This is a Python library that binds to Apache Arrow in-memory query engine DataFusion.

Like pyspark, it allows you to build a plan through SQL or a DataFrame API against in-memory data, parquet or CSV files, run it in a multi-threaded environment, and obtain the result back in Python.

It also allows you to use UDFs and UDAFs for complex operations.

The major advantage of this library over other execution engines is that this library achieves zero-copy between Python and its execution engine: there is no cost in using UDFs, UDAFs, and collecting the results to Python apart from having to lock the GIL when running those operations.

Its query engine, DataFusion, is written in Rust, which makes strong assumptions about thread safety and lack of memory leaks.

Technically, zero-copy is achieved via the c data interface.

pip install datafusion

from datafusion import SessionContext

ctx = SessionContext()

df = ctx.read_csv("pokemon.csv")

df.show()

• User Guide — reading data, building queries, tuning execution, and distributing work.
• Extension Guide — writing a library that plugs into DataFusion in Python: table providers, functions, extension codecs, and query planners.
• Contributor Guide — changing datafusion-python itself.

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user-guide/index
extension-guide/index
contributor-guide/index
API Reference <autoapi/index>
links