pandas: pd.concat 用法

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Python 3.6.5 (v3.6.5:f59c0932b4, Mar 28 2018, 17:00:18) [MSC v.1900 64 bit (AMD64)] on win32
Type "copyright", "credits" or "license()" for more information.
>>> import pandas as pd
>>> from pandas import Series
>>> import numpy as np
>>> s1=pd.Series(np.arange(10,13))
>>> s1
0
10
1
11
2
12
dtype: int32
>>> np.arange(10,13)
array([10, 11, 12])
>>> s2=pd.Series(np.arange(100,103))
>>> s2
0
100
1
101
2
102
dtype: int32
>>> pd.concat([s1,s2])
0
10
1
11
2
12
0
100
1
101
2
102
dtype: int32
>>> pd.concat([s1,s2],axis=1)
0
1
0
10
100
1
11
101
2
12
102
>>> pd.concat([s1,s2],keys=[1,2])
1
0
10
1
11
2
12
2
0
100
1
101
2
102
dtype: int32
>>> pd.concat([s1,s2], keys = [1,2],names = ['from','ID'])
from
ID
1
0
10
1
11
2
12
2
0
100
1
101
2
102
dtype: int32
>>> pd.concat([s1,s2], axis = 1,keys = ['s1','s2'],names = ['from','ID'])
s1
s2
0
10
100
1
11
101
2
12
102
>>> idx = 'this is a fake data'.split()
>>> idx
['this', 'is', 'a', 'fake', 'data']
>>> df1 = pd.DataFrame({'Country':['China','Japan','Germany','USA','UK'],'Team':['A','B','A','C','D']},index = idx)
>>> df1
Country Team
this
China
A
is
Japan
B
a
Germany
A
fake
USA
C
data
UK
D
>>> col = 'Country Team'.split()
>>> idx_2 = ['fake','world']
>>> values = [['KLR',100],['abc',200]]
>>> df2 = pd.DataFrame(values,index = idx_2, columns = col)
>>> df2
Country
Team
fake
KLR
100
world
abc
200
>>> pd.concat([df1,df2])
Country Team
this
China
A
is
Japan
B
a
Germany
A
fake
USA
C
data
UK
D
fake
KLR
100
world
abc
200
>>> pd.concat([df1,df2],axis=1)
Warning (from warnings module):
File "__main__", line 1
FutureWarning: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.
To accept the future behavior, pass 'sort=False'.
To retain the current behavior and silence the warning, pass 'sort=True'.
Country
Team Country
Team
a
Germany
A
NaN
NaN
data
UK
D
NaN
NaN
fake
USA
C
KLR
100.0
is
Japan
B
NaN
NaN
this
China
A
NaN
NaN
world
NaN
NaN
abc
200.0
>>> col = ['Team','SBF']
>>> idx_3= ['true','world']
>>> values3 = [['red','pm'],['orange','pl']]
>>> df3 = pd.DataFrame(values3,index = idx_3, columns = col)
>>> df3
Team SBF
true
red
pm
world
orange
pl
>>> df1
Country Team
this
China
A
is
Japan
B
a
Germany
A
fake
USA
C
data
UK
D
>>> pd.concat([df1,df3])
Country
SBF
Team
this
China
NaN
A
is
Japan
NaN
B
a
Germany
NaN
A
fake
USA
NaN
C
data
UK
NaN
D
true
NaN
pm
red
world
NaN
pl
orange
>>> df2
Country
Team
fake
KLR
100
world
abc
200
>>> pd.concat([df2,df3])
Country
SBF
Team
fake
KLR
NaN
100
world
abc
NaN
200
true
NaN
pm
red
world
NaN
pl
orange
>>> df3
Team SBF
true
red
pm
world
orange
pl
>>> df1
Country Team
this
China
A
is
Japan
B
a
Germany
A
fake
USA
C
data
UK
D
>>> df3
Team SBF
true
red
pm
world
orange
pl
>>> df3
Team SBF
true
red
pm
world
orange
pl
>>> df2
Country
Team
fake
KLR
100
world
abc
200
>>> pd.concat([df2,df3])
Country
SBF
Team
fake
KLR
NaN
100
world
abc
NaN
200
true
NaN
pm
red
world
NaN
pl
orange
>>> pd.concat([df2,df3],axis = 1)
Country
Team
Team
SBF
fake
KLR
100.0
NaN
NaN
true
NaN
NaN
red
pm
world
abc
200.0
orange
pl
>>> pd.concat([df1.Team,df2.Team,df3.Team])
this
A
is
B
a
A
fake
C
data
D
fake
100
world
200
true
red
world
orange
Name: Team, dtype: object
>>> pd.concat(df1['Team'],df2['Team'],df3['Team'])
Traceback (most recent call last):
File "<pyshell#43>", line 1, in <module>
pd.concat(df1['Team'],df2['Team'],df3['Team'])
