Improve some docstrings cross-referencing (see also) and error message (skrub-data#1688)

Co-authored-by: Riccardo Cappuzzo <7548232+rcap107@users.noreply.github.com>

Author: GaelVaroquaux

skrub/_apply_to_cols.py

@@ -18,11 +18,11 @@
     " estimators, its ``fit``, ``transform`` and ``fit_transform`` methods expect a"
     " single column (a pandas or polars Series) rather than a full dataframe. To apply"
     " this transformer to one or more columns in a dataframe, use it as a parameter in"
-    " a ``skrub.TableVectorizer`` or ``sklearn.compose.ColumnTransformer``. In the"
-    " ``ColumnTransformer``, pass a single column:"
-    " ``make_column_transformer(({class_name}(), 'col_name_1'), ({class_name}(),"
-    " 'col_name_2'))`` instead of ``make_column_transformer(({class_name}(),"
-    " ['col_name_1', 'col_name_2']))``."
+    " a ``skrub.ApplyToCols`` or a ``skrub.TableVectorizer``."
+    " To apply to all columns::\n\n"
+    "   ApplyToCol({class_name}())\n\n"
+    "To apply to selected columns::\n\n"
+    "   ApplyToCols({class_name}(), cols=['col_name_1', 'col_name_2'])"
 )
 _SINGLE_COL_PARAGRAPH = textwrap.fill(
     _SINGLE_COL_LINE, initial_indent="    ", subsequent_indent="    "
@@ -34,7 +34,7 @@ class RejectColumn(ValueError):
     """Used by single-column transformers to indicate they do not apply to a column.
 
     >>> import pandas as pd
-    >>> from skrub._to_datetime import ToDatetime
+    >>> from skrub import ToDatetime
     >>> df = pd.DataFrame(dict(a=['2020-02-02'], b=[12.5]))
     >>> ToDatetime().fit_transform(df['a'])
     0   2020-02-02
@@ -337,7 +337,7 @@ class ApplyToCols(TransformerMixin, BaseEstimator):
     The transformer can raise ``RejectColumn`` to indicate it cannot handle a
     given column.
 
-    >>> from skrub._to_datetime import ToDatetime
+    >>> from skrub import ToDatetime
     >>> df = pd.DataFrame(dict(birthday=["29/01/2024"], city=["London"]))
     >>> df
          birthday    city

skrub/_drop_uninformative.py

@@ -29,6 +29,13 @@ class DropUninformative(SingleColumnTransformer):
         Drop columns with a fraction of missing values larger than threshold. If None,
         keep the column even if all its values are missing.
 
+    See Also
+    --------
+    Cleaner :
+        A full-frame transformer (as opposed to single column) that can
+        drop columns with missing values.
+    DropCols : Dropping cols by name, dtypes, or general skrub selectors.
+
     Notes
     -----
     A column is considered to be "uninformative" if one or more of the following

skrub/_select_cols.py

@@ -19,6 +19,11 @@ class SelectCols(TransformerMixin, BaseEstimator):
         ``str``: ``"col_name"`` is the same as ``["col_name"]``. See the
         :ref:`selectors<user_guide_selectors>` user guide for more info on selectors.
 
+    See Also
+    --------
+    DropCols : Dropping cols by name, dtypes, or general skrub selectors.
+    Cleaner: Can be used to drop columns with too many NaNs.
+
     Examples
     --------
     >>> import pandas as pd
@@ -109,6 +114,11 @@ class DropCols(TransformerMixin, BaseEstimator):
         ``str``: ``"col_name"`` is the same as ``["col_name"]``. See the
         :ref:`selectors <user_guide_selectors>` user guide for more info on selectors.
 
+    See Also
+    --------
+    SelectCols : Selecting cols by name, dtypes, or general skrub selectors.
+    Cleaner: Can be used to drop columns with too many nulls (or NaNs).
+
     Examples
     --------
     >>> import pandas as pd