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DOC: Highlight dtype_backend with note #51037

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DOC: Highlight dtype_backend with note
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10 changes: 10 additions & 0 deletions pandas/core/tools/numeric.py
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Expand Up @@ -91,6 +91,16 @@ def to_numeric(
set to True, nullable dtypes are used for all dtypes that have a nullable
implementation, even if no nulls are present.
.. note::
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
.. versionadded:: 2.0.0
Returns
-------
ret
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16 changes: 9 additions & 7 deletions pandas/io/clipboards.py
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Expand Up @@ -36,13 +36,15 @@ def read_clipboard(
set to True, nullable dtypes are used for all dtypes that have a nullable
implementation, even if no nulls are present.
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
This is only implemented for the ``python``
engine.
.. note::
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
This is only implemented for the ``python``
engine.
.. versionadded:: 2.0
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8 changes: 8 additions & 0 deletions pandas/io/excel/_base.py
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Expand Up @@ -278,6 +278,14 @@
set to True, nullable dtypes are used for all dtypes that have a nullable
implementation, even if no nulls are present. Dtype takes precedence if given.
.. note::
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
.. versionadded:: 2.0
Returns
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12 changes: 7 additions & 5 deletions pandas/io/feather_format.py
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Expand Up @@ -131,11 +131,13 @@ def read_feather(
set to True, nullable dtypes are used for all dtypes that have a nullable
implementation, even if no nulls are present.
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
.. note::
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
.. versionadded:: 2.0
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12 changes: 7 additions & 5 deletions pandas/io/html.py
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Expand Up @@ -1145,11 +1145,13 @@ def read_html(
set to True, nullable dtypes are used for all dtypes that have a nullable
implementation, even if no nulls are present.
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
.. note::
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
.. versionadded:: 2.0
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12 changes: 7 additions & 5 deletions pandas/io/json/_json.py
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Expand Up @@ -643,11 +643,13 @@ def read_json(
set to True, nullable dtypes are used for all dtypes that have a nullable
implementation, even if no nulls are present.
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
.. note::
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
.. versionadded:: 2.0
Expand Down
21 changes: 10 additions & 11 deletions pandas/io/orc.py
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Expand Up @@ -59,20 +59,19 @@ def read_orc(
This mirrors the original behaviour of
:external+pyarrow:py:meth:`pyarrow.orc.ORCFile.read`.
use_nullable_dtypes : bool, default False
If True, use dtypes that use ``pd.NA`` as missing value indicator
for the resulting DataFrame.
Whether or not to use nullable dtypes as default when reading data. If
set to True, nullable dtypes are used for all dtypes that have a nullable
implementation, even if no nulls are present.
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
.. note::
.. versionadded:: 2.0.0
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
.. note
Currently only ``mode.dtype_backend`` set to ``"pyarrow"`` is supported.
.. versionadded:: 2.0
**kwargs
Any additional kwargs are passed to pyarrow.
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14 changes: 8 additions & 6 deletions pandas/io/parquet.py
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Expand Up @@ -497,13 +497,15 @@ def read_parquet(
.. versionadded:: 1.2.0
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
.. note::
.. versionadded:: 2.0.0
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
.. versionadded:: 2.0.0
**kwargs
Any additional kwargs are passed to the engine.
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26 changes: 19 additions & 7 deletions pandas/io/parsers/readers.py
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Expand Up @@ -401,13 +401,15 @@
set to True, nullable dtypes are used for all dtypes that have a nullable
implementation, even if no nulls are present.

The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
This is only implemented for the ``pyarrow`` or ``python``
engines.
.. note::

The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
This is only implemented for the ``pyarrow`` or ``python``
engines.

.. versionadded:: 2.0

Expand Down Expand Up @@ -1271,6 +1273,16 @@ def read_fwf(
set to True, nullable dtypes are used for all dtypes that have a nullable
implementation, even if no nulls are present.

.. note::

The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
This is only implemented for the ``pyarrow`` or ``python``
engines.

.. versionadded:: 2.0

**kwds : optional
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12 changes: 7 additions & 5 deletions pandas/io/xml.py
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Expand Up @@ -988,11 +988,13 @@ def read_xml(
set to True, nullable dtypes are used for all dtypes that have a nullable
implementation, even if no nulls are present.
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
.. note::
The nullable dtype implementation can be configured by calling
``pd.set_option("mode.dtype_backend", "pandas")`` to use
numpy-backed nullable dtypes or
``pd.set_option("mode.dtype_backend", "pyarrow")`` to use
pyarrow-backed nullable dtypes (using ``pd.ArrowDtype``).
.. versionadded:: 2.0
Expand Down