PERF: Use is_utc check to improve performance of dateutil UTC in DatetimeIndex methods

[mroeschke]

• passes git diff upstream/master -u -- "*.py" | flake8 --diff
• whatsnew entry

Benchmarks from timeseries.py and timestamp.py

       before           after         ratio
     [91d1c503]       [9cc84951]
-      32.6±0.7μs       6.89±0.2μs     0.21  timestamp.TimestampOps.time_normalize(tzutc())

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.

       before           after         ratio
     [91d1c503]       [9cc84951]
-      4.72±0.1μs      4.26±0.06μs     0.90  timeseries.DatetimeIndex.time_get('dst')
-      5.09±0.4ms      2.79±0.02ms     0.55  timeseries.DatetimeAccessor.time_dt_accessor_normalize(tzutc())
-        297±20μs       42.9±0.2μs     0.14  timeseries.TzLocalize.time_infer_dst(tzutc())

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.

[pep8speaks]

Hello @mroeschke! Thanks for submitting the PR.

• There are no PEP8 issues in the file asv_bench/benchmarks/timeseries.py !

• There are no PEP8 issues in the file asv_bench/benchmarks/timestamp.py !

• There are no PEP8 issues in the file pandas/core/arrays/datetimes.py !

• There are no PEP8 issues in the file pandas/core/indexes/datetimes.py !

[jreback]

lgtm. I bet this actually fixed a bug where a dateutil UTC value was not treated correctly?

[jbrockmendel]

Also datetime.timezones.utc in py3

[codecov]

Codecov Report

Merging #23772 into master will decrease coverage by <.01%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master   #23772      +/-   ##
==========================================
- Coverage   92.24%   92.24%   -0.01%     
==========================================
  Files         161      161              
  Lines       51433    51431       -2     
==========================================
- Hits        47446    47444       -2     
  Misses       3987     3987

Flag	Coverage Δ
#multiple	90.64% <100%> (-0.01%)	⬇️
#single	42.28% <14.28%> (-0.01%)	⬇️

Impacted Files	Coverage Δ
pandas/core/indexes/datetimes.py	96.2% <100%> (-0.01%)	⬇️
pandas/core/arrays/datetimes.py	98.5% <100%> (-0.01%)	⬇️

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[jreback]

thanks @mroeschke

as above, if you find some issues that now are closable because of the better checking (mine and @jbrockmendel comments). pls issue a PR with tests.