NumPy - pad() function

The NumPy pad() function is used to pad an array. This function contains an optional parameter mode, which can be used to specify string values (predefined style of padding) or a user supplied function for padding.

Syntax

numpy.pad(array, pad_width, mode='constant')

Parameters

`array`	`Required.` Specify the array (array_like of rank N) to pad.
`pad_width`	`Required.` Specify {sequence, array_like, int}. It specifies number of values padded to the edges of each axis. ((before_1, after_1), … (before_N, after_N)) unique pad widths for each axis. ((before, after),) yields same before and after pad for each axis. (pad,) or int is a shortcut for before = after = pad width for all axes.
`mode`	`Optional.` Specify one of the following string values or a user supplied function: 'constant': (default) Pads with a constant value. 'edge': Pads with the edge values of array. 'linear_ramp': Pads with the linear ramp between end_value and the array edge value. 'maximum': Pads with the maximum value of all or part of the vector along each axis. 'mean': Pads with the mean value of all or part of the vector along each axis. 'median': Pads with the median value of all or part of the vector along each axis. 'minimum': Pads with the minimum value of all or part of the vector along each axis. 'reflect': Pads with the reflection of the vector mirrored on the first and last values of the vector along each axis. 'symmetric': Pads with the reflection of the vector mirrored along the edge of the array. 'wrap': Pads with the wrap of the vector along the axis. The first values are used to pad the end and the end values are used to pad the beginning. 'empty': Pads with undefined values. <function>: Padding function.

Return Value

Returns the padded array of rank equal to array with shape increased according to pad_width.

Example:

The example below shows the usage of pad() function.

import numpy as np

x = [10, 20]

#padding with constant value
print("Padding with constant value:")
print(np.pad(x, (2,3), 'constant', constant_values=(5, 25)))

#padding with edge value
print("\nPadding with edge value:")
print(np.pad(x, (2,3), 'edge'))

#padding with maximum value
print("\nPadding with maximum value:")
print(np.pad(x, (2,3), 'maximum'))

#padding with minimum value
print("\nPadding with minimum value:")
print(np.pad(x, (2,3), 'minimum'))

#padding with mean value
print("\nPadding with mean value:")
print(np.pad(x, (2,3), 'mean'))

#padding with linear_ramp value
print("\nPadding with linear_ramp value:")
print(np.pad(x, (2,3), 'linear_ramp', end_values=(0, 0)))

The output of the above code will be:

Padding with constant value:
[ 5  5 10 20 25 25 25]

Padding with edge value:
[10 10 10 20 20 20 20]

Padding with maximum value:
[20 20 10 20 20 20 20]

Padding with minimum value:
[10 10 10 20 10 10 10]

Padding with mean value:
[15 15 10 20 15 15 15]

Padding with linear_ramp value:
[ 0  5 10 20 13  6  0]

Example:

Lets consider one more example where a 2-D array is padded with constant values.

import numpy as np

x = [[10, 20],
     [30, 40]]

#padding that array with 1
print("Padding with constant value:")
print(np.pad(x, ((2,3),(1,2)), 'constant', 
      constant_values=((1, 2),(3, 4))))

The output of the above code will be:

Padding with constant value:
[[ 3  1  1  4  4]
 [ 3  1  1  4  4]
 [ 3 10 20  4  4]
 [ 3 30 40  4  4]
 [ 3  2  2  4  4]
 [ 3  2  2  4  4]
 [ 3  2  2  4  4]]

❮ NumPy - Functions