REGR: revert "CLN: _consolidate_inplace less" / fix regression in fillna()
Reverts pandas-dev/pandas#34389

Closes #36495
do the asvs show any of the perf issues mentioned in #34389? The benchmark server isn't running at the moment, see https://github.com/pandas-dev/pandas/pull/34389#issuecomment-634654988 i was hoping you'd be able to run them locally.  (as mentioned in the other thread, im wrestling with hardware issues ATM) Sorry, I currently don't have time to run the full ASV suite. ive gotten asv working locally, will do a run on #34389 if were OK on holding off on merging this As I also said in #34389, running asv is not necessarily sufficient, as it not sure that the functions that are tested are actually covered (it's not that our benchmark suite is 100% covering all cases, and even then, they don't necessarily include cases that start with non-consolidated data). 
I think you need to specifically check the functions you changed and compare them with and without consolidation. Or, for some of them it might be sufficient to simply check in the code if it would matter or not. 
For example, I was looking at the `take` example, and `NDFrame.take` calls `BlockManager.take`, and the first thing that does is also consolidate inplace. 
So in such as case, of course the `consolidate_inplace` in `NDFrame.take` can be removed as a code clean-up, and it also won't matter anything performance wise. > So in such as case, of course the consolidate_inplace in NDFrame.take can be removed as a code clean-up, and it also won't matter anything performance wise.

Let's un-revert that here and whittle down the places that merit double-checking I'm not seeing any consistent pattern in asv results > Let's un-revert that here and whittle down the places that merit double-checking

Done @jbrockmendel what's the status here? (I re-reverted the ones discussed above)

Do we continue with this PR reverting those, or do you have time to check those cases? > Do we continue with this PR reverting those, or do you have time to check those cases?

Thanks for following up.  I've gotten my hardware issues resolved, will run a round of asvs on this. As mentioned before (https://github.com/pandas-dev/pandas/pull/34407#issuecomment-634881185) and discussed elsewhere, I am not sure that asv will give any useful information for those changes. > As mentioned before (#34407 (comment)) and discussed elsewhere, I am not sure that asv will give any useful information for those changes.

Neither am I.  But if they do show something, that will help us whittle down the places that need more manual attention. But what I want to say is: even if they don't show anything, each case still needs manual attention, as the asv's (AFAIK) don't include non-consolidated data.

For example, a small timing for `xs` (on master, so with the automatic consolidation removed):

```
In [2]: df = pd.DataFrame(index=list(range(10000))) 

In [3]: for i in range(10): 
   ...:     df[i] = np.random.randn(10000) 
   ...:    

In [6]: df2 = df._consolidate()                                                                                                                                                                                    

In [11]: %timeit df.xs(0)  
72 µs ± 2.33 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

In [12]: %timeit df2.xs(0)   
49.8 µs ± 547 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```

Do we care about this difference, I am not fully sure (that's to be discussed then). But it clearly has an impact.
 But what I want to say is: I haven't had my caffeine yet, and running asvs now doesn't preclude doing %timeits later Full asv run results below.  A lot of these look like they may be the result of not being rebased on master, so I'm going to do that and re-run.

