NumPy: Remove single-dimensional entries from a specified shape

Last update on August 29 2025 12:46:41 (UTC/GMT +8 hours)

Remove Single-Dimensional Entries

Write a NumPy program to remove single-dimensional entries from a specified shape.
Specified shape: (3, 1, 4).

Sample Solution:

Python Code:

# Importing the NumPy library with an alias 'np'
import numpy as np

# Creating a 3x1x4 array of zeros
x = np.zeros((3, 1, 4))

# Squeezing the array x to remove single-dimensional entries, 
# resulting in an array with shape (3, 4), and printing its shape
print(np.squeeze(x).shape)

Sample Output:

(3, 4)

Explanation:

In the above code -

x = np.zeros((3, 1, 4)): This line creates a 3-dimensional NumPy array named x with shape (3, 1, 4) filled with zeros. The middle dimension has a size of 1, which is redundant.

np.squeeze(x): Remove the redundant dimensions (dimensions with a size of 1) from the ‘x’ array. In this case, the second dimension (axis 1) with a size of 1 will be removed.

np.squeeze(x).shape: Get the shape of the array after the redundant dimensions have been removed. The new shape of the array will be (3, 4) because the middle dimension with a size of 1 is removed.

Finally print() function prints the shape of the squeezed array, which is (3, 4).

For more Practice: Solve these Related Problems:

Write a NumPy program to remove all singleton dimensions from an array using np.squeeze.
Apply np.squeeze on a (3,1,4) array and verify that the resulting shape is (3,4).
Create a function that automatically squeezes any input array and checks if the dimensions are reduced correctly.
Demonstrate the difference between np.squeeze and np.reshape by comparing outputs on arrays with single dimensions.

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