diff --git a/doc/source/getting_started/comparison/comparison_with_sql.rst b/doc/source/getting_started/comparison/comparison_with_sql.rst
index 6a03c06de3699..e3c8f8f5ccbcd 100644
--- a/doc/source/getting_started/comparison/comparison_with_sql.rst
+++ b/doc/source/getting_started/comparison/comparison_with_sql.rst
@@ -75,7 +75,7 @@ Filtering in SQL is done via a WHERE clause.
     LIMIT 5;
 
 DataFrames can be filtered in multiple ways; the most intuitive of which is using
-`boolean indexing <https://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing>`_.
+:ref:`boolean indexing <indexing.boolean>`
 
 .. ipython:: python
 
diff --git a/doc/source/user_guide/cookbook.rst b/doc/source/user_guide/cookbook.rst
index 4afdb14e5c39e..e51b5c9097951 100644
--- a/doc/source/user_guide/cookbook.rst
+++ b/doc/source/user_guide/cookbook.rst
@@ -794,8 +794,7 @@ The :ref:`Resample <timeseries.resampling>` docs.
 `Time grouping with some missing values
 <https://stackoverflow.com/questions/33637312/pandas-grouper-by-frequency-with-completeness-requirement>`__
 
-`Valid frequency arguments to Grouper
-<https://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__
+Valid frequency arguments to Grouper :ref:`Timeseries <timeseries.offset_aliases>`
 
 `Grouping using a MultiIndex
 <https://stackoverflow.com/questions/41483763/pandas-timegrouper-on-multiindex>`__