github_archive_query.sql

/* Fetch data from GitHub Archive using Google's BigQuery */
select actor, repository_language, count(repository_language) as pushes
from [githubarchive:github.timeline]
where type='PushEvent'
    and repository_language != ''
    and PARSE_UTC_USEC(created_at) >= PARSE_UTC_USEC('2012-01-01 00:00:00')
    and PARSE_UTC_USEC(created_at) < PARSE_UTC_USEC('2013-01-01 00:00:00')
group by actor, repository_language;

github_language_correlations.py

'''Calculate and plot the correlations between the most popular languages on GitHub.

Details on my blog: http://datahackermd.com/2013/language-use-on-github/

'''
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


def plot_correlation(dataframe, filename, title='', corr_type=''):
    lang_names = dataframe.columns.tolist()
    tick_indices = np.arange(0.5, len(lang_names) + 0.5)
    plt.figure()
    plt.pcolor(dataframe.values, cmap='RdBu', vmin=-.2, vmax=.2)
    colorbar = plt.colorbar()
    colorbar.set_label(corr_type)
    plt.title(title)
    plt.xticks(tick_indices, lang_names, rotation='vertical')
    plt.yticks(tick_indices, lang_names)
    plt.savefig(filename)


def main():
    pushes = pd.read_csv('stacked_language_by_user.csv').pivot(
        index='actor',
        columns='repository_language',
        values='pushes')

    popular = pushes.select(
			lambda x: np.sum(pushes[x]) > 50000, axis=1).fillna(0)

    pearson_corr = popular.corr()
    plot_correlation(
        pearson_corr,
        'pearson_language_correlation.svg',
        title='2012 GitHub Language Correlations',
        corr_type='Pearson\'s Correlation')

    spearman_corr = popular.corr(method='spearman')
    plot_correlation(
        spearman_corr,
        'spearman_language_correlation.svg',
        title='2012 GitHub Language Correlations',
        corr_type='Spearman\'s Rank Correlation')


if __name__ == '__main__':
    main()