std. dev. of 7 runs, 1,000 loops each)\n+124 us +- 177 ns per loop (mean +- std. dev. of 7 runs, 10,000 loops each)\n In [144]: ser = pd.Series(arr[:, 0])\n \n In [145]: %timeit ser.iloc[indexer]\n    .....: %timeit ser.take(indexer)\n    .....:\n-269 us +- 1.29 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)\n-259 us +- 2.03 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)\n+199 us +- 52.1 us per loop (mean +- std. dev. of 7 runs, 10,000 loops each)\n+369 us +- 63.9 us per loop (mean +- std. dev. of 7 runs, 1,000 loops each)\n *\b**\b**\b**\b**\b* I\bIn\bnd\bde\bex\bx t\bty\byp\bpe\bes\bs_\b#\b# *\b**\b**\b**\b**\b*\n We have discussed MultiIndex in the previous sections pretty extensively.\n Documentation about DatetimeIndex and PeriodIndex are shown _\bh_\be_\br_\be, and\n documentation about TimedeltaIndex is found _\bh_\be_\br_\be.\n In the following sub-sections we will highlight some other index types.\n *\b**\b**\b**\b* C\bCa\bat\bte\beg\bgo\bor\bri\bic\bca\bal\blI\bIn\bnd\bde\bex\bx_\b#\b# *\b**\b**\b**\b*\n _\bC_\ba_\bt_\be_\bg_\bo_\br_\bi_\bc_\ba_\bl_\bI_\bn_\bd_\be_\bx is a type of index that is useful for supporting indexing with\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/user_guide/enhancingperf.html", "source2": "./usr/share/doc/python-pandas-doc/html/user_guide/enhancingperf.html", "unified_diff": "@@ -592,31 +592,31 @@\n <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>\n <span class=\"gp\">   ...: </span>    <span class=\"k\">return</span> <span class=\"n\">s</span> <span class=\"o\">*</span> <span class=\"n\">dx</span>\n <span class=\"gp\">   ...: </span>\n </pre></div>\n </div>\n <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>\n <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)\n-<span class=\"go\">150 ms +- 5.27 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n+<span class=\"go\">177 ms +- 20.7 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n </pre></div>\n </div>\n <p>Let\u2019s take a look and see where the time is spent during this operation\n using the <a class=\"reference external\" href=\"https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-prun\">prun ipython magic function</a>:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"go\"># most time consuming 4 calls</span>\n <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\n-<span class=\"go\">         605946 function calls (605928 primitive calls) in 1.021 seconds</span>\n+<span class=\"go\">         605946 function calls (605928 primitive calls) in 0.296 seconds</span>\n \n <span class=\"go\">   Ordered by: internal time</span>\n <span class=\"go\">   List reduced from 159 to 4 due to restriction &lt;4&gt;</span>\n \n <span class=\"go\">   ncalls  tottime  percall  cumtime  percall filename:lineno(function)</span>\n-<span class=\"go\">     1000    0.531    0.001    0.905    0.001 &lt;ipython-input-4-c2a74e076cf0&gt;:1(integrate_f)</span>\n-<span class=\"go\">   552423    0.374    0.000    0.374    0.000 &lt;ipython-input-3-c138bdd570e3&gt;:1(f)</span>\n-<span class=\"go\">     3000    0.019    0.000    0.082    0.000 series.py:1095(__getitem__)</span>\n-<span class=\"go\">    16098    0.014    0.000    0.018    0.000 {built-in method builtins.isinstance}</span>\n+<span class=\"go\">     1000    0.178    0.000    0.259    0.000 &lt;ipython-input-4-c2a74e076cf0&gt;:1(integrate_f)</span>\n+<span class=\"go\">   552423    0.080    0.000    0.080    0.000 &lt;ipython-input-3-c138bdd570e3&gt;:1(f)</span>\n+<span class=\"go\">     3000    0.006    0.000    0.025    0.000 series.py:1095(__getitem__)</span>\n+<span class=\"go\">     3000    0.004    0.000    0.011    0.000 series.py:1220(_get_value)</span>\n </pre></div>\n </div>\n <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>,\n hence we\u2019ll concentrate our efforts cythonizing these two functions.</p>\n </section>\n <section id=\"plain-cython\">\n <span id=\"enhancingperf-plain\"></span><h3>Plain Cython<a class=\"headerlink\" href=\"#plain-cython\" title=\"Link to this heading\">#</a></h3>\n@@ -634,15 +634,15 @@\n <span class=\"gp\">   ...: </span>    for i in range(N):\n <span class=\"gp\">   ...: </span>        s += f_plain(a + i * dx)\n <span class=\"gp\">   ...: </span>    return s * dx\n <span class=\"gp\">   ...: </span>\n </pre></div>\n </div>\n <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)\n-<span class=\"go\">130 ms +- 359 us per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n+<span class=\"go\">182 ms +- 21.5 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n </pre></div>\n </div>\n <p>This has improved the performance compared to the pure Python approach by one-third.</p>\n </section>\n <section id=\"declaring-c-types\">\n <span id=\"enhancingperf-type\"></span><h3>Declaring C types<a class=\"headerlink\" href=\"#declaring-c-types\" title=\"Link to this heading\">#</a></h3>\n <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>\n@@ -658,36 +658,36 @@\n <span class=\"gp\">   ....: </span>    for i in range(N):\n <span class=\"gp\">   ....: </span>        s += f_typed(a + i * dx)\n <span class=\"gp\">   ....: </span>    return s * dx\n <span class=\"gp\">   ....: </span>\n </pre></div>\n </div>\n <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)\n-<span class=\"go\">22.8 ms +- 148 us per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n+<span class=\"go\">20.3 ms +- 93.9 us per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n </pre></div>\n </div>\n <p>Annotating the functions with C types yields an over ten times performance improvement compared to\n the original Python implementation.</p>\n </section>\n <section id=\"using-ndarray\">\n <span id=\"enhancingperf-ndarray\"></span><h3>Using ndarray<a class=\"headerlink\" href=\"#using-ndarray\" title=\"Link to this heading\">#</a></h3>\n <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\n the index and the series (three times for each row). These Python function calls are expensive and\n can be improved by passing an <code class=\"docutils literal notranslate\"><span class=\"pre\">np.ndarray</span></code>.</p>\n <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)\n-<span class=\"go\">         52523 function calls (52505 primitive calls) in 0.119 seconds</span>\n+<span class=\"go\">         52523 function calls (52505 primitive calls) in 0.066 seconds</span>\n \n <span class=\"go\">   Ordered by: internal time</span>\n <span class=\"go\">   List reduced from 157 to 4 due to restriction &lt;4&gt;</span>\n \n <span class=\"go\">   ncalls  tottime  percall  cumtime  percall filename:lineno(function)</span>\n-<span class=\"go\">     3000    0.019    0.000    0.084    0.000 series.py:1095(__getitem__)</span>\n-<span class=\"go\">    16098    0.014    0.000    0.018    0.000 {built-in method builtins.isinstance}</span>\n-<span class=\"go\">     3000    0.013    0.000    0.034    0.000 series.py:1220(_get_value)</span>\n-<span class=\"go\">     3000    0.012    0.000    0.021    0.000 indexing.py:2765(check_dict_or_set_indexers)</span>\n+<span class=\"go\">     3000    0.010    0.000    0.042    0.000 series.py:1095(__getitem__)</span>\n+<span class=\"go\">     3000    0.007    0.000    0.019    0.000 series.py:1220(_get_value)</span>\n+<span class=\"go\">     3000    0.007    0.000    0.008    0.000 base.py:3777(get_loc)</span>\n+<span class=\"go\">    16098    0.006    0.000    0.008    0.000 {built-in method builtins.isinstance}</span>\n </pre></div>\n </div>\n <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>\n <span class=\"gp\">   ....: </span>cimport numpy as np\n <span class=\"gp\">   ....: </span>import numpy as np\n <span class=\"gp\">   ....: </span>cdef double f_typed(double x) except? -2:\n <span class=\"gp\">   ....: </span>    return x * (x - 1)\n@@ -722,33 +722,33 @@\n </div>\n <p>This implementation creates an array of zeros and inserts the result\n 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\n 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>\n <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>\n calls are needed to utilize this function.</p>\n <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())\n-<span class=\"go\">2.42 ms +- 2.27 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n+<span class=\"go\">3.42 ms +- 20 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n </pre></div>\n </div>\n <p>Performance has improved from the prior implementation by almost ten times.</p>\n </section>\n <section id=\"disabling-compiler-directives\">\n <span id=\"enhancingperf-boundswrap\"></span><h3>Disabling compiler directives<a class=\"headerlink\" href=\"#disabling-compiler-directives\" title=\"Link to this heading\">#</a></h3>\n <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\u2019s <code class=\"docutils literal notranslate\"><span class=\"pre\">boundscheck</span></code>\n and <code class=\"docutils literal notranslate\"><span class=\"pre\">wraparound</span></code> checks can yield more performance.</p>\n <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())\n-<span class=\"go\">         78 function calls in 0.003 seconds</span>\n+<span class=\"go\">         78 function calls in 0.004 seconds</span>\n \n <span class=\"go\">   Ordered by: internal time</span>\n <span class=\"go\">   List reduced from 21 to 4 due to restriction &lt;4&gt;</span>\n \n <span class=\"go\">   ncalls  tottime  percall  cumtime  percall filename:lineno(function)</span>\n-<span class=\"go\">        1    0.002    0.002    0.003    0.003 &lt;string&gt;:1(&lt;module&gt;)</span>\n+<span class=\"go\">        1    0.003    0.003    0.004    0.004 &lt;string&gt;:1(&lt;module&gt;)</span>\n <span class=\"go\">        1    0.000    0.000    0.000    0.000 {method &#39;disable&#39; of &#39;_lsprof.Profiler&#39; objects}</span>\n-<span class=\"go\">        1    0.000    0.000    0.003    0.003 {built-in method builtins.exec}</span>\n+<span class=\"go\">        1    0.000    0.000    0.004    0.004 {built-in method builtins.exec}</span>\n <span class=\"go\">        3    0.000    0.000    0.000    0.000 frame.py:4062(__getitem__)</span>\n </pre></div>\n </div>\n <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>\n <span class=\"gp\">   ....: </span>cimport cython\n <span class=\"gp\">   ....: </span>cimport numpy as np\n <span class=\"gp\">   ....: </span>import numpy as np\n@@ -1180,19 +1180,19 @@\n 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\n optional dependency <code class=\"docutils literal notranslate\"><span class=\"pre\">numexpr</span></code> to be installed.</p>\n <p>The <code class=\"docutils literal notranslate\"><span class=\"pre\">'python'</span></code> engine is generally <em>not</em> useful except for testing\n other evaluation engines against it. You will achieve <strong>no</strong> performance\n 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\n incur a performance hit.</p>\n <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\n-<span class=\"go\">540 ms +- 41.9 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">622 ms +- 31 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n </pre></div>\n </div>\n <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;)\n-<span class=\"go\">513 ms +- 19.9 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">585 ms +- 27.6 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n </pre></div>\n </div>\n </section>\n <section id=\"the-dataframe-eval-method\">\n <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>\n <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\n evaluate an expression in the \u201ccontext\u201d 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>\n@@ -1307,39 +1307,39 @@\n <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>\n \n <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>\n </pre></div>\n </div>\n <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>\n <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\n-<span class=\"go\">545 ms +- 52.5 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">611 ms +- 45.4 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n </pre></div>\n </div>\n <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;)\n-<span class=\"go\">163 ms +- 7.86 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n+<span class=\"go\">201 ms +- 28.4 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n </pre></div>\n </div>\n <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>\n <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)\n-<span class=\"go\">32.6 ms +- 431 us per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n+<span class=\"go\">35.7 ms +- 1.29 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n </pre></div>\n </div>\n <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;)\n-<span class=\"go\">9.13 ms +- 30.6 us per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n+<span class=\"go\">17.2 ms +- 1.32 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)</span>\n </pre></div>\n </div>\n <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>\n <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>\n \n <span class=\"gp\">In [65]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> df1 + df2 + df3 + df4 + s\n-<span class=\"go\">810 ms +- 23.1 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">1.06 s +- 54.5 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n </pre></div>\n </div>\n <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;)\n-<span class=\"go\">155 ms +- 4.85 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)</span>\n+<span class=\"go\">229 ms +- 42.6 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n </pre></div>\n </div>\n <div class=\"admonition note\">\n <p class=\"admonition-title\">Note</p>\n <p>Operations such as</p>\n <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>\n <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>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -110,33 +110,32 @@\n    ...:     dx = (b - a) / N\n    ...:     for i in range(N):\n    ...:         s += f(a + i * dx)\n    ...:     return s * dx\n    ...:\n We achieve our result by using _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\ba_\bp_\bp_\bl_\by_\b(_\b) (row-wise):\n In [5]: %timeit df.apply(lambda x: integrate_f(x[\"a\"], x[\"b\"], x[\"N\"]), axis=1)\n-150 ms +- 5.27 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+177 ms +- 20.7 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n Let\u2019s take a look and see where the time is spent during this operation using\n the _\bp_\br_\bu_\bn_\b _\bi_\bp_\by_\bt_\bh_\bo_\bn_\b _\bm_\ba_\bg_\bi_\bc_\b _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn:\n # most time consuming 4 calls\n In [6]: %prun -l 4 df.apply(lambda x: integrate_f(x[\"a\"], x[\"b\"], x[\"N\"]),\n axis=1)  # noqa E999\n-         605946 function calls (605928 primitive calls) in 1.021 seconds\n+         605946 function calls (605928 primitive calls) in 0.296 seconds\n \n    Ordered by: internal time\n    List reduced from 159 to 4 due to restriction <4>\n \n    ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n-     1000    0.531    0.001    0.905    0.001 <ipython-input-4-c2a74e076cf0>:1\n+     1000    0.178    0.000    0.259    0.000 <ipython-input-4-c2a74e076cf0>:1\n (integrate_f)\n-   552423    0.374    0.000    0.374    0.000 <ipython-input-3-c138bdd570e3>:1\n+   552423    0.080    0.000    0.080    0.000 <ipython-input-3-c138bdd570e3>:1\n (f)\n-     3000    0.019    0.000    0.082    0.000 series.py:1095(__getitem__)\n-    16098    0.014    0.000    0.018    0.000 {built-in method\n-builtins.isinstance}\n+     3000    0.006    0.000    0.025    0.000 series.py:1095(__getitem__)\n+     3000    0.004    0.000    0.011    0.000 series.py:1220(_get_value)\n By far the majority of time is spend inside either integrate_f or f, hence\n we\u2019ll concentrate our efforts cythonizing these two functions.\n *\b**\b**\b**\b* P\bPl\bla\bai\bin\bn C\bCy\byt\bth\bho\bon\bn_\b#\b# *\b**\b**\b**\b*\n First we\u2019re going to need to import the Cython magic function to IPython:\n In [7]: %load_ext Cython\n Now, let\u2019s simply copy our functions over to Cython:\n In [8]: %%cython\n@@ -147,15 +146,15 @@\n    ...:     dx = (b - a) / N\n    ...:     for i in range(N):\n    ...:         s += f_plain(a + i * dx)\n    ...:     return s * dx\n    ...:\n In [9]: %timeit df.apply(lambda x: integrate_f_plain(x[\"a\"], x[\"b\"], x[\"N\"]),\n axis=1)\n-130 ms +- 359 us per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+182 ms +- 21.5 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n This has improved the performance compared to the pure Python approach by one-\n third.\n *\b**\b**\b**\b* D\bDe\bec\bcl\bla\bar\bri\bin\bng\bg C\bC t\bty\byp\bpe\bes\bs_\b#\b# *\b**\b**\b**\b*\n We can annotate the function variables and return types as well as use cdef and\n cpdef to improve performance:\n In [10]: %%cython\n    ....: cdef double f_typed(double x) except? -2:\n@@ -167,36 +166,35 @@\n    ....:     dx = (b - a) / N\n    ....:     for i in range(N):\n    ....:         s += f_typed(a + i * dx)\n    ....:     return s * dx\n    ....:\n In [11]: %timeit df.apply(lambda x: integrate_f_typed(x[\"a\"], x[\"b\"], x[\"N\"]),\n axis=1)\n-22.8 ms +- 148 us per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+20.3 ms +- 93.9 us per loop (mean +- std. dev. of 7 runs, 10 loops each)\n Annotating the functions with C types yields an over ten times performance\n improvement compared to the original Python implementation.\n *\b**\b**\b**\b* U\bUs\bsi\bin\bng\bg n\bnd\bda\bar\brr\bra\bay\by_\b#\b# *\b**\b**\b**\b*\n When re-profiling, time is spent creating a _\bS_\be_\br_\bi_\be_\bs from each row, and calling\n __getitem__ from both the index and the series (three times for each row).\n These Python function calls are expensive and can be improved by passing an\n np.ndarray.\n In [12]: %prun -l 4 df.apply(lambda x: integrate_f_typed(x[\"a\"], x[\"b\"], x\n [\"N\"]), axis=1)\n-         52523 function calls (52505 primitive calls) in 0.119 seconds\n+         52523 function calls (52505 primitive calls) in 0.066 seconds\n \n    Ordered by: internal time\n    List reduced from 157 to 4 due to restriction <4>\n \n    ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n-     3000    0.019    0.000    0.084    0.000 series.py:1095(__getitem__)\n-    16098    0.014    0.000    0.018    0.000 {built-in method\n+     3000    0.010    0.000    0.042    0.000 series.py:1095(__getitem__)\n+     3000    0.007    0.000    0.019    0.000 series.py:1220(_get_value)\n+     3000    0.007    0.000    0.008    0.000 base.py:3777(get_loc)\n+    16098    0.006    0.000    0.008    0.000 {built-in method\n builtins.isinstance}\n-     3000    0.013    0.000    0.034    0.000 series.py:1220(_get_value)\n-     3000    0.012    0.000    0.021    0.000 indexing.py:2765\n-(check_dict_or_set_indexers)\n In [13]: %%cython\n    ....: cimport numpy as np\n    ....: import numpy as np\n    ....: cdef double f_typed(double x) except? -2:\n    ....:     return x * (x - 1)\n    ....: cpdef double integrate_f_typed(double a, double b, int N):\n    ....:     cdef int i\n@@ -237,31 +235,31 @@\n This implementation creates an array of zeros and inserts the result of\n integrate_f_typed applied over each row. Looping over an ndarray is faster in\n Cython than looping over a _\bS_\be_\br_\bi_\be_\bs object.\n Since apply_integrate_f is typed to accept an np.ndarray, _\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bn_\bu_\bm_\bp_\by_\b(_\b)\n calls are needed to utilize this function.\n In [14]: %timeit apply_integrate_f(df[\"a\"].to_numpy(), df[\"b\"].to_numpy(), df\n [\"N\"].to_numpy())\n-2.42 ms +- 2.27 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+3.42 ms +- 20 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n Performance has improved from the prior implementation by almost ten times.\n *\b**\b**\b**\b* D\bDi\bis\bsa\bab\bbl\bli\bin\bng\bg c\bco\bom\bmp\bpi\bil\ble\ber\br d\bdi\bir\bre\bec\bct\bti\biv\bve\bes\bs_\b#\b# *\b**\b**\b**\b*\n The majority of the time is now spent in apply_integrate_f. Disabling Cython\u2019s\n boundscheck and wraparound checks can yield more performance.\n In [15]: %prun -l 4 apply_integrate_f(df[\"a\"].to_numpy(), df[\"b\"].to_numpy(),\n df[\"N\"].to_numpy())\n-         78 function calls in 0.003 seconds\n+         78 function calls in 0.004 seconds\n \n    Ordered by: internal time\n    List reduced from 21 to 4 due to restriction <4>\n \n    ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n-        1    0.002    0.002    0.003    0.003 <string>:1(<module>)\n+        1    0.003    0.003    0.004    0.004 <string>:1(<module>)\n         1    0.000    0.000    0.000    0.000 {method 'disable' of\n '_lsprof.Profiler' objects}\n-        1    0.000    0.000    0.003    0.003 {built-in method builtins.exec}\n+        1    0.000    0.000    0.004    0.004 {built-in method builtins.exec}\n         3    0.000    0.000    0.000    0.000 frame.py:4062(__getitem__)\n In [16]: %%cython\n    ....: cimport cython\n    ....: cimport numpy as np\n    ....: import numpy as np\n    ....: cdef np.float64_t f_typed(np.float64_t x) except? -2:\n    ....:     return x * (x - 1)\n@@ -648,17 +646,17 @@\n The 'numexpr' engine is the more performant engine that can yield performance\n improvements compared to standard Python syntax for large _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be. This\n engine requires the optional dependency numexpr to be installed.\n The 'python' engine is generally n\bno\bot\bt useful except for testing other evaluation\n engines against it. You will achieve n\bno\bo performance benefits using _\be_\bv_\ba_\bl_\b(_\b) with\n engine='python' and may incur a performance hit.\n In [40]: %timeit df1 + df2 + df3 + df4\n-540 ms +- 41.9 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+622 ms +- 31 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n In [41]: %timeit pd.eval(\"df1 + df2 + df3 + df4\", engine=\"python\")\n-513 ms +- 19.9 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+585 ms +- 27.6 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n *\b**\b**\b**\b* T\bTh\bhe\be _\bD\bD_\ba\ba_\bt\bt_\ba\ba_\bF\bF_\br\br_\ba\ba_\bm\bm_\be\be_\b.\b._\be\be_\bv\bv_\ba\ba_\bl\bl_\b(\b(_\b)\b) m\bme\bet\bth\bho\bod\bd_\b#\b# *\b**\b**\b**\b*\n In addition to the top level _\bp_\ba_\bn_\bd_\ba_\bs_\b._\be_\bv_\ba_\bl_\b(_\b) function you can also evaluate an\n expression in the \u201ccontext\u201d of a _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be.\n In [42]: df = pd.DataFrame(np.random.randn(5, 2), columns=[\"a\", \"b\"])\n \n In [43]: df.eval(\"a + b\")\n Out[43]:\n@@ -755,29 +753,29 @@\n _\bp_\ba_\bn_\bd_\ba_\bs_\b._\be_\bv_\ba_\bl_\b(_\b) works well with expressions containing large arrays.\n In [58]: nrows, ncols = 20000, 100\n \n In [59]: df1, df2, df3, df4 = [pd.DataFrame(np.random.randn(nrows, ncols)) for\n _ in range(4)]\n _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be arithmetic:\n In [60]: %timeit df1 + df2 + df3 + df4\n-545 ms +- 52.5 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+611 ms +- 45.4 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n In [61]: %timeit pd.eval(\"df1 + df2 + df3 + df4\")\n-163 ms +- 7.86 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+201 ms +- 28.4 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be comparison:\n In [62]: %timeit (df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)\n-32.6 ms +- 431 us per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+35.7 ms +- 1.29 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n In [63]: %timeit pd.eval(\"(df1 > 0) & (df2 > 0) & (df3 > 0) & (df4 > 0)\")\n-9.13 ms +- 30.6 us per loop (mean +- std. dev. of 7 runs, 100 loops each)\n+17.2 ms +- 1.32 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)\n _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be arithmetic with unaligned axes.\n In [64]: s = pd.Series(np.random.randn(50))\n \n In [65]: %timeit df1 + df2 + df3 + df4 + s\n-810 ms +- 23.1 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.06 s +- 54.5 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n In [66]: %timeit pd.eval(\"df1 + df2 + df3 + df4 + s\")\n-155 ms +- 4.85 ms per loop (mean +- std. dev. of 7 runs, 10 loops each)\n+229 ms +- 42.6 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n Note\n Operations such as\n 1 and 2  # would parse to 1 & 2, but should evaluate to 2\n 3 or 4  # would parse to 3 | 4, but should evaluate to 3\n ~1  # this is okay, but slower when using eval\n should be performed in Python. An exception will be raised if you try to\n perform any boolean/bitwise operations with scalar operands that are not of\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/user_guide/pyarrow.html", "source2": "./usr/share/doc/python-pandas-doc/html/user_guide/pyarrow.html", "unified_diff": "@@ -986,26 +986,19 @@\n <span class=\"n\">Cell</span> <span class=\"n\">In</span><span class=\"p\">[</span><span class=\"mi\">33</span><span class=\"p\">],</span> <span class=\"n\">line</span> <span class=\"mi\">1</span>\n <span class=\"ne\">----&gt; </span><span class=\"mi\">1</span> <span class=\"n\">table</span> <span class=\"o\">=</span> <span class=\"n\">pa</span><span class=\"o\">.</span><span class=\"n\">table</span><span class=\"p\">([</span><span class=\"n\">pa</span><span class=\"o\">.</span><span class=\"n\">array</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=\"nb\">type</span><span class=\"o\">=</span><span class=\"n\">pa</span><span class=\"o\">.</span><span class=\"n\">int64</span><span class=\"p\">())],</span> <span class=\"n\">names</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s2\">&quot;a&quot;</span><span class=\"p\">])</span>\n \n <span class=\"ne\">NameError</span>: name &#39;pa&#39; is not defined\n \n <span class=\"gp\">In [34]: </span><span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">table</span><span class=\"o\">.</span><span class=\"n\">to_pandas</span><span class=\"p\">(</span><span class=\"n\">types_mapper</span><span class=\"o\">=</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">ArrowDtype</span><span class=\"p\">)</span>\n <span class=\"gt\">---------------------------------------------------------------------------</span>\n-<span class=\"ne\">AttributeError</span><span class=\"g g-Whitespace\">                            </span>Traceback (most recent call last)\n-<span class=\"nn\">&lt;ipython-input-34-64ec62289cb4&gt;</span> in <span class=\"ni\">?</span><span class=\"nt\">()</span>\n+<span class=\"ne\">NameError</span><span class=\"g g-Whitespace\">                                 </span>Traceback (most recent call last)\n+<span class=\"n\">Cell</span> <span class=\"n\">In</span><span class=\"p\">[</span><span class=\"mi\">34</span><span class=\"p\">],</span> <span class=\"n\">line</span> <span class=\"mi\">1</span>\n <span class=\"ne\">----&gt; </span><span class=\"mi\">1</span> <span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">table</span><span class=\"o\">.</span><span class=\"n\">to_pandas</span><span class=\"p\">(</span><span class=\"n\">types_mapper</span><span class=\"o\">=</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">ArrowDtype</span><span class=\"p\">)</span>\n \n-<span class=\"nn\">/usr/lib/python3/dist-packages/pandas/core/generic.py</span> in <span class=\"ni\">?</span><span class=\"nt\">(self, name)</span>\n-<span class=\"g g-Whitespace\">   </span><span class=\"mi\">6295</span>             <span class=\"ow\">and</span> <span class=\"n\">name</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_accessors</span>\n-<span class=\"g g-Whitespace\">   </span><span class=\"mi\">6296</span>             <span class=\"ow\">and</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_info_axis</span><span class=\"o\">.</span><span class=\"n\">_can_hold_identifiers_and_holds_name</span><span class=\"p\">(</span><span class=\"n\">name</span><span class=\"p\">)</span>\n-<span class=\"g g-Whitespace\">   </span><span class=\"mi\">6297</span>         <span class=\"p\">):</span>\n-<span class=\"g g-Whitespace\">   </span><span class=\"mi\">6298</span>             <span class=\"k\">return</span> <span class=\"bp\">self</span><span class=\"p\">[</span><span class=\"n\">name</span><span class=\"p\">]</span>\n-<span class=\"ne\">-&gt; </span><span class=\"mi\">6299</span>         <span class=\"k\">return</span> <span class=\"nb\">object</span><span class=\"o\">.</span><span class=\"fm\">__getattribute__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">name</span><span class=\"p\">)</span>\n-\n-<span class=\"ne\">AttributeError</span>: &#39;DataFrame&#39; object has no attribute &#39;to_pandas&#39;\n+<span class=\"ne\">NameError</span>: name &#39;table&#39; is not defined\n \n <span class=\"gp\">In [35]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[35]: </span>\n <span class=\"go\">     a    b</span>\n <span class=\"go\">0  xxx  yyy</span>\n <span class=\"go\">1   \u00a1\u00a1   \u00a1\u00a1</span>\n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -526,27 +526,19 @@\n Cell In[33], line 1\n ----> 1 table = pa.table([pa.array([1, 2, 3], type=pa.int64())], names=[\"a\"])\n \n NameError: name 'pa' is not defined\n \n In [34]: df = table.to_pandas(types_mapper=pd.ArrowDtype)\n ---------------------------------------------------------------------------\n-AttributeError                            Traceback (most recent call last)\n-<ipython-input-34-64ec62289cb4> in ?()\n+NameError                                 Traceback (most recent call last)\n+Cell In[34], line 1\n ----> 1 df = table.to_pandas(types_mapper=pd.ArrowDtype)\n \n-/usr/lib/python3/dist-packages/pandas/core/generic.py in ?(self, name)\n-   6295             and name not in self._accessors\n-   6296             and self._info_axis._can_hold_identifiers_and_holds_name\n-(name)\n-   6297         ):\n-   6298             return self[name]\n--> 6299         return object.__getattribute__(self, name)\n-\n-AttributeError: 'DataFrame' object has no attribute 'to_pandas'\n+NameError: name 'table' is not defined\n \n In [35]: df\n Out[35]:\n      a    b\n 0  xxx  yyy\n 1   \u00a1\u00a1   \u00a1\u00a1\n \n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/user_guide/scale.html", "source2": "./usr/share/doc/python-pandas-doc/html/user_guide/scale.html", "unified_diff": "@@ -1086,16 +1086,16 @@\n <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>\n <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>\n <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>\n <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>\n <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>\n <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>\n <span class=\"gp\">   ....: </span>\n-<span class=\"go\">CPU times: user 162 us, sys: 460 us, total: 622 us</span>\n-<span class=\"go\">Wall time: 630 us</span>\n+<span class=\"go\">CPU times: user 271 us, sys: 259 us, total: 530 us</span>\n+<span class=\"go\">Wall time: 538 us</span>\n <span class=\"gh\">Out[32]: </span><span class=\"go\">Series([], dtype: int32)</span>\n </pre></div>\n </div>\n <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\n <code class=\"docutils literal notranslate\"><span class=\"pre\">chunksize</span></code> when reading a single file.</p>\n <p>Manually chunking is an OK option for workflows that don\u2019t\n 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\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -644,16 +644,16 @@\n    ....: files = pathlib.Path(\"data/timeseries/\").glob(\"ts*.parquet\")\n    ....: counts = pd.Series(dtype=int)\n    ....: for path in files:\n    ....:     df = pd.read_parquet(path)\n    ....:     counts = counts.add(df[\"name\"].value_counts(), fill_value=0)\n    ....: counts.astype(int)\n    ....:\n-CPU times: user 162 us, sys: 460 us, total: 622 us\n-Wall time: 630 us\n+CPU times: user 271 us, sys: 259 us, total: 530 us\n+Wall time: 538 us\n Out[32]: Series([], dtype: int32)\n Some readers, like _\bp_\ba_\bn_\bd_\ba_\bs_\b._\br_\be_\ba_\bd_\b__\bc_\bs_\bv_\b(_\b), offer parameters to control the chunksize\n when reading a single file.\n Manually chunking is an OK option for workflows that don\u2019t require too\n sophisticated of operations. Some operations, like _\bp_\ba_\bn_\bd_\ba_\bs_\b._\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be_\b._\bg_\br_\bo_\bu_\bp_\bb_\by_\b(_\b),\n are much harder to do chunkwise. In these cases, you may be better switching to\n a different library that implements these out-of-core algorithms for you.\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/user_guide/style.ipynb.gz", "source2": "./usr/share/doc/python-pandas-doc/html/user_guide/style.ipynb.gz", "unified_diff": null, "details": [{"source1": "style.ipynb", "source2": "style.