import quincy 17.2.0

[ceph.git] / ceph / src / arrow / docs / source / python / numpy.rst

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+.. Licensed to the Apache Software Foundation (ASF) under one
+.. or more contributor license agreements.  See the NOTICE file
+.. distributed with this work for additional information
+.. regarding copyright ownership.  The ASF licenses this file
+.. to you under the Apache License, Version 2.0 (the
+.. "License"); you may not use this file except in compliance
+.. with the License.  You may obtain a copy of the License at
+
+..   http://www.apache.org/licenses/LICENSE-2.0
+
+.. Unless required by applicable law or agreed to in writing,
+.. software distributed under the License is distributed on an
+.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+.. KIND, either express or implied.  See the License for the
+.. specific language governing permissions and limitations
+.. under the License.
+
+.. _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:: pycon
+
+   >>> import numpy as np
+   >>> import pyarrow as pa
+   >>> data = np.arange(10, dtype='int16')
+   >>> arr = pa.array(data)
+   >>> arr
+   <pyarrow.lib.Int16Array object at 0x7fb1d1e6ae58>
+   [
+     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:: pycon
+
+   >>> 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).