DataFrame合併 軸向鏈結concat

from pandas import

dataframe,series
import
pandas as pd
import
numpy as np
arr = np.arange(12).reshape((3,4))
print
(arr)
'''[[ 0  1  2  3]
[ 4  5  6  7]
[ 8  9 10 11]]
'''print(np.concatenate([arr,arr],axis=1))
'''[[ 0  1  2  3  0  1  2  3]
[ 4  5  6  7  4  5  6  7]
[ 8  9 10 11  8  9 10 11]]
'''s1 = series([0,1],index=['
a','b'
])s2 = series([2,3,4],index=['
c','
d','e'
])s3 = series([5,6],index=['
f','g'
])print
(s1)
'''a    0
b    1
dtype: int64
'''print
(s2)
'''c    2
d    3
e    4
dtype: int64
'''print
(s3)
'''f    5
g    6
dtype: int64
'''print
(pd.concat([s1,s2,s3]))
'''a    0
b    1
c    2
d    3
e    4
f    5
g    6
dtype: int64
'''#
concat是在axis=0上工作的，產生乙個新的series，如果傳入axis=1，結果會變成dataframe（axis=1是列）
print(pd.concat([s1,s2,s3],axis=1,sort=true))
'''0    1    2
a  0.0  nan  nan
b  1.0  nan  nan
c  nan  2.0  nan
d  nan  3.0  nan
e  nan  4.0  nan
f  nan  nan  5.0
g  nan  nan  6.0
'''s4 = series([0,1],index=['
a','b'
])s5 = series([2,3,4],index=['
a','
b','c'
])print(pd.concat([s4,s5]))  #
軸向鏈結
'''a    0
b    1
a    2
b    3
c    4
dtype: int64
'''print(pd.concat([s4,s5],axis=1,sort=true)) #
外連線，並集
'''0  1
a  0.0  2
b  1.0  3
c  nan  4
'''print(pd.concat([s4,s5],axis=1,join='
inner
')) #
內連線，交集
'''0  1
a  0  2
b  1  3
'''print(pd.concat([s4,s5],axis=1,join_axes=[['
a','
b','
c','
d']])) #
使用join_axes指定索引
'''0    1
a  0.0  2.0
b  1.0  3.0
c  nan  4.0
d  nan  nan
'''print(pd.concat([s1,s2,s3],keys=['
a','
b','
c'])) #
連線的片段在結果可使用keys區分
'''a  a    0
b    1
b  c    2
d    3
e    4
c  f    5
g    6
dtype: int64
'''#
如果axis=1，keys就會變成dataframe的列頭
print(pd.concat([s1,s2,s3],keys=['
a','
b','
c'],axis=1,sort=true))
'''a    b    c
a  0.0  nan  nan
b  1.0  nan  nan
c  nan  2.0  nan
d  nan  3.0  nan
e  nan  4.0  nan
f  nan  nan  5.0
g  nan  nan  6.0
'''df1 = dataframe(np.arange(6).reshape((3,2)),index=['
a','
b','
c'],columns=['
one','
two'
])df2 = dataframe(np.arange(4).reshape((2,2)),index=['
a','
c'],columns=['
three
','four'])
print
(df1)
'''one  two
a    0    1
b    2    3
c    4    5
'''print
(df2)
'''three  four
a      0     1
c      2     3
'''print(pd.concat([df1,df2],sort=true))
'''four  one  three  two
a   nan  0.0    nan  1.0
b   nan  2.0    nan  3.0
c   nan  4.0    nan  5.0
a   1.0  nan    0.0  nan
c   3.0  nan    2.0  nan
'''print(pd.concat([df1,df2],axis=1,sort=true))
'''one  two  three  four
a    0    1    0.0   1.0
b    2    3    nan   nan
c    4    5    2.0   3.0
'''print(pd.concat([df1,df2],axis=1,keys=['
level1
','level2
'],names=['
upper
','lower
'],sort=true))
'''upper level1     level2     
lower    one two  three four
a          0   1    0.0  1.0
b          2   3    nan  nan
c          4   5    2.0  3.0
'''

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