docs/source/user-guide/io/arrow.rst

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Arrow
=====

DataFusion implements the
`Apache Arrow PyCapsule interface <https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html>`_
for importing and exporting DataFrames with zero copy. With this feature, any Python
project that implements this interface can share data back and forth with DataFusion
with zero copy.

We can demonstrate using `pyarrow <https://arrow.apache.org/docs/python/index.html>`_.

Importing to DataFusion
-----------------------

Here we will create an Arrow table and import it to DataFusion.

To import an Arrow table, use :py:func:`datafusion.context.SessionContext.from_arrow`.
This will accept any Python object that implements
`__arrow_c_stream__ <https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html#arrowstream-export>`_
or `__arrow_c_array__ <https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html#arrowarray-export>`_
and returns a ``StructArray``. Common pyarrow sources you can use are:

- `Array <https://arrow.apache.org/docs/python/generated/pyarrow.Array.html>`_ (but it must return a Struct Array)
- `Record Batch <https://arrow.apache.org/docs/python/generated/pyarrow.RecordBatch.html>`_
- `Record Batch Reader <https://arrow.apache.org/docs/python/generated/pyarrow.RecordBatchReader.html>`_
- `Table <https://arrow.apache.org/docs/python/generated/pyarrow.Table.html>`_

.. ipython:: python

    from datafusion import SessionContext
    import pyarrow as pa

    data = {"a": [1, 2, 3], "b": [4, 5, 6]}
    table = pa.Table.from_pydict(data)

    ctx = SessionContext()
    df = ctx.from_arrow(table)
    df

Exporting from DataFusion
-------------------------

DataFusion DataFrames implement ``__arrow_c_stream__`` PyCapsule interface, so any
Python library that accepts these can import a DataFusion DataFrame directly.

Invoking ``__arrow_c_stream__`` triggers execution of the underlying query, but
batches are yielded incrementally rather than materialized all at once in memory.
Consumers can process the stream as it arrives. The stream executes lazily,
letting downstream readers pull batches on demand.


.. ipython:: python

    from datafusion import col, lit

    df = df.select((col("a") * lit(1.5)).alias("c"), lit("df").alias("d"))
    pa.table(df)