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pandas and third-party libraries can extend NumPy's type system (see :ref:`extending.extension-types`).
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The top-level :meth:`array` method can be used to create a new array, which may be
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array
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.. _api.arrays.arrow:
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PyArrow
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-------
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.. warning::
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This feature is experimental, and the API can change in a future release without warning.
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The :class:`arrays.ArrowExtensionArray` is backed by a :external+pyarrow:py:class:`pyarrow.ChunkedArray` with a
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:external+pyarrow:py:class:`pyarrow.DataType` instead of a NumPy array and data type. The ``.dtype`` of a :class:`arrays.ArrowExtensionArray`
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is an :class:`ArrowDtype`.
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`Pyarrow <https://arrow.apache.org/docs/python/index.html>`__ provides similar array and `data type <https://arrow.apache.org/docs/python/api/datatypes.html>`__
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support as NumPy including first-class nullability support for all data types, immutability and more.
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.. note::
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For string types (``pyarrow.string()``, ``string[pyarrow]``), PyArrow support is still facilitated
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by :class:`arrays.ArrowStringArray` and ``StringDtype("pyarrow")``. See the :ref:`string section <api.arrays.string>`
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below.
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While individual values in an :class:`arrays.ArrowExtensionArray` are stored as a PyArrow objects, scalars are **returned**
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as Python scalars corresponding to the data type, e.g. a PyArrow int64 will be returned as Python int, or :class:`NA` for missing
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