Putting it all together: make, git, and a Jupyter notebook

Discussion topic: why are we using ‘make’ for the word counting exercise?

Related discussion topic: how do we decided what to put in ‘make’ and what to put in a notebook?

Adding a data analysis narrative

Rather than doing a real data analysis, we’re just going to make a word cloud from the word count information. (If this were a Real Scientific Project, this is where you’d be doing your statistics and your graphing.)

Create an empty Python2 notebook named ‘wordcloud’ in the make-lesson directory; in it, add three cells.

First, install the wordcloud library:

!pip2 install wordcloud

then write & run some parsing code to generate the wordcloud object:

def load_frequencies(filename):
     for line in open(filename):
          word, count, freq = line.split()
          if len(word) < 5:
          freq = float(freq)
          yield word, freq

import wordcloud
wc = wordcloud.WordCloud(stopwords=wordcloud.STOPWORDS)

and finally show the wordcloud object:

%matplotlib inline
from pylab import imshow, axis


When you run this all, you should see a word cloud!

Now add and commit this, & push to github. In the terminal window, do:

git add wordcloud.ipynb
git commit -m "wordcloud notebook"
git push origin master


You have done the following:

  1. encapsulated your analysis in a Makefile that generates results from raw data;
  2. put your analysis in a git repository, and posted it to github;
  3. written an analysis notebook to generate figures from the results of running the make command;

At this point, you have a fully articulated analysis workflow with both a script-based data reduction and a Jupyter Notebook graphing analysis.

Moreover, anyone who has access to your git repository (which is everybody, in this instance) can both get your workflow and (with the proper software) run it.

At this point we can take advantage of a service called ‘mybinder’ which will actually let you run your analysis and your notebooks for free on the Google cloud.

Testing it out with mybinder

Take the URL of your github repository and paste it into the top of mybinder.org; you don’t need to configure any dependencies. Now click ‘make my binder’. Once it builds, click on the black-and-red button ‘launch binder’.

This will spin up an execution container, running Jupyter notebook, that has your analysis repo in it, with everything copied from your github repo.

The ‘launch binder’ URL is something you can give to other people so they can run your analysis, too.

To read more on mybinder, see my blog post.

Next: Running in a container environment

LICENSE: This documentation and all textual/graphic site content is licensed under the Creative Commons - 0 License (CC0) -- fork @ github. Presentations (PPT/PDF) and PDFs are the property of their respective owners and are under the terms indicated within the presentation.