BUG: Rolling apply on DataFrame with Datetime index returns NaN

doc/source/whatsnew/v0.21.0.txt

@@ -296,6 +296,8 @@ Groupby/Resample/Rolling
 - Bug in :func:`infer_freq` causing indices with 2-day gaps during the working week to be wrongly inferred as business daily (:issue:`16624`)
 - Bug in ``.rolling(...).quantile()`` which incorrectly used different defaults than :func:`Series.quantile()` and :func:`DataFrame.quantile()` (:issue:`9413`, :issue:`16211`)
 - Bug in ``groupby.transform()`` that would coerce boolean dtypes back to float (:issue:`16875`)
+- Bug in ``.rolling(...).apply(...)`` where when rolling apply on DataFrame
+  with Datetime index and min_periods set to be greater than 1 (:issue:`15305`)
 
 Sparse
 ^^^^^^

pandas/_libs/window.pyx

@@ -1427,16 +1427,17 @@ def roll_generic(ndarray[float64_t, cast=True] input,
     n = len(input)
     if n == 0:
         return input
-
+
+    counts = roll_sum(np.concatenate([np.isfinite(input).astype(float),
+                                      np.array([0.] * offset)]),
+                      win, minp, index, closed)[offset:]
+
     start, end, N, win, minp, is_variable = get_window_indexer(input, win,
                                                                minp, index,
                                                                closed,
                                                                floor=0)
-    output = np.empty(N, dtype=float)
 
-    counts = roll_sum(np.concatenate([np.isfinite(input).astype(float),
-                                      np.array([0.] * offset)]),
-                      win, minp, index, closed)[offset:]
+    output = np.empty(N, dtype=float)
 
     if is_variable:
 

pandas/tests/test_window.py

@@ -423,6 +423,27 @@ def test_constructor_with_timedelta_window(self):
             expected = df.rolling('3D').sum()
             tm.assert_frame_equal(result, expected)
 
+    def test_constructor_with_timedelta_window_and_minperiods(self):
+        # GH 15305
+        n = 10
+        df = pd.DataFrame({'value': np.arange(n)},
+                          index=pd.date_range('2015-12-24',
+                                              periods=n,
+                                              freq="D"))
+        expected_data = np.append([np.NaN, 1.], np.arange(3., 27., 3))
+        for window in [timedelta(days=3), pd.Timedelta(days=3)]:
+            result_roll_sum = df.rolling(window=window, min_periods=2).sum()
+            result_roll_generic = df.rolling(window=window,
+                                             min_periods=2).apply(sum)
+            expected = pd.DataFrame({'value': expected_data},
+                                    index=pd.date_range('2015-12-24',
+                                                        periods=n,
+                                                        freq="D"))
+            tm.assert_frame_equal(result_roll_sum, expected)
+            tm.assert_frame_equal(result_roll_generic, expected)
+            expected = df.rolling('3D', min_periods=2).sum()
+            tm.assert_frame_equal(result_roll_generic, expected)
+
     def test_numpy_compat(self):
         # see gh-12811
         r = rwindow.Rolling(Series([2, 4, 6]), window=2)