PERF: Define PeriodArray._values_for_argsort

[qwhelan]

This PR speeds up .groupby() and .set_index() operations involving a PeriodArray by 25-64x:

asv compare upstream/master HEAD -s --sort ratio

Benchmarks that have improved:

       before           after         ratio
     [08395af4]       [696b40f1]
     <period_array_argsort~1>       <parse_time_string>
-       4.77±0.1s          191±3ms     0.04  period.DataFramePeriodColumn.time_set_index
-       2.23±0.2s         35.6±2ms     0.02  groupby.Datelike.time_sum('period_range')

The underlying issue was that pd.core.algorithms.factorize() calls argsort() on the input arrays. Calling this resulted in raw Period objects being sorted via equality comparisons that also generated Offset objects. Assuming all elements of the array have the same frequency, we can simply sort the underlying ordinals and achieve the same result far faster.

• closes #xxxx
• tests added / passed
• passes git diff upstream/master -u -- "*.py" | flake8 --diff
• whatsnew entry

[pep8speaks]

Hello @qwhelan! Thanks for submitting the PR.

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

[codecov]

Codecov Report

Merging #24083 into master will increase coverage by 49.74%.
The diff coverage is 100%.

@@             Coverage Diff             @@
##           master   #24083       +/-   ##
===========================================
+ Coverage   42.51%   92.25%   +49.74%     
===========================================
  Files         161      161               
  Lines       51689    51691        +2     
===========================================
+ Hits        21974    47689    +25715     
+ Misses      29715     4002    -25713

Flag	Coverage Δ
#multiple	90.66% <100%> (?)
#single	42.51% <50%> (ø)	⬆️

Impacted Files	Coverage Δ
pandas/core/arrays/period.py	98.45% <100%> (+61.56%)	⬆️
pandas/core/computation/pytables.py	92.37% <0%> (+0.3%)	⬆️
pandas/io/pytables.py	92.3% <0%> (+0.92%)	⬆️
pandas/util/_test_decorators.py	93.24% <0%> (+4.05%)	⬆️
pandas/compat/__init__.py	58.36% <0%> (+8.17%)	⬆️
pandas/core/config_init.py	99.24% <0%> (+9.84%)	⬆️
pandas/core/reshape/util.py	100% <0%> (+11.53%)	⬆️
pandas/compat/numpy/__init__.py	92.85% <0%> (+14.28%)	⬆️
pandas/core/computation/common.py	85.71% <0%> (+14.28%)	⬆️
pandas/core/api.py	100% <0%> (+14.81%)	⬆️
... and 120 more

---

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[codecov]

Codecov Report

Merging #24083 into master will increase coverage by 49.74%.
The diff coverage is 100%.

@@             Coverage Diff             @@
##           master   #24083       +/-   ##
===========================================
+ Coverage   42.51%   92.25%   +49.74%     
===========================================
  Files         161      161               
  Lines       51689    51691        +2     
===========================================
+ Hits        21974    47689    +25715     
+ Misses      29715     4002    -25713

Flag	Coverage Δ
#multiple	90.66% <100%> (?)
#single	42.51% <50%> (ø)	⬆️

Impacted Files	Coverage Δ
pandas/core/arrays/period.py	98.45% <100%> (+61.56%)	⬆️
pandas/core/computation/pytables.py	92.37% <0%> (+0.3%)	⬆️
pandas/io/pytables.py	92.3% <0%> (+0.92%)	⬆️
pandas/util/_test_decorators.py	93.24% <0%> (+4.05%)	⬆️
pandas/compat/__init__.py	58.36% <0%> (+8.17%)	⬆️
pandas/core/config_init.py	99.24% <0%> (+9.84%)	⬆️
pandas/core/reshape/util.py	100% <0%> (+11.53%)	⬆️
pandas/compat/numpy/__init__.py	92.85% <0%> (+14.28%)	⬆️
pandas/core/computation/common.py	85.71% <0%> (+14.28%)	⬆️
pandas/core/api.py	100% <0%> (+14.81%)	⬆️
... and 120 more

---

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Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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[jreback]

thanks @qwhelan