Data Visualization

We will use the ggplot2 and Seaborn packages for plotting in R and Python respectively. Actually, ggplot2 is more mature than Seaborn which nonetheless allows to create several plots commonly used in data science. The low-level Matplotlib module will be used for low-level manipulation and further customization of plots in Python.

The Seaborn “nextgen” API

I decided to use the nextgen API of Seaborn because it has a lot of potential. Nonetheless, caution must be taken because it is in alpha release yet and everything can change. See here.

If you are comfortable with a ggplot2 syntax and grammar, then you can take a look at the plotnine Python package.

The data visualization part will cover the following topics:

• look for trends in your data with scatter plots and line plots;
• visualize distributions through bar plots, histograms, density plots, boxplots and violin plots;
• augment the information displayed in your plots with faceting;
• customize the theme of your plots.