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9985610875706213%", "Differences: {\"'cells'\": \"{1: {'metadata': {'execution': {'iopub.execute_input': '2026-05-03T19:44:33.845345Z', \"", "            \"'iopub.status.busy': '2026-05-03T19:44:33.845105Z', 'iopub.status.idle': \"", "            \"'2026-05-03T19:44:34.235274Z', 'shell.execute_reply': \"", "            \"'2026-05-03T19:44:34.234607Z'}}}, 3: {'metadata': {'execution': \"", "            \"{'iopub.execute_input': '2026-05-03T19:44:34.237753Z', 'iopub.status.busy': \"", "            \"'2026-05-03T19:44:34.237445Z', 'iopub.status.idle': '2026-05-03T19:44:3 [\u2026]"], "unified_diff": "@@ -39,18 +39,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 1,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:18.920313Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:18.920009Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:19.702226Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:19.701242Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:33.845345Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:33.845105Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.235274Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.234607Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"import matplotlib.pyplot\\n\",\n                 \"# We have this here to trigger matplotlib's font cache stuff.\\n\",\n@@ -77,36 +77,36 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 2,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:19.705752Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:19.705364Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.318721Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.317763Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.237753Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.237445Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.528179Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.527483Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"import pandas as pd\\n\",\n                 \"import numpy as np\\n\",\n                 \"import matplotlib as mpl\\n\"\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 3,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.322096Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.321680Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.425042Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.424082Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.530470Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.530173Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.587165Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.586556Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"# For reproducibility - this doesn't respect uuid_len or positionally-passed uuid but the places here that use that coincidentally bypass this anyway\\n\",\n                 \"from pandas.io.formats.style import Styler\\n\",\n@@ -123,18 +123,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 4,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.428297Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.427918Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.440991Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.440123Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.589156Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.588867Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.597950Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.597355Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -193,18 +193,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 5,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.499714Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.499332Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.514223Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.513357Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.631970Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.631728Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.642471Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.641896Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<div>\\n\",\n@@ -327,18 +327,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 6,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.517181Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.516875Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.530662Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.529810Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.644317Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.644077Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.654716Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.654140Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -448,18 +448,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 7,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.533464Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.533160Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.543093Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.542248Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.656598Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.656364Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.663708Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.663130Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -510,18 +510,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 8,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.545932Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.545629Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.556502Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.555664Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.665592Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.665358Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.673329Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.672749Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -585,18 +585,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 9,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.559316Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.558996Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.575254Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.574404Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.675228Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.674992Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.686679Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.686108Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -705,18 +705,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 10,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.578039Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.577737Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.589460Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.588595Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.688608Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.688370Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.696964Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.696388Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -787,18 +787,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 11,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.592397Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.592093Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.606142Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.605267Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.699080Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.698825Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.708942Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.708340Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"# Hidden cell to just create the below example: code is covered throughout the guide.\\n\",\n                 \"s = df.style\\\\\\n\",\n@@ -861,18 +861,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 12,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.608890Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.608587Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.619814Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.618944Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.710799Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.710563Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.718210Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.717645Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -1012,18 +1012,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 13,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.622623Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.622324Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.634730Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.633893Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.720110Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.719856Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.729165Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.728561Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -1083,18 +1083,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 14,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.637428Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.637135Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.645891Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.645031Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.731055Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.730821Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.736882Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.736307Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -1202,18 +1202,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 15,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.648717Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.648408Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.658401Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.657562Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.738791Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.738558Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.745365Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.744791Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -1296,18 +1296,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 16,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.661135Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.660797Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.669527Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.668681Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.747195Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.746963Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.753123Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.752542Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -1387,18 +1387,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 17,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.672389Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.672089Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.681622Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.680747Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.754988Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.754756Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.761280Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.760702Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -1483,18 +1483,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 18,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.684375Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.684072Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.692865Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.692035Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.763126Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.762895Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.769064Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.768475Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -1586,18 +1586,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 19,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.695673Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.695355Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.706094Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.705266Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.770944Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.770712Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.778416Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.777848Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -1687,18 +1687,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 20,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.708850Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.708555Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.717453Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.716597Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.780272Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.780021Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.786216Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.785652Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -1802,18 +1802,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 21,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.720195Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.719896Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.728354Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.727427Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.788136Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.787883Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.793826Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.793229Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"name\": \"stdout\",\n                     \"output_type\": \"stream\",\n                     \"text\": [\n@@ -1844,18 +1844,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 22,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.731244Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.730907Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.743796Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.742950Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.795745Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.795510Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.804471Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.803872Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -1956,18 +1956,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 23,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.746529Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.746215Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.755120Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.754290Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.806285Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.806052Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.812277Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.811685Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -2081,18 +2081,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 24,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.757907Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.757612Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.766598Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.765764Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.814172Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.813938Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.820178Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.819583Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -2204,18 +2204,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 25,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.769349Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.769050Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.780940Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.780105Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.822053Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.821822Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.830075Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.829499Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -2332,18 +2332,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 26,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.783718Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.783403Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.794132Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.793305Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.831923Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.831692Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.838993Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.838415Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -2465,18 +2465,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 27,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.796913Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.796605Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.809735Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.808867Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.840878Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.840644Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.849989Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.849410Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -2595,18 +2595,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 28,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.812446Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.812145Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.824505Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.823659Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.851849Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.851616Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.860420Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.859825Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -2732,18 +2732,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 29,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.827428Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.827113Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.844443Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.843566Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.862253Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.862020Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.874399Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.873828Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -2884,18 +2884,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 30,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.847214Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.846910Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.863523Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.862694Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.876262Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.876010Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.888238Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.887646Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -3069,18 +3069,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 31,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.866367Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.866070Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.886254Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.885408Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.890163Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.889932Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.904969Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.904392Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -3244,18 +3244,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 32,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.889056Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.888750Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.898325Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.897472Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.906838Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.906605Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.913093Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.912521Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -3357,18 +3357,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 33,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.901030Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.900728Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.909603Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.908786Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.914917Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.914685Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.920934Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.920359Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -3477,18 +3477,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 34,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.912507Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.912216Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.924930Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.924127Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.922831Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.922597Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.931417Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.930849Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -3615,18 +3615,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 35,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.927588Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.927285Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.938713Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.937887Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.933251Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.933018Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.940728Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.940158Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -3757,18 +3757,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 36,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.941562Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.941242Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.956457Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.955571Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.942639Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.942407Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.952888Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.952311Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -3904,18 +3904,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 37,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.959206Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.958899Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.970263Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.969367Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.954746Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.954515Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.962256Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.961689Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n@@ -4062,18 +4062,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 38,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.973158Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.972852Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:20.986436Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:20.985589Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.964199Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.963953Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.974042Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.973472Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<div>\\n\",\n@@ -4164,18 +4164,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 39,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:20.989229Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:20.988929Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.001390Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.000525Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.975982Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.975750Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.985030Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.984464Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -4255,18 +4255,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 40,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.004337Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.004037Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.018781Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.017929Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.986916Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.986681Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:34.997599Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:34.997027Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -4347,18 +4347,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 41,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.021619Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.021328Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.034171Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.033331Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:34.999535Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:34.999298Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.008769Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.008200Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -4440,18 +4440,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 42,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.037020Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.036720Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.050619Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.049763Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.010673Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.010440Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.020976Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.020404Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -4571,18 +4571,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 43,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.053704Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.053399Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.057763Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.056941Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.022953Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.022720Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.025983Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.025408Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"df4 = pd.DataFrame([[1,2],[3,4]])\\n\",\n                 \"s4 = df4.style\"\n             ]\n@@ -4599,18 +4599,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 44,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.060456Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.060165Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.064312Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.063460Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.027833Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.027599Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.030818Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.030238Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"from pandas.io.formats.style import Styler\\n\",\n                 \"s4 = Styler(df4, uuid_len=0, cell_ids=False)\"\n             ]\n@@ -4636,18 +4636,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 45,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.067086Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.066792Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.075377Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.074516Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.032658Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.032426Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.038647Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.038081Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -4705,18 +4705,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 46,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.078257Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.077956Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.084852Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.084019Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.040591Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.040357Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.045367Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.044800Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -4782,18 +4782,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 47,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.087605Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.087314Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.102431Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.101576Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.047270Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.047037Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.057973Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.057408Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -4931,18 +4931,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 48,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.105301Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.104999Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.121420Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.120556Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.059825Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.059592Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.071184Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.070611Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -5083,18 +5083,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 49,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.124267Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.123964Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.131344Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.130408Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.073147Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.072919Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.078223Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.077625Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"name\": \"stdout\",\n                     \"output_type\": \"stream\",\n                     \"text\": [\n@@ -5158,18 +5158,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 50,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.134025Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.133735Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.140340Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.139451Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.080108Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.079854Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.084651Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.084074Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -5260,18 +5260,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 51,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.143253Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.142947Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.152298Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.151400Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.086593Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.086361Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.092991Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.092416Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -5351,18 +5351,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 