This Data Visualization tutorial was created as a class assignment for CMU's Practical Data Science class. It uses Michael Waskom's fantastic seaborn statistical visualization library. The tutorial takes the form of a Jupyter .ipynb notebook. You can view the notebook here on GitHub, or view it here with nbviewer. I also recommend that you download the notebook yourself and try modifying some of the parameters to see how they can affect each of the visualization examples.
The examples used in this tutorial come from the U.S. Energy Information Agency's Residential Energy Consumption Survey (RECS). I have used unweighted individual household microdata. Since the data have not been properly weighted, take care with making any conclusions from the figures.