Clean up ufuncs post numpy bump

[h-vetinari]

Found an old branch lying around I had forgotten about after the numpy bump, which had been requested by @TomAugspurger in this comment

[codecov]

Codecov Report

Merging #26606 into master will increase coverage by <.01%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master   #26606      +/-   ##
==========================================
+ Coverage   91.85%   91.86%   +<.01%     
==========================================
  Files         174      174              
  Lines       50707    50693      -14     
==========================================
- Hits        46578    46568      -10     
+ Misses       4129     4125       -4

Flag	Coverage Δ
#multiple	90.4% <ø> (+0.01%)	⬆️
#single	41.76% <ø> (-0.09%)	⬇️

Impacted Files	Coverage Δ
pandas/core/sparse/frame.py	95.63% <ø> (-0.02%)	⬇️
pandas/core/arrays/sparse.py	93.48% <ø> (+0.41%)	⬆️
pandas/core/sparse/series.py	94.9% <ø> (+1.66%)	⬆️
pandas/io/gbq.py	78.94% <0%> (-10.53%)	⬇️
pandas/core/frame.py	97% <0%> (-0.12%)	⬇️

---

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 0dbb99e...a4db51b. Read the comment docs.

[codecov]

Codecov Report

Merging #26606 into master will increase coverage by <.01%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master   #26606      +/-   ##
==========================================
+ Coverage   91.85%   91.86%   +<.01%     
==========================================
  Files         174      174              
  Lines       50707    50693      -14     
==========================================
- Hits        46578    46568      -10     
+ Misses       4129     4125       -4

Flag	Coverage Δ
#multiple	90.4% <ø> (+0.01%)	⬆️
#single	41.76% <ø> (-0.09%)	⬇️

Impacted Files	Coverage Δ
pandas/core/sparse/frame.py	95.63% <ø> (-0.02%)	⬇️
pandas/core/arrays/sparse.py	93.48% <ø> (+0.41%)	⬆️
pandas/core/sparse/series.py	94.9% <ø> (+1.66%)	⬆️
pandas/io/gbq.py	78.94% <0%> (-10.53%)	⬇️
pandas/core/frame.py	97% <0%> (-0.12%)	⬇️

---

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 0dbb99e...a4db51b. Read the comment docs.

[jreback]

nice that this passes

are there any other array_wrap* leftover? (in any class)

[h-vetinari]

@jreback

There are a bunch left:

>findstr /L /S /N "__array_wrap__" pandas/*.py
pandas/core\generic.py:1481:        return self.__array_wrap__(arr)
pandas/core\generic.py:1493:        return self.__array_wrap__(arr)
pandas/core\generic.py:1498:            return self.__array_wrap__(arr)
pandas/core\generic.py:1951:    def __array_wrap__(self, result, context=None):
pandas/core\indexes\base.py:665:    def __array_wrap__(self, result, context=None):
pandas/core\indexes\interval.py:384:    def __array_wrap__(self, result, context=None):
pandas/core\indexes\period.py:461:    def __array_wrap__(self, result, context=None):
pandas/core\series.py:748:    def __array_wrap__(self, result, context=None):

>findstr /L /S /N "__array_prepare__" pandas/*.py
pandas/core\series.py:755:    def __array_prepare__(self, result, context=None):

>findstr /L /S /N "__array_priority__" pandas/*.py
pandas/core\arrays\base.py:973:       You may want to set ``__array_priority__`` if you want your
pandas/core\arrays\base.py:1028:       You may want to set ``__array_priority__`` if you want your
pandas/core\arrays\categorical.py:307:    __array_priority__ = 1000
pandas/core\arrays\datetimes.py:264:    __array_priority__ = 1000
pandas/core\arrays\integer.py:342:    __array_priority__ = 1000  # higher than ndarray so ops dispatch to us
pandas/core\arrays\numpy_.py:107:    __array_priority__ = 1000
pandas/core\arrays\period.py:132:    __array_priority__ = 1000
pandas/core\arrays\timedeltas.py:132:    __array_priority__ = 1000
pandas/core\base.py:646:    __array_priority__ = 1000
pandas/core\generic.py:1946:    __array_priority__ = 1000
pandas/tests\extension\decimal\array.py:52:    __array_priority__ = 1000
pandas/tests\extension\json\array.py:53:    __array_priority__ = 1000

I only touched the sparse-stuff (according to the comment I followed for this PR), and hadn't checked which other classes already implement __array_ufunc__. Now I did and not all of the above classes do:

>findstr /L /S /N "__array_ufunc__" pandas/*.py
pandas/core\arrays\numpy_.py:160:    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
pandas/core\arrays\numpy_.py:170:            # allow subclasses that don't override __array_ufunc__ to
pandas/core\arrays\sparse.py:1643:    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
pandas/core\sparse\series.py:115:    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
pandas/core\sparse\series.py:121:        result = self.values.__array_ufunc__(ufunc, method, *inputs, **kwargs)

[jreback]

these are fine as we have __array_ufunc__ in sparse; but we do not have in series/frame (and its non-trivial to add I briefly tried).