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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

A typical workflow with Git branching

A distributed version control system such as Git is designed for the complex and nonlinear workflows that are typical in interactive computing and exploratory research. A central concept is branching, which we will discuss in this recipe.

Getting ready

You need to work in a local Git repository for this recipe (see the previous recipe, Learning the basics of the distributed version control system Git).

How to do it...

  1. We go to the myproject repository and we create a new branch named newidea:
    $ pwd
    /home/cyrille/git/cookbook-2nd/chapter02
    $ cd myproject
    $ git branch newidea
    $ git branch
    * master
      newidea
    

    As indicated by the star *, we are still on the master branch.

  2. We switch to the newly-created newidea branch:
    $ git checkout newidea
    Switched to branch 'newidea'
    $ git branch
      Master
    * newidea
    
  3. We make changes to the code, for instance, by creating a new file:
    $ echo "print('new')" > newfile.py
    $ cat newfile.py
    print(&apos...
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