python 数据合并函数merge( )[通俗易懂]

大家好，又见面了，我是你们的朋友全栈君。

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