52,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.155104Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.154805Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.167547Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.166743Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.094865Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.094632Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.104201Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.103600Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -5443,18 +5443,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 53,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.170406Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.170114Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.186816Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.185965Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.106040Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.105807Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.118359Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.117791Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -5535,18 +5535,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 54,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.189622Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.189310Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:21.200799Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:21.199970Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.120244Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.119988Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.128493Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.127898Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -5631,18 +5631,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 55,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:21.203699Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:21.203389Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.022983Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.022058Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.130431Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.130198Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.631018Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.630414Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -5880,18 +5880,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 56,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.026034Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.025627Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.040183Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.039298Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.633136Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.632831Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.643010Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.642435Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -6112,18 +6112,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 57,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.043175Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.042866Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.052565Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.051693Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.644944Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.644709Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.651286Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.650710Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -6212,18 +6212,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 58,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.055337Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.055037Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.066341Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.065455Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.653237Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.653000Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.660928Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.660349Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -6415,18 +6415,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 59,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.069109Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.068804Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.094649Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.093802Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.662852Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.662617Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.681667Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.681095Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -6810,18 +6810,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 60,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.097525Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.097225Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.170764Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.169882Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.683595Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.683360Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.740934Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.740339Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"# Hide the construction of the display chart from the user\\n\",\n                 \"from IPython.display import HTML\\n\",\n@@ -6864,18 +6864,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 61,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.173933Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.173615Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.180508Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.179442Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.742835Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.742600Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.746902Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.746334Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"\\n\",\n@@ -7847,18 +7847,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 62,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.183549Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.183243Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.195041Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.194198Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.748853Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.748609Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.756640Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.756061Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -7968,18 +7968,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 63,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.197885Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.197588Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.208183Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.207359Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.758483Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.758249Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.765856Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.765286Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -8084,18 +8084,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 64,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.211178Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.210858Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.522033Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.521123Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.767847Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.767615Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.944574Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.943938Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"ModuleNotFoundError\",\n                     \"evalue\": \"No module named 'ipywidgets'\",\n                     \"output_type\": \"error\",\n@@ -8125,18 +8125,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 65,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.525097Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.524793Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.530005Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.529162Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.946645Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.946407Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:35.950109Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:35.949523Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"def magnify():\\n\",\n                 \"    return [dict(selector=\\\"th\\\",\\n\",\n                 \"                 props=[(\\\"font-size\\\", \\\"4pt\\\")]),\\n\",\n@@ -8151,18 +8151,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 66,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.532842Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.532544Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.616909Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.615981Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:35.951911Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:35.951677Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.000660Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.000071Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -10009,18 +10009,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 67,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.619998Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.619675Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.678423Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.677530Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.002551Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.002318Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.033963Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.033386Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -11817,18 +11817,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 68,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.682787Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.682479Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.750762Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.749852Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.036258Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.036006Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.074520Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.073947Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -13655,18 +13655,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 69,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.755248Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.754928Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.762293Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.761453Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.076759Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.076521Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.081778Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.081204Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -13705,18 +13705,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 70,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.764981Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.764687Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.771679Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.770822Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.083633Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.083401Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.088593Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.088007Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -13754,18 +13754,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 71,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.774381Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.774081Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.781038Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.780212Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.090435Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.090203Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.095366Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.094800Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -13836,18 +13836,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 72,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.783747Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.783425Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.946672Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.945718Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.097326Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.097091Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.202381Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.201770Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"df2.style.\\\\\\n\",\n                 \"    map(style_negative, props='color:red;').\\\\\\n\",\n                 \"    highlight_max(axis=0).\\\\\\n\",\n@@ -13915,18 +13915,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 73,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.950193Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.949771Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.957110Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.956211Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.204573Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.204273Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.209563Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.208968Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"name\": \"stdout\",\n                     \"output_type\": \"stream\",\n                     \"text\": [\n@@ -13971,18 +13971,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 74,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.959858Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.959539Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.967722Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.966866Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.211456Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.211224Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.217339Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.216766Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14022,18 +14022,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 75,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.970421Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.970124Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.977743Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.976889Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.219177Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.218943Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.224674Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.224100Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14088,18 +14088,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 76,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.980607Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.980309Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.987682Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.986818Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.226545Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.226312Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.231736Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.231170Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14148,18 +14148,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 77,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:22.990462Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:22.990171Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:22.998422Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:22.997578Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.233624Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.233390Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.239478Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.238908Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14213,18 +14213,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 78,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:23.001233Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:23.000908Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:23.009346Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:23.008511Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.241373Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.241141Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.247372Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.246810Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14284,18 +14284,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 79,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:23.012170Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:23.011866Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:23.020183Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:23.019344Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.249282Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.249050Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.255224Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.254650Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14373,36 +14373,36 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 80,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:23.023131Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:23.022830Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:23.027003Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:23.026148Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.257140Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.256910Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.260050Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.259453Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"from jinja2 import Environment, ChoiceLoader, FileSystemLoader\\n\",\n                 \"from IPython.display import HTML\\n\",\n                 \"from pandas.io.formats.style import Styler\"\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 81,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:23.029663Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:23.029358Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:23.033102Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:23.032260Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.261862Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.261629Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.264503Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.263900Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"# For reproducibility\\n\",\n                 \"Styler = StylerReproducible\\n\"\n@@ -14416,18 +14416,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 82,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:23.035855Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:23.035541Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:23.040567Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:23.039633Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.266320Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.266090Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.269987Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.269398Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"name\": \"stdout\",\n                     \"output_type\": \"stream\",\n                     \"text\": [\n@@ -14454,18 +14454,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 83,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:23.043444Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:23.043147Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:23.050987Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:23.050087Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.271845Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.271613Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.277273Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.276690Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"class MyStyler(Styler):\\n\",\n                 \"    env = Environment(\\n\",\n                 \"        loader=ChoiceLoader([\\n\",\n@@ -14488,18 +14488,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 84,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:23.054032Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:23.053728Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:23.085618Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:23.084737Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.279175Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.278942Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.297950Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.297387Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14661,18 +14661,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 85,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:23.088558Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:23.088249Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:23.096986Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:23.096158Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.299918Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.299684Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.305687Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.305122Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -14834,18 +14834,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 86,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:23.099768Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:23.099443Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:23.132965Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:23.132097Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.307549Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.307318Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.327497Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.326930Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<style type=\\\"text/css\\\">\\n\",\n@@ -15017,36 +15017,36 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 87,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:23.135838Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:23.135519Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:23.140491Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:23.139616Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.329441Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.329209Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.332652Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.332070Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"with open(\\\"templates/html_style_structure.html\\\") as f:\\n\",\n                 \"    style_structure = f.read()\"\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 88,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:23.143225Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:23.142924Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:23.148354Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:23.147506Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.334464Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.334232Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.338231Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.337668Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<!--\\n\",\n@@ -15106,36 +15106,36 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 89,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:23.151158Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:23.150819Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:23.155037Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:23.154157Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.340153Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.339904Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.343291Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.342717Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"with open(\\\"templates/html_table_structure.html\\\") as f:\\n\",\n                 \"    table_structure = f.read()\"\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 90,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:23.157694Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:23.157398Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:23.162782Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:23.161956Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.345119Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.344888Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.348861Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.348281Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"data\": {\n                         \"text/html\": [\n                             \"<!--\\n\",\n@@ -15208,18 +15208,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 91,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2025-03-31T12:10:23.165610Z\",\n-                    \"iopub.status.busy\": \"2025-03-31T12:10:23.165309Z\",\n-                    \"iopub.status.idle\": \"2025-03-31T12:10:23.169178Z\",\n-                    \"shell.execute_reply\": \"2025-03-31T12:10:23.168336Z\"\n+                    \"iopub.execute_input\": \"2026-05-03T19:44:36.350760Z\",\n+                    \"iopub.status.busy\": \"2026-05-03T19:44:36.350526Z\",\n+                    \"iopub.status.idle\": \"2026-05-03T19:44:36.353399Z\",\n+                    \"shell.execute_reply\": \"2026-05-03T19:44:36.352825Z\"\n                 },\n                 \"nbsphinx\": \"hidden\"\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"# # Hack to get the same style in the notebook as the\\n\",\n                 \"# # main site. This is hidden in the docs.\\n\",\n"}]}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.13.0.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.13.0.html", "unified_diff": "@@ -1613,20 +1613,20 @@\n <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>\n <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>\n <span class=\"gp\">   ....: </span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"go\"># eval with NumExpr backend</span>\n <span class=\"gp\">In [95]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> pd.eval(&#39;df1 + df2 + df3 + df4&#39;)\n-<span class=\"go\">237 ms +- 27.3 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">219 ms +- 38.5 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"go\"># pure Python evaluation</span>\n <span class=\"gp\">In [96]: </span><span class=\"o\">%</span><span class=\"k\">timeit</span> df1 + df2 + df3 + df4\n-<span class=\"go\">630 ms +- 60.1 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n+<span class=\"go\">690 ms +- 38 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)</span>\n </pre></div>\n </div>\n <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>\n </li>\n <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\n <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame.eval</span></code> method that evaluates an expression in the context of\n the <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code>. For example,</p>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -965,18 +965,18 @@\n \n       In [94]: df1, df2, df3, df4 = [pd.DataFrame(np.random.randn(nrows,\n       ncols))\n          ....:                       for _ in range(4)]\n          ....:\n       # eval with NumExpr backend\n       In [95]: %timeit pd.eval('df1 + df2 + df3 + df4')\n-      237 ms +- 27.3 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+      219 ms +- 38.5 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n       # pure Python evaluation\n       In [96]: %timeit df1 + df2 + df3 + df4\n-      630 ms +- 60.1 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+      690 ms +- 38 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n       For more details, see the _\bt_\bh_\be_\b _\bd_\bo_\bc_\bs\n     * Similar to pandas.eval, _\bD_\ba_\bt_\ba_\bF_\br_\ba_\bm_\be has a new DataFrame.eval method that\n       evaluates an expression in the context of the DataFrame. For example,\n       In [97]: df = pd.DataFrame(np.random.randn(10, 2), columns=['a', 'b'])\n \n       In [98]: df.eval('a + b')\n       Out[98]:\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.16.1.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.16.1.html", "unified_diff": "@@ -821,62 +821,64 @@\n facilitate replication. (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/2419\">GH 2419</a>)</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [1]: </span><span class=\"n\">example_series</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> <span class=\"mi\">5</span><span class=\"p\">])</span>\n \n <span class=\"go\"># When no arguments are passed, returns 1</span>\n <span class=\"gp\">In [2]: </span><span class=\"n\">example_series</span><span class=\"o\">.</span><span class=\"n\">sample</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[2]: </span>\n <span class=\"go\">3    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 1, dtype: int64</span>\n \n <span class=\"go\"># One may specify either a number of rows:</span>\n <span class=\"gp\">In [3]: </span><span class=\"n\">example_series</span><span class=\"o\">.</span><span class=\"n\">sample</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"o\">=</span><span class=\"mi\">3</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[3]: </span>\n <span class=\"go\">2    2</span>\n <span class=\"go\">1    1</span>\n <span class=\"go\">0    0</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 3, dtype: int64</span>\n \n <span class=\"go\"># Or a fraction of the rows:</span>\n <span class=\"gp\">In [4]: </span><span class=\"n\">example_series</span><span class=\"o\">.</span><span class=\"n\">sample</span><span class=\"p\">(</span><span class=\"n\">frac</span><span class=\"o\">=</span><span class=\"mf\">0.5</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[4]: </span>\n <span class=\"go\">1    1</span>\n <span class=\"go\">5    5</span>\n <span class=\"go\">3    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 3, dtype: int64</span>\n \n <span class=\"go\"># weights are accepted.</span>\n <span class=\"gp\">In [5]: </span><span class=\"n\">example_weights</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mf\">0.2</span><span class=\"p\">,</span> <span class=\"mf\">0.2</span><span class=\"p\">,</span> <span class=\"mf\">0.2</span><span class=\"p\">,</span> <span class=\"mf\">0.4</span><span class=\"p\">]</span>\n \n <span class=\"gp\">In [6]: </span><span class=\"n\">example_series</span><span class=\"o\">.</span><span class=\"n\">sample</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"o\">=</span><span class=\"mi\">3</span><span class=\"p\">,</span> <span class=\"n\">weights</span><span class=\"o\">=</span><span class=\"n\">example_weights</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[6]: </span>\n <span class=\"go\">2    2</span>\n <span class=\"go\">4    4</span>\n <span class=\"go\">3    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 3, dtype: int64</span>\n \n <span class=\"go\"># weights will also be normalized if they do not sum to one,</span>\n <span class=\"go\"># and missing values will be treated as zeros.</span>\n <span class=\"gp\">In [7]: </span><span class=\"n\">example_weights2</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"mf\">0.5</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">nan</span><span class=\"p\">]</span>\n \n <span class=\"gp\">In [8]: </span><span class=\"n\">example_series</span><span class=\"o\">.</span><span class=\"n\">sample</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">weights</span><span class=\"o\">=</span><span class=\"n\">example_weights2</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[8]: </span>\n <span class=\"go\">0    0</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 1, dtype: int64</span>\n </pre></div>\n </div>\n <p>When applied to a DataFrame, one may pass the name of a column to specify sampling weights\n when sampling from rows.