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# DataFusion in Python

This is a Python library that binds to [Apache Arrow](https://arrow.apache.org/) in-memory query engine [DataFusion](https://github.com/apache/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](https://www.rust-lang.org), which makes strong assumptions about thread safety and lack of memory leaks.

Technically, zero-copy is achieved via the [c data interface](https://arrow.apache.org/docs/format/CDataInterface.html).

## Install

```shell
pip install datafusion
```

## Example

```{code-cell} ipython3
from datafusion import SessionContext

ctx = SessionContext()

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

df.show()

```

## Where to go next

- **[User Guide](user-guide/index)** — reading data, building queries, tuning
  execution, and distributing work.
- **[Extension Guide](extension-guide/index)** — writing a library that plugs
  into DataFusion in Python: table providers, functions, extension codecs, and
  query planners.
- **[Contributor Guide](contributor-guide/index)** — changing
  datafusion-python itself.

```{toctree}
:hidden: true
:maxdepth: 1

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