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Introduction to Data Visualization in Python

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4 hours
5,000 XP
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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.
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  1. 1

    Customizing plots

    Free

    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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    Working with 2D arrays
    50 xp
    Generating meshes
    100 xp
    Array orientation
    50 xp
    Visualizing bivariate functions
    50 xp
    Contour & filled contour plots
    100 xp
    Modifying colormaps
    100 xp
    Visualizing bivariate distributions
    50 xp
    Using hist2d()
    100 xp
    Using hexbin()
    100 xp
    Working with images
    50 xp
    Loading, examining images
    100 xp
    Pseudocolor plot from image data
    100 xp
    Extent and aspect
    100 xp
    Rescaling pixel intensities
    100 xp
  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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    Visualizing regressions
    50 xp
    Simple linear regressions
    100 xp
    Plotting residuals of a regression
    100 xp
    Higher-order regressions
    100 xp
    Grouping linear regressions by hue
    100 xp
    Grouping linear regressions by row or column
    100 xp
    Visualizing univariate distributions
    50 xp
    Constructing strip plots
    100 xp
    Constructing swarm plots
    100 xp
    Constructing violin plots
    100 xp
    Visualizing multivariate distributions
    50 xp
    Plotting joint distributions (1)
    100 xp
    Plotting joint distributions (2)
    100 xp
    Plotting distributions pairwise (1)
    100 xp
    Plotting distributions pairwise (2)
    100 xp
    Visualizing correlations with a heatmap
    100 xp
  4. 4

    Analyzing time series and images

    This chapter ties together the skills gained so far through examining time series data and images. You’ll customize plots of stock data, generate histograms of image pixel intensities, and enhance image contrast through histogram equalization.

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    Visualizing time series
    50 xp
    Multiple time series on common axes
    100 xp
    Multiple time series slices (1)
    100 xp
    Multiple time series slices (2)
    100 xp
    Plotting an inset view
    100 xp
    Time series with moving windows
    50 xp
    Plotting moving averages
    100 xp
    Plotting moving standard deviations
    100 xp
    Interpreting moving statistics
    50 xp
    Histogram equalization in images
    50 xp
    Extracting a histogram from a grayscale image
    100 xp
    Cumulative Distribution Function from an image histogram
    100 xp
    Equalizing an image histogram
    100 xp
    Extracting histograms from a color image
    100 xp
    Extracting bivariate histograms from a color image
    100 xp
    Congratulations!
    50 xp
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GroupTraining 2 or more people?

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datasets

Automobile miles per gallonPercentage of bachelor's degrees awarded to women in the USAStocks

prerequisites

Intermediate Python
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