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ASV's look unaffected:

before           after         ratio
     [029907c9]       [b45f9193]
     <master>         <bug/rank_datetimelike>
       6.90±0.2ms       6.76±0.6ms     0.98  frame_methods.Rank.time_rank('float')
       2.63±0.3ms       2.39±0.2ms    ~0.91  frame_methods.Rank.time_rank('int')
         45.7±1ms         45.3±1ms     0.99  frame_methods.Rank.time_rank('object')
       2.47±0.2ms       2.22±0.1ms    ~0.90  frame_methods.Rank.time_rank('uint')
      10.8±0.09ms       10.8±0.8ms     1.01  series_methods.Rank.time_rank('float')
       8.10±0.2ms       8.26±0.3ms     1.02  series_methods.Rank.time_rank('int')
         51.3±2ms         51.2±1ms     1.00  series_methods.Rank.time_rank('object')
       8.76±0.6ms       8.30±0.2ms     0.95  series_methods.Rank.time_rank('uint')

@mzeitlin11 mzeitlin11 added Algos Non-arithmetic algos: value_counts, factorize, sorting, isin, clip, shift, diff Bug labels Mar 27, 2021
@@ -1109,6 +1108,7 @@ def group_rank(float64_t[:, ::1] out,
result = rank_1d(
values=values[:, 0],
labels=labels,
is_datetimelike=is_datetimelike,
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needs tests?

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Yep added test for groupby as well

@jreback jreback added this to the 1.3 milestone Mar 29, 2021
@jreback jreback merged commit a59987b into pandas-dev:master Mar 29, 2021
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jreback commented Mar 29, 2021

thanks @mzeitlin11

keep em coming on closing older issues!

@mzeitlin11 mzeitlin11 deleted the bug/rank_datetimelike branch March 29, 2021 15:51
vladu pushed a commit to vladu/pandas that referenced this pull request Apr 5, 2021
JulianWgs pushed a commit to JulianWgs/pandas that referenced this pull request Jul 3, 2021
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rank() for int64 gives NAN for most negative value
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