Python Data Visualization Programming in Practice

This is a hands-on guide to data visualization programming using Python. Centered on matplotlib and its companion scientific computing libraries (NumPy, SciPy, PIL, etc.), it walks through the complete visualization workflow—reading data, cleaning data, plotting and presenting, and adjusting and refining—starting from setting up the development environment. The book first covers importing data from local files, remote requests, CSV and JSON, and relational databases, explaining the basics of loading and organizing data. It then uses reproducible small examples to demonstrate how to draw line charts, bar charts, scatter plots, pie charts, histograms, and other common chart types, and introduces customization of titles, axes, ticks, legends, color schemes, and styles. The advanced section further covers 3D visualization, drawing charts with images and maps (including geographic data presentation), animation and interactive effects, and how to choose the right chart to express a point based on data type and analytical purpose. The book has 8 chapters organized as "preparing the working environment, understanding data, drawing and customizing charts, learning more charts and customization, creating 3D visualization charts, drawing charts with images and maps, using the right charts to understand data, and more matplotlib knowledge," presenting visualization effects through more than 60 methods. It adopts a "problem—solution—code—explanation" recipe format, so readers can experiment by following the code or look up specific techniques as needed. It is a tool-manual-style book for beginners through intermediate users.