Introduction to Data Visualization with Matplotlib
Learn how to create, customize, and share data visualizations using Matplotlib.
- 4 hours
- 14 Videos
- 44 Exercises
- 25,094 Participants
- 3,600 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.
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1
Introduction to Matplotlib
FreeThis chapter introduces the Matplotlib visualization library and demonstrates how to use it with data.
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Quantitative comparisons and statistical visualizations
Visualizations can be used to compare data in a quantitative manner. This chapter explains several methods for quantitative visualizations.
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Plotting time-series
Time series data is data that is recorded. Visualizing this type of data helps clarify trends and illuminates relationships between data.
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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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1
Introduction to Matplotlib
FreeThis chapter introduces the Matplotlib visualization library and demonstrates how to use it with data.
-
Plotting time-series
Time series data is data that is recorded. Visualizing this type of data helps clarify trends and illuminates relationships between data.
-
Quantitative comparisons and statistical visualizations
Visualizations can be used to compare data in a quantitative manner. This chapter explains several methods for quantitative visualizations.
-
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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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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Collaborators
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Chester Ismay
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Amy Peterson