DOC: Fixing some doc warnings (#26786)

Author: datapythonista

doc/source/reference/series.rst

@@ -472,6 +472,7 @@ strings and apply several methods to it. These can be accessed like
        Series.str
        Series.cat
        Series.dt
+       Series.sparse
        Index.str
 
 .. _api.series.cat:

doc/source/user_guide/missing_data.rst

@@ -466,7 +466,7 @@ at the new values.
 .. _missing_data.interp_limits:
 
 Interpolation Limits
-^^^^^^^^^^^^^^^^^^^^
+--------------------
 
 Like other pandas fill methods, :meth:`~DataFrame.interpolate` accepts a ``limit`` keyword
 argument. Use this argument to limit the number of consecutive ``NaN`` values

doc/source/user_guide/reshaping.rst

@@ -705,7 +705,7 @@ handling of NaN:
     you can use  ``df["cat_col"] = pd.Categorical(df["col"])`` or
     ``df["cat_col"] = df["col"].astype("category")``. For full docs on :class:`~pandas.Categorical`,
     see the :ref:`Categorical introduction <categorical>` and the
-    :ref:`API documentation <api.categorical>`.
+    :ref:`API documentation <api.arrays.categorical>`.
 
 Examples
 --------

doc/source/whatsnew/v0.20.0.rst

@@ -396,7 +396,7 @@ IntervalIndex
 
 pandas has gained an ``IntervalIndex`` with its own dtype, ``interval`` as well as the ``Interval`` scalar type. These allow first-class support for interval
 notation, specifically as a return type for the categories in :func:`cut` and :func:`qcut`. The ``IntervalIndex`` allows some unique indexing, see the
-:ref:`docs <indexing.intervallindex>`. (:issue:`7640`, :issue:`8625`)
+:ref:`docs <advanced.intervalindex>`. (:issue:`7640`, :issue:`8625`)
 
 .. warning::
 
@@ -1564,7 +1564,7 @@ Removal of prior version deprecations/changes
 
 - The ``pandas.rpy`` module is removed. Similar functionality can be accessed
   through the `rpy2 <https://rpy2.readthedocs.io/>`__ project.
-  See the :ref:`R interfacing docs <rpy>` for more details.
+  See the :ref:`R interfacing docs <https://pandas.pydata.org/pandas-docs/version/0.20/r_interface.html>`__ for more details.
 - The ``pandas.io.ga`` module with a ``google-analytics`` interface is removed (:issue:`11308`).
   Similar functionality can be found in the `Google2Pandas <https://github.com/panalysis/Google2Pandas>`__ package.
 - ``pd.to_datetime`` and ``pd.to_timedelta`` have dropped the ``coerce`` parameter in favor of ``errors`` (:issue:`13602`)

doc/source/whatsnew/v0.25.0.rst

@@ -72,7 +72,7 @@ column selection, the values can just be the functions to apply
 This type of aggregation is the recommended alternative to the deprecated behavior when passing
 a dict to a Series groupby aggregation (:ref:`whatsnew_0200.api_breaking.deprecate_group_agg_dict`).
 
-See :ref:`_groupby.aggregate.named` for more.
+See :ref:`groupby.aggregate.named` for more.
 
 .. _whatsnew_0250.enhancements.other:
 

pandas/core/arrays/datetimelike.py

@@ -212,7 +212,7 @@ class TimelikeOps:
 
             .. versionadded:: 0.24.0
 
-        nonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta, \
+        nonexistent : 'shift_forward', 'shift_backward', 'NaT', timedelta, \
 default 'raise'
             A nonexistent time does not exist in a particular timezone
             where clocks moved forward due to DST.

pandas/core/generic.py

@@ -10283,8 +10283,8 @@ def _doc_parms(cls):
 Returns
 -------
 %(name1)s or %(name2)s (if level specified)\
-%(see_also)s
-%(examples)s\
+%(see_also)s\
+%(examples)s
 """
 
 _num_ddof_doc = """
@@ -10427,7 +10427,8 @@ def _doc_parms(cls):
 
 Returns
 -------
-%(name1)s or %(name2)s\n
+%(name1)s or %(name2)s
+
 See Also
 --------
 core.window.Expanding.%(accum_func_name)s : Similar functionality
@@ -10788,10 +10789,10 @@ def _doc_parms(cls):
 Series([], dtype: bool)
 """
 
-_shared_docs['stat_func_example'] = """\
+_shared_docs['stat_func_example'] = """
+
 Examples
 --------
-
 >>> idx = pd.MultiIndex.from_arrays([
 ...     ['warm', 'warm', 'cold', 'cold'],
 ...     ['dog', 'falcon', 'fish', 'spider']],
@@ -10820,8 +10821,7 @@ def _doc_parms(cls):
 blooded
 warm    {level_output_0}
 cold    {level_output_1}
-Name: legs, dtype: int64
-"""
+Name: legs, dtype: int64"""
 
 _sum_examples = _shared_docs['stat_func_example'].format(
     stat_func='sum',
@@ -10831,6 +10831,7 @@ def _doc_parms(cls):
     level_output_1=8)
 
 _sum_examples += """
+
 By default, the sum of an empty or all-NA Series is ``0``.
 
 >>> pd.Series([]).sum()  # min_count=0 is the default
@@ -10849,8 +10850,7 @@ def _doc_parms(cls):
 0.0
 
 >>> pd.Series([np.nan]).sum(min_count=1)
-nan
-"""
+nan"""
 
 _max_examples = _shared_docs['stat_func_example'].format(
     stat_func='max',
@@ -10879,10 +10879,10 @@ def _doc_parms(cls):
 DataFrame.min : Return the minimum over the requested axis.
 DataFrame.max : Return the maximum over the requested axis.
 DataFrame.idxmin : Return the index of the minimum over the requested axis.
-DataFrame.idxmax : Return the index of the maximum over the requested axis.
-"""
+DataFrame.idxmax : Return the index of the maximum over the requested axis."""
+
+_prod_examples = """
 
-_prod_examples = """\
 Examples
 --------
 By default, the product of an empty or all-NA Series is ``1``
@@ -10902,8 +10902,7 @@ def _doc_parms(cls):
 1.0
 
 >>> pd.Series([np.nan]).prod(min_count=1)
-nan
-"""
+nan"""
 
 _min_count_stub = """\
 min_count : int, default 0