詳解Pandas中GroupBy對(duì)象的使用

更新時(shí)間：2022年06月10日 10:31:47 作者：周蘿卜

本文將探討如何在Python的Pandas庫中創(chuàng)建GroupBy對(duì)象以及該對(duì)象的工作原理。我們將詳細(xì)了解分組過程的每個(gè)步驟，以及我們可以從中提取哪些有用信息，需要的可以參考一下

今天，我們將探討如何在 Python 的 Pandas 庫中創(chuàng)建 GroupBy 對(duì)象以及該對(duì)象的工作原理。我們將詳細(xì)了解分組過程的每個(gè)步驟，可以將哪些方法應(yīng)用于 GroupBy 對(duì)象上，以及我們可以從中提取哪些有用信息

不要再觀望了，一起學(xué)起來吧

使用 Groupby 三個(gè)步驟

首先我們要知道，任何 groupby 過程都涉及以下 3 個(gè)步驟的某種組合：

根據(jù)定義的標(biāo)準(zhǔn)將原始對(duì)象分成組
對(duì)每個(gè)組應(yīng)用某些函數(shù)
整合結(jié)果

讓我先來大致瀏覽下今天用到的測(cè)試數(shù)據(jù)集

import?pandas?as?pd
import?numpy?as?np

pd.set_option('max_columns',?None)

df?=?pd.read_csv('complete.csv')
df?=?df[['awardYear',?'category',?'prizeAmount',?'prizeAmountAdjusted',?'name',?'gender',?'birth_continent']]
df.head()

Output:

   awardYear   category   prizeAmount   prizeAmountAdjusted   name   gender   birth_continent
0   2001   Economic Sciences   10000000   12295082   A. Michael Spence   male   North America
1   1975   Physics   630000   3404179   Aage N. Bohr   male   Europe
2   2004   Chemistry   10000000   11762861   Aaron Ciechanover   male   Asia
3   1982   Chemistry   1150000   3102518   Aaron Klug   male   Europe
4   1979   Physics   800000   2988048   Abdus Salam   male   Asia

將原始對(duì)象拆分為組

在這個(gè)階段，我們調(diào)用 pandas DataFrame.groupby() 函數(shù)。我們使用它根據(jù)預(yù)定義的標(biāo)準(zhǔn)將數(shù)據(jù)分組，沿行（默認(rèn)情況下，axis=0）或列（axis=1）。換句話說，此函數(shù)將標(biāo)簽映射到組的名稱。

例如，在我們的案例中，我們可以按獎(jiǎng)項(xiàng)類別對(duì)諾貝爾獎(jiǎng)的數(shù)據(jù)進(jìn)行分組：

grouped?=?df.groupby('category')

也可以使用多個(gè)列來執(zhí)行數(shù)據(jù)分組，傳遞一個(gè)列列表即可。讓我們首先按獎(jiǎng)項(xiàng)類別對(duì)我們的數(shù)據(jù)進(jìn)行分組，然后在每個(gè)創(chuàng)建的組中，我們將根據(jù)獲獎(jiǎng)年份應(yīng)用額外的分組：

grouped_category_year?=?df.groupby(['category',?'awardYear'])

現(xiàn)在，如果我們嘗試打印剛剛創(chuàng)建的兩個(gè) GroupBy 對(duì)象之一，我們實(shí)際上將看不到任何組：

print(grouped)

Output:

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000026083789DF0>

我們要注意的是，創(chuàng)建 GroupBy 對(duì)象成功與否，只檢查我們是否通過了正確的映射；在我們顯式地對(duì)該對(duì)象使用某些方法或提取其某些屬性之前，都不會(huì)真正執(zhí)行拆分-應(yīng)用-組合鏈的任何操作

為了簡要檢查生成的 GroupBy 對(duì)象并檢查組的拆分方式，我們可以從中提取組或索引屬性。它們都返回一個(gè)字典，其中鍵是創(chuàng)建的組，值是原始 DataFrame 中每個(gè)組的實(shí)例的軸標(biāo)簽列表（對(duì)于組屬性）或索引（對(duì)于索引屬性）：

grouped.indices

Output:

