Besides the most basic scatter and line plots, Matplotlib provides a versatile collection of plot types to serve different data visualization purposes. In this section, we will introduce the rationale of plot type selection and the usage of each type.
More native Matplotlib plot types
by Allen Yu, Claire Chung, Aldrin Yim
Matplotlib for Python Developers
- Title Page
- Copyright and Credits
- Dedication
- Packt Upsell
- Contributors
- Preface
- Introduction to Matplotlib
- Getting Started with Matplotlib
- Decorating Graphs with Plot Styles and Types
- Advanced Matplotlib
- Drawing Subplots
- Initiating a figure with plt.figure()
- Initiating subplots as axes with plt.subplot()
- Adding subplots with plt.figure.add_subplot()
- Initiating an array of subplots with plt.subplots()
- Setting the margin with plt.tight_layout()
- Aligning subplots of different dimensions with plt.subplot2grid()
- Drawing inset plots with fig.add_axes()
- Adjusting subplot dimensions post hoc with plt.subplots_adjust
- Adjusting axes and ticks
- More on Pandas-Matplotlib integration
- Expanding plot types with Seaborn 
- Image plotting
- Financial plotting
- 3D plots with Axes3D
- Geographical plotting
- Summary
- Drawing Subplots
- Embedding Matplotlib in GTK+3
- Embedding Matplotlib in Qt 5
- Embedding Matplotlib in wxWidgets Using wxPython
- Integrating Matplotlib with Web Applications
- Matplotlib in the Real World
- Integrating Data Visualization into the Workflow
More native Matplotlib plot types
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