numpy.pad() - ( Common NumPy Functions )

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Syntax

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

Example

import numpy as np

arr = np.array([[1, 2], [3, 4]])
padded_arr = np.pad(arr, ((2, 2), (3, 3)), 'constant')

print(padded_arr)

Output

array([[0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 1, 2, 0, 0],
       [0, 0, 0, 3, 4, 0, 0],
       [0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0]])

Explanation

The numpy.pad() function is used to pad an array with values. The function takes in three arguments: the input array, pad_width, and the padding mode.

pad_width is a tuple containing the number of values padded before and after the dimensions of the input array. The padding values are filled with zeros by default.

The padding mode argument determines how the padding values are filled in. The default mode is constant, meaning the values are filled with a constant value.

Use

The numpy.pad() function is useful for adding borders or padding to an image, as well as for resizing arrays. This function can be used in image processing applications, visualizing data, and data analysis.

Important Points

• The numpy.pad() function is used to pad an array
• pad_width is a tuple that specifies the padding width before and after each dimension of the input array
• The padding mode determines how the padding values are filled in
• The numpy.pad() function can be used in image processing applications, visualizing data, and data analysis

Summary

In conclusion, the numpy.pad() function is a useful NumPy function for padding and adding borders to arrays. It takes an array, pad width, and padding mode. pad_width is a tuple that specifies the padding width before and after each dimension of the input array. The padding mode determines how the padding values are filled in. This function can be used in image processing applications, visualizing data, and data analysis.