The pandas library itself performs does not perform data visualization. To perform this, pandas tightly integrates with other robust visualization libraries that are part of the Python ecosystem. The most common of these integrations is with matplotlib. This chapter will, therefore, focus its examples on matplotlib, but we will also point you to other possible libraries to try on your own. Two of these are worth mentioning.
Seaborn is another Python visualization library which is also based on matplotlib. It provides a high-level interface for rendering attractive statistical graphics. It has native support for NumPy and pandas data structures. The goal of Seaborn is to create matplotlib graphs that look a lot less scientific in nature. To learn about Seaborn, please visit the site at http://seaborn.pydata.org/index.html.
While both Seaborn and matplotlib...
