Added note about groupby excluding Decimal columns by default

Author: pdpark

doc/source/groupby.rst

@@ -521,6 +521,28 @@ 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