```
       before           after         ratio
     [bbb89cad]       [1228510e]
     <master>         <revert-34389-consolidate-less-1>
+         773±2μs      2.66±0.02ms     3.44  timeseries.DatetimeIndex.time_normalize('repeated')
+      77.2±0.9μs        256±0.7μs     3.31  timeseries.SortIndex.time_sort_index(True)
+         983±5μs      3.15±0.01ms     3.21  timeseries.DatetimeAccessor.time_dt_accessor_normalize('UTC')
+         984±4μs      3.15±0.01ms     3.20  timeseries.DatetimeAccessor.time_dt_accessor_normalize(tzutc())
+        988±10μs      3.12±0.02ms     3.16  timeseries.DatetimeAccessor.time_dt_accessor_normalize(None)
+        776±10μs      2.28±0.01ms     2.94  indexing.DataFrameNumericIndexing.time_bool_indexer
+         778±1μs         2.08±0ms     2.67  timeseries.DatetimeIndex.time_normalize('tz_naive')
+         332±9ns          750±4ns     2.26  tslibs.period.PeriodProperties.time_property('min', 'quarter')
+         348±5ns          761±2ns     2.19  tslibs.period.PeriodProperties.time_property('min', 'qyear')
+         355±6ns          767±4ns     2.16  tslibs.period.PeriodProperties.time_property('M', 'quarter')
+       235±0.3μs        498±0.4μs     2.12  indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.Int16Engine'>, <class 
'numpy.int16'>), 'monotonic_incr')
+         381±4ns          806±6ns     2.12  tslibs.period.PeriodProperties.time_property('min', 'month')
+        369±10ns          778±7ns     2.11  tslibs.period.PeriodProperties.time_property('M', 'qyear')
+         383±1ns          808±2ns     2.11  tslibs.period.PeriodProperties.time_property('min', 'day')
+         383±5ns          807±2ns     2.11  tslibs.period.PeriodProperties.time_property('min', 'minute')
+         383±7ns          805±4ns     2.10  tslibs.period.PeriodProperties.time_property('min', 'hour')
+         384±5ns          806±2ns     2.10  tslibs.period.PeriodProperties.time_property('min', 'second')
+         396±1ns          816±5ns     2.06  tslibs.period.PeriodProperties.time_property('min', 'dayofyear')
+         398±5ns          816±4ns     2.05  tslibs.period.PeriodProperties.time_property('min', 'year')
+        435±10ns          884±6ns     2.03  tslibs.period.PeriodProperties.time_property('min', 'daysinmonth')
+         400±1ns          812±9ns     2.03  tslibs.period.PeriodProperties.time_property('M', 'month')
+         401±5ns          811±3ns     2.02  tslibs.period.PeriodProperties.time_property('M', 'minute')
+         404±8ns          817±3ns     2.02  tslibs.period.PeriodProperties.time_property('M', 'day')
+         402±3ns          810±3ns     2.01  tslibs.period.PeriodProperties.time_property('min', 'dayofweek')
+         405±9ns          811±4ns     2.00  tslibs.period.PeriodProperties.time_property('M', 'hour')
+         419±4ns          836±4ns     1.99  tslibs.period.PeriodProperties.time_property('M', 'dayofyear')
+         457±6ns          909±3ns     1.99  tslibs.period.PeriodProperties.time_property('M', 'daysinmonth')
+         406±7ns          806±7ns     1.99  tslibs.period.PeriodProperties.time_property('M', 'second')
+         416±4ns          825±7ns     1.99  tslibs.period.PeriodProperties.time_property('M', 'year')
+         418±6ns          830±1ns     1.99  tslibs.period.PeriodProperties.time_property('M', 'dayofweek')
+        440±10ns          866±4ns     1.97  tslibs.period.PeriodProperties.time_property('min', 'is_leap_year')
+        449±20ns          877±5ns     1.95  tslibs.period.PeriodProperties.time_property('min', 'week')
+        469±20ns          892±3ns     1.90  tslibs.period.PeriodProperties.time_property('M', 'week')
+         465±6ns         873±10ns     1.88  tslibs.period.PeriodProperties.time_property('M', 'is_leap_year')
+     1.19±0.01μs      1.84±0.03μs     1.55  tslibs.offsets.OnOffset.time_on_offset(<DateOffset: days=2, months=2>)
+         325±1μs          493±2μs     1.52  indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.Int8Engine'>, <class '
numpy.int8'>), 'monotonic_incr')
+        25.3±2ms         37.6±1ms     1.49  gil.ParallelDatetimeFields.time_datetime_field_normalize
