Python 之 Pandas 分组(GroupBy)

任何分组(groupby)操作都涉及原始对象的以下操作之一。它们是 –

  • 分割对象
  • 应用一个函数
  • 结合的结果

在许多情况下,我们将数据分成多个集合,并在每个子集上应用一些函数。在应用函数中,可以执行以下操作 –

  • 聚合 – 计算汇总统计
  • 转换 – 执行一些特定于组的操作
  • 过滤 – 在某些情况下丢弃数据

下面来看看创建一个DataFrame对象并对其执行所有操作 –

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)
Python

执行上面示例代码,得到以下结果 –

    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
Shell

将数据拆分成组

Pandas对象可以分成任何对象。有多种方式来拆分对象,如 –

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

现在来看看如何将分组对象应用于DataFrame对象

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'))
Python

执行上面示例代码,得到以下结果 –

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

查看分组

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)
Python

执行上面示例代码,得到以下结果 –

{
'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')
}
Shell

示例

按多列分组 –

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)
Python

执行上面示例代码,得到以下结果 –

{
('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')
}
Shell

迭代遍历分组

使用groupby对象,可以遍历类似itertools.obj的对象。

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)
Python

执行上面示例代码,得到以下结果 –

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
Shell

默认情况下,groupby对象具有与分组名相同的标签名称。

选择一个分组

使用get_group()方法,可以选择一个组。参考以下示例代码 –

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))
Python

执行上面示例代码,得到以下结果 –

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

聚合

聚合函数为每个组返回单个聚合值。当创建了分组(group by)对象,就可以对分组数据执行多个聚合操作。

一个比较常用的是通过聚合或等效的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))
Python

执行上面示例代码,得到以下结果 –

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

另一种查看每个分组的大小的方法是应用size()函数 –

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))
Python

执行上面示例代码,得到以下结果 –

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

一次应用多个聚合函数

通过分组系列,还可以传递函数的列表或字典来进行聚合,并生成DataFrame作为输出 –

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)
Python

执行上面示例代码,得到以下结果 –

         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
Shell

转换

分组或列上的转换返回索引大小与被分组的索引相同的对象。因此,转换应该返回与组块大小相同的结果。

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))
Python

执行上面示例代码,得到以下结果 –

       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
Shell

过滤

过滤根据定义的标准过滤数据并返回数据的子集。filter()函数用于过滤数据。

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)
Python

执行上面示例代码,得到以下结果 –

    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
Shell

在上述过滤条件下,要求返回三次以上参加IPL的队伍。

原文出自【易百教程】,商业转载请联系作者获得授权,非商业转载请保留原文链接:https://www.yiibai.com/pandas/python_pandas_groupby.html

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