{'Chemistry': array([ 2, 3, 7, 9, 10, 11, 13, 14, 15, 17, 19, 39, 62,
64, 66, 71, 75, 80, 81, 86, 92, 104, 107, 112, 129, 135,
153, 169, 175, 178, 181, 188, 197, 199, 203, 210, 215, 223, 227,
239, 247, 249, 258, 264, 265, 268, 272, 274, 280, 282, 284, 289,
296, 298, 310, 311, 317, 318, 337, 341, 343, 348, 352, 357, 362,
365, 366, 372, 374, 384, 394, 395, 396, 415, 416, 419, 434, 440,
442, 444, 446, 448, 450, 455, 456, 459, 461, 463, 465, 469, 475,
504, 505, 508, 518, 522, 523, 524, 539, 549, 558, 559, 563, 567,
571, 572, 585, 591, 596, 599, 627, 630, 632, 641, 643, 644, 648,
659, 661, 666, 667, 668, 671, 673, 679, 681, 686, 713, 715, 717,
719, 720, 722, 723, 725, 726, 729, 732, 738, 742, 744, 746, 751,
756, 759, 763, 766, 773, 776, 798, 810, 813, 814, 817, 827, 828,
829, 832, 839, 848, 853, 855, 862, 866, 880, 885, 886, 888, 889,
892, 894, 897, 902, 904, 914, 915, 920, 921, 922, 940, 941, 943,
946, 947], dtype=int64),
'Economic Sciences': array([ 0, 5, 45, 46, 58, 90, 96, 139, 140, 145, 152, 156, 157,
180, 187, 193, 207, 219, 231, 232, 246, 250, 269, 279, 283, 295,
305, 324, 346, 369, 418, 422, 425, 426, 430, 432, 438, 458, 467,
476, 485, 510, 525, 527, 537, 538, 546, 580, 594, 595, 605, 611,
636, 637, 657, 669, 670, 678, 700, 708, 716, 724, 734, 737, 739,
745, 747, 749, 750, 753, 758, 767, 800, 805, 854, 856, 860, 864,
871, 882, 896, 912, 916, 924], dtype=int64),
'Literature': array([ 21, 31, 40, 49, 52, 98, 100, 101, 102, 111, 115, 142, 149,
159, 170, 177, 201, 202, 220, 221, 233, 235, 237, 253, 257, 259,
275, 277, 278, 286, 312, 315, 316, 321, 326, 333, 345, 347, 350,
355, 359, 364, 370, 373, 385, 397, 400, 403, 406, 411, 435, 439,
441, 454, 468, 479, 480, 482, 483, 492, 501, 506, 511, 516, 556,
569, 581, 602, 604, 606, 613, 614, 618, 631, 633, 635, 640, 652,
653, 655, 656, 665, 675, 683, 699, 761, 765, 771, 774, 777, 779,
780, 784, 786, 788, 796, 799, 803, 836, 840, 842, 850, 861, 867,
868, 878, 881, 883, 910, 917, 919, 927, 928, 929, 930, 936],
dtype=int64),
'Peace': array([ 6, 12, 16, 25, 26, 27, 34, 36, 44, 47, 48, 54, 61,
65, 72, 78, 79, 82, 95, 99, 116, 119, 120, 126, 137, 146,
151, 166, 167, 171, 200, 204, 205, 206, 209, 213, 225, 236, 240,
244, 255, 260, 266, 267, 270, 287, 303, 320, 329, 356, 360, 361,
377, 386, 387, 388, 389, 390, 391, 392, 393, 433, 447, 449, 471,
477, 481, 489, 491, 500, 512, 514, 517, 528, 529, 530, 533, 534,
540, 542, 544, 545, 547, 553, 555, 560, 562, 574, 578, 590, 593,