+        11.0±1μs       16.3±0.2μs     1.49  tslibs.period.PeriodUnaryMethods.time_asfreq('min')
+      27.6±0.2ms       40.8±0.5ms     1.48  timeseries.ToDatetimeFormat.time_different_offset
+     7.03±0.01ms       10.3±0.1ms     1.46  groupby.CountMultiInt.time_multi_int_nunique
+       129±0.4μs        188±0.6μs     1.46  timeseries.DatetimeIndex.time_normalize('dst')
+      10.4±0.9μs       15.0±0.2μs     1.45  tslibs.period.PeriodUnaryMethods.time_now('M')
+     5.91±0.02ms      8.42±0.04ms     1.42  arithmetic.OffsetArrayArithmetic.time_add_dti_offset(<SemiMonthEnd: day_of_month=15>)
+     5.94±0.04ms      8.45±0.01ms     1.42  arithmetic.OffsetArrayArithmetic.time_add_series_offset(<SemiMonthEnd: day_of_month=15>)
+     5.76±0.02ms      8.16±0.05ms     1.42  arithmetic.OffsetArrayArithmetic.time_add_dti_offset(<SemiMonthBegin: day_of_month=15>)
+         924±3μs         1.29±0ms     1.40  series_methods.NanOps.time_func('sum', 1000000, 'int8')
+     5.81±0.04ms      8.11±0.01ms     1.40  arithmetic.OffsetArrayArithmetic.time_add_series_offset(<SemiMonthBegin: day_of_month=15>)
+     5.44±0.04ms      7.58±0.03ms     1.39  arithmetic.OffsetArrayArithmetic.time_add_series_offset(<BusinessDay>)
+     5.42±0.02ms      7.53±0.01ms     1.39  arithmetic.OffsetArrayArithmetic.time_add_dti_offset(<BusinessDay>)
+        11.9±1μs       16.2±0.1μs     1.36  tslibs.period.PeriodUnaryMethods.time_asfreq('M')
+      1.30±0.01s       1.75±0.02s     1.35  groupby.Apply.time_copy_function_multi_col
+      26.2±0.1ms      34.4±0.07ms     1.31  timeseries.DatetimeIndex.time_normalize('tz_aware')
+     3.02±0.03μs      3.90±0.06μs     1.29  period.Indexing.time_get_loc
+     2.81±0.03ms      3.62±0.01ms     1.29  rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'int', 'min')
+     2.83±0.02ms      3.64±0.03ms     1.29  rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'int', 'max')
+     1.28±0.01ms      1.62±0.02ms     1.27  series_methods.NanOps.time_func('prod', 1000000, 'int8')
+      14.9±0.4μs       18.3±0.2μs     1.23  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<DateOffset: days=2, months=2>)
+     38.3±0.09μs       47.2±0.5μs     1.23  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<CustomBusinessDay>)
+      2.05±0.2μs       2.52±0.5μs     1.23  index_cached_properties.IndexCache.time_values('IntervalIndex')
+      15.1±0.4μs       18.4±0.1μs     1.22  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<DateOffset: days=2, months=2>)
+      35.5±0.2μs       43.5±0.1μs     1.22  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<CustomBusinessDay>)
+      15.4±0.5μs       18.8±0.3μs     1.22  tslibs.offsets.OffestDatetimeArithmetic.time_add(<DateOffset: days=2, months=2>)
+      13.4±0.4μs       16.4±0.3μs     1.22  period.Indexing.time_series_loc
+      19.9±0.1μs       24.0±0.4μs     1.21  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BusinessYearBegin: month=1>)
+      19.6±0.1μs       23.6±0.8μs     1.21  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<YearBegin: month=1>)
+      21.1±0.5μs       25.4±0.1μs     1.20  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<BusinessYearEnd: month=12>)
+      19.8±0.1μs       23.8±0.1μs     1.20  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BusinessYearEnd: month=12>)
+       144±0.2μs          173±2μs     1.20  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<CustomBusinessMonthBegin>)
+     1.44±0.05ms      1.72±0.09ms     1.20  period.PeriodIndexConstructor.time_from_pydatetime('D', True)
+     1.45±0.04ms       1.73±0.1ms     1.20  period.PeriodIndexConstructor.time_from_pydatetime('D', False)
+       521±0.9ms          624±2ms     1.20  groupby.Apply.time_copy_overhead_single_col
+     20.3±0.09μs       24.3±0.1μs     1.20  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<SemiMonthBegin: day_of_month=15>)
+       175±0.7μs          209±4μs     1.20  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<CustomBusinessMonthBegin>)
