Summary
We covered quite a lot in this chapter, starting with EDA and EDA plots, moving through visualizations and best practices for creating charts, and finishing with Plotly for making plots. Recall that for your first EDA step, it's not a bad idea to utilize one of the auto-EDA packages, such as pandas-profiling, that we covered here. With a few lines of code, we have an array of EDA statistics and charts for us to examine. But remember that we often need to create custom EDA figures and statistics, or use pandas filtering, groupby, and other methods to generate custom EDA insights.
EDA overlaps significantly with visualization, and much of EDA ends up being visualization. If our charts move beyond EDA to something we want to share with others, then we need to think more about visualization best practices. Remember in general we want to keep visualizations as simple as possible (avoid chart junk), use color and text properly, use the best methods to convey our data and...
