Closed
Description
$ 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')
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+ 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
- 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