Demo Tight Layout

Introduction

This lab is designed to provide a step-by-step guide for using Matplotlib, a Python library for creating visualizations. Matplotlib is a popular data visualization tool in the scientific and engineering communities. This tutorial will walk you through the process of creating visualizations using Matplotlib.

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.

Importing Matplotlib

Before we can start creating visualizations, we need to import Matplotlib.

import matplotlib.pyplot as plt

Here, we are importing the pyplot module of Matplotlib and aliasing it as plt. This is a common convention in the Matplotlib community.

Creating a Simple Plot

Now that we have imported Matplotlib, we can start creating visualizations. Let's start by creating a simple plot.

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.plot(x, y)
plt.show()

Here, we are creating two lists x and y that contain the x and y values for our plot. We then use the plot function to create a line plot of x and y. Finally, we use the show function to display the plot.

Customizing the Plot

Now that we have a basic plot, let's customize it.

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.plot(x, y, color='red', marker='o')
plt.title('My Plot')
plt.xlabel('X Axis Label')
plt.ylabel('Y Axis Label')
plt.show()

Here, we have added some customizations to our plot. We have changed the color of the line to red and added circular markers to each data point. We have also added a title and axis labels to our plot.

Creating Multiple Plots

We can also create multiple plots in the same figure.

x = [1, 2, 3, 4, 5]
y1 = [2, 4, 6, 8, 10]
y2 = [1, 3, 5, 7, 9]

plt.subplot(1, 2, 1)
plt.plot(x, y1)
plt.title('Plot 1')

plt.subplot(1, 2, 2)
plt.plot(x, y2)
plt.title('Plot 2')

plt.show()

Here, we are using the subplot function to create two plots side-by-side in the same figure. We pass three arguments to subplot: the number of rows, the number of columns, and the plot number. We then create a plot in each subplot.

Saving the Plot

Once we have created a plot, we can save it to a file.

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.plot(x, y)
plt.title('My Plot')
plt.xlabel('X Axis Label')
plt.ylabel('Y Axis Label')
plt.savefig('my_plot.png')

Here, we are using the savefig function to save our plot to a file named my_plot.png.

Summary

In this lab, we have learned how to use Matplotlib to create visualizations in Python. We started by importing Matplotlib and creating a simple plot. We then customized our plot by changing the color and adding a title and axis labels. We also learned how to create multiple plots in the same figure and how to save our plots to a file.