+     3.53±0.02ms       4.22±0.5ms     1.20  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'max')
+      56.3±0.7ms       67.3±0.8ms     1.19  series_methods.SeriesConstructor.time_constructor('dict')
+     21.4±0.08μs       25.5±0.3μs     1.19  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<YearEnd: month=12>)
+         128±1μs          152±2μs     1.19  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<CustomBusinessMonthEnd>)
+      20.2±0.2μs       24.1±0.3μs     1.19  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<BusinessYearBegin: month=1>)
+      19.7±0.1μs      23.5±0.07μs     1.19  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<BusinessYearEnd: month=12>)
+         124±1μs          148±4μs     1.19  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<CustomBusinessMonthEnd>)
+         160±1μs          190±2μs     1.19  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<CustomBusinessMonthBegin>)
+      18.6±0.1μs       22.0±0.3μs     1.19  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<MonthBegin>)
+     19.7±0.09μs       23.4±0.5μs     1.19  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<YearEnd: month=12>)
+      19.1±0.1μs       22.6±0.1μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BusinessMonthEnd>)
+     21.2±0.09μs       25.1±0.3μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<BusinessYearBegin: month=1>)
+      19.1±0.2μs       22.5±0.3μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<BusinessMonthEnd>)
+      20.1±0.2μs       23.8±0.2μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<SemiMonthEnd: day_of_month=15>)
+       167±0.7μs          198±1μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<CustomBusinessMonthEnd>)
+      16.9±0.1μs       20.0±0.2μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<SemiMonthBegin: day_of_month=15>)
+      19.6±0.3μs      23.1±0.07μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<QuarterBegin: startingMonth=3>)
+      21.4±0.1μs      25.3±0.07μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<SemiMonthBegin: day_of_month=15>)
+       142±0.1μs          167±2μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<CustomBusinessMonthBegin>)
+      18.8±0.1μs       22.2±0.2μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<MonthEnd>)
+     18.5±0.09μs      21.7±0.08μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<MonthBegin>)
+      16.7±0.1μs       19.7±0.2μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<YearBegin: month=1>)
+        37.1±1μs       43.6±0.1μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<DateOffset: days=2, months=2>)
+      17.7±0.2μs       20.8±0.4μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_add(<BusinessYearBegin: month=1>)
+     20.3±0.08μs       23.9±0.1μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<SemiMonthBegin: day_of_month=15>)
+      16.9±0.3μs       19.9±0.1μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessYearEnd: month=12>)
+      16.7±0.1μs       19.7±0.2μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BusinessMonthBegin>)
+     19.0±0.09μs      22.3±0.09μs     1.18  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<BusinessMonthBegin>)
+     21.1±0.03μs       24.8±0.6μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<YearBegin: month=1>)
+       162±0.5μs          190±3μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<CustomBusinessMonthBegin>)
+      16.7±0.1μs       19.6±0.2μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<YearEnd: month=12>)
+      20.1±0.1μs       23.6±0.3μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<YearEnd: month=12>)
+      17.2±0.1μs      20.1±0.05μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_add(<YearEnd: month=12>)
+     16.7±0.09μs      19.6±0.09μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<YearBegin: month=1>)
+     17.4±0.08μs       20.3±0.1μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_add(<YearBegin: month=1>)
+     28.2±0.09μs       32.9±0.3μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<CustomBusinessDay>)
