As we have seen in the previous paragraph, pandas can speed up exploring data visually since it wraps up into single commands what would have required an entire code snippet using matplotlib. The idea behind this is that unless you need to tailor and configure a special visualization, using a wrapper can allow you to create standard graphics faster.
Apart from pandas, other packages assemble low-level instructions from matplotlib into more user-friendly commands for specific representations and usage:
- Seaborn is a package that extends your visualization capabilities by providing you with a set of statistical plots useful for finding out trends and discriminating groups
- ggplot is a port of a popular R library, ggplot2 (ggplot2.tidyverse.org), based on the visualization grammar proposed in Leland Wilkinson's book, Grammar of Graphics...