File "C:Python36libsite-packagespandascorereshapeconcat.py", line 225, in concat
copy=copy, sort=sort)
File "C:Python36libsite-packagespandascorereshapeconcat.py", line 241, in __init__
'"{name}"'.format(name=type(objs).__name__))
TypeError: first argument must be an iterable of pandas objects, you passed an object of type "Series"
>>> pd.concat(df1[['Team']],df2[['Team']],df3[['Team']])
Traceback (most recent call last):
File "<pyshell#44>", line 1, in <module>
pd.concat(df1[['Team']],df2[['Team']],df3[['Team']])
File "C:Python36libsite-packagespandascorereshapeconcat.py", line 225, in concat
copy=copy, sort=sort)
File "C:Python36libsite-packagespandascorereshapeconcat.py", line 241, in __init__
'"{name}"'.format(name=type(objs).__name__))
TypeError: first argument must be an iterable of pandas objects, you passed an object of type "DataFrame"
>>> pd.concat([df2,df3])
Country
SBF
Team
fake
KLR
NaN
100
world
abc
NaN
200
true
NaN
pm
red
world
NaN
pl
orange
>>> pd.concat([df2,df3],join = 'inner')
Team
fake
100
world
200
true
red
world
orange
>>> pd.concat([df2,df3],join = 'outer')
Country
SBF
Team
fake
KLR
NaN
100
world
abc
NaN
200
true
NaN
pm
red
world
NaN
pl
orange
>>> df1
Country Team
this
China
A
is
Japan
B
a
Germany
A
fake
USA
C
data
UK
D
>>> df2
Country
Team
fake
KLR
100
world
abc
200
>>> df3
Team SBF
true
red
pm
world
orange
pl
>>> pd.concat([df2,df3])
Country
SBF
Team
fake
KLR
NaN
100
world
abc
NaN
200
true
NaN
pm
red
world
NaN
pl
orange
>>> pd.concat([df2,df3], ignore_index = True)
Country
SBF
Team
0
KLR
NaN
100
1
abc
NaN
200
2
NaN
pm
red
3
NaN
pl
orange
>>>

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Python 3.6.5 (v3.6.5:f59c0932b4, Mar 28 2018, 17:00:18) [MSC v.1900 64 bit (AMD64)] on win32
Type "copyright", "credits" or "license()" for more information.
>>> import pandas as pd
>>> from pandas import Series
>>> import numpy as np
>>> s1=pd.Series(np.arange(10,13))
>>> s1
0
10
1
11
2
12
dtype: int32
>>> np.arange(10,13)
array([10, 11, 12])
>>> s2=pd.Series(np.arange(100,103))
>>> s2
0
100
1
101
2
102
dtype: int32
>>> pd.concat([s1,s2])
0
10
1
11
2
12
0
100
1
101
2
102
dtype: int32
>>> pd.concat([s1,s2],axis=1)
0
1
0
10
100
1
11
101
2
12
102
>>> pd.concat([s1,s2],keys=[1,2])
1
0
10
1
11
2
12
2
0
100
1
101
2
102
dtype: int32
>>> pd.concat([s1,s2], keys = [1,2],names = ['from','ID'])
from
ID
1
0
10
1
11
2
12
2
0
100
1
101
2
102
dtype: int32
>>> pd.concat([s1,s2], axis = 1,keys = ['s1','s2'],names = ['from','ID'])
s1
s2
0
10
100
1
11
101
2
12
102
>>> idx = 'this is a fake data'.split()
>>> idx
['this', 'is', 'a', 'fake', 'data']
>>> df1 = pd.DataFrame({'Country':['China','Japan','Germany','USA','UK'],'Team':['A','B','A','C','D']},index = idx)
>>> df1
Country Team
this
China
A
is
Japan
B
a
Germany
A
fake
USA
C
data
UK
D
>>> col = 'Country Team'.split()
>>> idx_2 = ['fake','world']
>>> values = [['KLR',100],['abc',200]]
>>> df2 = pd.DataFrame(values,index = idx_2, columns = col)
>>> df2
Country
Team
fake
KLR
100
world
abc
200
>>> pd.concat([df1,df2])
Country Team
this
China
A
is
Japan
B
a
Germany
A
fake
USA
C
data
UK
D
fake
KLR
100
world
abc
200
>>> pd.concat([df1,df2],axis=1)
Warning (from warnings module):
File "__main__", line 1
FutureWarning: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.
To accept the future behavior, pass 'sort=False'.
To retain the current behavior and silence the warning, pass 'sort=True'.
