BUG: fix numpy min/max compat with index (#26125)

[Batalex]

• closes BUG: np.min(pd.Index) and np.max(pd.Index) raise a TypeError #26125
• 2 tests added
• passes git diff upstream/master -u -- "*.py" | flake8 --diff
• whatsnew entry

When working on the original issue, I came across something similar :

• RangeIndex had its own implementation of max/min with the same behavior
• Index & RangeIndex could both benefit from adding another compat call

In [5]: np.argmin(pd.Index([1,2,3]), out=0)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
venv\lib\site-packages\numpy\core\fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
     55     try:
---> 56         return getattr(obj, method)(*args, **kwds)
     57

TypeError: argmin() got an unexpected keyword argument 'out'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-5-5c9b894867ef> in <module>
----> 1 np.argmin(pd.Index([1,2,3]), out=0)

venv\lib\site-packages\numpy\core\fromnumeric.py in argmin(a, axis, out)
   1170
   1171     """
-> 1172     return _wrapfunc(a, 'argmin', axis=axis, out=out)
   1173
   1174

venv\lib\site-packages\numpy\core\fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
     64     # a downstream library like 'pandas'.
     65     except (AttributeError, TypeError):
---> 66         return _wrapit(obj, method, *args, **kwds)
     67
     68

venv\lib\site-packages\numpy\core\fromnumeric.py in _wrapit(obj, method, *args, **kwds)
     44     except AttributeError:
     45         wrap = None
---> 46     result = getattr(asarray(obj), method)(*args, **kwds)
     47     if wrap:
     48         if not isinstance(result, mu.ndarray):

TypeError: output must be an array

I changed it to be consistent with the rest of pandas ValueError: the 'out' parameter is not supported in the pandas implementation of argmax().

[codecov]

Codecov Report

Merging #26324 into master will decrease coverage by <.01%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master   #26324      +/-   ##
==========================================
- Coverage   92.04%   92.03%   -0.01%     
==========================================
  Files         175      175              
  Lines       52291    52297       +6     
==========================================
+ Hits        48132    48134       +2     
- Misses       4159     4163       +4

Flag	Coverage Δ
#multiple	90.59% <100%> (ø)	⬆️
#single	40.74% <50%> (-0.16%)	⬇️

Impacted Files	Coverage Δ
pandas/core/indexes/range.py	97.97% <100%> (+0.01%)	⬆️
pandas/core/base.py	98% <100%> (+0.01%)	⬆️
pandas/io/gbq.py	78.94% <0%> (-10.53%)	⬇️
pandas/core/frame.py	97.01% <0%> (-0.12%)	⬇️

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[codecov]

Codecov Report

Merging #26324 into master will decrease coverage by <.01%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master   #26324      +/-   ##
==========================================
- Coverage   92.04%   92.03%   -0.01%     
==========================================
  Files         175      175              
  Lines       52291    52297       +6     
==========================================
+ Hits        48132    48134       +2     
- Misses       4159     4163       +4

Flag	Coverage Δ
#multiple	90.59% <100%> (ø)	⬆️
#single	40.74% <50%> (-0.16%)	⬇️

Impacted Files	Coverage Δ
pandas/core/indexes/range.py	97.97% <100%> (+0.01%)	⬆️
pandas/core/base.py	98% <100%> (+0.01%)	⬆️
pandas/io/gbq.py	78.94% <0%> (-10.53%)	⬇️
pandas/core/frame.py	97.01% <0%> (-0.12%)	⬇️

---

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[jreback]

thanks @Batalex

[TomAugspurger]

I'm a bit surprised at the performance impact here http://pandas.pydata.org/speed/pandas/#index_object.Range.time_min (about 20-25%). It may be worth seeing if the performance of our numpy compat validators can be improved.

[TomAugspurger]

On second thought, maybe the relative perf slowdown isn't too surprising, since RangeIndex.min / max is basically free. So it looks like a large slowdown, but in absolute terms it's not so bad... Still may be worth profiling things to see if there's any easy wins to be had in the validators (as in theory those should be cheap too).