CLN: minor cleaning of Categorical.__init__

[topper-123]

At a certain point in the Categorical init, we should for clarity only use dtype and not intermix categories or ordered.

This small PR does that.

[codecov]

Codecov Report

Merging #24402 into master will decrease coverage by <.01%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master   #24402      +/-   ##
==========================================
- Coverage    92.3%    92.3%   -0.01%     
==========================================
  Files         162      162              
  Lines       51875    51871       -4     
==========================================
- Hits        47883    47879       -4     
  Misses       3992     3992

Flag	Coverage Δ
#multiple	90.71% <100%> (-0.01%)	⬇️
#single	42.99% <66.66%> (ø)	⬆️

Impacted Files	Coverage Δ
pandas/core/arrays/categorical.py	95.29% <100%> (-0.03%)	⬇️

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[codecov]

Codecov Report

Merging #24402 into master will decrease coverage by <.01%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master   #24402      +/-   ##
==========================================
- Coverage    92.3%    92.3%   -0.01%     
==========================================
  Files         162      163       +1     
  Lines       51875    51944      +69     
==========================================
+ Hits        47883    47946      +63     
- Misses       3992     3998       +6

Flag	Coverage Δ
#multiple	90.71% <100%> (ø)	⬆️
#single	42.99% <100%> (-0.01%)	⬇️

Impacted Files	Coverage Δ
pandas/core/arrays/categorical.py	95.3% <100%> (-0.02%)	⬇️
pandas/core/arrays/datetimes.py	97.77% <0%> (-0.47%)	⬇️
pandas/core/arrays/timedeltas.py	86.85% <0%> (-0.31%)	⬇️
pandas/core/arrays/datetimelike.py	96.35% <0%> (-0.16%)	⬇️
pandas/core/indexes/timedeltas.py	90.42% <0%> (-0.03%)	⬇️
pandas/core/arrays/period.py	98.49% <0%> (-0.01%)	⬇️
pandas/core/indexes/datetimes.py	96.33% <0%> (-0.01%)	⬇️
pandas/core/indexes/base.py	96.27% <0%> (ø)	⬆️
pandas/core/arrays/_ranges.py	98.66% <0%> (ø)
pandas/core/indexes/datetimelike.py	97.29% <0%> (ø)	⬆️
... and 2 more

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[jreback]

lgtm. can you run the asv's for categoricals to make sure nothing has changed; we recently had some optimization here which depended on certain checks.

[jschendel]

lgtm pending @jreback's asv comment

[topper-123]

I have some problem getting asv working. I don’t think this can have a performance effect now, after the change after @jschendel comments...

[jreback]

thanks @topper-123