How to Calculate the Rank of Matrix in Python using NumPy?

Learn to compute matrix rank effortlessly in Python with NumPy. Discover step-by-step instructions on calculating matrix rank. | ProjectPro

Understanding the rank of a matrix is fundamental in data science projects. It informs decisions related to dimensionality reduction, model construction, and the solvability of linear systems, ultimately contributing to the accuracy and reliability of data analysis and predictive modeling tasks. The rank essentially represents the maximum number of linearly independent rows or columns in the matrix. This tutorial provides practical examples and a process for computing a matrix's rank using NumPy

How to Find the Rank of a Matrix in Python? 

NumPy provides a simple method numpy.linalg.matrix_rank(), to compute the rank of a matrix. This function will output the rank of the matrix. However, installing NumPy is crucial if you haven't already (pip install numpy). 

Example - Get Rank of a Matrix Using NumPy 

Let’s now understand with an example - 

Step 1 - Loading Library

We have imported numpy which is needed.

    import numpy as np

Step 2 - Creating a Matrix

We have created a matrix using an np.array with different values.

    matrixA = np.array([[1, 2, 3, 23],

                       [4, 5, 6, 25],

                       [7, 8, 9, 28],

                       [10, 11, 12, 41]])

Step 3 - Calculating the Rank of a Matrix in NumPy  

We have calculated the rank of the matrix by using the numpy function np.linalg.matrix_rank and passing the matrix through it.

    print("The Rank of a Matrix: ", np.linalg.matrix_rank(matrixA))

So, the output comes as

The Rank of a Matrix:  3 

Practice more NumPy Operations with ProjectPro! 

Learning to find matrix rank with NumPy in Python is essential for folks getting into data analysis. NumPy makes it easy with its operations.  But to get good at it, you must practice with real projects. That's where ProjectPro comes in handy. They've got over 250 projects on data science, big data, and data engineering. Working on these projects will help you learn by doing, which is the best way to get good at something. So, exploring ProjectPro's projects is an excellent place to start if you want to enhance your data analysis skills.

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