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IPython Interactive Computing and Visualization Cookbook

You're reading from   IPython Interactive Computing and Visualization Cookbook Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and data science in the Jupyter Notebook

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781785888632
Length 548 pages
Edition 2nd Edition
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Author (1):
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Cyrille Rossant Cyrille Rossant
Author Profile Icon Cyrille Rossant
Cyrille Rossant
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Table of Contents (17) Chapters Close

Preface 1. A Tour of Interactive Computing with Jupyter and IPython FREE CHAPTER 2. Best Practices in Interactive Computing 3. Mastering the Jupyter Notebook 4. Profiling and Optimization 5. High-Performance Computing 6. Data Visualization 7. Statistical Data Analysis 8. Machine Learning 9. Numerical Optimization 10. Signal Processing 11. Image and Audio Processing 12. Deterministic Dynamical Systems 13. Stochastic Dynamical Systems 14. Graphs, Geometry, and Geographic Information Systems 15. Symbolic and Numerical Mathematics Index

Creating interactive web visualizations with Bokeh and HoloViews


Bokeh (http://bokeh.pydata.org/en/latest/) is a library for creating rich interactive visualizations in a browser. Plots are designed in Python, and they are rendered in the browser.

In this recipe, we will give a few short examples of interactive Bokeh figures in the Jupyter Notebook. We will also introduce HoloViews, which provides a high-level API for Bokeh and other plotting libraries.

Getting ready

Bokeh should be installed by default in Anaconda, but you can also install it manually by typing conda install bokeh in a Terminal.

To install HoloViews, type conda install -c ioam holoviews.

How to do it...

  1. Let's import NumPy and Bokeh. We need to call output_notebook() to tell Bokeh to render plots in the Jupyter Notebook.

    >>> import numpy as np
        import pandas as pd
        import bokeh
        import bokeh.plotting as bkh
        bkh.output_notebook()
  2. Let's create a scatter plot of random data:

    >>> f = bkh.figure(width=600...
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