PERF: Regressions since v0.21
[xref](https://github.com/pandas-dev/pandas/pull/18503#issuecomment-347167230)

```
$ asv continuous -f 1.1 81372093f1fdc0c07e4b45ba0f47b upstream/master

+        54.0±9μs       1.40±0.01s 25895.28  indexing.IntervalIndexing.time_loc_list
+       65.6±20μs       1.39±0.02s 21250.19  indexing.IntervalIndexing.time_getitem_list
+     14.2±0.04μs      1.51±0.03ms   106.31  categoricals.CategoricalSlicing.time_getitem_bool_array('monotonic_decr')
+      35.6±0.5ms       1.99±0.01s    55.86  offset.ApplyIndex.time_apply_index(<BusinessDay>)
+      36.4±0.3ms       1.98±0.02s    54.20  offset.ApplyIndex.time_apply_index(<SemiMonthEnd: day_of_month=15>)
+      36.9±0.7ms          1.99±0s    53.86  offset.ApplyIndex.time_apply_index(<SemiMonthBegin: day_of_month=15>)
+         443±1ns       22.9±0.2μs    51.76  timestamp.TimestampProperties.time_weekday_name(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
+         444±5ns       23.0±0.2μs    51.71  timestamp.TimestampProperties.time_weekday_name(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None)
+      22.3±0.5ms       1.04±0.01s    46.47  period.DataFramePeriodColumn.time_setitem_period_column
+     4.65±0.02ms          203±2ms    43.80  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<BusinessDay>)
+     4.87±0.06ms          202±1ms    41.57  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<SemiMonthBegin: day_of_month=15>)
+     5.01±0.09ms        202±0.6ms    40.33  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<SemiMonthEnd: day_of_month=15>)
+     5.15±0.03ms        204±0.9ms    39.65  offset.OffsetSeriesArithmetic.time_add_offset(<BusinessDay>)
+      13.8±0.1ms          522±2ms    37.99  timeseries.Iteration.time_iter_preexit(<function period_range at 0x1124ecea0>)
+     5.45±0.02ms          206±2ms    37.74  offset.OffsetSeriesArithmetic.time_add_offset(<SemiMonthBegin: day_of_month=15>)
+     5.51±0.02ms        206±0.7ms    37.43  offset.OffsetSeriesArithmetic.time_add_offset(<SemiMonthEnd: day_of_month=15>)
+       374±0.9ns      12.4±0.03μs    33.08  indexing.MethodLookup.time_lookup_ix
+     3.42±0.03ms        104±0.7ms    30.46  period.PeriodIndexConstructor.time_from_pydatetime('D')
+     1.71±0.01ms         50.1±1ms    29.26  indexing.CategoricalIndexIndexing.time_get_indexer_list('monotonic_decr')
+         438±3ns      8.69±0.04μs    19.85  timestamp.TimestampProperties.time_weekday_name(None, 'B')
+     5.01±0.08ms         99.1±4ms    19.78  timeseries.DatetimeIndex.time_timeseries_is_month_start('tz_aware')
+         444±2ns      8.65±0.08μs    19.50  timestamp.TimestampProperties.time_weekday_name(None, None)
+     8.82±0.09ms          170±2ms    19.24  multiindex_object.Values.time_datetime_level_values_copy
+      7.02±0.2μs        113±0.9μs    16.07  period.Indexing.time_get_loc
+      60.9±0.8ms          655±3ms    10.75  plotting.TimeseriesPlotting.time_plot_regular
+      6.36±0.1μs       67.8±0.5μs    10.67  period.Indexing.time_shallow_copy
+     7.30±0.02ms         75.1±3ms    10.29  frame_methods.Repr.time_frame_repr_wide
+     7.20±0.07μs       60.3±0.6μs     8.38  index_object.Indexing.time_slice('Int')
+      22.0±0.1ms          183±3ms     8.32  binary_ops.Ops2.time_frame_float_floor_by_zero
+     7.16±0.05μs       59.5±0.5μs     8.30  index_object.Indexing.time_slice_step('Int')
+       113±0.4μs        841±300μs     7.44  groupby.GroupByMethods.time_dtype_as_field('int', 'cummin', 'direct')
+      72.0±0.2μs        525±0.9μs     7.28  groupby.GroupByMethods.time_dtype_as_field('float', 'cummin', 'direct')
+      73.0±0.6μs          524±2μs     7.18  groupby.GroupByMethods.time_dtype_as_field('float', 'cummin', 'transformation')
+      73.1±0.7μs          519±3μs     7.10  groupby.GroupByMethods.time_dtype_as_field('float', 'cummax', 'transformation')
+      18.0±0.2μs        127±0.5μs     7.06  period.PeriodUnaryMethods.time_now('M')
+       116±0.9μs        814±300μs     7.03  groupby.GroupByMethods.time_dtype_as_field('int', 'cummax', 'transformation')
+      73.3±0.6μs          514±3μs     7.00  groupby.GroupByMethods.time_dtype_as_field('float', 'cummax', 'direct')
+      77.2±0.4μs          506±4μs     6.56  groupby.GroupByMethods.time_dtype_as_field('datetime', 'cummin', 'direct')
+      77.4±0.5μs          503±4μs     6.50  groupby.GroupByMethods.time_dtype_as_field('datetime', 'cummin', 'transformation')
+      81.3±0.3μs          528±5μs     6.49  groupby.GroupByMethods.time_dtype_as_field('float', 'cumprod', 'direct')
+      80.9±0.3μs          525±5μs     6.49  groupby.GroupByMethods.time_dtype_as_field('float', 'cumprod', 'transformation')
+        86.0±5μs          527±5μs     6.13  groupby.GroupByMethods.time_dtype_as_field('float', 'cumsum', 'direct')
+        86.8±5μs          531±3μs     6.12  groupby.GroupByMethods.time_dtype_as_field('float', 'cumsum', 'transformation')
+      18.3±0.1ms          112±7ms     6.11  frame_methods.Dropna.time_dropna('any', 1)
+      18.0±0.2ms          106±3ms     5.91  frame_methods.Dropna.time_dropna('any', 0)
+      30.9±0.7μs          178±1μs     5.78  period.PeriodUnaryMethods.time_asfreq('min')
+      31.1±0.4μs        178±0.9μs     5.73  period.PeriodUnaryMethods.time_asfreq('M')
+         116±1μs        628±200μs     5.40  groupby.GroupByMethods.time_dtype_as_field('int', 'cummax', 'direct')
+       107±0.3μs          570±5μs     5.31  groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'transformation')
+      64.8±0.1μs          341±4μs     5.27  period.PeriodProperties.time_property('M', 'end_time')
+         108±1μs          566±4μs     5.22  groupby.GroupByMethods.time_dtype_as_group('float', 'cummin', 'direct')
+         110±1μs          572±4μs     5.22  groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'direct')
+       109±0.5μs          567±4μs     5.20  groupby.GroupByMethods.time_dtype_as_group('float', 'cummin', 'transformation')
+       109±0.4μs          567±4μs     5.19  groupby.GroupByMethods.time_dtype_as_group('float', 'cummax', 'direct')
+         109±1μs          565±2μs     5.17  groupby.GroupByMethods.time_dtype_as_group('float', 'cummax', 'transformation')
+      65.7±0.7μs          340±1μs     5.17  period.PeriodProperties.time_property('min', 'end_time')
+         116±1μs         591±90μs     5.10  groupby.GroupByMethods.time_dtype_as_field('int', 'cummin', 'transformation')
+         116±2μs         578±10μs     5.00  groupby.GroupByMethods.time_dtype_as_group('int', 'cummin', 'transformation')
+         114±1μs          569±9μs     4.99  groupby.GroupByMethods.time_dtype_as_group('int', 'cummax', 'direct')
+       114±0.6μs          566±3μs     4.95  groupby.GroupByMethods.time_dtype_as_group('int', 'cummin', 'direct')
+         115±2μs          565±4μs     4.90  groupby.GroupByMethods.time_dtype_as_group('int', 'cummax', 'transformation')
+      32.2±0.1ms          155±4ms     4.83  eval.Eval.time_and('python', 1)
+      3.54±0.1μs       16.7±0.1μs     4.71  indexing.DataFrameStringIndexing.time_ix
+         124±4μs          583±6μs     4.68  groupby.GroupByMethods.time_dtype_as_group('float', 'cumsum', 'transformation')
+         125±4μs         583±30μs     4.68  groupby.GroupByMethods.time_dtype_as_field('int', 'cumsum', 'transformation')
+         124±5μs          573±2μs     4.63  groupby.GroupByMethods.time_dtype_as_group('int', 'cumsum', 'direct')
+         123±5μs          569±5μs     4.63  groupby.GroupByMethods.time_dtype_as_field('int', 'cumsum', 'direct')
+         124±6μs          574±5μs     4.62  groupby.GroupByMethods.time_dtype_as_group('int', 'cumsum', 'transformation')
+         128±5μs          577±3μs     4.52  groupby.GroupByMethods.time_dtype_as_group('float', 'cumsum', 'direct')
+      38.2±0.4ms          160±2ms     4.18  eval.Eval.time_and('python', 'all')
+      59.4±0.5μs          232±1μs     3.91  period.PeriodUnaryMethods.time_to_timestamp('min')
+      60.2±0.3μs          233±2μs     3.87  period.PeriodUnaryMethods.time_to_timestamp('M')
+      60.6±0.8μs        234±0.9μs     3.86  period.PeriodProperties.time_property('min', 'start_time')
+      60.5±0.5μs          232±2μs     3.84  period.PeriodProperties.time_property('M', 'start_time')
+      40.6±0.2ms          153±9ms     3.76  frame_methods.Dropna.time_dropna('all', 1)
+      38.4±0.3ms          144±9ms     3.75  frame_methods.Dropna.time_dropna('all', 0)
+     3.18±0.01μs       11.7±0.2μs     3.66  multiindex_object.GetLoc.time_string_get_loc
+     3.12±0.01ms       11.3±0.1ms     3.62  multiindex_object.GetLoc.time_small_get_loc_warm
+         102±3ms          360±4ms     3.52  groupby.Groups.time_series_groups('int64_large')
+     3.19±0.02ms      10.8±0.08ms     3.40  multiindex_object.GetLoc.time_med_get_loc_warm
+     27.3±0.08ms         90.3±2ms     3.30  binary_ops.Ops.time_frame_multi_and(False, 1)
+      51.2±0.4μs        169±0.7μs     3.30  period.Indexing.time_unique
+     3.36±0.09μs       11.1±0.1μs     3.30  multiindex_object.GetLoc.time_med_get_loc
+     5.58±0.02ms         18.3±2ms     3.28  frame_methods.Equals.time_frame_nonunique_equal
+      27.4±0.2ms         89.2±2ms     3.25  binary_ops.Ops.time_frame_multi_and(False, 'default')
+      53.7±0.5μs          172±2μs     3.21  period.PeriodUnaryMethods.time_now('min')
+     5.58±0.04ms         17.8±2ms     3.19  frame_methods.Equals.time_frame_nonunique_unequal
+      84.9±0.6μs          267±1μs     3.14  period.Algorithms.time_drop_duplicates('index')
+       227±0.9μs          696±8μs     3.06  groupby.GroupByMethods.time_dtype_as_field('int', 'cumprod', 'direct')
+         230±1μs          692±3μs     3.01  groupby.GroupByMethods.time_dtype_as_field('int', 'cumprod', 'transformation')
+         142±2μs          426±5μs     3.00  period.Indexing.time_intersection
+       139±0.9μs          415±4μs     2.98  ctors.SeriesConstructors.time_series_constructor(<function SeriesConstructors.<lambda> at 0x1133397b8>, False)
+      31.2±0.1ms         92.9±1ms     2.98  binary_ops.Ops.time_frame_multi_and(True, 1)
+       139±0.7μs          412±4μs     2.97  ctors.SeriesConstructors.time_series_constructor(<function SeriesConstructors.<lambda> at 0x1133396a8>, False)
+       139±0.7μs          410±3μs     2.96  ctors.SeriesConstructors.time_series_constructor(<function SeriesConstructors.<lambda> at 0x11332b730>, False)
+       246±0.8μs          723±2μs     2.94  groupby.GroupByMethods.time_dtype_as_group('float', 'cumprod', 'transformation')
+       151±0.4μs          442±5μs     2.93  ctors.SeriesConstructors.time_series_constructor(<function SeriesConstructors.<lambda> at 0x1133397b8>, True)
+       106±0.8μs        310±0.3μs     2.93  period.PeriodIndexConstructor.time_from_date_range('D')
+         150±1μs          437±5μs     2.92  ctors.SeriesConstructors.time_series_constructor(<function SeriesConstructors.<lambda> at 0x1133396a8>, True)
+       248±0.9μs          723±4μs     2.92  groupby.GroupByMethods.time_dtype_as_group('float', 'cumprod', 'direct')
+       150±0.7μs          437±3μs     2.91  ctors.SeriesConstructors.time_series_constructor(<function SeriesConstructors.<lambda> at 0x11332b730>, True)
+       139±0.6μs          403±3μs     2.90  ctors.SeriesConstructors.time_series_constructor(<function SeriesConstructors.<lambda> at 0x113339840>, False)
+     9.12±0.08μs       26.5±0.2μs     2.90  timestamp.TimestampProperties.time_is_quarter_end(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
+         151±2μs          435±4μs     2.89  ctors.SeriesConstructors.time_series_constructor(<function SeriesConstructors.<lambda> at 0x113339840>, True)
+     9.30±0.09μs       26.9±0.7μs     2.89  timestamp.TimestampProperties.time_is_year_start(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
+         249±2μs          718±6μs     2.88  groupby.GroupByMethods.time_dtype_as_group('int', 'cumprod', 'direct')
+         251±5μs          721±3μs     2.87  groupby.GroupByMethods.time_dtype_as_group('int', 'cumprod', 'transformation')
+      9.24±0.1μs       26.3±0.1μs     2.85  timestamp.TimestampProperties.time_is_year_end(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
+         142±5ms          404±7ms     2.84  groupby.Groups.time_series_groups('object_large')
+      21.1±0.1ms         59.9±2ms     2.83  groupby.ApplyDictReturn.time_groupby_apply_dict_return
+      9.24±0.1μs       26.1±0.2μs     2.82  timestamp.TimestampProperties.time_is_month_start(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
+      24.6±0.1μs       69.1±0.8μs     2.80  indexing.NumericSeriesIndexing.time_iloc_list_like(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
+      9.28±0.1μs       25.9±0.1μs     2.79  timestamp.TimestampProperties.time_is_quarter_start(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
+     24.6±0.08μs       68.7±0.1μs     2.79  indexing.NumericSeriesIndexing.time_iloc_list_like(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+      53.8±0.2μs        149±0.8μs     2.78  period.Indexing.time_series_loc
+        11.3±2ms       31.3±0.1ms     2.77  io.msgpack.MSGPack.time_read_msgpack
+      9.53±0.2μs       26.3±0.2μs     2.76  timestamp.TimestampProperties.time_is_leap_year(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
+     6.58±0.04μs       18.1±0.6μs     2.75  timestamp.TimestampAcrossDst.time_replace_across_dst
+      25.0±0.1μs         68.8±1μs     2.75  indexing.NumericSeriesIndexing.time_iloc_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+      36.5±0.2ms         99.3±1ms     2.72  binary_ops.Ops.time_frame_multi_and(True, 'default')
+         725±3μs      1.95±0.02ms     2.68  io.csv.ReadCSVParseDates.time_multiple_date
+      25.7±0.2μs       68.9±0.3μs     2.68  indexing.NumericSeriesIndexing.time_iloc_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+         330±2μs          882±2μs     2.68  period.Algorithms.time_value_counts('index')
+     9.77±0.05μs       25.9±0.2μs     2.65  timestamp.TimestampProperties.time_is_month_end(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
+      36.8±0.1ms        97.0±10ms     2.64  frame_methods.Interpolate.time_interpolate(None)
+        44.2±1ms          116±3ms     2.62  join_merge.MergeAsof.time_by_int
+      54.2±0.3μs          140±2μs     2.58  indexing.NumericSeriesIndexing.time_ix_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+       120±0.6ms          307±2ms     2.56  groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'transformation')
+      52.0±0.2ms          133±1ms     2.55  groupby.GroupByMethods.time_dtype_as_field('int', 'unique', 'direct')
+      76.5±0.7ms          195±2ms     2.55  groupby.GroupByMethods.time_dtype_as_group('int', 'unique', 'direct')
+       120±0.6ms          306±3ms     2.54  groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'direct')
+      77.2±0.4ms          196±3ms     2.54  groupby.GroupByMethods.time_dtype_as_group('int', 'unique', 'transformation')
+      53.8±0.4ms          137±3ms     2.54  groupby.GroupByMethods.time_dtype_as_field('float', 'unique', 'direct')
+      39.2±0.3μs       99.2±0.4μs     2.53  indexing.NumericSeriesIndexing.time_ix_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+      53.2±0.3ms          133±2ms     2.51  groupby.GroupByMethods.time_dtype_as_field('int', 'unique', 'transformation')
+        93.8±3ms        235±0.7ms     2.50  reshape.WideToLong.time_wide_to_long_big
+     7.76±0.04μs       19.4±0.1μs     2.49  timestamp.TimestampOps.time_replace_tz(None)
+       123±0.7ms          306±2ms     2.49  groupby.GroupByMethods.time_dtype_as_group('datetime', 'unique', 'transformation')
+      54.3±0.1ms          135±1ms     2.49  groupby.GroupByMethods.time_dtype_as_field('float', 'unique', 'transformation')
+         170±2μs          418±4μs     2.47  indexing.NonNumericSeriesIndexing.time_getitem_label_slice('datetime', 'nonunique_monotonic_inc')
+         125±1ms          305±2ms     2.45  groupby.GroupByMethods.time_dtype_as_group('datetime', 'unique', 'direct')
+      25.1±0.2μs       61.1±0.2μs     2.43  indexing.NumericSeriesIndexing.time_iloc_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+        1.90±0ms      4.56±0.03ms     2.40  binary_ops.Timeseries.time_timestamp_series_compare(None)
+     1.90±0.01ms      4.54±0.05ms     2.39  binary_ops.Timeseries.time_series_timestamp_compare(None)
+      25.6±0.5μs       60.8±0.2μs     2.38  indexing.NumericSeriesIndexing.time_iloc_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
+      25.6±0.1μs       60.4±0.2μs     2.36  indexing.NumericSeriesIndexing.time_iloc_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+      25.7±0.1μs       60.2±0.6μs     2.34  indexing.NumericSeriesIndexing.time_iloc_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+      8.03±0.1μs       18.8±0.2μs     2.34  ctors.SeriesDtypesConstructors.time_dtindex_from_series
+        844±10ms       1.97±0.03s     2.34  stat_ops.FrameMultiIndexOps.time_op([0, 1], 'mad')
+      13.1±0.2μs       30.4±0.3μs     2.33  timestamp.TimestampOps.time_replace_tz('US/Eastern')
+      68.1±0.5ms        157±0.6ms     2.30  groupby.GroupByMethods.time_dtype_as_field('object', 'unique', 'direct')
+      68.4±0.5ms          157±1ms     2.30  groupby.GroupByMethods.time_dtype_as_field('object', 'unique', 'transformation')
+      66.2±0.8ms          149±1ms     2.25  groupby.GroupByMethods.time_dtype_as_field('datetime', 'unique', 'direct')
+      33.9±0.2μs       75.9±0.2μs     2.24  indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+         171±3μs        381±0.8μs     2.23  multiindex_object.Values.time_datetime_level_values_sliced
+      65.8±0.6ms        145±0.5ms     2.21  groupby.GroupByMethods.time_dtype_as_field('datetime', 'unique', 'transformation')
+        98.4±1ms          217±2ms     2.20  stat_ops.FrameMultiIndexOps.time_op(1, 'mad')
+      34.7±0.2μs         76.3±1μs     2.20  indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
+         745±5μs      1.64±0.01ms     2.20  io.csv.ReadCSVParseDates.time_baseline
+      48.4±0.6μs          106±2μs     2.18  indexing.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+      34.6±0.2μs       75.2±0.2μs     2.18  indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+      34.8±0.3μs       75.6±0.7μs     2.17  indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+         816±8μs      1.77±0.02ms     2.17  indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime', 'unique_monotonic_inc')
+         494±5μs      1.06±0.01ms     2.15  groupby.GroupByMethods.time_dtype_as_group('object', 'unique', 'transformation')
+        500±10μs      1.07±0.01ms     2.14  groupby.GroupByMethods.time_dtype_as_group('object', 'unique', 'direct')
+      36.7±0.1μs       78.0±0.5μs     2.12  indexing.NumericSeriesIndexing.time_ix_scalar(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+      49.5±0.9μs          105±1μs     2.11  timeseries.AsOf.time_asof_single_early('DataFrame')
+      61.7±0.2ms          130±7ms     2.11  frame_methods.Interpolate.time_interpolate('infer')
+         880±6μs       1.84±0.1ms     2.09  frame_methods.Interpolate.time_interpolate_some_good(None)
+      43.1±0.3μs         89.6±1μs     2.08  indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
