Matplotlib Practice for Python Data Visualization

This is a technical tutorial on Python data visualization practice, centered on the official plotting library matplotlib. The book is organized into four parts—Getting Started, Advancing, Practice, and Extending—with 13 main chapters and 3 appendices. The Getting Started part begins with the basic components of a chart, explaining one by one the roles and parameters of commonly used functions such as plot(), scatter(), xlim(), xlabel(), grid(), axhline(), axvspan(), annotate(), text(), title(), and legend(); it then introduces statistical functions such as bar(), barh(), hist(), pie(), polar(), stem(), boxplot(), and errorbar(), and explains the drawing principles, suitable scenarios, and parameter adjustments for charts including bar charts, horizontal bar charts, stacked charts, block charts, line charts, histograms, pie charts, box plots, and error bar charts. The Advancing part focuses on refining chart styles, including axis tick styles, pointing and non-pointing annotations, shadow effects for titles and axis labels, and using subplot(), subplot2grid(), and subplots() to divide the canvas and share axes within the same area or across different sub-areas. The Practice part goes deeper into controlling axis position and display forms, as well as setting line and marker styles. The Extending part explains matplotlib's code-level and project-level configuration, font and text attribute settings, the use of color parameters and colormaps, and displaying and saving output graphics. The appendices add basic Python knowledge, NumPy basics, and matplotlib installation methods to help readers with no background get started. All example code uses only basic Python syntax, so readers can focus on visualization itself.