Convert Sparse ASVs

[TomAugspurger]

No description provided.

[codecov]

Codecov Report

Merging #26704 into master will decrease coverage by <.01%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master   #26704      +/-   ##
==========================================
- Coverage   91.88%   91.87%   -0.01%     
==========================================
  Files         174      174              
  Lines       50701    50701              
==========================================
- Hits        46588    46584       -4     
- Misses       4113     4117       +4

Flag	Coverage Δ
#multiple	90.41% <ø> (ø)	⬆️
#single	41.93% <ø> (-0.09%)	⬇️

Impacted Files	Coverage Δ
pandas/io/gbq.py	78.94% <0%> (-10.53%)	⬇️
pandas/core/frame.py	97% <0%> (-0.12%)	⬇️

---

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[codecov]

Codecov Report

Merging #26704 into master will increase coverage by <.01%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master   #26704      +/-   ##
==========================================
+ Coverage   91.71%   91.72%   +<.01%     
==========================================
  Files         178      178              
  Lines       50771    50779       +8     
==========================================
+ Hits        46567    46575       +8     
  Misses       4204     4204

Flag	Coverage Δ
#multiple	90.31% <ø> (ø)	⬆️
#single	41.21% <ø> (-0.07%)	⬇️

Impacted Files	Coverage Δ
pandas/io/gbq.py	78.94% <0%> (-10.53%)	⬇️
pandas/core/indexes/interval.py	96.11% <0%> (-0.32%)	⬇️
pandas/core/frame.py	96.88% <0%> (-0.12%)	⬇️
pandas/core/series.py	93.64% <0%> (+0.01%)	⬆️
pandas/util/testing.py	90.94% <0%> (+0.1%)	⬆️
pandas/io/excel/_base.py	91.92% <0%> (+0.11%)	⬆️
pandas/core/generic.py	94.1% <0%> (+0.19%)	⬆️
pandas/core/computation/expr.py	97.8% <0%> (+0.27%)	⬆️

---

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[gfyoung]

For my personal edification, why are we filtering these?

[TomAugspurger]

Keeps them from polluting the output of ASV runs. Alternatively, we just remove all the SparseDataFrame / SparseSeries asvs, or convert them to using DataFrame[sparse] / Series[Sparse]?

…

On Fri, Jun 7, 2019 at 1:11 PM gfyoung ***@***.***> wrote: For my personal edification, why are we filtering these? — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#26704?email_source=notifications&email_token=AAKAOIUNC3JSM2566JS5O73PZKQFHA5CNFSM4HVWCALKYY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGODXGSZRQ#issuecomment-499985606>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AAKAOIT4R4CV454P5IQP74TPZKQFHANCNFSM4HVWCALA> .

[jreback]

Alternatively, we just remove all the SparseDataFrame / SparseSeries asvs,
or convert them to using DataFrame[sparse] / Series[Sparse]?

should do this, can open an issue?

is this mergable (or failing for some other reason)?

[TomAugspurger]

Updated to remove the filter and change the SparseSeries / SparseFrame tests to use regular dataframe / series.

[TomAugspurger]

FYI, all green if anyone has a chance to take a quick look.

[jreback]

+1 on this, though means we lose asv's on previous versions for sparse (which I think is ok).

cc @topper-123 @qwhelan

[topper-123]

+1 for me. Logical step, now that sparse(DataFrame|Series) is deprecated.

[qwhelan]

+1, if loss of old benchmark comparisons is a concern it would probably make sense to set up a regular backfill job on the speed site. asv could make this quite a bit easier, but just covering every major point release would go pretty far. Happy to work on this starting in about a week if there's interest.

[jreback]

thanks @TomAugspurger