+      36.4±0.2μs         75.4±2μs     2.07  indexing.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+      43.0±0.2μs       88.2±0.3μs     2.05  indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+      43.9±0.3μs       88.6±0.4μs     2.02  indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+      28.4±0.5μs       57.0±0.6μs     2.01  indexing.NumericSeriesIndexing.time_ix_scalar(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+      44.0±0.3μs       88.2±0.6μs     2.00  indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+      1.64±0.02s       3.28±0.08s     2.00  sparse.SparseDataFrameConstructor.time_constructor
+      80.4±0.9ms        160±0.6ms     1.99  join_merge.MergeAsof.time_by_object
+      32.5±0.2μs         64.6±1μs     1.98  indexing.NumericSeriesIndexing.time_ix_scalar(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+      47.8±0.2μs       93.6±0.9μs     1.96  groupby.GroupByMethods.time_dtype_as_group('float', 'size', 'transformation')
+      47.8±0.7μs         93.5±1μs     1.96  groupby.GroupByMethods.time_dtype_as_field('int', 'size', 'transformation')
+      47.7±0.6μs       93.3±0.2μs     1.96  groupby.GroupByMethods.time_dtype_as_group('datetime', 'size', 'transformation')
+      8.76±0.1ms       17.0±0.2ms     1.94  frame_methods.Repr.time_repr_tall
+      1.19±0.01s       2.30±0.01s     1.94  timeseries.ToDatetimeNONISO8601.time_different_offset
+     1.66±0.01ms      3.23±0.01ms     1.94  reshape.SimpleReshape.time_stack
+      47.9±0.2μs       92.4±0.9μs     1.93  groupby.GroupByMethods.time_dtype_as_field('float', 'size', 'direct')
+      47.7±0.3μs         92.2±1μs     1.93  groupby.GroupByMethods.time_dtype_as_field('object', 'size', 'direct')
+      47.9±0.3μs         92.4±1μs     1.93  groupby.GroupByMethods.time_dtype_as_field('datetime', 'size', 'direct')
+      47.6±0.1μs       91.9±0.5μs     1.93  groupby.GroupByMethods.time_dtype_as_field('int', 'size', 'direct')
+      47.5±0.2μs       91.6±0.5μs     1.93  groupby.GroupByMethods.time_dtype_as_field('float', 'size', 'transformation')
+      48.0±0.5μs       92.2±0.8μs     1.92  groupby.GroupByMethods.time_dtype_as_group('float', 'size', 'direct')
+      48.0±0.3μs       92.2±0.5μs     1.92  groupby.GroupByMethods.time_dtype_as_group('int', 'size', 'direct')
+      47.9±0.6μs       91.7±0.5μs     1.92  groupby.GroupByMethods.time_dtype_as_field('datetime', 'size', 'transformation')
+      48.3±0.2μs       92.5±0.4μs     1.91  groupby.GroupByMethods.time_dtype_as_group('datetime', 'size', 'direct')
+      48.1±0.2μs       92.0±0.9μs     1.91  groupby.GroupByMethods.time_dtype_as_group('int', 'size', 'transformation')
+      48.3±0.2μs       91.8±0.7μs     1.90  groupby.GroupByMethods.time_dtype_as_group('object', 'size', 'transformation')
+      82.2±0.5μs          156±2μs     1.90  indexing.CategoricalIndexIndexing.time_getitem_bool_array('monotonic_decr')
+      48.1±0.3μs       91.4±0.5μs     1.90  groupby.GroupByMethods.time_dtype_as_field('object', 'size', 'transformation')
+      63.0±0.4μs          119±5μs     1.90  groupby.GroupByMethods.time_dtype_as_field('int', 'count', 'direct')
+      63.2±0.8μs          119±1μs     1.88  groupby.GroupByMethods.time_dtype_as_field('int', 'count', 'transformation')
+       230±0.8ms          432±3ms     1.87  groupby.Transform.time_transform_lambda_max
+      62.7±0.8μs          117±1μs     1.86  groupby.GroupByMethods.time_dtype_as_group('int', 'count', 'transformation')
+      62.1±0.5μs        115±0.6μs     1.86  groupby.GroupByMethods.time_dtype_as_field('float', 'count', 'transformation')
+      61.8±0.2μs        115±0.8μs     1.85  groupby.GroupByMethods.time_dtype_as_field('float', 'count', 'direct')
+      63.4±0.6μs        118±0.5μs     1.85  groupby.GroupByMethods.time_dtype_as_group('datetime', 'count', 'direct')
+      63.6±0.1μs          118±1μs     1.85  groupby.GroupByMethods.time_dtype_as_group('datetime', 'count', 'transformation')
+      25.7±0.2μs       47.5±0.4μs     1.85  indexing.NumericSeriesIndexing.time_ix_scalar(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+      63.0±0.6μs          116±1μs     1.85  groupby.GroupByMethods.time_dtype_as_group('int', 'count', 'direct')
+     13.6±0.08μs       25.1±0.4μs     1.85  ctors.SeriesDtypesConstructors.time_index_from_array_floats
+     2.92±0.02ms         5.39±1ms     1.85  gil.ParallelRolling.time_rolling('var')
+      49.3±0.3μs       91.0±0.6μs     1.85  groupby.GroupByMethods.time_dtype_as_group('object', 'size', 'direct')
+      63.8±0.4μs        117±0.7μs     1.84  groupby.GroupByMethods.time_dtype_as_group('float', 'count', 'transformation')
+        63.5±1μs          116±3μs     1.83  groupby.GroupByMethods.time_dtype_as_group('float', 'count', 'direct')
+      95.6±0.9μs          174±6μs     1.83  frame_methods.GetDtypeCounts.time_frame_get_dtype_counts
+         972±3μs      1.77±0.01ms     1.82  io.csv.ReadCSVDInferDatetimeFormat.time_read_csv(False, 'ymd')
+      61.7±0.4μs        112±0.7μs     1.82  groupby.GroupByMethods.time_dtype_as_field('datetime', 'count', 'transformation')
+      63.4±0.7μs        115±0.3μs     1.82  groupby.GroupByMethods.time_dtype_as_group('object', 'count', 'transformation')
+      61.6±0.4μs        112±0.8μs     1.81  groupby.GroupByMethods.time_dtype_as_field('datetime', 'count', 'direct')
+      64.4±0.6μs          115±1μs     1.79  groupby.GroupByMethods.time_dtype_as_group('object', 'count', 'direct')
+        29.1±2ms         51.9±1ms     1.78  binary_ops.Ops.time_frame_comparison(False, 1)
+        28.7±2ms       51.1±0.6ms     1.78  binary_ops.Ops.time_frame_comparison(False, 'default')
+     18.6±0.09μs       33.0±0.4μs     1.77  ctors.SeriesDtypesConstructors.time_dtindex_from_index_with_series
+     2.48±0.03ms      4.40±0.02ms     1.77  reindex.DropDuplicates.time_frame_drop_dups_bool(True)
+        1.08±0ms      1.90±0.02ms     1.76  io.csv.ReadCSVDInferDatetimeFormat.time_read_csv(False, 'iso8601')
+      14.8±0.1μs       26.2±0.2μs     1.76  inference.ToNumeric.time_from_float('ignore')
+         378±3μs         660±10μs     1.74  indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+      15.0±0.1μs       25.9±0.4μs     1.73  inference.ToNumeric.time_from_float('coerce')
+     2.00±0.01ms       3.45±0.1ms     1.73  frame_methods.Interpolate.time_interpolate_some_good('infer')
+     3.44±0.01μs      5.94±0.02μs     1.72  inference.ToNumericDowncast.time_downcast('int32', None)
+      29.2±0.2μs       50.3±0.7μs     1.72  ctors.SeriesConstructors.time_series_constructor(<function SeriesConstructors.<lambda> at 0x11332b7b8>, True)
+      84.8±0.4μs          145±1μs     1.71  indexing.NumericSeriesIndexing.time_ix_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+      19.9±0.3μs       33.9±0.2μs     1.71  ctors.SeriesDtypesConstructors.time_index_from_array_string
+         116±1ms          197±9ms     1.70  frame_methods.Iteration.time_iterrows
+       154±0.2μs          260±5μs     1.69  ctors.SeriesConstructors.time_series_constructor(<function SeriesConstructors.<lambda> at 0x11332b840>, True)
+         178±1ms          301±5ms     1.69  sparse.SparseDataFrameConstructor.time_from_scipy
+         254±1μs          427±5μs     1.68  indexing.NumericSeriesIndexing.time_ix_list_like(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+     1.00±0.01ms      1.69±0.01ms     1.68  timeseries.ResampleDataFrame.time_method('max')
+      80.1±0.9μs          134±2μs     1.67  indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('datetime', 'nonunique_monotonic_inc')
+      70.6±0.6μs          117±1μs     1.66  indexing.NumericSeriesIndexing.time_ix_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+     3.02±0.02ms      5.02±0.01ms     1.66  reindex.DropDuplicates.time_frame_drop_dups_bool(False)
+         196±2ns          324±2ns     1.65  multiindex_object.Integer.time_is_monotonic
+     1.02±0.01ms      1.69±0.03ms     1.65  timeseries.ResampleDataFrame.time_method('min')
+         142±3ms          234±2ms     1.65  indexing.NumericSeriesIndexing.time_getitem_lists(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+         140±2ms          231±3ms     1.65  indexing.NumericSeriesIndexing.time_loc_array(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+         123±1μs         203±70μs     1.65  groupby.GroupByMethods.time_dtype_as_field('int', 'first', 'transformation')
+      24.9±0.2μs       40.9±0.5μs     1.64  indexing.NumericSeriesIndexing.time_iloc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+      72.4±0.3μs          119±2μs     1.64  series_methods.SeriesConstructor.time_constructor(None)
+         241±1μs          395±4μs     1.64  indexing.NumericSeriesIndexing.time_ix_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+         141±3ms          231±3ms     1.64  indexing.NumericSeriesIndexing.time_getitem_array(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+     1.71±0.04ms      2.79±0.01ms     1.63  reshape.Melt.time_melt_dataframe
+     3.36±0.02μs      5.47±0.02μs     1.63  offset.OnOffset.time_on_offset(<MonthBegin>)
+       140±0.6ms          228±2ms     1.63  indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+       143±0.8μs          231±3μs     1.62  ctors.SeriesConstructors.time_series_constructor(<function SeriesConstructors.<lambda> at 0x11332b840>, False)
+       141±0.9ms          229±3ms     1.62  indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+     2.90±0.01μs       4.68±0.2μs     1.62  categoricals.CategoricalSlicing.time_getitem_scalar('non_monotonic')
+         107±1μs          173±2μs     1.61  timeseries.DatetimeIndex.time_unique('dst')
+       123±0.9ms          198±3ms     1.61  groupby.GroupByMethods.time_dtype_as_group('int', 'skew', 'transformation')
+     10.3±0.03μs       16.5±0.2μs     1.61  offset.OffestDatetimeArithmetic.time_apply(<DateOffset: days=2, months=2>)
+         191±2ms          306±1ms     1.60  groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'direct')
+      24.9±0.2μs       39.9±0.2μs     1.60  indexing.NumericSeriesIndexing.time_iloc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+       124±0.4ms          198±1ms     1.59  groupby.GroupByMethods.time_dtype_as_group('int', 'skew', 'direct')
+      9.81±0.1ms       15.6±0.7ms     1.59  eval.Query.time_query_datetime_column
+      73.9±0.9ms        117±0.8ms     1.59  sparse.SparseDataFrameConstructor.time_from_dict
+       192±0.8ms          304±4ms     1.59  groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'transformation')
+      40.9±0.5μs       64.9±0.3μs     1.58  timeseries.SortIndex.time_get_slice(False)
+     4.25±0.03ms      6.72±0.03ms     1.58  categoricals.Rank.time_rank_int
+      81.2±0.2ms        128±0.9ms     1.57  groupby.GroupByMethods.time_dtype_as_field('float', 'skew', 'transformation')
+     1.43±0.01ms      2.26±0.01ms     1.57  io.csv.ReadCSVDInferDatetimeFormat.time_read_csv(True, 'iso8601')
+      51.6±0.5μs       81.1±0.8μs     1.57  indexing.NumericSeriesIndexing.time_ix_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+         448±3μs          703±9μs     1.57  indexing.MultiIndexing.time_series_ix
+        1.40±0ms      2.19±0.01ms     1.57  io.csv.ReadCSVDInferDatetimeFormat.time_read_csv(True, 'ymd')
+         227±2μs         354±20μs     1.56  frame_ctor.FromRecords.time_frame_from_records_generator(1000)
+        82.1±1ms          128±1ms     1.56  groupby.GroupByMethods.time_dtype_as_field('float', 'skew', 'direct')
+     3.20±0.01ms       4.99±0.2ms     1.56  frame_methods.Apply.time_apply_pass_thru
+      85.1±0.6ms        132±0.7ms     1.56  groupby.GroupByMethods.time_dtype_as_field('int', 'skew', 'direct')
+      85.1±0.6ms          132±1ms     1.55  groupby.GroupByMethods.time_dtype_as_field('int', 'skew', 'transformation')
+       111±0.9μs          171±2μs     1.55  indexing.DataFrameNumericIndexing.time_iloc_dups
+     4.41±0.05ms      6.82±0.03ms     1.55  categoricals.Rank.time_rank_int_cat_ordered
+     4.44±0.04ms      6.82±0.06ms     1.54  categoricals.Rank.time_rank_string_cat_ordered
+     3.86±0.02ms         5.93±2ms     1.53  gil.ParallelRolling.time_rolling('skew')
+        83.1±1ms          127±9ms     1.53  frame_methods.Apply.time_apply_axis_1
+     4.58±0.05ms      6.99±0.07ms     1.53  categoricals.Rank.time_rank_int_cat
+      6.00±0.1μs      9.17±0.04μs     1.53  timestamp.TimestampOps.time_replace_None('US/Eastern')
+        83.5±2μs        127±0.6μs     1.53  groupby.GroupByMethods.time_dtype_as_field('float', 'first', 'direct')
+         111±2ms          169±4ms     1.52  sparse.SparseSeriesToFrame.time_series_to_frame
+      22.9±0.2ms         34.7±1ms     1.52  frame_methods.Equals.time_frame_object_unequal
+     6.12±0.07ms       9.28±0.4ms     1.52  frame_methods.Apply.time_apply_lambda_mean
+        82.2±1μs          124±4μs     1.51  indexing.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+      83.3±0.3μs          126±1μs     1.51  groupby.GroupByMethods.time_dtype_as_field('float', 'first', 'transformation')
+     2.33±0.01ms      3.52±0.05ms     1.51  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'max')
+         287±2ms         431±10ms     1.50  groupby.GroupByMethods.time_dtype_as_field('int', 'mad', 'direct')
+     2.37±0.03ms      3.57±0.04ms     1.50  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'max')
+     2.70±0.03ms      4.06±0.04ms     1.50  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'std')
+         652±4ms          978±5ms     1.50  groupby.GroupByMethods.time_dtype_as_group('float', 'mad', 'transformation')
+     2.80±0.05ms      4.19±0.02ms     1.50  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'std')
+      23.8±0.1ms         35.6±9ms     1.50  gil.ParallelFactorize.time_loop(2)
+     2.79±0.01ms      4.18±0.01ms     1.50  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'std')
+        529±10μs          793±7μs     1.50  indexing.MultiIndexing.time_frame_ix
+         159±1ms          238±1ms     1.50  timeseries.ToDatetimeISO8601.time_iso8601_tz_spaceformat
+     2.74±0.03ms      4.10±0.03ms     1.50  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'std')
+     2.35±0.01ms      3.50±0.02ms     1.49  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'min')
+        85.2±4μs          127±1μs     1.49  groupby.GroupByMethods.time_dtype_as_field('float', 'max', 'direct')
+         287±2ms         427±10ms     1.49  groupby.GroupByMethods.time_dtype_as_field('int', 'mad', 'transformation')
+        87.0±4μs          129±2μs     1.48  groupby.GroupByMethods.time_dtype_as_field('float', 'min', 'transformation')
+      58.3±0.1μs        86.4±10μs     1.48  frame_methods.Dtypes.time_frame_dtypes
+        87.9±4μs          130±1μs     1.48  groupby.GroupByMethods.time_dtype_as_field('float', 'min', 'direct')
+        87.8±4μs          130±1μs     1.48  groupby.GroupByMethods.time_dtype_as_field('float', 'max', 'transformation')
+     2.40±0.01ms      3.54±0.02ms     1.48  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'min')
+         324±1μs          478±1μs     1.48  groupby.GroupByMethods.time_dtype_as_field('float', 'sem', 'direct')
+       283±0.8ms          417±3ms     1.47  groupby.GroupByMethods.time_dtype_as_field('float', 'mad', 'direct')
+         663±2ms          978±5ms     1.47  groupby.GroupByMethods.time_dtype_as_group('float', 'mad', 'direct')
+         282±2ms          415±2ms     1.47  groupby.GroupByMethods.time_dtype_as_field('float', 'mad', 'transformation')
+         424±2ms          621±3ms     1.47  groupby.GroupByMethods.time_dtype_as_group('int', 'mad', 'direct')
+        84.4±5μs        124±0.5μs     1.47  groupby.GroupByMethods.time_dtype_as_field('float', 'prod', 'transformation')
+         176±1μs          258±3μs     1.47  groupby.GroupByMethods.time_dtype_as_field('object', 'last', 'direct')
+         425±4ms          621±4ms     1.46  groupby.GroupByMethods.time_dtype_as_group('int', 'mad', 'transformation')
+         325±5μs          476±8μs     1.46  groupby.GroupByMethods.time_dtype_as_field('float', 'sem', 'transformation')
+        84.2±4μs        123±0.6μs     1.46  groupby.GroupByMethods.time_dtype_as_field('float', 'prod', 'direct')
+      81.1±0.7μs          118±1μs     1.46  groupby.GroupByMethods.time_dtype_as_field('float', 'last', 'transformation')
+      25.1±0.3ms       36.5±0.8ms     1.46  strings.Methods.time_get
+       284±0.8μs          412±3μs     1.45  multiindex_object.Duplicates.time_remove_unused_levels
+      81.4±0.3μs          118±1μs     1.45  groupby.GroupByMethods.time_dtype_as_field('float', 'last', 'direct')
+         333±2μs          482±2μs     1.45  indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+         177±1μs          256±3μs     1.45  groupby.GroupByMethods.time_dtype_as_field('object', 'last', 'transformation')
+         708±4μs      1.02±0.01ms     1.45  timeseries.ResampleDataFrame.time_method('mean')
+     1.08±0.01ms      1.56±0.02ms     1.45  sparse.FromCoo.time_sparse_series_from_coo
+       121±0.5μs          174±1μs     1.44  groupby.GroupByMethods.time_dtype_as_group('datetime', 'min', 'direct')
+        85.1±5μs        122±0.9μs     1.44  groupby.GroupByMethods.time_dtype_as_field('float', 'sum', 'transformation')
+         182±2μs        262±0.5μs     1.44  groupby.GroupByMethods.time_dtype_as_field('object', 'first', 'direct')
+         742±4μs      1.07±0.01ms     1.44  indexing.PanelIndexing.time_subset
+       112±0.3μs          161±8μs     1.43  groupby.GroupByMethods.time_dtype_as_field('int', 'last', 'direct')
+      9.18±0.1μs       13.2±0.1μs     1.43  timestamp.TimestampConstruction.time_parse_iso8601_tz
+       125±0.1μs        179±0.8μs     1.43  groupby.GroupByMethods.time_dtype_as_field('float', 'std', 'transformation')
+         107±1μs        154±0.7μs     1.43  groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'transformation')
+       182±0.6μs          259±1μs     1.43  groupby.GroupByMethods.time_dtype_as_field('object', 'first', 'transformation')
+         376±2μs          537±4μs     1.43  groupby.GroupByMethods.time_dtype_as_group('float', 'sem', 'direct')
+     1.57±0.02ms      2.24±0.05ms     1.43  io.csv.ReadCSVFloatPrecision.time_read_csv(';', '_', None)
+     2.38±0.01ms      3.39±0.04ms     1.43  categoricals.Concat.time_union
+        91.2±5μs        130±0.6μs     1.42  groupby.GroupByMethods.time_dtype_as_field('float', 'var', 'direct')
+        85.4±5μs        121±0.9μs     1.42  groupby.GroupByMethods.time_dtype_as_field('float', 'sum', 'direct')
+     6.63±0.03ms       9.42±0.5ms     1.42  frame_methods.Apply.time_apply_np_mean
+       108±0.5μs        153±0.4μs     1.42  groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'direct')
+        1.58±0ms      2.24±0.02ms     1.42  io.csv.ReadCSVFloatPrecision.time_read_csv(',', '_', 'high')
+       121±0.5μs        172±0.5μs     1.42  groupby.GroupByMethods.time_dtype_as_field('object', 'count', 'direct')
+       118±0.6μs          167±2μs     1.42  groupby.GroupByMethods.time_dtype_as_group('float', 'first', 'direct')
+         785±7μs      1.11±0.01ms     1.42  period.Indexing.time_align
+       121±0.4μs          172±1μs     1.42  groupby.GroupByMethods.time_dtype_as_group('datetime', 'max', 'transformation')