603, 607, 608, 609, 612, 615, 616, 617, 619, 620, 628, 634, 639,
642, 664, 677, 688, 697, 703, 705, 710, 727, 736, 787, 793, 795,
806, 823, 846, 847, 852, 865, 875, 876, 877, 895, 926, 934, 935,
937, 944, 948, 949], dtype=int64),
'Physics': array([ 1, 4, 8, 20, 23, 24, 30, 32, 38, 51, 59, 60, 67,
68, 69, 70, 74, 84, 89, 97, 103, 105, 108, 109, 114, 117,
118, 122, 125, 127, 128, 130, 133, 141, 143, 144, 155, 162, 163,
164, 165, 168, 173, 174, 176, 179, 183, 195, 212, 214, 216, 222,
224, 228, 230, 234, 238, 241, 243, 251, 256, 263, 271, 276, 291,
292, 297, 301, 306, 307, 308, 323, 327, 328, 330, 335, 336, 338,
349, 351, 353, 354, 363, 367, 375, 376, 378, 381, 382, 398, 399,
402, 404, 405, 408, 410, 412, 413, 420, 421, 424, 428, 429, 436,
445, 451, 453, 457, 460, 462, 470, 472, 487, 495, 498, 499, 509,
513, 515, 521, 526, 532, 535, 536, 541, 548, 550, 552, 557, 561,
564, 565, 566, 573, 576, 577, 579, 583, 586, 588, 592, 601, 610,
621, 622, 623, 629, 647, 650, 651, 654, 658, 674, 676, 682, 684,
690, 691, 693, 694, 695, 696, 698, 702, 707, 711, 714, 721, 730,
731, 735, 743, 752, 755, 770, 772, 775, 781, 785, 790, 792, 797,
801, 802, 808, 822, 833, 834, 835, 844, 851, 870, 872, 879, 884,
887, 890, 893, 900, 901, 903, 905, 907, 908, 909, 913, 925, 931,
932, 933, 938, 942, 945], dtype=int64),
'Physiology or Medicine': array([ 18, 22, 28, 29, 33, 35, 37, 41, 42, 43, 50, 53, 55,
56, 57, 63, 73, 76, 77, 83, 85, 87, 88, 91, 93, 94,
106, 110, 113, 121, 123, 124, 131, 132, 134, 136, 138, 147, 148,
150, 154, 158, 160, 161, 172, 182, 184, 185, 186, 189, 190, 191,
192, 194, 196, 198, 208, 211, 217, 218, 226, 229, 242, 245, 248,
252, 254, 261, 262, 273, 281, 285, 288, 290, 293, 294, 299, 300,
302, 304, 309, 313, 314, 319, 322, 325, 331, 332, 334, 339, 340,
342, 344, 358, 368, 371, 379, 380, 383, 401, 407, 409, 414, 417,
423, 427, 431, 437, 443, 452, 464, 466, 473, 474, 478, 484, 486,
488, 490, 493, 494, 496, 497, 502, 503, 507, 519, 520, 531, 543,
551, 554, 568, 570, 575, 582, 584, 587, 589, 597, 598, 600, 624,
625, 626, 638, 645, 646, 649, 660, 662, 663, 672, 680, 685, 687,
689, 692, 701, 704, 706, 709, 712, 718, 728, 733, 740, 741, 748,
754, 757, 760, 762, 764, 768, 769, 778, 782, 783, 789, 791, 794,
804, 807, 809, 811, 812, 815, 816, 818, 819, 820, 821, 824, 825,
826, 830, 831, 837, 838, 841, 843, 845, 849, 857, 858, 859, 863,
869, 873, 874, 891, 898, 899, 906, 911, 918, 923, 939], dtype=int64)}

