numpy.squeeze() in Python Last Updated : 15 Apr, 2025 Summarize Comments Improve Suggest changes Share Like Article Like Report The numpy.squeeze() is a useful Python function, which is utilized for the removal of single-dimensional elements from the shape of a NumPy array. It comes in very handy when you have to discard redundant dimensions (like a dimension with size 1) after operations that introduce extra dimensions.Basic usage of numpy.squeeze() Python import numpy as np in_arr = np.array([[[2, 2, 2], [2, 2, 2]]]) print ("Input array : ", in_arr) print("Shape of input array : ", in_arr.shape) out_arr = np.squeeze(in_arr) print ("output squeezed array : ", out_arr) print("Shape of output array : ", out_arr.shape) Output : Input array : [[[2 2 2] [2 2 2]]]Shape of input array : (1, 2, 3)output squeezed array : [[2 2 2] [2 2 2]]Shape of output array : (2, 3)Explanation:Input: A 3D array of shape (1, 2, 3)—the first dimension has size 1.Output: The numpy.squeeze() function removes the first dimension of size 1, resulting in a 2D array of shape (2, 3).Syntax of numpy.squeeze() in Pythonnumpy.squeeze(arr, axis=None ) Parameters: arr: Input array. axis: Selects a subset of the single-dimensional entries in the shape. If an axis is selected with shape entry greater than one, an error is raised. Return Type: The function returns a new array, which is a view of the input array with the single-dimensional entries removed from its shapeExample 1: Using the axis parameter to squeeze a specific dimension Python import numpy as np in_arr = geek.arange(9).reshape(1, 3, 3) print ("Input array : ", in_arr) out_arr = np.squeeze(in_arr, axis = 0) print ("output array : ", out_arr) print("The shapes of Input and Output array : ") print(in_arr.shape, out_arr.shape) Output : Input array : [[[0 1 2] [3 4 5] [6 7 8]]]output array : [[0 1 2] [3 4 5] [6 7 8]]The shapes of Input and Output array : (1, 3, 3) (3, 3)Explanation: The input array has shape (1, 3, 3). By specifying axis = 0, we remove the first dimension, resulting in an output array with shape (3, 3).Example 3: Error when trying to squeeze a non-singleton dimension Python import numpy as np in_arr = np.arange(9).reshape(1, 3, 3) print("Input array: ", in_arr) # Trying to squeeze the axis that doesn't have size 1 try: out_arr = np.squeeze(in_arr, axis=1) except ValueError as e: print("Error: ", e Output : Input array: [[[0 1 2] [3 4 5] [6 7 8]]] Error: cannot select an axis to squeeze out which has size not equal to oneExplanation: The input array has a shape of (1, 3, 3). The attempt to squeeze axis = 1, which corresponds to the second dimension with size 3, results in a ValueError, as the dimension is not of size 1. 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