+         374±3μs          528±4μs     1.41  groupby.GroupByMethods.time_dtype_as_field('int', 'sem', 'direct')
+       115±0.7μs        163±0.9μs     1.41  groupby.GroupByMethods.time_dtype_as_group('float', 'last', 'direct')
+     2.66±0.02ms      3.76±0.05ms     1.41  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'min')
+         376±4μs          532±6μs     1.41  groupby.GroupByMethods.time_dtype_as_field('int', 'sem', 'transformation')
+       109±0.7μs          154±2μs     1.41  groupby.GroupByMethods.time_dtype_as_group('object', 'first', 'transformation')
+        2.71±0ms      3.83±0.05ms     1.41  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'max')
+     1.51±0.02ms      2.13±0.07ms     1.41  io.csv.ReadCSVFloatPrecision.time_read_csv(';', '.', 'high')
+         112±1μs          158±4μs     1.41  groupby.GroupByMethods.time_dtype_as_field('int', 'last', 'transformation')
+     2.99±0.05μs       4.21±0.4μs     1.41  categoricals.CategoricalSlicing.time_getitem_scalar('monotonic_incr')
+         122±1μs        171±0.5μs     1.41  groupby.GroupByMethods.time_dtype_as_group('datetime', 'max', 'direct')
+      15.5±0.2ms       21.8±0.3ms     1.41  io.msgpack.MSGPack.time_write_msgpack
+       120±0.8μs          168±1μs     1.41  groupby.GroupByMethods.time_dtype_as_group('datetime', 'first', 'direct')
+       116±0.7μs          163±1μs     1.41  groupby.GroupByMethods.time_dtype_as_group('float', 'last', 'transformation')
+         221±3ms        310±0.9ms     1.41  frame_methods.Duplicated.time_frame_duplicated_wide
+      47.4±0.6ms       66.6±0.8ms     1.41  index_object.IndexAppend.time_append_range_list
+     1.57±0.01ms      2.21±0.06ms     1.40  io.csv.ReadCSVFloatPrecision.time_read_csv(';', '_', 'high')
+         238±3μs          334±6μs     1.40  frame_ctor.FromRecords.time_frame_from_records_generator(None)
+       113±0.7μs          159±1μs     1.40  groupby.GroupByMethods.time_dtype_as_group('int', 'last', 'transformation')
+        1.57±0ms      2.21±0.05ms     1.40  io.csv.ReadCSVFloatPrecision.time_read_csv(',', '_', None)
+       120±0.8μs          168±3μs     1.40  groupby.GroupByMethods.time_dtype_as_group('datetime', 'first', 'transformation')
+         435±3μs         609±10μs     1.40  groupby.GroupByMethods.time_dtype_as_group('int', 'sem', 'transformation')
+         246±1μs         344±30μs     1.40  groupby.GroupByMethods.time_dtype_as_field('int', 'head', 'transformation')
+       114±0.6μs        160±0.5μs     1.40  groupby.GroupByMethods.time_dtype_as_group('int', 'last', 'direct')
+         127±4μs          177±4μs     1.40  groupby.GroupByMethods.time_dtype_as_field('int', 'max', 'transformation')
+         126±1μs        176±0.8μs     1.40  groupby.GroupByMethods.time_dtype_as_field('float', 'std', 'direct')
+         382±2μs          533±8μs     1.40  groupby.GroupByMethods.time_dtype_as_group('float', 'sem', 'transformation')
+        86.5±5μs        121±0.5μs     1.39  groupby.GroupByMethods.time_dtype_as_field('float', 'mean', 'direct')
+         121±4μs          168±1μs     1.39  groupby.GroupByMethods.time_dtype_as_field('object', 'count', 'transformation')
+         899±4μs      1.25±0.02ms     1.39  groupby.SumMultiLevel.time_groupby_sum_multiindex
+       110±0.4μs        153±0.8μs     1.39  groupby.GroupByMethods.time_dtype_as_field('float', 'median', 'direct')
+        92.7±4μs        129±0.8μs     1.39  groupby.GroupByMethods.time_dtype_as_field('float', 'var', 'transformation')
+       125±0.6μs          175±2μs     1.39  groupby.GroupByMethods.time_dtype_as_group('int', 'first', 'direct')
+         419±1μs         582±30μs     1.39  categoricals.CategoricalSlicing.time_getitem_list('non_monotonic')
+     1.60±0.03ms      2.22±0.07ms     1.39  io.csv.ReadCSVFloatPrecision.time_read_csv(',', '_', 'round_trip')
+      13.5±0.3μs      18.7±0.06μs     1.39  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<DateOffset: days=2, months=2>)
+       119±0.4μs          166±1μs     1.39  groupby.GroupByMethods.time_dtype_as_group('float', 'first', 'transformation')
+       111±0.8μs          154±3μs     1.39  groupby.GroupByMethods.time_dtype_as_group('object', 'first', 'direct')
+         128±4μs          177±7μs     1.38  groupby.GroupByMethods.time_dtype_as_field('int', 'max', 'direct')
+     2.70±0.04ms      3.74±0.04ms     1.38  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'max')
+       124±0.5μs         172±40μs     1.38  groupby.GroupByMethods.time_dtype_as_field('int', 'first', 'direct')
+       171±0.8μs          236±2μs     1.38  groupby.GroupByMethods.time_dtype_as_field('int', 'std', 'direct')
+         123±1μs          171±1μs     1.38  groupby.GroupByMethods.time_dtype_as_group('datetime', 'min', 'transformation')
+         110±1μs          152±1μs     1.38  groupby.GroupByMethods.time_dtype_as_field('float', 'median', 'transformation')
+         127±4μs          175±1μs     1.38  groupby.GroupByMethods.time_dtype_as_group('int', 'max', 'transformation')
+         119±1μs          164±2μs     1.38  groupby.GroupByMethods.time_dtype_as_group('datetime', 'last', 'direct')
+      26.5±0.1ms       36.5±0.1ms     1.38  join_merge.Concat.time_concat_small_frames(0)
+     2.75±0.04ms      3.79±0.07ms     1.38  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'min')
+         127±4μs          175±1μs     1.38  groupby.GroupByMethods.time_dtype_as_field('int', 'min', 'transformation')
+      18.0±0.4μs       24.7±0.4μs     1.37  ctors.SeriesConstructors.time_series_constructor(<function SeriesConstructors.<lambda> at 0x11332b7b8>, False)
+       118±0.5μs        162±0.5μs     1.37  groupby.GroupByMethods.time_dtype_as_group('datetime', 'last', 'transformation')
+     1.55±0.03ms      2.13±0.01ms     1.37  io.csv.ReadCSVFloatPrecision.time_read_csv(';', '_', 'round_trip')
+         174±1μs          239±1μs     1.37  groupby.GroupByMethods.time_dtype_as_group('float', 'std', 'direct')
+        1.53±0ms      2.10±0.04ms     1.37  io.csv.ReadCSVFloatPrecision.time_read_csv(',', '.', 'high')
+         124±4μs          170±1μs     1.37  groupby.GroupByMethods.time_dtype_as_group('float', 'min', 'direct')
+         169±1μs          232±1μs     1.37  groupby.GroupByMethods.time_dtype_as_field('int', 'std', 'transformation')
+     1.60±0.01ms      2.19±0.05ms     1.37  io.csv.ReadCSVFloatPrecision.time_read_csv(',', '.', None)
+         440±2μs          602±4μs     1.37  groupby.GroupByMethods.time_dtype_as_group('int', 'sem', 'direct')
+         127±5μs          174±2μs     1.37  groupby.GroupByMethods.time_dtype_as_group('int', 'max', 'direct')
+     1.60±0.03ms      2.18±0.07ms     1.36  io.csv.ReadCSVFloatPrecision.time_read_csv(';', '.', None)
+      5.63±0.2ms      7.69±0.06ms     1.36  strings.Cat.time_cat(0, None, None, 0.001)
+         127±4μs        173±0.9μs     1.36  groupby.GroupByMethods.time_dtype_as_group('int', 'min', 'transformation')
+         124±5μs        169±0.9μs     1.36  groupby.GroupByMethods.time_dtype_as_group('float', 'min', 'transformation')
+         127±1μs        172±0.6μs     1.36  groupby.GroupByMethods.time_dtype_as_group('int', 'first', 'transformation')
+      43.8±0.2μs       59.6±0.5μs     1.36  indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+         134±8μs          183±4μs     1.36  groupby.GroupByMethods.time_dtype_as_field('int', 'var', 'direct')
+         205±2μs          279±5μs     1.36  timeseries.DatetimeIndex.time_normalize('dst')
+         420±2μs         572±40μs     1.36  categoricals.CategoricalSlicing.time_getitem_list('monotonic_incr')
+         126±4μs          172±4μs     1.36  groupby.GroupByMethods.time_dtype_as_group('float', 'max', 'transformation')
+     6.00±0.01ms      8.16±0.03ms     1.36  categoricals.Rank.time_rank_string_cat
+        89.3±5μs        122±0.4μs     1.36  groupby.GroupByMethods.time_dtype_as_field('float', 'mean', 'transformation')
+         136±8μs          185±4μs     1.36  groupby.GroupByMethods.time_dtype_as_group('float', 'var', 'transformation')
+     3.41±0.01ms      4.64±0.01ms     1.36  rolling.Methods.time_rolling('Series', 1000, 'float', 'std')
+      55.3±0.4ms       75.2±0.3ms     1.36  stat_ops.Correlation.time_corr('spearman')
+         129±4μs        175±0.3μs     1.36  groupby.GroupByMethods.time_dtype_as_field('int', 'min', 'direct')
+      17.6±0.2ms       23.9±0.2ms     1.35  stat_ops.FrameMultiIndexOps.time_op(0, 'kurt')
+     3.50±0.02ms      4.73±0.01ms     1.35  rolling.Methods.time_rolling('Series', 10, 'int', 'std')
+     3.49±0.01ms      4.72±0.02ms     1.35  rolling.Methods.time_rolling('Series', 1000, 'int', 'std')
+     7.50±0.03μs      10.1±0.05μs     1.35  offset.OnOffset.time_on_offset(<YearEnd: month=12>)
+         175±1μs          237±2μs     1.35  groupby.GroupByMethods.time_dtype_as_group('float', 'std', 'transformation')
+      3.45±0.1ms      4.65±0.03ms     1.35  rolling.Methods.time_rolling('Series', 10, 'float', 'std')
+      29.9±0.2μs       40.2±0.4μs     1.35  offset.OffestDatetimeArithmetic.time_subtract(<DateOffset: days=2, months=2>)
+         130±4μs        174±0.9μs     1.35  groupby.GroupByMethods.time_dtype_as_group('int', 'min', 'direct')
+     6.77±0.03μs      9.11±0.05μs     1.34  index_object.Indexing.time_get_loc('Int')
+      9.66±0.1ms       13.0±0.4ms     1.34  categoricals.CategoricalSlicing.time_getitem_bool_array('non_monotonic')
+      10.1±0.2ms      13.6±0.03ms     1.34  timedelta.TimedeltaOps.time_add_td_ts
+      44.3±0.3μs       59.3±0.5μs     1.34  indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+        76.2±2ms        102±0.9ms     1.34  stat_ops.FrameMultiIndexOps.time_op(1, 'kurt')
+     1.35±0.01ms      1.80±0.05ms     1.34  join_merge.Merge.time_merge_dataframe_integer_key(False)
+         127±4μs          170±1μs     1.33  groupby.GroupByMethods.time_dtype_as_group('float', 'max', 'direct')
+        1.33±0ms      1.78±0.06ms     1.33  groupby.Datelike.time_sum('date_range')
+     5.32±0.02ms       7.09±0.2ms     1.33  reindex.DropDuplicates.time_frame_drop_dups(True)
+         293±4ms         391±30ms     1.33  frame_methods.Nunique.time_frame_nunique
+      96.4±0.7μs        128±0.7μs     1.33  join_merge.Concat.time_concat_empty_right(0)
+      61.7±0.1μs       81.9±0.6μs     1.33  indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+     10.8±0.08ms       14.3±0.2ms     1.33  categoricals.Constructor.time_regular
+         106±2ms        140±0.6ms     1.32  index_object.IndexAppend.time_append_obj_list
+      48.5±0.9μs       64.1±0.1μs     1.32  frame_ctor.FromNDArray.time_frame_from_ndarray
+         329±6μs          434±1μs     1.32  timeseries.ResetIndex.time_reest_datetimeindex(None)
+        98.5±2μs        130±0.4μs     1.32  join_merge.Concat.time_concat_empty_left(0)
+      12.7±0.1ms      16.7±0.07ms     1.32  reshape.PivotTable.time_pivot_table
+         139±6μs          184±3μs     1.32  groupby.GroupByMethods.time_dtype_as_group('float', 'var', 'direct')
+      62.9±0.4μs       82.8±0.5μs     1.32  inference.ToNumeric.time_from_str('ignore')
+      50.1±0.6μs       65.8±0.6μs     1.32  indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('datetime', 'unique_monotonic_inc')
+         248±9μs          326±8μs     1.31  groupby.GroupByMethods.time_dtype_as_group('int', 'prod', 'direct')
+         218±2μs          286±2μs     1.31  groupby.GroupByMethods.time_dtype_as_group('object', 'head', 'transformation')
+     10.5±0.08μs       13.8±0.3μs     1.31  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<YearBegin: month=1>)
+      7.04±0.2μs       9.23±0.2μs     1.31  index_object.Indexing.time_get_loc_sorted('Int')
+       216±0.9μs          283±3μs     1.31  groupby.GroupByMethods.time_dtype_as_group('object', 'head', 'direct')
+     7.26±0.04ms       9.52±0.1ms     1.31  indexing.InsertColumns.time_assign_with_setitem
+      59.8±0.4μs         78.3±3μs     1.31  frame_ctor.FromSeries.time_mi_series
+      88.5±0.3μs          115±1μs     1.30  groupby.GroupByMethods.time_dtype_as_field('int', 'shift', 'direct')
+         598±2μs         780±50μs     1.30  frame_methods.Quantile.time_frame_quantile(1)
+       226±0.7μs          294±2μs     1.30  groupby.GroupByMethods.time_dtype_as_group('object', 'tail', 'transformation')
+     2.15±0.02ms      2.80±0.01ms     1.30  groupby.Transform.time_transform_multi_key4
+         226±9μs          294±5μs     1.30  groupby.GroupByMethods.time_dtype_as_field('int', 'prod', 'transformation')
+     3.10±0.02ms      4.04±0.03ms     1.30  io.sas.SAS.time_read_msgpack('xport')
+       241±0.7μs          313±2μs     1.30  groupby.GroupByMethods.time_dtype_as_group('int', 'std', 'transformation')
+      88.6±0.7μs          115±1μs     1.30  groupby.GroupByMethods.time_dtype_as_field('int', 'shift', 'transformation')
+      80.4±0.1μs        105±0.9μs     1.30  groupby.GroupByMethods.time_dtype_as_field('datetime', 'shift', 'direct')
+         229±2μs          298±5μs     1.30  groupby.GroupByMethods.time_dtype_as_group('object', 'tail', 'direct')
+         866±9μs      1.13±0.01ms     1.30  series_methods.ValueCounts.time_value_counts('int')
+         629±4μs          817±5μs     1.30  reindex.DropDuplicates.time_series_drop_dups_int(False)
+     3.75±0.01ms      4.86±0.04ms     1.30  rolling.Pairwise.time_pairwise(1000, 'corr', False)
+         226±1μs          294±2μs     1.30  groupby.GroupByMethods.time_dtype_as_group('float', 'head', 'direct')
+       237±0.3μs          308±3μs     1.30  groupby.GroupByMethods.time_dtype_as_group('datetime', 'tail', 'transformation')
+      89.5±0.2μs          116±1μs     1.30  groupby.GroupByMethods.time_dtype_as_group('int', 'shift', 'transformation')
+      13.0±0.2μs       16.8±0.1μs     1.29  offset.OffestDatetimeArithmetic.time_add(<DateOffset: days=2, months=2>)
+     5.16±0.09ms      6.68±0.03ms     1.29  groupby.Transform.time_transform_multi_key2
+      89.8±0.4μs          116±1μs     1.29  groupby.GroupByMethods.time_dtype_as_group('int', 'shift', 'direct')
+       114±0.5μs        147±0.8μs     1.29  inference.NumericInferOps.time_subtract(<class 'numpy.int8'>)
+      7.83±0.1ms       10.1±0.2ms     1.29  stat_ops.FrameOps.time_op('mad', 'float', 0, False)
+       228±0.6μs          294±3μs     1.29  groupby.GroupByMethods.time_dtype_as_group('float', 'head', 'transformation')
+        71.9±2μs       92.7±0.6μs     1.29  indexing.NumericSeriesIndexing.time_ix_scalar(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+         399±8μs          515±2μs     1.29  timeseries.ResetIndex.time_reest_datetimeindex('US/Eastern')
+         237±1μs          306±2μs     1.29  groupby.GroupByMethods.time_dtype_as_group('datetime', 'tail', 'direct')
+         236±1μs          305±2μs     1.29  groupby.GroupByMethods.time_dtype_as_group('float', 'tail', 'transformation')
+      74.7±0.9μs       96.2±0.9μs     1.29  groupby.GroupByMethods.time_dtype_as_field('float', 'shift', 'direct')
+      80.4±0.8μs        104±0.6μs     1.29  groupby.GroupByMethods.time_dtype_as_field('datetime', 'shift', 'transformation')
+      75.5±0.9μs       97.1±0.6μs     1.29  groupby.GroupByMethods.time_dtype_as_field('float', 'shift', 'transformation')
+         242±3μs          311±2μs     1.29  groupby.GroupByMethods.time_dtype_as_group('int', 'std', 'direct')
+     2.06±0.01ms      2.64±0.06ms     1.28  io.csv.ReadCSVFloatPrecision.time_read_csv(';', '.', 'round_trip')
+         456±3ms          586±7ms     1.28  stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'mad')
+     5.76±0.08ms      7.40±0.09ms     1.28  reindex.DropDuplicates.time_frame_drop_dups_na(True)
+      58.9±0.2μs       75.7±0.8μs     1.28  timeseries.SortIndex.time_sort_index(True)
+         229±1μs          294±2μs     1.28  groupby.GroupByMethods.time_dtype_as_group('datetime', 'head', 'direct')
+         249±3μs          320±4μs     1.28  groupby.GroupByMethods.time_dtype_as_group('int', 'head', 'direct')
+         142±8μs        182±0.8μs     1.28  groupby.GroupByMethods.time_dtype_as_field('int', 'var', 'transformation')
+      45.7±0.5μs       58.6±0.6μs     1.28  indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+         228±2μs          292±3μs     1.28  groupby.GroupByMethods.time_dtype_as_group('datetime', 'head', 'transformation')
+         245±7μs          315±1μs     1.28  groupby.GroupByMethods.time_dtype_as_field('int', 'sum', 'transformation')
+     2.61±0.04ms      3.35±0.01ms     1.28  rolling.Pairwise.time_pairwise(1000, 'cov', False)
+       250±0.9μs          320±1μs     1.28  groupby.GroupByMethods.time_dtype_as_field('datetime', 'head', 'transformation')
+      71.5±0.6ms       91.7±0.6ms     1.28  join_merge.Concat.time_concat_series(1)
+         162±2ms          207±2ms     1.28  stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'kurt')
+       114±0.5μs          147±1μs     1.28  inference.NumericInferOps.time_subtract(<class 'numpy.uint8'>)
+         117±2μs        150±0.5μs     1.28  inference.NumericInferOps.time_add(<class 'numpy.int8'>)
+     13.4±0.04ms       17.1±0.2ms     1.28  join_merge.Concat.time_concat_series(0)
+     7.82±0.05ms      10.0±0.05ms     1.28  stat_ops.FrameOps.time_op('mad', 'float', 0, True)
+     2.07±0.02ms      2.66±0.08ms     1.28  io.csv.ReadCSVFloatPrecision.time_read_csv(',', '.', 'round_trip')
+         239±1μs          305±3μs     1.28  groupby.GroupByMethods.time_dtype_as_group('float', 'tail', 'direct')
+         249±8μs          319±2μs     1.28  groupby.GroupByMethods.time_dtype_as_group('int', 'sum', 'direct')
+         204±9μs          260±4μs     1.28  groupby.GroupByMethods.time_dtype_as_group('int', 'var', 'direct')
+     1.58±0.01ms      2.02±0.01ms     1.28  join_merge.Merge.time_merge_dataframe_integer_key(True)
+         565±4μs         722±10μs     1.28  groupby.GroupByMethods.time_dtype_as_group('object', 'value_counts', 'transformation')
+         248±1μs          317±5μs     1.28  groupby.GroupByMethods.time_dtype_as_field('int', 'head', 'direct')
+       148±0.9ms          188±1ms     1.28  replace.Convert.time_replace('DataFrame', 'Timedelta')
+         568±1μs          724±4μs     1.28  groupby.GroupByMethods.time_dtype_as_group('object', 'value_counts', 'direct')
+     1.07±0.01ms      1.37±0.01ms     1.27  groupby.SumBools.time_groupby_sum_booleans
+         257±2μs          328±4μs     1.27  groupby.GroupByMethods.time_dtype_as_field('int', 'tail', 'transformation')
+      29.8±0.3ms       37.9±0.3ms     1.27  stat_ops.FrameMultiIndexOps.time_op(0, 'mad')
+         258±2μs          329±3μs     1.27  groupby.GroupByMethods.time_dtype_as_group('int', 'tail', 'direct')
+      3.62±0.03s       4.61±0.02s     1.27  period.DataFramePeriodColumn.time_set_index
+         649±2ms         826±10ms     1.27  stat_ops.FrameMultiIndexOps.time_op([0, 1], 'kurt')
+         251±8μs          319±4μs     1.27  groupby.GroupByMethods.time_dtype_as_group('int', 'sum', 'transformation')
+         149±1ms          190±1ms     1.27  replace.Convert.time_replace('DataFrame', 'Timestamp')
+         225±9μs        286±0.6μs     1.27  groupby.GroupByMethods.time_dtype_as_field('int', 'mean', 'transformation')
