.. _sphx_glr_auto_examples_plot_labels.py:


======
Labels
======

In this small examples, we compare labels between the different projects:

    - scikit-learn
    - watchtower





.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/images/sphx_glr_plot_labels_001.png
            :scale: 47

    *

      .. image:: /auto_examples/images/sphx_glr_plot_labels_002.png
            :scale: 47


.. rst-class:: sphx-glr-script-out

 Out::

    Updating repository: https://api.github.com/repos/scikit-learn/scikit-learn/commits
    Params: {'since': '2017-02-01', 'per_page': 100}
    Updating repository: https://api.github.com/repos/docathon/watchtower/commits
    Params: {'since': '2017-02-01', 'per_page': 100}




|


.. code-block:: python

    import numpy as np
    import pandas as pd
    from watchtower import GithubDatabase
    import matplotlib.pyplot as plt

    projects = (("scikit-learn", "scikit-learn"),
                ("docathon", "watchtower"))
    db = GithubDatabase(verbose=True)

    update_issues = True  # Do we update the database
    since = '2017-02-01'

    for user, project in projects:
        if update_issues is True:
            db.update(user, project, since=since)

        proj = db.load(user, project)
        if proj.issues is None:
            print('No data for {}'.format(project))
            continue
        all_issues = proj.issues
        open_issues = all_issues.query('state == "open"')

        # Extract the names of the labels
        all_labels = np.array([name for names in all_issues['label_names'].values
                               for name in names])
        open_labels = np.array([name for names in open_issues['label_names'].values
                                for name in names])
        unique_labels = np.unique(all_labels)
        counts = dict()
        for label in unique_labels:
            n_instances = np.sum(all_labels == label)
            n_open = np.sum(open_labels == label)
            counts[label] = (n_instances, n_open)
        counts = pd.DataFrame(counts).T
        counts.columns = ['all', 'open']
        counts = counts.sort_values('all', ascending=False)

        fig = plt.figure(figsize=(14, 5))
        ax = fig.add_axes([0.1, 0.3, 0.8, 0.6])
        ax.grid("off")
        ax.spines['right'].set_color('none')
        ax.spines['left'].set_color('none')
        ax.spines['top'].set_color('none')
        ax.xaxis.set_ticks_position('bottom')
        ax.yaxis.set_ticks_position('left')

        for column in counts.columns:
            ixs = range(counts.shape[0])
            ax.bar(ixs, counts[column], label=column)
        ax.set_xticks(ixs)
        ax.set_xticklabels(counts.index, rotation=90, fontsize="x-small")

        yticks = ax.get_yticks()
        for l in yticks:
            ax.axhline(l, linewidth=0.75, zorder=-10, color="0.5")
        ax.set_yticks(yticks)
        ax.set_title(project, fontweight="bold")

        ax.legend()
    plt.show()

**Total running time of the script:** ( 0 minutes  3.375 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_labels.py <plot_labels.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_labels.ipynb <plot_labels.ipynb>`

.. rst-class:: sphx-glr-signature

    `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_