Commit faf97858ace6ddff6056cd75fc82631c11be0509

[github-actions]

Commit Range

Subsequent benchmarks may have skipped some commits. The link above lists the commits that are between the two benchmark runs where the regression was identified.

• algorithms.Quantile.time_quantile
  • quantile=0, interpolation='linear', dtype='int64' - 33.527% (482.879us)
• algorithms.Quantile.time_quantile
  • quantile=0, interpolation='midpoint', dtype='int64' - 30.460% (444.238us)
• algorithms.Quantile.time_quantile
  • quantile=0.5, interpolation='linear', dtype='int64' - 33.593% (460.584us)
• algorithms.Quantile.time_quantile
  • quantile=0.5, interpolation='linear', dtype='uint64' - 22.015% (300.035us)
• algorithms.Quantile.time_quantile
  • quantile=0.5, interpolation='midpoint', dtype='int64' - 32.279% (452.590us)
• algorithms.Quantile.time_quantile
  • quantile=0.5, interpolation='midpoint', dtype='uint64' - 21.613% (301.728us)
• algorithms.Quantile.time_quantile
  • quantile=1, interpolation='linear', dtype='int64' - 25.568% (286.547us)
• algorithms.Quantile.time_quantile
  • quantile=1, interpolation='midpoint', dtype='int64' - 26.154% (292.853us)
• arithmetic.CategoricalComparisons.time_categorical_op
  • op='eq' - 34.173% (7.850us)
• arithmetic.CategoricalComparisons.time_categorical_op
  • op='gt' - 31.296% (7.294us)
• arithmetic.IntFrameWithScalar.time_frame_op_with_scalar
  • dtype=<class 'numpy.int64'>, scalar=3.0, op= - 14.664% (5.580ms)
• arithmetic.IntFrameWithScalar.time_frame_op_with_scalar
  • dtype=<class 'numpy.int64'>, scalar=np.float64(5.0), op= - 14.701% (5.557ms)
• arithmetic.NumericInferOps.time_divide
  • dtype=<class 'numpy.uint64'> - 20.605% (157.314us)
• hash_functions.NumericSeriesIndexing.time_loc_slice
  • dtype=<class 'numpy.uint64'>, N=100000 - 24.650% (35.384us)
• hash_functions.NumericSeriesIndexing.time_loc_slice
  • dtype=<class 'numpy.uint64'>, N=1000000 - 34.258% (667.594us)
• hash_functions.NumericSeriesIndexing.time_loc_slice
  • dtype=<class 'numpy.uint64'>, N=500000 - 32.494% (259.093us)
• indexing.DataFrameNumericIndexing.time_loc
  • dtype=<class 'numpy.uint64'>, index_structure='nonunique_monotonic_inc' - 17.737% (33.623us)
• indexing.MultiIndexing.time_loc_partial_key_bool_indexer
  • unique_levels=False - 13.140% (53.493us)
• indexing.MultiIndexing.time_loc_partial_key_bool_indexer
  • unique_levels=True - 16.735% (67.437us)
• indexing.NumericSeriesIndexing.time_getitem_scalar
  • dtype=<class 'numpy.uint64'>, index_structure='nonunique_monotonic_inc' - 35.350% (680.371us)
• indexing.NumericSeriesIndexing.time_loc_scalar
  • dtype=<class 'numpy.uint64'>, index_structure='nonunique_monotonic_inc' - 33.185% (649.180us)
• indexing.NumericSeriesIndexing.time_loc_slice
  • dtype=<class 'numpy.uint64'>, index_structure='nonunique_monotonic_inc' - 32.142% (648.470us)
• indexing_engines.MaskedNumericEngineIndexing.time_get_loc
  • engine_and_dtype=(<class 'pandas._libs.index.MaskedInt16Engine'>, 'Int16'), index_type='monotonic_decr', unique=True, N=100000 - 11.885% (31.801us)
• indexing_engines.MaskedNumericEngineIndexing.time_get_loc
  • engine_and_dtype=(<class 'pandas._libs.index.MaskedInt16Engine'>, 'Int16'), index_type='monotonic_incr', unique=True, N=100000 - 9.939% (8.892us)
• indexing_engines.MaskedNumericEngineIndexing.time_get_loc
  • engine_and_dtype=(<class 'pandas._libs.index.MaskedInt8Engine'>, 'Int8'), index_type='monotonic_incr', unique=True, N=100000 - 15.604% (14.554us)
• indexing_engines.MaskedNumericEngineIndexing.time_get_loc
  • engine_and_dtype=(<class 'pandas._libs.index.MaskedUInt8Engine'>, 'UInt8'), index_type='monotonic_incr', unique=True, N=100000 - 15.799% (14.737us)
• indexing_engines.MaskedNumericEngineIndexing.time_get_loc_near_middle
  • engine_and_dtype=(<class 'pandas._libs.index.MaskedInt16Engine'>, 'Int16'), index_type='monotonic_decr', unique=True, N=100000 - 15.082% (40.264us)
• indexing_engines.MaskedNumericEngineIndexing.time_get_loc_near_middle
  • engine_and_dtype=(<class 'pandas._libs.index.MaskedInt16Engine'>, 'Int16'), index_type='monotonic_incr', unique=True, N=100000 - 25.137% (20.359us)
• indexing_engines.MaskedNumericEngineIndexing.time_get_loc_near_middle
  • engine_and_dtype=(<class 'pandas._libs.index.MaskedInt8Engine'>, 'Int8'), index_type='monotonic_incr', unique=True, N=100000 - 14.624% (13.691us)
• indexing_engines.MaskedNumericEngineIndexing.time_get_loc_near_middle