+         158±2ms          200±2ms     1.27  stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'skew')
+         247±9μs          313±2μs     1.27  groupby.GroupByMethods.time_dtype_as_field('int', 'sum', 'direct')
+         255±9μs          323±4μs     1.27  groupby.GroupByMethods.time_dtype_as_group('float', 'mean', 'transformation')
+         259±3μs          328±1μs     1.27  groupby.GroupByMethods.time_dtype_as_field('int', 'tail', 'direct')
+     8.74±0.06μs      11.1±0.06μs     1.27  offset.OffestDatetimeArithmetic.time_apply(<YearEnd: month=12>)
+      64.7±0.5μs         82.0±2μs     1.27  indexing.NonNumericSeriesIndexing.time_get_value('datetime', 'nonunique_monotonic_inc')
+        51.4±1μs       65.2±0.3μs     1.27  timeseries.SortIndex.time_get_slice(True)
+       256±0.6μs          324±2μs     1.27  groupby.GroupByMethods.time_dtype_as_field('float', 'tail', 'transformation')
+        73.6±1μs       93.3±0.8μs     1.27  series_methods.Clip.time_clip
+         743±1μs          942±3μs     1.27  reindex.DropDuplicates.time_series_drop_dups_string(False)
+     2.57±0.02ms      3.26±0.01ms     1.27  rolling.Pairwise.time_pairwise(None, 'cov', False)
+         247±1μs        313±0.4μs     1.27  groupby.GroupByMethods.time_dtype_as_group('int', 'head', 'transformation')
+      10.6±0.1μs       13.4±0.3μs     1.27  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<YearEnd: month=12>)
+         687±3μs         870±10μs     1.27  groupby.GroupByMethods.time_dtype_as_group('datetime', 'value_counts', 'direct')
+      86.4±0.4μs          109±1μs     1.27  groupby.GroupByMethods.time_dtype_as_field('datetime', 'min', 'direct')
+     1.18±0.02μs      1.49±0.01μs     1.27  index_object.Indexing.time_get('Int')
+       116±0.5μs        147±0.5μs     1.26  inference.NumericInferOps.time_add(<class 'numpy.uint8'>)
+         698±6μs         882±20μs     1.26  groupby.GroupByMethods.time_dtype_as_field('int', 'value_counts', 'direct')
+        206±10μs          260±2μs     1.26  groupby.GroupByMethods.time_dtype_as_group('int', 'var', 'transformation')
+         251±7μs          317±5μs     1.26  groupby.GroupByMethods.time_dtype_as_group('int', 'prod', 'transformation')
+       108±0.4μs          136±1μs     1.26  groupby.GroupByMethods.time_dtype_as_field('object', 'shift', 'transformation')
+        32.1±1ms       40.5±0.5ms     1.26  io.csv.ReadCSVCategorical.time_convert_direct
+      82.7±0.5μs        104±0.8μs     1.26  groupby.GroupByMethods.time_dtype_as_group('object', 'shift', 'direct')
+         782±3μs          986±2μs     1.26  groupby.GroupByMethods.time_dtype_as_field('float', 'value_counts', 'direct')
+      83.4±0.4μs        105±0.6μs     1.26  groupby.GroupByMethods.time_dtype_as_group('object', 'shift', 'transformation')
+       107±0.4μs          135±2μs     1.26  groupby.GroupByMethods.time_dtype_as_field('object', 'shift', 'direct')
+         260±1μs          327±3μs     1.26  groupby.GroupByMethods.time_dtype_as_field('datetime', 'tail', 'direct')
+         252±1μs          317±2μs     1.26  groupby.GroupByMethods.time_dtype_as_field('datetime', 'head', 'direct')
+       247±0.9μs          311±3μs     1.26  groupby.GroupByMethods.time_dtype_as_field('float', 'head', 'direct')
+       259±0.9μs          326±3μs     1.26  groupby.GroupByMethods.time_dtype_as_group('int', 'tail', 'transformation')
+         150±5μs          188±4μs     1.26  indexing.AssignTimeseriesIndex.time_frame_assign_timeseries_index
+     2.52±0.01ms      3.17±0.03ms     1.26  io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(',', '_', 'high')
+        598±10ns          751±8ns     1.26  index_object.Indexing.time_get('String')
+         269±1μs          338±3μs     1.26  groupby.GroupByMethods.time_dtype_as_group('float', 'median', 'transformation')
+        226±10μs          283±2μs     1.26  groupby.GroupByMethods.time_dtype_as_field('int', 'mean', 'direct')
+      8.34±0.1ms      10.5±0.05ms     1.26  stat_ops.Rank.time_rank('Series', True)
+         257±2μs          322±2μs     1.25  groupby.GroupByMethods.time_dtype_as_field('float', 'tail', 'direct')
+     7.99±0.05ms       10.0±0.2ms     1.25  stat_ops.FrameOps.time_op('mad', 'int', 0, True)
+     2.62±0.03ms      3.28±0.01ms     1.25  rolling.Pairwise.time_pairwise(10, 'cov', False)
+         261±8μs          328±1μs     1.25  join_merge.Append.time_append_homogenous
+     8.01±0.05ms       10.0±0.1ms     1.25  stat_ops.FrameOps.time_op('mad', 'int', 0, False)
+      20.8±0.2ms       26.1±0.4ms     1.25  join_merge.MergeAsof.time_on_int
+         785±7μs          983±6μs     1.25  groupby.GroupByMethods.time_dtype_as_field('float', 'value_counts', 'transformation')
+         269±1μs          336±2μs     1.25  groupby.GroupByMethods.time_dtype_as_group('float', 'median', 'direct')
+       248±0.7μs          310±1μs     1.25  groupby.GroupByMethods.time_dtype_as_field('float', 'head', 'transformation')
+      40.6±0.1μs       50.8±0.5μs     1.25  indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string', 'nonunique_monotonic_inc')
+         260±8μs          326±1μs     1.25  groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'transformation')
+      57.7±0.6ms       72.1±0.3ms     1.25  io.sas.SAS.time_read_msgpack('sas7bdat')
+     7.36±0.08μs      9.19±0.06μs     1.25  index_object.Indexing.time_slice_step('Float')
+        88.5±1μs          111±2μs     1.25  groupby.GroupByMethods.time_dtype_as_field('datetime', 'min', 'transformation')
+        258±10μs          323±5μs     1.25  groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'direct')
+      8.31±0.1ms      10.4±0.04ms     1.25  stat_ops.Rank.time_rank('Series', False)
+     2.52±0.01ms      3.15±0.01ms     1.25  io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(',', '_', 'round_trip')
+     3.85±0.04ms      4.80±0.05ms     1.25  rolling.Pairwise.time_pairwise(None, 'corr', False)
+     2.51±0.02ms      3.13±0.01ms     1.25  io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(';', '_', 'high')
+     1.79±0.02ms      2.23±0.03ms     1.25  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'count')
+      84.6±0.1μs        105±0.6μs     1.25  groupby.GroupByMethods.time_dtype_as_group('float', 'shift', 'transformation')
+         700±3μs          872±3μs     1.25  groupby.GroupByMethods.time_dtype_as_field('int', 'value_counts', 'transformation')
+       264±0.8μs          329±3μs     1.25  groupby.GroupByMethods.time_dtype_as_field('datetime', 'tail', 'transformation')
+      41.1±0.1μs       51.2±0.5μs     1.25  indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string', 'unique_monotonic_inc')
+      85.5±0.8μs        106±0.3μs     1.24  groupby.GroupByMethods.time_dtype_as_group('float', 'shift', 'direct')
+     2.53±0.01ms      3.15±0.02ms     1.24  io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(';', '_', None)
+     3.85±0.04ms      4.79±0.01ms     1.24  rolling.Pairwise.time_pairwise(10, 'corr', False)
+      85.4±0.5μs        106±0.4μs     1.24  groupby.GroupByMethods.time_dtype_as_group('datetime', 'shift', 'direct')
+         151±1μs          188±2μs     1.24  inference.NumericInferOps.time_add(<class 'numpy.int16'>)
+     2.53±0.02ms      3.14±0.01ms     1.24  io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(';', '_', 'round_trip')
+     1.20±0.05μs      1.49±0.02μs     1.24  index_object.Indexing.time_get('Float')
+        259±10μs          321±2μs     1.24  groupby.GroupByMethods.time_dtype_as_group('float', 'mean', 'direct')
+         232±8μs          288±2μs     1.24  groupby.GroupByMethods.time_dtype_as_group('int', 'mean', 'direct')
+         649±9μs          805±4μs     1.24  groupby.GroupByMethods.time_dtype_as_field('object', 'value_counts', 'transformation')
+      67.9±0.3μs       84.3±0.9μs     1.24  indexing.NonNumericSeriesIndexing.time_getitem_label_slice('datetime', 'unique_monotonic_inc')
+      6.25±0.1ms      7.75±0.02ms     1.24  groupby.Transform.time_transform_multi_key1
+         680±6μs         844±20μs     1.24  groupby.GroupByMethods.time_dtype_as_group('int', 'value_counts', 'direct')
+         649±2μs          806±1μs     1.24  groupby.GroupByMethods.time_dtype_as_field('object', 'value_counts', 'direct')
+         689±5μs          854±4μs     1.24  groupby.GroupByMethods.time_dtype_as_group('datetime', 'value_counts', 'transformation')
+         261±2μs          323±5μs     1.24  groupby.GroupByMethods.time_dtype_as_field('int', 'median', 'direct')
+     2.65±0.05ms       3.28±0.1ms     1.24  io.csv.ReadUint64Integers.time_read_uint64
+     9.29±0.07ms       11.5±0.2ms     1.24  frame_methods.MaskBool.time_frame_mask_floats
+     5.57±0.06ms      6.90±0.04ms     1.24  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'std')
+     1.88±0.01ms      2.33±0.07ms     1.24  stat_ops.FrameMultiIndexOps.time_op(1, 'prod')
+         258±2μs          319±2μs     1.24  groupby.GroupByMethods.time_dtype_as_field('int', 'median', 'transformation')
+     14.8±0.07ms      18.3±0.08ms     1.24  reindex.DropDuplicates.time_frame_drop_dups(False)
+      78.5±0.5μs       97.1±0.7μs     1.24  groupby.GroupByMethods.time_dtype_as_field('datetime', 'last', 'direct')
+      51.0±0.5ms       63.1±0.7ms     1.24  stat_ops.SeriesMultiIndexOps.time_op(1, 'mad')
+      66.3±0.5μs         81.9±1μs     1.24  indexing.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+     1.43±0.01ms      1.76±0.03ms     1.23  stat_ops.SeriesMultiIndexOps.time_op(0, 'sum')
+      8.69±0.1μs      10.7±0.05μs     1.23  offset.OffestDatetimeArithmetic.time_apply(<YearBegin: month=1>)
+     1.00±0.01ms         1.23±0ms     1.23  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'sum')
+        86.2±1μs        106±0.6μs     1.23  groupby.GroupByMethods.time_dtype_as_group('datetime', 'shift', 'transformation')
+      73.2±0.6ms       90.2±0.9ms     1.23  frame_methods.ToHTML.time_to_html_mixed
+     2.24±0.02ms      2.76±0.06ms     1.23  stat_ops.FrameMultiIndexOps.time_op(0, 'var')
+      7.63±0.4μs      9.38±0.09μs     1.23  index_object.Indexing.time_slice('Float')
+         996±5μs      1.22±0.01ms     1.23  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'sum')
+      85.5±0.7μs        105±0.9μs     1.23  groupby.GroupByMethods.time_dtype_as_field('datetime', 'first', 'direct')
+      83.6±0.8μs          103±2μs     1.23  groupby.GroupByMethods.time_dtype_as_field('datetime', 'max', 'transformation')
+         135±1μs          165±1μs     1.23  join_merge.Concat.time_concat_empty_right(1)
+         264±7μs          324±3μs     1.23  groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'direct')
+         152±2μs          187±4μs     1.23  inference.NumericInferOps.time_multiply(<class 'numpy.uint8'>)
+      83.2±0.6μs        102±0.4μs     1.23  groupby.GroupByMethods.time_dtype_as_field('datetime', 'max', 'direct')
+     4.27±0.05ms      5.24±0.04ms     1.23  stat_ops.FrameMultiIndexOps.time_op(0, 'sem')
+     2.57±0.03ms      3.15±0.03ms     1.23  io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(',', '_', None)
+         153±1μs          187±3μs     1.23  inference.NumericInferOps.time_multiply(<class 'numpy.uint16'>)
+         293±4μs          360±1μs     1.23  groupby.GroupByMethods.time_dtype_as_field('object', 'tail', 'transformation')
+         152±2μs          186±2μs     1.23  inference.NumericInferOps.time_add(<class 'numpy.uint16'>)
+      9.09±0.1ms       11.1±0.1ms     1.23  stat_ops.Rank.time_average_old('Series', True)
+       682±0.9μs          836±7μs     1.23  groupby.GroupByMethods.time_dtype_as_group('float', 'value_counts', 'transformation')
+         681±3μs          835±5μs     1.23  groupby.GroupByMethods.time_dtype_as_group('float', 'value_counts', 'direct')
+     22.6±0.05μs       27.7±0.1μs     1.22  indexing.NonNumericSeriesIndexing.time_getitem_scalar('datetime', 'nonunique_monotonic_inc')
+         286±2μs          350±2μs     1.22  groupby.GroupByMethods.time_dtype_as_field('object', 'head', 'transformation')
+         683±2μs         836±10μs     1.22  groupby.GroupByMethods.time_dtype_as_group('int', 'value_counts', 'transformation')
+      79.4±0.3μs         97.2±1μs     1.22  groupby.GroupByMethods.time_dtype_as_field('datetime', 'last', 'transformation')
+     10.2±0.03ms      12.4±0.03ms     1.22  gil.ParallelRolling.time_rolling('std')
+         155±2μs          190±3μs     1.22  inference.NumericInferOps.time_multiply(<class 'numpy.int16'>)
+      26.6±0.2μs      32.5±0.07μs     1.22  indexing.NumericSeriesIndexing.time_iloc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+     1.81±0.01ms      2.22±0.02ms     1.22  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'count')
+     3.29±0.02ms       4.02±0.2ms     1.22  binary_ops.Ops.time_frame_mult(False, 'default')
+         238±5μs          290±3μs     1.22  groupby.GroupByMethods.time_dtype_as_group('int', 'mean', 'transformation')
+     5.67±0.02ms      6.94±0.02ms     1.22  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'std')
+     9.03±0.07ms       11.0±0.2ms     1.22  stat_ops.Rank.time_average_old('Series', False)
+     1.84±0.02ms      2.24±0.02ms     1.22  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'count')
+       295±0.9μs          360±3μs     1.22  groupby.GroupByMethods.time_dtype_as_field('object', 'tail', 'direct')
+     1.85±0.02ms      2.25±0.04ms     1.22  stat_ops.FrameMultiIndexOps.time_op(1, 'mean')
+         263±3ms          321±3ms     1.22  groupby.Apply.time_copy_overhead_single_col
+         266±9μs          325±2μs     1.22  groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'transformation')
+     9.14±0.07μs       11.2±0.4μs     1.22  timestamp.TimestampProperties.time_is_year_start(None, 'B')
+     3.30±0.06ms       4.03±0.2ms     1.22  binary_ops.Ops.time_frame_add(False, 1)
+     4.28±0.04ms      5.21±0.06ms     1.22  stat_ops.FrameMultiIndexOps.time_op(1, 'sem')
+     1.79±0.03ms      2.18±0.01ms     1.22  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'count')
+      13.5±0.1μs       16.4±0.3μs     1.22  indexing.NumericSeriesIndexing.time_getitem_scalar(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+      85.8±0.6μs          104±1μs     1.22  groupby.GroupByMethods.time_dtype_as_field('datetime', 'first', 'transformation')
+      43.6±0.7ms         53.1±1ms     1.22  frame_methods.Equals.time_frame_object_equal
+       135±0.7μs          165±2μs     1.22  join_merge.Concat.time_concat_empty_left(1)
+        1.04±0ms         1.27±0ms     1.22  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'sum')
+      27.4±0.4ms         33.4±2ms     1.22  binary_ops.Timeseries.time_timestamp_ops_diff_with_shift('US/Eastern')
+         151±1μs          184±4μs     1.22  inference.NumericInferOps.time_subtract(<class 'numpy.uint16'>)
+     1.46±0.02ms      1.77±0.02ms     1.22  stat_ops.SeriesMultiIndexOps.time_op(1, 'mean')
+      5.72±0.1ms       6.95±0.1ms     1.22  strings.Cat.time_cat(0, ',', '-', 0.001)
+       153±0.7μs          186±2μs     1.21  inference.NumericInferOps.time_multiply(<class 'numpy.int8'>)
+        1.05±0ms         1.27±0ms     1.21  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'sum')
+     3.30±0.05ms       4.01±0.2ms     1.21  binary_ops.Ops.time_frame_add(False, 'default')
+     1.83±0.01ms      2.22±0.02ms     1.21  stat_ops.FrameMultiIndexOps.time_op(0, 'mean')
+     5.40±0.06ms      6.56±0.01ms     1.21  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'std')
+         157±2μs          191±2μs     1.21  indexing.DataFrameStringIndexing.time_boolean_rows
+       285±0.9μs          346±5μs     1.21  groupby.GroupByMethods.time_dtype_as_field('object', 'head', 'direct')
+         262±3μs          318±2μs     1.21  groupby.GroupByMethods.time_dtype_as_group('int', 'median', 'direct')
+      5.50±0.1ms      6.67±0.05ms     1.21  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'std')
+         228±4μs          276±2μs     1.21  join_merge.JoinNonUnique.time_join_non_unique_equal
+     9.06±0.04μs       11.0±0.1μs     1.21  timestamp.TimestampProperties.time_is_leap_year(None, 'B')
+      9.27±0.1μs       11.2±0.1μs     1.21  timestamp.TimestampProperties.time_is_quarter_start(None, 'B')
+      5.62±0.1ms      6.81±0.08ms     1.21  strings.Cat.time_cat(0, None, '-', 0.001)
+     1.45±0.02ms      1.75±0.01ms     1.21  stat_ops.SeriesMultiIndexOps.time_op(1, 'prod')
+     1.50±0.03ms      1.81±0.04ms     1.21  stat_ops.SeriesMultiIndexOps.time_op(0, 'mean')
+      5.69±0.1ms      6.88±0.07ms     1.21  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'std')
+      48.9±0.4μs       59.1±0.3μs     1.21  indexing.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+      17.8±0.3μs       21.5±0.4μs     1.21  index_object.Indexing.time_get_loc('Float')
+     1.45±0.02ms      1.75±0.02ms     1.21  stat_ops.SeriesMultiIndexOps.time_op(1, 'sum')
+     5.80±0.02ms      7.00±0.08ms     1.21  frame_methods.MaskBool.time_frame_mask_bools
+         238±9μs          287±1μs     1.21  groupby.GroupByMethods.time_dtype_as_field('int', 'prod', 'direct')
+         151±1μs          183±3μs     1.21  inference.NumericInferOps.time_subtract(<class 'numpy.int16'>)
+      73.0±0.3μs         88.0±5μs     1.21  frame_methods.GetNumericData.time_frame_get_numeric_data
+        306±20ms          369±4ms     1.21  groupby.GroupStrings.time_multi_columns
+         138±1μs        167±0.8μs     1.21  groupby.GroupByMethods.time_dtype_as_group('float', 'cumcount', 'transformation')
+         264±4μs          318±2μs     1.21  groupby.GroupByMethods.time_dtype_as_group('int', 'median', 'transformation')
+     3.11±0.03ms      3.75±0.02ms     1.21  io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(',', '.', None)
+     9.17±0.09μs       11.1±0.1μs     1.20  timestamp.TimestampProperties.time_is_month_start(None, 'B')
+         192±2ms        231±0.9ms     1.20  strings.Split.time_split(True)
+        1.45±0ms      1.75±0.01ms     1.20  stat_ops.SeriesMultiIndexOps.time_op(0, 'prod')
+     3.63±0.05ms       4.37±0.3ms     1.20  binary_ops.Ops.time_frame_add(True, 1)
+       132±0.2μs          159±2μs     1.20  groupby.GroupByMethods.time_dtype_as_group('object', 'cumcount', 'transformation')
+     3.18±0.05μs      3.83±0.07μs     1.20  indexing.CategoricalIndexIndexing.time_getitem_scalar('monotonic_incr')
+     3.12±0.02ms      3.75±0.03ms     1.20  io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(';', '.', None)
+     3.63±0.03ms       4.36±0.3ms     1.20  binary_ops.Ops.time_frame_mult(True, 1)
+     4.13±0.02ms      4.96±0.06ms     1.20  groupby.Apply.time_scalar_function_single_col
+         407±2μs         488±30μs     1.20  frame_methods.Quantile.time_frame_quantile(0)
+     3.14±0.03ms      3.77±0.02ms     1.20  io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(',', '.', 'high')
+         151±1ms          181±3ms     1.20  binary_ops.Ops2.time_frame_float_div_by_zero
+         945±6μs         1.13±0ms     1.20  reshape.SparseIndex.time_unstack
+     3.11±0.04ms      3.73±0.02ms     1.20  io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(';', '.', 'high')
+     2.20±0.05ms      2.63±0.05ms     1.20  timeseries.AsOf.time_asof_nan_single('DataFrame')
+         138±1μs          166±1μs     1.20  groupby.GroupByMethods.time_dtype_as_group('float', 'cumcount', 'direct')
