Mastering Matplotlib Visualization for Data Analysis

Introduction

In this lab, you will learn how to use Matplotlib library to create various plots. Matplotlib is a Python library used for visualizing data. It is built on top of the NumPy and SciPy libraries and allows you to create a wide range of visualizations such as line charts, scatter plots, and bar charts.

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`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/PlottingDataGroup(["`Plotting Data`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/PlotCustomizationGroup(["`Plot Customization`"]) python(("`Python`")) -.-> python/DataStructuresGroup(["`Data Structures`"]) python(("`Python`")) -.-> python/ModulesandPackagesGroup(["`Modules and Packages`"]) python(("`Python`")) -.-> python/PythonStandardLibraryGroup(["`Python Standard Library`"]) python(("`Python`")) -.-> python/DataScienceandMachineLearningGroup(["`Data Science and Machine Learning`"]) matplotlib/BasicConceptsGroup -.-> matplotlib/importing_matplotlib("`Importing Matplotlib`") matplotlib/BasicConceptsGroup -.-> matplotlib/figures_axes("`Understanding Figures and Axes`") matplotlib/PlottingDataGroup -.-> matplotlib/line_plots("`Line Plots`") matplotlib/PlottingDataGroup -.-> matplotlib/scatter_plots("`Scatter Plots`") matplotlib/PlottingDataGroup -.-> matplotlib/bar_charts("`Bar Charts`") matplotlib/PlotCustomizationGroup -.-> matplotlib/titles_labels("`Adding Titles and Labels`") python/DataStructuresGroup -.-> python/lists("`Lists`") python/DataStructuresGroup -.-> python/tuples("`Tuples`") python/ModulesandPackagesGroup -.-> python/importing_modules("`Importing Modules`") python/ModulesandPackagesGroup -.-> python/standard_libraries("`Common Standard Libraries`") python/PythonStandardLibraryGroup -.-> python/math_random("`Math and Random`") python/DataScienceandMachineLearningGroup -.-> python/numerical_computing("`Numerical Computing`") python/DataScienceandMachineLearningGroup -.-> python/data_visualization("`Data Visualization`") subgraph Lab Skills matplotlib/importing_matplotlib -.-> lab-48980{{"`Matplotlib Visualization Techniques for Data Analysis`"}} matplotlib/figures_axes -.-> lab-48980{{"`Matplotlib Visualization Techniques for Data Analysis`"}} matplotlib/line_plots -.-> lab-48980{{"`Matplotlib Visualization Techniques for Data Analysis`"}} matplotlib/scatter_plots -.-> lab-48980{{"`Matplotlib Visualization Techniques for Data Analysis`"}} matplotlib/bar_charts -.-> lab-48980{{"`Matplotlib Visualization Techniques for Data Analysis`"}} matplotlib/titles_labels -.-> lab-48980{{"`Matplotlib Visualization Techniques for Data Analysis`"}} python/lists -.-> lab-48980{{"`Matplotlib Visualization Techniques for Data Analysis`"}} python/tuples -.-> lab-48980{{"`Matplotlib Visualization Techniques for Data Analysis`"}} python/importing_modules -.-> lab-48980{{"`Matplotlib Visualization Techniques for Data Analysis`"}} python/standard_libraries -.-> lab-48980{{"`Matplotlib Visualization Techniques for Data Analysis`"}} python/math_random -.-> lab-48980{{"`Matplotlib Visualization Techniques for Data Analysis`"}} python/numerical_computing -.-> lab-48980{{"`Matplotlib Visualization Techniques for Data Analysis`"}} python/data_visualization -.-> lab-48980{{"`Matplotlib Visualization Techniques for Data Analysis`"}} end

Import Matplotlib and Numpy libraries

Before we can start creating plots, we need to import the Matplotlib and Numpy libraries. We can do this by running the following code:

import matplotlib.pyplot as plt
import numpy as np

Create a Simple Line Plot

In this step, we will create a simple line plot using Matplotlib. We will start by generating some data to plot using the NumPy linspace() function and the cos() function. Then, we will use the plot() function to create the plot.

t = np.linspace(0.0, 1.0, 100)
s = np.cos(4 * np.pi * t) + 2

plt.plot(t, s)
plt.show()

Customize the Plot

In this step, we will customize the plot by adding labels to the x and y axes, and a title to the plot.

plt.plot(t, s)

plt.xlabel('time (s)')
plt.ylabel('Velocity (degrees/sec)')
plt.title('Cosine Wave')
plt.show()

Create a Scatter Plot

In this step, we will create a scatter plot using Matplotlib. We will start by generating some random data to plot using the NumPy random() function. Then, we will use the scatter() function to create the plot.

x = np.random.randn(100)
y = np.random.randn(100)

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

Create a Bar Chart

In this step, we will create a bar chart using Matplotlib. We will start by generating some data to plot using the NumPy random() function. Then, we will use the bar() function to create the plot.

x = ['A', 'B', 'C', 'D', 'E']
y = np.random.randint(1, 10, 5)

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

Summary

In this lab, you learned the basics of using Matplotlib to create various types of plots such as line plots, scatter plots, and bar charts. You also learned how to customize the plots by adding labels to the x and y axes, and titles to the plot. With these skills, you can now create your own plots to visualize your data.

Matplotlib Visualization Techniques for Data Analysis