关于程序员:Pandas-GroupBy-深度总结

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明天,咱们将探讨如何在 Python 的 Pandas 库中创立 GroupBy 对象以及该对象的工作原理。咱们将具体理解分组过程的每个步骤,能够将哪些办法利用于 GroupBy 对象上,以及咱们能够从中提取哪些有用信息

不要再张望了,一起学起来吧

应用 Groupby 三个步骤

首先文末要晓得,任何 groupby 过程都波及以下 3 个步骤的某种组合:

  • 依据定义的规范将原始对象分成组
  • 对每个组利用某些函数
  • 整合后果

让我先来大抵浏览下明天用到的测试数据集

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

将原始对象拆分为组

在这个阶段,咱们调用 pandas DataFrame.groupby() 函数。咱们应用它依据预约义的规范将数据分组,沿行(默认状况下,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,在咱们的案例中,咱们能够按奖项类别对诺贝尔奖的数据进行分组:

grouped = df.groupby('category')

也能够应用多个列来执行数据分组,传递一个列列表即可。让咱们首先按奖项类别对咱们的数据进行分组,而后在每个创立的组中,咱们将依据获奖年份利用额定的分组:

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

当初,如果咱们尝试打印刚刚创立的两个 GroupBy 对象之一,咱们实际上将看不到任何组:

print(grouped)

Output:

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

咱们要留神的是,创立 GroupBy 对象胜利与否,只查看咱们是否通过了正确的映射;在咱们显式地对该对象应用某些办法或提取其某些属性之前,都不会真正执行拆分 - 利用 - 组合链的任何操作

为了简要查看生成的 GroupBy 对象并检查组的拆分形式,咱们能够从中提取组或索引属性。它们都返回一个字典,其中键是创立的组,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,咱们能够从中提取 ngroups 属性或调用 Python 规范库的 len 函数:

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

Output:

6
6

如果咱们须要可视化每个组的所有或局部条目,那么能够遍历 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   

相同,如果咱们想以 DataFrame 的模式抉择单个组,咱们应该在 GroupBy 对象上应用 get_group() 办法:

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

按组利用函数

在拆分原始数据并查看后果组之后,咱们能够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)并为许多数据点输入单个数字
  • Transformation(变换):按组进行一些操作,例如计算每个组的 z -score
  • Filtration(过滤):依据预约义的条件回绝某些组,例如组大小、平均值、中位数或总和,还能够包含从每个组中过滤掉特定的行

Aggregation

要聚合 GroupBy 对象的数据(即按组计算汇总统计量),咱们能够在对象上应用 agg() 办法:

# 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

下面的代码生成一个 DataFrame,其中组名作为其新索引,每个数字列的平均值作为分组

咱们能够间接在 GroupBy 对象上利用其余相应的 Pandas 办法,而不仅仅是应用 agg() 办法。最罕用的办法是 mean()median()mode()sum()size()count()min()max()std()var()(计算每个的方差 group)、describe()(按组输入描述性统计信息)和 nunique()(给出每个组中惟一值的数量)

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

通常状况下咱们只对某些特定列或列的统计信息感兴趣,因而咱们须要指定它们。在下面的例子中,咱们相对不想总结所有年份,相应的咱们可能心愿按奖品类别对奖品价值求和。为此咱们能够抉择 GroupBy 对象的 PrizeAmountAdjusted 列,就像咱们抉择 DataFrame 的列,而后对其利用 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

对于下面的代码片段,咱们能够在抉择必要的列之前应用对 GroupBy 对象利用函数的等效语法:grouped.sum()['prizeAmountAdjusted']。然而后面的语法更可取,因为它的性能更好,尤其是在大型数据集上,成果更为显著

如果咱们须要聚合两列或更多列的数据,咱们应用单方括号:

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

能够一次将多个函数利用于 GroupBy 对象的一列或多列。为此咱们再次须要 agg() 办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,咱们能够思考通过传递字典将不同的聚合函数利用于 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

Transformation

与聚合办法不同,转换方法返回一个新的 DataFrame,其形态和索引与原始 DataFrame 雷同,但具备转换后的各个值。这里须要留神的是,transformation 肯定不能批改原始 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 办法是 transform()。例如它能够帮忙计算每个组的 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

应用转换方法,咱们还能够用组均值、中位数、众数或任何其余值替换缺失数据:

grouped['gender'].transform(lambda x: x.fillna(x.mode()[0]))

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

咱们当然还能够应用其余一些 Pandas 办法来转换 GroupBy 对象的数据:bfill()ffill()diff()pct_change()rank()shift()quantile()

Filtration

过滤办法依据预约义的条件从每个组中抛弃组或特定行,并返回原始数据的子集。例如咱们可能心愿只保留所有组中某个列的值,其中该列的组均值大于预约义值。在咱们的 DataFrame 的状况下,让咱们过滤掉所有组均值小于 7,000,000 的 prizeAmountAdjusted 列,并在输入中仅保留该列:

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备超过肯定数量元素的组:

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,咱们应用了 filter() 办法,将 lambda 函数作为参数传递。这样的函数,利用于整个组,依据该组与预约义统计条件的比拟后果返回 TrueFalse。换句话说,filter() 办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,还能够从每个组中抛弃某些行。这里有一些有用的办法是 first()last()nth()。将其中一个利用于 GroupBy 对象会相应地返回每个组的第一个 / 最初一个 / 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() 办法,咱们必须传递示意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

下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办法是 head()tail(),别离返回每个组的第一 / 最初 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

整合后果

split-apply-combine 链的最初一个阶段——合并后果——由 Ppandas 在后盾执行。它包含获取在 GroupBy 对象上执行的所有操作的输入并将它们重新组合在一起,生成新的数据结构,例如 Series 或 DataFrame。将此数据结构调配给一个变量,咱们能够用它来解决其余工作

总结

明天咱们介绍了应用 pandas groupby 函数和应用后果对象的许多常识

  • 分组过程所包含的步骤
  • split-apply-combine 链是如何一步一步工作的
  • 如何创立 GroupBy 对象
  • 如何简要查看 GroupBy 对象
  • GroupBy 对象的属性
  • 可利用于 GroupBy 对象的操作
  • 如何按组计算汇总统计量以及可用于此目标的办法
  • 如何一次将多个函数利用于 GroupBy 对象的一列或多列
  • 如何将不同的聚合函数利用于 GroupBy 对象的不同列
  • 如何以及为什么要转换原始 DataFrame 中的值
  • 如何过滤 GroupBy 对象的组或每个组的特定行
  • Pandas 如何组合分组过程的后果
  • 分组过程产生的数据结构

好了,这就是明天分享的全部内容,喜爱就点个赞吧~

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