pandas数据合并_series合并

目录

1.merge数据合并

1.1merge默认合并数据

1.2左连接和右连接

1.3 merge通过多个键合并

2.concat数据连接

2.1两个Series的数据连接

2.2两个DataFrame的数据连接

3.combine_first合并数据

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1.merge数据合并

import pandas as pd
import numpy as np

1.1merge默认合并数据

merge合并时默认是内连接（inner）

price = pd.DataFrame({'fruit':['apple','grape','orange','orange'],'price':[8,7,9,11]})
amount = pd.DataFrame({'fruit':['apple','grape','orange'],'amout':[5,11,8]})
display(price,amount,pd.merge(price,amount))
#------------------------------------------------------------------------
    fruit	price
0	apple	8
1	grape	7
2	orange	9
3	orange	11
    fruit	amout
0	apple	5
1	grape	11
2	orange	8
    fruit	price	amout
0	apple	8	    5
1	grape	7	    11
2	orange	9	    8
3	orange	11	    8

1.2左连接和右连接

pd.merge(price,amount,how = 'left')#左连接
pd.merge(price,amount,how = 'right'）#右连接

1.3 merge通过多个键合并

left = pd.DataFrame({'key1':['one','one','two'],'key2':['a','b','a'],'value1':range(3)})
right = pd.DataFrame({'key1':['one','one','two','two'],'key2':['a','a','a','b'],'value2':range(4)})
display(left,right,pd.merge(left,right,on = ['key1','key2'],how = 'left'))
#--------------------------------------------------------------------------------
    key1	key2	value1
0	one	    a	    0
1	one	    b	    1
2	two	    a	    2
    key1	key2	value2
0	one	    a	    0
1	one	    a	    1
2	two	    a	    2
3	two	    b	    3
    key1	key2	value1	value2
0	one	    a	    0	    0.0
1	one	    a	    0	    1.0
2	one	    b	    1	    NaN
3	two	    a	    2	    2.0

2.concat数据连接

concat方法默认情况下会按行的方向堆叠数据，如果在列向上连接，设置axis=1。

2.1两个Series的数据连接

s1 = pd.Series([0,1],index = ['a','b'])
s2 = pd.Series([2,3,4],index = ['a','d','e'])
s3 = pd.Series([5,6],index = ['f','g'])
print(pd.concat([s1,s2,s3]))  #Series行合并
#----------------------------------------------
a    0
b    1
a    2
d    3
e    4
f    5
g    6

2.2两个DataFrame的数据连接

data1 = pd.DataFrame(np.arange(6).reshape(2,3),columns = list('abc'))
data2 = pd.DataFrame(np.arange(20,26).reshape(2,3),columns = list('ayz'))
data = pd.concat([data1,data2],axis = 0,sort=False)
display(data1,data2,data)
#--------------------------------------------------------------------------
    a	b	c
0	0	1	2
1	3	4	5
    a	y	z
0	20	21	22
1	23	24	25
    a	b	c	y	    z
0	0	1.0	2.0	NaN	    NaN
1	3	4.0	5.0	NaN	    NaN
0	20	NaN	NaN	21.0	22.0
1	23	NaN	NaN	24.0	25.0

3.combine_first合并数据

如果需要合并的两个DataFrame存在重复索引，可以使用combine_first方法。

s6.combine_first(s5)
#-----------------------
    0	1
a	0.0	0.0
b	1.0	5.0
f	NaN	5.0
g	NaN	6.0