+      25.9±0.2ms         31.0±1ms     1.20  eval.Query.time_query_datetime_index
+     3.30±0.02ms       3.95±0.2ms     1.20  binary_ops.Ops.time_frame_mult(False, 1)
+         427±1μs         511±60μs     1.20  frame_methods.Isnull.time_isnull_floats_no_null
+         159±1μs          190±9μs     1.19  groupby.GroupByMethods.time_dtype_as_field('int', 'cumcount', 'transformation')
+      27.0±0.3μs       32.2±0.2μs     1.19  indexing.NumericSeriesIndexing.time_iloc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+     4.29±0.03ms       5.12±0.7ms     1.19  groupby.Categories.time_groupby_extra_cat_nosort
+         133±1μs          159±1μs     1.19  groupby.GroupByMethods.time_dtype_as_group('object', 'cumcount', 'direct')
+     2.87±0.01ms      3.42±0.03ms     1.19  timeseries.ToDatetimeISO8601.time_iso8601_nosep
+     9.08±0.06ms       10.8±0.2ms     1.19  groupby.MultiColumn.time_cython_sum
+      9.17±0.1μs      10.9±0.08μs     1.19  timestamp.TimestampProperties.time_is_quarter_end(None, 'B')
+     4.52±0.02ms      5.39±0.02ms     1.19  io.csv.ReadCSVDInferDatetimeFormat.time_read_csv(True, 'custom')
+     3.14±0.01ms      3.74±0.02ms     1.19  io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(';', '.', 'round_trip')
+      38.8±0.2ms       46.2±0.4ms     1.19  algorithms.Factorize.time_factorize_float(True)
+         659±2μs          783±3μs     1.19  groupby.GroupByMethods.time_dtype_as_field('datetime', 'value_counts', 'direct')
+     3.15±0.03μs       3.74±0.1μs     1.19  indexing.CategoricalIndexIndexing.time_getitem_scalar('non_monotonic')
+     9.08±0.05μs      10.8±0.04μs     1.19  timestamp.TimestampProperties.time_is_month_end(None, 'B')
+      63.9±0.2ms       75.8±0.9ms     1.19  indexing.NumericSeriesIndexing.time_ix_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+     3.12±0.01ms      3.71±0.04ms     1.19  io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(',', '.', 'round_trip')
+         138±1μs          164±3μs     1.19  groupby.GroupByMethods.time_dtype_as_group('datetime', 'cumcount', 'direct')
+     1.87±0.01ms      2.22±0.02ms     1.19  stat_ops.FrameMultiIndexOps.time_op(1, 'sum')
+      15.6±0.3ms      18.5±0.08ms     1.19  strings.Methods.time_len
+         159±1μs          189±1μs     1.18  groupby.GroupByMethods.time_dtype_as_field('datetime', 'cumcount', 'direct')
+     5.12±0.05ms       6.06±0.2ms     1.18  strings.Cat.time_cat(0, None, '-', 0.0)
+     2.91±0.04ms      3.44±0.02ms     1.18  timeseries.ToDatetimeISO8601.time_iso8601
+         662±4μs          782±4μs     1.18  groupby.GroupByMethods.time_dtype_as_field('datetime', 'value_counts', 'transformation')
+     2.28±0.02ms      2.70±0.03ms     1.18  stat_ops.FrameMultiIndexOps.time_op(1, 'var')
+      14.3±0.1ms       16.9±0.8ms     1.18  gil.ParallelReadCSV.time_read_csv('object')
+      8.09±0.1ms       9.55±0.1ms     1.18  groupby.MultiColumn.time_col_select_numpy_sum
+         984±4ns      1.16±0.01μs     1.18  timestamp.TimestampConstruction.time_parse_iso8601_no_tz
+     2.85±0.05ms      3.36±0.01ms     1.18  timeseries.ToDatetimeISO8601.time_iso8601_format_no_sep
+      86.7±0.4ms          102±2ms     1.18  groupby.DateAttributes.time_len_groupby_object
+     2.07±0.01ms      2.43±0.02ms     1.18  series_methods.IsIn.time_isin('object')
+       159±0.5μs        187±0.5μs     1.18  groupby.GroupByMethods.time_dtype_as_field('float', 'cumcount', 'direct')
+     18.4±0.06μs       21.6±0.3μs     1.18  index_object.Indexing.time_get_loc_sorted('Float')
+     13.1±0.08ms         15.4±2ms     1.18  eval.Eval.time_mult('python', 1)
+     5.89±0.09ms      6.92±0.03ms     1.18  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'std')
+         159±1μs          187±2μs     1.18  groupby.GroupByMethods.time_dtype_as_field('float', 'cumcount', 'transformation')
+         155±2ms          182±3ms     1.17  binary_ops.Ops2.time_frame_int_div_by_zero
+     6.31±0.01ms      7.41±0.06ms     1.17  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'std')
+     5.06±0.05ms      5.94±0.08ms     1.17  strings.Cat.time_cat(0, None, None, 0.0)
+      63.0±0.2μs       74.0±0.4μs     1.17  offset.OffestDatetimeArithmetic.time_add_10(<DateOffset: days=2, months=2>)
+     1.16±0.01ms      1.36±0.01ms     1.17  algorithms.Hashing.time_series_int
+     2.37±0.02ms      2.78±0.01ms     1.17  stat_ops.FrameMultiIndexOps.time_op(1, 'std')
+     4.50±0.04ms      5.28±0.01ms     1.17  groupby.Transform.time_transform_multi_key3
+     1.90±0.01ms      2.22±0.01ms     1.17  groupby.TransformNaN.time_first
+       163±0.6μs          191±1μs     1.17  groupby.GroupByMethods.time_dtype_as_group('int', 'cumcount', 'direct')
+         412±2μs          483±4μs     1.17  reindex.Reindex.time_reindex_columns
+     1.81±0.04ms      2.12±0.02ms     1.17  reindex.DropDuplicates.time_frame_drop_dups_int(True)
+         544±3μs          637±3μs     1.17  ctors.SeriesConstructors.time_series_constructor(<class 'list'>, True)
+     2.24±0.02ms      2.63±0.05ms     1.17  groupby.CountMultiInt.time_multi_int_count
+         160±2μs        187±0.4μs     1.17  groupby.GroupByMethods.time_dtype_as_field('int', 'cumcount', 'direct')
+     1.22±0.01ms      1.43±0.04ms     1.17  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'nearest')
+      18.1±0.2ms       21.2±0.1ms     1.17  reindex.DropDuplicates.time_frame_drop_dups_na(False)
+      20.6±0.4ms       24.1±0.2ms     1.17  stat_ops.SeriesMultiIndexOps.time_op(1, 'kurt')
+         529±2μs          619±8μs     1.17  ctors.SeriesConstructors.time_series_constructor(<class 'list'>, False)
+      53.6±0.3μs       62.6±0.3μs     1.17  frame_methods.XS.time_frame_xs(0)
+     6.26±0.05ms      7.31±0.04ms     1.17  rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'std')
+     5.34±0.05ms       6.23±0.1ms     1.17  strings.Cat.time_cat(0, ',', '-', 0.0)
+     6.25±0.06ms      7.29±0.02ms     1.17  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'std')
+       159±0.4μs          185±2μs     1.17  groupby.GroupByMethods.time_dtype_as_field('object', 'cumcount', 'direct')
+     2.97±0.03ms      3.46±0.01ms     1.17  timeseries.ToDatetimeISO8601.time_iso8601_format
+         360±3μs          419±3μs     1.17  reindex.ReindexMethod.time_reindex_method('pad')
+         184±1μs          214±3μs     1.17  indexing.DataFrameStringIndexing.time_boolean_rows_object
+     1.99±0.03ms      2.31±0.05ms     1.16  stat_ops.FrameMultiIndexOps.time_op(0, 'prod')
+     2.14±0.04ms      2.49±0.06ms     1.16  timeseries.AsOf.time_asof_single('DataFrame')
+         386±9μs          449±3μs     1.16  timeseries.DatetimeIndex.time_unique('repeated')
+     7.98±0.02ms       9.27±0.2ms     1.16  ctors.MultiIndexConstructor.time_multiindex_from_iterables
+     1.17±0.02ms      1.36±0.01ms     1.16  algorithms.Hashing.time_series_float
+     1.94±0.01ms      2.25±0.04ms     1.16  stat_ops.SeriesMultiIndexOps.time_op(1, 'std')
+      6.15±0.2ms      7.12±0.06ms     1.16  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'std')
+     1.90±0.04ms      2.20±0.03ms     1.16  stat_ops.SeriesMultiIndexOps.time_op(0, 'var')
+         306±1ms          354±9ms     1.16  stat_ops.FrameMultiIndexOps.time_op([0, 1], 'skew')
+         217±5μs          251±2μs     1.16  timeseries.DatetimeIndex.time_add_timedelta('dst')
+     8.92±0.04ms       10.3±0.1ms     1.16  stat_ops.SeriesMultiIndexOps.time_op(0, 'mad')
+     1.94±0.01ms      2.25±0.03ms     1.16  stat_ops.SeriesMultiIndexOps.time_op(0, 'std')
+     9.14±0.04ms      10.6±0.08ms     1.16  join_merge.Merge.time_merge_2intkey(False)
+     5.50±0.09ms       6.35±0.1ms     1.16  strings.Cat.time_cat(0, ',', None, 0.0)
+     2.05±0.03ms      2.37±0.04ms     1.16  binary_ops.Ops.time_frame_comparison(True, 1)
+         162±2μs          187±2μs     1.16  groupby.GroupByMethods.time_dtype_as_group('int', 'cumcount', 'transformation')
+     1.16±0.02ms         1.34±0ms     1.16  algorithms.Hashing.time_series_timedeltas
+     1.90±0.03ms      2.20±0.02ms     1.16  stat_ops.FrameMultiIndexOps.time_op(0, 'sum')
+     2.38±0.01ms      2.75±0.02ms     1.15  stat_ops.FrameMultiIndexOps.time_op(0, 'std')
+         209±4μs          241±5μs     1.15  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<MonthEnd>)
+      35.1±0.5μs       40.5±0.3μs     1.15  indexing.NumericSeriesIndexing.time_loc_scalar(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+       117±0.9ms        135±0.7ms     1.15  replace.Convert.time_replace('Series', 'Timedelta')
+     6.10±0.05ms      7.04±0.02ms     1.15  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'std')
+      63.3±0.4ms         73.1±2ms     1.15  indexing.NumericSeriesIndexing.time_ix_array(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
+     1.54±0.01ms      1.77±0.01ms     1.15  timeseries.ResampleDatetetime64.time_resample
+         160±2μs          184±1μs     1.15  groupby.GroupByMethods.time_dtype_as_field('object', 'cumcount', 'transformation')
+     1.16±0.01ms      1.33±0.01ms     1.15  algorithms.Hashing.time_series_dates
+     4.90±0.05ms       5.64±0.1ms     1.15  io.csv.ReadUint64Integers.time_read_uint64_neg_values
+     1.91±0.01ms      2.20±0.05ms     1.15  stat_ops.SeriesMultiIndexOps.time_op(1, 'var')
+         687±3ms          790±4ms     1.15  join_merge.MergeAsof.time_multiby
+         251±1μs          288±4μs     1.15  inference.NumericInferOps.time_subtract(<class 'numpy.uint32'>)
+         214±4μs          246±2μs     1.15  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<Day>)
+         181±1μs          208±2μs     1.15  strings.Encode.time_encode_decode
+      15.0±0.3ms         17.2±2ms     1.15  eval.Eval.time_mult('numexpr', 'all')
+         163±3μs          188±1μs     1.15  groupby.GroupByMethods.time_dtype_as_field('datetime', 'cumcount', 'transformation')
+         117±1ms        134±0.7ms     1.15  replace.Convert.time_replace('Series', 'Timestamp')
+      54.9±0.8ms       62.9±0.7ms     1.15  join_merge.MergeOrdered.time_merge_ordered
+     2.62±0.01ms      3.00±0.04ms     1.14  stat_ops.SeriesMultiIndexOps.time_op(1, 'sem')
+         204±1μs        233±0.5μs     1.14  groupby.GroupByMethods.time_dtype_as_group('object', 'nunique', 'transformation')
+         203±2μs          232±3μs     1.14  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<MonthBegin>)
+        1.02±0ms      1.16±0.01ms     1.14  rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'mean')
+      29.0±0.6ms         33.1±2ms     1.14  eval.Eval.time_chained_cmp('python', 'all')
+     1.48±0.04ms      1.69±0.05ms     1.14  timeseries.DatetimeIndex.time_add_timedelta('tz_aware')
+       203±0.5μs          232±2μs     1.14  groupby.GroupByMethods.time_dtype_as_group('object', 'nunique', 'direct')
+     4.67±0.03ms       5.32±0.1ms     1.14  stat_ops.SeriesMultiIndexOps.time_op(0, 'skew')
+     4.72±0.03ms      5.38±0.04ms     1.14  stat_ops.SeriesMultiIndexOps.time_op(0, 'kurt')
+     4.84±0.04ms      5.50±0.02ms     1.14  join_merge.Merge.time_merge_dataframe_integer_2key(False)
+      19.3±0.2μs       21.9±0.3μs     1.14  indexing.NumericSeriesIndexing.time_getitem_scalar(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+     2.64±0.01ms      3.00±0.02ms     1.14  stat_ops.SeriesMultiIndexOps.time_op(0, 'sem')
+         449±2μs          509±6μs     1.13  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<DateOffset: days=2, months=2>)
+     1.47±0.01ms      1.67±0.02ms     1.13  series_methods.IsInForObjects.time_isin_long_series_short_values
+        1.23±0ms         1.39±0ms     1.13  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'nearest')
+         258±2μs          292±6μs     1.13  inference.NumericInferOps.time_add(<class 'numpy.uint32'>)
+         493±6μs          558±4μs     1.13  indexing.DataFrameNumericIndexing.time_bool_indexer
+     1.07±0.01ms      1.21±0.01ms     1.13  rolling.Methods.time_rolling('DataFrame', 10, 'int', 'mean')
+     1.82±0.02ms      2.05±0.02ms     1.13  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'sum')
+     5.86±0.03ms      6.62±0.02ms     1.13  stat_ops.FrameMultiIndexOps.time_op([0, 1], 'sem')
+       107±0.4μs          121±1μs     1.13  inference.ToNumericDowncast.time_downcast('int32', 'float')
+        846±20μs         956±20μs     1.13  series_methods.IsInForObjects.time_isin_short_series_long_values
+      65.6±0.5μs       74.1±0.3μs     1.13  indexing.DataFrameNumericIndexing.time_loc
+     1.23±0.01ms      1.39±0.01ms     1.13  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'nearest')
+      19.6±0.2ms       22.1±0.3ms     1.13  eval.Eval.time_chained_cmp('python', 1)
+     1.83±0.02ms      2.07±0.04ms     1.13  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'sum')
+      2.05±0.01s       2.31±0.02s     1.13  groupby.GroupByMethods.time_dtype_as_group('float', 'describe', 'direct')
+     1.81±0.02ms      2.04±0.03ms     1.13  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'sum')
+     1.61±0.05ms      1.82±0.01ms     1.13  groupby.GroupManyLabels.time_sum(1)
+     1.23±0.01ms      1.39±0.01ms     1.13  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'midpoint')
+      6.53±0.1ms      7.36±0.03ms     1.13  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'std')
+     1.24±0.01ms         1.39±0ms     1.13  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'lower')
+     13.1±0.06ms       14.7±0.2ms     1.13  eval.Eval.time_add('python', 1)
+     1.39±0.02μs      1.57±0.01μs     1.13  timestamp.TimestampConstruction.time_parse_today
+         885±3ms         995±20ms     1.13  groupby.GroupByMethods.time_dtype_as_field('int', 'describe', 'transformation')
+     1.26±0.04ms      1.42±0.04ms     1.12  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'linear')
+        1.32±0ms      1.48±0.02ms     1.12  rolling.Methods.time_rolling('Series', 10, 'float', 'sum')
+      69.5±0.5ms       78.2±0.7ms     1.12  io.hdf.HDFStoreDataFrame.time_read_store_table_mixed
+     1.23±0.01ms         1.38±0ms     1.12  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'midpoint')
+        1.23±0ms      1.38±0.01ms     1.12  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'lower')
+     1.98±0.01ms      2.22±0.03ms     1.12  binary_ops.Timeseries.time_series_timestamp_compare('US/Eastern')
+     1.04±0.01ms      1.17±0.01ms     1.12  rolling.Methods.time_rolling('DataFrame', 10, 'float', 'mean')
+     1.86±0.01ms      2.09±0.02ms     1.12  rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'sum')
+         1.31±0s       1.47±0.05s     1.12  groupby.GroupByMethods.time_dtype_as_group('int', 'describe', 'direct')
+     1.23±0.01ms         1.38±0ms     1.12  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'higher')
+     1.07±0.01ms         1.20±0ms     1.12  rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'mean')
+        1.24±0ms      1.39±0.01ms     1.12  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'linear')
+     5.05±0.02ms      5.66±0.02ms     1.12  categoricals.Concat.time_concat
+       228±0.8μs          255±1μs     1.12  groupby.GroupByMethods.time_dtype_as_field('datetime', 'nunique', 'direct')
+     4.96±0.09ms       5.56±0.1ms     1.12  io.csv.ReadUint64Integers.time_read_uint64_na_values
+     2.78±0.03ms      3.11±0.04ms     1.12  gil.ParallelRolling.time_rolling('mean')
+      58.6±0.5ms       65.6±0.2ms     1.12  frame_ctor.FromDicts.time_nested_dict_int64
+     1.36±0.01ms      1.52±0.02ms     1.12  rolling.Methods.time_rolling('Series', 1000, 'int', 'sum')
+      19.7±0.2ms       22.0±0.8ms     1.12  frame_methods.Iteration.time_iteritems
+        1.15±0ms      1.28±0.06ms     1.12  index_object.Ops.time_divide('float')
+        1.32±0ms      1.48±0.01ms     1.12  rolling.Methods.time_rolling('Series', 1000, 'float', 'sum')
+     1.97±0.01ms      2.20±0.02ms     1.12  binary_ops.Timeseries.time_timestamp_series_compare('US/Eastern')
+      2.06±0.01s       2.30±0.01s     1.12  groupby.GroupByMethods.time_dtype_as_group('float', 'describe', 'transformation')
+     1.97±0.03ms      2.20±0.02ms     1.12  timeseries.ResampleSeries.time_resample('period', '5min', 'ohlc')
+     1.65±0.02ms      1.85±0.03ms     1.12  timeseries.ResampleSeries.time_resample('period', '1D', 'ohlc')
+     1.36±0.01ms      1.52±0.01ms     1.12  rolling.Methods.time_rolling('Series', 10, 'int', 'sum')
+     1.23±0.01ms      1.38±0.01ms     1.12  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'higher')
+     1.23±0.01ms      1.37±0.01ms     1.12  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'nearest')
+      22.3±0.2ms       24.9±0.1ms     1.12  join_merge.Merge.time_merge_2intkey(True)
+     1.80±0.02ms      2.01±0.03ms     1.12  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'sum')
+         989±6μs      1.10±0.01ms     1.12  replace.FillNa.time_replace(True)
+      2.30±0.02s       2.56±0.01s     1.11  io.json.ReadJSON.time_read_json('index', 'int')
+     2.20±0.01ms      2.45±0.06ms     1.11  groupby.TransformBools.time_transform_mean
+        1.24±0ms      1.38±0.01ms     1.11  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'lower')
+     1.87±0.02ms      2.08±0.01ms     1.11  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'sum')
+        1.23±0ms      1.37±0.01ms     1.11  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'higher')
+      12.0±0.3μs      13.4±0.07μs     1.11  indexing.NumericSeriesIndexing.time_getitem_scalar(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+      2.30±0.03s       2.56±0.03s     1.11  io.json.ReadJSON.time_read_json('index', 'datetime')
+      6.92±0.1ms       7.69±0.2ms     1.11  binary_ops.Ops.time_frame_mult(True, 'default')
+     1.25±0.01ms      1.39±0.01ms     1.11  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'midpoint')
+      1.32±0.01s       1.46±0.01s     1.11  groupby.GroupByMethods.time_dtype_as_group('int', 'describe', 'transformation')
+     10.0±0.06ms       11.1±0.5ms     1.11  eval.Eval.time_mult('numexpr', 1)
+       303±0.6μs          337±2μs     1.11  groupby.GroupByMethods.time_dtype_as_group('int', 'nunique', 'transformation')
+     1.24±0.01ms         1.38±0ms     1.11  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'lower')
+     1.24±0.01ms         1.37±0ms     1.11  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'midpoint')
+     1.75±0.03ms      1.94±0.01ms     1.11  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'midpoint')
+         250±4μs          277±2μs     1.11  reindex.Reindex.time_reindex_dates
+         897±4ms         993±10ms     1.11  groupby.GroupByMethods.time_dtype_as_field('int', 'describe', 'direct')
+       161±0.5μs        178±0.5μs     1.11  reindex.Fillna.time_float_32('backfill')
+      17.3±0.4ms       19.1±0.1ms     1.11  frame_ctor.FromDictwithTimestamp.time_dict_with_timestamp_offsets(<Nano>)
+      45.0±0.5ms       49.7±0.7ms     1.10  stat_ops.FrameMultiIndexOps.time_op(1, 'skew')
+      20.7±0.3ms       22.9±0.1ms     1.10  frame_ctor.FromDictwithTimestamp.time_dict_with_timestamp_offsets(<Hour>)
+     1.60±0.01ms      1.76±0.04ms     1.10  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'lower')
+      22.3±0.3ms       24.6±0.4ms     1.10  frame_methods.SortIndexByColumns.time_frame_sort_values_by_columns