+      20.4±0.2μs       23.8±0.2μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<SemiMonthEnd: day_of_month=15>)
+      17.1±0.3μs      20.0±0.09μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessYearBegin: month=1>)
+         109±1μs          127±2μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<CustomBusinessMonthEnd>)
+      20.1±0.1μs       23.5±0.3μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<BusinessQuarterBegin: startingMonth=3>)
+      17.5±0.1μs       20.5±0.3μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_add(<BusinessYearEnd: month=12>)
+      19.8±0.1μs       23.1±0.2μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<YearBegin: month=1>)
+       162±0.6μs          189±2μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_add(<CustomBusinessMonthBegin>)
+     4.19±0.04μs       4.89±0.1μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<Day>)
+      16.9±0.1μs       19.7±0.1μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessMonthEnd>)
+      17.6±0.1μs      20.6±0.06μs     1.17  tslibs.offsets.OffestDatetimeArithmetic.time_add(<SemiMonthEnd: day_of_month=15>)
+      19.8±0.2μs       23.1±0.2μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<BusinessMonthBegin>)
+     5.28±0.02μs      6.14±0.03μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<Day>)
+     5.22±0.04ms      6.08±0.08ms     1.16  tslibs.offsets.OnOffset.time_on_offset(<CustomBusinessMonthEnd>)
+     16.7±0.05μs       19.4±0.1μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<MonthBegin>)
+      4.21±0.3μs       4.89±0.5μs     1.16  index_cached_properties.IndexCache.time_shape('IntervalIndex')
+     28.4±0.08μs       33.1±0.6μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<CustomBusinessDay>)
+     5.56±0.03ms       6.45±0.1ms     1.16  tslibs.offsets.OnOffset.time_on_offset(<CustomBusinessMonthBegin>)
+     16.6±0.06μs       19.3±0.4μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<YearEnd: month=12>)
+      21.4±0.2μs       24.9±0.3μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<BusinessQuarterEnd: startingMonth=3>)
+     5.39±0.02μs      6.25±0.03μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<Day>)
+     21.4±0.07μs       24.8±0.1μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<SemiMonthEnd: day_of_month=15>)
+      19.9±0.1μs       23.1±0.1μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BusinessQuarterBegin: startingMonth=3>)
+      19.7±0.1μs      22.9±0.06μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<MonthEnd>)
+      17.2±0.2μs       19.9±0.3μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<SemiMonthEnd: day_of_month=15>)
+      1.57±0.02s       1.81±0.03s     1.16  arithmetic.OffsetArrayArithmetic.time_add_dti_offset(<CustomBusinessMonthBegin>)
+      1.58±0.01s       1.83±0.02s     1.16  arithmetic.OffsetArrayArithmetic.time_add_series_offset(<CustomBusinessMonthBegin>)
+         109±1μs          126±1μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_add(<CustomBusinessMonthEnd>)
+      17.0±0.2μs       19.6±0.3μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<SemiMonthBegin: day_of_month=15>)
+     19.4±0.08μs       22.4±0.2μs     1.16  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BusinessDay>)
+      18.2±0.2μs       21.0±0.1μs     1.15  indexing.NumericSeriesIndexing.time_loc_scalar(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+      20.1±0.2μs       23.3±0.2μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<BusinessQuarterEnd: startingMonth=3>)
+      17.2±0.1μs       19.9±0.3μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BusinessYearBegin: month=1>)
+      17.2±0.1μs      19.8±0.06μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BusinessQuarterEnd: startingMonth=3>)
+      20.2±0.2μs       23.2±0.1μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BusinessQuarterEnd: startingMonth=3>)
+         1.07±0s       1.24±0.02s     1.15  arithmetic.OffsetArrayArithmetic.time_add_dti_offset(<CustomBusinessMonthEnd>)
+      16.6±0.1μs      19.2±0.05μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<MonthEnd>)