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [9]: </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=\"s2\">&quot;col1&quot;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mi\">9</span><span class=\"p\">,</span> <span class=\"mi\">8</span><span class=\"p\">,</span> <span class=\"mi\">7</span><span class=\"p\">,</span> <span class=\"mi\">6</span><span class=\"p\">],</span> <span class=\"s2\">&quot;weight_column&quot;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mf\">0.5</span><span class=\"p\">,</span> <span class=\"mf\">0.4</span><span class=\"p\">,</span> <span class=\"mf\">0.1</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">]})</span>\n \n <span class=\"gp\">In [10]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">sample</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"o\">=</span><span class=\"mi\">3</span><span class=\"p\">,</span> <span class=\"n\">weights</span><span class=\"o\">=</span><span class=\"s2\">&quot;weight_column&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[10]: </span>\n <span class=\"go\">   col1  weight_column</span>\n <span class=\"go\">0     9            0.5</span>\n <span class=\"go\">1     8            0.4</span>\n <span class=\"go\">2     7            0.1</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"string-methods-enhancements\">\n <span id=\"whatsnew-0161-enhancements-string\"></span><h3>String methods enhancements<a class=\"headerlink\" href=\"#string-methods-enhancements\" title=\"Link to this heading\">#</a></h3>\n <p><a class=\"reference internal\" href=\"v0.16.0.html#whatsnew-0160-enhancements-string\"><span class=\"std std-ref\">Continuing from v0.16.0</span></a>, the following\n enhancements make string operations easier and more consistent with standard python string operations.</p>\n@@ -898,24 +900,24 @@\n \n <span class=\"gp\">In [15]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[15]: </span>\n <span class=\"go\">a1    0</span>\n <span class=\"go\">a2    1</span>\n <span class=\"go\">b1    2</span>\n <span class=\"go\">b2    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 4, dtype: int64</span>\n \n <span class=\"gp\">In [16]: </span><span class=\"n\">idx</span><span class=\"o\">.</span><span class=\"n\">str</span><span class=\"o\">.</span><span class=\"n\">startswith</span><span class=\"p\">(</span><span class=\"s2\">&quot;a&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[16]: </span><span class=\"go\">array([ True,  True, False, False])</span>\n \n <span class=\"gp\">In [17]: </span><span class=\"n\">s</span><span class=\"p\">[</span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"o\">.</span><span class=\"n\">str</span><span class=\"o\">.</span><span class=\"n\">startswith</span><span class=\"p\">(</span><span class=\"s2\">&quot;a&quot;</span><span class=\"p\">)]</span>\n <span class=\"gh\">Out[17]: </span>\n <span class=\"go\">a1    0</span>\n <span class=\"go\">a2    1</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 2, dtype: int64</span>\n </pre></div>\n </div>\n </li>\n <li><p>The following new methods are accessible via <code class=\"docutils literal notranslate\"><span class=\"pre\">.str</span></code> accessor to apply the function to each values. (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/9766\">GH 9766</a>, <a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/9773\">GH 9773</a>, <a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/10031\">GH 10031</a>, <a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/10045\">GH 10045</a>, <a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/10052\">GH 10052</a>)</p>\n <div class=\"pst-scrollable-table-container\"><table class=\"table\">\n <thead>\n <tr class=\"row-odd\"><th class=\"head\"></th>\n@@ -947,24 +949,26 @@\n \n <span class=\"go\"># return Series</span>\n <span class=\"gp\">In [19]: </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=\"s2\">&quot;,&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[19]: </span>\n <span class=\"go\">0    [a, b]</span>\n <span class=\"go\">1    [a, c]</span>\n <span class=\"go\">2    [b, c]</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n \n <span class=\"go\"># return DataFrame</span>\n <span class=\"gp\">In [20]: </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=\"s2\">&quot;,&quot;</span><span class=\"p\">,</span> <span class=\"n\">expand</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[20]: </span>\n <span class=\"go\">   0  1</span>\n <span class=\"go\">0  a  b</span>\n <span class=\"go\">1  a  c</span>\n <span class=\"go\">2  b  c</span>\n \n+<span class=\"go\">[3 rows x 2 columns]</span>\n+\n <span class=\"gp\">In [21]: </span><span class=\"n\">idx</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=\"s2\">&quot;a,b&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;a,c&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;b,c&quot;</span><span class=\"p\">])</span>\n \n <span class=\"go\"># return Index</span>\n <span class=\"gp\">In [22]: </span><span class=\"n\">idx</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=\"s2\">&quot;,&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[22]: </span><span class=\"go\">Index([[&#39;a&#39;, &#39;b&#39;], [&#39;a&#39;, &#39;c&#39;], [&#39;b&#39;, &#39;c&#39;]], dtype=&#39;object&#39;)</span>\n \n <span class=\"go\"># return MultiIndex</span>\n@@ -1003,14 +1007,16 @@\n \n <span class=\"gp\">In [28]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">drop</span><span class=\"p\">([</span><span class=\"s2\">&quot;A&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;X&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=\"n\">errors</span><span class=\"o\">=</span><span class=\"s2\">&quot;ignore&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[28]: </span>\n <span class=\"go\">          B         C</span>\n <span class=\"go\">0 -0.706771 -1.039575</span>\n <span class=\"go\">1 -0.424972  0.567020</span>\n <span class=\"go\">2 -1.087401 -0.673690</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n </li>\n <li><p>Add support for separating years and quarters using dashes, for\n example 2014-Q1.  (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/9688\">GH 9688</a>)</p></li>\n <li><p>Allow conversion of values with dtype <code class=\"docutils literal notranslate\"><span class=\"pre\">datetime64</span></code> or <code class=\"docutils literal notranslate\"><span class=\"pre\">timedelta64</span></code> to strings using <code class=\"docutils literal notranslate\"><span class=\"pre\">astype(str)</span></code> (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/9757\">GH 9757</a>)</p></li>\n <li><p><code class=\"docutils literal notranslate\"><span class=\"pre\">get_dummies</span></code> function now accepts <code class=\"docutils literal notranslate\"><span class=\"pre\">sparse</span></code> keyword.  If set to <code class=\"docutils literal notranslate\"><span class=\"pre\">True</span></code>, the return <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code> is sparse, e.g. <code class=\"docutils literal notranslate\"><span class=\"pre\">SparseDataFrame</span></code>. (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/8823\">GH 8823</a>)</p></li>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -267,61 +267,63 @@\n sampling, and for setting seed values to facilitate replication. (_\bG_\bH_\b _\b2_\b4_\b1_\b9)\n In [1]: example_series = pd.Series([0, 1, 2, 3, 4, 5])\n \n # When no arguments are passed, returns 1\n In [2]: example_series.sample()\n Out[2]:\n 3    3\n-dtype: int64\n+Length: 1, dtype: int64\n \n # One may specify either a number of rows:\n In [3]: example_series.sample(n=3)\n Out[3]:\n 2    2\n 1    1\n 0    0\n-dtype: int64\n+Length: 3, dtype: int64\n \n # Or a fraction of the rows:\n In [4]: example_series.sample(frac=0.5)\n Out[4]:\n 1    1\n 5    5\n 3    3\n-dtype: int64\n+Length: 3, dtype: int64\n \n # weights are accepted.\n In [5]: example_weights = [0, 0, 0.2, 0.2, 0.2, 0.4]\n \n In [6]: example_series.sample(n=3, weights=example_weights)\n Out[6]:\n 2    2\n 4    4\n 3    3\n-dtype: int64\n+Length: 3, dtype: int64\n \n # weights will also be normalized if they do not sum to one,\n # and missing values will be treated as zeros.\n In [7]: example_weights2 = [0.5, 0, 0, 0, None, np.nan]\n \n In [8]: example_series.sample(n=1, weights=example_weights2)\n Out[8]:\n 0    0\n-dtype: int64\n+Length: 1, dtype: int64\n When applied to a DataFrame, one may pass the name of a column to specify\n sampling weights when sampling from rows.\n In [9]: df = pd.DataFrame({\"col1\": [9, 8, 7, 6], \"weight_column\": [0.5, 0.4,\n 0.1, 0]})\n \n In [10]: df.sample(n=3, weights=\"weight_column\")\n Out[10]:\n    col1  weight_column\n 0     9            0.5\n 1     8            0.4\n 2     7            0.1\n+\n+[3 rows x 2 columns]\n *\b**\b**\b**\b* S\bSt\btr\bri\bin\bng\bg m\bme\bet\bth\bho\bod\bds\bs e\ben\bnh\bha\ban\bnc\bce\bem\bme\ben\bnt\bts\bs_\b#\b# *\b**\b**\b**\b*\n _\bC_\bo_\bn_\bt_\bi_\bn_\bu_\bi_\bn_\bg_\b _\bf_\br_\bo_\bm_\b _\bv_\b0_\b._\b1_\b6_\b._\b0, the following enhancements make string operations\n easier and more consistent with standard python string operations.\n     * Added StringMethods (.str accessor) to Index (_\bG_\bH_\b _\b9_\b0_\b6_\b8)\n       The .str accessor is now available for both Series and Index.\n       In [11]: idx = pd.Index([\" jack\", \"jill \", \" jesse \", \"frank\"])\n \n@@ -337,24 +339,24 @@\n \n       In [15]: s\n       Out[15]:\n       a1    0\n       a2    1\n       b1    2\n       b2    3\n-      dtype: int64\n+      Length: 4, dtype: int64\n \n       In [16]: idx.str.startswith(\"a\")\n       Out[16]: array([ True,  True, False, False])\n \n       In [17]: s[s.index.str.startswith(\"a\")]\n       Out[17]:\n       a1    0\n       a2    1\n-      dtype: int64\n+      Length: 2, dtype: int64\n     * The following new methods are accessible via .str accessor to apply the\n       function to each values. (_\bG_\bH_\b _\b9_\b7_\b6_\b6, _\bG_\bH_\b _\b9_\b7_\b7_\b3, _\bG_\bH_\b _\b1_\b0_\b0_\b3_\b1, _\bG_\bH_\b _\b1_\b0_\b0_\b4_\b5, _\bG_\bH_\b _\b1_\b0_\b0_\b5_\b2)\n                               M\bMe\bet\bth\bho\bod\bds\bs\n       capitalize() swapcase() normalize() partition() rpartition()\n       index()      rindex()   translate()\n     * split now takes expand keyword to specify whether to expand\n       dimensionality. return_type is deprecated. (_\bG_\bH_\b _\b9_\b8_\b4_\b7)\n@@ -362,24 +364,26 @@\n \n       # return Series\n       In [19]: s.str.split(\",\")\n       Out[19]:\n       0    [a, b]\n       1    [a, c]\n       2    [b, c]\n-      dtype: object\n+      Length: 3, dtype: object\n \n       # return DataFrame\n       In [20]: s.str.split(\",\", expand=True)\n       Out[20]:\n          0  1\n       0  a  b\n       1  a  c\n       2  b  c\n \n+      [3 rows x 2 columns]\n+\n       In [21]: idx = pd.Index([\"a,b\", \"a,c\", \"b,c\"])\n \n       # return Index\n       In [22]: idx.str.split(\",\")\n       Out[22]: Index([['a', 'b'], ['a', 'c'], ['b', 'c']], dtype='object')\n \n       # return MultiIndex\n@@ -420,14 +424,16 @@\n \n       In [28]: df.drop([\"A\", \"X\"], axis=1, errors=\"ignore\")\n       Out[28]:\n                 B         C\n       0 -0.706771 -1.039575\n       1 -0.424972  0.567020\n       2 -1.087401 -0.673690\n+\n+      [3 rows x 2 columns]\n     * Add support for separating years and quarters using dashes, for example\n       2014-Q1. (_\bG_\bH_\b _\b9_\b6_\b8_\b8)\n     * Allow conversion of values with dtype datetime64 or timedelta64 to\n       strings using astype(str) (_\bG_\bH_\b _\b9_\b7_\b5_\b7)\n     * get_dummies function now accepts sparse keyword. If set to True, the\n       return DataFrame is sparse, e.g. SparseDataFrame. (_\bG_\bH_\b _\b8_\b8_\b2_\b3)\n     * Period now accepts datetime64 as value input. (_\bG_\bH_\b _\b9_\b0_\b5_\b4)\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.17.0.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.17.0.html", "unified_diff": "@@ -769,35 +769,37 @@\n <span class=\"gp\">In [2]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[2]: </span>\n <span class=\"go\">           A                         B                         C</span>\n <span class=\"go\">0 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00+01:00</span>\n <span class=\"go\">1 2013-01-02 2013-01-02 00:00:00-05:00 2013-01-02 00:00:00+01:00</span>\n <span class=\"go\">2 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-03 00:00:00+01:00</span>\n \n+<span class=\"go\">[3 rows x 3 columns]</span>\n+\n <span class=\"gp\">In [3]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[3]: </span>\n <span class=\"go\">A                datetime64[ns]</span>\n <span class=\"go\">B    datetime64[ns, US/Eastern]</span>\n <span class=\"go\">C           datetime64[ns, CET]</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n </pre></div>\n </div>\n <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\">B</span>\n <span class=\"gh\">Out[4]: </span>\n <span class=\"go\">0   2013-01-01 00:00:00-05:00</span>\n <span class=\"go\">1   2013-01-02 00:00:00-05:00</span>\n <span class=\"go\">2   2013-01-03 00:00:00-05:00</span>\n-<span class=\"go\">Name: B, dtype: datetime64[ns, US/Eastern]</span>\n+<span class=\"go\">Name: B, Length: 3, dtype: datetime64[ns, US/Eastern]</span>\n \n <span class=\"gp\">In [5]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">B</span><span class=\"o\">.</span><span class=\"n\">dt</span><span class=\"o\">.</span><span class=\"n\">tz_localize</span><span class=\"p\">(</span><span class=\"kc\">None</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[5]: </span>\n <span class=\"go\">0   2013-01-01</span>\n <span class=\"go\">1   2013-01-02</span>\n <span class=\"go\">2   2013-01-03</span>\n-<span class=\"go\">Name: B, dtype: datetime64[ns]</span>\n+<span class=\"go\">Name: B, Length: 3, dtype: datetime64[ns]</span>\n </pre></div>\n </div>\n <p>This uses a new-dtype representation as well, that is very similar in look-and-feel to its numpy cousin <code class=\"docutils literal notranslate\"><span class=\"pre\">datetime64[ns]</span></code></p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [6]: </span><span class=\"n\">df</span><span class=\"p\">[</span><span class=\"s2\">&quot;B&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">dtype</span>\n <span class=\"gh\">Out[6]: </span><span class=\"go\">datetime64[ns, US/Eastern]</span>\n \n <span class=\"gp\">In [7]: </span><span class=\"nb\">type</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">[</span><span class=\"s2\">&quot;B&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">dtype</span><span class=\"p\">)</span>\n@@ -876,43 +878,43 @@\n \n <span class=\"gp\">In [14]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[14]: </span>\n <span class=\"go\">0   2013-01-01</span>\n <span class=\"go\">1   2013-01-02</span>\n <span class=\"go\">2   2013-01-03</span>\n <span class=\"go\">3   2013-01-04</span>\n-<span class=\"go\">dtype: datetime64[ns]</span>\n+<span class=\"go\">Length: 4, dtype: datetime64[ns]</span>\n \n <span class=\"gp\">In [15]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">dt</span><span class=\"o\">.</span><span class=\"n\">strftime</span><span class=\"p\">(</span><span class=\"s2\">&quot;%Y/%m/</span><span class=\"si\">%d</span><span class=\"s2\">&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[15]: </span>\n <span class=\"go\">0    2013/01/01</span>\n <span class=\"go\">1    2013/01/02</span>\n <span class=\"go\">2    2013/01/03</span>\n <span class=\"go\">3    2013/01/04</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 4, dtype: object</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"go\"># PeriodIndex</span>\n <span class=\"gp\">In [16]: </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\">pd</span><span class=\"o\">.</span><span class=\"n\">period_range</span><span class=\"p\">(</span><span class=\"s2\">&quot;20130101&quot;</span><span class=\"p\">,</span> <span class=\"n\">periods</span><span class=\"o\">=</span><span class=\"mi\">4</span><span class=\"p\">))</span>\n \n <span class=\"gp\">In [17]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[17]: </span>\n <span class=\"go\">0    2013-01-01</span>\n <span class=\"go\">1    2013-01-02</span>\n <span class=\"go\">2    2013-01-03</span>\n <span class=\"go\">3    2013-01-04</span>\n-<span class=\"go\">dtype: period[D]</span>\n+<span class=\"go\">Length: 4, dtype: period[D]</span>\n \n <span class=\"gp\">In [18]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">dt</span><span class=\"o\">.</span><span class=\"n\">strftime</span><span class=\"p\">(</span><span class=\"s2\">&quot;%Y/%m/</span><span class=\"si\">%d</span><span class=\"s2\">&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[18]: </span>\n <span class=\"go\">0    2013/01/01</span>\n <span class=\"go\">1    2013/01/02</span>\n <span class=\"go\">2    2013/01/03</span>\n <span class=\"go\">3    2013/01/04</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 4, dtype: object</span>\n </pre></div>\n </div>\n <p>The string format is as the python standard library and details can be found <a class=\"reference external\" href=\"https://docs.python.org/2/library/datetime.html#strftime-and-strptime-behavior\">here</a></p>\n </section>\n <section id=\"series-dt-total-seconds\">\n <h4>Series.dt.total_seconds<a class=\"headerlink\" href=\"#series-dt-total-seconds\" title=\"Link to this heading\">#</a></h4>\n <p><code class=\"docutils literal notranslate\"><span class=\"pre\">pd.Series</span></code> of type <code class=\"docutils literal notranslate\"><span class=\"pre\">timedelta64</span></code> has new method <code class=\"docutils literal notranslate\"><span class=\"pre\">.dt.total_seconds()</span></code> returning the duration of the timedelta in seconds (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/10817\">GH 10817</a>)</p>\n@@ -921,23 +923,23 @@\n \n <span class=\"gp\">In [20]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[20]: </span>\n <span class=\"go\">0   0 days 00:01:00</span>\n <span class=\"go\">1   1 days 00:01:00</span>\n <span class=\"go\">2   2 days 00:01:00</span>\n <span class=\"go\">3   3 days 00:01:00</span>\n-<span class=\"go\">dtype: timedelta64[ns]</span>\n+<span class=\"go\">Length: 4, dtype: timedelta64[ns]</span>\n \n <span class=\"gp\">In [21]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">dt</span><span class=\"o\">.</span><span class=\"n\">total_seconds</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[21]: </span>\n <span class=\"go\">0        60.0</span>\n <span class=\"go\">1     86460.0</span>\n <span class=\"go\">2    172860.0</span>\n <span class=\"go\">3    259260.0</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 4, dtype: float64</span>\n </pre></div>\n </div>\n </section>\n </section>\n <section id=\"period-frequency-enhancement\">\n <span id=\"whatsnew-0170-periodfreq\"></span><h3>Period frequency enhancement<a class=\"headerlink\" href=\"#period-frequency-enhancement\" title=\"Link to this heading\">#</a></h3>\n <p><code class=\"docutils literal notranslate\"><span class=\"pre\">Period</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">PeriodIndex</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">period_range</span></code> can now accept multiplied freq. Also, <code class=\"docutils literal notranslate\"><span class=\"pre\">Period.freq</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">PeriodIndex.freq</span></code> are now stored as a <code class=\"docutils literal notranslate\"><span class=\"pre\">DateOffset</span></code> instance like <code class=\"docutils literal notranslate\"><span class=\"pre\">DatetimeIndex</span></code>, and not as <code class=\"docutils literal notranslate\"><span class=\"pre\">str</span></code> (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/7811\">GH 7811</a>)</p>\n@@ -1018,25 +1020,29 @@\n <span class=\"gh\">Out[32]: </span>\n <span class=\"go\">col1  foo    bar   </span>\n <span class=\"go\">col2    a  b   a  b</span>\n <span class=\"go\">i1 i2              </span>\n <span class=\"go\">j  l    1  2   3  4</span>\n <span class=\"go\">   k    5  6   7  8</span>\n \n+<span class=\"go\">[2 rows x 4 columns]</span>\n+\n <span class=\"gp\">In [33]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">to_excel</span><span class=\"p\">(</span><span class=\"s2\">&quot;test.xlsx&quot;</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [34]: </span><span class=\"n\">df</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_excel</span><span class=\"p\">(</span><span class=\"s2\">&quot;test.xlsx&quot;</span><span class=\"p\">,</span> <span class=\"n\">header</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=\"n\">index_col</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>\n \n <span class=\"gp\">In [35]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[35]: </span>\n <span class=\"go\">col1  foo    bar   </span>\n <span class=\"go\">col2    a  b   a  b</span>\n <span class=\"go\">i1 i2              </span>\n <span class=\"go\">j  l    1  2   3  4</span>\n <span class=\"go\">   k    5  6   7  8</span>\n+\n+<span class=\"go\">[2 rows x 4 columns]</span>\n </pre></div>\n </div>\n <p>Previously, it was necessary to specify the <code class=\"docutils literal notranslate\"><span class=\"pre\">has_index_names</span></code> argument in <code class=\"docutils literal notranslate\"><span class=\"pre\">read_excel</span></code>,\n if the serialized data had index names.  For version 0.17.0 the output format of <code class=\"docutils literal notranslate\"><span class=\"pre\">to_excel</span></code>\n has been changed to make this keyword unnecessary - the change is shown below.</p>\n <p><strong>Old</strong></p>\n <img alt=\"../_images/old-excel-index.png\" src=\"../_images/old-excel-index.png\" />\n@@ -1073,23 +1079,27 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [36]: </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=\"sa\">u</span><span class=\"s2\">&quot;\u56fd\u7c4d&quot;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"s2\">&quot;UK&quot;</span><span class=\"p\">,</span> <span class=\"sa\">u</span><span class=\"s2\">&quot;\u65e5\u672c&quot;</span><span class=\"p\">],</span> <span class=\"sa\">u</span><span class=\"s2\">&quot;\u540d\u524d&quot;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"s2\">&quot;Alice&quot;</span><span class=\"p\">,</span> <span class=\"sa\">u</span><span class=\"s2\">&quot;\u3057\u306e\u3076&quot;</span><span class=\"p\">]})</span>\n \n <span class=\"gp\">In [37]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[37]: </span>\n <span class=\"go\">   \u56fd\u7c4d     \u540d\u524d</span>\n <span class=\"go\">0  UK  Alice</span>\n <span class=\"go\">1  \u65e5\u672c    \u3057\u306e\u3076</span>\n+\n+<span class=\"go\">[2 rows x 2 columns]</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [38]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">set_option</span><span class=\"p\">(</span><span class=\"s2\">&quot;display.unicode.east_asian_width&quot;</span><span class=\"p\">,</span> <span class=\"kc\">True</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [39]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[39]: </span>\n <span class=\"go\">   \u56fd\u7c4d    \u540d\u524d</span>\n <span class=\"go\">0    UK   Alice</span>\n <span class=\"go\">1  \u65e5\u672c  \u3057\u306e\u3076</span>\n+\n+<span class=\"go\">[2 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>For further details, see <a class=\"reference internal\" href=\"../user_guide/options.html#options-east-asian-width\"><span class=\"std std-ref\">here</span></a></p>\n </section>\n <section id=\"other-enhancements\">\n <span id=\"whatsnew-0170-enhancements-other\"></span><h3>Other enhancements<a class=\"headerlink\" href=\"#other-enhancements\" title=\"Link to this heading\">#</a></h3>\n <ul>\n@@ -1121,14 +1131,16 @@\n <span class=\"gp\">In [42]: </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\">on</span><span class=\"o\">=</span><span class=\"s2\">&quot;col1&quot;</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\">indicator</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[42]: </span>\n <span class=\"go\">   col1 col_left  col_right      _merge</span>\n <span class=\"go\">0     0        a        NaN   left_only</span>\n <span class=\"go\">1     1        b        2.0        both</span>\n <span class=\"go\">2     2      NaN        2.0  right_only</span>\n <span class=\"go\">3     2      NaN        2.0  right_only</span>\n+\n+<span class=\"go\">[4 rows x 4 columns]</span>\n </pre></div>\n </div>\n <p>For more, see the <a class=\"reference internal\" href=\"../user_guide/merging.html#merging-indicator\"><span class=\"std std-ref\">updated docs</span></a></p>\n </li>\n <li><p><code class=\"docutils literal notranslate\"><span class=\"pre\">pd.to_numeric</span></code> is a new function to coerce strings to numbers (possibly with coercion) (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/11133\">GH 11133</a>)</p></li>\n <li><p><code class=\"docutils literal notranslate\"><span class=\"pre\">pd.merge</span></code> will now allow duplicate column names if they are not merged upon (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/10639\">GH 10639</a>).</p></li>\n <li><p><code class=\"docutils literal notranslate\"><span class=\"pre\">pd.pivot</span></code> will now allow passing index as <code class=\"docutils literal notranslate\"><span class=\"pre\">None</span></code> (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/3962\">GH 3962</a>).</p></li>\n@@ -1150,14 +1162,16 @@\n </div>\n <p>New behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [46]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">concat</span><span class=\"p\">([</span><span class=\"n\">foo</span><span class=\"p\">,</span> <span class=\"n\">bar</span><span class=\"p\">,</span> <span class=\"n\">baz</span><span class=\"p\">],</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[46]: </span>\n <span class=\"go\">   foo  0  1</span>\n <span class=\"go\">0    1  1  4</span>\n <span class=\"go\">1    2  2  5</span>\n+\n+<span class=\"go\">[2 rows x 3 columns]</span>\n </pre></div>\n </div>\n </li>\n <li><p><code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code> has gained the <code class=\"docutils literal notranslate\"><span class=\"pre\">nlargest</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">nsmallest</span></code> methods (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/10393\">GH 10393</a>)</p></li>\n <li><p>Add a <code class=\"docutils literal notranslate\"><span class=\"pre\">limit_direction</span></code> keyword argument that works with <code class=\"docutils literal notranslate\"><span class=\"pre\">limit</span></code> to enable <code class=\"docutils literal notranslate\"><span class=\"pre\">interpolate</span></code> to fill <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code> values forward, backward, or both (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/9218\">GH 9218</a>, <a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/10420\">GH 10420</a>, <a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/11115\">GH 11115</a>)</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [47]: </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\">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=\"mi\">5</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\">np</span><span class=\"o\">.</span><span class=\"n\">nan</span><span class=\"p\">,</span> <span class=\"mi\">13</span><span class=\"p\">])</span>\n \n@@ -1166,15 +1180,15 @@\n <span class=\"go\">0     NaN</span>\n <span class=\"go\">1     5.0</span>\n <span class=\"go\">2     5.0</span>\n <span class=\"go\">3     7.0</span>\n <span class=\"go\">4     NaN</span>\n <span class=\"go\">5    11.0</span>\n <span class=\"go\">6    13.0</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 7, dtype: float64</span>\n </pre></div>\n </div>\n </li>\n <li><p>Added a <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame.round</span></code> method to round the values to a variable number of decimal places (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/10568\">GH 10568</a>).</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [49]: </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>\n <span class=\"gp\">   ....: </span>    <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">random</span><span class=\"o\">.</span><span class=\"n\">random</span><span class=\"p\">([</span><span class=\"mi\">3</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">]),</span>\n <span class=\"gp\">   ....: </span>    <span class=\"n\">columns</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;C&quot;</span><span class=\"p\">],</span>\n@@ -1185,75 +1199,85 @@\n <span class=\"gp\">In [50]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[50]: </span>\n <span class=\"go\">               A         B         C</span>\n <span class=\"go\">first   0.126970  0.966718  0.260476</span>\n <span class=\"go\">second  0.897237  0.376750  0.336222</span>\n <span class=\"go\">third   0.451376  0.840255  0.123102</span>\n \n+<span class=\"go\">[3 rows x 3 columns]</span>\n+\n <span class=\"gp\">In [51]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">round</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[51]: </span>\n <span class=\"go\">           A     B     C</span>\n <span class=\"go\">first   0.13  0.97  0.26</span>\n <span class=\"go\">second  0.90  0.38  0.34</span>\n <span class=\"go\">third   0.45  0.84  0.12</span>\n \n+<span class=\"go\">[3 rows x 3 columns]</span>\n+\n <span class=\"gp\">In [52]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">round</span><span class=\"p\">({</span><span class=\"s2\">&quot;A&quot;</span><span class=\"p\">:</span> <span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"s2\">&quot;C&quot;</span><span class=\"p\">:</span> <span class=\"mi\">2</span><span class=\"p\">})</span>\n <span class=\"gh\">Out[52]: </span>\n <span class=\"go\">          A         B     C</span>\n <span class=\"go\">first   0.0  0.966718  0.26</span>\n <span class=\"go\">second  1.0  0.376750  0.34</span>\n <span class=\"go\">third   0.0  0.840255  0.12</span>\n+\n+<span class=\"go\">[3 rows x 3 columns]</span>\n </pre></div>\n </div>\n </li>\n <li><p><code class=\"docutils literal notranslate\"><span class=\"pre\">drop_duplicates</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">duplicated</span></code> now accept a <code class=\"docutils literal notranslate\"><span class=\"pre\">keep</span></code> keyword to target first, last, and all duplicates. The <code class=\"docutils literal notranslate\"><span class=\"pre\">take_last</span></code> keyword is deprecated, see <a class=\"reference internal\" href=\"#whatsnew-0170-deprecations\"><span class=\"std std-ref\">here</span></a> (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/6511\">GH 6511</a>, <a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/8505\">GH 8505</a>)</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [53]: </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=\"s2\">&quot;A&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;B&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;C&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;D&quot;</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [54]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">drop_duplicates</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[54]: </span>\n <span class=\"go\">0    A</span>\n <span class=\"go\">1    B</span>\n <span class=\"go\">2    C</span>\n <span class=\"go\">5    D</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 4, dtype: object</span>\n \n <span class=\"gp\">In [55]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">drop_duplicates</span><span class=\"p\">(</span><span class=\"n\">keep</span><span class=\"o\">=</span><span class=\"s2\">&quot;last&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[55]: </span>\n <span class=\"go\">2    C</span>\n <span class=\"go\">3    A</span>\n <span class=\"go\">4    B</span>\n <span class=\"go\">5    D</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 4, dtype: object</span>\n \n <span class=\"gp\">In [56]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">drop_duplicates</span><span class=\"p\">(</span><span class=\"n\">keep</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[56]: </span>\n <span class=\"go\">2    C</span>\n <span class=\"go\">5    D</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 2, dtype: object</span>\n </pre></div>\n </div>\n </li>\n <li><p>Reindex now has a <code class=\"docutils literal notranslate\"><span class=\"pre\">tolerance</span></code> argument that allows for finer control of <a class=\"reference internal\" href=\"../user_guide/basics.html#basics-limits-on-reindex-fill\"><span class=\"std std-ref\">Limits on filling while reindexing</span></a> (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/10411\">GH 10411</a>):</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [57]: </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=\"s2\">&quot;x&quot;</span><span class=\"p\">:</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mi\">5</span><span class=\"p\">),</span> <span class=\"s2\">&quot;t&quot;</span><span class=\"p\">:</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">date_range</span><span class=\"p\">(</span><span class=\"s2\">&quot;2000-01-01&quot;</span><span class=\"p\">,</span> <span class=\"n\">periods</span><span class=\"o\">=</span><span class=\"mi\">5</span><span class=\"p\">)})</span>\n \n <span class=\"gp\">In [58]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">reindex</span><span class=\"p\">([</span><span class=\"mf\">0.1</span><span class=\"p\">,</span> <span class=\"mf\">1.9</span><span class=\"p\">,</span> <span class=\"mf\">3.5</span><span class=\"p\">],</span> <span class=\"n\">method</span><span class=\"o\">=</span><span class=\"s2\">&quot;nearest&quot;</span><span class=\"p\">,</span> <span class=\"n\">tolerance</span><span class=\"o\">=</span><span class=\"mf\">0.2</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[58]: </span>\n <span class=\"go\">       x          t</span>\n <span class=\"go\">0.1  0.0 2000-01-01</span>\n <span class=\"go\">1.9  2.0 2000-01-03</span>\n <span class=\"go\">3.5  NaN        NaT</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>When used on a <code class=\"docutils literal notranslate\"><span class=\"pre\">DatetimeIndex</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">TimedeltaIndex</span></code> or <code class=\"docutils literal notranslate\"><span class=\"pre\">PeriodIndex</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">tolerance</span></code> will coerced into a <code class=\"docutils literal notranslate\"><span class=\"pre\">Timedelta</span></code> if possible. This allows you to specify tolerance with a string:</p>\n <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\">df</span><span class=\"o\">.