There are many advantages in creating data visualization with code so that the visualization streamlines into part of the result generation pipeline. Let's have a look at some of the key advantages of the Matplotlib library.
- 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
Merits of Matplotlib
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