+         303±2μs          334±3μs     1.10  groupby.GroupByMethods.time_dtype_as_group('int', 'nunique', 'direct')
+         238±4ms         262±10ms     1.10  frame_methods.GetDtypeCounts.time_info
+         298±1μs          328±5μs     1.10  groupby.GroupByMethods.time_dtype_as_group('datetime', 'nunique', 'direct')
+         894±6ms          984±8ms     1.10  groupby.GroupByMethods.time_dtype_as_field('float', 'describe', 'direct')


SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```

Speedups
<details>

```
-         246±2ns        224±0.8ns     0.91  timedelta.TimedeltaProperties.time_timedelta_nanoseconds
-      42.2±0.1ms       38.3±0.2ms     0.91  io.sql.WriteSQLDtypes.time_to_sql_dataframe_column('sqlalchemy', 'bool')
-        795±10μs          722±7μs     0.91  indexing.NumericSeriesIndexing.time_ix_array(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
-     7.47±0.05μs      6.78±0.06μs     0.91  offset.OnOffset.time_on_offset(<BusinessMonthBegin>)
-        70.4±3ms       63.8±0.2ms     0.91  join_merge.ConcatDataFrames.time_f_ordered(1, False)
-      33.9±0.3μs       30.7±0.9μs     0.91  offset.OffestDatetimeArithmetic.time_subtract_10(<Day>)
-     2.14±0.01ms      1.94±0.04ms     0.91  groupby.Datelike.time_sum('date_range_tz')
-      8.59±0.1μs       7.74±0.1μs     0.90  indexing.NumericSeriesIndexing.time_iloc_scalar(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
-      40.2±0.2ms      36.0±0.08ms     0.90  io.sql.WriteSQLDtypes.time_to_sql_dataframe_column('sqlalchemy', 'string')
-     6.35±0.09μs      5.69±0.06μs     0.90  timedelta.TimedeltaConstructor.time_from_datetime_timedelta
-     39.3±0.07ms      35.2±0.05ms     0.90  io.sql.WriteSQLDtypes.time_to_sql_dataframe_column('sqlalchemy', 'float')
-         133±3ms          119±4ms     0.89  gil.ParallelGroupbyMethods.time_loop(4, 'var')
-      39.0±0.1ms       34.8±0.2ms     0.89  io.sql.WriteSQLDtypes.time_to_sql_dataframe_column('sqlalchemy', 'int')
-       143±0.9ms          128±1ms     0.89  categoricals.Constructor.time_with_nan
-     2.71±0.02ms      2.42±0.04ms     0.89  frame_methods.NSort.time_nlargest_one_column('last')
-      28.4±0.3ms       25.3±0.2ms     0.89  stat_ops.FrameOps.time_op('kurt', 'int', 1, False)
-      7.94±0.2μs      7.06±0.07μs     0.89  offset.OnOffset.time_on_offset(<SemiMonthEnd: day_of_month=15>)
-        97.7±1μs       86.7±0.5μs     0.89  index_object.SetOperations.time_operation('datetime', 'union')
-     8.58±0.04μs      7.59±0.05μs     0.88  indexing.NumericSeriesIndexing.time_iloc_scalar(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
-     8.56±0.04μs      7.54±0.03μs     0.88  indexing.NumericSeriesIndexing.time_iloc_scalar(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
-        1.13±0ms          994±4μs     0.88  indexing.CategoricalIndexIndexing.time_getitem_bool_array('non_monotonic')
-      5.51±0.1ms      4.84±0.03ms     0.88  timeseries.Factorize.time_factorize(None)
-      8.63±0.1μs      7.59±0.04μs     0.88  indexing.NumericSeriesIndexing.time_iloc_scalar(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
-        72.2±2ms       63.4±0.3ms     0.88  join_merge.ConcatDataFrames.time_f_ordered(1, True)
-      16.4±0.5μs       14.4±0.2μs     0.88  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessDay>)
-      20.5±0.3ms       17.8±0.2ms     0.87  timeseries.DatetimeIndex.time_normalize('tz_aware')
-      18.3±0.4μs       15.9±0.2μs     0.87  offset.OffestDatetimeArithmetic.time_add_10(<BusinessDay>)
-         114±3ms         98.5±3ms     0.86  gil.ParallelGroupbyMethods.time_loop(4, 'prod')
-       125±0.9μs          107±1μs     0.86  offset.OffestDatetimeArithmetic.time_add(<CustomBusinessMonthBegin>)
-     14.1±0.06μs      12.1±0.05μs     0.86  offset.OffestDatetimeArithmetic.time_apply(<BusinessDay>)
-         139±2μs          119±2μs     0.86  offset.OffestDatetimeArithmetic.time_add_10(<CustomBusinessMonthBegin>)
-      18.6±0.1μs       15.9±0.4μs     0.86  timeseries.AsOf.time_asof_single('Series')
-      9.41±0.1ms       8.03±0.2ms     0.85  strings.Cat.time_cat(0, ',', None, 0.001)
-       124±0.2μs        105±0.5μs     0.85  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<CustomBusinessMonthBegin>)
-         192±1ms          163±1ms     0.85  io.stata.Stata.time_read_stata('tc')
-         193±1ms          163±3ms     0.84  io.stata.Stata.time_read_stata('td')
-      9.00±0.3μs      7.57±0.05μs     0.84  timeseries.AsOf.time_asof_single_early('Series')
-         105±1ms         88.6±1ms     0.84  indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
-      36.2±0.2ms       30.4±0.2ms     0.84  plotting.TimeseriesPlotting.time_plot_irregular
-      5.95±0.1ms      4.97±0.09ms     0.84  timeseries.Factorize.time_factorize('Asia/Tokyo')
-       122±0.8μs        101±0.3μs     0.83  offset.OffestDatetimeArithmetic.time_apply(<CustomBusinessMonthBegin>)
-      37.4±0.3μs       30.9±0.1μs     0.83  indexing.NumericSeriesIndexing.time_loc_scalar(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
-       129±0.9ms          107±2ms     0.82  offset.OffsetSeriesArithmetic.time_add_offset(<CustomBusinessMonthBegin>)
-      35.4±0.5ms       28.9±0.5ms     0.82  plotting.TimeseriesPlotting.time_plot_regular_compat
-         130±1ms        106±0.8ms     0.81  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<CustomBusinessMonthBegin>)
-      15.1±0.3μs       12.3±0.2μs     0.81  timedelta.TimedeltaConstructor.time_from_components
-      23.5±0.1μs      19.0±0.03μs     0.81  offset.OffestDatetimeArithmetic.time_subtract_10(<BusinessDay>)
-     4.69±0.03ms       3.79±0.2ms     0.81  frame_methods.NSort.time_nsmallest_two_columns('first')
-        11.1±2ms       8.93±0.2ms     0.80  strings.Cat.time_cat(0, ',', None, 0.15)
-         120±1μs         95.9±2μs     0.80  offset.OffestDatetimeArithmetic.time_subtract_10(<CustomBusinessMonthBegin>)
-         116±1μs       92.1±0.6μs     0.79  offset.OffestDatetimeArithmetic.time_subtract(<CustomBusinessMonthBegin>)
-      16.3±0.4μs       12.9±0.1μs     0.79  offset.OffestDatetimeArithmetic.time_add(<BusinessDay>)
-      9.86±0.2ms      7.81±0.03ms     0.79  series_methods.ValueCounts.time_value_counts('object')
-     4.80±0.02ms      3.76±0.07ms     0.78  frame_methods.NSort.time_nsmallest_two_columns('last')
-         365±5ns          284±2ns     0.78  indexing.MethodLookup.time_lookup_iloc
-     1.83±0.05ms      1.42±0.02ms     0.77  series_methods.NSort.time_nlargest('last')
-         140±2ms          108±3ms     0.77  reshape.Unstack.time_without_last_row
-         373±3ns          284±2ns     0.76  indexing.MethodLookup.time_lookup_loc
-     1.25±0.01ms         946±20μs     0.76  stat_ops.SeriesOps.time_op('median', 'int', True)
-     4.99±0.04μs      3.78±0.05μs     0.76  timeseries.DatetimeIndex.time_get('dst')
-      19.0±0.1ms       14.3±0.4ms     0.76  algorithms.Factorize.time_factorize_int(True)
-     1.25±0.01ms          941±8μs     0.75  stat_ops.SeriesOps.time_op('median', 'int', False)
-     5.04±0.09μs      3.79±0.04μs     0.75  timeseries.DatetimeIndex.time_get('tz_naive')
-      20.9±0.2ms       15.6±0.6ms     0.75  index_object.SetOperations.time_operation('strings', 'symmetric_difference')
-        10.8±1ms       8.05±0.2ms     0.74  timeseries.AsOf.time_asof_nan('DataFrame')
-     1.78±0.01ms      1.33±0.02ms     0.74  series_methods.NSort.time_nlargest('first')
-        94.6±1ms         69.9±4ms     0.74  frame_methods.Describe.time_series_describe
-         319±1ms          235±6ms     0.74  frame_methods.Describe.time_dataframe_describe
-      11.9±0.1ms      8.78±0.02ms     0.74  io.hdf.HDFStoreDataFrame.time_store_info
-       103±0.2μs       74.6±0.3μs     0.73  indexing.NumericSeriesIndexing.time_loc_scalar(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
-      21.7±0.2μs       15.8±0.2μs     0.73  offset.OffestDatetimeArithmetic.time_subtract(<YearBegin: month=1>)
-      54.1±0.8μs       39.1±0.3μs     0.72  indexing.NumericSeriesIndexing.time_loc_scalar(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
-       736±200μs          527±1μs     0.72  timeseries.InferFreq.time_infer_freq(None)
-     5.11±0.09ms      3.66±0.02ms     0.72  offset.OnOffset.time_on_offset(<CustomBusinessMonthBegin>)
-         188±2ms        133±0.3ms     0.71  timeseries.DatetimeIndex.time_to_pydatetime('tz_aware')
-     1.08±0.01ms         765±10μs     0.71  indexing.NumericSeriesIndexing.time_getitem_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
-      10.9±0.1ms      7.59±0.02ms     0.70  inference.DateInferOps.time_subtract_datetimes
-      22.2±0.1μs       15.5±0.2μs     0.70  offset.OffestDatetimeArithmetic.time_subtract(<YearEnd: month=12>)
-     2.37±0.02ms      1.64±0.03ms     0.69  groupby.RankWithTies.time_rank_ties('int64', 'dense')
-      21.2±0.5μs      14.6±0.09μs     0.69  offset.OffestDatetimeArithmetic.time_add_10(<YearBegin: month=1>)
-         282±3ns          194±1ns     0.69  timedelta.TimedeltaProperties.time_timedelta_days
-     2.37±0.01ms      1.63±0.01ms     0.69  groupby.RankWithTies.time_rank_ties('int64', 'min')
-        1.74±0ms      1.19±0.01ms     0.69  series_methods.NSort.time_nsmallest('first')
-         145±2ms       99.2±0.5ms     0.68  indexing.NumericSeriesIndexing.time_getitem_lists(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
-     11.1±0.06ms       7.55±0.1ms     0.68  frame_methods.ToString.time_to_string_floats
-      1.14±0.3ms          777±4μs     0.68  timeseries.InferFreq.time_infer_freq('B')
-     2.37±0.03ms      1.61±0.01ms     0.68  groupby.RankWithTies.time_rank_ties('int64', 'average')
-     2.38±0.01ms      1.62±0.02ms     0.68  groupby.RankWithTies.time_rank_ties('int64', 'max')
-     2.40±0.01ms      1.63±0.01ms     0.68  groupby.RankWithTies.time_rank_ties('float32', 'first')
-        17.0±1ms       11.4±0.1ms     0.67  categoricals.ValueCounts.time_value_counts(False)
-     2.41±0.02ms      1.61±0.01ms     0.67  groupby.RankWithTies.time_rank_ties('int64', 'first')
-     2.41±0.02ms      1.61±0.01ms     0.67  groupby.RankWithTies.time_rank_ties('float64', 'first')
-     1.54±0.01ms      1.03±0.01ms     0.67  series_methods.NSort.time_nsmallest('last')
-     6.44±0.03ms      4.28±0.05ms     0.67  stat_ops.FrameOps.time_op('median', 'int', 0, False)
-         173±1μs        115±0.7μs     0.66  offset.OffestDatetimeArithmetic.time_subtract(<CustomBusinessMonthEnd>)
-     2.47±0.02ms      1.63±0.01ms     0.66  groupby.RankWithTies.time_rank_ties('float64', 'dense')
-     6.53±0.03ms      4.30±0.04ms     0.66  stat_ops.FrameOps.time_op('median', 'int', 0, True)
-     2.46±0.01ms      1.62±0.02ms     0.66  groupby.RankWithTies.time_rank_ties('float64', 'max')
-     2.46±0.02ms      1.61±0.01ms     0.66  groupby.RankWithTies.time_rank_ties('float64', 'min')
-     2.44±0.01ms      1.59±0.01ms     0.65  groupby.RankWithTies.time_rank_ties('datetime64', 'dense')
-     2.48±0.01ms      1.62±0.01ms     0.65  groupby.RankWithTies.time_rank_ties('float64', 'average')
-     2.48±0.02ms      1.61±0.01ms     0.65  groupby.RankWithTies.time_rank_ties('float32', 'max')
-     2.46±0.02ms      1.60±0.01ms     0.65  groupby.RankWithTies.time_rank_ties('datetime64', 'average')
-     2.45±0.02ms      1.59±0.02ms     0.65  groupby.RankWithTies.time_rank_ties('datetime64', 'min')
-        2.49±0ms      1.61±0.01ms     0.65  groupby.RankWithTies.time_rank_ties('float32', 'min')
-     2.50±0.02ms      1.61±0.01ms     0.64  groupby.RankWithTies.time_rank_ties('float32', 'dense')
-     2.49±0.03ms      1.60±0.02ms     0.64  groupby.RankWithTies.time_rank_ties('datetime64', 'first')
-     2.51±0.03ms         1.61±0ms     0.64  groupby.RankWithTies.time_rank_ties('float32', 'average')
-         304±2ns        195±0.9ns     0.64  timedelta.TimedeltaProperties.time_timedelta_microseconds
-     5.25±0.05ms      3.35±0.02ms     0.64  offset.OnOffset.time_on_offset(<CustomBusinessMonthEnd>)
-        948±20μs         605±10μs     0.64  indexing.NumericSeriesIndexing.time_loc_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
-     2.53±0.07ms      1.61±0.02ms     0.64  groupby.RankWithTies.time_rank_ties('datetime64', 'max')
-     3.32±0.01ms      2.10±0.01ms     0.63  ctors.SeriesConstructors.time_series_constructor(<function SeriesConstructors.<lambda> at 0x11332b6a8>, True)
-         105±1ms       66.0±0.6ms     0.63  index_object.IndexAppend.time_append_int_list
-      25.7±0.2μs      16.1±0.08μs     0.62  offset.OffestDatetimeArithmetic.time_subtract(<QuarterEnd: startingMonth=3>)
-     2.38±0.01ms      1.47±0.01ms     0.62  period.Algorithms.time_value_counts('series')
-      29.1±0.5μs       17.9±0.1μs     0.62  offset.OffestDatetimeArithmetic.time_subtract_10(<QuarterEnd: startingMonth=3>)
-         471±3ms          284±2ms     0.60  indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime', 'nonunique_monotonic_inc')
-       141±0.7μs       84.5±0.3μs     0.60  offset.OffestDatetimeArithmetic.time_subtract_10(<CustomBusinessMonthEnd>)
-      50.1±0.5μs       30.0±0.5μs     0.60  categoricals.IsMonotonic.time_categorical_series_is_monotonic_increasing
-      26.8±0.2μs       15.9±0.3μs     0.59  offset.OffestDatetimeArithmetic.time_subtract(<BusinessQuarterEnd: startingMonth=3>)
-      49.7±0.6μs       29.0±0.2μs     0.58  categoricals.IsMonotonic.time_categorical_series_is_monotonic_decreasing
-     30.7±0.08μs       17.8±0.1μs     0.58  offset.OffestDatetimeArithmetic.time_subtract_10(<BusinessQuarterEnd: startingMonth=3>)
-     3.13±0.03ms      1.81±0.02ms     0.58  indexing.DataFrameNumericIndexing.time_loc_dups
-      23.9±0.7μs       13.8±0.1μs     0.58  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessQuarterEnd: startingMonth=3>)
-      29.7±0.4μs      17.2±0.09μs     0.58  offset.OffestDatetimeArithmetic.time_subtract_10(<YearBegin: month=1>)
-       137±0.5μs       78.2±0.9μs     0.57  offset.OffestDatetimeArithmetic.time_add_10(<CustomBusinessMonthEnd>)
-      26.6±0.3μs       15.2±0.3μs     0.57  offset.OffestDatetimeArithmetic.time_add_10(<QuarterEnd: startingMonth=3>)
-       359±0.9ms          205±2ms     0.57  reindex.Reindex.time_reindex_multiindex
-      23.8±0.2μs       13.6±0.5μs     0.57  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<QuarterEnd: startingMonth=3>)
-        20.2±1ms       11.5±0.1ms     0.57  categoricals.ValueCounts.time_value_counts(True)
-     20.2±0.07μs      11.5±0.05μs     0.57  offset.OffestDatetimeArithmetic.time_apply(<QuarterEnd: startingMonth=3>)
-         346±2ns          196±1ns     0.57  timedelta.TimedeltaProperties.time_timedelta_seconds
-      86.2±0.8ms         48.6±3ms     0.56  reshape.Unstack.time_full_product
-      41.5±0.2ms       23.3±0.2ms     0.56  categoricals.Constructor.time_all_nan
-         379±1ms          211±2ms     0.56  series_methods.SeriesConstructor.time_constructor('dict')
-         544±7ns          303±2ns     0.56  timestamp.TimestampProperties.time_freqstr(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
-     13.6±0.07ms       7.37±0.4ms     0.54  binary_ops.Timeseries.time_timestamp_ops_diff('US/Eastern')
-       141±0.6ms         75.9±1ms     0.54  indexing.NumericSeriesIndexing.time_getitem_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
-     22.7±0.08μs      12.2±0.06μs     0.54  offset.OffestDatetimeArithmetic.time_add(<QuarterEnd: startingMonth=3>)
-      21.4±0.2μs       11.4±0.2μs     0.53  offset.OffestDatetimeArithmetic.time_apply(<BusinessQuarterEnd: startingMonth=3>)
-       141±0.6ms       75.4±0.6ms     0.53  indexing.NumericSeriesIndexing.time_loc_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
-         294±3μs          156±2μs     0.53  indexing.CategoricalIndexIndexing.time_getitem_bool_array('monotonic_incr')
-      23.8±0.1μs      12.5±0.05μs     0.53  offset.OffestDatetimeArithmetic.time_add(<BusinessQuarterEnd: startingMonth=3>)
-       128±0.9ms         66.8±2ms     0.52  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<CustomBusinessMonthEnd>)
-        582±50ns          303±4ns     0.52  timestamp.TimestampProperties.time_freqstr(None, 'B')
-       126±0.8μs       65.1±0.3μs     0.52  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<CustomBusinessMonthEnd>)
-      31.3±0.2μs      16.0±0.04μs     0.51  offset.OffestDatetimeArithmetic.time_subtract(<QuarterBegin: startingMonth=3>)
-      35.3±0.2μs       18.0±0.3μs     0.51  offset.OffestDatetimeArithmetic.time_subtract_10(<QuarterBegin: startingMonth=3>)
-         129±1ms       64.8±0.8ms     0.50  offset.OffsetSeriesArithmetic.time_add_offset(<CustomBusinessMonthEnd>)
-         122±1μs       61.3±0.9μs     0.50  offset.OffestDatetimeArithmetic.time_apply(<CustomBusinessMonthEnd>)
-       124±0.6μs       62.1±0.4μs     0.50  offset.OffestDatetimeArithmetic.time_add(<CustomBusinessMonthEnd>)
-      30.2±0.1μs      14.9±0.05μs     0.49  offset.OffestDatetimeArithmetic.time_add_10(<YearEnd: month=12>)
-      32.7±0.6μs       16.2±0.1μs     0.49  offset.OffestDatetimeArithmetic.time_subtract(<BusinessQuarterBegin: startingMonth=3>)
-      36.2±0.2μs       17.7±0.1μs     0.49  offset.OffestDatetimeArithmetic.time_subtract_10(<BusinessQuarterBegin: startingMonth=3>)
-      31.7±0.1μs       15.4±0.2μs     0.49  offset.OffestDatetimeArithmetic.time_add_10(<QuarterBegin: startingMonth=3>)
-     1.39±0.01ms          671±8μs     0.48  series_methods.Map.time_map('dict')
-      95.7±0.5ms       45.8±0.3ms     0.48  rolling.Quantile.time_quantile('Series', 10, 'float', 0.5, 'linear')
-      95.6±0.8ms       45.4±0.4ms     0.48  rolling.Quantile.time_quantile('Series', 10, 'float', 0.5, 'midpoint')
-      28.3±0.3μs       13.4±0.1μs     0.47  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<MonthBegin>)
-      28.7±0.2μs      13.6±0.06μs     0.47  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<QuarterBegin: startingMonth=3>)
-      36.9±0.1μs       17.5±0.1μs     0.47  offset.OffestDatetimeArithmetic.time_subtract_10(<YearEnd: month=12>)
-      95.2±0.4ms       44.4±0.2ms     0.47  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0.5, 'midpoint')
-      96.3±0.4ms       44.8±0.6ms     0.47  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0.5, 'linear')
-      95.2±0.7ms       44.1±0.5ms     0.46  rolling.Quantile.time_quantile('Series', 10, 'float', 0.5, 'higher')
-      28.8±0.3μs      13.3±0.07μs     0.46  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessYearBegin: month=1>)
-      28.7±0.3μs      13.2±0.06μs     0.46  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessMonthEnd>)
-        95.3±1ms       43.9±0.3ms     0.46  rolling.Quantile.time_quantile('Series', 10, 'float', 0.5, 'lower')
-      30.0±0.2μs      13.7±0.08μs     0.46  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessQuarterBegin: startingMonth=3>)
-      33.7±0.3μs       15.4±0.6μs     0.46  offset.OffestDatetimeArithmetic.time_add_10(<BusinessQuarterBegin: startingMonth=3>)
-      95.3±0.1ms       43.1±0.3ms     0.45  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0.5, 'higher')
-      96.0±0.3ms      43.4±0.09ms     0.45  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0.5, 'nearest')
