Introduction to Data Visualization with Matplotlib

Learn how to create, customize, and share data visualizations using Matplotlib.

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4 Hours14 Videos44 Exercises79,489 Learners
3600 XP

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Course Description

Visualizing data in plots and figures exposes the underlying patterns in the data and provides insights. Good visualizations also help you communicate your data to others, and are useful to data analysts and other consumers of the data. In this course, you will learn how to use Matplotlib, a powerful Python data visualization library. Matplotlib provides the building blocks to create rich visualizations of many different kinds of datasets. You will learn how to create visualizations for different kinds of data and how to customize, automate, and share these visualizations.

  1. 1

    Introduction to Matplotlib

    Free

    This chapter introduces the Matplotlib visualization library and demonstrates how to use it with data.

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    Introduction to data visualization with Matplotlib
    50 xp
    Using the matplotlib.pyplot interface
    100 xp
    Adding data to an Axes object
    100 xp
    Customizing your plots
    50 xp
    Customizing data appearance
    100 xp
    Customizing axis labels and adding titles
    100 xp
    Small multiples
    50 xp
    Creating a grid of subplots
    50 xp
    Creating small multiples with plt.subplots
    100 xp
    Small multiples with shared y axis
    100 xp
  2. 4

    Sharing visualizations with others

    This chapter shows you how to share your visualizations with others: how to save your figures as files, how to adjust their look and feel, and how to automate their creation based on input data.

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In the following tracks

Data Analyst Data Scientist Data Visualization

Collaborators

Chester IsmayAmy Peterson
Ariel Rokem Headshot

Ariel Rokem

Senior Data Scientist, University of Washington

Ariel Rokem is a Data Scientist at the University of Washington eScience Institute. He received a PhD in neuroscience from UC Berkeley, and postdoctoral training in computational neuroimaging at Stanford. In his work, he develops data science algorithms and tools, and applies them to analysis of neural data. He is also a contributor to multiple open-source software projects in the scientific Python ecosystem.
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