DOC: whatsnew & linked edits (#24649)

Author: jreback

doc/source/advanced.rst

@@ -921,7 +921,7 @@ If you need integer based selection, you should use ``iloc``:
 
    dfir.iloc[0:5]
 
-.. _advanced.intervallindex:
+.. _advanced.intervalindex:
 
 IntervalIndex
 ~~~~~~~~~~~~~

doc/source/api/arrays.rst

@@ -330,13 +330,13 @@ a :class:`pandas.api.types.CategoricalDtype`.
    :toctree: generated/
    :template: autosummary/class_without_autosummary.rst
 
-   api.types.CategoricalDtype
+   CategoricalDtype
 
 .. autosummary::
    :toctree: generated/
 
-   api.types.CategoricalDtype.categories
-   api.types.CategoricalDtype.ordered
+   CategoricalDtype.categories
+   CategoricalDtype.ordered
 
 Categorical data can be stored in a :class:`pandas.Categorical`
 

doc/source/basics.rst

@@ -64,37 +64,37 @@ NumPy's type system to add support for custom arrays
 (see :ref:`basics.dtypes`).
 
 To get the actual data inside a :class:`Index` or :class:`Series`, use
-the **array** property
+the ``.array`` property
 
 .. ipython:: python
 
    s.array
    s.index.array
 
 :attr:`~Series.array` will always be an :class:`~pandas.api.extensions.ExtensionArray`.
-The exact details of what an ``ExtensionArray`` is and why pandas uses them is a bit
+The exact details of what an :class:`~pandas.api.extensions.ExtensionArray` is and why pandas uses them is a bit
 beyond the scope of this introduction. See :ref:`basics.dtypes` for more.
 
 If you know you need a NumPy array, use :meth:`~Series.to_numpy`
-or :meth:`numpy.asarray`.
+or :meth:`numpy.ndarray.asarray`.
 
 .. ipython:: python
 
    s.to_numpy()
    np.asarray(s)
 
 When the Series or Index is backed by
-an :class:`~pandas.api.extension.ExtensionArray`, :meth:`~Series.to_numpy`
+an :class:`~pandas.api.extensions.ExtensionArray`, :meth:`~Series.to_numpy`
 may involve copying data and coercing values. See :ref:`basics.dtypes` for more.
 
 :meth:`~Series.to_numpy` gives some control over the ``dtype`` of the
-resulting :class:`ndarray`. For example, consider datetimes with timezones.
+resulting :class:`numpy.ndarray`. For example, consider datetimes with timezones.
 NumPy doesn't have a dtype to represent timezone-aware datetimes, so there
 are two possibly useful representations:
 
-1. An object-dtype :class:`ndarray` with :class:`Timestamp` objects, each
+1. An object-dtype :class:`numpy.ndarray` with :class:`Timestamp` objects, each
    with the correct ``tz``
-2. A ``datetime64[ns]`` -dtype :class:`ndarray`, where the values have
+2. A ``datetime64[ns]`` -dtype :class:`numpy.ndarray`, where the values have
    been converted to UTC and the timezone discarded
 
 Timezones may be preserved with ``dtype=object``
@@ -106,6 +106,8 @@ Timezones may be preserved with ``dtype=object``
 
 Or thrown away with ``dtype='datetime64[ns]'``
 
+.. ipython:: python
+
    ser.to_numpy(dtype="datetime64[ns]")
 
 Getting the "raw data" inside a :class:`DataFrame` is possibly a bit more
@@ -137,7 +139,7 @@ drawbacks:
 
 1. When your Series contains an :ref:`extension type <extending.extension-types>`, it's
    unclear whether :attr:`Series.values` returns a NumPy array or the extension array.
-   :attr:`Series.array` will always return an ``ExtensionArray``, and will never
+   :attr:`Series.array` will always return an :class:`~pandas.api.extensions.ExtensionArray`, and will never
    copy data. :meth:`Series.to_numpy` will always return a NumPy array,
    potentially at the cost of copying / coercing values.
 2. When your DataFrame contains a mixture of data types, :attr:`DataFrame.values` may