  • engine_and_dtype=(<class 'pandas._libs.index.MaskedUInt8Engine'>, 'UInt8'), index_type='monotonic_incr', unique=True, N=100000 - 19.549% (17.544us)
• indexing_engines.NumericEngineIndexing.time_get_loc
  • engine_and_dtype=(<class 'pandas._libs.index.Int16Engine'>, <class 'numpy.int16'>), index_type='monotonic_decr', unique=True, N=100000 - 12.090% (28.128us)
• indexing_engines.NumericEngineIndexing.time_get_loc
  • engine_and_dtype=(<class 'pandas._libs.index.Int16Engine'>, <class 'numpy.int16'>), index_type='monotonic_decr', unique=True, N=2000000 - 15.826% (716.688us)
• indexing_engines.NumericEngineIndexing.time_get_loc
  • engine_and_dtype=(<class 'pandas._libs.index.Int16Engine'>, <class 'numpy.int16'>), index_type='monotonic_incr', unique=True, N=100000 - 31.532% (16.954us)
• indexing_engines.NumericEngineIndexing.time_get_loc
  • engine_and_dtype=(<class 'pandas._libs.index.Int16Engine'>, <class 'numpy.int16'>), index_type='monotonic_incr', unique=True, N=2000000 - 44.514% (457.390us)
• indexing_engines.NumericEngineIndexing.time_get_loc
  • engine_and_dtype=(<class 'pandas._libs.index.Int8Engine'>, <class 'numpy.int8'>), index_type='monotonic_incr', unique=True, N=100000 - 27.470% (16.956us)
• indexing_engines.NumericEngineIndexing.time_get_loc
  • engine_and_dtype=(<class 'pandas._libs.index.Int8Engine'>, <class 'numpy.int8'>), index_type='monotonic_incr', unique=True, N=2000000 - 26.075% (312.650us)
• indexing_engines.NumericEngineIndexing.time_get_loc
  • engine_and_dtype=(<class 'pandas._libs.index.UInt8Engine'>, <class 'numpy.uint8'>), index_type='monotonic_incr', unique=True, N=100000 - 28.331% (17.456us)
• indexing_engines.NumericEngineIndexing.time_get_loc
  • engine_and_dtype=(<class 'pandas._libs.index.UInt8Engine'>, <class 'numpy.uint8'>), index_type='monotonic_incr', unique=True, N=2000000 - 27.257% (322.015us)
• indexing_engines.NumericEngineIndexing.time_get_loc_near_middle
  • engine_and_dtype=(<class 'pandas._libs.index.Int16Engine'>, <class 'numpy.int16'>), index_type='monotonic_decr', unique=True, N=100000 - 14.860% (34.141us)
• indexing_engines.NumericEngineIndexing.time_get_loc_near_middle
  • engine_and_dtype=(<class 'pandas._libs.index.Int16Engine'>, <class 'numpy.int16'>), index_type='monotonic_decr', unique=True, N=2000000 - 15.219% (694.191us)
• indexing_engines.NumericEngineIndexing.time_get_loc_near_middle
  • engine_and_dtype=(<class 'pandas._libs.index.Int16Engine'>, <class 'numpy.int16'>), index_type='monotonic_incr', unique=True, N=100000 - 38.782% (20.826us)
• indexing_engines.NumericEngineIndexing.time_get_loc_near_middle
  • engine_and_dtype=(<class 'pandas._libs.index.Int16Engine'>, <class 'numpy.int16'>), index_type='monotonic_incr', unique=True, N=2000000 - 48.925% (509.168us)
• indexing_engines.NumericEngineIndexing.time_get_loc_near_middle
  • engine_and_dtype=(<class 'pandas._libs.index.Int8Engine'>, <class 'numpy.int8'>), index_type='monotonic_decr', unique=False, N=100000 - 10.064% (11.539us)
• indexing_engines.NumericEngineIndexing.time_get_loc_near_middle
  • engine_and_dtype=(<class 'pandas._libs.index.Int8Engine'>, <class 'numpy.int8'>), index_type='monotonic_incr', unique=True, N=100000 - 27.682% (17.051us)
• indexing_engines.NumericEngineIndexing.time_get_loc_near_middle
  • engine_and_dtype=(<class 'pandas._libs.index.Int8Engine'>, <class 'numpy.int8'>), index_type='monotonic_incr', unique=True, N=2000000 - 24.250% (295.635us)
• indexing_engines.NumericEngineIndexing.time_get_loc_near_middle
  • engine_and_dtype=(<class 'pandas._libs.index.UInt8Engine'>, <class 'numpy.uint8'>), index_type='monotonic_decr', unique=False, N=100000 - 7.999% (9.235us)
• indexing_engines.NumericEngineIndexing.time_get_loc_near_middle
  • engine_and_dtype=(<class 'pandas._libs.index.UInt8Engine'>, <class 'numpy.uint8'>), index_type='monotonic_incr', unique=True, N=100000 - 27.190% (16.851us)
• indexing_engines.NumericEngineIndexing.time_get_loc_near_middle
  • engine_and_dtype=(<class 'pandas._libs.index.UInt8Engine'>, <class 'numpy.uint8'>), index_type='monotonic_incr', unique=True, N=2000000 - 25.203% (303.401us)
• series_methods.All.time_all
  • N=1000000, case='fast', dtype='boolean' - 84.768% (315.209us)
• series_methods.All.time_all
  • N=1000000, case='slow', dtype='boolean' - 77.745% (307.341us)
• series_methods.Any.time_any
  • N=1000000, case='fast', dtype='boolean' - 85.118% (315.044us)
• series_methods.Any.time_any
  • N=1000000, case='slow', dtype='boolean' - 80.474% (312.320us)