Python:NumPy .squeeze()

Numpy’s .squeeze() is used to remove dimensions of size 1 from an array, returning a reshaped array without those singleton dimensions.

Syntax

ndarray.squeeze(axis=None)

Parameters:

• axis: Specifies which axis or axes to squeeze. If set to None (default), all dimensions of size 1 are removed. If any specified axis is not of size 1, it raises a ValueError.

Return value:

Returns a view of the input array with the specified singleton dimensions removed.

Example 1: Removing All Singleton Dimensions Using .squeeze()

In this example, .squeeze() is used without the axis parameter to remove all dimensions of size 1 from a 3D array:

import numpy as np

np_array = np.array([[[1, 2, 3], [1, 2, 3]]])
print(np_array.shape)  # (1, 2, 3)

squeezed_array = np.squeeze(np_array)
print(squeezed_array.shape)

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The output of this code is:

(2, 3)

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Example 2: Removing a Specific Dimension Using .squeeze()

In this example, .squeeze(axis=0) removes the first dimension explicitly from an array with shape (1, 2, 3):

import numpy as np

np_array = np.array([[[1, 2, 3], [1, 2, 3]]])
squeezed_array = np.squeeze(np_array, axis=0)

# Output shape after squeezing
print(squeezed_array.shape)
print(squeezed_array)

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The output of this code is:

(2, 3)
[[1 2 3]
 [1 2 3]]

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Only axis 0 is removed since it has size 1, resulting in a 2D array.

Codebyte Example: Removing Multiple Dimensions With a Tuple Using .squeeze()

In this example, .squeeze(axis=(0, 2)) removes both the first and third dimensions from a shape (1, 3, 1):

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Axes 0 and 2, both of size 1, will be removed, leaving a flat array of shape (3,).