Pandas | 18 GroupBy 分组

时间 2019-11-10

标签 pandas groupby 分组繁體版

原文原文链接

任何分组(groupby)操做都涉及原始对象的如下操做之一:python

分割对象
应用一个函数
结合的结果

在许多状况下，咱们将数据分红多个集合，并在每一个子集上应用一些函数。在应用函数中，能够执行如下操做:shell

聚合 - 计算汇总统计
转换 - 执行一些特定于组的操做
过滤 - 在某些状况下丢弃数据

下面来看看建立一个DataFrame对象并对其执行全部操做 -ide

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)

输出结果：函数

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

1、将数据拆分红组

Pandas对象能够分红任何对象。有多种方式来拆分对象，如 -spa

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

如今来看看如何将分组对象应用于DataFrame对象code

示例orm

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'))

输出结果：对象

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

2、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)

输出结果：

{ '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') }

按多列分组blog

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)

输出结果：索引

{ ('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') }

3、迭代遍历分组

使用groupby对象，能够遍历相似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)
    print ('\n')

输出结果：

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

默认状况下，groupby对象具备与分组名相同的标签名称。

4、选择一个分组

使用get_group()方法，能够选择一个组。参考如下示例代码 -

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))

输出结果：

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

5、聚合

聚合函数为每一个组返回单个聚合值。当建立了分组(group by)对象，就能够对分组数据执行多个聚合操做。

应用单个聚合函数

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

输出结果：

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

另外一种查看每一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))

输出结果：

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

经过分组系列，还能够传递函数的列表或字典来进行聚合，并生成DataFrame做为输出

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)

print(df)
print('\n')

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

输出结果：

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


         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

6、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。所以，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))

输出结果：

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

7、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)

输出结果：

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

在上述过滤条件下，要求返回三次以上参加IPL的队伍。