</span><span class=\"n\">set_index</span><span class=\"p\">(</span><span class=\"s2\">&quot;t&quot;</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [60]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">reindex</span><span class=\"p\">(</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">to_datetime</span><span class=\"p\">([</span><span class=\"s2\">&quot;1999-12-31&quot;</span><span class=\"p\">]),</span> <span class=\"n\">method</span><span class=\"o\">=</span><span class=\"s2\">&quot;nearest&quot;</span><span class=\"p\">,</span> <span class=\"n\">tolerance</span><span class=\"o\">=</span><span class=\"s2\">&quot;1 day&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[60]: </span>\n <span class=\"go\">            x</span>\n <span class=\"go\">1999-12-31  0</span>\n+\n+<span class=\"go\">[1 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p><code class=\"docutils literal notranslate\"><span class=\"pre\">tolerance</span></code> is also exposed by the lower level <code class=\"docutils literal notranslate\"><span class=\"pre\">Index.get_indexer</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">Index.get_loc</span></code> methods.</p>\n </li>\n <li><p>Added functionality to use the <code class=\"docutils literal notranslate\"><span class=\"pre\">base</span></code> argument when resampling a <code class=\"docutils literal notranslate\"><span class=\"pre\">TimeDeltaIndex</span></code> (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/10530\">GH 10530</a>)</p></li>\n <li><p><code class=\"docutils literal notranslate\"><span class=\"pre\">DatetimeIndex</span></code> can be instantiated using strings contains <code class=\"docutils literal notranslate\"><span class=\"pre\">NaT</span></code> (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/7599\">GH 7599</a>)</p></li>\n <li><p><code class=\"docutils literal notranslate\"><span class=\"pre\">to_datetime</span></code> can now accept the <code class=\"docutils literal notranslate\"><span class=\"pre\">yearfirst</span></code> keyword (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/7599\">GH 7599</a>)</p></li>\n@@ -1434,18 +1458,18 @@\n </div>\n <div class=\"admonition note\">\n <p class=\"admonition-title\">Note</p>\n <p>If you want to perform calculations based on today\u2019s 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>\n <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>\n \n <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>\n-<span class=\"gh\">Out[66]: </span><span class=\"go\">Timestamp(&#39;2025-03-31 00:07:15.692651&#39;)</span>\n+<span class=\"gh\">Out[66]: </span><span class=\"go\">Timestamp(&#39;2026-05-04 09:45:17.903170&#39;)</span>\n \n <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>\n-<span class=\"gh\">Out[67]: </span><span class=\"go\">Timestamp(&#39;2026-03-31 00:07:15.694263&#39;)</span>\n+<span class=\"gh\">Out[67]: </span><span class=\"go\">Timestamp(&#39;2027-05-04 09:45:17.904676&#39;)</span>\n </pre></div>\n </div>\n </div>\n </section>\n </section>\n <section id=\"changes-to-index-comparisons\">\n <h3>Changes to Index comparisons<a class=\"headerlink\" href=\"#changes-to-index-comparisons\" title=\"Link to this heading\">#</a></h3>\n@@ -1494,38 +1518,38 @@\n <span class=\"gp\">In [70]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">iloc</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"kc\">None</span>\n \n <span class=\"gp\">In [71]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[71]: </span>\n <span class=\"go\">0    0.0</span>\n <span class=\"go\">1    NaN</span>\n <span class=\"go\">2    2.0</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 3, dtype: float64</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [5]: </span><span class=\"n\">s</span> <span class=\"o\">==</span> <span class=\"kc\">None</span>\n <span class=\"go\">TypeError: Could not compare &lt;type &#39;NoneType&#39;&gt; type with Series</span>\n </pre></div>\n </div>\n <p>New behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [72]: </span><span class=\"n\">s</span> <span class=\"o\">==</span> <span class=\"kc\">None</span>\n <span class=\"gh\">Out[72]: </span>\n <span class=\"go\">0    False</span>\n <span class=\"go\">1    False</span>\n <span class=\"go\">2    False</span>\n-<span class=\"go\">dtype: bool</span>\n+<span class=\"go\">Length: 3, dtype: bool</span>\n </pre></div>\n </div>\n <p>Usually you simply want to know which values are null.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [73]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">isnull</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[73]: </span>\n <span class=\"go\">0    False</span>\n <span class=\"go\">1     True</span>\n <span class=\"go\">2    False</span>\n-<span class=\"go\">dtype: bool</span>\n+<span class=\"go\">Length: 3, dtype: bool</span>\n </pre></div>\n </div>\n <div class=\"admonition warning\">\n <p class=\"admonition-title\">Warning</p>\n <p>You generally will want to use <code class=\"docutils literal notranslate\"><span class=\"pre\">isnull/notnull</span></code> for these types of comparisons, as <code class=\"docutils literal notranslate\"><span class=\"pre\">isnull/notnull</span></code> tells you which elements are null. One has to be\n mindful that <code class=\"docutils literal notranslate\"><span class=\"pre\">nan's</span></code> don\u2019t compare equal, but <code class=\"docutils literal notranslate\"><span class=\"pre\">None's</span></code> do. Note that pandas/numpy uses the fact that <code class=\"docutils literal notranslate\"><span class=\"pre\">np.nan</span> <span class=\"pre\">!=</span> <span class=\"pre\">np.nan</span></code>, and treats <code class=\"docutils literal notranslate\"><span class=\"pre\">None</span></code> like <code class=\"docutils literal notranslate\"><span class=\"pre\">np.nan</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [74]: </span><span class=\"kc\">None</span> <span class=\"o\">==</span> <span class=\"kc\">None</span>\n@@ -1548,14 +1572,16 @@\n \n <span class=\"gp\">In [77]: </span><span class=\"n\">df_with_missing</span>\n <span class=\"gh\">Out[77]: </span>\n <span class=\"go\">   col1  col2</span>\n <span class=\"go\">0   0.0   1.0</span>\n <span class=\"go\">1   NaN   NaN</span>\n <span class=\"go\">2   2.0   NaN</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [27]:</span>\n <span class=\"go\">df_with_missing.to_hdf(&#39;file.h5&#39;,</span>\n <span class=\"go\">                       key=&#39;df_with_missing&#39;,</span>\n <span class=\"go\">                       format=&#39;table&#39;,</span>\n <span class=\"go\">                       mode=&#39;w&#39;)</span>\n@@ -1573,14 +1599,16 @@\n \n <span class=\"gp\">In [79]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_hdf</span><span class=\"p\">(</span><span class=\"s2\">&quot;file.h5&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;df_with_missing&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[79]: </span>\n <span class=\"go\">   col1  col2</span>\n <span class=\"go\">0   0.0   1.0</span>\n <span class=\"go\">1   NaN   NaN</span>\n <span class=\"go\">2   2.0   NaN</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>See the <a class=\"reference internal\" href=\"../user_guide/io.html#io-hdf5\"><span class=\"std std-ref\">docs</span></a> for more details.</p>\n </section>\n <section id=\"changes-to-display-precision-option\">\n <span id=\"whatsnew-0170-api-breaking-display-precision\"></span><h3>Changes to <code class=\"docutils literal notranslate\"><span class=\"pre\">display.precision</span></code> option<a class=\"headerlink\" href=\"#changes-to-display-precision-option\" title=\"Link to this heading\">#</a></h3>\n <p>The <code class=\"docutils literal notranslate\"><span class=\"pre\">display.precision</span></code> option has been clarified to refer to decimal places (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/10451\">GH 10451</a>).</p>\n@@ -1600,14 +1628,16 @@\n regular formatting as well as scientific notation, similar to how numpy\u2019s <code class=\"docutils literal notranslate\"><span class=\"pre\">precision</span></code> print option works.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [80]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">set_option</span><span class=\"p\">(</span><span class=\"s2\">&quot;display.precision&quot;</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [81]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"p\">({</span><span class=\"s2\">&quot;x&quot;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"mf\">123.456789</span><span class=\"p\">]})</span>\n <span class=\"gh\">Out[81]: </span>\n <span class=\"go\">        x</span>\n <span class=\"go\">0  123.46</span>\n+\n+<span class=\"go\">[1 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p>To preserve output behavior with prior versions the default value of <code class=\"docutils literal notranslate\"><span class=\"pre\">display.precision</span></code> has been reduced to <code class=\"docutils literal notranslate\"><span class=\"pre\">6</span></code>\n from <code class=\"docutils literal notranslate\"><span class=\"pre\">7</span></code>.</p>\n </section>\n <section id=\"changes-to-categorical-unique\">\n <span id=\"whatsnew-0170-api-breaking-categorical-unique\"></span><h3>Changes to <code class=\"docutils literal notranslate\"><span class=\"pre\">Categorical.unique</span></code><a class=\"headerlink\" href=\"#changes-to-categorical-unique\" title=\"Link to this heading\">#</a></h3>\n@@ -1781,14 +1811,16 @@\n <span class=\"gh\">Out[90]: </span>\n <span class=\"go\">                   A         B</span>\n <span class=\"go\">2013-01-01  0.471435 -1.190976</span>\n <span class=\"go\">2013-01-02  1.432707 -0.312652</span>\n <span class=\"go\">2013-01-03 -0.720589  0.887163</span>\n <span class=\"go\">2013-01-04  0.859588 -0.636524</span>\n <span class=\"go\">2013-01-05  0.015696 -2.242685</span>\n+\n+<span class=\"go\">[5 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>Previously</p>\n <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\">df</span><span class=\"o\">.</span><span class=\"n\">A</span>\n <span class=\"go\">FutureWarning: TimeSeries broadcasting along DataFrame index by default is deprecated.</span>\n <span class=\"go\">Please use DataFrame.&lt;op&gt; to explicitly broadcast arithmetic operations along the index</span>\n \n@@ -1806,14 +1838,16 @@\n <span class=\"gh\">Out[91]: </span>\n <span class=\"go\">                   A         B</span>\n <span class=\"go\">2013-01-01  0.942870 -0.719541</span>\n <span class=\"go\">2013-01-02  2.865414  1.120055</span>\n <span class=\"go\">2013-01-03 -1.441177  0.166574</span>\n <span class=\"go\">2013-01-04  1.719177  0.223065</span>\n <span class=\"go\">2013-01-05  0.031393 -2.226989</span>\n+\n+<span class=\"go\">[5 rows x 2 columns]</span>\n </pre></div>\n </div>\n </li>\n <li><p>Remove <code class=\"docutils literal notranslate\"><span class=\"pre\">table</span></code> keyword in <code class=\"docutils literal notranslate\"><span class=\"pre\">HDFStore.put/append</span></code>, in favor of using <code class=\"docutils literal notranslate\"><span class=\"pre\">format=</span></code> (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/4645\">GH 4645</a>)</p></li>\n <li><p>Remove <code class=\"docutils literal notranslate\"><span class=\"pre\">kind</span></code> in <code class=\"docutils literal notranslate\"><span class=\"pre\">read_excel/ExcelFile</span></code> as its unused (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/4712\">GH 4712</a>)</p></li>\n <li><p>Remove <code class=\"docutils literal notranslate\"><span class=\"pre\">infer_type</span></code> keyword from <code class=\"docutils literal notranslate\"><span class=\"pre\">pd.read_html</span></code> as its unused (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/4770\">GH 4770</a>, <a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/7032\">GH 7032</a>)</p></li>\n <li><p>Remove <code class=\"docutils literal notranslate\"><span class=\"pre\">offset</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">timeRule</span></code> keywords from <code class=\"docutils literal notranslate\"><span class=\"pre\">Series.tshift/shift</span></code>, in favor of <code class=\"docutils literal notranslate\"><span class=\"pre\">freq</span></code> (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/4853\">GH 4853</a>, <a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/4864\">GH 4864</a>)</p></li>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -222,33 +222,35 @@\n In [2]: df\n Out[2]:\n            A                         B                         C\n 0 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00+01:00\n 1 2013-01-02 2013-01-02 00:00:00-05:00 2013-01-02 00:00:00+01:00\n 2 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-03 00:00:00+01:00\n \n+[3 rows x 3 columns]\n+\n In [3]: df.dtypes\n Out[3]:\n A                datetime64[ns]\n B    datetime64[ns, US/Eastern]\n C           datetime64[ns, CET]\n-dtype: object\n+Length: 3, dtype: object\n In [4]: df.B\n Out[4]:\n 0   2013-01-01 00:00:00-05:00\n 1   2013-01-02 00:00:00-05:00\n 2   2013-01-03 00:00:00-05:00\n-Name: B, dtype: datetime64[ns, US/Eastern]\n+Name: B, Length: 3, dtype: datetime64[ns, US/Eastern]\n \n In [5]: df.B.dt.tz_localize(None)\n Out[5]:\n 0   2013-01-01\n 1   2013-01-02\n 2   2013-01-03\n-Name: B, dtype: datetime64[ns]\n+Name: B, Length: 3, dtype: datetime64[ns]\n This uses a new-dtype representation as well, that is very similar in look-and-\n feel to its numpy cousin datetime64[ns]\n In [6]: df[\"B\"].dtype\n Out[6]: datetime64[ns, US/Eastern]\n \n In [7]: type(df[\"B\"].dtype)\n Out[7]: pandas.core.dtypes.dtypes.DatetimeTZDtype\n@@ -324,64 +326,64 @@\n \n In [14]: s\n Out[14]:\n 0   2013-01-01\n 1   2013-01-02\n 2   2013-01-03\n 3   2013-01-04\n-dtype: datetime64[ns]\n+Length: 4, dtype: datetime64[ns]\n \n In [15]: s.dt.strftime(\"%Y/%m/%d\")\n Out[15]:\n 0    2013/01/01\n 1    2013/01/02\n 2    2013/01/03\n 3    2013/01/04\n-dtype: object\n+Length: 4, dtype: object\n # PeriodIndex\n In [16]: s = pd.Series(pd.period_range(\"20130101\", periods=4))\n \n In [17]: s\n Out[17]:\n 0    2013-01-01\n 1    2013-01-02\n 2    2013-01-03\n 3    2013-01-04\n-dtype: period[D]\n+Length: 4, dtype: period[D]\n \n In [18]: s.dt.strftime(\"%Y/%m/%d\")\n Out[18]:\n 0    2013/01/01\n 1    2013/01/02\n 2    2013/01/03\n 3    2013/01/04\n-dtype: object\n+Length: 4, dtype: object\n The string format is as the python standard library and details can be found\n _\bh_\be_\br_\be\n *\b**\b**\b* S\bSe\ber\bri\bie\bes\bs.\b.d\bdt\bt.\b.t\bto\bot\bta\bal\bl_\b_s\bse\bec\bco\bon\bnd\bds\bs_\b#\b# *\b**\b**\b*\n pd.Series of type timedelta64 has new method .dt.total_seconds() returning the\n duration of the timedelta in seconds (_\bG_\bH_\b _\b1_\b0_\b8_\b1_\b7)\n # TimedeltaIndex\n In [19]: s = pd.Series(pd.timedelta_range(\"1 minutes\", periods=4))\n \n In [20]: s\n Out[20]:\n 0   0 days 00:01:00\n 1   1 days 00:01:00\n 2   2 days 00:01:00\n 3   3 days 00:01:00\n-dtype: timedelta64[ns]\n+Length: 4, dtype: timedelta64[ns]\n \n In [21]: s.dt.total_seconds()\n Out[21]:\n 0        60.0\n 1     86460.0\n 2    172860.0\n 3    259260.0\n-dtype: float64\n+Length: 4, dtype: float64\n *\b**\b**\b**\b* P\bPe\ber\bri\bio\bod\bd f\bfr\bre\beq\bqu\bue\ben\bnc\bcy\by e\ben\bnh\bha\ban\bnc\bce\bem\bme\ben\bnt\bt_\b#\b# *\b**\b**\b**\b*\n Period, PeriodIndex and period_range can now accept multiplied freq. Also,\n Period.freq and PeriodIndex.freq are now stored as a DateOffset instance like\n DatetimeIndex, and not as str (_\bG_\bH_\b _\b7_\b8_\b1_\b1)\n A multiplied freq represents a span of corresponding length. The example below\n creates a period of 3 days. Addition and subtraction will shift the period by\n its span.\n@@ -447,25 +449,29 @@\n Out[32]:\n col1  foo    bar\n col2    a  b   a  b\n i1 i2\n j  l    1  2   3  4\n    k    5  6   7  8\n \n+[2 rows x 4 columns]\n+\n In [33]: df.to_excel(\"test.xlsx\")\n \n In [34]: df = pd.read_excel(\"test.xlsx\", header=[0, 1], index_col=[0, 1])\n \n In [35]: df\n Out[35]:\n col1  foo    bar\n col2    a  b   a  b\n i1 i2\n j  l    1  2   3  4\n    k    5  6   7  8\n+\n+[2 rows x 4 columns]\n Previously, it was necessary to specify the has_index_names argument in\n read_excel, if the serialized data had index names. For version 0.17.0 the\n output format of to_excel has been changed to make this keyword unnecessary -\n the change is shown below.\n O\bOl\bld\bd\n [../_images/old-excel-index.png]\n N\bNe\bew\bw\n@@ -500,21 +506,25 @@\n In [36]: df = pd.DataFrame({u\"\u56fd\u7c4d\": [\"UK\", u\"\u65e5\u672c\"], u\"\u540d\u524d\": [\"Alice\", u\"\u3057\u306e\u3076\"]})\n \n In [37]: df\n Out[37]:\n    \u56fd\u7c4d     \u540d\u524d\n 0  UK  Alice\n 1  \u65e5\u672c    \u3057\u306e\u3076\n+\n+[2 rows x 2 columns]\n In [38]: pd.set_option(\"display.unicode.east_asian_width\", True)\n \n In [39]: df\n Out[39]:\n    \u56fd\u7c4d    \u540d\u524d\n 0    UK   Alice\n 1  \u65e5\u672c  \u3057\u306e\u3076\n+\n+[2 rows x 2 columns]\n For further details, see _\bh_\be_\br_\be\n *\b**\b**\b**\b* O\bOt\bth\bhe\ber\br e\ben\bnh\bha\ban\bnc\bce\bem\bme\ben\bnt\bts\bs_\b#\b# *\b**\b**\b**\b*\n     * Support for openpyxl >= 2.2. The API for style support is now stable (_\bG_\bH\n       _\b1_\b0_\b1_\b2_\b5)\n     * merge now accepts the argument indicator which adds a Categorical-type\n       column (by default called _merge) to the output object that takes on the\n       values (_\bG_\bH_\b _\b8_\b7_\b9_\b0)\n@@ -529,14 +539,16 @@\n       In [42]: pd.merge(df1, df2, on=\"col1\", how=\"outer\", indicator=True)\n       Out[42]:\n          col1 col_left  col_right      _merge\n       0     0        a        NaN   left_only\n       1     1        b        2.0        both\n       2     2      NaN        2.0  right_only\n       3     2      NaN        2.0  right_only\n+\n+      [4 rows x 4 columns]\n       For more, see the _\bu_\bp_\bd_\ba_\bt_\be_\bd_\b _\bd_\bo_\bc_\bs\n     * pd.to_numeric is a new function to coerce strings to numbers (possibly\n       with coercion) (_\bG_\bH_\b _\b1_\b1_\b1_\b3_\b3)\n     * pd.merge will now allow duplicate column names if they are not merged\n       upon (_\bG_\bH_\b _\b1_\b0_\b6_\b3_\b9).\n     * pd.pivot will now allow passing index as None (_\bG_\bH_\b _\b3_\b9_\b6_\b2).\n     * pd.concat will now use existing Series names if provided (_\bG_\bH_\b _\b1_\b0_\b6_\b9_\b8).\n@@ -553,14 +565,16 @@\n          1  2  2  5\n       New behavior:\n       In [46]: pd.concat([foo, bar, baz], axis=1)\n       Out[46]:\n          foo  0  1\n       0    1  1  4\n       1    2  2  5\n+\n+      [2 rows x 3 columns]\n     * DataFrame has gained the nlargest and nsmallest methods (_\bG_\bH_\b _\b1_\b0_\b3_\b9_\b3)\n     * Add a limit_direction keyword argument that works with limit to enable\n       interpolate to fill NaN values forward, backward, or both (_\bG_\bH_\b _\b9_\b2_\b1_\b8, _\bG_\bH\n       _\b1_\b0_\b4_\b2_\b0, _\bG_\bH_\b _\b1_\b1_\b1_\b1_\b5)\n       In [47]: ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan, np.nan, 13])\n \n       In [48]: ser.interpolate(limit=1, limit_direction=\"both\")\n@@ -568,15 +582,15 @@\n       0     NaN\n       1     5.0\n       2     5.0\n       3     7.0\n       4     NaN\n       5    11.0\n       6    13.0\n-      dtype: float64\n+      Length: 7, dtype: float64\n     * Added a DataFrame.round method to round the values to a variable number\n       of decimal places (_\bG_\bH_\b _\b1_\b0_\b5_\b6_\b8).\n       In [49]: df = pd.DataFrame(\n          ....:     np.random.random([3, 3]),\n          ....:     columns=[\"A\", \"B\", \"C\"],\n          ....:     index=[\"first\", \"second\", \"third\"],\n          ....: )\n@@ -585,74 +599,84 @@\n       In [50]: df\n       Out[50]:\n                      A         B         C\n       first   0.126970  0.966718  0.260476\n       second  0.897237  0.376750  0.336222\n       third   0.451376  0.840255  0.123102\n \n+      [3 rows x 3 columns]\n+\n       In [51]: df.round(2)\n       Out[51]:\n                  A     B     C\n       first   0.13  0.97  0.26\n       second  0.90  0.38  0.34\n       third   0.45  0.84  0.12\n \n+      [3 rows x 3 columns]\n+\n       In [52]: df.round({\"A\": 0, \"C\": 2})\n       Out[52]:\n                 A         B     C\n       first   0.0  0.966718  0.26\n       second  1.0  0.376750  0.34\n       third   0.0  0.840255  0.12\n+\n+      [3 rows x 3 columns]\n     * drop_duplicates and duplicated now accept a keep keyword to target first,\n       last, and all duplicates. The take_last keyword is deprecated, see _\bh_\be_\br_\be\n       (_\bG_\bH_\b _\b6_\b5_\b1_\b1, _\bG_\bH_\b _\b8_\b5_\b0_\b5)\n       In [53]: s = pd.Series([\"A\", \"B\", \"C\", \"A\", \"B\", \"D\"])\n \n       In [54]: s.drop_duplicates()\n       Out[54]:\n       0    A\n       1    B\n       2    C\n       5    D\n-      dtype: object\n+      Length: 4, dtype: object\n \n       In [55]: s.drop_duplicates(keep=\"last\")\n       Out[55]:\n       2    C\n       3    A\n       4    B\n       5    D\n-      dtype: object\n+      Length: 4, dtype: object\n \n       In [56]: s.drop_duplicates(keep=False)\n       Out[56]:\n       2    C\n       5    D\n-      dtype: object\n+      Length: 2, dtype: object\n     * Reindex now has a tolerance argument that allows for finer control of\n       _\bL_\bi_\bm_\bi_\bt_\bs_\b _\bo_\bn_\b _\bf_\bi_\bl_\bl_\bi_\bn_\bg_\b _\bw_\bh_\bi_\bl_\be_\b _\br_\be_\bi_\bn_\bd_\be_\bx_\bi_\bn_\bg (_\bG_\bH_\b _\b1_\b0_\b4_\b1_\b1):\n       In [57]: df = pd.DataFrame({\"x\": range(5), \"t\": pd.date_range(\"2000-01-\n       01\", periods=5)})\n \n       In [58]: df.reindex([0.1, 1.9, 3.5], method=\"nearest\", tolerance=0.2)\n       Out[58]:\n              x          t\n       0.1  0.0 2000-01-01\n       1.9  2.0 2000-01-03\n       3.5  NaN        NaT\n+\n+      [3 rows x 2 columns]\n       When used on a DatetimeIndex, TimedeltaIndex or PeriodIndex, tolerance\n       will coerced into a Timedelta if possible. This allows you to specify\n       tolerance with a string:\n       In [59]: df = df.set_index(\"t\")\n \n       In [60]: df.reindex(pd.to_datetime([\"1999-12-31\"]), method=\"nearest\",\n       tolerance=\"1 day\")\n       Out[60]:\n                   x\n       1999-12-31  0\n+\n+      [1 rows x 1 columns]\n       tolerance is also exposed by the lower level Index.get_indexer and\n       Index.get_loc methods.\n     * Added functionality to use the base argument when resampling a\n       TimeDeltaIndex (_\bG_\bH_\b _\b1_\b0_\b5_\b3_\b0)\n     * DatetimeIndex can be instantiated using strings contains NaT (_\bG_\bH_\b _\b7_\b5_\b9_\b9)\n     * to_datetime can now accept the yearfirst keyword (_\bG_\bH_\b _\b7_\b5_\b9_\b9)\n     * pandas.tseries.offsets larger than the Day offset can now be used with a\n@@ -790,18 +814,18 @@\n freq=None)\n Note\n If you want to perform calculations based on today\u2019s date, use Timestamp.now()\n and pandas.tseries.offsets.\n In [65]: import pandas.tseries.offsets as offsets\n \n In [66]: pd.Timestamp.now()\n-Out[66]: Timestamp('2025-03-31 00:07:15.692651')\n+Out[66]: Timestamp('2026-05-04 09:45:17.903170')\n \n In [67]: pd.Timestamp.now() + offsets.DateOffset(years=1)\n-Out[67]: Timestamp('2026-03-31 00:07:15.694263')\n+Out[67]: Timestamp('2027-05-04 09:45:17.904676')\n *\b**\b**\b**\b* C\bCh\bha\ban\bng\bge\bes\bs t\bto\bo I\bIn\bnd\bde\bex\bx c\bco\bom\bmp\bpa\bar\bri\bis\bso\bon\bns\bs_\b#\b# *\b**\b**\b**\b*\n Operator equal on Index should behavior similarly to Series (_\bG_\bH_\b _\b9_\b9_\b4_\b7, _\bG_\bH_\b _\b1_\b0_\b6_\b3_\b7)\n Starting in v0.17.0, comparing Index objects of different lengths will raise a\n ValueError. This is to be consistent with the behavior of Series.\n Previous behavior:\n In [2]: pd.Index([1, 2, 3]) == pd.Index([1, 4, 5])\n Out[2]: array([ True, False, False], dtype=bool)\n@@ -835,32 +859,32 @@\n In [70]: s.iloc[1] = None\n \n In [71]: s\n Out[71]:\n 0    0.0\n 1    NaN\n 2    2.0\n-dtype: float64\n+Length: 3, dtype: float64\n Previous behavior:\n In [5]: s == None\n TypeError: Could not compare <type 'NoneType'> type with Series\n New behavior:\n In [72]: s == None\n Out[72]:\n 0    False\n 1    False\n 2    False\n-dtype: bool\n+Length: 3, dtype: bool\n Usually you simply want to know which values are null.\n In [73]: s.isnull()\n Out[73]:\n 0    False\n 1     True\n 2    False\n-dtype: bool\n+Length: 3, dtype: bool\n Warning\n You generally will want to use isnull/notnull for these types of comparisons,\n as isnull/notnull tells you which elements are null. One has to be mindful that\n nan's don\u2019t compare equal, but None's do. Note that pandas/numpy uses the fact\n that np.nan != np.nan, and treats None like np.nan.\n In [74]: None == None\n Out[74]: True\n@@ -880,14 +904,16 @@\n \n In [77]: df_with_missing\n Out[77]:\n    col1  col2\n 0   0.0   1.0\n 1   NaN   NaN\n 2   2.0   NaN\n+\n+[3 rows x 2 columns]\n In [27]:\n df_with_missing.to_hdf('file.h5',\n                        key='df_with_missing',\n                        format='table',\n                        mode='w')\n \n In [28]: pd.read_hdf('file.h5', 'df_with_missing')\n@@ -902,14 +928,16 @@\n \n In [79]: pd.read_hdf(\"file.h5\", \"df_with_missing\")\n Out[79]:\n    col1  col2\n 0   0.0   1.0\n 1   NaN   NaN\n 2   2.0   NaN\n+\n+[3 rows x 2 columns]\n See the _\bd_\bo_\bc_\bs for more details.\n *\b**\b**\b**\b* C\bCh\bha\ban\bng\bge\bes\bs t\bto\bo d\bdi\bis\bsp\bpl\bla\bay\by.\b.p\bpr\bre\bec\bci\bis\bsi\bio\bon\bn o\bop\bpt\bti\bio\bon\bn_\b#\b# *\b**\b**\b**\b*\n The display.precision option has been clarified to refer to decimal places (_\bG_\bH\n _\b1_\b0_\b4_\b5_\b1).\n Earlier versions of pandas would format floating point numbers to have one less\n decimal place than the value in display.precision.\n In [1]: pd.set_option('display.precision', 2)\n@@ -926,14 +954,16 @@\n notation, similar to how numpy\u2019s precision print option works.\n In [80]: pd.set_option(\"display.precision\", 2)\n \n In [81]: pd.DataFrame({\"x\": [123.456789]})\n Out[81]:\n         x\n 0  123.46\n+\n+[1 rows x 1 columns]\n To preserve output behavior with prior versions the default value of\n display.precision has been reduced to 6 from 7.\n *\b**\b**\b**\b* C\bCh\bha\ban\bng\bge\bes\bs t\bto\bo C\bCa\bat\bte\beg\bgo\bor\bri\bic\bca\bal\bl.\b.u\bun\bni\biq\bqu\bue\be_\b#\b# *\b**\b**\b**\b*\n Categorical.unique now returns new Categoricals with categories and codes that\n are unique, rather than returning np.array (_\bG_\bH_\b _\b1_\b0_\b5_\b0_\b8)\n     * unordered category: values and categories are sorted by appearance order.\n     * ordered category: values are sorted by appearance order, categories keep\n@@ -1058,14 +1088,16 @@\n       Out[90]:\n                          A         B\n       2013-01-01  0.471435 -1.190976\n       2013-01-02  1.432707 -0.312652\n       2013-01-03 -0.720589  0.887163\n       2013-01-04  0.859588 -0.636524\n       2013-01-05  0.015696 -2.242685\n+\n+      [5 rows x 2 columns]\n       Previously\n       In [3]: df + df.A\n       FutureWarning: TimeSeries broadcasting along DataFrame index by default\n       is deprecated.\n       Please use DataFrame.<op> to explicitly broadcast arithmetic operations\n       along the index\n \n@@ -1081,14 +1113,16 @@\n       Out[91]:\n                          A         B\n       2013-01-01  0.942870 -0.719541\n       2013-01-02  2.865414  1.120055\n       2013-01-03 -1.441177  0.166574\n       2013-01-04  1.719177  0.223065\n       2013-01-05  0.031393 -2.226989\n+\n+      [5 rows x 2 columns]\n     * Remove table keyword in HDFStore.put/append, in favor of using format=\n       (_\bG_\bH_\b _\b4_\b6_\b4_\b5)\n     * Remove kind in read_excel/ExcelFile as its unused (_\bG_\bH_\b _\b4_\b7_\b1_\b2)\n     * Remove infer_type keyword from pd.read_html as its unused (_\bG_\bH_\b _\b4_\b7_\b7_\b0, _\bG_\bH\n       _\b7_\b0_\b3_\b2)\n     * Remove offset and timeRule keywords from Series.tshift/shift, in favor of\n       freq (_\bG_\bH_\b _\b4_\b8_\b5_\b3, _\bG_\bH_\b _\b4_\b8_\b6_\b4)\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.17.1.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.17.1.html", "unified_diff": "@@ -1642,45 +1642,45 @@\n \n <span class=\"gp\">In [10]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[10]: </span>\n <span class=\"go\">0    a</span>\n <span class=\"go\">1    a</span>\n <span class=\"go\">2    b</span>\n <span class=\"go\">3    b</span>\n-<span class=\"go\">dtype: category</span>\n+<span class=\"go\">Length: 4, dtype: category</span>\n <span class=\"go\">Categories (2, object): [&#39;a&#39;, &#39;b&#39;]</span>\n \n <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\">contains</span><span class=\"p\">(</span><span class=\"s2\">&quot;a&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[11]: </span>\n <span class=\"go\">0     True</span>\n <span class=\"go\">1     True</span>\n <span class=\"go\">2    False</span>\n <span class=\"go\">3    False</span>\n-<span class=\"go\">dtype: bool</span>\n+<span class=\"go\">Length: 4, dtype: bool</span>\n \n <span class=\"gp\">In [12]: </span><span class=\"n\">date</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\">pd</span><span class=\"o\">.</span><span class=\"n\">date_range</span><span class=\"p\">(</span><span class=\"s2\">&quot;1/1/2015&quot;</span><span class=\"p\">,</span> <span class=\"n\">periods</span><span class=\"o\">=</span><span class=\"mi\">5</span><span class=\"p\">))</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"s2\">&quot;category&quot;</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [13]: </span><span class=\"n\">date</span>\n <span class=\"gh\">Out[13]: </span>\n <span class=\"go\">0   2015-01-01</span>\n <span class=\"go\">1   2015-01-02</span>\n <span class=\"go\">2   2015-01-03</span>\n <span class=\"go\">3   2015-01-04</span>\n <span class=\"go\">4   2015-01-05</span>\n-<span class=\"go\">dtype: category</span>\n+<span class=\"go\">Length: 5, dtype: category</span>\n <span class=\"go\">Categories (5, datetime64[ns]): [2015-01-01, 2015-01-02, 2015-01-03, 2015-01-04, 2015-01-05]</span>\n \n <span class=\"gp\">In [14]: </span><span class=\"n\">date</span><span class=\"o\">.</span><span class=\"n\">dt</span><span class=\"o\">.