Introduction to Data Visualization in Python

Learn complex data visualization techniques using Matplotlib and seaborn.

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4 Hours14 Videos58 Exercises141,269 Learners
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Course Description

This course extends your existing Python skills to provide a stronger foundation in data visualization in Python. You’ll get a broader coverage of the Matplotlib library and an overview of seaborn, a package for statistical graphics. Topics covered include customizing graphics, plotting two-dimensional arrays (like pseudocolor plots, contour plots, and images), statistical graphics (like visualizing distributions and regressions), and working with time series and image data.

  1. 1

    Customizing plots


    Following a review of basic plotting with Matplotlib, this chapter delves into customizing plots using Matplotlib. This includes overlaying plots, making subplots, controlling axes, adding legends and annotations, and using different plot styles.

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    Plotting multiple graphs
    50 xp
    Multiple plots on single axis
    100 xp
    Using axes()
    100 xp
    Using subplot() (1)
    100 xp
    Using subplot() (2)
    100 xp
    Customizing axes
    50 xp
    Using xlim(), ylim()
    100 xp
    Using axis()
    100 xp
    Legends, annotations, and styles
    50 xp
    Using legend()
    100 xp
    Using annotate()
    100 xp
    Modifying styles
    100 xp
  2. 2

    Plotting 2D arrays

    This chapter showcases various techniques for visualizing two-dimensional arrays. This includes the use, presentation, and orientation of grids for representing two-variable functions followed by discussions of pseudocolor plots, contour plots, color maps, two-dimensional histograms, and images.

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  3. 3

    Statistical plots with Seaborn

    This is a high-level tour of the seaborn plotting library for producing statistical graphics in Python. We’ll cover seaborn tools for computing and visualizing linear regressions, as well as tools for visualizing univariate distributions (like strip, swarm, and violin plots) and multivariate distributions (like joint plots, pair plots, and heatmaps). We’ll also discuss grouping categories in plots.

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