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Learning IPython for Interactive Computing and Data Visualization - Second Edition by Cyrille Rossant

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Chapter 4. Interactive Plotting and Graphical Interfaces

In the previous chapter, we created a few plots with matplotlib and seaborn. In this chapter, we'll look at these libraries in more detail. We'll also discuss some of the many other visualization libraries in Python, with a particular emphasis on those that integrate with the Jupyter Notebook.

We will cover the following topics:

  • Choosing a plotting backend
  • matplotlib and seaborn essentials
  • Image processing
  • Further plotting and visualization libraries

Choosing a plotting backend

There are different ways to display a plot in the Jupyter Notebook.

Inline plots

So far, we have created plots within the Notebook using the matplotlib inline mode. This is activated with the %matplotlib inline magic command ...

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