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鍍金池/ 教程/ 數(shù)據(jù)分析&挖掘/ Pandas分組（GroupBy）
Pandas教程
Pandas注意事項(xiàng)＆竅門(mén)
Pandas IO工具
Pandas重建索引
Pandas稀疏數(shù)據(jù)
Pandas時(shí)間差（Timedelta）
Pandas聚合
Pandas字符串和文本數(shù)據(jù)
Pandas分類(lèi)數(shù)據(jù)
Pandas索引和選擇數(shù)據(jù)
Pandas基本功能
Pandas系列
Pandas數(shù)據(jù)幀（DataFrame）
Pandas日期功能
Pandas缺失數(shù)據(jù)
Pandas與SQL比較
Pandas迭代
Pandas合并/連接
Pandas選項(xiàng)和自定義
Pandas級(jí)聯(lián)
Pandas可視化
Pandas數(shù)據(jù)結(jié)構(gòu)
Pandas環(huán)境安裝配置
Pandas統(tǒng)計(jì)函數(shù)
Pandas窗口函數(shù)
Pandas面板（Panel）
Pandas排序
Pandas函數(shù)應(yīng)用
Pandas快速入門(mén)
Pandas描述性統(tǒng)計(jì)
Pandas分組（GroupBy）

Pandas分組（GroupBy）

任何分組(groupby)操作都涉及原始對(duì)象的以下操作之一。它們是 -

  • 分割對(duì)象
  • 應(yīng)用一個(gè)函數(shù)
  • 結(jié)合的結(jié)果

在許多情況下，我們將數(shù)據(jù)分成多個(gè)集合，并在每個(gè)子集上應(yīng)用一些函數(shù)。在應(yīng)用函數(shù)中，可以執(zhí)行以下操作 -

  • 聚合 - 計(jì)算匯總統(tǒng)計(jì)
  • 轉(zhuǎn)換 - 執(zhí)行一些特定于組的操作
  • 過(guò)濾 - 在某些情況下丟棄數(shù)據(jù)

下面來(lái)看看創(chuàng)建一個(gè)DataFrame對(duì)象并對(duì)其執(zhí)行所有操作 -

import pandas as pd

ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
         'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
         'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
         'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
         'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)

print (df)

執(zhí)行上面示例代碼，得到以下結(jié)果 - 

    Points  Rank    Team  Year
0      876     1  Riders  2014
1      789     2  Riders  2015
2      863     2  Devils  2014
3      673     3  Devils  2015
4      741     3   Kings  2014
5      812     4   kings  2015
6      756     1   Kings  2016
7      788     1   Kings  2017
8      694     2  Riders  2016
9      701     4  Royals  2014
10     804     1  Royals  2015
11     690     2  Riders  2017

將數(shù)據(jù)拆分成組

Pandas對(duì)象可以分成任何對(duì)象。有多種方式來(lái)拆分對(duì)象，如 -

  • obj.groupby(‘key’)
  • obj.groupby([‘key1’,’key2’])
  • obj.groupby(key,axis=1)

現(xiàn)在來(lái)看看如何將分組對(duì)象應(yīng)用于DataFrame對(duì)象

示例

import pandas as pd

ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
         'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
         'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
         'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
         'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)

print (df.groupby('Team'))

執(zhí)行上面示例代碼，得到以下結(jié)果 - 

<pandas.core.groupby.DataFrameGroupBy object at 0x00000245D60AD518>

查看分組

import pandas as pd
ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
         'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
         'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
         'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],           'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)

print (df.groupby('Team').groups)

執(zhí)行上面示例代碼，得到以下結(jié)果 -

{
'Devils': Int64Index([2, 3], dtype='int64'), 
'Kings': Int64Index([4, 6, 7], dtype='int64'), 
'Riders': Int64Index([0, 1, 8, 11], dtype='int64'), 
'Royals': Int64Index([9, 10], dtype='int64'), 
'kings': Int64Index([5], dtype='int64')
}

示例

按多列分組 -

import pandas as pd
ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
         'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
         'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
         'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
         'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)
print (df.groupby(['Team','Year']).groups)

執(zhí)行上面示例代碼，得到以下結(jié)果 -

{
('Devils', 2014): Int64Index([2], dtype='int64'), 
('Devils', 2015): Int64Index([3], dtype='int64'), 
('Kings', 2014): Int64Index([4], dtype='int64'),
('Kings', 2016): Int64Index([6], dtype='int64'),
('Kings', 2017): Int64Index([7], dtype='int64'), 
('Riders', 2014): Int64Index([0], dtype='int64'), 
('Riders', 2015): Int64Index([1], dtype='int64'), 
('Riders', 2016): Int64Index([8], dtype='int64'), 
('Riders', 2017): Int64Index([11], dtype='int64'),
('Royals', 2014): Int64Index([9], dtype='int64'), 
('Royals', 2015): Int64Index([10], dtype='int64'), 
('kings', 2015): Int64Index([5], dtype='int64')
}

迭代遍歷分組

使用groupby對(duì)象，可以遍歷類(lèi)似itertools.obj的對(duì)象。

import pandas as pd

ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
         'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
         'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
         'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
         'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)

grouped = df.groupby('Year')

for name,group in grouped:
    print (name)
    print (group)