要查找 GroupBy 對(duì)象中的組數(shù)，我們可以從中提取 ngroups 屬性或調(diào)用 Python 標(biāo)準(zhǔn)庫的 len 函數(shù)：

print(grouped.ngroups)
print(len(grouped))

Output:

6
6

如果我們需要可視化每個(gè)組的所有或部分條目，那么可以遍歷 GroupBy 對(duì)象：

for?name,?entries?in?grouped:
????print(f'First?2?entries?for?the?"{name}"?category:')
????print(30*'-')
????print(entries.head(2),?'\n\n')

Output:

First 2 entries for the "Chemistry" category:
------------------------------
awardYear category prizeAmount prizeAmountAdjusted name \
2 2004 Chemistry 10000000 11762861 Aaron Ciechanover
3 1982 Chemistry 1150000 3102518 Aaron Klug

gender birth_continent
2 male Asia
3 male Europe

First 2 entries for the "Economic Sciences" category:
------------------------------
awardYear category prizeAmount prizeAmountAdjusted \
0 2001 Economic Sciences 10000000 12295082
5 2019 Economic Sciences 9000000 9000000

name gender birth_continent
0 A. Michael Spence male North America
5 Abhijit Banerjee male Asia

First 2 entries for the "Literature" category:
------------------------------
awardYear category prizeAmount prizeAmountAdjusted \
21 1957 Literature 208629 2697789
31 1970 Literature 400000 3177966

name gender birth_continent
21 Albert Camus male Africa
31 Alexandr Solzhenitsyn male Europe

First 2 entries for the "Peace" category:
------------------------------
awardYear category prizeAmount prizeAmountAdjusted \
6 2019 Peace 9000000 9000000
12 1980 Peace 880000 2889667

name gender birth_continent
6 Abiy Ahmed Ali male Africa
12 Adolfo Pérez Esquivel male South America

First 2 entries for the "Physics" category:
------------------------------
awardYear category prizeAmount prizeAmountAdjusted name gender \
1 1975 Physics 630000 3404179 Aage N. Bohr male
4 1979 Physics 800000 2988048 Abdus Salam male

birth_continent
1 Europe
4 Asia

First 2 entries for the "Physiology or Medicine" category:
------------------------------
awardYear category prizeAmount prizeAmountAdjusted \
18 1963 Physiology or Medicine 265000 2839286
22 1974 Physiology or Medicine 550000 3263449

name gender birth_continent
18 Alan Hodgkin male Europe
22 Albert Claude male Europe

相反，如果我們想以 DataFrame 的形式選擇單個(gè)組，我們應(yīng)該在 GroupBy 對(duì)象上使用 get_group() 方法：

grouped.get_group('Economic?Sciences')

Output:

   awardYear   category   prizeAmount   prizeAmountAdjusted   name   gender   birth_continent
0   2001   Economic Sciences   10000000   12295082   A. Michael Spence   male   North America
5   2019   Economic Sciences   9000000   9000000   Abhijit Banerjee   male   Asia
45   2012   Economic Sciences   8000000   8361204   Alvin E. Roth   male   North America
46   1998   Economic Sciences   7600000   9713701   Amartya Sen   male   Asia
58   2015   Economic Sciences   8000000   8384572   Angus Deaton   male   Europe
…   …   …   …   …   …   …   …
882   2002   Economic Sciences   10000000   12034660   Vernon L. Smith   male   North America
896   1973   Economic Sciences   510000   3331882   Wassily Leontief   male   Europe
912   2018   Economic Sciences   9000000   9000000   William D. Nordhaus   male   North America
916   1990   Economic Sciences   4000000   6329114   William F. Sharpe   male   North America
924   1996   Economic Sciences   7400000   9490424   William Vickrey   male   North America

按組應(yīng)用函數(shù)

在拆分原始數(shù)據(jù)并檢查結(jié)果組之后，我們可以對(duì)每個(gè)組執(zhí)行以下操作之一或其組合：

Aggregation（聚合）：計(jì)算每個(gè)組的匯總統(tǒng)計(jì)量（例如，組大小、平均值、中位數(shù)或總和）并為許多數(shù)據(jù)點(diǎn)輸出單個(gè)數(shù)字
Transformation（變換）：按組進(jìn)行一些操作，例如計(jì)算每個(gè)組的z-score
Filtration（過濾）：根據(jù)預(yù)定義的條件拒絕某些組，例如組大小、平均值、中位數(shù)或總和，還可以包括從每個(gè)組中過濾掉特定的行

Aggregation

要聚合 GroupBy 對(duì)象的數(shù)據(jù)（即按組計(jì)算匯總統(tǒng)計(jì)量），我們可以在對(duì)象上使用 agg() 方法：

