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numpy.shape() function will return the shape of the given array in tuple format. The shape of the array is the elements in each dimension. Dimension is the count of indices that are required to access or refer to an individual element of the array.
For example, if the array has the shape (2, 4) means that the array has 2 dimensions and for each dimension, there are 4 elements.
The Syntax of
numpy.shape() method is:
numpy.shape() accepts a single argument as shown below
- arr (array_like) – The input array for fetching the shape
The return value of the shape is a tuple that gives the length of corresponding array dimensions.
Example 1: Fetching the shape of a NumPy array
# import numpy to be used as np import numpy as np # initialize the array array_in = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) # print numpy array print("The input array is:\n\n", array_in) # shape of the array print("\nThe shape of the array is:\n\n", np.shape(array_in))
The input array is: [[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]] The shape of the array is: (3, 4)
Example 2: Creating an array using ndmin and verifying the last dimension
import numpy as np # ndim attribute is used to return an integer to fetch the dimension of the array array_in = np.array([1, 2, 3, 4], ndmin=5) # print array print("The input array is:\n", array_in) # shape of the array print("\nshape of array is :\n", np.shape(array_in))
The input array is: [[[[[1 2 3 4]]]]] shape of array is : (1, 1, 1, 1, 4)
ndim() attribute will return an integer that will contain the dimension of the array to be created.