Hi，大家好，我是编程小6，很荣幸遇见你，我把这些年在开发过程中遇到的问题或想法写出来，今天说一说
python中数据合并_python数据提取和合并,希望能够帮助你!!!。

python中的merge函数与sql中的 join 用法非常类似，以下是merge( )函数中的参数：

merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)

一、左右连接键名一样

import pandas as pd df1=pd.DataFrame({'key':['a','b','a','b','b'],'value1':range(5)}) df2=pd.DataFrame({'key':['a','c','c','c','c'],'value2':range(5)}) display(df1,df2,pd.merge(df1,df2))

df1

 key value1 0 a 0 1 b 1 2 a 2 3 b 3 4 b 4

df2

 key value2 0 a 0 1 c 1 2 c 2 3 c 3 4 c 4

pd.merge(df1,df2) ##以df1、df2中相同的列名key进行连接,默认how='inner', pd.merge(df1,df2,on='key',how='inner')

 key value1 value2 0 a 0 0 1 a 2 0

pd.merge(df1,df2,how='outer') ##  全连接，取并集

 key value1 value2 0 a 0.0 0.0 1 a 2.0 0.0 2 b 1.0 NaN 3 b 3.0 NaN 4 b 4.0 NaN 5 c NaN 1.0 6 c NaN 2.0 7 c NaN 3.0 8 c NaN 4.0

pd.merge(df1,df2,how='left')  ### 左连接，左边取全部，右边取部分，没有值则用NaN填充

 key value1 value2 0 a 0 0.0 1 b 1 NaN 2 a 2 0.0 3 b 3 NaN 4 b 4 NaN

pd.merge(df1,df2,how='right') ###  右连接，右边取全部，左边取部分，没有值则用NaN填充

 key value1 value2 0 a 0.0 0 1 a 2.0 0 2 c NaN 1 3 c NaN 2 4 c NaN 3 5 c NaN 4

二、左右连接键名不一样

如果两个DataFrame的左右连接键的列名不一样，可以用left_on，right_on来进行指定

df3=pd.DataFrame({'lkey':['a','b','a','b','b'],'data1':range(5)}) df4=pd.DataFrame({'rkey':['a','c','c','c','c'],'data2':range(5)})

df3

 lkey data1 0 a 0 1 b 1 2 a 2 3 b 3 4 b 4

df4

 rkey data2 0 a 0 1 c 1 2 c 2 3 c 3 4 c 4

pd.merge(df3,df4,left_on='lkey',right_on='rkey')   ### 内连接，默认how='inner'

 lkey data1 rkey data2 0 a 0 a 0 1 a 2 a 0

pd.merge(df3,df4,left_on='lkey',right_on='lkey',how='outer')  ### 全连接

 lkey data1 rkey data2 0 a 0.0 a 0.0 1 a 2.0 a 0.0 2 b 1.0 NaN NaN 3 b 3.0 NaN NaN 4 b 4.0 NaN NaN 5 NaN NaN c 1.0 6 NaN NaN c 2.0 7 NaN NaN c 3.0 8 NaN NaN c 4.0

pd.merge(df3,df4,left_on='lkey',right_on='rkey',how='left')  ### 左连接

 lkey data1 rkey data2 0 a 0 a 0.0 1 b 1 NaN NaN 2 a 2 a 0.0 3 b 3 NaN NaN 4 b 4 NaN NaN

pd.merge(df3,df4,left_on='lkey',right_on='rkey',how='right')  ### 右连接

 lkey data1 rkey data2 0 a 0.0 a 0 1 a 2.0 a 0 2 NaN NaN c 1 3 NaN NaN c 2 4 NaN NaN c 3 5 NaN NaN c 4

三、索引作为连接键

df5=pd.DataFrame(np.arange(12).reshape(3,4),index=list('abc'),columns=['v1','v2','v3','v4']) df6=pd.DataFrame(np.arange(12,24,1).reshape(3,4),index=list('abd'),columns=['v5','v6','v7','v8'])

df5

 v1 v2 v3 v4 a 0 1 2 3 b 4 5 6 7 c 8 9 10 11

df6

 v5 v6 v7 v8 a 12 13 14 15 b 16 17 18 19 d 20 21 22 23

pd.merge(df5,df6,left_index=True,right_index=True)

 v1 v2 v3 v4 v5 v6 v7 v8 a 0 1 2 3 12 13 14 15 b 4 5 6 7 16 17 18 19

今天的分享到此就结束了，感谢您的阅读，如果确实帮到您，您可以动动手指转发给其他人。