#?Showing?only?1?decimal?for?all?float?numbers
pd.options.display.float_format?=?'{:.1f}'.format

grouped.agg(np.mean)

Output:

   awardYear   prizeAmount   prizeAmountAdjusted
category
Chemistry   1972.3   3629279.4   6257868.1
Economic Sciences   1996.1   6105845.2   7837779.2
Literature   1960.9   2493811.2   5598256.3
Peace   1964.5   3124879.2   6163906.9
Physics   1971.1   3407938.6   6086978.2
Physiology or Medicine   1970.4   3072972.9   5738300.7

上面的代碼生成一個(gè) DataFrame，其中組名作為其新索引，每個(gè)數(shù)字列的平均值作為分組

我們可以直接在 GroupBy 對(duì)象上應(yīng)用其他相應(yīng)的 Pandas 方法，而不僅僅是使用 agg() 方法。最常用的方法是 mean()、median()、mode()、sum()、size()、count()、min()、max()、std()、var()（計(jì)算每個(gè)的方差 group)、describe()（按組輸出描述性統(tǒng)計(jì)信息）和 nunique()（給出每個(gè)組中唯一值的數(shù)量）

grouped.sum()

Output:

   awardYear   prizeAmount   prizeAmountAdjusted
category
Chemistry   362912   667787418   1151447726
Economic Sciences   167674   512891000   658373449
Literature   227468   289282102   649397731
Peace   263248   418733807   825963521
Physics   419837   725890928   1296526352
Physiology or Medicine   431508   672981066   1256687857

通常情況下我們只對(duì)某些特定列或列的統(tǒng)計(jì)信息感興趣，因此我們需要指定它們。在上面的例子中，我們絕對(duì)不想總結(jié)所有年份，相應(yīng)的我們可能希望按獎(jiǎng)品類別對(duì)獎(jiǎng)品價(jià)值求和。為此我們可以選擇 GroupBy 對(duì)象的 PrizeAmountAdjusted 列，就像我們選擇 DataFrame 的列，然后對(duì)其應(yīng)用 sum() 函數(shù)：

grouped['prizeAmountAdjusted'].sum()

Output:

category
Chemistry 1151447726
Economic Sciences 658373449
Literature 649397731
Peace 825963521
Physics 1296526352
Physiology or Medicine 1256687857
Name: prizeAmountAdjusted, dtype: int64

對(duì)于上面的代碼片段，我們可以在選擇必要的列之前使用對(duì) GroupBy 對(duì)象應(yīng)用函數(shù)的等效語法：grouped.sum()['prizeAmountAdjusted']。但是前面的語法更可取，因?yàn)樗男阅芨茫绕涫窃诖笮蛿?shù)據(jù)集上，效果更為明顯

如果我們需要聚合兩列或更多列的數(shù)據(jù)，我們使用雙方括號(hào)：

grouped[['prizeAmount',?'prizeAmountAdjusted']].sum()

Output:

   prizeAmount   prizeAmountAdjusted
category
Chemistry   667787418   1151447726
Economic Sciences   512891000   658373449
Literature   289282102   649397731
Peace   418733807   825963521
Physics   725890928   1296526352
Physiology or Medicine   672981066   1256687857

可以一次將多個(gè)函數(shù)應(yīng)用于 GroupBy 對(duì)象的一列或多列。為此我們?cè)俅涡枰?nbsp;agg() 方法和感興趣的函數(shù)列表：

grouped[['prizeAmount',?'prizeAmountAdjusted']].agg([np.sum,?np.mean,?np.std])

Output:

   prizeAmount   prizeAmountAdjusted
sum   mean   std   sum   mean   std
category
Chemistry   667787418   3629279.4   4070588.4   1151447726   6257868.1   3276027.2
Economic Sciences   512891000   6105845.2   3787630.1   658373449   7837779.2   3313153.2
Literature   289282102   2493811.2   3653734.0   649397731   5598256.3   3029512.1
Peace   418733807   3124879.2   3934390.9   825963521   6163906.9   3189886.1
Physics   725890928   3407938.6   4013073.0   1296526352   6086978.2   3294268.5
Physiology or Medicine   672981066   3072972.9   3898539.3   1256687857   5738300.7   3241781.0

