DOC: Add missing punctuation to pandas documentation (#60982)

* DOC: Add missing punctuation in text.rst

* DOC: Also add missing punctuation in arrays.rst

doc/source/reference/arrays.rst

@@ -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
@@ -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:
 
@@ -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/

doc/source/user_guide/text.rst

@@ -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.
@@ -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
 
@@ -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,
@@ -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