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 4df2
key value2
0 a 0
1 c 1
2 c 2
3 c 3
4 c 4pd.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 0pd.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 4.0pd.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 NaNpd.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 4df4
rkey data2
0 a 0
1 c 1
2 c 2
3 c 3
4 c 4pd.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 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 4.0pd.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 11df6
v5 v6 v7 v8
a 12 13 14 15
b 16 17 18 19
d 20 21 22 23pd.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