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. 2

    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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    Plotting time-series data
    50 xp
    Read data with a time index
    100 xp
    Plot time-series data
    100 xp
    Using a time index to zoom in
    100 xp
    Plotting time-series with different variables
    50 xp
    Plotting two variables
    100 xp
    Defining a function that plots time-series data
    100 xp
    Using a plotting function
    100 xp
    Annotating time-series data
    50 xp
    Annotating a plot of time-series data
    100 xp
    Plotting time-series: putting it all together
    100 xp
  3. 3

    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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    Quantitative comparisons: bar-charts
    50 xp
    Bar chart
    100 xp
    Stacked bar chart
    100 xp
    Quantitative comparisons: histograms
    50 xp
    Creating histograms
    100 xp
    "Step" histogram
    100 xp
    Statistical plotting
    50 xp
    Adding error-bars to a bar chart
    100 xp
    Adding error-bars to a plot
    100 xp
    Creating boxplots
    100 xp
    Quantitative comparisons: scatter plots
    50 xp
    Simple scatter plot
    100 xp
    Encoding time by color
    100 xp
  4. 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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    Preparing your figures to share with others
    50 xp
    Selecting a style for printing
    50 xp
    Switching between styles
    100 xp
    Saving your visualizations
    50 xp
    Saving a file several times
    100 xp
    Save a figure with different sizes
    100 xp
    Automating figures from data
    50 xp
    Unique values of a column
    100 xp
    Automate your visualization
    100 xp
    Where to go next
    50 xp

In the following tracks

Data Analyst Data Scientist Data Visualization

Datasets

Seattle weatherAustin weatherClimate dataMedals by countryMedalist weights

Collaborators

Chester IsmayAmy Peterson

Prerequisites

Introduction to Python
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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