ENH: add quantile method to resample

doc/source/api.rst

@@ -2352,6 +2352,7 @@ Computations / Descriptive Stats
    Resampler.std
    Resampler.sum
    Resampler.var
+   Resampler.quantile
 
 Style
 -----

doc/source/whatsnew/v0.24.0.txt

@@ -183,6 +183,7 @@ Other Enhancements
 - :class:`Series` and :class:`DataFrame` now support :class:`Iterable` in constructor (:issue:`2193`)
 - :class:`DatetimeIndex` gained :attr:`DatetimeIndex.timetz` attribute. Returns local time with timezone information. (:issue:`21358`)
 - :class:`Resampler` now is iterable like :class:`GroupBy` (:issue:`15314`).
+- :ref:`Series.resample` and :ref:`DataFrame.resample` have gained the :meth:`Resampler.quantile` (:issue:`15023`).
 
 .. _whatsnew_0240.api_breaking:
 

pandas/core/groupby/groupby.py

@@ -1168,7 +1168,12 @@ def var(self, ddof=1, *args, **kwargs):
         """
         nv.validate_groupby_func('var', args, kwargs)
         if ddof == 1:
-            return self._cython_agg_general('var', **kwargs)
+            try:
+                return self._cython_agg_general('var', **kwargs)
+            except Exception:
+                f = lambda x: x.var(ddof=ddof, **kwargs)
+                with _group_selection_context(self):
+                    return self._python_agg_general(f)
         else:
             f = lambda x: x.var(ddof=ddof, **kwargs)
             with _group_selection_context(self):

pandas/core/resample.py

@@ -766,6 +766,24 @@ def size(self):
             result = pd.Series([], index=result.index, dtype='int64')
         return result
 
+    def quantile(self, q=0.5, **kwargs):
+        """
+        Return value at the given quantile.
+
+        .. versionadded:: 0.24.0
+
+        Parameters
+        ----------
+        q : float or array-like, default 0.5 (50% quantile)
+
+        See Also
+        --------
+        Series.quantile
+        DataFrame.quantile
+        DataFrameGroupBy.quantile
+        """
+        return self._downsample('quantile', q=q, **kwargs)
+
 
 # downsample methods
 for method in ['sum', 'prod']:
@@ -1060,7 +1078,8 @@ def _downsample(self, how, **kwargs):
 
         if is_subperiod(ax.freq, self.freq):
             # Downsampling
-            return self._groupby_and_aggregate(how, grouper=self.grouper)
+            return self._groupby_and_aggregate(how, grouper=self.grouper,
+                                               **kwargs)
         elif is_superperiod(ax.freq, self.freq):
             if how == 'ohlc':
                 # GH #13083

pandas/tests/test_resample.py

@@ -41,7 +41,7 @@
 
 # The various methods we support
 downsample_methods = ['min', 'max', 'first', 'last', 'sum', 'mean', 'sem',
-                      'median', 'prod', 'var', 'ohlc']
+                      'median', 'prod', 'var', 'ohlc', 'quantile']
 upsample_methods = ['count', 'size']
 series_methods = ['nunique']
 resample_methods = downsample_methods + upsample_methods + series_methods
@@ -782,6 +782,15 @@ def test_resampler_is_iterable(self):
             assert rk == gk
             assert_series_equal(rv, gv)
 
+    def test_resample_quantile(self):
+        # GH 15023
+        s = self.create_series()
+        q = 0.75
+        freq = 'H'
+        result = s.resample(freq).quantile(q)
+        expected = s.resample(freq).agg(lambda x: x.quantile(q))
+        tm.assert_series_equal(result, expected)
+
 
 class TestDatetimeIndex(Base):
     _index_factory = lambda x: date_range