</span><span class=\"n\">day</span>\n <span class=\"gh\">Out[14]: </span>\n <span class=\"go\">0    1</span>\n <span class=\"go\">1    2</span>\n <span class=\"go\">2    3</span>\n <span class=\"go\">3    4</span>\n <span class=\"go\">4    5</span>\n-<span class=\"go\">dtype: int32</span>\n+<span class=\"go\">Length: 5, dtype: int32</span>\n </pre></div>\n </div>\n </li>\n <li><p><code class=\"docutils literal notranslate\"><span class=\"pre\">pivot_table</span></code> now has a <code class=\"docutils literal notranslate\"><span class=\"pre\">margins_name</span></code> argument so you can use something other than the default of \u2018All\u2019 (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/3335\">GH 3335</a>)</p></li>\n <li><p>Implement export of <code class=\"docutils literal notranslate\"><span class=\"pre\">datetime64[ns,</span> <span class=\"pre\">tz]</span></code> dtypes with a fixed HDF5 store (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/11411\">GH 11411</a>)</p></li>\n <li><p>Pretty printing sets (e.g. in DataFrame cells) now uses set literal syntax (<code class=\"docutils literal notranslate\"><span class=\"pre\">{x,</span> <span class=\"pre\">y}</span></code>) instead of\n Legacy Python syntax (<code class=\"docutils literal notranslate\"><span class=\"pre\">set([x,</span> <span class=\"pre\">y])</span></code>) (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/11215\">GH 11215</a>)</p></li>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -229,47 +229,47 @@\n \n       In [10]: s\n       Out[10]:\n       0    a\n       1    a\n       2    b\n       3    b\n-      dtype: category\n+      Length: 4, dtype: category\n       Categories (2, object): ['a', 'b']\n \n       In [11]: s.str.contains(\"a\")\n       Out[11]:\n       0     True\n       1     True\n       2    False\n       3    False\n-      dtype: bool\n+      Length: 4, dtype: bool\n \n       In [12]: date = pd.Series(pd.date_range(\"1/1/2015\", periods=5)).astype\n       (\"category\")\n \n       In [13]: date\n       Out[13]:\n       0   2015-01-01\n       1   2015-01-02\n       2   2015-01-03\n       3   2015-01-04\n       4   2015-01-05\n-      dtype: category\n+      Length: 5, dtype: category\n       Categories (5, datetime64[ns]): [2015-01-01, 2015-01-02, 2015-01-03,\n       2015-01-04, 2015-01-05]\n \n       In [14]: date.dt.day\n       Out[14]:\n       0    1\n       1    2\n       2    3\n       3    4\n       4    5\n-      dtype: int32\n+      Length: 5, dtype: int32\n     * pivot_table now has a margins_name argument so you can use something\n       other than the default of \u2018All\u2019 (_\bG_\bH_\b _\b3_\b3_\b3_\b5)\n     * Implement export of datetime64[ns, tz] dtypes with a fixed HDF5 store (_\bG_\bH\n       _\b1_\b1_\b4_\b1_\b1)\n     * Pretty printing sets (e.g. in DataFrame cells) now uses set literal\n       syntax ({x, y}) instead of Legacy Python syntax (set([x, y])) (_\bG_\bH_\b _\b1_\b1_\b2_\b1_\b5)\n     * Improve the error message in pandas.io.gbq.to_gbq() when a streaming\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.18.0.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.18.0.html", "unified_diff": "@@ -749,14 +749,16 @@\n <span class=\"go\">3  3 -0.312652</span>\n <span class=\"go\">4  4 -0.720589</span>\n <span class=\"go\">5  5  0.887163</span>\n <span class=\"go\">6  6  0.859588</span>\n <span class=\"go\">7  7 -0.636524</span>\n <span class=\"go\">8  8  0.015696</span>\n <span class=\"go\">9  9 -2.242685</span>\n+\n+<span class=\"go\">[10 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [8]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">rolling_mean</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">,</span> <span class=\"n\">window</span><span class=\"o\">=</span><span class=\"mi\">3</span><span class=\"p\">)</span>\n <span class=\"go\">        FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with</span>\n <span class=\"go\">                       DataFrame.rolling(window=3,center=False).mean()</span>\n <span class=\"gh\">Out[8]:</span>\n@@ -798,14 +800,16 @@\n <span class=\"go\">3  2.0 -0.023640</span>\n <span class=\"go\">4  3.0  0.133155</span>\n <span class=\"go\">5  4.0 -0.048693</span>\n <span class=\"go\">6  5.0  0.342054</span>\n <span class=\"go\">7  6.0  0.370076</span>\n <span class=\"go\">8  7.0  0.079587</span>\n <span class=\"go\">9  8.0 -0.954504</span>\n+\n+<span class=\"go\">[10 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>They provide getitem accessors</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [7]: </span><span class=\"n\">r</span><span class=\"p\">[</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[7]: </span>\n <span class=\"go\">0    NaN</span>\n <span class=\"go\">1    NaN</span>\n@@ -813,15 +817,15 @@\n <span class=\"go\">3    2.0</span>\n <span class=\"go\">4    3.0</span>\n <span class=\"go\">5    4.0</span>\n <span class=\"go\">6    5.0</span>\n <span class=\"go\">7    6.0</span>\n <span class=\"go\">8    7.0</span>\n <span class=\"go\">9    8.0</span>\n-<span class=\"go\">Name: A, dtype: float64</span>\n+<span class=\"go\">Name: A, Length: 10, dtype: float64</span>\n </pre></div>\n </div>\n <p>And multiple aggregations</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [8]: </span><span class=\"n\">r</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;mean&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;std&#39;</span><span class=\"p\">],</span>\n <span class=\"gp\">   ...: </span>       <span class=\"s1\">&#39;B&#39;</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"s1\">&#39;mean&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;std&#39;</span><span class=\"p\">]})</span>\n <span class=\"gp\">   ...: </span>\n <span class=\"gh\">Out[8]: </span>\n@@ -833,14 +837,16 @@\n <span class=\"go\">3  2.0  1.0 -0.023640  1.335505</span>\n <span class=\"go\">4  3.0  1.0  0.133155  1.143778</span>\n <span class=\"go\">5  4.0  1.0 -0.048693  0.835747</span>\n <span class=\"go\">6  5.0  1.0  0.342054  0.920379</span>\n <span class=\"go\">7  6.0  1.0  0.370076  0.871850</span>\n <span class=\"go\">8  7.0  1.0  0.079587  0.750099</span>\n <span class=\"go\">9  8.0  1.0 -0.954504  1.162285</span>\n+\n+<span class=\"go\">[10 rows x 4 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"changes-to-rename\">\n <span id=\"whatsnew-0180-enhancements-rename\"></span><h3>Changes to rename<a class=\"headerlink\" href=\"#changes-to-rename\" title=\"Link to this heading\">#</a></h3>\n <p><code class=\"docutils literal notranslate\"><span class=\"pre\">Series.rename</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">NDFrame.rename_axis</span></code> can now take a scalar or list-like\n argument for altering the Series or axis <em>name</em>, in addition to their old behaviors of altering labels. (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/9494\">GH 9494</a>, <a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/11965\">GH 11965</a>)</p>\n@@ -849,15 +855,15 @@\n <span class=\"gp\">In [10]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">rename</span><span class=\"p\">(</span><span class=\"s1\">&#39;newname&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[10]: </span>\n <span class=\"go\">0    1.150036</span>\n <span class=\"go\">1    0.991946</span>\n <span class=\"go\">2    0.953324</span>\n <span class=\"go\">3   -2.021255</span>\n <span class=\"go\">4   -0.334077</span>\n-<span class=\"go\">Name: newname, dtype: float64</span>\n+<span class=\"go\">Name: newname, Length: 5, dtype: float64</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [11]: </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=\"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\">5</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">))</span>\n \n <span class=\"gp\">In [12]: </span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">rename_axis</span><span class=\"p\">(</span><span class=\"s2\">&quot;indexname&quot;</span><span class=\"p\">)</span>\n <span class=\"gp\">   ....: </span>   <span class=\"o\">.</span><span class=\"n\">rename_axis</span><span class=\"p\">(</span><span class=\"s2\">&quot;columns_name&quot;</span><span class=\"p\">,</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"s2\">&quot;columns&quot;</span><span class=\"p\">))</span>\n <span class=\"gp\">   ....: </span>\n@@ -865,14 +871,16 @@\n <span class=\"go\">columns_name         0         1</span>\n <span class=\"go\">indexname                       </span>\n <span class=\"go\">0             0.002118  0.405453</span>\n <span class=\"go\">1             0.289092  1.321158</span>\n <span class=\"go\">2            -1.546906 -0.202646</span>\n <span class=\"go\">3            -0.655969  0.193421</span>\n <span class=\"go\">4             0.553439  1.318152</span>\n+\n+<span class=\"go\">[5 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>The new functionality works well in method chains. Previously these methods only accepted functions or dicts mapping a <em>label</em> to a new label.\n This continues to work as before for function or dict-like values.</p>\n </section>\n <section id=\"range-index\">\n <span id=\"whatsnew-0180-enhancements-rangeindex\"></span><h3>Range Index<a class=\"headerlink\" href=\"#range-index\" title=\"Link to this heading\">#</a></h3>\n@@ -929,24 +937,26 @@\n <p>Extracting a regular expression with one group returns a Series if\n <code class=\"docutils literal notranslate\"><span class=\"pre\">expand=False</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [16]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"s1\">&#39;a1&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;b2&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;c3&#39;</span><span class=\"p\">])</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;[ab](\\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>\n <span class=\"gh\">Out[16]: </span>\n <span class=\"go\">0      1</span>\n <span class=\"go\">1      2</span>\n <span class=\"go\">2    NaN</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n </pre></div>\n </div>\n <p>It returns a <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code> with one column if <code class=\"docutils literal notranslate\"><span class=\"pre\">expand=True</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [17]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"s1\">&#39;a1&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;b2&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;c3&#39;</span><span class=\"p\">])</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;[ab](\\d)&#39;</span><span class=\"p\">,</span> <span class=\"n\">expand</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[17]: </span>\n <span class=\"go\">     0</span>\n <span class=\"go\">0    1</span>\n <span class=\"go\">1    2</span>\n <span class=\"go\">2  NaN</span>\n+\n+<span class=\"go\">[3 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p>Calling on an <code class=\"docutils literal notranslate\"><span class=\"pre\">Index</span></code> with a regex with exactly one capture group\n returns an <code class=\"docutils literal notranslate\"><span class=\"pre\">Index</span></code> if <code class=\"docutils literal notranslate\"><span class=\"pre\">expand=False</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [18]: </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=\"s2\">&quot;a1&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;b2&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;c3&quot;</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"s2\">&quot;A11&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;B22&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;C33&quot;</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [19]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">index</span>\n@@ -959,14 +969,16 @@\n <p>It returns a <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code> with one column if <code class=\"docutils literal notranslate\"><span class=\"pre\">expand=True</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [21]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">index</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=\"s2\">&quot;(?P&lt;letter&gt;[a-zA-Z])&quot;</span><span class=\"p\">,</span> <span class=\"n\">expand</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[21]: </span>\n <span class=\"go\">  letter</span>\n <span class=\"go\">0      A</span>\n <span class=\"go\">1      B</span>\n <span class=\"go\">2      C</span>\n+\n+<span class=\"go\">[3 rows x 1 columns]</span>\n </pre></div>\n </div>\n <p>Calling on an <code class=\"docutils literal notranslate\"><span class=\"pre\">Index</span></code> with a regex with more than one capture group\n raises <code class=\"docutils literal notranslate\"><span class=\"pre\">ValueError</span></code> if <code class=\"docutils literal notranslate\"><span class=\"pre\">expand=False</span></code>.</p>\n <div class=\"highlight-python notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">&gt;&gt;&gt; </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">index</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=\"s2\">&quot;(?P&lt;letter&gt;[a-zA-Z])([0-9]+)&quot;</span><span class=\"p\">,</span> <span class=\"n\">expand</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n <span class=\"go\">ValueError: only one regex group is supported with Index</span>\n </pre></div>\n@@ -974,14 +986,16 @@\n <p>It returns a <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code> if <code class=\"docutils literal notranslate\"><span class=\"pre\">expand=True</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [22]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">index</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=\"s2\">&quot;(?P&lt;letter&gt;[a-zA-Z])([0-9]+)&quot;</span><span class=\"p\">,</span> <span class=\"n\">expand</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[22]: </span>\n <span class=\"go\">  letter   1</span>\n <span class=\"go\">0      A  11</span>\n <span class=\"go\">1      B  22</span>\n <span class=\"go\">2      C  33</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>In summary, <code class=\"docutils literal notranslate\"><span class=\"pre\">extract(expand=True)</span></code> always returns a <code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame</span></code>\n with a row for every subject string, and a column for every capture\n group.</p>\n </section>\n <section id=\"addition-of-str-extractall\">\n@@ -992,32 +1006,36 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [23]: </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=\"s2\">&quot;a1a2&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;b1&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;c1&quot;</span><span class=\"p\">],</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;C&quot;</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [24]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[24]: </span>\n <span class=\"go\">A    a1a2</span>\n <span class=\"go\">B      b1</span>\n <span class=\"go\">C      c1</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 3, dtype: object</span>\n \n <span class=\"gp\">In [25]: </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=\"s2\">&quot;(?P&lt;letter&gt;[ab])(?P&lt;digit&gt;\\d)&quot;</span><span class=\"p\">,</span> <span class=\"n\">expand</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[25]: </span>\n <span class=\"go\">  letter digit</span>\n <span class=\"go\">A      a     1</span>\n <span class=\"go\">B      b     1</span>\n <span class=\"go\">C    NaN   NaN</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>The <code class=\"docutils literal notranslate\"><span class=\"pre\">extractall</span></code> method returns all matches.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [26]: </span><span class=\"n\">s</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;(?P&lt;letter&gt;[ab])(?P&lt;digit&gt;\\d)&quot;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[26]: </span>\n <span class=\"go\">        letter digit</span>\n <span class=\"go\">  match             </span>\n <span class=\"go\">A 0          a     1</span>\n <span class=\"go\">  1          a     2</span>\n <span class=\"go\">B 0          b     1</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"changes-to-str-cat\">\n <span id=\"whatsnew-0180-enhancements-strcat\"></span><h3>Changes to str.cat<a class=\"headerlink\" href=\"#changes-to-str-cat\" title=\"Link to this heading\">#</a></h3>\n <p>The method <code class=\"docutils literal notranslate\"><span class=\"pre\">.str.cat()</span></code> concatenates the members of a <code class=\"docutils literal notranslate\"><span class=\"pre\">Series</span></code>. Before, if <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code> values were present in the Series, calling <code class=\"docutils literal notranslate\"><span class=\"pre\">.str.cat()</span></code> on it would return <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code>, unlike the rest of the <code class=\"docutils literal notranslate\"><span class=\"pre\">Series.str.*</span></code> API. This behavior has been amended to ignore <code class=\"docutils literal notranslate\"><span class=\"pre\">NaN</span></code> values by default. (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/11435\">GH 11435</a>).</p>\n <p>A new, friendlier <code class=\"docutils literal notranslate\"><span class=\"pre\">ValueError</span></code> is added to protect against the mistake of supplying the <code class=\"docutils literal notranslate\"><span class=\"pre\">sep</span></code> as an arg, rather than as a kwarg. (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/11334\">GH 11334</a>).</p>\n@@ -1100,22 +1118,22 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [42]: </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\">dr</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [43]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[43]: </span>\n <span class=\"go\">0   2013-01-01 09:12:56.123400-05:00</span>\n <span class=\"go\">1   2013-01-02 09:12:56.123400-05:00</span>\n <span class=\"go\">2   2013-01-03 09:12:56.123400-05:00</span>\n-<span class=\"go\">dtype: datetime64[ns, US/Eastern]</span>\n+<span class=\"go\">Length: 3, dtype: datetime64[ns, US/Eastern]</span>\n \n <span class=\"gp\">In [44]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">dt</span><span class=\"o\">.</span><span class=\"n\">round</span><span class=\"p\">(</span><span class=\"s1\">&#39;D&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[44]: </span>\n <span class=\"go\">0   2013-01-01 00:00:00-05:00</span>\n <span class=\"go\">1   2013-01-02 00:00:00-05:00</span>\n <span class=\"go\">2   2013-01-03 00:00:00-05:00</span>\n-<span class=\"go\">dtype: datetime64[ns, US/Eastern]</span>\n+<span class=\"go\">Length: 3, dtype: datetime64[ns, US/Eastern]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"formatting-of-integers-in-floatindex\">\n <h3>Formatting of integers in FloatIndex<a class=\"headerlink\" href=\"#formatting-of-integers-in-floatindex\" title=\"Link to this heading\">#</a></h3>\n <p>Integers in <code class=\"docutils literal notranslate\"><span class=\"pre\">FloatIndex</span></code>, e.g. 1., are now formatted with a decimal point and a <code class=\"docutils literal notranslate\"><span class=\"pre\">0</span></code> digit, e.g. <code class=\"docutils literal notranslate\"><span class=\"pre\">1.0</span></code> (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/11713\">GH 11713</a>)\n This change not only affects the display to the console, but also the output of IO methods like <code class=\"docutils literal notranslate\"><span class=\"pre\">.to_csv</span></code> or <code class=\"docutils literal notranslate\"><span class=\"pre\">.to_html</span></code>.</p>\n@@ -1142,15 +1160,15 @@\n <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=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mf\">3.</span><span class=\"p\">))</span>\n \n <span class=\"gp\">In [46]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[46]: </span>\n <span class=\"go\">0.0    1</span>\n <span class=\"go\">1.0    2</span>\n <span class=\"go\">2.0    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 3, dtype: int64</span>\n \n <span class=\"gp\">In [47]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">index</span>\n <span class=\"gh\">Out[47]: </span><span class=\"go\">Index([0.0, 1.0, 2.0], dtype=&#39;float64&#39;)</span>\n \n <span class=\"gp\">In [48]: </span><span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">to_csv</span><span class=\"p\">(</span><span class=\"n\">path_or_buf</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">,</span> <span class=\"n\">header</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">))</span>\n <span class=\"go\">0.0,1</span>\n <span class=\"go\">1.0,2</span>\n@@ -1187,25 +1205,25 @@\n <span class=\"gp\">   ....: </span>                   <span class=\"s1\">&#39;b&#39;</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\">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\">dtype</span><span class=\"o\">=</span><span class=\"s1\">&#39;uint32&#39;</span><span class=\"p\">)})</span>\n <span class=\"gp\">   ....: </span>\n \n <span class=\"gp\">In [50]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[50]: </span>\n <span class=\"go\">a     int64</span>\n <span class=\"go\">b    uint32</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 2, dtype: object</span>\n \n <span class=\"gp\">In [51]: </span><span class=\"n\">ix</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"p\">[</span><span class=\"s1\">&#39;a&#39;</span><span class=\"p\">]</span> <span class=\"o\">==</span> <span class=\"mi\">1</span>\n \n <span class=\"gp\">In [52]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">ix</span><span class=\"p\">,</span> <span class=\"s1\">&#39;b&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"n\">ix</span><span class=\"p\">,</span> <span class=\"s1\">&#39;b&#39;</span><span class=\"p\">]</span>\n \n <span class=\"gp\">In [53]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">dtypes</span>\n <span class=\"gh\">Out[53]: </span>\n <span class=\"go\">a     int64</span>\n <span class=\"go\">b    uint32</span>\n-<span class=\"go\">dtype: object</span>\n+<span class=\"go\">Length: 2, dtype: object</span>\n </pre></div>\n </div>\n <p>When a DataFrame\u2019s integer slice is partially updated with a new slice of floats that could potentially be down-casted to integer without losing precision, the dtype of the slice will be set to float instead of integer.</p>\n <p>Previous behavior:</p>\n <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\">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=\"nb\">range</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"p\">,</span><span class=\"mi\">10</span><span class=\"p\">))</span><span class=\"o\">.</span><span class=\"n\">reshape</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">,</span><span class=\"mi\">3</span><span class=\"p\">),</span>\n <span class=\"go\">                          columns=list(&#39;abc&#39;),</span>\n <span class=\"go\">                          index=[[4,4,8], [8,10,12]])</span>\n@@ -1236,22 +1254,26 @@\n <span class=\"gp\">In [55]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[55]: </span>\n <span class=\"go\">      a  b  c</span>\n <span class=\"go\">4 8   1  2  3</span>\n <span class=\"go\">  10  4  5  6</span>\n <span class=\"go\">8 12  7  8  9</span>\n \n+<span class=\"go\">[3 rows x 3 columns]</span>\n+\n <span class=\"gp\">In [56]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">loc</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=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">([</span><span class=\"mf\">0.</span><span class=\"p\">,</span> <span class=\"mf\">1.</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [57]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[57]: </span>\n <span class=\"go\">      a  b  c</span>\n <span class=\"go\">4 8   1  2  0</span>\n <span class=\"go\">  10  4  5  1</span>\n <span class=\"go\">8 12  7  8  9</span>\n+\n+<span class=\"go\">[3 rows x 3 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"method-to-xarray\">\n <span id=\"whatsnew-0180-enhancements-xarray\"></span><h3>Method to_xarray<a class=\"headerlink\" href=\"#method-to-xarray\" title=\"Link to this heading\">#</a></h3>\n <p>In a future version of pandas, we will be deprecating <code class=\"docutils literal notranslate\"><span class=\"pre\">Panel</span></code> and other &gt; 2 ndim objects. In order to provide for continuity,\n all <code class=\"docutils literal notranslate\"><span class=\"pre\">NDFrame</span></code> objects have gained the <code class=\"docutils literal notranslate\"><span class=\"pre\">.to_xarray()</span></code> method in order to convert to <code class=\"docutils literal notranslate\"><span class=\"pre\">xarray</span></code> objects, which has\n@@ -1373,15 +1395,15 @@\n <span class=\"go\">TypeError: can only operate on a datetimes for subtraction,</span>\n <span class=\"go\">           but the operator [__add__] was passed</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [66]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Series</span><span class=\"p\">([</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">NaT</span><span class=\"p\">],</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"s1\">&#39;&lt;m8[ns]&#39;</span><span class=\"p\">)</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\">pd</span><span class=\"o\">.</span><span class=\"n\">NaT</span><span class=\"p\">],</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"s1\">&#39;&lt;m8[ns]&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[66]: </span>\n <span class=\"go\">0   NaT</span>\n-<span class=\"go\">dtype: timedelta64[ns]</span>\n+<span class=\"go\">Length: 1, dtype: timedelta64[ns]</span>\n </pre></div>\n </div>\n <p><code class=\"docutils literal notranslate\"><span class=\"pre\">Timedelta</span></code> division by <code class=\"docutils literal notranslate\"><span class=\"pre\">floats</span></code> now works.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [67]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Timedelta</span><span class=\"p\">(</span><span class=\"s1\">&#39;1s&#39;</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"mf\">2.0</span>\n <span class=\"gh\">Out[67]: </span><span class=\"go\">Timedelta(&#39;0 days 00:00:00.500000&#39;)</span>\n </pre></div>\n </div>\n@@ -1389,22 +1411,22 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [68]: </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\">pd</span><span class=\"o\">.</span><span class=\"n\">timedelta_range</span><span class=\"p\">(</span><span class=\"s1\">&#39;1 day&#39;</span><span class=\"p\">,</span> <span class=\"n\">periods</span><span class=\"o\">=</span><span class=\"mi\">3</span><span class=\"p\">))</span>\n \n <span class=\"gp\">In [69]: </span><span class=\"n\">ser</span>\n <span class=\"gh\">Out[69]: </span>\n <span class=\"go\">0   1 days</span>\n <span class=\"go\">1   2 days</span>\n <span class=\"go\">2   3 days</span>\n-<span class=\"go\">dtype: timedelta64[ns]</span>\n+<span class=\"go\">Length: 3, dtype: timedelta64[ns]</span>\n \n <span class=\"gp\">In [70]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">Timestamp</span><span class=\"p\">(</span><span class=\"s1\">&#39;2012-01-01&#39;</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">ser</span>\n <span class=\"gh\">Out[70]: </span>\n <span class=\"go\">0   2011-12-31</span>\n <span class=\"go\">1   2011-12-30</span>\n <span class=\"go\">2   2011-12-29</span>\n-<span class=\"go\">dtype: datetime64[ns]</span>\n+<span class=\"go\">Length: 3, dtype: datetime64[ns]</span>\n </pre></div>\n </div>\n <p><code class=\"docutils literal notranslate\"><span class=\"pre\">NaT.isoformat()</span></code> now returns <code class=\"docutils literal notranslate\"><span class=\"pre\">'NaT'</span></code>. This change allows\n <code class=\"docutils literal notranslate\"><span class=\"pre\">pd.Timestamp</span></code> to rehydrate any timestamp like object from its isoformat\n (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/12300\">GH 12300</a>).</p>\n </section>\n <section id=\"changes-to-msgpack\">\n@@ -1468,23 +1490,25 @@\n <p>New signature</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [71]: </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=\"o\">.</span><span class=\"n\">rank</span><span class=\"p\">(</span><span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"n\">method</span><span class=\"o\">=</span><span class=\"s1\">&#39;average&#39;</span><span class=\"p\">,</span> <span class=\"n\">numeric_only</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n <span class=\"gp\">   ....: </span>                      <span class=\"n\">na_option</span><span class=\"o\">=</span><span class=\"s1\">&#39;keep&#39;</span><span class=\"p\">,</span> <span class=\"n\">ascending</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">pct</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n <span class=\"gp\">   ....: </span>\n <span class=\"gh\">Out[71]: </span>\n <span class=\"go\">0    1.0</span>\n <span class=\"go\">1    2.0</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 2, dtype: float64</span>\n \n <span class=\"gp\">In [72]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">DataFrame</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=\"o\">.</span><span class=\"n\">rank</span><span class=\"p\">(</span><span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"n\">method</span><span class=\"o\">=</span><span class=\"s1\">&#39;average&#39;</span><span class=\"p\">,</span> <span class=\"n\">numeric_only</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span>\n <span class=\"gp\">   ....: </span>                         <span class=\"n\">na_option</span><span class=\"o\">=</span><span class=\"s1\">&#39;keep&#39;</span><span class=\"p\">,</span> <span class=\"n\">ascending</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">pct</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n <span class=\"gp\">   ....: </span>\n <span class=\"gh\">Out[72]: </span>\n <span class=\"go\">     0</span>\n <span class=\"go\">0  1.0</span>\n <span class=\"go\">1  2.0</span>\n+\n+<span class=\"go\">[2 rows x 1 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"bug-in-quarterbegin-with-n-0\">\n <h3>Bug in QuarterBegin with n=0<a class=\"headerlink\" href=\"#bug-in-quarterbegin-with-n-0\" title=\"Link to this heading\">#</a></h3>\n <p>In previous versions, the behavior of the QuarterBegin offset was inconsistent\n depending on the date when the <code class=\"docutils literal notranslate\"><span class=\"pre\">n</span></code> parameter was 0. (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/11406\">GH 11406</a>)</p>\n@@ -1540,14 +1564,16 @@\n <span class=\"go\">2010-01-01 09:00:03  0.683463  0.712702  0.370251  0.561196</span>\n <span class=\"go\">2010-01-01 09:00:04  0.503083  0.013768  0.772827  0.882641</span>\n <span class=\"go\">2010-01-01 09:00:05  0.364886  0.615396  0.075381  0.368824</span>\n <span class=\"go\">2010-01-01 09:00:06  0.933140  0.651378  0.397203  0.788730</span>\n <span class=\"go\">2010-01-01 09:00:07  0.316836  0.568099  0.869127  0.436173</span>\n <span class=\"go\">2010-01-01 09:00:08  0.802148  0.143767  0.704261  0.704581</span>\n <span class=\"go\">2010-01-01 09:00:09  0.218792  0.924868  0.442141  0.909316</span>\n+\n+<span class=\"go\">[10 rows x 4 columns]</span>\n </pre></div>\n </div>\n <p><strong>Previous API</strong>:</p>\n <p>You would write a resampling operation that immediately evaluates. If a <code class=\"docutils literal notranslate\"><span class=\"pre\">how</span></code> parameter was not provided, it\n would default to <code class=\"docutils literal notranslate\"><span class=\"pre\">how='mean'</span></code>.</p>\n <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\">resample</span><span class=\"p\">(</span><span class=\"s1\">&#39;2s&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[6]:</span>\n@@ -1587,58 +1613,68 @@\n <span class=\"gh\">Out[84]: </span>\n <span class=\"go\">                            A         B         C         D</span>\n <span class=\"go\">2010-01-01 09:00:00  0.485748  0.447351  0.357096  0.793615</span>\n <span class=\"go\">2010-01-01 09:00:02  0.820801  0.794317  0.364034  0.531096</span>\n <span class=\"go\">2010-01-01 09:00:04  0.433985  0.314582  0.424104  0.625733</span>\n <span class=\"go\">2010-01-01 09:00:06  0.624988  0.609738  0.633165  0.612452</span>\n <span class=\"go\">2010-01-01 09:00:08  0.510470  0.534317  0.573201  0.806949</span>\n+\n+<span class=\"go\">[5 rows x 4 columns]</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [85]: </span><span class=\"n\">r</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[85]: </span>\n <span class=\"go\">                            A         B         C         D</span>\n <span class=\"go\">2010-01-01 09:00:00  0.971495  0.894701  0.714192  1.587231</span>\n <span class=\"go\">2010-01-01 09:00:02  1.641602  1.588635  0.728068  1.062191</span>\n <span class=\"go\">2010-01-01 09:00:04  0.867969  0.629165  0.848208  1.251465</span>\n <span class=\"go\">2010-01-01 09:00:06  1.249976  1.219477  1.266330  1.224904</span>\n <span class=\"go\">2010-01-01 09:00:08  1.020940  1.068634  1.146402  1.613897</span>\n+\n+<span class=\"go\">[5 rows x 4 columns]</span>\n </pre></div>\n </div>\n <p>Furthermore, resample now supports <code class=\"docutils literal notranslate\"><span class=\"pre\">getitem</span></code> operations to perform the resample on specific columns.