Interactive Course

Introduction to Matplotlib

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

  • 4 hours
  • 14 Videos
  • 44 Exercises
  • 5,146 Participants
  • 3,600 XP

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Course Description

A picture is worth a thousand words. Visualizing data in plots and figures exposes the underlying patterns in the data and provides insights. Good visualizations also help you to communicate about your data with others. So good visualizations are useful both to data analysts and to 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 different kinds of 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.

  2. Plotting time-series

    Time-series data are data that are recorded. Visualizing this kind of data helps clarify trends and understand relationships between different data.

  3. Quantitative comparisons and statistical visualizations

    Visualizations can be used to compare different data in a quantitative manner. This chapter shows several methods for quantitative visualizations.

  4. Sharing visualizations with others

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

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Ariel Rokem
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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