Interpreting Git with Python


#1

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Here’s looking at you Git, data science style

This notebook is based on examples from GitPandas, a project to create handy wrappers for the GitPython library, in order to use Pandas data structures for analysing git repositories.

The basic use of this is to make GitHub-style pretty project graphs on your own processor time. It was prompted by a team working on a similar idea at Hackergarten Bern 3/2017, who used Python to parse the output of the git command, and I wanted to explore an alternative approach. Or at least just have some fun poking at Git:

Image © Jamie Hewlett via fabiola garcia

We begin with some basic initialisations. You might like to install Anaconda to set up an environment with something like:

conda create --name gitdatasci python=3 numpy pandas bokeh jupyter

…or use your own approach to set up the essential dependencies for this type of analysis. Then run:

pip install gitpandas

…and put a Git repository somewhere you can find on your filesystem, as you’ll see below. Finally:

jupyter notebook

…will open a web browser where you can make a notebook like this one - which you are free (as in beer) to download and reuse.

We start by loading the relevant modules:

import os
from gitpandas import ProjectDirectory, Repository
import numpy as np
from pandas import set_option
from bokeh.plotting import figure, output_notebook, show

set_option('display.max_rows', 500)
set_option('display.max_columns', 500)
set_option('display.width', 1000)

output_notebook() # are we happy?

Baby steps

Okay, now let’s grab a locally checked out git project - say, https://github.com/okfn/licenses.git which I’ve put in the parent folder. Create a basic repository object and do some basic checks.

path = os.path.abspath('../licenses')
p = ProjectDirectory(working_dir=path)
r = Repository(path)

# is it an empty repository? expect False

r.is_bare()
False

Data collection

Now let’s extract some useful information from that famous Git database. From the commit history, which we filter somewhat, we can get the git logs:

ignore_dirs = [
    'docs/*',
    'tests/*',
]

# gets a data frame in the format [ date, author, committer, message, lines, insertions, deletions, net(?) ]

ch = r.commit_history('HEAD', limit=None, include_globs=['*.py'], ignore_globs=ignore_dirs)
ch.head()
author committer message lines insertions deletions net
date
2014-08-11 23:11:59 Mike Linksvayer Mike Linksvayer minimally get deploy script working with renam... 24 12 12 0
2014-04-12 16:56:23 Rufus Pollock Rufus Pollock Merge branch 'master' into gh-pages\n 67 40 27 13
2013-12-06 00:26:38 Mike Linksvayer Mike Linksvayer Merge pull request #29 from enyst/gov\n\nUpdat... 2 1 1 0
2013-12-05 23:56:13 enyst enyst Re-deploy with the changes to gov licenses. (f... 2 1 1 0
2013-12-04 16:47:37 Mike Linksvayer Mike Linksvayer Merge pull request #26 from enyst/retired\n\nU... 31 23 8 15

Data wrangling

Now let us start putting dots together. What do everyones contributions look like? For that we shall group by “committer” (notice the duplicates).

attr = ch.reindex(columns=['committer', 'lines', 'insertions', 'deletions']).groupby(['committer'])
attr = attr.agg({
    'lines': np.sum,
    'insertions': np.sum,
    'deletions': np.sum
})
attr
lines insertions deletions
committer
David Read 2 1 1
Mike Linksvayer 101 57 44
Rufus Pollock 1127 329 798
david.read@okfn.org 174 162 12
dread 9 6 3
enyst 79 46 33
johnbywater 334 253 81
rgrp 421 317 104
ww@eu8.okfn.org 4 2 2
ww@styx.org 97 91 6

Data visualisation

At this point, we can sort and visualise the committers in a simple Bokeh plot, made even simpler thanks to bkcharts (though it sounds like we could try something like Holoviews next).

from bkcharts import Donut

pie_chart = Donut(attr)
show(pie_chart)

Rock and roll.

Thanks for watching!

Now go listen to some music …

Image CC BY 2.0 Brad Barrish via Wikipedia


IPython (or R) notebooks developed (in hacking events and alike)