Shape Manipulation in NumPy
Example 1: Shape of Arrays
Printing the shape of the multidimensional array. In this example, two NumPy arrays arr1
and arr2
are created, representing a 2D array and a 3D array, respectively. The shape of each array is printed, revealing their dimensions and sizes along each dimension.
Python3
import numpy as npy # creating a 2-d array arr1 = npy.array([[ 1 , 3 , 5 , 7 ], [ 2 , 4 , 6 , 8 ]]) # creating a 3-d array arr2 = npy.array([[[ 1 , 2 ], [ 3 , 4 ]], [[ 5 , 6 ], [ 7 , 8 ]]]) print (arr1.shape) print (arr2.shape) |
Output:
(2, 4)
(2, 2,2)
Example 2: Shape of Array Using ndim
In this example, we are creating an array using ndmin using a vector with values 2,4,6,8,10 and verifying the value of last dimension.
python3
import numpy as npy # creating an array of 6 dimension # using ndim arr = npy.array([ 2 , 4 , 6 , 8 , 10 ], ndmin = 6 ) # printing array print (arr) # verifying the value of last dimension # as 5 print ( 'shape of an array :' , arr.shape) |
Output:
[[[[[[ 2 4 6 8 10]]]]]]
shape of an array : (1, 1, 1, 1, 1, 5)
Example 3: Shape of Array of Tuples
In this example, we’ll create a NumPy array where each element is a tuple. We’ll also demonstrate how to determine the shape of such an array.
Python3
import numpy as np # Create an array of tuples array_of_tuples = np.array([( 1 , 2 ), ( 3 , 4 ), ( 5 , 6 ), ( 7 , 8 )]) # Display the array print ( "Array of Tuples:" ) print (array_of_tuples) # Determine and display the shape shape = array_of_tuples.shape print ( "\nShape of Array:" , shape) |
Output:
Array of Tuples:
[[1 2]
[3 4]
[5 6]
[7 8]]
Shape of Array: (4, 2)
NumPy Array Shape
The shape of an array can be defined as the number of elements in each dimension. Dimension is the number of indices or subscripts, that we require in order to specify an individual element of an array.