Distimate documentation
========================

Distimate approximates and plots common statistical functions from histograms.

Distimate can aggregate empirical distributions of random variables.
The distributions are represented as histograms with user-defined bucket edges.
This is especially useful when working with large datasets
that can be aggregated to histograms at database level.

.. plot::

    import distimate
    import matplotlib.pyplot as plt

    edges = [0, 1, 2, 5, 10, 15, 20, 50]
    values = [291, 10, 143, 190, 155, 60, 90, 34, 27]
    dist = distimate.Distribution.from_histogram(edges, values)

    plt.title(f"x̃={dist.quantile(0.5):.2f}")
    plt.xlim(0, 50)
    plt.ylim(0, 1)
    plt.plot(dist.cdf.x, dist.cdf.y, label="CDF")
    plt.plot(dist.pdf.x, dist.pdf.y, label="PDF")
    plt.legend(loc="lower right")


Features:

* Histogram creation and merging
* Probability density function (PDF)
* Cumulative distribution function (CDF or ECDF)
* Quantile (percentile) function
* Pandas integration.

Distimate is hosted at `GitHub <http://github.com/akamai/distimate>`_ and
it can be installed from `PyPI <https://pypi.org/project/distimate/>`_.
This documentation is available online at `Read the Docs <https://distimate.readthedocs.io/>`_.


Table of Contents
-----------------

.. toctree::

    install
    tutorial
    api
    develop

.. toctree::
    :maxdepth: 1

    faq
    license


Indices and tables
..................

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`