rolling.quantile returns an interpolated result

pandas/core/window.pyx

@@ -1348,6 +1348,7 @@ def roll_quantile(ndarray[float64_t, cast=True] input, int64_t win,
         bint is_variable
         ndarray[int64_t] start, end
         ndarray[double_t] output
+        double qlow, qhigh, vlow, vhigh
 
     if quantile < 0.0 or quantile > 1.0:
         raise ValueError("quantile value {0} not in [0, 1]".format(quantile))
@@ -1391,7 +1392,19 @@ def roll_quantile(ndarray[float64_t, cast=True] input, int64_t win,
 
         if nobs >= minp:
             idx = int(quantile * <double>(nobs - 1))
-            output[i] = skiplist.get(idx)
+
+            # Exactly last point
+            if idx == nobs - 1:
+                output[i] = skiplist.get(idx)
+
+            # Interpolated percentile
+            else:
+                qlow = (<double> idx) / (<double>(nobs - 1))
+                qhigh = (<double> (idx + 1)) / (<double>(nobs - 1))
+                vlow = skiplist.get(idx)
+                vhigh = skiplist.get(idx+1)
+
+                output[i] = vlow + (vhigh - vlow)*(quantile - qlow)/(qhigh - qlow)
         else:
             output[i] = NaN
 

pandas/tests/test_window.py

@@ -1079,7 +1079,18 @@ def scoreatpercentile(a, per):
             values = np.sort(a, axis=0)
 
             idx = per / 1. * (values.shape[0] - 1)
-            return values[int(idx)]
+
+            if int(idx) == values.shape[0] - 1:
+                retval = values[-1]
+
+            else:
+                qlow = int(idx) / (values.shape[0] - 1)
+                qhig = (int(idx) + 1) / (values.shape[0] - 1)
+                vlow = values[int(idx)]
+                vhig = values[int(idx + 1)]
+                retval = vlow + (vhig - vlow)*(per - qlow)/(qhig - qlow)
+
+            return retval
 
         for q in qs:
 
@@ -3514,7 +3525,7 @@ def test_ragged_quantile(self):
 
         result = df.rolling(window='2s', min_periods=1).quantile(0.5)
         expected = df.copy()
-        expected['B'] = [0.0, 1, 1.0, 3.0, 3.0]
+        expected['B'] = [0.0, 1, 1.5, 3.0, 3.5]
         tm.assert_frame_equal(result, expected)
 
     def test_ragged_std(self):