CLN: ASV eval benchmark

[mroeschke]

• Added np.random.seed(1234) in setup classes where random data is created xref BENCH: put in np.random.seed on vbenches #8144

• Ran flake8 and replaced star imports

• Removed some initialization of variables within the time_* methods

asv run -b ^eval
[  0.00%] ·· Benchmarking conda-py3.6-Cython-matplotlib-numexpr-numpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 14.29%] ··· Running eval.Eval.time_add                                                                                  41.8±0.7ms;...
[ 28.57%] ··· Running eval.Eval.time_and                                                                                  55.5±0.4ms;...
[ 42.86%] ··· Running eval.Eval.time_chained_cmp                                                                            48.7±1ms;...
[ 57.14%] ··· Running eval.Eval.time_mult                                                                                   38.5±1ms;...
[ 71.43%] ··· Running eval.Query.time_query_datetime_column                                                                       19.1ms
[ 85.71%] ··· Running eval.Query.time_query_datetime_index                                                                        51.9ms
[100.00%] ··· Running eval.Query.time_query_with_boolean_selection                                                                66.9ms

[codecov]

Codecov Report

Merging #18500 into master will increase coverage by 0.02%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master   #18500      +/-   ##
==========================================
+ Coverage    91.3%   91.32%   +0.02%     
==========================================
  Files         163      163              
  Lines       49781    49781              
==========================================
+ Hits        45451    45463      +12     
+ Misses       4330     4318      -12

Flag	Coverage Δ
#multiple	89.12% <ø> (+0.02%)	⬆️
#single	40.72% <ø> (ø)	⬆️

Impacted Files	Coverage Δ
pandas/plotting/_converter.py	65.25% <0%> (+1.81%)	⬆️

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[jreback]

FYI the random see is already defined in pandas_vb_common I would actually remove it entirely from benchmark suite (except for there). (and put a note in each file that its already defined).

separate PR for this.

[jorisvandenbossche]

When you are cleaning up, you can also remove such unneeded self.'s, for things that are only used in init (they are a left over from the autogeneration of this code when the benchmarks were translated to asv)

[mroeschke]

@jreback I think the random seed should be included in the setup function instead so that the same data is generated for each benchmark. If the seed is global then different random data is generated for each benchmark.

You can also include a module-level setup function, which will be run for every benchmark within the module, prior to any setup assigned specifically to each function.

[jreback]

ok then let’s remove from the global name space (or maybe put a comment there) and add to setup as u r already doing

[jorisvandenbossche]

I think the module level setup is a good idea