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [86]: </span><span class=\"n\">r</span><span class=\"p\">[[</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">,</span><span class=\"s1\">&#39;C&#39;</span><span class=\"p\">]]</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[86]: </span>\n <span class=\"go\">                            A         C</span>\n <span class=\"go\">2010-01-01 09:00:00  0.485748  0.357096</span>\n <span class=\"go\">2010-01-01 09:00:02  0.820801  0.364034</span>\n <span class=\"go\">2010-01-01 09:00:04  0.433985  0.424104</span>\n <span class=\"go\">2010-01-01 09:00:06  0.624988  0.633165</span>\n <span class=\"go\">2010-01-01 09:00:08  0.510470  0.573201</span>\n+\n+<span class=\"go\">[5 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>and <code class=\"docutils literal notranslate\"><span class=\"pre\">.aggregate</span></code> type operations.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [87]: </span><span class=\"n\">r</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=\"s1\">&#39;mean&#39;</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>\n <span class=\"gh\">Out[87]: </span>\n <span class=\"go\">                            A         B</span>\n <span class=\"go\">2010-01-01 09:00:00  0.485748  0.894701</span>\n <span class=\"go\">2010-01-01 09:00:02  0.820801  1.588635</span>\n <span class=\"go\">2010-01-01 09:00:04  0.433985  0.629165</span>\n <span class=\"go\">2010-01-01 09:00:06  0.624988  1.219477</span>\n <span class=\"go\">2010-01-01 09:00:08  0.510470  1.068634</span>\n+\n+<span class=\"go\">[5 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>These accessors can of course, be combined</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [88]: </span><span class=\"n\">r</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=\"o\">.</span><span class=\"n\">agg</span><span class=\"p\">([</span><span class=\"s1\">&#39;mean&#39;</span><span class=\"p\">,</span><span class=\"s1\">&#39;sum&#39;</span><span class=\"p\">])</span>\n <span class=\"gh\">Out[88]: </span>\n <span class=\"go\">                            A                   B          </span>\n <span class=\"go\">                         mean       sum      mean       sum</span>\n <span class=\"go\">2010-01-01 09:00:00  0.485748  0.971495  0.447351  0.894701</span>\n <span class=\"go\">2010-01-01 09:00:02  0.820801  1.641602  0.794317  1.588635</span>\n <span class=\"go\">2010-01-01 09:00:04  0.433985  0.867969  0.314582  0.629165</span>\n <span class=\"go\">2010-01-01 09:00:06  0.624988  1.249976  0.609738  1.219477</span>\n <span class=\"go\">2010-01-01 09:00:08  0.510470  1.020940  0.534317  1.068634</span>\n+\n+<span class=\"go\">[5 rows x 4 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"upsampling\">\n <h4>Upsampling<a class=\"headerlink\" href=\"#upsampling\" title=\"Link to this heading\">#</a></h4>\n <p>Upsampling operations take you from a lower frequency to a higher frequency. These are now\n performed with the <code class=\"docutils literal notranslate\"><span class=\"pre\">Resampler</span></code> objects with <code class=\"xref py py-meth docutils literal notranslate\"><span class=\"pre\">backfill()</span></code>,\n@@ -1741,26 +1777,28 @@\n <span class=\"gh\">Out[89]: </span>\n <span class=\"go\">                            A         B         C         D</span>\n <span class=\"go\">2010-01-01 09:00:00  0.191519  0.272593  0.276464  0.785359</span>\n <span class=\"go\">2010-01-01 09:00:02  0.683463  0.712702  0.357817  0.500995</span>\n <span class=\"go\">2010-01-01 09:00:04  0.364886  0.013768  0.075381  0.368824</span>\n <span class=\"go\">2010-01-01 09:00:06  0.316836  0.568099  0.397203  0.436173</span>\n <span class=\"go\">2010-01-01 09:00:08  0.218792  0.143767  0.442141  0.704581</span>\n+\n+<span class=\"go\">[5 rows x 4 columns]</span>\n </pre></div>\n </div>\n <p>The good news is the return dimensions will differ between the new API and the old API, so this should loudly raise\n an exception.</p>\n <p>To replicate the original operation</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [90]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">resample</span><span class=\"p\">(</span><span class=\"s1\">&#39;2s&#39;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"n\">min</span><span class=\"p\">()</span>\n <span class=\"gh\">Out[90]: </span>\n <span class=\"go\">A    0.433985</span>\n <span class=\"go\">B    0.314582</span>\n <span class=\"go\">C    0.357096</span>\n <span class=\"go\">D    0.531096</span>\n-<span class=\"go\">dtype: float64</span>\n+<span class=\"go\">Length: 4, dtype: float64</span>\n </pre></div>\n </div>\n </div>\n </section>\n </section>\n <section id=\"changes-to-eval\">\n <h3>Changes to eval<a class=\"headerlink\" href=\"#changes-to-eval\" title=\"Link to this heading\">#</a></h3>\n@@ -1772,14 +1810,16 @@\n <span class=\"gh\">Out[92]: </span>\n <span class=\"go\">      a  b</span>\n <span class=\"go\">0   0.0  0</span>\n <span class=\"go\">1   2.5  1</span>\n <span class=\"go\">2   5.0  2</span>\n <span class=\"go\">3   7.5  3</span>\n <span class=\"go\">4  10.0  4</span>\n+\n+<span class=\"go\">[5 rows x 2 columns]</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [12]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">eval</span><span class=\"p\">(</span><span class=\"s1\">&#39;c = a + b&#39;</span><span class=\"p\">)</span>\n <span class=\"go\">FutureWarning: eval expressions containing an assignment currentlydefault to operating inplace.</span>\n <span class=\"go\">This will change in a future version of pandas, use inplace=True to avoid this warning.</span>\n \n <span class=\"gp\">In [13]: </span><span class=\"n\">df</span>\n@@ -1799,42 +1839,50 @@\n <span class=\"go\">      a  b     c</span>\n <span class=\"go\">0   0.0  0   0.0</span>\n <span class=\"go\">1   2.5  1   3.5</span>\n <span class=\"go\">2   5.0  2   7.0</span>\n <span class=\"go\">3   7.5  3  10.5</span>\n <span class=\"go\">4  10.0  4  14.0</span>\n \n+<span class=\"go\">[5 rows x 3 columns]</span>\n+\n <span class=\"gp\">In [94]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">eval</span><span class=\"p\">(</span><span class=\"s1\">&#39;d = c - b&#39;</span><span class=\"p\">,</span> <span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[94]: </span>\n <span class=\"go\">      a  b     c     d</span>\n <span class=\"go\">0   0.0  0   0.0   0.0</span>\n <span class=\"go\">1   2.5  1   3.5   2.5</span>\n <span class=\"go\">2   5.0  2   7.0   5.0</span>\n <span class=\"go\">3   7.5  3  10.5   7.5</span>\n <span class=\"go\">4  10.0  4  14.0  10.0</span>\n \n+<span class=\"go\">[5 rows x 4 columns]</span>\n+\n <span class=\"gp\">In [95]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[95]: </span>\n <span class=\"go\">      a  b     c</span>\n <span class=\"go\">0   0.0  0   0.0</span>\n <span class=\"go\">1   2.5  1   3.5</span>\n <span class=\"go\">2   5.0  2   7.0</span>\n <span class=\"go\">3   7.5  3  10.5</span>\n <span class=\"go\">4  10.0  4  14.0</span>\n \n+<span class=\"go\">[5 rows x 3 columns]</span>\n+\n <span class=\"gp\">In [96]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">eval</span><span class=\"p\">(</span><span class=\"s1\">&#39;d = c - b&#39;</span><span class=\"p\">,</span> <span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [97]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[97]: </span>\n <span class=\"go\">      a  b     c     d</span>\n <span class=\"go\">0   0.0  0   0.0   0.0</span>\n <span class=\"go\">1   2.5  1   3.5   2.5</span>\n <span class=\"go\">2   5.0  2   7.0   5.0</span>\n <span class=\"go\">3   7.5  3  10.5   7.5</span>\n <span class=\"go\">4  10.0  4  14.0  10.0</span>\n+\n+<span class=\"go\">[5 rows x 4 columns]</span>\n </pre></div>\n </div>\n <div class=\"admonition warning\">\n <p class=\"admonition-title\">Warning</p>\n <p>For backwards compatibility, <code class=\"docutils literal notranslate\"><span class=\"pre\">inplace</span></code> defaults to <code class=\"docutils literal notranslate\"><span class=\"pre\">True</span></code> if not specified.\n This will change in a future version of pandas. If your code depends on an\n inplace assignment you should update to explicitly set <code class=\"docutils literal notranslate\"><span class=\"pre\">inplace=True</span></code></p>\n@@ -1842,21 +1890,25 @@\n <p>The <code class=\"docutils literal notranslate\"><span class=\"pre\">inplace</span></code> keyword parameter was also added the <code class=\"docutils literal notranslate\"><span class=\"pre\">query</span></code> method.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [98]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">query</span><span class=\"p\">(</span><span class=\"s1\">&#39;a &gt; 5&#39;</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[98]: </span>\n <span class=\"go\">      a  b     c     d</span>\n <span class=\"go\">3   7.5  3  10.5   7.5</span>\n <span class=\"go\">4  10.0  4  14.0  10.0</span>\n \n+<span class=\"go\">[2 rows x 4 columns]</span>\n+\n <span class=\"gp\">In [99]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">query</span><span class=\"p\">(</span><span class=\"s1\">&#39;a &gt; 5&#39;</span><span class=\"p\">,</span> <span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [100]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[100]: </span>\n <span class=\"go\">      a  b     c     d</span>\n <span class=\"go\">3   7.5  3  10.5   7.5</span>\n <span class=\"go\">4  10.0  4  14.0  10.0</span>\n+\n+<span class=\"go\">[2 rows x 4 columns]</span>\n </pre></div>\n </div>\n <div class=\"admonition warning\">\n <p class=\"admonition-title\">Warning</p>\n <p>Note that the default value for <code class=\"docutils literal notranslate\"><span class=\"pre\">inplace</span></code> in a <code class=\"docutils literal notranslate\"><span class=\"pre\">query</span></code>\n is <code class=\"docutils literal notranslate\"><span class=\"pre\">False</span></code>, which is consistent with prior versions.</p>\n </div>\n@@ -1865,25 +1917,29 @@\n assignments are valid for multi-line expressions.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [101]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[101]: </span>\n <span class=\"go\">      a  b     c     d</span>\n <span class=\"go\">3   7.5  3  10.5   7.5</span>\n <span class=\"go\">4  10.0  4  14.0  10.0</span>\n \n+<span class=\"go\">[2 rows x 4 columns]</span>\n+\n <span class=\"gp\">In [102]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">eval</span><span class=\"p\">(</span><span class=\"s2\">&quot;&quot;&quot;</span>\n <span class=\"gp\">   .....: </span><span class=\"s2\">e = d + a</span>\n <span class=\"gp\">   .....: </span><span class=\"s2\">f = e - 22</span>\n <span class=\"gp\">   .....: </span><span class=\"s2\">g = f / 2.0&quot;&quot;&quot;</span><span class=\"p\">,</span> <span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n <span class=\"gp\">   .....: </span>\n \n <span class=\"gp\">In [103]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[103]: </span>\n <span class=\"go\">      a  b     c     d     e    f    g</span>\n <span class=\"go\">3   7.5  3  10.5   7.5  15.0 -7.0 -3.5</span>\n <span class=\"go\">4  10.0  4  14.0  10.0  20.0 -2.0 -1.0</span>\n+\n+<span class=\"go\">[2 rows x 7 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"other-api-changes\">\n <span id=\"whatsnew-0180-api\"></span><h3>Other API changes<a class=\"headerlink\" href=\"#other-api-changes\" title=\"Link to this heading\">#</a></h3>\n <ul>\n <li><p><code class=\"docutils literal notranslate\"><span class=\"pre\">DataFrame.between_time</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">Series.between_time</span></code> now only parse a fixed set of time strings. Parsing of date strings is no longer supported and raises a <code class=\"docutils literal notranslate\"><span class=\"pre\">ValueError</span></code>. (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/11818\">GH 11818</a>)</p>\n@@ -1964,24 +2020,24 @@\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [104]: </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=\"mi\">4</span><span class=\"p\">,</span> <span class=\"mi\">5</span><span class=\"p\">,</span> <span class=\"mi\">6</span><span class=\"p\">])</span>\n \n <span class=\"gp\">In [105]: </span><span class=\"n\">s</span>\n <span class=\"gh\">Out[105]: </span>\n <span class=\"go\">4    1</span>\n <span class=\"go\">5    2</span>\n <span class=\"go\">6    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 3, dtype: int64</span>\n \n <span class=\"gp\">In [106]: </span><span class=\"n\">s2</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=\"nb\">list</span><span class=\"p\">(</span><span class=\"s1\">&#39;abc&#39;</span><span class=\"p\">))</span>\n \n <span class=\"gp\">In [107]: </span><span class=\"n\">s2</span>\n <span class=\"gh\">Out[107]: </span>\n <span class=\"go\">a    1</span>\n <span class=\"go\">b    2</span>\n <span class=\"go\">c    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 3, dtype: int64</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"go\"># this is label indexing</span>\n <span class=\"gp\">In [2]: </span><span class=\"n\">s</span><span class=\"p\">[</span><span class=\"mf\">5.0</span><span class=\"p\">]</span>\n <span class=\"go\">FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point</span>\n <span class=\"gh\">Out[2]: </span><span class=\"go\">2</span>\n@@ -2028,26 +2084,26 @@\n <span class=\"gp\">In [111]: </span><span class=\"n\">s_copy</span><span class=\"p\">[</span><span class=\"mf\">5.0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mi\">10</span>\n \n <span class=\"gp\">In [112]: </span><span class=\"n\">s_copy</span>\n <span class=\"gh\">Out[112]: </span>\n <span class=\"go\">4     1</span>\n <span class=\"go\">5    10</span>\n <span class=\"go\">6     3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 3, dtype: int64</span>\n \n <span class=\"gp\">In [113]: </span><span class=\"n\">s_copy</span> <span class=\"o\">=</span> <span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">copy</span><span class=\"p\">()</span>\n \n <span class=\"gp\">In [114]: </span><span class=\"n\">s_copy</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"mf\">5.0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mi\">10</span>\n \n <span class=\"gp\">In [115]: </span><span class=\"n\">s_copy</span>\n <span class=\"gh\">Out[115]: </span>\n <span class=\"go\">4     1</span>\n <span class=\"go\">5    10</span>\n <span class=\"go\">6     3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 3, dtype: int64</span>\n </pre></div>\n </div>\n <p>Positional setting with <code class=\"docutils literal notranslate\"><span class=\"pre\">.ix</span></code> and a float indexer will ADD this value to the index, rather than previously setting the value by position.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [3]: </span><span class=\"n\">s2</span><span class=\"o\">.</span><span class=\"n\">ix</span><span class=\"p\">[</span><span class=\"mf\">1.0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mi\">10</span>\n <span class=\"gp\">In [4]: </span><span class=\"n\">s2</span>\n <span class=\"gh\">Out[4]:</span>\n <span class=\"go\">a       1</span>\n@@ -2058,35 +2114,35 @@\n </pre></div>\n </div>\n <p>Slicing will also coerce integer-like floats to integers for a non-<code class=\"docutils literal notranslate\"><span class=\"pre\">Float64Index</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [116]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"mf\">5.0</span><span class=\"p\">:</span><span class=\"mi\">6</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[116]: </span>\n <span class=\"go\">5    2</span>\n <span class=\"go\">6    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 2, dtype: int64</span>\n </pre></div>\n </div>\n <p>Note that for floats that are NOT coercible to ints, the label based bounds will be excluded</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [117]: </span><span class=\"n\">s</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</span><span class=\"mf\">5.1</span><span class=\"p\">:</span><span class=\"mi\">6</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[117]: </span>\n <span class=\"go\">6    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 1, dtype: int64</span>\n </pre></div>\n </div>\n <p>Float indexing on a <code class=\"docutils literal notranslate\"><span class=\"pre\">Float64Index</span></code> is unchanged.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [118]: </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=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mf\">3.</span><span class=\"p\">))</span>\n \n <span class=\"gp\">In [119]: </span><span class=\"n\">s</span><span class=\"p\">[</span><span class=\"mf\">1.0</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[119]: </span><span class=\"go\">np.int64(2)</span>\n \n <span class=\"gp\">In [120]: </span><span class=\"n\">s</span><span class=\"p\">[</span><span class=\"mf\">1.0</span><span class=\"p\">:</span><span class=\"mf\">2.5</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[120]: </span>\n <span class=\"go\">1.0    2</span>\n <span class=\"go\">2.0    3</span>\n-<span class=\"go\">dtype: int64</span>\n+<span class=\"go\">Length: 2, dtype: int64</span>\n </pre></div>\n </div>\n </section>\n <section id=\"removal-of-prior-version-deprecations-changes\">\n <span id=\"whatsnew-0180-prior-deprecations\"></span><h3>Removal of prior version deprecations/changes<a class=\"headerlink\" href=\"#removal-of-prior-version-deprecations-changes\" title=\"Link to this heading\">#</a></h3>\n <ul class=\"simple\">\n <li><p>Removal of <code class=\"docutils literal notranslate\"><span class=\"pre\">rolling_corr_pairwise</span></code> in favor of <code class=\"docutils literal notranslate\"><span class=\"pre\">.rolling().corr(pairwise=True)</span></code> (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/4950\">GH 4950</a>)</p></li>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -199,14 +199,16 @@\n 3  3 -0.312652\n 4  4 -0.720589\n 5  5  0.887163\n 6  6  0.859588\n 7  7 -0.636524\n 8  8  0.015696\n 9  9 -2.242685\n+\n+[10 rows x 2 columns]\n Previous behavior:\n In [8]: pd.rolling_mean(df, window=3)\n         FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be\n removed in a future version, replace with\n                        DataFrame.rolling(window=3,center=False).mean()\n Out[8]:\n     A         B\n@@ -241,28 +243,30 @@\n 3  2.0 -0.023640\n 4  3.0  0.133155\n 5  4.0 -0.048693\n 6  5.0  0.342054\n 7  6.0  0.370076\n 8  7.0  0.079587\n 9  8.0 -0.954504\n+\n+[10 rows x 2 columns]\n They provide getitem accessors\n In [7]: r['A'].mean()\n Out[7]:\n 0    NaN\n 1    NaN\n 2    1.0\n 3    2.0\n 4    3.0\n 5    4.0\n 6    5.0\n 7    6.0\n 8    7.0\n 9    8.0\n-Name: A, dtype: float64\n+Name: A, Length: 10, dtype: float64\n And multiple aggregations\n In [8]: r.agg({'A': ['mean', 'std'],\n    ...:        'B': ['mean', 'std']})\n    ...:\n Out[8]:\n      A              B\n   mean  std      mean       std\n@@ -272,41 +276,45 @@\n 3  2.0  1.0 -0.023640  1.335505\n 4  3.0  1.0  0.133155  1.143778\n 5  4.0  1.0 -0.048693  0.835747\n 6  5.0  1.0  0.342054  0.920379\n 7  6.0  1.0  0.370076  0.871850\n 8  7.0  1.0  0.079587  0.750099\n 9  8.0  1.0 -0.954504  1.162285\n+\n+[10 rows x 4 columns]\n *\b**\b**\b**\b* C\bCh\bha\ban\bng\bge\bes\bs t\bto\bo r\bre\ben\bna\bam\bme\be_\b#\b# *\b**\b**\b**\b*\n Series.rename and NDFrame.rename_axis can now take a scalar or list-like\n argument for altering the Series or axis n\bna\bam\bme\be, in addition to their old\n behaviors of altering labels. (_\bG_\bH_\b _\b9_\b4_\b9_\b4, _\bG_\bH_\b _\b1_\b1_\b9_\b6_\b5)\n In [9]: s = pd.Series(np.random.randn(5))\n \n In [10]: s.rename('newname')\n Out[10]:\n 0    1.150036\n 1    0.991946\n 2    0.953324\n 3   -2.021255\n 4   -0.334077\n-Name: newname, dtype: float64\n+Name: newname, Length: 5, dtype: float64\n In [11]: df = pd.DataFrame(np.random.randn(5, 2))\n \n In [12]: (df.rename_axis(\"indexname\")\n    ....:    .rename_axis(\"columns_name\", axis=\"columns\"))\n    ....:\n Out[12]:\n columns_name         0         1\n indexname\n 0             0.002118  0.405453\n 1             0.289092  1.321158\n 2            -1.546906 -0.202646\n 3            -0.655969  0.193421\n 4             0.553439  1.318152\n+\n+[5 rows x 2 columns]\n The new functionality works well in method chains. Previously these methods\n only accepted functions or dicts mapping a l\bla\bab\bbe\bel\bl to a new label. This continues\n to work as before for function or dict-like values.\n *\b**\b**\b**\b* R\bRa\ban\bng\bge\be I\bIn\bnd\bde\bex\bx_\b#\b# *\b**\b**\b**\b*\n A RangeIndex has been added to the Int64Index sub-classes to support a memory\n saving alternative for common use cases. This has a similar implementation to\n the python range object (xrange in python 2), in that it only stores the start,\n@@ -359,22 +367,24 @@\n Extracting a regular expression with one group returns a Series if\n expand=False.\n In [16]: pd.Series(['a1', 'b2', 'c3']).str.extract(r'[ab](\\d)', expand=False)\n Out[16]:\n 0      1\n 1      2\n 2    NaN\n-dtype: object\n+Length: 3, dtype: object\n It returns a DataFrame with one column if expand=True.\n In [17]: pd.Series(['a1', 'b2', 'c3']).str.extract(r'[ab](\\d)', expand=True)\n Out[17]:\n      0\n 0    1\n 1    2\n 2  NaN\n+\n+[3 rows x 1 columns]\n Calling on an Index with a regex with exactly one capture group returns an\n Index if expand=False.\n In [18]: s = pd.Series([\"a1\", \"b2\", \"c3\"], [\"A11\", \"B22\", \"C33\"])\n \n In [19]: s.index\n Out[19]: Index(['A11', 'B22', 'C33'], dtype='object')\n \n@@ -383,53 +393,61 @@\n It returns a DataFrame with one column if expand=True.\n In [21]: s.index.str.extract(\"(?P<letter>[a-zA-Z])\", expand=True)\n Out[21]:\n   letter\n 0      A\n 1      B\n 2      C\n+\n+[3 rows x 1 columns]\n Calling on an Index with a regex with more than one capture group raises\n ValueError if expand=False.\n >>> s.index.str.extract(\"(?P<letter>[a-zA-Z])([0-9]+)\", expand=False)\n ValueError: only one regex group is supported with Index\n It returns a DataFrame if expand=True.\n In [22]: s.index.str.extract(\"(?P<letter>[a-zA-Z])([0-9]+)\", expand=True)\n Out[22]:\n   letter   1\n 0      A  11\n 1      B  22\n 2      C  33\n+\n+[3 rows x 2 columns]\n In summary, extract(expand=True) always returns a DataFrame with a row for\n every subject string, and a column for every capture group.\n *\b**\b**\b**\b* A\bAd\bdd\bdi\bit\bti\bio\bon\bn o\bof\bf s\bst\btr\br.\b.e\bex\bxt\btr\bra\bac\bct\bta\bal\bll\bl_\b#\b# *\b**\b**\b**\b*\n The _\b._\bs_\bt_\br_\b._\be_\bx_\bt_\br_\ba_\bc_\bt_\ba_\bl_\bl method was added (_\bG_\bH_\b _\b1_\b1_\b3_\b8_\b6). Unlike extract, which returns\n only the first match.\n In [23]: s = pd.Series([\"a1a2\", \"b1\", \"c1\"], [\"A\", \"B\", \"C\"])\n \n In [24]: s\n Out[24]:\n A    a1a2\n B      b1\n C      c1\n-dtype: object\n+Length: 3, dtype: object\n \n In [25]: s.str.extract(r\"(?P<letter>[ab])(?P<digit>\\d)\", expand=False)\n Out[25]:\n   letter digit\n A      a     1\n B      b     1\n C    NaN   NaN\n+\n+[3 rows x 2 columns]\n The extractall method returns all matches.\n In [26]: s.str.extractall(r\"(?P<letter>[ab])(?P<digit>\\d)\")\n Out[26]:\n         letter digit\n   match\n A 0          a     1\n   1          a     2\n B 0          b     1\n+\n+[3 rows x 2 columns]\n *\b**\b**\b**\b* C\bCh\bha\ban\bng\bge\bes\bs t\bto\bo s\bst\btr\br.\b.c\bca\bat\bt_\b#\b# *\b**\b**\b**\b*\n The method .str.cat() concatenates the members of a Series. Before, if NaN\n values were present in the Series, calling .str.cat() on it would return NaN,\n unlike the rest of the Series.str.* API. This behavior has been amended to\n ignore NaN values by default. (_\bG_\bH_\b _\b1_\b1_\b4_\b3_\b5).\n A new, friendlier ValueError is added to protect against the mistake of\n supplying the sep as an arg, rather than as a kwarg. (_\bG_\bH_\b _\b1_\b1_\b3_\b3_\b4).\n@@ -505,22 +523,22 @@\n In [42]: s = pd.Series(dr)\n \n In [43]: s\n Out[43]:\n 0   2013-01-01 09:12:56.123400-05:00\n 1   2013-01-02 09:12:56.123400-05:00\n 2   2013-01-03 09:12:56.123400-05:00\n-dtype: datetime64[ns, US/Eastern]\n+Length: 3, dtype: datetime64[ns, US/Eastern]\n \n In [44]: s.dt.round('D')\n Out[44]:\n 0   2013-01-01 00:00:00-05:00\n 1   2013-01-02 00:00:00-05:00\n 2   2013-01-03 00:00:00-05:00\n-dtype: datetime64[ns, US/Eastern]\n+Length: 3, dtype: datetime64[ns, US/Eastern]\n *\b**\b**\b**\b* F\bFo\bor\brm\bma\bat\btt\bti\bin\bng\bg o\bof\bf i\bin\bnt\bte\beg\bge\ber\brs\bs i\bin\bn F\bFl\blo\boa\bat\btI\bIn\bnd\bde\bex\bx_\b#\b# *\b**\b**\b**\b*\n Integers in FloatIndex, e.g. 1., are now formatted with a decimal point and a 0\n digit, e.g. 1.0 (_\bG_\bH_\b _\b1_\b1_\b7_\b1_\b3) This change not only affects the display to the\n console, but also the output of IO methods like .to_csv or .to_html.\n Previous behavior:\n In [2]: s = pd.Series([1, 2, 3], index=np.arange(3.))\n \n@@ -542,15 +560,15 @@\n In [45]: s = pd.Series([1, 2, 3], index=np.arange(3.))\n \n In [46]: s\n Out[46]:\n 0.0    1\n 1.0    2\n 2.0    3\n-dtype: int64\n+Length: 3, dtype: int64\n \n In [47]: s.index\n Out[47]: Index([0.0, 1.0, 2.0], dtype='float64')\n \n In [48]: print(s.to_csv(path_or_buf=None, header=False))\n 0.0,1\n 1.0,2\n@@ -582,25 +600,25 @@\n    ....:                    'b': pd.Series([100, 200, 300], dtype='uint32')})\n    ....:\n \n In [50]: df.dtypes\n Out[50]:\n a     int64\n b    uint32\n-dtype: object\n+Length: 2, dtype: object\n \n In [51]: ix = df['a'] == 1\n \n In [52]: df.loc[ix, 'b'] = df.loc[ix, 'b']\n \n In [53]: df.dtypes\n Out[53]:\n a     int64\n b    uint32\n-dtype: object\n+Length: 2, dtype: object\n When a DataFrame\u2019s integer slice is partially updated with a new slice of\n floats that could potentially be down-casted to integer without losing\n precision, the dtype of the slice will be set to float instead of integer.\n Previous behavior:\n In [4]: df = pd.DataFrame(np.array(range(1,10)).reshape(3,3),\n                           columns=list('abc'),\n                           index=[[4,4,8], [8,10,12]])\n@@ -629,22 +647,26 @@\n In [55]: df\n Out[55]:\n       a  b  c\n 4 8   1  2  3\n   10  4  5  6\n 8 12  7  8  9\n \n+[3 rows x 3 columns]\n+\n In [56]: df.loc[4, 'c'] = np.array([0., 1.])\n \n In [57]: df\n Out[57]:\n       a  b  c\n 4 8   1  2  0\n   10  4  5  1\n 8 12  7  8  9\n+\n+[3 rows x 3 columns]\n *\b**\b**\b**\b* M\bMe\bet\bth\bho\bod\bd t\bto\bo_\b_x\bxa\bar\brr\bra\bay\by_\b#\b# *\b**\b**\b**\b*\n In a future version of pandas, we will be deprecating Panel and other > 2 ndim\n objects. In order to provide for continuity, all NDFrame objects have gained\n the .to_xarray() method in order to convert to xarray objects, which has a\n pandas-like interface for > 2 ndim. (_\bG_\bH_\b _\b1_\b1_\b9_\b7_\b2)\n See the _\bx_\ba_\br_\br_\ba_\by_\b _\bf_\bu_\bl_\bl_\b-_\bd_\bo_\bc_\bu_\bm_\be_\bn_\bt_\ba_\bt_\bi_\bo_\bn_\b _\bh_\be_\br_\be.\n In [1]: p = Panel(np.arange(2*3*4).reshape(2,3,4))\n@@ -761,34 +783,34 @@\n [ns]')\n TypeError: can only operate on a datetimes for subtraction,\n            but the operator [__add__] was passed\n In [66]: pd.Series([pd.NaT], dtype='<m8[ns]') + pd.Series([pd.NaT], dtype='<m8\n [ns]')\n Out[66]:\n 0   NaT\n-dtype: timedelta64[ns]\n+Length: 1, dtype: timedelta64[ns]\n Timedelta division by floats now works.\n In [67]: pd.Timedelta('1s') / 2.0\n Out[67]: Timedelta('0 days 00:00:00.500000')\n Subtraction by Timedelta in a Series by a Timestamp works (_\bG_\bH_\b _\b1_\b1_\b9_\b2_\b5)\n In [68]: ser = pd.Series(pd.timedelta_range('1 day', periods=3))\n \n In [69]: ser\n Out[69]:\n 0   1 days\n 1   2 days\n 2   3 days\n-dtype: timedelta64[ns]\n+Length: 3, dtype: timedelta64[ns]\n \n In [70]: pd.Timestamp('2012-01-01') - ser\n Out[70]:\n 0   2011-12-31\n 1   2011-12-30\n 2   2011-12-29\n-dtype: datetime64[ns]\n+Length: 3, dtype: datetime64[ns]\n NaT.isoformat() now returns 'NaT'. This change allows pd.Timestamp to rehydrate\n any timestamp like object from its isoformat (_\bG_\bH_\b _\b1_\b2_\b3_\b0_\b0).\n *\b**\b**\b**\b* C\bCh\bha\ban\bng\bge\bes\bs t\bto\bo m\bms\bsg\bgp\bpa\bac\bck\bk_\b#\b# *\b**\b**\b**\b*\n Forward incompatible changes in msgpack writing format were made over 0.17.0\n and 0.18.0; older versions of pandas cannot read files packed by newer versions\n (_\bG_\bH_\b _\b1_\b2_\b1_\b2_\b9, _\bG_\bH_\b _\b1_\b0_\b5_\b2_\b7)\n Bugs in to_msgpack and read_msgpack introduced in 0.17.0 and fixed in 0.18.0,\n@@ -825,23 +847,25 @@\n New signature\n In [71]: pd.Series([0,1]).rank(axis=0, method='average', numeric_only=False,\n    ....:                       na_option='keep', ascending=True, pct=False)\n    ....:\n Out[71]:\n 0    1.0\n 1    2.0\n-dtype: float64\n+Length: 2, dtype: float64\n \n In [72]: pd.DataFrame([0,1]).rank(axis=0, method='average', numeric_only=False,\n    ....:                          na_option='keep', ascending=True, pct=False)\n    ....:\n Out[72]:\n      0\n 0  1.0\n 1  2.0\n+\n+[2 rows x 1 columns]\n *\b**\b**\b**\b* B\bBu\bug\bg i\bin\bn Q\bQu\bua\bar\brt\bte\ber\brB\bBe\beg\bgi\bin\bn w\bwi\bit\bth\bh n\bn=\b=0\b0_\b#\b# *\b**\b**\b**\b*\n In previous versions, the behavior of the QuarterBegin offset was inconsistent\n depending on the date when the n parameter was 0. (_\bG_\bH_\b _\b1_\b1_\b4_\b0_\b6)\n The general semantics of anchored offsets for n=0 is to not move the date when\n it is an anchor point (e.g., a quarter start date), and otherwise roll forward\n to the next anchor point.\n In [73]: d = pd.Timestamp('2014-02-01')\n@@ -887,14 +911,16 @@\n 2010-01-01 09:00:03  0.683463  0.712702  0.370251  0.561196\n 2010-01-01 09:00:04  0.503083  0.013768  0.772827  0.882641\n 2010-01-01 09:00:05  0.364886  0.615396  0.075381  0.368824\n 2010-01-01 09:00:06  0.933140  0.651378  0.397203  0.788730\n 2010-01-01 09:00:07  0.316836  0.568099  0.869127  0.436173\n 2010-01-01 09:00:08  0.802148  0.143767  0.704261  0.704581\n 2010-01-01 09:00:09  0.218792  0.924868  0.442141  0.909316\n+\n+[10 rows x 4 columns]\n P\bPr\bre\bev\bvi\bio\bou\bus\bs A\bAP\bPI\bI:\n You would write a resampling operation that immediately evaluates. If a how\n parameter was not provided, it would default to how='mean'.\n In [6]: df.resample('2s')\n Out[6]:\n                          A         B         C         D\n 2010-01-01 09:00:00  0.485748  0.447351  0.357096  0.793615\n@@ -925,51 +951,61 @@\n Out[84]:\n                             A         B         C         D\n 2010-01-01 09:00:00  0.485748  0.447351  0.357096  0.793615\n 2010-01-01 09:00:02  0.820801  0.794317  0.364034  0.531096\n 2010-01-01 09:00:04  0.433985  0.314582  0.424104  0.625733\n 2010-01-01 09:00:06  0.624988  0.609738  0.633165  0.612452\n 2010-01-01 09:00:08  0.510470  0.534317  0.573201  0.806949\n+\n+[5 rows x 4 columns]\n In [85]: r.sum()\n Out[85]:\n                             A         B         C         D\n 2010-01-01 09:00:00  0.971495  0.894701  0.714192  1.587231\n 2010-01-01 09:00:02  1.641602  1.588635  0.728068  1.062191\n 2010-01-01 09:00:04  0.867969  0.629165  0.848208  1.251465\n 2010-01-01 09:00:06  1.249976  1.219477  1.266330  1.224904\n 2010-01-01 09:00:08  1.020940  1.068634  1.146402  1.613897\n+\n+[5 rows x 4 columns]\n Furthermore, resample now supports getitem operations to perform the resample\n on specific columns.\n In [86]: r[['A','C']].mean()\n Out[86]:\n                             A         C\n 2010-01-01 09:00:00  0.485748  0.357096\n 2010-01-01 09:00:02  0.820801  0.364034\n 2010-01-01 09:00:04  0.433985  0.424104\n 2010-01-01 09:00:06  0.624988  0.633165\n 2010-01-01 09:00:08  0.510470  0.573201\n+\n+[5 rows x 2 columns]\n and .aggregate type operations.