Added note about groupby excluding Decimal columns by default

Author: Patrick Park

doc/source/groupby.rst

@@ -989,6 +989,31 @@ Note that ``df.groupby('A').colname.std().`` is more efficient than
 is only interesting over one column (here ``colname``), it may be filtered 
 *before* applying the aggregation function.
 
+.. note::
+   Decimal and object columns are also "nuisance" columns. They are excluded from aggregate functions automatically in groupby.
+
+   If you do wish to include decimal or object columns in an aggregation with other non-nuisance data types, you must do so explicitly.
+
+.. ipython:: python
+
+    from decimal import Decimal
+    dec = pd.DataFrame(
+        {'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')]
+        },
+        columns=['id','int_column','dec_column']
+    )
+
+    # Decimal columns can be sum'd explicitly by themselves...
+    dec.groupby(['id'], as_index=False)['dec_column'].sum()
+
+    # ...but cannot be combined with standard data types or they will be excluded
+    dec.groupby(['id'], as_index=False)['int_column','dec_column'].sum()
+
+    # Use .agg function to aggregate over standard and "nuisance" data types at the same time
+    dec.groupby(['id'], as_index=False).agg({'int_column': 'sum', 'dec_column': 'sum'})
+
 .. _groupby.missing:
 
 NA and NaT group handling