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Introduction to Data Visualization with Matplotlib

78 reviews

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

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4 Hours14 Videos44 Exercises
152,216 Learners

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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


    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 Scientist with PythonData Scientist Professional with PythonData Visualization with Python


Collaborator's avatar
Chester Ismay
Collaborator's avatar
Amy Peterson
Ariel Rokem HeadshotAriel 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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  • Anya A.
    6 days

    Well explained, awesome code labs

  • Emiliano G.
    22 days

    Great course

  • Noel C.
    about 1 month


  • Mohammed B.
    about 1 month


  • Thomas S.
    2 months

    Clear, intuitive, and simple to follow instructions, along with interactive tasks. An excellent teaching platform for learning how to use Matplotlib.

"Well explained, awesome code labs"

Anya A.

"Great course"

Emiliano G.


Noel C.


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