Interactive Course

Introduction to Data Visualization with Python

Learn more complex data visualization techniques using Matplotlib and Seaborn.

4 hours
14 Videos
58 Exercises
77,041 Participants
5,000 XP

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Course Description

This course extends Intermediate Python for Data Science to provide a stronger foundation in data visualization in Python. The course provides 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 (e.g., pseudocolor plots, contour plots, images, etc.), statistical graphics (e.g., visualizing distributions & regressions), and working with time series and image data.

Prerequisites:

Introduction to Data Visualization with Python

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.

This is a high-level tour of the Seaborn plotting library for producing statistical graphics in Python. The tour covers Seaborn tools for computing and visualizing linear regressions as well as tools for visualizing univariate distributions (e.g., strip, swarm, and violin plots) and multivariate distributions (e.g., joint plots, pair plots, and heatmaps). This also includes a discussion of grouping categories in plots.

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.

This chapter ties together the skills gained so far through examining time series data and images. This involves customizing plots of stock data, generating histograms of image pixel intensities, and enhancing image contrast through histogram equalization.

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.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now
Statistical plots with Seaborn

This is a high-level tour of the Seaborn plotting library for producing statistical graphics in Python. The tour covers Seaborn tools for computing and visualizing linear regressions as well as tools for visualizing univariate distributions (e.g., strip, swarm, and violin plots) and multivariate distributions (e.g., joint plots, pair plots, and heatmaps). This also includes a discussion of grouping categories in plots.
View Chapter Details Play Chapter Now
Analyzing time series and images

This chapter ties together the skills gained so far through examining time series data and images. This involves customizing plots of stock data, generating histograms of image pixel intensities, and enhancing image contrast through histogram equalization.
View Chapter Details Play Chapter Now

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Introduction to Data Visualization with Python

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Course Description

Prerequisites:

Customizing plots

Plotting multiple graphs

Multiple plots on single axis

Using axes()

Using subplot() (1)

Using subplot() (2)

Customizing axes

Using xlim(), ylim()

Using axis()

Legends, annotations, and styles

Using legend()

Using annotate()

Modifying styles

Statistical plots with Seaborn

Visualizing regressions

Simple linear regressions

Plotting residuals of a regression

Higher-order regressions

Grouping linear regressions by hue

Grouping linear regressions by row or column

Visualizing univariate distributions

Constructing strip plots

Constructing swarm plots

Constructing violin plots

Visualizing multivariate distributions

Plotting joint distributions (1)

Plotting joint distributions (2)

Plotting distributions pairwise (1)

Plotting distributions pairwise (2)

Visualizing correlations with a heatmap

Plotting 2D arrays

Working with 2D arrays

Generating meshes

Array orientation

Visualizing bivariate functions

Contour & filled contour plots

Modifying colormaps

Visualizing bivariate distributions

Using hist2d()

Using hexbin()

Working with images

Loading, examining images

Pseudocolor plot from image data

Extent and aspect

Rescaling pixel intensities

Analyzing time series and images

Visualizing time series

Multiple time series on common axes

Multiple time series slices (1)

Multiple time series slices (2)

Plotting an inset view

Time series with moving windows

Plotting moving averages

Plotting moving standard deviations

Interpreting moving statistics

Histogram equalization in images

Extracting a histogram from a grayscale image

Cumulative Distribution Function from an image histogram

Equalizing an image histogram

Extracting histograms from a color image

Extracting bivariate histograms from a color image

Congratulations!!