執(zhí)行上面示例代碼，得到以下結(jié)果 - 

2014
   Points  Rank    Team  Year
0     876     1  Riders  2014
2     863     2  Devils  2014
4     741     3   Kings  2014
9     701     4  Royals  2014
2015
    Points  Rank    Team  Year
1      789     2  Riders  2015
3      673     3  Devils  2015
5      812     4   kings  2015
10     804     1  Royals  2015
2016
   Points  Rank    Team  Year
6     756     1   Kings  2016
8     694     2  Riders  2016
2017
    Points  Rank    Team  Year
7      788     1   Kings  2017
11     690     2  Riders  2017

默認(rèn)情況下，groupby對(duì)象具有與分組名相同的標(biāo)簽名稱。

選擇一個(gè)分組

使用get_group()方法，可以選擇一個(gè)組。參考以下示例代碼 - 

import pandas as pd

ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
         'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
         'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
         'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
         'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)

grouped = df.groupby('Year')
print (grouped.get_group(2014))

執(zhí)行上面示例代碼，得到以下結(jié)果 - 

   Points  Rank    Team  Year
0     876     1  Riders  2014
2     863     2  Devils  2014
4     741     3   Kings  2014
9     701     4  Royals  2014

聚合

聚合函數(shù)為每個(gè)組返回單個(gè)聚合值。當(dāng)創(chuàng)建了分組(group by)對(duì)象，就可以對(duì)分組數(shù)據(jù)執(zhí)行多個(gè)聚合操作。

一個(gè)比較常用的是通過(guò)聚合或等效的agg方法聚合 -

import pandas as pd
import numpy as np

ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
         'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
         'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
         'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
         'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)

grouped = df.groupby('Year')
print (grouped['Points'].agg(np.mean))

執(zhí)行上面示例代碼，得到以下結(jié)果 - 

Year
2014    795.25
2015    769.50
2016    725.00
2017    739.00
Name: Points, dtype: float64

另一種查看每個(gè)分組的大小的方法是應(yīng)用size()函數(shù) -

import pandas as pd
import numpy as np

ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
         'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
         'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
         'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
         'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)
grouped = df.groupby('Team')
print (grouped.agg(np.size))

執(zhí)行上面示例代碼，得到以下結(jié)果 - 

Team                      
Devils       2     2     2
Kings        3     3     3
Riders       4     4     4
Royals       2     2     2
kings        1     1     1

一次應(yīng)用多個(gè)聚合函數(shù)

通過(guò)分組系列，還可以傳遞函數(shù)的列表或字典來(lái)進(jìn)行聚合，并生成DataFrame作為輸出 -

import pandas as pd
import numpy as np

ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
         'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
         'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
         'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
         'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)

grouped = df.groupby('Team')
agg = grouped['Points'].agg([np.sum, np.mean, np.std])
print (agg)

執(zhí)行上面示例代碼，得到以下結(jié)果 - 

         sum        mean         std
Team                                
Devils  1536  768.000000  134.350288
Kings   2285  761.666667   24.006943
Riders  3049  762.250000   88.567771
Royals  1505  752.500000   72.831998
kings    812  812.000000         NaN

轉(zhuǎn)換

分組或列上的轉(zhuǎn)換返回索引大小與被分組的索引相同的對(duì)象。因此，轉(zhuǎn)換應(yīng)該返回與組塊大小相同的結(jié)果。

import pandas as pd
import numpy as np

ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
         'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
         'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
         'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
         'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)

grouped = df.groupby('Team')
score = lambda x: (x - x.mean()) / x.std()*10
print (grouped.transform(score))

執(zhí)行上面示例代碼，得到以下結(jié)果 - 

       Points       Rank       Year
0   12.843272 -15.000000 -11.618950
1    3.020286   5.000000  -3.872983
2    7.071068  -7.071068  -7.071068
3   -7.071068   7.071068   7.071068
4   -8.608621  11.547005 -10.910895
5         NaN        NaN        NaN
6   -2.360428  -5.773503   2.182179
7   10.969049  -5.773503   8.728716
8   -7.705963   5.000000   3.872983
9   -7.071068   7.071068  -7.071068
10   7.071068  -7.071068   7.071068
11  -8.157595   5.000000  11.618950

過(guò)濾

過(guò)濾根據(jù)定義的標(biāo)準(zhǔn)過(guò)濾數(shù)據(jù)并返回?cái)?shù)據(jù)的子集。filter()函數(shù)用于過(guò)濾數(shù)據(jù)。

import pandas as pd
import numpy as np
ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
         'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
         'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
         'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
         'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)
filter = df.groupby('Team').filter(lambda x: len(x) >= 3)

print (filter)

執(zhí)行上面示例代碼，得到以下結(jié)果 - 

    Points  Rank    Team  Year
0      876     1  Riders  2014
1      789     2  Riders  2015
4      741     3   Kings  2014
6      756     1   Kings  2016
7      788     1   Kings  2017
8      694     2  Riders  2016
11     690     2  Riders  2017

在上述過(guò)濾條件下，要求返回三次以上參加IPL的隊(duì)伍。


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