3-3 groupby操作

 Pandas章节应用的数据可以在以下链接下载：  https://files.cnblogs.com/files/AI-robort/Titanic_Data-master.zip

In [1]:

import pandas as pd
df=pd.DataFrame({'key':['A','B','C','A','B','C','A','B','C'],
                 'data':[0,5,10,5,10,15,10,15,20]})
df

Out[1]:

 

	key	data
0	A	0
1	B	5
2	C	10
3	A	5
4	B	10
5	C	15
6	A	10
7	B	15
8	C	20

In [3]:

for key in['A','B','C']:
     print(key,df[df['key']==key].sum())#求每个key值的求和

 

A key     AAA
data     15
dtype: object
B key     BBB
data     30
dtype: object
C key     CCC
data     45
dtype: object

In [4]:

df.groupby('key').sum()#和上面的分组是一样的

Out[4]:

 

	data
key
A	15
B	30
C	45

In [7]:

import numpy as np
df.groupby('key').aggregate(np.mean)#aggregate是执行操作，如np的sum 、mean等

Out[7]:

 

	data
key
A	5
B	10
C	15

In [8]:

df1=pd.read_csv('./Titanic_Data-master/Titanic_Data-master/train.csv')

In [13]:

df1.groupby('Sex')['Age'].mean()#统计性别对应的年龄的均值

Out[13]:

Sex
female    27.915709
male      30.726645
Name: Age, dtype: float64

In [14]:

df1.groupby('Sex')['Survived'].mean()#统计性别对应的获救的平均概率

Out[14]:

Sex
female    0.742038
male      0.188908
Name: Survived, dtype: float64