此外，我們可以考慮通過傳遞字典將不同的聚合函數(shù)應(yīng)用于 GroupBy 對(duì)象的不同列：

grouped.agg({'prizeAmount':?[np.sum,?np.size],?'prizeAmountAdjusted':?np.mean})

Output:

   prizeAmount   prizeAmountAdjusted
sum   size   mean
category
Chemistry   667787418   184   6257868.1
Economic Sciences   512891000   84   7837779.2
Literature   289282102   116   5598256.3
Peace   418733807   134   6163906.9
Physics   725890928   213   6086978.2
Physiology or Medicine   672981066   219   5738300.7

Transformation

與聚合方法不同，轉(zhuǎn)換方法返回一個(gè)新的 DataFrame，其形狀和索引與原始 DataFrame 相同，但具有轉(zhuǎn)換后的各個(gè)值。這里需要注意的是，transformation 一定不能修改原始 DataFrame 中的任何值，也就是這些操作不能原地執(zhí)行

轉(zhuǎn)換 GroupBy 對(duì)象數(shù)據(jù)的最常見的 Pandas 方法是 transform()。例如它可以幫助計(jì)算每個(gè)組的 z-score：

grouped[['prizeAmount',?'prizeAmountAdjusted']].transform(lambda?x:?(x?-?x.mean())?/?x.std())

Output:

   prizeAmount   prizeAmountAdjusted
0   1.0   1.3
1   -0.7   -0.8
2   1.6   1.7
3   -0.6   -1.0
4   -0.6   -0.9
…   …   …
945   -0.7   -0.8
946   -0.8   -1.1
947   -0.9   0.3
948   -0.5   -1.0
949   -0.7   -1.0

使用轉(zhuǎn)換方法，我們還可以用組均值、中位數(shù)、眾數(shù)或任何其他值替換缺失數(shù)據(jù)：

Output:

0 male
1 male
2 male
3 male
4 male
...
945 male
946 male
947 female
948 male
949 male
Name: gender, Length: 950, dtype: object

我們當(dāng)然還可以使用其他一些 Pandas 方法來轉(zhuǎn)換 GroupBy 對(duì)象的數(shù)據(jù)：bfill()、ffill()、diff()、pct_change()、rank()、shift()、quantile()等

Filtration

過濾方法根據(jù)預(yù)定義的條件從每個(gè)組中丟棄組或特定行，并返回原始數(shù)據(jù)的子集。例如我們可能希望只保留所有組中某個(gè)列的值，其中該列的組均值大于預(yù)定義值。在我們的 DataFrame 的情況下，讓我們過濾掉所有組均值小于 7,000,000 的prizeAmountAdjusted 列，并在輸出中僅保留該列：

grouped['prizeAmountAdjusted'].filter(lambda?x:?x.mean()?>?7000000)

Output:

0 12295082
5 9000000
45 8361204
46 9713701
58 8384572
...
882 12034660
896 3331882
912 9000000
916 6329114
924 9490424
Name: prizeAmountAdjusted, Length: 84, dtype: int64

另一個(gè)例子是過濾掉具有超過一定數(shù)量元素的組：

grouped['prizeAmountAdjusted'].filter(lambda?x:?len(x)?<?100)

Output:

0 12295082
5 9000000
45 8361204
46 9713701
58 8384572
...
882 12034660
896 3331882
912 9000000
916 6329114
924 9490424
Name: prizeAmountAdjusted, Length: 84, dtype: int64

在上述兩個(gè)操作中，我們使用了 filter() 方法，將 lambda 函數(shù)作為參數(shù)傳遞。這樣的函數(shù)，應(yīng)用于整個(gè)組，根據(jù)該組與預(yù)定義統(tǒng)計(jì)條件的比較結(jié)果返回 True 或 False。換句話說，filter()方法中的函數(shù)決定了哪些組保留在新的 DataFrame 中

