Pandas GroupBy


任何 groupby 操作都涉及原始对象的以下操作之一。他们是 -

  • 分割 对象

  • 应用 功能

  • 结合 结果

在很多情况下,我们将数据拆分为集合,并在每个子集上应用一些功能。在应用功能中,我们可以执行以下操作 -

  • Aggregation - 计算总结统计

  • Transformation - 执行一些特定于组的操作

  • Filtration - 在某些情况下丢弃数据

让我们现在创建一个DataFrame对象并对其执行所有操作 -

#import the pandas library
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

输出 如下 -

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

将数据拆分成组

Pandas物体可以分成任何物体。有多种方式来分割对象,如 -

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

现在让我们看看如何将分组对象应用于DataFrame对象

# import the pandas library
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')

输出 如下 -

<pandas.core.groupby.DataFrameGroupBy object at 0x7fa46a977e50>

查看组

# import the pandas library
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

输出 如下 -

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

多列 分组 -

# import the pandas library
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

输出 如下 -

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

通过组遍历

有了 groupby 对象,我们可以遍历类似itertools.obj的对象。

# import the pandas library
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

输出 如下 -

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

默认情况下, groupby 对象具有与组名相同的标签名称。

选择一个组

使用 get_group() 方法,我们可以选择一个组。

# import the pandas library
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)

输出 如下 -

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

聚合函数为每个组返回单个聚合值。一旦创建了 分组 对象,就可以对分组数据执行多个聚合操作。

显而易见的是通过聚合或等价的 agg 方法进行聚合-

# import the pandas library
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)

输出 如下 -

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

另一种查看每个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)

输出 如下 -

Points   Rank   Year
Team
Devils        2      2      2
Kings         3      3      3
Riders        4      4      4
Royals        2      2      2
kings         1      1      1

一次应用多个聚合函数

通过分组序列,您还可以传递 函数列表字典 来进行聚合,并生成DataFrame作为输出 -

# import the pandas library
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['Points'].agg([np.sum, np.mean, np.std])

输出 如下 -

Team      sum      mean          std
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

转换

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

# import the pandas library
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)

输出 如下 -

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

过滤

过滤根据定义的标准过滤数据并返回数据的子集。所述 过滤器() 函数是用来筛选数据。

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)
print df.groupby('Team').filter(lambda x: len(x) >= 3)

输出 如下 -

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

在上述过滤条件下,我们要求将参加三次或三次以上参赛的球队归还IPL。