-      95.4±0.5ms       43.0±0.5ms     0.45  rolling.Quantile.time_quantile('Series', 10, 'float', 0.5, 'nearest')
-      57.8±0.8ms         26.1±1ms     0.45  binary_ops.Timeseries.time_timestamp_ops_diff_with_shift(None)
-      95.4±0.3ms       42.9±0.4ms     0.45  rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0.5, 'lower')
-     1.52±0.02ms          683±3μs     0.45  offset.OffsetSeriesArithmetic.time_add_offset(<QuarterBegin: startingMonth=3>)
-      95.1±0.3ms       42.6±0.5ms     0.45  rolling.Quantile.time_quantile('Series', 10, 'int', 0.5, 'linear')
-     29.0±0.06μs       12.9±0.2μs     0.45  offset.OffestDatetimeArithmetic.time_add(<BusinessQuarterBegin: startingMonth=3>)
-     4.48±0.03ms      1.99±0.03ms     0.44  ctors.SeriesConstructors.time_series_constructor(<function SeriesConstructors.<lambda> at 0x11332b6a8>, False)
-      32.3±0.1μs       14.3±0.3μs     0.44  offset.OffestDatetimeArithmetic.time_add_10(<MonthBegin>)
-      95.9±0.7ms       42.3±0.2ms     0.44  rolling.Quantile.time_quantile('Series', 10, 'int', 0.5, 'midpoint')
-      95.2±0.6ms       41.9±0.6ms     0.44  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0.5, 'linear')
-     25.3±0.05μs      11.1±0.06μs     0.44  offset.OffestDatetimeArithmetic.time_apply(<QuarterBegin: startingMonth=3>)
-      36.4±0.6μs       16.0±0.5μs     0.44  offset.OffestDatetimeArithmetic.time_subtract_10(<MonthBegin>)
-     28.1±0.05μs       12.3±0.2μs     0.44  offset.OffestDatetimeArithmetic.time_add(<QuarterBegin: startingMonth=3>)
-      25.2±0.4μs       11.0±0.2μs     0.44  offset.OffestDatetimeArithmetic.time_apply(<BusinessMonthEnd>)
-      95.9±0.3ms       41.5±0.1ms     0.43  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0.5, 'midpoint')
-      26.3±0.2μs       11.4±0.1μs     0.43  offset.OffestDatetimeArithmetic.time_apply(<BusinessQuarterBegin: startingMonth=3>)
-     1.56±0.02ms          675±3μs     0.43  offset.OffsetSeriesArithmetic.time_add_offset(<YearBegin: month=1>)
-      33.5±0.3μs       14.5±0.3μs     0.43  offset.OffestDatetimeArithmetic.time_subtract(<MonthBegin>)
-      25.0±0.3μs      10.8±0.03μs     0.43  offset.OffestDatetimeArithmetic.time_apply(<MonthBegin>)
-      94.4±0.8ms       40.6±0.4ms     0.43  rolling.Quantile.time_quantile('Series', 10, 'int', 0.5, 'nearest')
-      33.8±0.2μs       14.5±0.4μs     0.43  offset.OffestDatetimeArithmetic.time_add_10(<BusinessMonthEnd>)
-      95.3±0.8ms       40.7±0.4ms     0.43  rolling.Quantile.time_quantile('Series', 10, 'int', 0.5, 'lower')
-      95.4±0.3ms       40.6±0.9ms     0.43  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0.5, 'higher')
-      95.9±0.9ms       40.7±0.1ms     0.42  rolling.Quantile.time_quantile('Series', 10, 'int', 0.5, 'higher')
-      28.1±0.2μs       11.9±0.1μs     0.42  offset.OffestDatetimeArithmetic.time_add(<MonthBegin>)
-      34.9±0.4μs       14.7±0.3μs     0.42  offset.OffestDatetimeArithmetic.time_subtract(<BusinessMonthEnd>)
-     28.8±0.08μs      12.1±0.04μs     0.42  offset.OffestDatetimeArithmetic.time_add(<BusinessYearBegin: month=1>)
-      95.3±0.8ms      39.9±0.08ms     0.42  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0.5, 'lower')
-     31.9±0.04μs       13.4±0.1μs     0.42  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessMonthBegin>)
-      28.6±0.5μs       11.9±0.2μs     0.42  offset.OffestDatetimeArithmetic.time_add(<BusinessMonthEnd>)
-      26.5±0.2μs      11.0±0.07μs     0.42  offset.OffestDatetimeArithmetic.time_apply(<BusinessYearBegin: month=1>)
-      38.9±0.3μs      16.1±0.08μs     0.41  offset.OffestDatetimeArithmetic.time_subtract_10(<BusinessMonthBegin>)
-      95.7±0.3ms       39.5±0.4ms     0.41  rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0.5, 'nearest')
-      38.7±0.5μs       15.9±0.1μs     0.41  offset.OffestDatetimeArithmetic.time_subtract_10(<BusinessMonthEnd>)
-      35.5±0.3μs       14.5±0.3μs     0.41  offset.OffestDatetimeArithmetic.time_subtract(<BusinessMonthBegin>)
-      48.9±0.5μs       19.3±0.2μs     0.39  offset.OffestDatetimeArithmetic.time_subtract_10(<SemiMonthBegin: day_of_month=15>)
-      50.9±0.6ms       20.0±0.2ms     0.39  indexing.CategoricalIndexIndexing.time_get_indexer_list('monotonic_incr')
-         197±2ms       77.1±0.8ms     0.39  indexing.NumericSeriesIndexing.time_getitem_lists(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
-        49.7±1μs       19.3±0.1μs     0.39  offset.OffestDatetimeArithmetic.time_subtract_10(<SemiMonthEnd: day_of_month=15>)
-         193±3ms       74.1±0.6ms     0.38  indexing.NumericSeriesIndexing.time_loc_array(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
-      32.0±0.7μs       12.2±0.2μs     0.38  offset.OffestDatetimeArithmetic.time_add(<BusinessMonthBegin>)
-         196±2ms       74.5±0.7ms     0.38  indexing.NumericSeriesIndexing.time_getitem_array(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
-     1.86±0.01ms          708±6μs     0.38  period.Algorithms.time_drop_duplicates('series')
-      29.2±0.2μs      11.0±0.06μs     0.38  offset.OffestDatetimeArithmetic.time_apply(<BusinessMonthBegin>)
-        43.3±2μs       16.3±0.1μs     0.38  offset.OffestDatetimeArithmetic.time_subtract(<BusinessYearBegin: month=1>)
-      40.0±0.5μs       15.1±0.2μs     0.38  offset.OffestDatetimeArithmetic.time_add_10(<BusinessYearBegin: month=1>)
-      37.5±0.4μs      14.1±0.07μs     0.38  offset.OffestDatetimeArithmetic.time_add_10(<BusinessMonthBegin>)
-      39.7±0.2μs       14.9±0.2μs     0.38  offset.OffestDatetimeArithmetic.time_add_10(<BusinessYearEnd: month=12>)
-        42.1±2μs       15.7±0.3μs     0.37  offset.OffestDatetimeArithmetic.time_subtract(<BusinessYearEnd: month=12>)
-      46.8±0.6μs       17.2±0.4μs     0.37  offset.OffestDatetimeArithmetic.time_subtract(<SemiMonthBegin: day_of_month=15>)
-      44.9±0.1μs       16.3±0.1μs     0.36  offset.OffestDatetimeArithmetic.time_add_10(<SemiMonthBegin: day_of_month=15>)
-         1.75±0s          630±3ms     0.36  reshape.GetDummies.time_get_dummies_1d_sparse
-      47.7±0.5μs       17.0±0.2μs     0.36  offset.OffestDatetimeArithmetic.time_subtract(<SemiMonthEnd: day_of_month=15>)
-      50.0±0.5μs       17.8±0.3μs     0.36  offset.OffestDatetimeArithmetic.time_subtract_10(<BusinessYearEnd: month=12>)
-      46.0±0.2μs       16.2±0.1μs     0.35  offset.OffestDatetimeArithmetic.time_add_10(<SemiMonthEnd: day_of_month=15>)
-      50.1±0.4μs       17.4±0.4μs     0.35  offset.OffestDatetimeArithmetic.time_subtract_10(<BusinessYearBegin: month=1>)
-        43.8±2μs      14.2±0.04μs     0.32  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<SemiMonthEnd: day_of_month=15>)
-        44.2±3μs      14.2±0.09μs     0.32  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<SemiMonthBegin: day_of_month=15>)
-      41.8±0.2μs       13.2±0.1μs     0.32  offset.OffestDatetimeArithmetic.time_add(<SemiMonthBegin: day_of_month=15>)
-      39.3±0.5μs      12.0±0.07μs     0.31  offset.OffestDatetimeArithmetic.time_apply(<SemiMonthBegin: day_of_month=15>)
-      52.7±0.6μs       15.9±0.1μs     0.30  offset.OffestDatetimeArithmetic.time_subtract_10(<MonthEnd>)
-        43.3±1μs       13.0±0.2μs     0.30  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<MonthEnd>)
-      43.1±0.6μs      12.9±0.08μs     0.30  offset.OffestDatetimeArithmetic.time_add(<SemiMonthEnd: day_of_month=15>)
-         233±3ms         69.4±2ms     0.30  rolling.Quantile.time_quantile('Series', 1000, 'float', 0.5, 'linear')
-      49.2±0.2μs      14.7±0.07μs     0.30  offset.OffestDatetimeArithmetic.time_subtract(<MonthEnd>)
-         233±3ms         69.3±1ms     0.30  rolling.Quantile.time_quantile('Series', 1000, 'float', 0.5, 'midpoint')
-         232±2ms       67.6±0.5ms     0.29  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0.5, 'linear')
-      40.4±0.3μs      11.8±0.09μs     0.29  offset.OffestDatetimeArithmetic.time_apply(<SemiMonthEnd: day_of_month=15>)
-      48.1±0.8μs       14.0±0.3μs     0.29  offset.OffestDatetimeArithmetic.time_add_10(<MonthEnd>)
-        52.4±1μs       15.2±0.3μs     0.29  offset.OffestDatetimeArithmetic.time_add_10(<BusinessQuarterEnd: startingMonth=3>)
-     71.1±0.09μs       20.6±0.2μs     0.29  indexing.CategoricalIndexIndexing.time_getitem_list_like('non_monotonic')
-         235±3ms       67.0±0.1ms     0.29  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0.5, 'midpoint')
-      70.4±0.3μs       20.1±0.1μs     0.29  indexing.CategoricalIndexIndexing.time_getitem_list_like('monotonic_incr')
-         230±2ms         65.5±4ms     0.28  rolling.Quantile.time_quantile('Series', 1000, 'float', 0.5, 'nearest')
-      42.2±0.1μs       11.9±0.1μs     0.28  offset.OffestDatetimeArithmetic.time_add(<MonthEnd>)
-         205±1ms       57.3±0.2ms     0.28  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0.5, 'midpoint')
-         234±2ms       65.2±0.3ms     0.28  rolling.Quantile.time_quantile('Series', 1000, 'float', 0.5, 'lower')
-         233±3ms         64.7±2ms     0.28  rolling.Quantile.time_quantile('Series', 1000, 'float', 0.5, 'higher')
-         208±2ms       57.5±0.5ms     0.28  rolling.Quantile.time_quantile('Series', 1000, 'int', 0.5, 'midpoint')
-        233±10ms       64.4±0.8ms     0.28  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0.5, 'nearest')
-      39.3±0.3μs      10.8±0.05μs     0.28  offset.OffestDatetimeArithmetic.time_apply(<MonthEnd>)
-       206±0.9ms       56.7±0.6ms     0.27  rolling.Quantile.time_quantile('Series', 1000, 'int', 0.5, 'linear')
-         205±1ms       55.6±0.4ms     0.27  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0.5, 'linear')
-         236±3ms       63.0±0.3ms     0.27  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0.5, 'lower')
-      50.6±0.5μs      13.3±0.03μs     0.26  offset.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessYearEnd: month=12>)
-        242±10ms         63.7±1ms     0.26  rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0.5, 'higher')
-         625±4μs          157±1μs     0.25  groupby.GroupByMethods.time_dtype_as_group('object', 'bfill', 'direct')
-         810±3μs          203±3μs     0.25  series_methods.Map.time_map('Series')
-         625±6μs          156±1μs     0.25  groupby.GroupByMethods.time_dtype_as_group('object', 'ffill', 'direct')
-         628±2μs        156±0.6μs     0.25  groupby.GroupByMethods.time_dtype_as_group('object', 'ffill', 'transformation')
-        636±10μs          156±2μs     0.25  groupby.GroupByMethods.time_dtype_as_group('object', 'bfill', 'transformation')
-      49.1±0.5μs       12.0±0.1μs     0.24  offset.OffestDatetimeArithmetic.time_add(<BusinessYearEnd: month=12>)
-         206±1ms       49.8±0.5ms     0.24  rolling.Quantile.time_quantile('Series', 1000, 'int', 0.5, 'lower')
-         206±1ms       49.5±0.7ms     0.24  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0.5, 'nearest')
-     5.56±0.03ms      1.34±0.01ms     0.24  series_methods.Dir.time_dir_strings
-         206±2ms       49.5±0.3ms     0.24  rolling.Quantile.time_quantile('Series', 1000, 'int', 0.5, 'nearest')
-       206±0.7ms       49.2±0.3ms     0.24  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0.5, 'higher')
-         206±2ms       49.1±0.6ms     0.24  rolling.Quantile.time_quantile('Series', 1000, 'int', 0.5, 'higher')
-      46.7±0.6μs      11.1±0.09μs     0.24  offset.OffestDatetimeArithmetic.time_apply(<BusinessYearEnd: month=12>)
-       206±0.9ms       48.7±0.5ms     0.24  rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0.5, 'lower')
-     1.04±0.02ms          242±2μs     0.23  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<QuarterBegin: startingMonth=3>)
-     1.06±0.01ms          239±1μs     0.22  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<YearBegin: month=1>)
-         302±1μs       67.4±0.4μs     0.22  groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'transformation')
-         302±1μs       66.9±0.9μs     0.22  groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'direct')
-         302±3μs       66.8±0.6μs     0.22  groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'direct')
-         305±2μs       67.0±0.4μs     0.22  groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'transformation')
-         512±9ms          111±2ms     0.22  timeseries.DatetimeIndex.time_to_date('tz_aware')
-         528±5ms          111±2ms     0.21  timeseries.DatetimeIndex.time_to_time('tz_aware')
-      17.2±0.2ms      3.49±0.09ms     0.20  groupby.Datelike.time_sum('period_range')
-         457±3ms         92.0±1ms     0.20  multiindex_object.GetLoc.time_large_get_loc_warm
-      63.2±0.3μs       12.5±0.1μs     0.20  indexing.CategoricalIndexIndexing.time_getitem_slice('monotonic_decr')
-     2.47±0.06ms        460±0.6μs     0.19  offset.ApplyIndex.time_apply_index(<QuarterBegin: startingMonth=3>)
-     2.37±0.02ms          416±2μs     0.18  offset.ApplyIndex.time_apply_index(<YearBegin: month=1>)
-      76.8±0.4μs       12.2±0.1μs     0.16  indexing.CategoricalIndexIndexing.time_getitem_slice('monotonic_incr')
-      76.7±0.3μs      11.9±0.04μs     0.16  indexing.CategoricalIndexIndexing.time_getitem_slice('non_monotonic')
-     3.64±0.01ms         540±10μs     0.15  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.UInt64Engine'>, <class 'numpy.float64'>, 'non_monotonic')
-        58.2±2ms       8.51±0.1ms     0.15  categoricals.Isin.time_isin_categorical('object')
-     3.65±0.01ms         527±10μs     0.14  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.UInt64Engine'>, <class 'numpy.float64'>, 'monotonic_decr')
-      45.7±0.5μs      5.97±0.04μs     0.13  offset.OnOffset.time_on_offset(<QuarterEnd: startingMonth=3>)
-     3.54±0.01ms          414±3μs     0.12  indexing.CategoricalIndexIndexing.time_get_loc_scalar('non_monotonic')
-     3.59±0.02ms          417±5μs     0.12  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.Int64Engine'>, <class 'numpy.float64'>, 'non_monotonic')
-     3.59±0.01ms          415±8μs     0.12  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.Int64Engine'>, <class 'numpy.float64'>, 'monotonic_decr')
-     5.11±0.04ms          588±4μs     0.12  timeseries.DatetimeIndex.time_to_time('dst')
-     6.05±0.06ms          678±1μs     0.11  offset.OffsetSeriesArithmetic.time_add_offset(<QuarterEnd: startingMonth=3>)
-        3.51±0ms          392±1μs     0.11  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.UInt64Engine'>, <class 'numpy.int64'>, 'monotonic_decr')
-         145±1ms      15.9±0.03ms     0.11  timeseries.DatetimeIndex.time_to_time('repeated')
-       145±0.7ms      15.9±0.03ms     0.11  timeseries.DatetimeIndex.time_to_time('tz_naive')
-     3.52±0.04ms          385±5μs     0.11  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.UInt64Engine'>, <class 'numpy.int64'>, 'non_monotonic')
-     3.57±0.01ms         390±10μs     0.11  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.Int64Engine'>, <class 'numpy.uint64'>, 'non_monotonic')
-     3.56±0.01ms          388±3μs     0.11  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.Int64Engine'>, <class 'numpy.uint64'>, 'monotonic_decr')
-     6.30±0.08ms          682±3μs     0.11  offset.OffsetSeriesArithmetic.time_add_offset(<YearEnd: month=12>)
-      5.07±0.1ms          515±2μs     0.10  timeseries.DatetimeIndex.time_to_date('dst')
-         142±1ms      13.9±0.02ms     0.10  timeseries.DatetimeIndex.time_to_date('repeated')
-         143±2ms      13.9±0.05ms     0.10  timeseries.DatetimeIndex.time_to_date('tz_naive')
-      91.7±0.4ms      8.06±0.05ms     0.09  inference.DateInferOps.time_timedelta_plus_datetime
-     6.15±0.01ms         510±10μs     0.08  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.Float64Engine'>, <class 'numpy.uint64'>, 'monotonic_decr')
-      67.0±0.8ms      5.01±0.09ms     0.07  sparse.Arithmetic.time_divide(0.1, nan)
-     3.36±0.01ms          245±3μs     0.07  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.UInt64Engine'>, <class 'numpy.uint64'>, 'monotonic_decr')
-     3.37±0.01ms          244±3μs     0.07  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.UInt64Engine'>, <class 'numpy.uint64'>, 'non_monotonic')
-     3.42±0.01ms          248±8μs     0.07  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.Int64Engine'>, <class 'numpy.int64'>, 'monotonic_decr')
-     7.20±0.08ms          516±7μs     0.07  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.Float64Engine'>, <class 'numpy.uint64'>, 'non_monotonic')
-        66.7±1ms      4.73±0.02ms     0.07  sparse.Arithmetic.time_divide(0.01, nan)
-        3.42±0ms          240±2μs     0.07  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.Int64Engine'>, <class 'numpy.int64'>, 'non_monotonic')
-     6.07±0.02ms          415±5μs     0.07  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.Float64Engine'>, <class 'numpy.int64'>, 'monotonic_decr')
-      7.17±0.2ms         417±10μs     0.06  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.Float64Engine'>, <class 'numpy.int64'>, 'non_monotonic')
-      74.1±0.5ms      3.71±0.09ms     0.05  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'max')
-      74.2±0.2ms      3.65±0.05ms     0.05  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'min')
-      65.4±0.9ms      3.18±0.07ms     0.05  sparse.Arithmetic.time_add(0.01, nan)
-      65.6±0.7ms      3.17±0.03ms     0.05  sparse.Arithmetic.time_add(0.1, nan)
-     74.3±0.09ms      3.55±0.02ms     0.05  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'min')
-      74.6±0.4ms      3.54±0.03ms     0.05  rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'max')
-      2.88±0.02s          136±2ms     0.05  plotting.Plotting.time_frame_plot
-      73.8±0.5ms      3.49±0.05ms     0.05  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'max')
-      2.82±0.01s         131±10ms     0.05  plotting.Plotting.time_series_plot
-      73.9±0.4ms      3.38±0.03ms     0.05  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'min')
-      73.6±0.1ms      3.33±0.01ms     0.05  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'max')
-      74.0±0.4ms      3.33±0.05ms     0.04  rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'min')
-     5.50±0.05ms          243±2μs     0.04  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<QuarterEnd: startingMonth=3>)
-     8.19±0.07μs          352±3ns     0.04  timestamp.TimestampProperties.time_is_month_end(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None)
-        5.89±0ms          252±3μs     0.04  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.Float64Engine'>, <class 'numpy.float64'>, 'monotonic_decr')
-     8.11±0.07μs          346±3ns     0.04  timestamp.TimestampProperties.time_is_month_end(None, None)
-     5.80±0.04ms          243±3μs     0.04  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<YearEnd: month=12>)
-     7.98±0.09μs          298±1ns     0.04  timestamp.TimestampProperties.time_week(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
-      8.06±0.1μs        301±0.9ns     0.04  timestamp.TimestampProperties.time_week(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None)
-     7.99±0.08μs          298±1ns     0.04  timestamp.TimestampProperties.time_week(None, None)
