When many people hear of matplotlib, they think rudimentary graphs that will need to be touched up in photoshop. This tutorial aims to correct that notion by showing attendees how to generate polished data visualizations by teaching them how to exploit the functionality provided by various matplotlib libraries, such as color, ticker, cm, axes, and basemap/cartopy and modify the visualizations provided by pandas. Using various standard [exploratory data analytics graphs][1] as examples, this tutorial will teach the user how to work with the various components that go into a matplotlib figure. The matplotlib object model will be described in detail, including a discussion on subplots, layouts, and figures. Next comes a discussion on manipulating markers, lines, and color, creating custom colormaps, and customizing labels and ticks in all sorts of ways, including changing the size, font, color, and placement. The talk will wrap up with an introduction to plotting simple GIS data, such as points and polygons and how to customize Pandas visualizations. [1]: http://www.itl.nist.gov/div898/handbook/eda/section3/eda33.htm