Pallas documentation
====================

Pallas makes querying AWS Athena easy.

It is especially valuable for analyses in Jupyter Notebook,
but it is designed to be generic and usable in any application.


Main features:

* Friendly interface to AWS Athena.
* Caching – local and remote cache for reproducible results.
* Performance – Large results are downloaded directly from S3.
* Pandas integration - Conversion to DataFrame with appropriate dtypes.
* Optional white space normalization for better caching.
* Kills queries on KeyboardInterrupt.


.. code-block:: python

    import pallas
    athena = pallas.environ_setup()
    df = athena.execute("SELECT 'Hello world!'").to_df()


Pallas is hosted at `GitHub <http://github.com/akamai/pallas>`_ and
it can be installed from `PyPI <https://pypi.org/project/pallas/>`_.

This documentation is available online at `Read the Docs <https://pallas.readthedocs.io/>`_.


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

.. toctree::

    install
    tutorial
    api
    develop
    alternatives
    license
    changelog


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

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