除了過濾掉整個(gè)組之外，還可以從每個(gè)組中丟棄某些行。這里有一些有用的方法是 first()、last() 和 nth()。將其中一個(gè)應(yīng)用于 GroupBy 對(duì)象會(huì)相應(yīng)地返回每個(gè)組的第一個(gè)/最后一個(gè)/第 n 個(gè)條目：

grouped.last()

Output:

   awardYear   prizeAmount   prizeAmountAdjusted   name   gender   birth_continent
category
Chemistry   1911   140695   7327865   Marie Curie   female   Europe
Economic Sciences   1996   7400000   9490424   William Vickrey   male   North America
Literature   1968   350000   3052326   Yasunari Kawabata   male   Asia
Peace   1963   265000   2839286   International Committee of the Red Cross   male   Asia
Physics   1972   480000   3345725   John Bardeen   male   North America
Physiology or Medicine   2016   8000000   8301051   Yoshinori Ohsumi   male   Asia

對(duì)于 nth() 方法，我們必須傳遞表示要為每個(gè)組返回的條目索引的整數(shù)：

grouped.nth(1)

Output:

   awardYear   prizeAmount   prizeAmountAdjusted   name   gender   birth_continent
category
Chemistry   1982   1150000   3102518   Aaron Klug   male   Europe
Economic Sciences   2019   9000000   9000000   Abhijit Banerjee   male   Asia
Literature   1970   400000   3177966   Alexandr Solzhenitsyn   male   Europe
Peace   1980   880000   2889667   Adolfo Pérez Esquivel   male   South America
Physics   1979   800000   2988048   Abdus Salam   male   Asia
Physiology or Medicine   1974   550000   3263449   Albert Claude   male   Europe

上面的代碼收集了所有組的第二個(gè)條目

另外兩個(gè)過濾每個(gè)組中的行的方法是 head() 和 tail()，分別返回每個(gè)組的第一/最后 n 行（默認(rèn)為 5）：

grouped.head(3)

Output:

   awardYear   category   prizeAmount   prizeAmountAdjusted   name   gender   birth_continent
0   2001   Economic Sciences   10000000   12295082   A. Michael Spence   male   North America
1   1975   Physics   630000   3404179   Aage N. Bohr   male   Europe
2   2004   Chemistry   10000000   11762861   Aaron Ciechanover   male   Asia
3   1982   Chemistry   1150000   3102518   Aaron Klug   male   Europe
4   1979   Physics   800000   2988048   Abdus Salam   male   Asia
5   2019   Economic Sciences   9000000   9000000   Abhijit Banerjee   male   Asia
6   2019   Peace   9000000   9000000   Abiy Ahmed Ali   male   Africa
7   2009   Chemistry   10000000   10958504   Ada E. Yonath   female   Asia
8   2011   Physics   10000000   10545557   Adam G. Riess   male   North America
12   1980   Peace   880000   2889667   Adolfo Pérez Esquivel   male   South America
16   2007   Peace   10000000   11301989   Al Gore   male   North America
18   1963   Physiology or Medicine   265000   2839286   Alan Hodgkin   male   Europe
21   1957   Literature   208629   2697789   Albert Camus   male   Africa
22   1974   Physiology or Medicine   550000   3263449   Albert Claude   male   Europe
28   1937   Physiology or Medicine   158463   4716161   Albert Szent-Györgyi   male   Europe
31   1970   Literature   400000   3177966   Alexandr Solzhenitsyn   male   Europe
40   2013   Literature   8000000   8365867   Alice Munro   female   North America
45   2012   Economic Sciences   8000000   8361204   Alvin E. Roth   male   North America

整合結(jié)果

split-apply-combine 鏈的最后一個(gè)階段——合并結(jié)果——由Ppandas 在后臺(tái)執(zhí)行。它包括獲取在 GroupBy 對(duì)象上執(zhí)行的所有操作的輸出并將它們重新組合在一起，生成新的數(shù)據(jù)結(jié)構(gòu)，例如 Series 或 DataFrame。將此數(shù)據(jù)結(jié)構(gòu)分配給一個(gè)變量，我們可以用它來解決其他任務(wù)