在Pandas Dataframe中按分组进行多重聚合 [英] Multiple aggregation in group by in Pandas Dataframe

问题描述

SQL : Select Max(A) , Min (B) , C from Table group by C 

我想在数据框上的熊猫中执行相同的操作.我离这更近了:

I want to do the same operation in pandas on a dataframe. The closer I got was till :

DF2= DF1.groupby(by=['C']).max() 

我在两列中最多获取的地方，在分组时如何做一个以上的操作.

where I land up getting max of both the columns , how do i do more than one operation while grouping by.

推荐答案

尝试agg()函数:

import numpy as np
import pandas as pd


df = pd.DataFrame(np.random.randint(0,5,size=(20, 3)), columns=list('ABC'))
print(df)

print(df.groupby('C').agg({'A': max, 'B':min}))

输出:

    A  B  C
0   2  3  0
1   2  2  1
2   4  0  1
3   0  1  4
4   3  3  2
5   0  4  3
6   2  4  2
7   3  4  0
8   4  2  2
9   3  2  1
10  2  3  1
11  4  1  0
12  4  3  2
13  0  0  1
14  3  1  1
15  4  1  1
16  0  0  0
17  4  0  1
18  3  4  0
19  0  2  4
   A  B
C
0  4  0
1  4  0
2  4  2
3  0  4
4  0  1

或者，您可能要检查 pandas.read_sql_query()功能...

Alternatively you may want to check pandas.read_sql_query() function...

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