Python Pandas - GroupBy 操作

任何 groupby 操作都涉及对原始对象的以下操作之一。他们是 −

分割对象
应用函数
组合结果

在许多情况下，我们将数据拆分为多个集合，并对每个子集应用一些功能。在应用功能中，我们可以执行以下操作 −

聚合 − 计算汇总统计量
变换 − 执行一些特定于组的操作
过滤 − 在某些条件下丢弃数据

现在让我们创建一个 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

它的输出如下 −

    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

将数据分组

Pandas 对象可以拆分为它们的任何对象。有多种方法可以拆分对象，例如 −

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

示例

# 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')

它的输出如下 −

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

查看群组

# 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

它的输出如下 −

{'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')}

示例

Group by 多列 −

# 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

它的输出如下 −

{('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')}

遍历组

有了 groupby 对象，我们可以像 itertools.obj 一样遍历对象。

# 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

它的输出如下 −

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 对象的标签名称与组名称相同。

选择一个组

使用 get_group() 方法，我们可以选择单个组。

# 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)

它的输出如下 −

   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一个聚合值。一旦创建了 group by 对象，就可以对分组数据执行多个聚合操作。

一个明显的方法是通过聚合或等效的 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)

它的输出如下 −

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

查看每个组大小的另一种方法是应用 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)

Attribute Access in Python Pandas
grouped = df.groupby('Team')
print grouped.agg(np.size)

它的输出如下 −

         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

使用分组系列，您还可以传递 list 或 函数字典 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])

它的输出如下 −

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

对组或列的转换会返回一个对象，该对象的索引大小与正在分组的对象的大小相同。因此，转换应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)

它的输出如下 −

       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)

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

它的输出如下 −

    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

在上述过滤条件中，我们要求返回参加过 3 次或以上 IPL 的团队。

Pandas 教程

Pandas 其他

Pandas 实例

Python Pandas - GroupBy 操作

将数据分组

示例

查看群组

示例

遍历组

选择一个组

聚合

一次应用多个聚合函数

转换

过滤

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