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In [1]:

    
import numpy as np
import pandas as pd
from pandas import Series, DataFrame



In [3]:

    
arr1 = np.arange(9).reshape(3,3)



In [5]:

    
np.concatenate([arr1, arr1], axis=1)









    Out[5]:





array([[0, 1, 2, 0, 1, 2],
       [3, 4, 5, 3, 4, 5],
       [6, 7, 8, 6, 7, 8]])



In [6]:

    
np.concatenate([arr1, arr1], axis=0)









    Out[6]:





array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8],
       [0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])



In [7]:

    
ser1 = Series([0,1,2], index=['T', 'U', 'V'])
ser2 = Series([3,4], index=['X', 'Y'])



In [9]:

    
pd.concat([ser1,ser2], axis=0)









    Out[9]:





T    0
U    1
V    2
X    3
Y    4
dtype: int64



In [10]:

    
pd.concat([ser1,ser2], axis=1)



In [11]:

    
pd.concat([ser1,ser2], axis=0, keys=['cat1', 'cat2'])









    Out[11]:





cat1  T    0
      U    1
      V    2
cat2  X    3
      Y    4
dtype: int64



In [14]:

    
dframe1 = DataFrame(np.random.randn(4,3), columns=['X', 'Y', 'Z'])



In [15]:

    
dframe2 = DataFrame(np.random.randn(3,3), columns=['Y', 'Q', 'X'])



In [16]:

    
pd.concat([dframe1, dframe2])



In [17]:

    
pd.concat([dframe1, ser1])



In [ ]:

	0	1
T	0.0	NaN
U	1.0	NaN
V	2.0	NaN
X	NaN	3.0
Y	NaN	4.0

	Q	X	Y	Z
0	NaN	-0.249939	0.722401	-1.706570
1	NaN	1.818554	0.279813	0.865695
2	NaN	0.609562	-0.433680	1.386421
3	NaN	-0.550459	0.520273	-1.539290
0	0.300158	-0.112388	1.183463	NaN
1	1.558093	-1.046223	1.491392	NaN
2	0.757474	0.419251	1.734149	NaN

	0	X	Y	Z
0	NaN	-0.249939	0.722401	-1.706570
1	NaN	1.818554	0.279813	0.865695
2	NaN	0.609562	-0.433680	1.386421
3	NaN	-0.550459	0.520273	-1.539290
T	0.0	NaN	NaN	NaN
U	1.0	NaN	NaN	NaN
V	2.0	NaN	NaN	NaN