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 `_. 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 `. Learning Daft ------------- :doc:`Learn Daft ` 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 `. Keep up to date --------------- Keep up to date with the latest features and fixes in Daft with our :doc:`Release Notes `. 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 `. .. toctree:: :hidden: :maxdepth: 1 Home install 10-min learn/index api_docs/index Release Notes Telemetry dataframe_comparison technical_architecture benchmarks/index .. Indices and tables .. ================== .. * :ref:`genindex` .. * :ref:`modindex` .. * :ref:`search`