CLN: rework apply_loffset a bit to make it applied only once

xref #13218

Author: jreback

pandas/tseries/resample.py

@@ -319,15 +319,11 @@ def aggregate(self, arg, *args, **kwargs):
  self._set_binner()
  result, how = self._aggregate(arg, *args, **kwargs)
  if result is None:
- return self._groupby_and_aggregate(arg,
- *args,
- **kwargs)
-
- # if arg was a string, _aggregate called resampler's _downsample or
- # _groupby_and_agg methods, which would've already applied the loffset
- if not isinstance(arg, compat.string_types):
- result = self._apply_loffset(result)
+ result = self._groupby_and_aggregate(arg,
+ *args,
+ **kwargs)
 
+ result = self._apply_loffset(result)
  return result
 
  agg = aggregate
@@ -410,31 +406,31 @@ def _groupby_and_aggregate(self, how, grouper=None, *args, **kwargs):
  result = grouped.apply(how, *args, **kwargs)
 
  result = self._apply_loffset(result)
-
  return self._wrap_result(result)
 
  def _apply_loffset(self, result):
  """
  if loffset is set, offset the result index
 
+ This is NOT an idempotent routine, it will be applied
+ exactly once to the result.
+
  Parameters
  ----------
  result : Series or DataFrame
  the result of resample
  """
- loffset = self.loffset
- if isinstance(loffset, compat.string_types):
- loffset = to_offset(self.loffset)
 
  needs_offset = (
- isinstance(loffset, (DateOffset, timedelta)) and
+ isinstance(self.loffset, (DateOffset, timedelta)) and
  isinstance(result.index, DatetimeIndex) and
  len(result.index) > 0
  )
 
  if needs_offset:
- result.index = result.index + loffset
+ result.index = result.index + self.loffset
 
+ self.loffset = None
  return result
 
  def _get_resampler_for_grouping(self, groupby, **kwargs):
@@ -445,6 +441,7 @@ def _wrap_result(self, result):
  """ potentially wrap any results """
  if isinstance(result, com.ABCSeries) and self._selection is not None:
  result.name = self._selection
+
  return result
 
  def pad(self, limit=None):
@@ -706,7 +703,6 @@ def _downsample(self, how, **kwargs):
  self.grouper, axis=self.axis).aggregate(how, **kwargs)
 
  result = self._apply_loffset(result)
-
  return self._wrap_result(result)
 
  def _adjust_binner_for_upsample(self, binner):
@@ -810,11 +806,7 @@ def aggregate(self, arg, *args, **kwargs):
  if result is None:
  result = self._downsample(arg, *args, **kwargs)
 
- # if arg was a string, _aggregate called resamplers' _downsample or
- # _groupby_and_agg methods, which would've already applied the loffset
- if not isinstance(arg, compat.string_types):
- result = self._apply_loffset(result)
-
+ result = self._apply_loffset(result)
  return result
 
  agg = aggregate
@@ -1022,7 +1014,10 @@ def __init__(self, freq='Min', closed=None, label=None, how='mean',
  self.convention = convention or 'E'
  self.convention = self.convention.lower()
 
+ if isinstance(loffset, compat.string_types):
+ loffset = to_offset(loffset)
  self.loffset = loffset
+
  self.how = how
  self.fill_method = fill_method
  self.limit = limit

pandas/tseries/tests/test_resample.py

@@ -777,19 +777,26 @@ def test_resample_loffset_arg_type(self):
  expected_index = self.create_index(df.index[0],
  periods=len(df.index) / 2,
  freq='2D')
+
  # loffset coreces PeriodIndex to DateTimeIndex
  if isinstance(expected_index, PeriodIndex):
  expected_index = expected_index.to_timestamp()
+
  expected_index += timedelta(hours=2)
  expected = DataFrame({'value': expected_means}, index=expected_index)
+
  for arg in ['mean', {'value': 'mean'}, ['mean']]:
+
  result_agg = df.resample('2D', loffset='2H').agg(arg)
+
  with tm.assert_produces_warning(FutureWarning,
  check_stacklevel=False):
  result_how = df.resample('2D', how=arg, loffset='2H')
+
  if isinstance(arg, list):
  expected.columns = pd.MultiIndex.from_tuples([('value',
  'mean')])
+
  # GH 13022, 7687 - TODO: fix resample w/ TimedeltaIndex
  if isinstance(expected.index, TimedeltaIndex):
  with tm.assertRaises(AssertionError):