From 5831b8e466b9895cc4839f1bd1fa2fae604df45a Mon Sep 17 00:00:00 2001
From: Jeff Reback <jeff@reback.net>
Date: Fri, 13 Jan 2017 21:16:50 -0500
Subject: [PATCH] BUG: no need to validate monotonicity when groupby-rolling

closes #15130
---
 doc/source/whatsnew/v0.20.0.txt |  2 +-
 pandas/core/window.py           | 49 ++++++++++++++++++++++++---------
 pandas/tests/test_window.py     | 20 ++++++++++++++
 3 files changed, 57 insertions(+), 14 deletions(-)

diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt
index f2d64a059282f..f9380e3e87f1d 100644
--- a/doc/source/whatsnew/v0.20.0.txt
+++ b/doc/source/whatsnew/v0.20.0.txt
@@ -392,7 +392,7 @@ Bug Fixes
 
 
 
-
+- Bug in ``.groupby(...).rolling(...)`` when the ``on`` is specified and using a ``DatetimeIndex`` (:issue:`15130`)
 
 
 
diff --git a/pandas/core/window.py b/pandas/core/window.py
index b7276aed506de..5700329921d16 100644
--- a/pandas/core/window.py
+++ b/pandas/core/window.py
@@ -1025,19 +1025,8 @@ def validate(self):
         if (self.is_datetimelike and
                 isinstance(self.window, (compat.string_types, DateOffset))):
 
-            # must be monotonic for on
-            if not self._on.is_monotonic:
-                formatted = self.on or 'index'
-                raise ValueError("{0} must be "
-                                 "monotonic".format(formatted))
-
-            from pandas.tseries.frequencies import to_offset
-            try:
-                freq = to_offset(self.window)
-            except (TypeError, ValueError):
-                raise ValueError("passed window {0} in not "
-                                 "compat with a datetimelike "
-                                 "index".format(self.window))
+            self._validate_monotonic()
+            freq = self._validate_freq()
 
             # we don't allow center
             if self.center:
@@ -1058,6 +1047,23 @@ def validate(self):
         elif self.window < 0:
             raise ValueError("window must be non-negative")
 
+    def _validate_monotonic(self):
+        """ validate on is monotonic """
+        if not self._on.is_monotonic:
+            formatted = self.on or 'index'
+            raise ValueError("{0} must be "
+                             "monotonic".format(formatted))
+
+    def _validate_freq(self):
+        """ validate & return our freq """
+        from pandas.tseries.frequencies import to_offset
+        try:
+            return to_offset(self.window)
+        except (TypeError, ValueError):
+            raise ValueError("passed window {0} in not "
+                             "compat with a datetimelike "
+                             "index".format(self.window))
+
     @Substitution(name='rolling')
     @Appender(SelectionMixin._see_also_template)
     @Appender(SelectionMixin._agg_doc)
@@ -1175,6 +1181,23 @@ class RollingGroupby(_GroupByMixin, Rolling):
     def _constructor(self):
         return Rolling
 
+    def _gotitem(self, key, ndim, subset=None):
+
+        # we are resetting the actual object here so our
+        # index is carried thru to the selected obj
+        # when we do the splitting for the groupby
+        if self.on is not None:
+            self._groupby.obj = self._groupby.obj.set_index(self._on)
+            self.on = None
+        return super(RollingGroupby, self)._gotitem(key, ndim, subset=subset)
+
+    def _validate_monotonic(self):
+        """
+        validate on is monotonic;
+        we don't care for groupby.rolling
+        """
+        pass
+
 
 class Expanding(_Rolling_and_Expanding):
     """
diff --git a/pandas/tests/test_window.py b/pandas/tests/test_window.py
index 22c3cee95f314..1afd5dad404c8 100644
--- a/pandas/tests/test_window.py
+++ b/pandas/tests/test_window.py
@@ -3616,3 +3616,23 @@ def agg_by_day(x):
                 agg_by_day).reset_index(level=0, drop=True)
 
             tm.assert_frame_equal(result, expected)
+
+    def test_groupby_monotonic(self):
+
+        # GH 15130
+        # we don't need to validate monotonicity when grouping
+
+        data = [
+            ['David', '1/1/2015', 100], ['David', '1/5/2015', 500],
+            ['David', '5/30/2015', 50], ['David', '7/25/2015', 50],
+            ['Ryan', '1/4/2014', 100], ['Ryan', '1/19/2015', 500],
+            ['Ryan', '3/31/2016', 50], ['Joe', '7/1/2015', 100],
+            ['Joe', '9/9/2015', 500], ['Joe', '10/15/2015', 50]]
+
+        df = pd.DataFrame(data=data, columns=['name', 'date', 'amount'])
+        df['date'] = pd.to_datetime(df['date'])
+
+        expected = df.set_index('date').groupby('name').apply(
+            lambda x: x.rolling('180D')['amount'].sum())
+        result = df.groupby('name').rolling('180D', on='date')['amount'].sum()
+        tm.assert_series_equal(result, expected)