Find the number of rows and columns of a given matrix using NumPy

The shape attribute of a NumPy array returns a tuple representing the dimensions of the array. For a two-dimensional array, the shape tuple contains two values: the number of rows and the number of columns.

In this article, let's discuss methods used to find dimensions of the matrix.

How to Find the Number of Rows and Columns of a Matrix?

We can find matrix dimension with three ways:

• Using shape Attribute
• Using Indexing
• Using numpy.reshape()

Way 1: Using .shape Attribute

Here we are finding the number of rows and columns of a given matrix using Numpy.shape.

import numpy as np
matrix = np.array([[9, 9, 9], [8, 8, 8]])

dimensions = matrix.shape
rows, columns = dimensions

print("Rows:", rows)
print("Columns:", columns)

Output:

Rows: 2 
Columns: 3

Way 2: Using Indexing

Here we are finding the number of rows and columns of a given matrix using Indexing.

import numpy as np

matrix = np.array([[4, 3, 2], [8, 7, 6]])
rows = matrix.shape[0]
columns = matrix.shape[1]

print("Rows:", rows)
print("Columns:", columns)

Output:

Rows: 2 
Columns: 3

Way 3: Using numpy.reshape()

Here we are using numpy.reshape() to find number of rows and columns of a matrix, numpy.reshape in NumPy is used for changing the shape of an array without modifying the underlying data.

When using np.arange(start, stop), remember that the stop element is not included in the generated array. So, np.arange(1, 10) will create an array with values from 1 to 9 (inclusive).

import numpy as np
matrix= np.arange(1,10).reshape((3, 3))

print(matrix) # Original matrix
print(matrix.shape) # Number of rows and columns of the said matrix

Output:

[[1 2 3]
[4 5 6]
[7 8 9]]
(3,3)