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DOC: Add missing punctuation to pandas documentation #60982

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Feb 24, 2025
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DOC: Add missing punctuation to pandas documentation #60982

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528b17b
DOC: Add missing punctuation in text.rst
star1327p Feb 22, 2025
5a92506
DOC: Also add missing punctuation in arrays.rst
star1327p Feb 22, 2025
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6 changes: 3 additions & 3 deletions doc/source/reference/arrays.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ is an :class:`ArrowDtype`.
support as NumPy including first-class nullability support for all data types, immutability and more.

The table below shows the equivalent pyarrow-backed (``pa``), pandas extension, and numpy (``np``) types that are recognized by pandas.
Pyarrow-backed types below need to be passed into :class:`ArrowDtype` to be recognized by pandas e.g. ``pd.ArrowDtype(pa.bool_())``
Pyarrow-backed types below need to be passed into :class:`ArrowDtype` to be recognized by pandas e.g. ``pd.ArrowDtype(pa.bool_())``.

=============================================== ========================== ===================
PyArrow type pandas extension type NumPy type
Expand Down Expand Up @@ -114,7 +114,7 @@ values.

ArrowDtype

For more information, please see the :ref:`PyArrow user guide <pyarrow>`
For more information, please see the :ref:`PyArrow user guide <pyarrow>`.

.. _api.arrays.datetime:

Expand Down Expand Up @@ -495,7 +495,7 @@ a :class:`CategoricalDtype`.
CategoricalDtype.categories
CategoricalDtype.ordered

Categorical data can be stored in a :class:`pandas.Categorical`
Categorical data can be stored in a :class:`pandas.Categorical`:

.. autosummary::
:toctree: api/
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12 changes: 6 additions & 6 deletions doc/source/user_guide/text.rst
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Expand Up @@ -13,7 +13,7 @@ Text data types

There are two ways to store text data in pandas:

1. ``object`` -dtype NumPy array.
1. ``object`` dtype NumPy array.
2. :class:`StringDtype` extension type.

We recommend using :class:`StringDtype` to store text data.
Expand All @@ -40,20 +40,20 @@ to significantly increase the performance and lower the memory overhead of
and parts of the API may change without warning.

For backwards-compatibility, ``object`` dtype remains the default type we
infer a list of strings to
infer a list of strings to:

.. ipython:: python
pd.Series(["a", "b", "c"])
To explicitly request ``string`` dtype, specify the ``dtype``
To explicitly request ``string`` dtype, specify the ``dtype``:

.. ipython:: python
pd.Series(["a", "b", "c"], dtype="string")
pd.Series(["a", "b", "c"], dtype=pd.StringDtype())
Or ``astype`` after the ``Series`` or ``DataFrame`` is created
Or ``astype`` after the ``Series`` or ``DataFrame`` is created:

.. ipython:: python
Expand Down Expand Up @@ -88,7 +88,7 @@ Behavior differences
^^^^^^^^^^^^^^^^^^^^

These are places where the behavior of ``StringDtype`` objects differ from
``object`` dtype
``object`` dtype:

l. For ``StringDtype``, :ref:`string accessor methods<api.series.str>`
that return **numeric** output will always return a nullable integer dtype,
Expand All @@ -102,7 +102,7 @@ l. For ``StringDtype``, :ref:`string accessor methods<api.series.str>`
s.str.count("a")
s.dropna().str.count("a")
Both outputs are ``Int64`` dtype. Compare that with object-dtype
Both outputs are ``Int64`` dtype. Compare that with object-dtype:

.. ipython:: python
Expand Down
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