+     17.0±0.05μs       19.6±0.5μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_add(<BusinessDay>)
+     20.4±0.08μs       23.5±0.1μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<QuarterEnd: startingMonth=3>)
+     17.5±0.08μs      20.1±0.09μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_add(<QuarterEnd: startingMonth=3>)
+      4.53±0.1μs      5.21±0.07μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_add(<Day>)
+      17.6±0.1μs       20.3±0.4μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_add(<BusinessQuarterBegin: startingMonth=3>)
+         1.09±0s       1.26±0.03s     1.15  arithmetic.OffsetArrayArithmetic.time_add_series_offset(<CustomBusinessMonthEnd>)
+     17.0±0.04μs       19.6±0.2μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BusinessYearEnd: month=12>)
+      16.9±0.1μs       19.4±0.3μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessMonthBegin>)
+      17.1±0.1μs       19.6±0.1μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BusinessQuarterBegin: startingMonth=3>)
+      16.6±0.2μs       19.0±0.1μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BusinessDay>)
+      19.8±0.1μs       22.7±0.2μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<BusinessMonthEnd>)
+      16.9±0.1μs       19.3±0.1μs     1.15  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<QuarterBegin: startingMonth=3>)
+     19.2±0.07μs       21.9±0.2μs     1.14  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BusinessMonthBegin>)
+      20.7±0.1μs       23.7±0.1μs     1.14  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<BusinessDay>)
+     17.6±0.09μs       20.1±0.2μs     1.14  tslibs.offsets.OffestDatetimeArithmetic.time_add(<BusinessMonthEnd>)
+     17.1±0.05μs       19.6±0.1μs     1.14  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<QuarterEnd: startingMonth=3>)
+       108±0.6μs          124±3μs     1.14  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<CustomBusinessMonthEnd>)
+     17.1±0.07μs      19.5±0.09μs     1.14  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<QuarterEnd: startingMonth=3>)
+      19.9±0.1μs       22.8±0.2μs     1.14  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<QuarterBegin: startingMonth=3>)
+     4.33±0.05μs      4.94±0.07μs     1.14  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<Day>)
+     7.05±0.05ms      8.03±0.05ms     1.14  io.hdf.HDFStoreDataFrame.time_query_store_table
+      16.9±0.1μs       19.2±0.2μs     1.14  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<QuarterBegin: startingMonth=3>)
+      16.5±0.3μs       18.7±0.4μs     1.14  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessDay>)
+     19.5±0.08μs       22.2±0.2μs     1.14  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<BusinessDay>)
+     17.3±0.09μs       19.7±0.4μs     1.14  tslibs.offsets.OffestDatetimeArithmetic.time_add(<MonthEnd>)
+     10.7±0.03ms      12.2±0.08ms     1.14  groupby.MultiColumn.time_col_select_numpy_sum
+      21.5±0.1μs       24.4±0.2μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<QuarterEnd: startingMonth=3>)
+     17.1±0.08μs       19.4±0.1μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BusinessMonthEnd>)
+      17.2±0.1μs       19.5±0.5μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<SemiMonthEnd: day_of_month=15>)
+      17.9±0.2μs       20.2±0.1μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_add(<SemiMonthBegin: day_of_month=15>)
+     20.4±0.09μs       23.1±0.2μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<QuarterEnd: startingMonth=3>)
+      16.6±0.1μs       18.7±0.2μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_apply(<MonthBegin>)
+      19.8±0.3μs      22.4±0.09μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<MonthBegin>)
+      17.2±0.2μs       19.4±0.2μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessQuarterEnd: startingMonth=3>)
+     17.2±0.06μs       19.4±0.1μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessQuarterBegin: startingMonth=3>)
+         301±6ms          341±2ms     1.13  arithmetic.OffsetArrayArithmetic.time_add_dti_offset(<CustomBusinessDay>)
