Mastering Matplotlib Scatter Plots: A Step-by-Step Guide

Introduction

This step-by-step lab will guide you through using Python's Matplotlib library to create visualizations. Matplotlib is a data visualization library that allows users to create a wide range of visualizations, including line plots, scatter plots, and histograms. In this lab, we will create a simple scatter plot 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.

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Skills Graph

%%%%{init: {'theme':'neutral'}}%%%% flowchart RL python(("`Python`")) -.-> python/BasicConceptsGroup(["`Basic Concepts`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/BasicConceptsGroup(["`Basic Concepts`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/PlottingDataGroup(["`Plotting Data`"]) python(("`Python`")) -.-> python/ControlFlowGroup(["`Control Flow`"]) python(("`Python`")) -.-> python/DataStructuresGroup(["`Data Structures`"]) python(("`Python`")) -.-> python/FunctionsGroup(["`Functions`"]) python(("`Python`")) -.-> python/ModulesandPackagesGroup(["`Modules and Packages`"]) python(("`Python`")) -.-> python/PythonStandardLibraryGroup(["`Python Standard Library`"]) python(("`Python`")) -.-> python/DataScienceandMachineLearningGroup(["`Data Science and Machine Learning`"]) python/BasicConceptsGroup -.-> python/comments("`Comments`") matplotlib/BasicConceptsGroup -.-> matplotlib/importing_matplotlib("`Importing Matplotlib`") matplotlib/BasicConceptsGroup -.-> matplotlib/figures_axes("`Understanding Figures and Axes`") matplotlib/PlottingDataGroup -.-> matplotlib/line_plots("`Line Plots`") python/ControlFlowGroup -.-> python/for_loops("`For Loops`") python/DataStructuresGroup -.-> python/tuples("`Tuples`") python/FunctionsGroup -.-> python/function_definition("`Function Definition`") 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 python/comments -.-> lab-48633{{"`Python Matplotlib Scatter Plot Creation`"}} matplotlib/importing_matplotlib -.-> lab-48633{{"`Python Matplotlib Scatter Plot Creation`"}} matplotlib/figures_axes -.-> lab-48633{{"`Python Matplotlib Scatter Plot Creation`"}} matplotlib/line_plots -.-> lab-48633{{"`Python Matplotlib Scatter Plot Creation`"}} python/for_loops -.-> lab-48633{{"`Python Matplotlib Scatter Plot Creation`"}} python/tuples -.-> lab-48633{{"`Python Matplotlib Scatter Plot Creation`"}} python/function_definition -.-> lab-48633{{"`Python Matplotlib Scatter Plot Creation`"}} python/importing_modules -.-> lab-48633{{"`Python Matplotlib Scatter Plot Creation`"}} python/standard_libraries -.-> lab-48633{{"`Python Matplotlib Scatter Plot Creation`"}} python/math_random -.-> lab-48633{{"`Python Matplotlib Scatter Plot Creation`"}} python/numerical_computing -.-> lab-48633{{"`Python Matplotlib Scatter Plot Creation`"}} python/data_visualization -.-> lab-48633{{"`Python Matplotlib Scatter Plot Creation`"}} end

Import libraries

Before we start creating our visualization, we need to import the necessary libraries. In this example, we will be using numpy and matplotlib.pyplot.

import matplotlib.pyplot as plt
import numpy as np

Create data

Next, we will create some random data to use in our visualization. In this example, we will create two arrays of random data using numpy.

## Fixing random state for reproducibility
np.random.seed(19680801)

x = np.random.rand(20)
y = 1e7 * np.random.rand(20)

Create the plot

Now that we have our data, we can create our plot using Matplotlib. In this example, we will create a scatter plot using the plot() function.

fig, ax = plt.subplots()
plt.plot(x, y, 'o')

Format the plot

To make our plot more readable, we can format it using Matplotlib's formatting functions. In this example, we will format the y-axis labels to display values in millions.

def millions(x):
    return '$%1.1fM' % (x * 1e-6)

ax.fmt_ydata = millions

Display the plot

Finally, we can display our plot using Matplotlib's show() function.

plt.show()

Summary

In this lab, we learned how to use Matplotlib to create a scatter plot. We also learned how to format the plot and display it. Matplotlib is a powerful library that can be used to create a wide range of visualizations.