\n In [87]: r.agg({'A' : 'mean', 'B' : 'sum'})\n Out[87]:\n                             A         B\n 2010-01-01 09:00:00  0.485748  0.894701\n 2010-01-01 09:00:02  0.820801  1.588635\n 2010-01-01 09:00:04  0.433985  0.629165\n 2010-01-01 09:00:06  0.624988  1.219477\n 2010-01-01 09:00:08  0.510470  1.068634\n+\n+[5 rows x 2 columns]\n These accessors can of course, be combined\n In [88]: r[['A','B']].agg(['mean','sum'])\n Out[88]:\n                             A                   B\n                          mean       sum      mean       sum\n 2010-01-01 09:00:00  0.485748  0.971495  0.447351  0.894701\n 2010-01-01 09:00:02  0.820801  1.641602  0.794317  1.588635\n 2010-01-01 09:00:04  0.433985  0.867969  0.314582  0.629165\n 2010-01-01 09:00:06  0.624988  1.249976  0.609738  1.219477\n 2010-01-01 09:00:08  0.510470  1.020940  0.534317  1.068634\n+\n+[5 rows x 4 columns]\n *\b**\b**\b* U\bUp\bps\bsa\bam\bmp\bpl\bli\bin\bng\bg_\b#\b# *\b**\b**\b*\n Upsampling operations take you from a lower frequency to a higher frequency.\n These are now performed with the Resampler objects with backfill(), ffill(),\n fillna() and asfreq() methods.\n In [89]: s = pd.Series(np.arange(5, dtype='int64'),\n               index=pd.date_range('2010-01-01', periods=5, freq='Q'))\n \n@@ -1059,37 +1095,41 @@\n Out[89]:\n                             A         B         C         D\n 2010-01-01 09:00:00  0.191519  0.272593  0.276464  0.785359\n 2010-01-01 09:00:02  0.683463  0.712702  0.357817  0.500995\n 2010-01-01 09:00:04  0.364886  0.013768  0.075381  0.368824\n 2010-01-01 09:00:06  0.316836  0.568099  0.397203  0.436173\n 2010-01-01 09:00:08  0.218792  0.143767  0.442141  0.704581\n+\n+[5 rows x 4 columns]\n The good news is the return dimensions will differ between the new API and the\n old API, so this should loudly raise an exception.\n To replicate the original operation\n In [90]: df.resample('2s').mean().min()\n Out[90]:\n A    0.433985\n B    0.314582\n C    0.357096\n D    0.531096\n-dtype: float64\n+Length: 4, dtype: float64\n *\b**\b**\b**\b* C\bCh\bha\ban\bng\bge\bes\bs t\bto\bo e\bev\bva\bal\bl_\b#\b# *\b**\b**\b**\b*\n In prior versions, new columns assignments in an eval expression resulted in an\n inplace change to the DataFrame. (_\bG_\bH_\b _\b9_\b2_\b9_\b7, _\bG_\bH_\b _\b8_\b6_\b6_\b4, _\bG_\bH_\b _\b1_\b0_\b4_\b8_\b6)\n In [91]: df = pd.DataFrame({'a': np.linspace(0, 10, 5), 'b': range(5)})\n \n In [92]: df\n Out[92]:\n       a  b\n 0   0.0  0\n 1   2.5  1\n 2   5.0  2\n 3   7.5  3\n 4  10.0  4\n+\n+[5 rows x 2 columns]\n In [12]: df.eval('c = a + b')\n FutureWarning: eval expressions containing an assignment currentlydefault to\n operating inplace.\n This will change in a future version of pandas, use inplace=True to avoid this\n warning.\n \n In [13]: df\n@@ -1107,83 +1147,99 @@\n       a  b     c\n 0   0.0  0   0.0\n 1   2.5  1   3.5\n 2   5.0  2   7.0\n 3   7.5  3  10.5\n 4  10.0  4  14.0\n \n+[5 rows x 3 columns]\n+\n In [94]: df.eval('d = c - b', inplace=False)\n Out[94]:\n       a  b     c     d\n 0   0.0  0   0.0   0.0\n 1   2.5  1   3.5   2.5\n 2   5.0  2   7.0   5.0\n 3   7.5  3  10.5   7.5\n 4  10.0  4  14.0  10.0\n \n+[5 rows x 4 columns]\n+\n In [95]: df\n Out[95]:\n       a  b     c\n 0   0.0  0   0.0\n 1   2.5  1   3.5\n 2   5.0  2   7.0\n 3   7.5  3  10.5\n 4  10.0  4  14.0\n \n+[5 rows x 3 columns]\n+\n In [96]: df.eval('d = c - b', inplace=True)\n \n In [97]: df\n Out[97]:\n       a  b     c     d\n 0   0.0  0   0.0   0.0\n 1   2.5  1   3.5   2.5\n 2   5.0  2   7.0   5.0\n 3   7.5  3  10.5   7.5\n 4  10.0  4  14.0  10.0\n+\n+[5 rows x 4 columns]\n Warning\n For backwards compatibility, inplace defaults to True if not specified. This\n will change in a future version of pandas. If your code depends on an inplace\n assignment you should update to explicitly set inplace=True\n The inplace keyword parameter was also added the query method.\n In [98]: df.query('a > 5')\n Out[98]:\n       a  b     c     d\n 3   7.5  3  10.5   7.5\n 4  10.0  4  14.0  10.0\n \n+[2 rows x 4 columns]\n+\n In [99]: df.query('a > 5', inplace=True)\n \n In [100]: df\n Out[100]:\n       a  b     c     d\n 3   7.5  3  10.5   7.5\n 4  10.0  4  14.0  10.0\n+\n+[2 rows x 4 columns]\n Warning\n Note that the default value for inplace in a query is False, which is\n consistent with prior versions.\n eval has also been updated to allow multi-line expressions for multiple\n assignments. These expressions will be evaluated one at a time in order. Only\n assignments are valid for multi-line expressions.\n In [101]: df\n Out[101]:\n       a  b     c     d\n 3   7.5  3  10.5   7.5\n 4  10.0  4  14.0  10.0\n \n+[2 rows x 4 columns]\n+\n In [102]: df.eval(\"\"\"\n    .....: e = d + a\n    .....: f = e - 22\n    .....: g = f / 2.0\"\"\", inplace=True)\n    .....:\n \n In [103]: df\n Out[103]:\n       a  b     c     d     e    f    g\n 3   7.5  3  10.5   7.5  15.0 -7.0 -3.5\n 4  10.0  4  14.0  10.0  20.0 -2.0 -1.0\n+\n+[2 rows x 7 columns]\n *\b**\b**\b**\b* O\bOt\bth\bhe\ber\br A\bAP\bPI\bI c\bch\bha\ban\bng\bge\bes\bs_\b#\b# *\b**\b**\b**\b*\n     * DataFrame.between_time and Series.between_time now only parse a fixed set\n       of time strings. Parsing of date strings is no longer supported and\n       raises a ValueError. (_\bG_\bH_\b _\b1_\b1_\b8_\b1_\b8)\n       In [107]: s = pd.Series(range(10), pd.date_range('2015-01-01', freq='H',\n       periods=10))\n \n@@ -1279,24 +1335,24 @@\n In [104]: s = pd.Series([1, 2, 3], index=[4, 5, 6])\n \n In [105]: s\n Out[105]:\n 4    1\n 5    2\n 6    3\n-dtype: int64\n+Length: 3, dtype: int64\n \n In [106]: s2 = pd.Series([1, 2, 3], index=list('abc'))\n \n In [107]: s2\n Out[107]:\n a    1\n b    2\n c    3\n-dtype: int64\n+Length: 3, dtype: int64\n Previous behavior:\n # this is label indexing\n In [2]: s[5.0]\n FutureWarning: scalar indexers for index type Int64Index should be integers and\n not floating point\n Out[2]: 2\n \n@@ -1342,26 +1398,26 @@\n In [111]: s_copy[5.0] = 10\n \n In [112]: s_copy\n Out[112]:\n 4     1\n 5    10\n 6     3\n-dtype: int64\n+Length: 3, dtype: int64\n \n In [113]: s_copy = s.copy()\n \n In [114]: s_copy.loc[5.0] = 10\n \n In [115]: s_copy\n Out[115]:\n 4     1\n 5    10\n 6     3\n-dtype: int64\n+Length: 3, dtype: int64\n Positional setting with .ix and a float indexer will ADD this value to the\n index, rather than previously setting the value by position.\n In [3]: s2.ix[1.0] = 10\n In [4]: s2\n Out[4]:\n a       1\n b       2\n@@ -1370,32 +1426,32 @@\n dtype: int64\n Slicing will also coerce integer-like floats to integers for a non-\n Float64Index.\n In [116]: s.loc[5.0:6]\n Out[116]:\n 5    2\n 6    3\n-dtype: int64\n+Length: 2, dtype: int64\n Note that for floats that are NOT coercible to ints, the label based bounds\n will be excluded\n In [117]: s.loc[5.1:6]\n Out[117]:\n 6    3\n-dtype: int64\n+Length: 1, dtype: int64\n Float indexing on a Float64Index is unchanged.\n In [118]: s = pd.Series([1, 2, 3], index=np.arange(3.))\n \n In [119]: s[1.0]\n Out[119]: np.int64(2)\n \n In [120]: s[1.0:2.5]\n Out[120]:\n 1.0    2\n 2.0    3\n-dtype: int64\n+Length: 2, dtype: int64\n *\b**\b**\b**\b* R\bRe\bem\bmo\bov\bva\bal\bl o\bof\bf p\bpr\bri\bio\bor\br v\bve\ber\brs\bsi\bio\bon\bn d\bde\bep\bpr\bre\bec\bca\bat\bti\bio\bon\bns\bs/\b/c\bch\bha\ban\bng\bge\bes\bs_\b#\b# *\b**\b**\b**\b*\n     * Removal of rolling_corr_pairwise in favor of .rolling().corr\n       (pairwise=True) (_\bG_\bH_\b _\b4_\b9_\b5_\b0)\n     * Removal of expanding_corr_pairwise in favor of .expanding().corr\n       (pairwise=True) (_\bG_\bH_\b _\b4_\b9_\b5_\b0)\n     * Removal of DataMatrix module. This was not imported into the pandas\n       namespace in any event (_\bG_\bH_\b _\b1_\b2_\b1_\b1_\b1)\n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.18.1.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.18.1.html", "unified_diff": "@@ -724,15 +724,15 @@\n \n <span class=\"gp\">In [6]: </span><span class=\"n\">dt</span> <span class=\"o\">+</span> <span class=\"n\">bhour_us</span>\n <span class=\"gh\">Out[6]: </span><span class=\"go\">Timestamp(&#39;2014-01-17 16:00:00&#39;)</span>\n </pre></div>\n </div>\n <p>Tuesday after MLK Day (Monday is skipped because it\u2019s a holiday)</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [7]: </span><span class=\"n\">dt</span> <span class=\"o\">+</span> <span class=\"n\">bhour_us</span> <span class=\"o\">*</span> <span class=\"mi\">2</span>\n-<span class=\"gh\">Out[7]: </span><span class=\"go\">Timestamp(&#39;2014-01-21 09:00:00&#39;)</span>\n+<span class=\"gh\">Out[7]: </span><span class=\"go\">Timestamp(&#39;2014-01-20 09:00:00&#39;)</span>\n </pre></div>\n </div>\n </section>\n <section id=\"method-groupby-syntax-with-window-and-resample-operations\">\n <span id=\"whatsnew-0181-deferred-ops\"></span><h3>Method <code class=\"docutils literal notranslate\"><span class=\"pre\">.groupby(..)</span></code> syntax with window and resample operations<a class=\"headerlink\" href=\"#method-groupby-syntax-with-window-and-resample-operations\" title=\"Link to this heading\">#</a></h3>\n <p><code class=\"docutils literal notranslate\"><span class=\"pre\">.groupby(...)</span></code> has been enhanced to provide convenient syntax when working with <code class=\"docutils literal notranslate\"><span class=\"pre\">.rolling(..)</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">.expanding(..)</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">.resample(..)</span></code> per group, see (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/12486\">GH 12486</a>, <a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/12738\">GH 12738</a>).</p>\n <p>You can now use <code class=\"docutils literal notranslate\"><span class=\"pre\">.rolling(..)</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">.expanding(..)</span></code> as methods on groupbys. These return another deferred object (similar to what <code class=\"docutils literal notranslate\"><span class=\"pre\">.rolling()</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">.expanding()</span></code> do on ungrouped pandas objects). You can then operate on these <code class=\"docutils literal notranslate\"><span class=\"pre\">RollingGroupby</span></code> objects in a similar manner.</p>\n@@ -835,14 +835,16 @@\n <span class=\"gh\">Out[12]: </span>\n <span class=\"go\">            group  val</span>\n <span class=\"go\">date                  </span>\n <span class=\"go\">2016-01-03      1    5</span>\n <span class=\"go\">2016-01-10      1    6</span>\n <span class=\"go\">2016-01-17      2    7</span>\n <span class=\"go\">2016-01-24      2    8</span>\n+\n+<span class=\"go\">[4 rows x 2 columns]</span>\n </pre></div>\n </div>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"n\">In</span><span class=\"p\">[</span><span class=\"mi\">1</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=\"s2\">&quot;group&quot;</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\">resample</span><span class=\"p\">(</span><span class=\"s2\">&quot;1D&quot;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">ffill</span><span class=\"p\">())</span>\n <span class=\"n\">Out</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">]:</span>\n                   <span class=\"n\">group</span>  <span class=\"n\">val</span>\n <span class=\"n\">group</span> <span class=\"n\">date</span>\n <span class=\"mi\">1</span>     <span class=\"mi\">2016</span><span class=\"o\">-</span><span class=\"mi\">01</span><span class=\"o\">-</span><span class=\"mi\">03</span>      <span class=\"mi\">1</span>    <span class=\"mi\">5</span>\n@@ -905,48 +907,54 @@\n \n <span class=\"gp\">In [14]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">where</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\">&gt;</span> <span class=\"mi\">4</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=\"mi\">10</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[14]: </span>\n <span class=\"go\">    A   B  C</span>\n <span class=\"go\">0  11  14  7</span>\n <span class=\"go\">1  12   5  8</span>\n <span class=\"go\">2  13   6  9</span>\n+\n+<span class=\"go\">[3 rows x 3 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"methods-loc-iloc-ix\">\n <h4>Methods <code class=\"docutils literal notranslate\"><span class=\"pre\">.loc[]</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">.iloc[]</span></code>, <code class=\"docutils literal notranslate\"><span class=\"pre\">.ix[]</span></code><a class=\"headerlink\" href=\"#methods-loc-iloc-ix\" title=\"Link to this heading\">#</a></h4>\n <p>These can accept a callable, and a tuple of callable as a slicer. The callable\n can return a valid boolean indexer or anything which is valid for these indexer\u2019s input.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"go\"># callable returns bool indexer</span>\n <span class=\"gp\">In [15]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">loc</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\">A</span> <span class=\"o\">&gt;=</span> <span class=\"mi\">2</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\">sum</span><span class=\"p\">()</span> <span class=\"o\">&gt;</span> <span class=\"mi\">10</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[15]: </span>\n <span class=\"go\">   B  C</span>\n <span class=\"go\">1  5  8</span>\n <span class=\"go\">2  6  9</span>\n \n+<span class=\"go\">[2 rows x 2 columns]</span>\n+\n <span class=\"go\"># callable returns list of labels</span>\n <span class=\"gp\">In [16]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">loc</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=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</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>\n <span class=\"gh\">Out[16]: </span>\n <span class=\"go\">   A  B</span>\n <span class=\"go\">1  2  5</span>\n <span class=\"go\">2  3  6</span>\n+\n+<span class=\"go\">[2 rows x 2 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"indexing-with\">\n <h4>Indexing with <code class=\"docutils literal notranslate\"><span class=\"pre\">[]</span></code><a class=\"headerlink\" href=\"#indexing-with\" title=\"Link to this heading\">#</a></h4>\n <p>Finally, you can use a callable in <code class=\"docutils literal notranslate\"><span class=\"pre\">[]</span></code> indexing of Series, DataFrame and Panel.\n The callable must return a valid input for <code class=\"docutils literal notranslate\"><span class=\"pre\">[]</span></code> indexing depending on its\n class and index type.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [17]: </span><span class=\"n\">df</span><span class=\"p\">[</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"s2\">&quot;A&quot;</span><span class=\"p\">]</span>\n <span class=\"gh\">Out[17]: </span>\n <span class=\"go\">0    1</span>\n <span class=\"go\">1    2</span>\n <span class=\"go\">2    3</span>\n-<span class=\"go\">Name: A, dtype: int64</span>\n+<span class=\"go\">Name: A, Length: 3, dtype: int64</span>\n </pre></div>\n </div>\n <p>Using these methods / indexers, you can chain data selection operations\n without using temporary variable.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [18]: </span><span class=\"n\">bb</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=\"s2\">&quot;data/baseball.csv&quot;</span><span class=\"p\">,</span> <span class=\"n\">index_col</span><span class=\"o\">=</span><span class=\"s2\">&quot;id&quot;</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [19]: </span><span class=\"p\">(</span><span class=\"n\">bb</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">([</span><span class=\"s2\">&quot;year&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;team&quot;</span><span class=\"p\">])</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">(</span><span class=\"n\">numeric_only</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[</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\">r</span> <span class=\"o\">&gt;</span> <span class=\"mi\">100</span><span class=\"p\">])</span>\n@@ -1050,30 +1058,32 @@\n <span class=\"gp\">   ....: </span>\n \n <span class=\"gp\">In [21]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[21]: </span>\n <span class=\"go\">   year  month  day  hour</span>\n <span class=\"go\">0  2015      2    4     2</span>\n <span class=\"go\">1  2016      3    5     3</span>\n+\n+<span class=\"go\">[2 rows x 4 columns]</span>\n </pre></div>\n </div>\n <p>Assembling using the passed frame.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [22]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">to_datetime</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[22]: </span>\n <span class=\"go\">0   2015-02-04 02:00:00</span>\n <span class=\"go\">1   2016-03-05 03:00:00</span>\n-<span class=\"go\">dtype: datetime64[ns]</span>\n+<span class=\"go\">Length: 2, dtype: datetime64[ns]</span>\n </pre></div>\n </div>\n <p>You can pass only the columns that you need to assemble.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [23]: </span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">to_datetime</span><span class=\"p\">(</span><span class=\"n\">df</span><span class=\"p\">[[</span><span class=\"s2\">&quot;year&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;month&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;day&quot;</span><span class=\"p\">]])</span>\n <span class=\"gh\">Out[23]: </span>\n <span class=\"go\">0   2015-02-04</span>\n <span class=\"go\">1   2016-03-05</span>\n-<span class=\"go\">dtype: datetime64[ns]</span>\n+<span class=\"go\">Length: 2, dtype: datetime64[ns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"other-enhancements\">\n <span id=\"whatsnew-0181-other\"></span><h3>Other enhancements<a class=\"headerlink\" href=\"#other-enhancements\" title=\"Link to this heading\">#</a></h3>\n <ul>\n <li><p><code class=\"docutils literal notranslate\"><span class=\"pre\">pd.read_csv()</span></code> now supports <code class=\"docutils literal notranslate\"><span class=\"pre\">delim_whitespace=True</span></code> for the Python engine (<a class=\"extlink-issue reference external\" href=\"https://github.com/pandas-dev/pandas/issues/12958\">GH 12958</a>)</p></li>\n@@ -1158,14 +1168,16 @@\n \n <span class=\"gp\">In [30]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[30]: </span>\n <span class=\"go\">   A  B</span>\n <span class=\"go\">0  a  1</span>\n <span class=\"go\">1  b  2</span>\n <span class=\"go\">2  a  3</span>\n+\n+<span class=\"go\">[3 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <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\">groupby</span><span class=\"p\">(</span><span class=\"s1\">&#39;A&#39;</span><span class=\"p\">,</span> <span class=\"n\">as_index</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)[</span><span class=\"s1\">&#39;B&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">nth</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[3]:</span>\n <span class=\"go\">0    1</span>\n <span class=\"go\">1    2</span>\n@@ -1179,21 +1191,21 @@\n </pre></div>\n </div>\n <p>New behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [31]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"s2\">&quot;A&quot;</span><span class=\"p\">,</span> <span class=\"n\">as_index</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)[</span><span class=\"s2\">&quot;B&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">nth</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[31]: </span>\n <span class=\"go\">0    1</span>\n <span class=\"go\">1    2</span>\n-<span class=\"go\">Name: B, dtype: int64</span>\n+<span class=\"go\">Name: B, Length: 2, dtype: int64</span>\n \n <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=\"s2\">&quot;A&quot;</span><span class=\"p\">,</span> <span class=\"n\">as_index</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)[</span><span class=\"s2\">&quot;B&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">nth</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[32]: </span>\n <span class=\"go\">0    1</span>\n <span class=\"go\">1    2</span>\n-<span class=\"go\">Name: B, dtype: int64</span>\n+<span class=\"go\">Name: B, Length: 2, dtype: int64</span>\n </pre></div>\n </div>\n <p>Furthermore, previously, a <code class=\"docutils literal notranslate\"><span class=\"pre\">.groupby</span></code> would always sort, regardless if <code class=\"docutils literal notranslate\"><span class=\"pre\">sort=False</span></code> was passed with <code class=\"docutils literal notranslate\"><span class=\"pre\">.nth()</span></code>.</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [33]: </span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">random</span><span class=\"o\">.</span><span class=\"n\">seed</span><span class=\"p\">(</span><span class=\"mi\">1234</span><span class=\"p\">)</span>\n \n <span class=\"gp\">In [34]: </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=\"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\">100</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">),</span> <span class=\"n\">columns</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>\n \n@@ -1225,21 +1237,25 @@\n <span class=\"gh\">Out[36]: </span>\n <span class=\"go\">           a         b  c</span>\n <span class=\"go\">2  -0.720589  0.887163  2</span>\n <span class=\"go\">3   0.859588 -0.636524  3</span>\n <span class=\"go\">7  -0.334077  0.002118  0</span>\n <span class=\"go\">21  0.036142 -2.074978  1</span>\n \n+<span class=\"go\">[4 rows x 3 columns]</span>\n+\n <span class=\"gp\">In [37]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"s2\">&quot;c&quot;</span><span class=\"p\">,</span> <span class=\"n\">sort</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">nth</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n <span class=\"gh\">Out[37]: </span>\n <span class=\"go\">           a         b  c</span>\n <span class=\"go\">2  -0.720589  0.887163  2</span>\n <span class=\"go\">3   0.859588 -0.636524  3</span>\n <span class=\"go\">7  -0.334077  0.002118  0</span>\n <span class=\"go\">21  0.036142 -2.074978  1</span>\n+\n+<span class=\"go\">[4 rows x 3 columns]</span>\n </pre></div>\n </div>\n </section>\n <section id=\"numpy-function-compatibility\">\n <span id=\"whatsnew-0181-numpy-compatibility\"></span><h3>NumPy function compatibility<a class=\"headerlink\" href=\"#numpy-function-compatibility\" title=\"Link to this heading\">#</a></h3>\n <p>Compatibility between pandas array-like methods (e.g. <code class=\"docutils literal notranslate\"><span class=\"pre\">sum</span></code> and <code class=\"docutils literal notranslate\"><span class=\"pre\">take</span></code>) and their <code class=\"docutils literal notranslate\"><span class=\"pre\">numpy</span></code>\n counterparts has been greatly increased by augmenting the signatures of the <code class=\"docutils literal notranslate\"><span class=\"pre\">pandas</span></code> methods so\n@@ -1274,14 +1290,16 @@\n <span class=\"gp\">   ....: </span>\n \n <span class=\"gp\">In [39]: </span><span class=\"n\">df</span>\n <span class=\"gh\">Out[39]: </span>\n <span class=\"go\">        date  value</span>\n <span class=\"go\">0 2000-10-10     10</span>\n <span class=\"go\">1 2000-11-10     13</span>\n+\n+<span class=\"go\">[2 rows x 2 columns]</span>\n </pre></div>\n </div>\n <p>Previous behavior:</p>\n <div class=\"highlight-ipython notranslate\"><div class=\"highlight\"><pre><span></span><span class=\"gp\">In [1]: </span><span class=\"n\">df</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">TimeGrouper</span><span class=\"p\">(</span><span class=\"n\">key</span><span class=\"o\">=</span><span class=\"s1\">&#39;date&#39;</span><span class=\"p\">,</span>\n <span class=\"gp\">   ...: </span>                          <span class=\"n\">freq</span><span class=\"o\">=</span><span class=\"s1\">&#39;M&#39;</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\">value</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">())</span>\n <span class=\"gh\">Out[1]:</span>\n <span class=\"go\">...</span>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -173,15 +173,15 @@\n \n In [5]: dt = datetime.datetime(2014, 1, 17, 15)\n \n In [6]: dt + bhour_us\n Out[6]: Timestamp('2014-01-17 16:00:00')\n Tuesday after MLK Day (Monday is skipped because it\u2019s a holiday)\n In [7]: dt + bhour_us * 2\n-Out[7]: Timestamp('2014-01-21 09:00:00')\n+Out[7]: Timestamp('2014-01-20 09:00:00')\n *\b**\b**\b**\b* M\bMe\bet\bth\bho\bod\bd .\b.g\bgr\bro\bou\bup\bpb\bby\by(\b(.\b..\b.)\b) s\bsy\byn\bnt\bta\bax\bx w\bwi\bit\bth\bh w\bwi\bin\bnd\bdo\bow\bw a\ban\bnd\bd r\bre\bes\bsa\bam\bmp\bpl\ble\be o\bop\bpe\ber\bra\bat\bti\bio\bon\bns\bs_\b#\b# *\b**\b**\b**\b*\n .groupby(...) has been enhanced to provide convenient syntax when working with\n .rolling(..), .expanding(..) and .resample(..) per group, see (_\bG_\bH_\b _\b1_\b2_\b4_\b8_\b6, _\bG_\bH\n _\b1_\b2_\b7_\b3_\b8).\n You can now use .rolling(..) and .expanding(..) as methods on groupbys. These\n return another deferred object (similar to what .rolling() and .expanding() do\n on ungrouped pandas objects). You can then operate on these RollingGroupby\n@@ -281,14 +281,16 @@\n Out[12]:\n             group  val\n date\n 2016-01-03      1    5\n 2016-01-10      1    6\n 2016-01-17      2    7\n 2016-01-24      2    8\n+\n+[4 rows x 2 columns]\n In[1]: df.groupby(\"group\").apply(lambda x: x.resample(\"1D\").ffill())\n Out[1]:\n                   group  val\n group date\n 1     2016-01-03      1    5\n       2016-01-04      1    5\n       2016-01-05      1    5\n@@ -338,41 +340,47 @@\n \n In [14]: df.where(lambda x: x > 4, lambda x: x + 10)\n Out[14]:\n     A   B  C\n 0  11  14  7\n 1  12   5  8\n 2  13   6  9\n+\n+[3 rows x 3 columns]\n *\b**\b**\b* M\bMe\bet\bth\bho\bod\bds\bs .\b.l\blo\boc\bc[\b[]\b],\b, .\b.i\bil\blo\boc\bc[\b[]\b],\b, .\b.i\bix\bx[\b[]\b]_\b#\b# *\b**\b**\b*\n These can accept a callable, and a tuple of callable as a slicer. The callable\n can return a valid boolean indexer or anything which is valid for these\n indexer\u2019s input.\n # callable returns bool indexer\n In [15]: df.loc[lambda x: x.A >= 2, lambda x: x.sum() > 10]\n Out[15]:\n    B  C\n 1  5  8\n 2  6  9\n \n+[2 rows x 2 columns]\n+\n # callable returns list of labels\n In [16]: df.loc[lambda x: [1, 2], lambda x: [\"A\", \"B\"]]\n Out[16]:\n    A  B\n 1  2  5\n 2  3  6\n+\n+[2 rows x 2 columns]\n *\b**\b**\b* I\bIn\bnd\bde\bex\bxi\bin\bng\bg w\bwi\bit\bth\bh [\b[]\b]_\b#\b# *\b**\b**\b*\n Finally, you can use a callable in [] indexing of Series, DataFrame and Panel.\n The callable must return a valid input for [] indexing depending on its class\n and index type.\n In [17]: df[lambda x: \"A\"]\n Out[17]:\n 0    1\n 1    2\n 2    3\n-Name: A, dtype: int64\n+Name: A, Length: 3, dtype: int64\n Using these methods / indexers, you can chain data selection operations without\n using temporary variable.\n In [18]: bb = pd.read_csv(\"data/baseball.csv\", index_col=\"id\")\n \n In [19]: (bb.groupby([\"year\", \"team\"]).sum(numeric_only=True).loc[lambda df:\n df.r > 100])\n Out[19]:\n@@ -478,26 +486,28 @@\n    ....:\n \n In [21]: df\n Out[21]:\n    year  month  day  hour\n 0  2015      2    4     2\n 1  2016      3    5     3\n+\n+[2 rows x 4 columns]\n Assembling using the passed frame.\n In [22]: pd.to_datetime(df)\n Out[22]:\n 0   2015-02-04 02:00:00\n 1   2016-03-05 03:00:00\n-dtype: datetime64[ns]\n+Length: 2, dtype: datetime64[ns]\n You can pass only the columns that you need to assemble.\n In [23]: pd.to_datetime(df[[\"year\", \"month\", \"day\"]])\n Out[23]:\n 0   2015-02-04\n 1   2016-03-05\n-dtype: datetime64[ns]\n+Length: 2, dtype: datetime64[ns]\n *\b**\b**\b**\b* O\bOt\bth\bhe\ber\br e\ben\bnh\bha\ban\bnc\bce\bem\bme\ben\bnt\bts\bs_\b#\b# *\b**\b**\b**\b*\n     * pd.read_csv() now supports delim_whitespace=True for the Python engine\n       (_\bG_\bH_\b _\b1_\b2_\b9_\b5_\b8)\n     * pd.read_csv() now supports opening ZIP files that contains a single CSV,\n       via extension inference or explicit compression='zip' (_\bG_\bH_\b _\b1_\b2_\b1_\b7_\b5)\n     * pd.read_csv() now supports opening files using xz compression, via\n       extension inference or explicit compression='xz' is specified; xz\n@@ -589,14 +599,16 @@\n \n In [30]: df\n Out[30]:\n    A  B\n 0  a  1\n 1  b  2\n 2  a  3\n+\n+[3 rows x 2 columns]\n Previous behavior:\n In [3]: df.groupby('A', as_index=True)['B'].nth(0)\n Out[3]:\n 0    1\n 1    2\n Name: B, dtype: int64\n \n@@ -606,21 +618,21 @@\n 1    2\n Name: B, dtype: int64\n New behavior:\n In [31]: df.groupby(\"A\", as_index=True)[\"B\"].nth(0)\n Out[31]:\n 0    1\n 1    2\n-Name: B, dtype: int64\n+Name: B, Length: 2, dtype: int64\n \n In [32]: df.groupby(\"A\", as_index=False)[\"B\"].nth(0)\n Out[32]:\n 0    1\n 1    2\n-Name: B, dtype: int64\n+Name: B, Length: 2, dtype: int64\n Furthermore, previously, a .groupby would always sort, regardless if sort=False\n was passed with .nth().\n In [33]: np.random.seed(1234)\n \n In [34]: df = pd.DataFrame(np.random.randn(100, 2), columns=[\"a\", \"b\"])\n \n In [35]: df[\"c\"] = np.random.randint(0, 4, 100)\n@@ -647,21 +659,25 @@\n Out[36]:\n            a         b  c\n 2  -0.720589  0.887163  2\n 3   0.859588 -0.636524  3\n 7  -0.334077  0.002118  0\n 21  0.036142 -2.074978  1\n \n+[4 rows x 3 columns]\n+\n In [37]: df.groupby(\"c\", sort=False).nth(1)\n Out[37]:\n            a         b  c\n 2  -0.720589  0.887163  2\n 3   0.859588 -0.636524  3\n 7  -0.334077  0.002118  0\n 21  0.036142 -2.074978  1\n+\n+[4 rows x 3 columns]\n *\b**\b**\b**\b* N\bNu\bum\bmP\bPy\by f\bfu\bun\bnc\bct\bti\bio\bon\bn c\bco\bom\bmp\bpa\bat\bti\bib\bbi\bil\bli\bit\bty\by_\b#\b# *\b**\b**\b**\b*\n Compatibility between pandas array-like methods (e.g. sum and take) and their\n numpy counterparts has been greatly increased by augmenting the signatures of\n the pandas methods so as to accept arguments that can be passed in from numpy,\n even if they are not necessarily used in the pandas implementation (_\bG_\bH_\b _\b1_\b2_\b6_\b4_\b4,\n _\bG_\bH_\b _\b1_\b2_\b6_\b3_\b8, _\bG_\bH_\b _\b1_\b2_\b6_\b8_\b7)\n     * .searchsorted() for Index and TimedeltaIndex now accept a sorter argument\n@@ -687,14 +703,16 @@\n    ....:\n \n In [39]: df\n Out[39]:\n         date  value\n 0 2000-10-10     10\n 1 2000-11-10     13\n+\n+[2 rows x 2 columns]\n Previous behavior:\n In [1]: df.groupby(pd.TimeGrouper(key='date',\n    ...:                           freq='M')).apply(lambda x: x.value.sum())\n Out[1]:\n ...\n TypeError: cannot concatenate a non-NDFrame object\n \n"}]}, {"source1": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.24.0.html", "source2": "./usr/share/doc/python-pandas-doc/html/whatsnew/v0.24.0.html", "unified_diff": "@@ -820,18 +820,18 @@\n a new ndarray of period objects each time.</p>\n <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>\n <span class=\"gh\">Out[18]: </span>\n <span class=\"go\">array([Period(&#39;2000-01-01&#39;, &#39;D&#39;), Period(&#39;2000-01-02&#39;, &#39;D&#39;),</span>\n <span class=\"go\">       Period(&#39;2000-01-03&#39;, &#39;D&#39;), Period(&#39;2000-01-04&#39;, &#39;D&#39;)], dtype=object)</span>\n \n <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>\n-<span class=\"gh\">Out[19]: </span><span class=\"go\">3773406912</span>\n+<span class=\"gh\">Out[19]: </span><span class=\"go\">3788646784</span>\n \n <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>\n-<span class=\"gh\">Out[20]: </span><span class=\"go\">3879182560</span>\n+<span class=\"gh\">Out[20]: </span><span class=\"go\">3786966512</span>\n </pre></div>\n </div>\n <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>\n <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>\n <span class=\"gh\">Out[21]: </span>\n <span class=\"go\">array([Period(&#39;2000-01-01&#39;, &#39;D&#39;), Period(&#39;2000-01-02&#39;, &#39;D&#39;),</span>\n <span class=\"go\">       Period(&#39;2000-01-03&#39;, &#39;D&#39;), Period(&#39;2000-01-04&#39;, &#39;D&#39;)], dtype=object)</span>\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -268,18 +268,18 @@\n each time.\n In [18]: idx.values\n Out[18]:\n array([Period('2000-01-01', 'D'), Period('2000-01-02', 'D'),\n        Period('2000-01-03', 'D'), Period('2000-01-04', 'D')], dtype=object)\n \n In [19]: id(idx.values)\n-Out[19]: 3773406912\n+Out[19]: 3788646784\n \n In [20]: id(idx.values)\n-Out[20]: 3879182560\n+Out[20]: 3786966512\n If you need an actual NumPy array, use _\bS_\be_\br_\bi_\be_\bs_\b._\bt_\bo_\b__\bn_\bu_\bm_\bp_\by_\b(_\b) or Index.to_numpy().\n In [21]: idx.to_numpy()\n Out[21]:\n array([Period('2000-01-01', 'D'), Period('2000-01-02', 'D'),\n        Period('2000-01-03', 'D'), Period('2000-01-04', 'D')], dtype=object)\n \n In [22]: pd.Series(idx).to_numpy()\n"}]}]}]}]}]}