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numpy.pad() function in Python

Last Updated : 01 Oct, 2020

numpy.pad() function is used to pad the Numpy arrays. Sometimes there is a need to perform padding in Numpy arrays, then numPy.pad() function is used. The function returns the padded array of rank equal to the given array and the shape will increase according to pad_width.

Syntax: numpy.pad(array, pad_width, mode=’constant’, **kwargs)

Parameters :

array: the array to pad

pad_width: This parameter defines the number of values that are padded to the edges of each axis.
mode : str or function(optional)

**kwargs: allows you to pass keyword variable length of argument to a function. It is used when we want to handle the named argument in a function.

Return:
A padded array of rank equal to an array with shape increased according to pad_width.

Example 1:

Python3

# Python program to explain 
# working of numpy.pad() function 
import numpy as np 
  
  
arr = [1, 3, 2, 5, 4] 
  
# padding array using CONSTANT mode 
pad_arr = np.pad(arr, (3, 2), 'constant',  
                 constant_values=(6, 4)) 
  
print(pad_arr) 

Output:

[6 6 6 1 3 2 5 4 4 4]

Example 2:

Python3

# Python program to explain 
# working of numpy.pad() function 
import numpy as np 
  
  
arr = [1, 3, 2, 5, 4]  
  
# padding array using 'linear_ramp' mode 
pad_arr = np.pad(arr, (3, 2), 'linear_ramp', 
                 end_values=(-4, 5))    
  
print(pad_arr) 

Output:

[-4 -2 -1  1  3  2  5  4  4  5]

Example 3:

Python3

# Python program to explain 
# working of numpy.pad() function 
import numpy as np 
  
  
arr = [1, 3, 9, 5, 4] 
  
# padding array using 'maximum' mode 
pad_arr = np.pad(arr, (3,), 'maximum') 
  
print(pad_arr) 

Output:

[9 9 9 1 3 9 5 4 9 9 9]

Example 4:

Python3

# Python program to explain 
# working of numpy.pad() function 
import numpy as np 
  
  
arr = [[1, 3],[5, 8]]  
  
# padding array using 'minimum' mode 
pad_arr = np.pad(arr, (3,), 'minimum')        
  
print(pad_arr) 

Output:

[[1 1 1 1 3 1 1 1]
[1 1 1 1 3 1 1 1]
[1 1 1 1 3 1 1 1]
[1 1 1 1 3 1 1 1]
[5 5 5 5 8 5 5 5]
[1 1 1 1 3 1 1 1]
[1 1 1 1 3 1 1 1]
[1 1 1 1 3 1 1 1]]

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