14 Visualization
This chapter covers
- Installing the Matplotlib library for data visualization
- Rendering graphs and charts with pandas and Matplotlib
- Applying color templates to visualizations
Text-based DataFrame summaries are helpful, but many times, a story can best be told by a visualization. A line chart can quickly communicate a trend over time; a bar graph can distinctly identify unique categories and their counts; a pie chart can represent proportions in an easily digestible manner, and so on. Fortunately, pandas seamlessly integrates with many popular Python data visualization libraries, including Matplotlib, seaborn, and ggplot. In this chapter, we’ll learn how to use Matplotlib to render dynamic charts from our Series and DataFrame ...
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