RLS: 1.3.1

Tracking issue for the 1.3.1 release.

https://github.com/pandas-dev/pandas/milestone/87

Currently scheduled for July 25, 2021 (date flexible on severity of regressions)

List of open regressions: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3ARegression

significant performance regressions between 1.2.5 and 1.3.0

       before           after         ratio
     [7c48ff44]       [f00ed8f4]
     <v1.2.5^0>       <v1.3.0^0>
+     2.43±0.03ms         28.4±1ms    11.66  indexing.InsertColumns.time_assign_list_like_with_setitem
+     5.79±0.08ms       29.9±0.4ms     5.17  frame_methods.MaskBool.time_frame_mask_bools
+      88.7±0.9μs          453±5μs     5.11  inference.ToTimedelta.time_convert_int
+        82.6±2ms          386±1ms     4.67  frame_ctor.FromDicts.time_nested_dict_int64
+        363±10μs      1.26±0.01ms     3.48  stat_ops.FrameOps.time_op('prod', 'int', 1)
+         537±7μs      1.84±0.01ms     3.43  stat_ops.FrameOps.time_op('mean', 'int', 1)
+         341±3μs      1.09±0.01ms     3.21  stat_ops.FrameOps.time_op('sum', 'int', 1)
+        643±10μs      1.95±0.01ms     3.03  rolling.EWMMethods.time_ewm('Series', 1000, 'float', 'mean')
+         626±6μs      1.88±0.03ms     3.00  rolling.EWMMethods.time_ewm('Series', 10, 'float', 'mean')
+         678±9μs      2.00±0.04ms     2.95  rolling.EWMMethods.time_ewm('Series', 10, 'int', 'mean')
+        696±10μs      2.01±0.01ms     2.89  rolling.EWMMethods.time_ewm('Series', 1000, 'int', 'mean')
+         592±9μs      1.68±0.01ms     2.84  stat_ops.FrameOps.time_op('mean', 'int', 0)
+        758±10μs      2.11±0.02ms     2.79  rolling.EWMMethods.time_ewm('DataFrame', 10, 'float', 'mean')
+     1.34±0.01ms       3.73±0.1ms     2.78  stat_ops.FrameOps.time_op('std', 'int', 0)
+        804±10μs      2.21±0.03ms     2.75  rolling.EWMMethods.time_ewm('DataFrame', 10, 'int', 'mean')
+         479±4μs      1.32±0.04ms     2.75  stat_ops.FrameOps.time_op('sum', 'int', 0)
+         213±4ns          583±8ns     2.74  tslibs.timestamp.TimestampProperties.time_freqstr(None, 'B')
+         218±2ns          592±4ns     2.72  tslibs.timestamp.TimestampProperties.time_freqstr(<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>, 'B')
+     1.32±0.03ms      3.58±0.04ms     2.70  stat_ops.FrameOps.time_op('var', 'int', 0)
+         221±2ns          595±9ns     2.69  tslibs.timestamp.TimestampProperties.time_freqstr(tzlocal(), 'B')
+         216±2ns          582±6ns     2.69  tslibs.timestamp.TimestampProperties.time_freqstr(datetime.timezone(datetime.timedelta(seconds=3600)), 'B')
+         212±4ns         567±10ns     2.68  tslibs.timestamp.TimestampProperties.time_freqstr(tzfile('/usr/share/zoneinfo/Asia/Tokyo'), 'B')

cc @pandas-dev/pandas-core @pandas-dev/pandas-triage

[simonjayhawkins]

-      83.3±0.3ms      4.42±0.08ms     0.05  frame_methods.Fillna.time_frame_fillna(False, 'pad', 'Float64')
-        97.6±2ms       4.81±0.1ms     0.05  frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'Int64')
-      95.3±0.3ms      4.43±0.07ms     0.05  frame_methods.Fillna.time_frame_fillna(True, 'pad', 'Int64')
-        97.0±1ms      4.48±0.09ms     0.05  frame_methods.Fillna.time_frame_fillna(False, 'pad', 'Int64')
-      27.7±0.2ms      1.27±0.01ms     0.05  rolling.GroupbyEWM.time_groupby_method('std')
-     17.9±0.09ms         786±10μs     0.04  arithmetic.Ops2.time_frame_float_div_by_zero
-         189±2ms      4.88±0.03ms     0.03  indexing.NumericSeriesIndexing.time_getitem_lists(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
-      62.8±0.3ms      1.05±0.01ms     0.02  reshape.ReshapeExtensionDtype.time_unstack_fast('datetime64[ns, US/Pacific]')
-     2.27±0.03ms       37.2±0.8μs     0.02  categoricals.Concat.time_append_overlapping_index
-       228±0.5ms      3.45±0.03ms     0.02  rolling.GroupbyEWM.time_groupby_method('corr')
-         224±2ms      3.26±0.02ms     0.01  rolling.GroupbyEWM.time_groupby_method('cov')
-         189±2ms      2.43±0.01ms     0.01  indexing.NumericSeriesIndexing.time_getitem_array(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
-         189±2ms      2.30±0.01ms     0.01  indexing.NumericSeriesIndexing.time_loc_array(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
-      50.6±0.3μs         600±50ns     0.01  index_cached_properties.IndexCache.time_shape('RangeIndex')
-        51.4±2μs         600±50ns     0.01  index_cached_properties.IndexCache.time_shape('Int64Index')
-      69.6±0.6ms          455±6μs     0.01  indexing.DatetimeIndexIndexing.time_get_indexer_mismatched_tz
-      49.8±0.3ms          305±2μs     0.01  indexing.CategoricalIndexIndexing.time_get_indexer_list('monotonic_decr')
-      49.4±0.4ms          298±3μs     0.01  indexing.CategoricalIndexIndexing.time_get_indexer_list('monotonic_incr')
-         187±4ms      1.13±0.01ms     0.01  indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
-      49.6±0.3ms          290±2μs     0.01  indexing.CategoricalIndexIndexing.time_get_indexer_list('non_monotonic')
-         188±2ms         1.01±0ms     0.01  indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
-         634±6μs      2.16±0.02μs     0.00  inference.ToNumericDowncast.time_downcast('datetime64', None)
-      55.1±0.1ms          155±2μs     0.00  reshape.ReshapeExtensionDtype.time_transpose('datetime64[ns, US/Pacific]')
-       205±0.4ms          223±4μs     0.00  hash_functions.UniqueForLargePyObjectInts.time_unique
-      3.02±0.1ms      1.08±0.02μs     0.00  categoricals.Indexing.time_unique

[jbrockmendel]

for morale purposes, any perf improvements?