Backport PR #45612: DOC: Improve reshaping.rst (#45669)

Co-authored-by: Matthew Roeschke <[email protected]>

Author: meeseeksmachine

doc/source/user_guide/reshaping.rst

@@ -631,6 +631,10 @@ def factorize(
     cut : Discretize continuous-valued array.
     unique : Find the unique value in an array.
 
+    Notes
+    -----
+    Reference :ref:`the user guide <reshaping.factorize>` for more examples.
+
     Examples
     --------
     These examples all show factorize as a top-level method like

pandas/core/algorithms.py

@@ -7780,6 +7780,8 @@ def groupby(
         For finer-tuned control, see hierarchical indexing documentation along
         with the related stack/unstack methods.
 
+        Reference :ref:`the user guide <reshaping.pivot>` for more examples.
+
         Examples
         --------
         >>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
@@ -7938,6 +7940,10 @@ def pivot(self, index=None, columns=None, values=None) -> DataFrame:
         wide_to_long : Wide panel to long format. Less flexible but more
             user-friendly than melt.
 
+        Notes
+        -----
+        Reference :ref:`the user guide <reshaping.pivot>` for more examples.
+
         Examples
         --------
         >>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
@@ -8094,6 +8100,8 @@ def stack(self, level: Level = -1, dropna: bool = True):
         vertically on top of each other (in the index of the
         dataframe).
 
+        Reference :ref:`the user guide <reshaping.stacking>` for more examples.
+
         Examples
         --------
         **Single level columns**
@@ -8273,6 +8281,8 @@ def explode(
         result in a np.nan for that row. In addition, the ordering of rows in the
         output will be non-deterministic when exploding sets.
 
+        Reference :ref:`the user guide <reshaping.explode>` for more examples.
+
         Examples
         --------
         >>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],
@@ -8372,6 +8382,10 @@ def unstack(self, level: Level = -1, fill_value=None):
         DataFrame.stack : Pivot a level of the column labels (inverse operation
             from `unstack`).
 
+        Notes
+        -----
+        Reference :ref:`the user guide <reshaping.stacking>` for more examples.
+
         Examples
         --------
         >>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),

pandas/core/frame.py

@@ -589,6 +589,8 @@ def crosstab(
     In the event that there aren't overlapping indexes an empty DataFrame will
     be returned.
 
+    Reference :ref:`the user guide <reshaping.crosstabulations>` for more examples.
+
     Examples
     --------
     >>> a = np.array(["foo", "foo", "foo", "foo", "bar", "bar",

pandas/core/reshape/pivot.py

@@ -857,6 +857,10 @@ def get_dummies(
     --------
     Series.str.get_dummies : Convert Series to dummy codes.
 
+    Notes
+    -----
+    Reference :ref:`the user guide <reshaping.dummies>` for more examples.
+
     Examples
     --------
     >>> s = pd.Series(list('abca'))

pandas/core/reshape/reshape.py

@@ -145,6 +145,8 @@ def cut(
     Any NA values will be NA in the result. Out of bounds values will be NA in
     the resulting Series or Categorical object.
 
+    Reference :ref:`the user guide <reshaping.tile.cut>` for more examples.
+
     Examples
     --------
     Discretize into three equal-sized bins.

pandas/core/reshape/tile.py

@@ -4078,6 +4078,8 @@ def explode(self, ignore_index: bool = False) -> Series:
         result in a np.nan for that row. In addition, the ordering of elements in
         the output will be non-deterministic when exploding sets.
 
+        Reference :ref:`the user guide <reshaping.explode>` for more examples.
+
         Examples
         --------
         >>> s = pd.Series([[1, 2, 3], 'foo', [], [3, 4]])
@@ -4127,6 +4129,10 @@ def unstack(self, level=-1, fill_value=None) -> DataFrame:
         DataFrame
             Unstacked Series.
 
+        Notes
+        -----
+        Reference :ref:`the user guide <reshaping.stacking>` for more examples.
+
         Examples
         --------
         >>> s = pd.Series([1, 2, 3, 4],

pandas/core/series.py

@@ -195,6 +195,10 @@
 DataFrame.explode : Explode a DataFrame from list-like
         columns to long format.
 
+Notes
+-----
+Reference :ref:`the user guide <reshaping.melt>` for more examples.
+
 Examples
 --------
 >>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},