Moved note about exclusion of Decimal columns from agg functions to automatic-exclusion-of-nuisance-columns section

Author: pdpark

doc/source/groupby.rst

@@ -521,28 +521,6 @@ a trivial example is ``df.groupby('A').agg(lambda ser: 1)``. Note that
 :meth:`~pd.core.groupby.DataFrameGroupBy.nth` can act as a reducer *or* a
 filter, see :ref:`here <groupby.nth>`.
 
-   Decimal columns are "nuisance" columns that .agg automatically excludes in groupby.
-
-   If you do wish to aggregate them you must do so explicitly:
-
-.. ipython:: python
-
-    from decimal import Decimal
-    dec = pd.DataFrame(
-            {'name': ['foo', 'bar', 'foo', 'bar'], 
-                'title': ['boo', 'far', 'boo', 'far'], 
-                'id': [123, 456, 123, 456], 
-                'int_column': [1, 2, 3, 4], 
-                'dec_column1': [Decimal('0.50'), Decimal('0.15'), Decimal('0.25'), Decimal('0.40')], 
-                'dec_column2': [Decimal('0.20'), Decimal('0.30'), Decimal('0.55'), Decimal('0.60')]
-            },
-        columns=['name','title','id','int_column','dec_column1','dec_column2']
-        )
-
-    dec.groupby(['name', 'title', 'id'], as_index=False).sum()
-
-    dec.groupby(['name', 'title', 'id'], as_index=False).agg({'dec_column1': 'sum', 'dec_column2': 'sum'})
-
 .. _groupby.aggregate.multifunc:
 
 Applying multiple functions at once
@@ -1034,6 +1012,42 @@ The returned dtype of the grouped will *always* include *all* of the categories
    s = pd.Series([1, 1, 1]).groupby(pd.Categorical(['a', 'a', 'a'], categories=['a', 'b']), observed=False).count()
    s.index.dtype
 
+.. note::
+   Decimal columns are also "nuisance" columns. They are excluded from aggregate functions automatically in groupby.
+
+   If you do wish to include decimal columns in the aggregation, you must do so explicitly:
+
+.. ipython:: python
+
+    from decimal import Decimal
+    dec = pd.DataFrame(
+                {'name': ['foo', 'bar', 'foo', 'bar'],
+                    'title': ['boo', 'far', 'boo', 'far'],
+                    'id': [123, 456, 123, 456],
+                    'int_column': [1, 2, 3, 4],
+                    'dec_column1': [Decimal('0.50'), Decimal('0.15'), Decimal('0.25'), Decimal('0.40')],
+                    'dec_column2': [Decimal('0.20'), Decimal('0.30'), Decimal('0.55'), Decimal('0.60')]
+                },
+            columns=['name','title','id','int_column','dec_column1','dec_column2']
+            )
+
+    dec.head()
+
+    dec.dtypes
+
+    # Decimal columns excluded from sum by default
+    dec.groupby(['name', 'title', 'id'], as_index=False).sum()
+
+    # Decimal columns can be sum'd explicitly by themselves...
+    dec.groupby(['name', 'title', 'id'], as_index=False)['dec_column1','dec_column2'].sum()
+
+    # ...but cannot be combined with standard data types or they will be excluded
+    dec.groupby(['name', 'title', 'id'], as_index=False)['int_column','dec_column1','dec_column2'].sum()
+
+    # Use .agg function to aggregate over standard and "nuisance" data types at the same time
+    dec.groupby(['name', 'title', 'id'], as_index=False).agg({'int_column': 'sum', 'dec_column1': 'sum', 'dec_column2': 'sum'})
+
+
 .. _groupby.missing:
 
 NA and NaT group handling