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NumPy | resize method

schedule Aug 11, 2023
Last updated
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Numpy's resize(~) method returns a new Numpy array with the desired shape. If the reshaped array contains more values than the original array, then numbers will be repeated.

This is equivalent to Numpy's reshape(~) method, just without the order parameter.

Parameters

1. a | array-like

The input array.

2. new_shape | int or tuple of int

The desired shape of the array.

Return value

A new Numpy array with the desired shape.

WARNING

The behaviour of a.resize(~) is different

This documentation covers the method np.resize(~), which has a different behaviour from a.resize(~) where a is the source array.

  • Firstly, the a.resize(~) method performs the resizing in-place, that is, the original array is directly modified without the creation of a new array.

  • Secondly, instead of numbers being repeated in cases where the reshaped array contains more values than the original array, zeros are added.

Examples

Going from 1D to 2D

a = np.array([4,5,6,7])
np.resize(a, (2,2))
array([[4, 5],
[6, 7]])

Case when values are repeated

Values are repeated when resized array contains more values than the original array:

np.resize([4,5], (2,2))
array([[4, 5],
[4, 5]])

Notice how the numbers are simply repeated.

Going from 2D to 1D

Consider the following:

a = np.array([[1,2],[3,4]])
a
array([[1, 2],
[3, 4]])

To obtain the 1D representation:

np.resize(a, 4)
array([1, 2, 3, 4])
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Published by Isshin Inada
Edited by 0 others
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