2018-08-05 16:09:58 -04:00
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.. Licensed to the Apache Software Foundation (ASF) under one
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.. or more contributor license agreements. See the NOTICE file
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.. distributed with this work for additional information
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.. regarding copyright ownership. The ASF licenses this file
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.. to you under the Apache License, Version 2.0 (the
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.. "License"); you may not use this file except in compliance
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.. with the License. You may obtain a copy of the License at
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.. http://www.apache.org/licenses/LICENSE-2.0
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.. Unless required by applicable law or agreed to in writing,
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.. software distributed under the License is distributed on an
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.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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.. KIND, either express or implied. See the License for the
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.. specific language governing permissions and limitations
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.. under the License.
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.. _numpy_interop:
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2018-12-05 19:54:57 -06:00
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NumPy Integration
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=================
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2018-08-05 16:09:58 -04:00
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PyArrow allows converting back and forth from
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`NumPy <https://www.numpy.org/>`_ arrays to Arrow :ref:`Arrays <data.array>`.
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NumPy to Arrow
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--------------
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To convert a NumPy array to Arrow, one can simply call the :func:`pyarrow.array`
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factory function.
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2026-01-30 09:09:33 +01:00
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.. code-block:: python
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2018-08-05 16:09:58 -04:00
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>>> import numpy as np
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>>> import pyarrow as pa
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>>> data = np.arange(10, dtype='int16')
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>>> arr = pa.array(data)
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>>> arr
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2026-01-30 09:09:33 +01:00
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<pyarrow.lib.Int16Array object at ...>
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2018-08-05 16:09:58 -04:00
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[
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0,
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1,
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2,
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3,
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4,
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5,
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6,
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7,
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8,
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9
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]
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Converting from NumPy supports a wide range of input dtypes, including
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structured dtypes or strings.
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Arrow to NumPy
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--------------
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In the reverse direction, it is possible to produce a view of an Arrow Array
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for use with NumPy using the :meth:`~pyarrow.Array.to_numpy` method.
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This is limited to primitive types for which NumPy has the same physical
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representation as Arrow, and assuming the Arrow data has no nulls.
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2026-01-30 09:09:33 +01:00
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.. code-block:: python
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2018-08-05 16:09:58 -04:00
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>>> import numpy as np
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>>> import pyarrow as pa
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>>> arr = pa.array([4, 5, 6], type=pa.int32())
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>>> view = arr.to_numpy()
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>>> view
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array([4, 5, 6], dtype=int32)
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For more complex data types, you have to use the :meth:`~pyarrow.Array.to_pandas`
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method (which will construct a Numpy array with Pandas semantics for, e.g.,
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representation of null values).
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