Fixed additional passing args to resample().apply

Author: discort

doc/source/whatsnew/v0.24.0.txt

@@ -650,7 +650,7 @@ Groupby/Resample/Rolling
   ``SeriesGroupBy`` when the grouping variable only contains NaNs and numpy version < 1.13 (:issue:`21956`).
 - Multiple bugs in :func:`pandas.core.Rolling.min` with ``closed='left'` and a
   datetime-like index leading to incorrect results and also segfault. (:issue:`21704`)
--
+- Bug in :meth:`Resampler.apply` when passing an additional argument to applied func (:issue:`14615`).
 
 Sparse
 ^^^^^^

pandas/core/resample.py

@@ -239,7 +239,9 @@ def aggregate(self, arg, *args, **kwargs):
         self._set_binner()
         result, how = self._aggregate(arg, *args, **kwargs)
         if result is None:
+            grouper = None
             result = self._groupby_and_aggregate(arg,
+                                                 grouper,
                                                  *args,
                                                  **kwargs)
 
@@ -852,7 +854,7 @@ def __init__(self, obj, *args, **kwargs):
         self._groupby.grouper.mutated = True
         self.groupby = copy.copy(parent.groupby)
 
-    def _apply(self, f, **kwargs):
+    def _apply(self, f, *args, **kwargs):
         """
         dispatch to _upsample; we are stripping all of the _upsample kwargs and
         performing the original function call on the grouped object

pandas/tests/test_resample.py

@@ -2165,6 +2165,20 @@ def test_resample_datetime_values(self):
         res = df['timestamp'].resample('2D').first()
         tm.assert_series_equal(res, exp)
 
+    def test_resample_apply_with_additional_args(self):
+        def f(data, add_arg):
+            return np.mean(data) * add_arg
+
+        multiplier = 10
+        result = self.series.resample('D').apply(f, multiplier)
+        expected = self.series.resample('D').mean().multiply(multiplier)
+        tm.assert_series_equal(result, expected)
+
+        # Testing as kwarg
+        result = self.series.resample('D').apply(f, add_arg=multiplier)
+        expected = self.series.resample('D').mean().multiply(multiplier)
+        tm.assert_series_equal(result, expected)
+
 
 class TestPeriodIndex(Base):
     _index_factory = lambda x: period_range