Daft Documentation
==================

Daft is a **fast and scalable Python dataframe** for complex data and machine learning workloads.

.. NOTE::

   *Daft is currently in its Beta release phase - please expect bugs and rapid improvements to the project. We welcome user feedback/feature requests in our* `Discussions forums <https://github.com/Eventual-Inc/Daft/discussions>`_.

Installing Daft
---------------

To install Daft, run this from your terminal:

``pip install getdaft``

Learn about other more advanced installation options in our :doc:`Installation Guide <install>`.


Learning Daft
-------------

:doc:`Learn Daft <learn/index>` with examples and tutorials. We cover common use-cases such as:

1. Read a CSV into a Daft dataframe
2. Work with images using the Pillow library in Daft
3. Downloading data from URLs

Use Daft
--------

Refer to the :doc:`Daft API Documentation <api_docs/index>`.

Keep up to date
---------------

Keep up to date with the latest features and fixes in Daft with our :doc:`Release Notes <https://github.com/Eventual-Inc/Daft/releases>`.

Compare Daft to alternatives
----------------------------

Evaluating Daft for a new project or system? Read more about why you should choose Daft over alternatives such as Pandas, Modin and PySpark in our :doc:`Dataframe Comparison <dataframe_comparison>`.

.. toctree::
   :hidden:
   :maxdepth: 1

   Home <self>
   install
   10-min
   learn/index
   api_docs/index
   Release Notes <https://github.com/Eventual-Inc/Daft/releases>
   Telemetry <telemetry>
   dataframe_comparison
   technical_architecture
   benchmarks/index


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

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