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DOC: fix minor typos and grammar missing_data.rst #57929

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Mar 20, 2024
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DOC: fix minor typos and grammar missing_data.rst #57929

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8 changes: 4 additions & 4 deletions doc/source/user_guide/missing_data.rst
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Expand Up @@ -88,7 +88,7 @@ To detect these missing value, use the :func:`isna` or :func:`notna` methods.

.. warning::

Experimental: the behaviour of :class:`NA`` can still change without warning.
Experimental: the behaviour of :class:`NA` can still change without warning.

Starting from pandas 1.0, an experimental :class:`NA` value (singleton) is
available to represent scalar missing values. The goal of :class:`NA` is provide a
Expand All @@ -105,7 +105,7 @@ dtype, it will use :class:`NA`:
s[2]
s[2] is pd.NA

Currently, pandas does not yet use those data types using :class:`NA` by default
Currently, pandas does not use those data types using :class:`NA` by default in
a :class:`DataFrame` or :class:`Series`, so you need to specify
the dtype explicitly. An easy way to convert to those dtypes is explained in the
:ref:`conversion section <missing_data.NA.conversion>`.
Expand Down Expand Up @@ -253,8 +253,8 @@ Conversion
^^^^^^^^^^

If you have a :class:`DataFrame` or :class:`Series` using ``np.nan``,
:meth:`Series.convert_dtypes` and :meth:`DataFrame.convert_dtypes`
in :class:`DataFrame` that can convert data to use the data types that use :class:`NA`
:meth:`DataFrame.convert_dtypes` and :meth:`Series.convert_dtypes`, respectively,
will convert your data to use the nullable data types supporting :class:`NA`,
such as :class:`Int64Dtype` or :class:`ArrowDtype`. This is especially helpful after reading
in data sets from IO methods where data types were inferred.

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