Pandas Concatenating data

by Online Tutorials Library July 14, 2022

Concatenating the data

NumPy’s concatenate data is used to concatenate two arrays either row-wise or column-wise. It can take two or more arrays of the same shape and it concatenates row-wise as a default type i.e. axis=0.

Example1:

  # import numpy  import numpy as np  arr1 = np.arange(9)  arr1  arr2d_1 = array.reshape((3,3))  arr2d_1  arr2d_1 = np.arange(10,19).reshape(3,3)  arr2d_1      # concatenate 2 numpy arrays: row-wise  np.concatenate((arr2d_1, arr2d_2))  

Output:

array([[ 0,  1,  2],         [ 3,  4,  5],         [ 6,  7,  8],         [10, 11, 12],         [13, 14, 15],         [16, 17, 18]])

Example2:

  import pandas as pd    one = pd.DataFrame({‘Name’: [‘Parker’, ‘Phill’, ‘Smith’],’id’:[108,119,127]},index=[‘A’,’B’,’C’])  two = pd.DataFrame({‘Name’: [‘Terry’, ‘Jones’, ‘John’],                        ‘id’:[102,125,112]},  index=[‘A’,’B’,’C’])  print(pd.concat([one,two]))  

Output:

    Name     id  A   Parker   108  B   Phill    119  C   Smith    127  A   Terry    102  B   Jones    125  C   John     112

Example3:

  import pandas as pd  one = pd.DataFrame({‘Name’: [‘Parker’, ‘Phill’, ‘Smith’],’id’:[108,119,127]},index=[‘A’,’B’,’C’])  two = pd.DataFrame({‘Name’: [‘Terry’, ‘Jones’, ‘John’],                        ‘id’:[102,125,112]},  index=[‘A’,’B’,’C’])  print(pd.concat([one,two],keys=[‘x’,’y’]))  

Output:

 Name   id  x A  Parker  108  B   Phill119   C   Smith  127  y A   Terry  102   B   Jones  125  C    John  112

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Pandas Concatenating data

Concatenating the data

Example1:

Example2:

Example3:

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