BUG: Fix initialization of DataFrame from dict with NaN as key by toobaz · Pull Request #18600 · pandas-dev/pandas (original) (raw)

uhm... those asv results also seem pretty unstable:

      before           after         ratio
     [d163de70]       [f7447b3f]
+      30.7±0.2ms         41.1±3ms     1.34  frame_ctor.FromDictwithTimestampOffsets.time_frame_ctor('QuarterBegin', 1)
+      29.4±0.1ms         39.1±4ms     1.33  frame_ctor.FromDictwithTimestampOffsets.time_frame_ctor('CBMonthBegin', 2)
+      30.7±0.7ms         40.4±3ms     1.32  frame_ctor.FromDictwithTimestampOffsets.time_frame_ctor('BDay', 2)
+      31.1±0.7ms         39.1±5ms     1.26  frame_ctor.FromDictwithTimestampOffsets.time_frame_ctor('SemiMonthEnd', 2)
+      30.4±0.1ms         38.0±4ms     1.25  frame_ctor.FromDictwithTimestampOffsets.time_frame_ctor('Hour', 2)
-        33.8±1ms       30.4±0.8ms     0.90  frame_ctor.FromDictwithTimestampOffsets.time_frame_ctor('Micro', 1)
-      48.3±0.6ms       42.6±0.8ms     0.88  frame_ctor.FromDicts.time_frame_ctor_nested_dict
-        23.8±1ms       20.5±0.2ms     0.86  frame_ctor.FromDictwithTimestampOffsets.time_frame_ctor('FY5253Quarter_1', 2)
-      41.2±0.7ms         30.4±2ms     0.74  frame_ctor.FromDictwithTimestampOffsets.time_frame_ctor('CustomBusinessHour', 2)
-      8.35±0.9ms      6.05±0.01ms     0.72  frame_ctor.FromDictwithTimestampOffsets.time_frame_ctor('FY5253_2', 2)
-        42.4±2ms       30.5±0.5ms     0.72  frame_ctor.FromDictwithTimestampOffsets.time_frame_ctor('BMonthEnd', 2)

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.

I'll try to sort manually and see how it goes.