WARN: fix performance warning failure on numpy master

pandas/tseries/holiday.py

@@ -1,3 +1,5 @@
+import warnings
+
 from pandas import DateOffset, DatetimeIndex, Series, Timestamp
 from pandas.compat import add_metaclass
 from datetime import datetime, timedelta
@@ -192,10 +194,10 @@ def dates(self, start_date, end_date, return_name=False):
         """
         start_date = Timestamp(start_date)
         end_date = Timestamp(end_date)
-        
-        filter_start_date = start_date 
+
+        filter_start_date = start_date
         filter_end_date = end_date
-        
+
         if self.year is not None:
             dt = Timestamp(datetime(self.year, self.month, self.day))
             if return_name:
@@ -208,22 +210,22 @@ def dates(self, start_date, end_date, return_name=False):
         if self.days_of_week is not None:
             holiday_dates = holiday_dates[np.in1d(holiday_dates.dayofweek,
                                                   self.days_of_week)]
-            
+
         if self.start_date is not None:
             filter_start_date = max(self.start_date.tz_localize(filter_start_date.tz), filter_start_date)
         if self.end_date is not None:
             filter_end_date = min(self.end_date.tz_localize(filter_end_date.tz), filter_end_date)
-        holiday_dates = holiday_dates[(holiday_dates >= filter_start_date) & 
+        holiday_dates = holiday_dates[(holiday_dates >= filter_start_date) &
                                       (holiday_dates <= filter_end_date)]
         if return_name:
             return Series(self.name, index=holiday_dates)
         return holiday_dates
-        
-        
+
+
     def _reference_dates(self, start_date, end_date):
         """
         Get reference dates for the holiday.
-        
+
         Return reference dates for the holiday also returning the year
         prior to the start_date and year following the end_date.  This ensures
         that any offsets to be applied will yield the holidays within
@@ -238,13 +240,13 @@ def _reference_dates(self, start_date, end_date):
         year_offset = DateOffset(years=1)
         reference_start_date = Timestamp(
             datetime(start_date.year-1, self.month, self.day))
-        
+
         reference_end_date = Timestamp(
             datetime(end_date.year+1, self.month, self.day))
         # Don't process unnecessary holidays
-        dates = DatetimeIndex(start=reference_start_date, end=reference_end_date, 
+        dates = DatetimeIndex(start=reference_start_date, end=reference_end_date,
                               freq=year_offset, tz=start_date.tz)
-        
+
         return dates
 
     def _apply_rule(self, dates):
@@ -269,7 +271,11 @@ def _apply_rule(self, dates):
             else:
                 offsets = self.offset
             for offset in offsets:
-                dates += offset
+
+                # if we are adding a non-vectorized value
+                # ignore the PerformanceWarnings:
+                with warnings.catch_warnings(record=True):
+                    dates += offset
         return dates
 
 holiday_calendars = {}
@@ -327,12 +333,12 @@ def __init__(self, name=None, rules=None):
 
         if rules is not None:
             self.rules = rules
-            
+
     def rule_from_name(self, name):
         for rule in self.rules:
             if rule.name == name:
                 return rule
-            
+
         return None
 
     def holidays(self, start=None, end=None, return_name=False):

pandas/tseries/tests/test_timeseries.py

@@ -2637,35 +2637,32 @@ def test_datetime64_with_DateOffset(self):
                 assert_func(klass([x - op for x in s]), s - op)
 
 
-            # split by fast/slow path to test perf warning
-            off = {False:
-                   ['YearBegin', ('YearBegin', {'month': 5}),
-                    'YearEnd', ('YearEnd', {'month': 5}),
-                    'MonthBegin', 'MonthEnd', 'Week', ('Week', {'weekday': 3}),
-                    'BusinessDay', 'BDay', 'QuarterEnd', 'QuarterBegin'],
-                   PerformanceWarning:
-                   ['CustomBusinessDay', 'CDay', 'CBMonthEnd','CBMonthBegin',
-                    'BMonthBegin', 'BMonthEnd', 'BusinessHour', 'BYearBegin',
-                    'BYearEnd','BQuarterBegin', ('LastWeekOfMonth', {'weekday':2}),
-                    ('FY5253Quarter', {'qtr_with_extra_week': 1, 'startingMonth': 1,
-                                       'weekday': 2, 'variation': 'nearest'}),
-                    ('FY5253',{'weekday': 0, 'startingMonth': 2, 'variation': 'nearest'}),
-                    ('WeekOfMonth', {'weekday': 2, 'week': 2}), 'Easter',
-                    ('DateOffset', {'day': 4}), ('DateOffset', {'month': 5})]}
+            # assert these are equal on a piecewise basis
+            offsets = ['YearBegin', ('YearBegin', {'month': 5}),
+                       'YearEnd', ('YearEnd', {'month': 5}),
+                       'MonthBegin', 'MonthEnd', 'Week', ('Week', {'weekday': 3}),
+                       'BusinessDay', 'BDay', 'QuarterEnd', 'QuarterBegin',
+                       'CustomBusinessDay', 'CDay', 'CBMonthEnd','CBMonthBegin',
+                       'BMonthBegin', 'BMonthEnd', 'BusinessHour', 'BYearBegin',
+                       'BYearEnd','BQuarterBegin', ('LastWeekOfMonth', {'weekday':2}),
+                       ('FY5253Quarter', {'qtr_with_extra_week': 1, 'startingMonth': 1,
+                                          'weekday': 2, 'variation': 'nearest'}),
+                       ('FY5253',{'weekday': 0, 'startingMonth': 2, 'variation': 'nearest'}),
+                       ('WeekOfMonth', {'weekday': 2, 'week': 2}), 'Easter',
+                       ('DateOffset', {'day': 4}), ('DateOffset', {'month': 5})]
 
             for normalize in (True, False):
-                for warning, offsets in off.items():
-                    for do in offsets:
-                        if isinstance(do, tuple):
-                            do, kwargs = do
-                        else:
-                            do = do
-                            kwargs = {}
-                        op = getattr(pd.offsets,do)(5, normalize=normalize, **kwargs)
-                        with tm.assert_produces_warning(warning):
-                            assert_func(klass([x + op for x in s]), s + op)
-                            assert_func(klass([x - op for x in s]), s - op)
-                            assert_func(klass([op + x for x in s]), op + s)
+                for do in offsets:
+                    if isinstance(do, tuple):
+                        do, kwargs = do
+                    else:
+                        do = do
+                        kwargs = {}
+                    op = getattr(pd.offsets,do)(5, normalize=normalize, **kwargs)
+                    assert_func(klass([x + op for x in s]), s + op)
+                    assert_func(klass([x - op for x in s]), s - op)
+                    assert_func(klass([op + x for x in s]), op + s)
+
     # def test_add_timedelta64(self):
     #     rng = date_range('1/1/2000', periods=5)
     #     delta = rng.values[3] - rng.values[1]