这篇“pandas中GroupBy对象如何使用”文章的知识点大部分人都不太理解,所以小编给大家总结了以下内容,内容详细,步骤清晰,具有一定的借鉴价值,希望大家阅读完这篇文章能有所收获,下面我们一起来看看这篇“Pandas中GroupBy对象
这篇“pandas中GroupBy对象如何使用”文章的知识点大部分人都不太理解,所以小编给大家总结了以下内容,内容详细,步骤清晰,具有一定的借鉴价值,希望大家阅读完这篇文章能有所收获,下面我们一起来看看这篇“Pandas中GroupBy对象如何使用”文章吧。
首先我们要知道,任何 groupby 过程都涉及以下 3 个步骤的某种组合:
根据定义的标准将原始对象分成组
对每个组应用某些函数
整合结果
让我先来大致浏览下今天用到的测试数据集
import pandas as pdimport numpy as nppd.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
在这个阶段,我们调用 pandas DataFrame.groupby() 函数。我们使用它根据预定义的标准将数据分组,沿行(默认情况下,axis=0)或列(axis=1)。换句话说,此函数将标签映射到组的名称。
例如,在我们的案例中,我们可以按奖项类别对诺贝尔奖的数据进行分组:
grouped = df.groupby('category')
也可以使用多个列来执行数据分组,传递一个列列表即可。让我们首先按奖项类别对我们的数据进行分组,然后在每个创建的组中,我们将根据获奖年份应用额外的分组:
grouped_category_year = df.groupby(['category', 'awardYear'])
现在,如果我们尝试打印刚刚创建的两个 GroupBy 对象之一,我们实际上将看不到任何组:
print(grouped)
Output:
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000026083789DF0>
我们要注意的是,创建 GroupBy 对象成功与否,只检查我们是否通过了正确的映射;在我们显式地对该对象使用某些方法或提取其某些属性之前,都不会真正执行拆分-应用-组合链的任何操作
为了简要检查生成的 GroupBy 对象并检查组的拆分方式,我们可以从中提取组或索引属性。它们都返回一个字典,其中键是创建的组,值是原始 DataFrame 中每个组的实例的轴标签列表(对于组属性)或索引(对于索引属性):
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 对象中的组数,我们可以从中提取 ngroups 属性或调用 python 标准库的 len 函数:
print(grouped.ngroups)print(len(grouped))
Output:
6
6
如果我们需要可视化每个组的所有或部分条目,那么可以遍历 GroupBy 对象:
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,如果我们想以 DataFrame 的形式选择单个组,我们应该在 GroupBy 对象上使用 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
在拆分原始数据并检查结果组之后,我们可以对每个组执行以下操作之一或其组合:
Aggregation(聚合):计算每个组的汇总统计量(例如,组大小、平均值、中位数或总和)并为许多数据点输出单个数字
TransfORMation(变换):按组进行一些操作,例如计算每个组的z-score
Filtration(过滤):根据预定义的条件拒绝某些组,例如组大小、平均值、中位数或总和,还可以包括从每个组中过滤掉特定的行
要聚合 GroupBy 对象的数据(即按组计算汇总统计量),我们可以在对象上使用 agg()
方法:
# Showing only 1 decimal for all float numberspd.options.display.float_format = '{:.1f}'.formatgrouped.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
上面的代码生成一个 DataFrame,其中组名作为其新索引,每个数字列的平均值作为分组
我们可以直接在 GroupBy 对象上应用其他相应的 Pandas 方法,而不仅仅是使用 agg()
方法。最常用的方法是 mean()
、median()
、mode()
、sum()
、size()
、count()
、min()
、max()
、std()
、var()
(计算每个的方差 group)、describe()
(按组输出描述性统计信息)和 nunique()
(给出每个组中唯一值的数量)
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
通常情况下我们只对某些特定列或列的统计信息感兴趣,因此我们需要指定它们。在上面的例子中,我们绝对不想总结所有年份,相应的我们可能希望按奖品类别对奖品价值求和。为此我们可以选择 GroupBy 对象的 PrizeAmountAdjusted 列,就像我们选择 DataFrame 的列,然后对其应用 sum() 函数:
grouped['prizeAmountAdjusted'].sum()
Output:
category
Chemistry 1151447726
Economic Sciences 658373449
Literature 649397731
Peace 825963521
Physics 1296526352
Physiology or Medicine 1256687857
Name: prizeAmountAdjusted, dtype: int64
对于上面的代码片段,我们可以在选择必要的列之前使用对 GroupBy 对象应用函数的等效语法:grouped.sum()['prizeAmountAdjusted']
。但是前面的语法更可取,因为它的性能更好,尤其是在大型数据集上,效果更为明显
如果我们需要聚合两列或更多列的数据,我们使用双方括号:
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
可以一次将多个函数应用于 GroupBy 对象的一列或多列。为此我们再次需要 agg()
方法和感兴趣的函数列表:
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,我们可以考虑通过传递字典将不同的聚合函数应用于 GroupBy 对象的不同列:
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
与聚合方法不同,转换方法返回一个新的 DataFrame,其形状和索引与原始 DataFrame 相同,但具有转换后的各个值。这里需要注意的是,transformation 一定不能修改原始 DataFrame 中的任何值,也就是这些操作不能原地执行
转换 GroupBy 对象数据的最常见的 Pandas 方法是 transform()
。例如它可以帮助计算每个组的 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
使用转换方法,我们还可以用组均值、中位数、众数或任何其他值替换缺失数据:
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
我们当然还可以使用其他一些 Pandas 方法来转换 GroupBy 对象的数据:bfill()
、ffill()
、diff()
、pct_change()
、rank()
、shift()
、quantile()
等
过滤方法根据预定义的条件从每个组中丢弃组或特定行,并返回原始数据的子集。例如我们可能希望只保留所有组中某个列的值,其中该列的组均值大于预定义值。在我们的 DataFrame 的情况下,让我们过滤掉所有组均值小于 7,000,000 的prizeAmountAdjusted 列,并在输出中仅保留该列:
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
另一个例子是过滤掉具有超过一定数量元素的组:
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
在上述两个操作中,我们使用了 filter()
方法,将 lambda
函数作为参数传递。这样的函数,应用于整个组,根据该组与预定义统计条件的比较结果返回 True
或 False
。换句话说,filter()
方法中的函数决定了哪些组保留在新的 DataFrame 中
除了过滤掉整个组之外,还可以从每个组中丢弃某些行。这里有一些有用的方法是 first()
、last()
和 nth()
。将其中一个应用于 GroupBy 对象会相应地返回每个组的第一个/最后一个/第 n 个条目:
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
对于 nth()
方法,我们必须传递表示要为每个组返回的条目索引的整数:
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
上面的代码收集了所有组的第二个条目
另外两个过滤每个组中的行的方法是 head()
和 tail()
,分别返回每个组的第一/最后 n 行(默认为 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
split-apply-combine 链的最后一个阶段——合并结果——由Ppandas 在后台执行。它包括获取在 GroupBy 对象上执行的所有操作的输出并将它们重新组合在一起,生成新的数据结构,例如 Series 或 DataFrame。将此数据结构分配给一个变量,我们可以用它来解决其他任务
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本文标题: Pandas中GroupBy对象如何使用
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