Interactive Course

Introduction to Data Visualization in Python

Learn complex data visualization techniques using Matplotlib and seaborn.

4 hours
14 Videos
58 Exercises
124,965 Participants
5,000 XP

Create Your Free Account

By pressing Continue you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.

Loved by learners at thousands of top companies:

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.

Introduction to Data Visualization in 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. 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.

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. You’ll customize plots of stock data, generate histograms of image pixel intensities, and enhance 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. 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.
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. You’ll customize plots of stock data, generate histograms of image pixel intensities, and enhance image contrast through histogram equalization.
View Chapter Details Play Chapter Now

What do other learners have to say?

“I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden

Harvard Business School

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA

Team Anaconda

Data Science Training

This course was created in collaboration with Anaconda. With over 6 million users, the open source Anaconda Distribution is the fastest and easiest way to do Python data science and machine learning. It's the industry standard for developing, testing, and training on a single machine.

If you type... We will search for...

Press enter to see all search results

Courses

Tracks

Instructors

Introduction to Data Visualization in Python

Create Your Free Account

Loved by learners at thousands of top companies:

Course Description

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!