numpy.resize() in Python

numpy.resize() in Python

The numpy.resize() returns a new array with the specified shape. If the new array is larger than the original array, then the new array is filled with repeated copies of a. 

Syntax

numpy.resize(a, new_shape)

Parameter

The numpy.resize() parameter consists of two parameters, which are as follows:

a : This parameter represents the array to be resized.

new_shape: This parameter represents the shape of the resized array.

Return

This function returns the new array that is formed from the data in the old array, repeated if necessary to fill out the required number of elements. The data are repeated in the order that they are stored in memory.

Example 1

# Python program explaining
# numpy.resize() function
import numpy as np
# passing input array
inp_arr = np.array([7, 2, 3, 9, 5, 8])
print("Input array:")
print(inp_arr) 
# Reshaping the input array permanently
inp_arr.resize(2, 3)
print("After reshaping the array: ")
print(inp_arr)

Output

Input array:
[7 2 3 9 5 8]
After reshaping the array:
[[7 2 3]
[9 5 8]]

Example 2

# Python program explaining
# numpy.resize() function
import numpy as np
# input array
inp_arr = np.array([11, 12, 13, 14, 15, 16])
# Required values 12, existing values 6
inp_arr.resize(4, 3)
print(inp_arr)

Output

[[11 12 13]
[14 15 16]
[ 0  0  0]
[ 0  0  0]]