.resize()

The NumPy .resize() function returns a new array with the given shape and size. If the new size is larger than the original, the data is repeated cyclically; if smaller, the data is truncated. This function does not modify the original array.

Note: When only the shape needs to be changed without altering the size, .reshape() is recommended for better performance and clearer intent.

Syntax

numpy.resize(a, new_shape)

Parameters:

• a: The array to resize.
• new_shape: An integer or tuple of integers defining the desired shape. A single integer creates a 1D array of that length, while a tuple defines a multi-dimensional shape.

Return value:

• resized_array: A new array of the specified shape. If the new size is larger than the original, the data is repeated cyclically. If smaller, the data is truncated. The original array remains unchanged.

Example

This example demonstrates how to resize a 1D array with 3 elements into a 2D array with 6 elements:

import numpy as np

# Create a 1D array with 3 elements
arr = np.array([1, 2, 3])

# Resize the 1D array to a 2D array with 2 rows and 3 columns
resized_arr = np.resize(arr, (2, 3))

# Print the results
print("Original array:")
print(arr)
print("\nResized array:")
print(resized_arr)

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The output of this code is:

Original array:
[1 2 3]

Resized array:
[[1 2 3]
 [1 2 3]]

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Codebyte Example

Run this code to see how .resize() works with different sizes and shapes:

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