Country
Team Country
Team
a
Germany
A
NaN
NaN
data
UK
D
NaN
NaN
fake
USA
C
KLR
100.0
is
Japan
B
NaN
NaN
this
China
A
NaN
NaN
world
NaN
NaN
abc
200.0
>>> col = ['Team','SBF']
>>> idx_3= ['true','world']
>>> values3 = [['red','pm'],['orange','pl']]
>>> df3 = pd.DataFrame(values3,index = idx_3, columns = col)
>>> df3
Team SBF
true
red
pm
world
orange
pl
>>> df1
Country Team
this
China
A
is
Japan
B
a
Germany
A
fake
USA
C
data
UK
D
>>> pd.concat([df1,df3])
Country
SBF
Team
this
China
NaN
A
is
Japan
NaN
B
a
Germany
NaN
A
fake
USA
NaN
C
data
UK
NaN
D
true
NaN
pm
red
world
NaN
pl
orange
>>> df2
Country
Team
fake
KLR
100
world
abc
200
>>> pd.concat([df2,df3])
Country
SBF
Team
fake
KLR
NaN
100
world
abc
NaN
200
true
NaN
pm
red
world
NaN
pl
orange
>>> df3
Team SBF
true
red
pm
world
orange
pl
>>> df1
Country Team
this
China
A
is
Japan
B
a
Germany
A
fake
USA
C
data
UK
D
>>> df3
Team SBF
true
red
pm
world
orange
pl
>>> df3
Team SBF
true
red
pm
world
orange
pl
>>> df2
Country
Team
fake
KLR
100
world
abc
200
>>> pd.concat([df2,df3])
Country
SBF
Team
fake
KLR
NaN
100
world
abc
NaN
200
true
NaN
pm
red
world
NaN
pl
orange
>>> pd.concat([df2,df3],axis = 1)
Country
Team
Team
SBF
fake
KLR
100.0
NaN
NaN
true
NaN
NaN
red
pm
world
abc
200.0
orange
pl
>>> pd.concat([df1.Team,df2.Team,df3.Team])
this
A
is
B
a
A
fake
C
data
D
fake
100
world
200
true
red
world
orange
Name: Team, dtype: object
>>> pd.concat(df1['Team'],df2['Team'],df3['Team'])
Traceback (most recent call last):
File "<pyshell#43>", line 1, in <module>
pd.concat(df1['Team'],df2['Team'],df3['Team'])
File "C:Python36libsite-packagespandascorereshapeconcat.py", line 225, in concat
copy=copy, sort=sort)
File "C:Python36libsite-packagespandascorereshapeconcat.py", line 241, in __init__
'"{name}"'.format(name=type(objs).__name__))
TypeError: first argument must be an iterable of pandas objects, you passed an object of type "Series"
>>> pd.concat(df1[['Team']],df2[['Team']],df3[['Team']])
Traceback (most recent call last):
File "<pyshell#44>", line 1, in <module>
pd.concat(df1[['Team']],df2[['Team']],df3[['Team']])
File "C:Python36libsite-packagespandascorereshapeconcat.py", line 225, in concat
copy=copy, sort=sort)
File "C:Python36libsite-packagespandascorereshapeconcat.py", line 241, in __init__
'"{name}"'.format(name=type(objs).__name__))
TypeError: first argument must be an iterable of pandas objects, you passed an object of type "DataFrame"
>>> pd.concat([df2,df3])
Country
SBF
Team
fake
KLR
NaN
100
world
abc
NaN
200
true
NaN
pm
red
world
NaN
pl
orange
>>> pd.concat([df2,df3],join = 'inner')
Team
fake
100
world
200
true
red
world
orange
>>> pd.concat([df2,df3],join = 'outer')
Country
SBF
Team
fake
KLR
NaN
100
world
abc
NaN
200
true
NaN
pm
red
world
NaN
pl
orange
>>> df1
Country Team
this
China
A
is
Japan
B
a
Germany
A
fake
USA
C
data
UK
D
>>> df2
Country
Team
fake
KLR
100
world
abc
200
>>> df3
Team SBF
true
red
pm
world
orange
pl
>>> pd.concat([df2,df3])
Country
SBF
Team
fake
KLR
NaN
100
world
abc
NaN
200
true
NaN
pm
red
world
NaN
pl
orange
>>> pd.concat([df2,df3], ignore_index = True)
Country
SBF
Team
0
KLR
NaN
100
1
abc
NaN
200
2
NaN
pm
red
3
NaN
pl
orange
>>>

参考：https://blog.csdn.net/ycyrym/article/details/105127980
https://blog.csdn.net/Asher117/article/details/84799845
https://www.cnblogs.com/wzdLY/p/9673767.html
https://blog.csdn.net/Asher117/article/details/84725199
https://blog.csdn.net/stevenkwong/article/details/52528616