-      6.77±0.3ms          250±3μs     0.04  indexing_engines.NumericEngineIndexing.time_get_loc(<class 'pandas._libs.index.Float64Engine'>, <class 'numpy.float64'>, 'non_monotonic')
-     7.88±0.06μs          290±2ns     0.04  timestamp.TimestampProperties.time_dayofyear(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
-     8.10±0.04μs        297±0.8ns     0.04  timestamp.TimestampProperties.time_week(None, 'B')
-     8.03±0.08μs          293±1ns     0.04  timestamp.TimestampProperties.time_dayofyear(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None)
-     7.97±0.03μs        290±0.7ns     0.04  timestamp.TimestampProperties.time_dayofyear(None, 'B')
-     7.89±0.04μs          287±1ns     0.04  timestamp.TimestampProperties.time_dayofyear(None, None)
-     8.06±0.04μs          275±7ns     0.03  timestamp.TimestampProperties.time_days_in_month(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None)
-     8.05±0.07μs          274±5ns     0.03  timestamp.TimestampProperties.time_days_in_month(None, 'B')
-     8.23±0.09μs         276±10ns     0.03  timestamp.TimestampProperties.time_is_year_end(None, None)
-     8.14±0.07μs        269±0.7ns     0.03  timestamp.TimestampProperties.time_days_in_month(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
-     8.24±0.09μs          272±4ns     0.03  timestamp.TimestampProperties.time_days_in_month(None, None)
-     8.04±0.02μs          260±6ns     0.03  timestamp.TimestampProperties.time_quarter(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, 'B')
-     7.92±0.03μs          255±2ns     0.03  timestamp.TimestampProperties.time_quarter(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None)
-     8.18±0.06μs          260±1ns     0.03  timestamp.TimestampProperties.time_is_year_start(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None)
-     8.22±0.05μs          261±5ns     0.03  timestamp.TimestampProperties.time_is_quarter_end(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None)
-      8.29±0.2μs        262±0.7ns     0.03  timestamp.TimestampProperties.time_is_year_end(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None)
-     8.13±0.06μs        257±0.6ns     0.03  timestamp.TimestampProperties.time_quarter(None, 'B')
-     8.32±0.04μs          261±5ns     0.03  timestamp.TimestampProperties.time_is_quarter_end(None, None)
-      7.97±0.1μs          250±2ns     0.03  timestamp.TimestampProperties.time_quarter(None, None)
-      8.39±0.1μs          263±4ns     0.03  timestamp.TimestampProperties.time_is_leap_year(None, None)
-      8.18±0.2μs          256±6ns     0.03  timestamp.TimestampProperties.time_is_year_start(None, None)
-     8.28±0.05μs        258±0.2ns     0.03  timestamp.TimestampProperties.time_is_leap_year(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None)
-     8.15±0.07μs          253±3ns     0.03  timestamp.TimestampProperties.time_is_quarter_start(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None)
-     8.14±0.04μs          253±1ns     0.03  timestamp.TimestampProperties.time_is_quarter_start(None, None)
-     8.21±0.07μs          254±3ns     0.03  timestamp.TimestampProperties.time_is_month_start(None, None)
-     8.40±0.09μs          257±1ns     0.03  timestamp.TimestampProperties.time_is_month_start(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None)
-      25.7±0.1ms          694±5μs     0.03  offset.OffsetSeriesArithmetic.time_add_offset(<BusinessQuarterEnd: startingMonth=3>)
-      31.2±0.2ms          694±2μs     0.02  offset.OffsetSeriesArithmetic.time_add_offset(<BusinessMonthEnd>)
-      31.4±0.1ms          685±2μs     0.02  offset.OffsetSeriesArithmetic.time_add_offset(<BusinessYearBegin: month=1>)
-      31.5±0.2ms          688±7μs     0.02  offset.OffsetSeriesArithmetic.time_add_offset(<BusinessQuarterBegin: startingMonth=3>)
-      1.82±0.01s       37.6±0.2ms     0.02  stat_ops.FrameOps.time_op('median', 'float', 1, False)
-      1.83±0.01s       37.4±0.2ms     0.02  stat_ops.FrameOps.time_op('median', 'float', 1, True)
-      1.82±0.01s      36.1±0.08ms     0.02  stat_ops.FrameOps.time_op('median', 'int', 1, False)
-     34.6±0.06ms          686±5μs     0.02  offset.OffsetSeriesArithmetic.time_add_offset(<BusinessMonthBegin>)
-      1.83±0.07s       36.1±0.2ms     0.02  stat_ops.FrameOps.time_op('median', 'int', 1, True)
-        55.7±1ms          702±5μs     0.01  offset.OffsetSeriesArithmetic.time_add_offset(<BusinessYearEnd: month=12>)
-      45.9±0.7ms          529±1μs     0.01  offset.ApplyIndex.time_apply_index(<YearEnd: month=12>)
-      43.4±0.6ms          490±1μs     0.01  offset.ApplyIndex.time_apply_index(<QuarterEnd: startingMonth=3>)
-      70.4±0.3ms          749±2μs     0.01  groupby.GroupByMethods.time_dtype_as_field('float', 'rank', 'transformation')
-      70.5±0.5ms          750±3μs     0.01  groupby.GroupByMethods.time_dtype_as_field('float', 'rank', 'direct')
-        73.1±1ms         768±10μs     0.01  groupby.GroupByMethods.time_dtype_as_field('int', 'rank', 'direct')
-      72.6±0.9ms          760±8μs     0.01  groupby.GroupByMethods.time_dtype_as_field('int', 'rank', 'transformation')
-      19.6±0.2μs          201±1ns     0.01  timedelta.DatetimeAccessor.time_dt_accessor
-     25.8±0.05ms        258±0.9μs     0.01  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<BusinessQuarterEnd: startingMonth=3>)
-      55.3±0.4ms          541±8μs     0.01  indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
-         183±7ms      1.77±0.03ms     0.01  index_object.Indexing.time_get_loc_non_unique('Float')
-      1.66±0.01s       14.9±0.1ms     0.01  rolling.Pairwise.time_pairwise(1000, 'corr', True)
-      1.67±0.01s      14.5±0.08ms     0.01  rolling.Pairwise.time_pairwise(10, 'corr', True)
-      1.68±0.01s      14.5±0.04ms     0.01  rolling.Pairwise.time_pairwise(None, 'corr', True)
-         169±2ms         1.45±0ms     0.01  index_object.Indexing.time_get_loc_non_unique_sorted('Float')
-      30.6±0.2ms          258±2μs     0.01  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<BusinessMonthEnd>)
-      1.65±0.02s       13.4±0.1ms     0.01  rolling.Pairwise.time_pairwise(1000, 'cov', True)
-      31.7±0.5ms          252±1μs     0.01  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<BusinessYearBegin: month=1>)
-         1.66±0s       13.1±0.1ms     0.01  rolling.Pairwise.time_pairwise(10, 'cov', True)
-      31.5±0.5ms          248±3μs     0.01  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<BusinessQuarterBegin: startingMonth=3>)
-      1.67±0.03s      12.9±0.06ms     0.01  rolling.Pairwise.time_pairwise(None, 'cov', True)
-      54.8±0.5ms          419±3μs     0.01  indexing.NumericSeriesIndexing.time_ix_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
-         268±4ms      2.03±0.02ms     0.01  multiindex_object.Integer.time_get_indexer
-      57.7±0.9ms          430±5μs     0.01  indexing.NumericSeriesIndexing.time_ix_list_like(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
-      31.8±0.6μs          234±4ns     0.01  categoricals.IsMonotonic.time_categorical_index_is_monotonic_decreasing
-      58.4±0.3ms          425±5μs     0.01  indexing.NumericSeriesIndexing.time_ix_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
-       106±0.7ms          766±3μs     0.01  groupby.GroupByMethods.time_dtype_as_group('int', 'rank', 'direct')
-      31.8±0.3μs          230±2ns     0.01  categoricals.IsMonotonic.time_categorical_index_is_monotonic_increasing
-       106±0.6ms          759±2μs     0.01  groupby.GroupByMethods.time_dtype_as_group('int', 'rank', 'transformation')
-      15.9±0.1μs          114±1ns     0.01  timeseries.DatetimeAccessor.time_dt_accessor
-      34.3±0.3ms          241±2μs     0.01  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<BusinessMonthBegin>)
-      54.6±0.3ms          377±3μs     0.01  indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
-      57.5±0.3ms        388±100μs     0.01  groupby.GroupByMethods.time_dtype_as_field('int', 'ffill', 'direct')
-      97.3±0.3ms          657±4μs     0.01  groupby.GroupByMethods.time_dtype_as_field('datetime', 'rank', 'transformation')
-      98.2±0.9ms          661±4μs     0.01  groupby.GroupByMethods.time_dtype_as_field('datetime', 'rank', 'direct')
-     1.87±0.01ms      11.1±0.07μs     0.01  categoricals.Contains.time_categorical_contains
-      57.2±0.2ms         328±90μs     0.01  groupby.GroupByMethods.time_dtype_as_field('int', 'bfill', 'direct')
-       257±0.7ms         1.47±0ms     0.01  index_object.Indexing.time_get_loc_non_unique_sorted('Int')
-         259±8ms      1.47±0.03ms     0.01  index_object.Indexing.time_get_loc_non_unique('Int')
-      57.5±0.6ms         323±90μs     0.01  groupby.GroupByMethods.time_dtype_as_field('int', 'ffill', 'transformation')
-     1.91±0.01ms      10.2±0.08μs     0.01  offset.OnOffset.time_on_offset(<BusinessYearEnd: month=12>)
-     1.28±0.01ms      6.08±0.06μs     0.00  offset.OnOffset.time_on_offset(<BusinessQuarterBegin: startingMonth=3>)
-         130±2ms          606±9μs     0.00  indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
-       164±0.3ms          763±5μs     0.00  groupby.GroupByMethods.time_dtype_as_group('float', 'rank', 'transformation')
-        55.5±1ms          257±2μs     0.00  offset.OffsetDatetimeIndexArithmetic.time_add_offset(<BusinessYearEnd: month=12>)
-     1.23±0.01ms       5.66±0.1μs     0.00  offset.OnOffset.time_on_offset(<QuarterBegin: startingMonth=3>)
-       165±0.8ms          761±2μs     0.00  groupby.GroupByMethods.time_dtype_as_group('float', 'rank', 'direct')
-         167±2ms          767±2μs     0.00  groupby.GroupByMethods.time_dtype_as_group('datetime', 'rank', 'transformation')
-         169±1ms         773±10μs     0.00  groupby.GroupByMethods.time_dtype_as_group('datetime', 'rank', 'direct')
-     1.51±0.03ms       6.76±0.1μs     0.00  offset.OnOffset.time_on_offset(<BusinessMonthEnd>)
-       132±0.8ms          587±6μs     0.00  indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
-      57.6±0.4ms          252±8μs     0.00  groupby.GroupByMethods.time_dtype_as_field('int', 'bfill', 'transformation')
-     1.55±0.01ms      6.00±0.02μs     0.00  offset.OnOffset.time_on_offset(<BusinessQuarterEnd: startingMonth=3>)
-       129±0.3ms          401±6μs     0.00  indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
-      84.1±0.2ms          253±2μs     0.00  groupby.GroupByMethods.time_dtype_as_group('int', 'bfill', 'direct')
-      84.4±0.6ms          252±3μs     0.00  groupby.GroupByMethods.time_dtype_as_group('int', 'bfill', 'transformation')
-      83.9±0.5ms          250±3μs     0.00  groupby.GroupByMethods.time_dtype_as_group('int', 'ffill', 'transformation')
-      85.5±0.7ms          252±1μs     0.00  groupby.GroupByMethods.time_dtype_as_group('int', 'ffill', 'direct')
-         131±3ms          384±2μs     0.00  indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
-        1.46±0ms      3.77±0.02μs     0.00  offset.OnOffset.time_on_offset(<BusinessYearBegin: month=1>)
-      58.7±0.8ms          143±4μs     0.00  groupby.GroupByMethods.time_dtype_as_field('object', 'any', 'transformation')
-      58.0±0.4ms          135±3μs     0.00  groupby.GroupByMethods.time_dtype_as_field('object', 'all', 'direct')
-         250±2ms          579±6μs     0.00  groupby.GroupByMethods.time_dtype_as_field('int', 'pct_change', 'direct')
-      58.3±0.4ms        134±0.7μs     0.00  groupby.GroupByMethods.time_dtype_as_field('object', 'any', 'direct')
-       248±0.8ms          572±3μs     0.00  groupby.GroupByMethods.time_dtype_as_field('int', 'pct_change', 'transformation')
-      58.2±0.1ms        132±0.7μs     0.00  groupby.GroupByMethods.time_dtype_as_field('object', 'all', 'transformation')
-      1.61±0.01s      3.52±0.03ms     0.00  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'max')
-         1.61±0s      3.51±0.07ms     0.00  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'min')
-         1.62±0s      3.47±0.03ms     0.00  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'max')
-         1.62±0s      3.45±0.04ms     0.00  rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'min')
-      98.5±0.5ms          207±2μs     0.00  groupby.GroupByMethods.time_dtype_as_field('float', 'bfill', 'transformation')
-        99.5±1ms          208±2μs     0.00  groupby.GroupByMethods.time_dtype_as_field('float', 'bfill', 'direct')
-      98.9±0.6ms          206±1μs     0.00  groupby.GroupByMethods.time_dtype_as_field('float', 'ffill', 'direct')
-      1.61±0.01s      3.33±0.04ms     0.00  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'min')
-      99.8±0.6ms          205±2μs     0.00  groupby.GroupByMethods.time_dtype_as_field('float', 'ffill', 'transformation')
-      1.61±0.01s      3.30±0.03ms     0.00  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'max')
-      1.61±0.01s      3.25±0.04ms     0.00  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'max')
-         1.61±0s      3.23±0.03ms     0.00  rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'min')
-         281±2ms          521±3μs     0.00  groupby.GroupByMethods.time_dtype_as_field('float', 'pct_change', 'direct')
-       158±0.6ms          291±1μs     0.00  groupby.GroupByMethods.time_dtype_as_field('object', 'ffill', 'direct')
-       159±0.9ms          294±5μs     0.00  groupby.GroupByMethods.time_dtype_as_field('object', 'bfill', 'transformation')
-       160±0.8ms          292±2μs     0.00  groupby.GroupByMethods.time_dtype_as_field('object', 'bfill', 'direct')
-         280±2ms          510±3μs     0.00  groupby.GroupByMethods.time_dtype_as_field('float', 'pct_change', 'transformation')
-         610±4ms         1.10±0ms     0.00  timedelta.DatetimeAccessor.time_timedelta_days
-         161±2ms          290±3μs     0.00  groupby.GroupByMethods.time_dtype_as_field('object', 'ffill', 'transformation')
-       132±0.7ms          235±2μs     0.00  groupby.GroupByMethods.time_dtype_as_group('float', 'ffill', 'transformation')
-       133±0.7ms          237±2μs     0.00  groupby.GroupByMethods.time_dtype_as_group('float', 'bfill', 'transformation')
-         626±2ms      1.11±0.01ms     0.00  timedelta.DatetimeAccessor.time_timedelta_nanoseconds
-       132±0.5ms          234±3μs     0.00  groupby.GroupByMethods.time_dtype_as_group('float', 'ffill', 'direct')
-       133±0.8ms        236±0.9μs     0.00  groupby.GroupByMethods.time_dtype_as_group('float', 'bfill', 'direct')
-         639±6ms      1.12±0.03ms     0.00  timedelta.DatetimeAccessor.time_timedelta_seconds
-        643±20ms      1.10±0.01ms     0.00  timedelta.DatetimeAccessor.time_timedelta_microseconds
-         363±3ms          578±5μs     0.00  groupby.GroupByMethods.time_dtype_as_group('int', 'pct_change', 'direct')
-         366±2ms          570±2μs     0.00  groupby.GroupByMethods.time_dtype_as_group('int', 'pct_change', 'transformation')
-      48.4±0.2ms         67.9±1μs     0.00  groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'transformation')
-       151±0.7ms          211±1μs     0.00  groupby.GroupByMethods.time_dtype_as_field('datetime', 'bfill', 'direct')
-         152±2ms          212±2μs     0.00  groupby.GroupByMethods.time_dtype_as_field('datetime', 'ffill', 'direct')
-       152±0.9ms        211±0.9μs     0.00  groupby.GroupByMethods.time_dtype_as_field('datetime', 'bfill', 'transformation')
-      49.1±0.3ms       68.0±0.4μs     0.00  groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'transformation')
-         151±2ms          210±1μs     0.00  groupby.GroupByMethods.time_dtype_as_field('datetime', 'ffill', 'transformation')
-      48.0±0.2ms       66.4±0.3μs     0.00  groupby.GroupByMethods.time_dtype_as_field('float', 'any', 'transformation')
-      48.0±0.4ms       66.1±0.2μs     0.00  groupby.GroupByMethods.time_dtype_as_field('float', 'all', 'direct')
-      49.1±0.4ms       67.2±0.6μs     0.00  groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'direct')
-      49.2±0.5ms       67.2±0.5μs     0.00  groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'direct')
-      48.9±0.6ms       66.8±0.7μs     0.00  groupby.GroupByMethods.time_dtype_as_field('float', 'any', 'direct')
-      48.1±0.2ms         65.7±1μs     0.00  groupby.GroupByMethods.time_dtype_as_field('float', 'all', 'transformation')
-      52.9±0.2ms       65.5±0.4μs     0.00  groupby.GroupByMethods.time_dtype_as_field('datetime', 'any', 'transformation')
-      53.3±0.1ms       65.0±0.3μs     0.00  groupby.GroupByMethods.time_dtype_as_field('datetime', 'any', 'direct')
-      53.5±0.3ms       64.6±0.7μs     0.00  groupby.GroupByMethods.time_dtype_as_field('datetime', 'all', 'transformation')
-      53.5±0.2ms       64.4±0.4μs     0.00  groupby.GroupByMethods.time_dtype_as_field('datetime', 'all', 'direct')
-       137±0.2ms          161±1μs     0.00  groupby.GroupByMethods.time_dtype_as_group('datetime', 'bfill', 'direct')
-       135±0.6ms          158±1μs     0.00  groupby.GroupByMethods.time_dtype_as_group('datetime', 'ffill', 'direct')
-         135±1ms        157±0.6μs     0.00  groupby.GroupByMethods.time_dtype_as_group('datetime', 'ffill', 'transformation')
-         137±2ms          157±1μs     0.00  groupby.GroupByMethods.time_dtype_as_group('datetime', 'bfill', 'transformation')
-     1.87±0.01ms      2.08±0.07μs     0.00  categoricals.Contains.time_categorical_index_contains
-         565±1ms          562±5μs     0.00  groupby.GroupByMethods.time_dtype_as_group('float', 'pct_change', 'direct')
-         565±2ms          555±3μs     0.00  groupby.GroupByMethods.time_dtype_as_group('float', 'pct_change', 'transformation')
-      70.9±0.2ms         68.7±1μs     0.00  groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'transformation')
-      71.6±0.6ms       68.5±0.5μs     0.00  groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'direct')
-      72.1±0.3ms       68.0±0.5μs     0.00  groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'direct')
-      72.7±0.6ms       68.1±0.6μs     0.00  groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'transformation')
-       111±0.8ms       69.5±0.4μs     0.00  groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'direct')
-         112±1ms       68.8±0.2μs     0.00  groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'transformation')
-       112±0.8ms       69.0±0.9μs     0.00  groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'direct')
-         112±1ms       68.2±0.3μs     0.00  groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'transformation')
-       114±0.4ms       68.9±0.2μs     0.00  groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'transformation')
-       115±0.4ms       69.7±0.3μs     0.00  groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'direct')
-       115±0.9ms       69.4±0.7μs     0.00  groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'transformation')
-         116±1ms       69.4±0.4μs     0.00  groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'direct')
-      1.49±0.01s          471±5μs     0.00  series_methods.IsInForObjects.time_isin_nans
-       290±0.6ms       19.7±0.2μs     0.00  multiindex_object.GetLoc.time_large_get_loc
-        937±30ms      2.28±0.01μs     0.00  series_methods.SeriesGetattr.time_series_datetimeindex_repr
```

</details