pandas資料分組運算 groupby

（1）、按列分組

import pandas as pd

import numpy as np
df = dataframe() 
df

data1   data2   key1    key2

0 -1.488061 -0.002241
aone
10.707773
0.338733
atwo
2 -1.689161
0.647643 b one
30.987463 -0.584322 b two
4 -0.560973 -1.147191
aone

group1 = df.groupby('key1')  

[x for x in group1]

[('a',       data1     data2 key1 key2

0 -1.488061 -0.002241 a one
10.707773
0.338733 a two
4 -0.560973 -1.147191 a one), 
('b', data1 data2 key1 key2
2 -1.689161
0.647643 b one
30.987463 -0.584322 b two)]

group2 = df.groupby(['key1','key2'])  

[x for x in group2]

[(('a', 'one'),       data1     data2 key1 key2

0 -1.488061 -0.002241 a one
4 -0.560973 -1.147191 a one),
(('a', 'two'), data1 data2 key1 key2
10.707773
0.338733 a two),
(('b', 'one'), data1 data2 key1 key2
2 -1.689161
0.647643 b one),
(('b', 'two'), data1 data2 key1 key2
30.987463 -0.584322 b two)]

推導式[x for x in group1]用於顯示分組內容

(2）、分組統計

對分組group1、group2分別應用size()、sum()、count()等統計函式，可分別統計分組的數量、不同列的分組和、不同列的分組數量。

group1.size()

key1

a 3
b 2
dtype: int64

group1.sum()

data1   data2

key1 
a -1.341260 -0.810698
b -0.701698
0.063321

group2.size()

key1  key2

aone
2two
1b one
1two
1dtype: int64

group2.count()

data1   data2

key1 key2 
aone22
two1
1b one11
two1
1

agg(func)可對分組後的某一列或者多個列的資料應用func函式，也可推廣到同時作用於多個列和多個函式上。

例：對分組後的』data1』列求均值

group1['data1'].agg('mean')

key1

a -0.447087
b -0.350849
name: data1, dtype: float64

group1['data1','data2'].agg(['mean','sum'])

data1            data2

mean sum mean sum
key1 
a -0.447087 -1.341260 -0.270233 -0.810698
b -0.350849 -0.701698
0.031660
0.063321

data1       data2

key1 
a -0.447087 -0.270233
b -0.350849
0.031660

data1       data2

key1 key2 
aone -1.024517 -0.574716
two0.707773
0.338733
b one -1.689161
0.647643
two0.987463 -0.584322

通過reset_index()函式可以將groupby()的分組結果轉換成dataframe物件，進而儲存。

group1['data1','data2'].agg(['mean','sum']).reset_index()

key1   data1                    data2

mean sum mean sum
0a -0.447087 -1.341260 -0.270233 -0.810698
1 b -0.350849 -0.701698
0.031660
0.063321

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