TST/DEPR: remove .ix from tests\indexing\multiindex\test_loc.py

[simonjayhawkins]

follow on from #26438

i leave this as draft until #26438 is merged, to prevent this one being merged first.

not used iloc for the expected where dropping levels would be required, used df.values instead.

[jreback]

@simonjayhawkins can you rebase

[codecov]

Codecov Report

Merging #26451 into master will decrease coverage by <.01%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master   #26451      +/-   ##
==========================================
- Coverage   91.74%   91.73%   -0.01%     
==========================================
  Files         174      174              
  Lines       50746    50746              
==========================================
- Hits        46555    46551       -4     
- Misses       4191     4195       +4

Flag	Coverage Δ
#multiple	90.23% <ø> (ø)	⬆️
#single	41.69% <ø> (-0.09%)	⬇️

Impacted Files	Coverage Δ
pandas/io/gbq.py	78.94% <0%> (-10.53%)	⬇️
pandas/core/frame.py	97.02% <0%> (-0.12%)	⬇️

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[codecov]

Codecov Report

Merging #26451 into master will decrease coverage by <.01%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master   #26451      +/-   ##
==========================================
- Coverage   91.74%   91.73%   -0.01%     
==========================================
  Files         174      174              
  Lines       50746    50748       +2     
==========================================
- Hits        46555    46553       -2     
- Misses       4191     4195       +4

Flag	Coverage Δ
#multiple	90.24% <ø> (ø)	⬆️
#single	41.69% <ø> (-0.09%)	⬇️

Impacted Files	Coverage Δ
pandas/io/gbq.py	78.94% <0%> (-10.53%)	⬇️
pandas/core/frame.py	97.02% <0%> (-0.12%)	⬇️
pandas/core/internals/blocks.py	94.08% <0%> (ø)	⬆️

---

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

generally we don't want to use DataFrame.values instead prefer .iloc

[jreback]

use .iloc

[jreback]

same, don't directly use .values

[simonjayhawkins]

generally we don't want to use DataFrame.values instead prefer .iloc

and then follow with droplevel?

[jreback]

and then follow with droplevel?

yes I think the 2nd is more idiomatic

In [8]: pd.DataFrame(df.values[[0, 1]], index=['X', 'X'], columns=df.columns)                                                                                                                                                                                                      
Out[8]: 
          i                   j
          A         A         B
X  1.394995 -0.547172  0.790848
X  0.233443 -0.124027  1.298484

In [9]: df.iloc[[0,1]].droplevel(0)                                                                                                                                                                                                                                                
Out[9]: 
          i                   j
          A         A         B
X  1.394995 -0.547172  0.790848
X  0.233443 -0.124027  1.298484

[simonjayhawkins]

the straight sub fails a couple of tests, i've just commited that to show the failing cases.

next commit using droplevel.

[jreback]

thanks @simonjayhawkins nice clean up!