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.. _numpy_interop:

NumPy Integration
=================

PyArrow allows converting back and forth from
`NumPy <https://www.numpy.org/>`_ arrays to Arrow :ref:`Arrays <data.array>`.

NumPy to Arrow
--------------

To convert a NumPy array to Arrow, one can simply call the :func:`pyarrow.array`
factory function.

.. code-block:: python

   >>> import numpy as np
   >>> import pyarrow as pa
   >>> data = np.arange(10, dtype='int16')
   >>> arr = pa.array(data)
   >>> arr
   <pyarrow.lib.Int16Array object at ...>
   [
     0,
     1,
     2,
     3,
     4,
     5,
     6,
     7,
     8,
     9
   ]

Converting from NumPy supports a wide range of input dtypes, including
structured dtypes or strings.

Arrow to NumPy
--------------

In the reverse direction, it is possible to produce a view of an Arrow Array
for use with NumPy using the :meth:`~pyarrow.Array.to_numpy` method.
This is limited to primitive types for which NumPy has the same physical
representation as Arrow, and assuming the Arrow data has no nulls.

.. code-block:: python

   >>> import numpy as np
   >>> import pyarrow as pa
   >>> arr = pa.array([4, 5, 6], type=pa.int32())
   >>> view = arr.to_numpy()
   >>> view
   array([4, 5, 6], dtype=int32)

For more complex data types, you have to use the :meth:`~pyarrow.Array.to_pandas`
method (which will construct a Numpy array with Pandas semantics for, e.g.,
representation of null values).