Seaborn Learning Path
Learn everything you need to know about the popular data visualization library.
Seaborn is a Python data visualization library used for making statistical graphs. While the library can make any number of graphs, it specializes in making complex statistical graphs beautiful and simple.
The library is meant to help you explore and understand your data. Because data in Python often comes in the form of a Pandas DataFrame, Seaborn integrates nicely with Pandas. The library even handles many statistical aggregations for you in a simple, plain-English way.
Seaborn is built on top of Matplotlib. Because of this, you gain access to the entire functionality and customizability of Matplotlib. However, it provides high-level functions to help you easily produce consistently attractive visualizations.
Learn how to create figure-level plots that allow you to create all kinds of relational plots, including scatterplots and lineplots.
Create meaningful scatterplots that help explore the relationship between two variables, including how to add color, change sizes and shapes.
Create helpful lineplots that show the relationship between one variable and a continuous variable, including how to add color and customize aggregation measures.
Learn how to create figure-level plots that allow you to create all kinds of categorical plots, including barplots and boxplots.
Learn how to aggregate categorical data using bars that represent measures such as the mean and customize them using error bars and conditional coloring.
Create box and whisker plots that allow you to understand your categorical data by exploring the interquartile range, extreme values, and more.
Build on what you learned from boxplots and add more detail around relative distributions using violin plots.
Create a scatterplot that adds jitter to the dots so that you can better understand the distribution of different data points along categorical data.
Create bee and swarm plots that can be combined with box and whisker plots to give a sense of the underlying distribution.
Create bar plots that provide counts of data in their bars, giving readers good insight into the number of observations in each category.
Create point plots that provide similar functions as bar plots, but provide more information, such as error bars around a measure of central tendency.
Learn how to create figure-level plots that allow you to create all kinds of distribution plots, histograms and KDE plots.
Create incredible histograms using an easy function that allows you to add significant amounts of detail.
Build kernel density estimate plots that smooth out the underlying estimated function of your distribution.
Add marginal distributions to your plots and learn how to add them to other graphs to gain even more insight.
Learn how to build empirical cumulative distribution functions that illustrate how data are cumulatively distributed.
Building beautiful multi-grid plots that allow you to build small-multiples of charts or combine different chart types into one.
Learn how to plot pairwise relationships in a dataset so that each variable in a dataset will be plotted against each other variable in the dataset.
Learn how to add and customize titles in Seaborn plots, as well as in multi-grid plots. Also learn how to customize the axis labels of your visualizations.
Learn how to customize Seaborn legends, including changing how they appear and even move them outside of your plot.
Seaborn makes removing spines (the borders of your visualization) simple and intuitive, using this dedicate function.