Matplotlib | Python Data Visualization | 2D Matrix Visualization

Introduction

Matplotlib is a popular data visualization library in Python. It can be used to create a variety of different types of plots and charts. One of the functions provided by Matplotlib is matshow(), which can be used to visualize a 2D matrix or array as a color-coded image. In this lab, we will go through the steps to use matshow() to visualize a 2D array.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Skills Graph

%%%%{init: {'theme':'neutral'}}%%%% flowchart RL matplotlib(("`Matplotlib`")) -.-> matplotlib/BasicConceptsGroup(["`Basic Concepts`"]) python(("`Python`")) -.-> python/ModulesandPackagesGroup(["`Modules and Packages`"]) python(("`Python`")) -.-> python/DataScienceandMachineLearningGroup(["`Data Science and Machine Learning`"]) python(("`Python`")) -.-> python/FunctionsGroup(["`Functions`"]) matplotlib/BasicConceptsGroup -.-> matplotlib/importing_matplotlib("`Importing Matplotlib`") matplotlib/BasicConceptsGroup -.-> matplotlib/figures_axes("`Understanding Figures and Axes`") python/ModulesandPackagesGroup -.-> python/importing_modules("`Importing Modules`") python/DataScienceandMachineLearningGroup -.-> python/numerical_computing("`Numerical Computing`") python/DataScienceandMachineLearningGroup -.-> python/data_visualization("`Data Visualization`") python/FunctionsGroup -.-> python/build_in_functions("`Build-in Functions`") subgraph Lab Skills matplotlib/importing_matplotlib -.-> lab-48827{{"`Visualize 2D Arrays with Matplotlib`"}} matplotlib/figures_axes -.-> lab-48827{{"`Visualize 2D Arrays with Matplotlib`"}} python/importing_modules -.-> lab-48827{{"`Visualize 2D Arrays with Matplotlib`"}} python/numerical_computing -.-> lab-48827{{"`Visualize 2D Arrays with Matplotlib`"}} python/data_visualization -.-> lab-48827{{"`Visualize 2D Arrays with Matplotlib`"}} python/build_in_functions -.-> lab-48827{{"`Visualize 2D Arrays with Matplotlib`"}} end

Import Required Libraries

To use matshow(), we will need to import the Matplotlib library. We will also use NumPy to create a 2D array for visualization.

import matplotlib.pyplot as plt
import numpy as np

Create the 2D Array

Next, we will create a 2D array using NumPy. In this example, we will create a diagonal matrix with values ranging from 0 to 14.

a = np.diag(range(15))

Visualize the 2D Array using `matshow()`

Now we can use matshow() to visualize the 2D array as a color-coded image.

plt.matshow(a)
plt.show()

Interpret the Visualization

In the visualization generated by matshow(), each value in the 2D array is represented by a color. The color map used by default in Matplotlib is a gradient from blue to red, with blue representing the lowest values and red representing the highest values. In this particular visualization, the diagonal of the matrix is white, which indicates that the values on the diagonal are the highest in the matrix.

Summary

In this lab, we have learned how to use matshow() in Matplotlib to visualize a 2D array as a color-coded image. We first imported the required libraries, then created a 2D array using NumPy, and finally used matshow() to visualize the array. By interpreting the visualization, we can gain insights into the values and structure of the 2D array.

Visualize 2D Arrays with Matplotlib