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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).

Timezone-aware Timestamps
~~~~~~~~~~~~~~~~~~~~~~~~~

NumPy's ``datetime64`` type does not support timezones. When converting a
timezone-aware Arrow timestamp array to NumPy via :meth:`~pyarrow.Array.to_numpy`,
the timezone information is silently dropped:

.. code-block:: python

   >>> arr = pa.array([1735689600, 1735689600], type=pa.timestamp("s", tz="UTC"))
   >>> arr.type
   TimestampType(timestamp[s, tz=UTC])
   >>> arr.to_numpy()
   array(['2025-01-01T00:00:00', '2025-01-01T00:00:00'],
         dtype='datetime64[s]')

If you need to preserve timezone information, there are two alternatives:

* Convert to a Pandas Series, which supports timezone-aware ``datetime64`` dtypes:

  .. code-block:: python

     >>> arr.to_pandas()
     0   2025-01-01 00:00:00+00:00
     1   2025-01-01 00:00:00+00:00
     dtype: datetime64[s, UTC]

  To get a NumPy array while preserving timezone information, use
  ``timestamp_as_object=True``:

  .. code-block:: python

     >>> arr.to_pandas(timestamp_as_object=True).to_numpy()  # doctest: +ELLIPSIS
     array([datetime.datetime(2025, 1, 1, 0, 0, tzinfo=...),
            datetime.datetime(2025, 1, 1, 0, 0, tzinfo=...)],
           dtype=object)

  .. note::

     For nested types (e.g., list arrays containing timestamps),
     ``to_pandas()`` may not preserve timezone information. Structs and maps
     do retain timezones, but lists currently do not. See
     `GH-41162 <https://github.com/apache/arrow/issues/41162>`_ for details.

* Convert to Python ``datetime`` objects, which carry ``tzinfo``:

  .. code-block:: python

     >>> arr.to_pylist()  # doctest: +SKIP
     [datetime.datetime(2025, 1, 1, 0, 0, tzinfo=zoneinfo.ZoneInfo(key='UTC')),
      datetime.datetime(2025, 1, 1, 0, 0, tzinfo=zoneinfo.ZoneInfo(key='UTC'))]