+      21.4±0.1μs       24.2±0.1μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<QuarterBegin: startingMonth=3>)
+     17.3±0.08μs      19.6±0.09μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_add(<QuarterBegin: startingMonth=3>)
+         965±5μs         1.09±0ms     1.13  arithmetic.ApplyIndex.time_apply_index(<DateOffset: days=2, months=2>)
+     21.5±0.06μs       24.3±0.1μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<BusinessQuarterBegin: startingMonth=3>)
+      19.1±0.1μs       21.6±0.2μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<MonthEnd>)
+      17.4±0.2μs       19.7±0.3μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_add(<BusinessMonthBegin>)
+      17.0±0.1μs       19.2±0.1μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<MonthEnd>)
+     17.9±0.05μs       20.2±0.1μs     1.13  tslibs.offsets.OffestDatetimeArithmetic.time_add(<BusinessQuarterEnd: startingMonth=3>)
+     6.13±0.02μs      6.87±0.05μs     1.12  tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<Day>)
+      5.90±0.2μs      6.59±0.02μs     1.12  indexing.NonNumericSeriesIndexing.time_getitem_scalar('period', 'non_monotonic')
+     10.7±0.05ms       12.0±0.1ms     1.12  stat_ops.Rank.time_rank('Series', False)
+      20.5±0.2μs       22.9±0.2μs     1.12  indexing.NumericSeriesIndexing.time_loc_scalar(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+         305±6ms          339±9ms     1.11  arithmetic.OffsetArrayArithmetic.time_add_series_offset(<CustomBusinessDay>)
+     17.4±0.08μs      19.4±0.09μs     1.11  tslibs.offsets.OffestDatetimeArithmetic.time_add(<MonthBegin>)
+     10.8±0.03ms      12.0±0.06ms     1.11  stat_ops.Rank.time_rank('Series', True)
+        2.26±0ms       2.51±0.4ms     1.11  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'sum')
+      29.6±0.3μs      32.9±0.06μs     1.11  tslibs.offsets.OffestDatetimeArithmetic.time_add(<CustomBusinessDay>)
+        1.09±0ms         1.21±0ms     1.11  arithmetic.OffsetArrayArithmetic.time_add_dti_offset(<DateOffset: days=2, months=2>)
+     2.26±0.01ms       2.49±0.4ms     1.10  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'sum')
+     3.31±0.02ms      3.65±0.03ms     1.10  rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'float', 'max')
+     3.39±0.01ms       3.74±0.5ms     1.10  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'std')
-         278±2ms          253±2ms     0.91  io.json.ReadJSONLines.time_read_json_lines_concat('datetime')
-        873±40ns         793±20ns     0.91  index_cached_properties.IndexCache.time_is_monotonic('RangeIndex')
-      6.20±0.2μs       5.62±0.3μs     0.91  index_cached_properties.IndexCache.time_engine('TimedeltaIndex')
-      3.47±0.2ms      3.13±0.04ms     0.90  rolling.Apply.time_rolling('Series', 300, 'int', <function Apply.<lambda> at 0x7f62bd249d08>, True)
-     6.88±0.03ms      6.21±0.06ms     0.90  frame_methods.Apply.time_apply_pass_thru
-     1.41±0.05μs      1.26±0.04μs     0.90  index_cached_properties.IndexCache.time_inferred_type('DatetimeIndex')
-     1.07±0.08μs         950±30ns     0.89  index_cached_properties.IndexCache.time_is_monotonic_decreasing('Int64Index')
-        515±10ns         459±20ns     0.89  index_cached_properties.IndexCache.time_is_all_dates('Int64Index')
-     1.06±0.01ms          937±4μs     0.89  frame_methods.Quantile.time_frame_quantile(1)
-        81.4±1μs       72.1±0.3μs     0.89  tslibs.timestamp.TimestampOps.time_ceil(None)
-        83.1±3μs       73.5±0.6μs     0.88  tslibs.period.PeriodUnaryMethods.time_to_timestamp('M')
-        80.6±2μs       71.3±0.6μs     0.88  tslibs.timestamp.TimestampOps.time_floor(None)
-     1.13±0.08μs      1.00±0.06μs     0.88  index_cached_properties.IndexCache.time_is_all_dates('Float64Index')
-        83.9±3μs       74.1±0.3μs     0.88  tslibs.period.PeriodProperties.time_property('M', 'start_time')
-        83.8±4μs       73.6±0.2μs     0.88  tslibs.period.PeriodUnaryMethods.time_to_timestamp('min')
-        85.5±3μs         75.0±1μs     0.88  tslibs.period.PeriodProperties.time_property('min', 'start_time')
