FIX - leftover fixes from other PRs

CHANGES.rst

@@ -54,6 +54,8 @@ Changes
   :pr:`2096` by :user:`Ayesha Siddiqua <siddiqua-tamk>`.
 - The :class:`TableReport` can now be exported in markdown format with ``.markdown``.
   :pr:`2048` by :user:`Riccardo Cappuzzo <rcap107>`.
+- The minimum required version of matplotlib has been increased from 3.4.3 to 3.6.1.
+  :pr:`2159` by :user:`Riccardo Cappuzzo <rcap107>`.
 
 Bugfixes
 --------

skrub/_agg_joiner.py

@@ -188,7 +188,7 @@ class AggJoiner(TransformerMixin, BaseEstimator):
         not be reflected in the transformed output. If you need to update the
         auxiliary table, you will need to refit the transformer.
 
-        Consider using the :ref:`skrub Data Ops <_user_guide_data_ops_index>`
+        Consider using the :ref:`skrub Data Ops <user_guide_data_ops_index>`
         and a standard dataframe library (Pandas or Polars) to perform the
         aggregation instead.
 

skrub/_joiner.py

@@ -96,12 +96,11 @@ class Joiner(TransformerMixin, BaseEstimator):
         Additionally, the auxiliary table is stored in memory as part of the state
         of the transformer, which can lead to high memory usage if the auxiliary
         table is large.
-        Moreover, it is frozen in memory after fitting, which means that if the
-        auxiliary table is modified after fitting, the changes will not be reflected
+        Moreover, the auxiliary table is frozen in memory after fitting, which means
+        that if it is modified after fitting, the changes will not be reflected
         in the transformed output. If you need to update the auxiliary table, you
         will need to refit the transformer.
 
-
     Parameters
     ----------
     aux_table : dataframe

skrub/_multi_agg_joiner.py

@@ -29,8 +29,9 @@ class MultiAggJoiner(TransformerMixin, BaseEstimator):
     .. warning::
         The auxiliary table is stored in memory as part of the state of the transformer,
         which can lead to high memory usage if the auxiliary table is large.
-        Consider using the skrub Data Ops and a standard dataframe library (Pandas
-        or Polars) to perform the aggregation instead.
+        Consider using the :ref:`skrub Data Ops <user_guide_data_ops_index>` and
+        a standard dataframe library (Pandas or Polars) to perform the aggregation
+        instead.
 
     Parameters
     ----------