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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() |
| |
| ``` |
| |
| ```{toctree} |
| :hidden: true |
| :maxdepth: 1 |
| |
| user-guide/index |
| contributor-guide/index |
| API Reference <autoapi/index> |
| links |
| ``` |