NumPy: Count the number of dimensions, number of elements and number of bytes for each element in a given array

NumPy: Array Object Exercise-133 with Solution

Write a NumPy program to count the number of dimensions, number of elements and number of bytes for each element in a given array.

Sample Solution:

Python Code:

# Importing the NumPy library and aliasing it as 'np'
import numpy as np

# Printing a message indicating the original array will be displayed
print("\nOriginal arrays:")

# Creating a NumPy array 'x' containing two nested lists with integers from 0 to 23 and shaping it into a 2x12 array
x = np.array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11],
              [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]])

# Displaying the original 2D array 'x'
print(x)

# Printing a message indicating the number of dimensions of the array using x.ndim
print("\nNumber of dimensions:")
print(x.ndim)

# Printing a message indicating the total number of elements in the array using x.size
print("Number of elements:")
print(x.size)

# Printing a message indicating the number of bytes for each element in the array using x.itemsize
print("Number of bytes for each element in the said array:")
print(x.itemsize)

Sample Output:

Original arrays:
[[ 0  1  2  3  4  5  6  7  8  9 10 11]
 [12 13 14 15 16 17 18 19 20 21 22 23]]

Number of dimensions:
2
Number of elements:
24
Number of bytes for each element in the said array:
8

Explanation:

In the above exercise -

x = np.array([...]): This line creates a 2-dimensional NumPy array x with the given elements.

print(x.ndim): It prints the number of dimensions of the array x.

print(x.size): It prints the total number of elements in the array x. In this case, it will print 24 because x has 2 rows and 12 columns (2 * 12 = 24 elements).

print(x.itemsize): It prints the size in bytes of each element of the array x. In this case, since the elements are integers, it will typically print 4 or 8 depending on the default integer size of the platform (32-bit or 64-bit).

Pictorial Presentation:

Python-Numpy Code Editor:

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