-        71.3±2μs       62.3±0.6μs     0.87  dtypes.Dtypes.time_pandas_dtype('period[D]')
-      1.68±0.02s          1.47±0s     0.87  groupby.GroupByMethods.time_dtype_as_field('float', 'describe', 'direct')
-        722±40ns         630±20ns     0.87  index_cached_properties.IndexCache.time_is_monotonic_increasing('Int64Index')
-      1.69±0.01s          1.47±0s     0.87  groupby.GroupByMethods.time_dtype_as_field('float', 'describe', 'transformation')
-      2.39±0.02s       2.08±0.01s     0.87  groupby.GroupByMethods.time_dtype_as_group('int', 'describe', 'direct')
-      3.70±0.03s          3.21±0s     0.87  groupby.GroupByMethods.time_dtype_as_group('float', 'describe', 'transformation')
-      2.38±0.02s          2.07±0s     0.87  groupby.GroupByMethods.time_dtype_as_group('int', 'describe', 'transformation')
-      3.74±0.2μs      3.24±0.06μs     0.87  index_cached_properties.IndexCache.time_shape('DatetimeIndex')
-            198M             171M     0.87  io.json.ReadJSONLines.peakmem_read_json_lines_concat('datetime')
-            198M             171M     0.87  io.json.ReadJSONLines.peakmem_read_json_lines_concat('int')
-      1.62±0.01s          1.41±0s     0.87  groupby.GroupByMethods.time_dtype_as_field('int', 'describe', 'transformation')
-      4.03±0.2μs       3.49±0.1μs     0.87  index_cached_properties.IndexCache.time_engine('UInt64Index')
-      3.71±0.03s       3.21±0.01s     0.87  groupby.GroupByMethods.time_dtype_as_group('float', 'describe', 'direct')
-        517±20ns         447±10ns     0.86  index_cached_properties.IndexCache.time_inferred_type('Int64Index')
-      1.62±0.01s          1.40±0s     0.86  groupby.GroupByMethods.time_dtype_as_field('int', 'describe', 'direct')
-     1.58±0.06μs      1.36±0.03μs     0.86  index_cached_properties.IndexCache.time_is_all_dates('MultiIndex')
-     2.13±0.08μs       1.80±0.1μs     0.85  index_cached_properties.IndexCache.time_shape('UInt64Index')
-     1.22±0.05ms         974±20μs     0.80  arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('le')
-         540±4μs          414±2μs     0.77  frame_methods.Quantile.time_frame_quantile(0)
-      1.57±0.2μs      1.18±0.05μs     0.76  index_cached_properties.IndexCache.time_values('UInt64Index')
-      1.80±0.3μs      1.29±0.04μs     0.72  index_cached_properties.IndexCache.time_values('DatetimeIndex')
-         146±4μs       92.5±0.3μs     0.63  tslibs.period.PeriodProperties.time_property('M', 'end_time')
-        13.2±1μs      8.00±0.04μs     0.60  tslibs.timestamp.TimestampOps.time_normalize(<UTC>)
-      13.1±0.6μs       7.88±0.1μs     0.60  tslibs.timestamp.TimestampOps.time_normalize(None)
-      13.5±0.8μs      8.02±0.04μs     0.59  tslibs.timestamp.TimestampOps.time_normalize(tzutc())
-         161±4μs       81.4±0.7μs     0.51  tslibs.period.PeriodProperties.time_property('min', 'end_time')
``` @jorisvandenbossche is this still active? I milestoned this 1.1.1 since it reverted a PR that caused a regression #35488. #35578 has been merged (and backported) to fix #35488 so am removing milestone. @jorisvandenbossche is this still active/needed? There are still open regression reports related to this, eg https://github.com/pandas-dev/pandas/issues/35521 @jorisvandenbossche do you want to push for this in 1.1.2 or can I move to 1.1.3 The revert for the issue mentioned above was merged in a separate PR, so moving to 1.1.3 (would need to take the time to investigate the others) another issue surfaced #36495 if someone wants to merge master can consider this will add test and release note for #36495 here too (if green) ```
=========================== short test summary info ============================
FAILED pandas/tests/groupby/test_groupby.py::test_groupby_duplicate_columns
FAILED pandas/tests/groupby/aggregate/test_aggregate.py::test_agg_split_object_part_datetime
=== 2 failed, 72815 passed, 4137 skipped, 1129 xfailed in 506.02s (0:08:26) ====
``` it appears that #35839 and #35854 have been built on top of the changes that are being reverted. moving to 1.1.4