Hello all,
A question:
Many ndarray methods (eg sum, mean, any, min) have a "keepdims" keyword
argument, but ndarray subclass methods sometimes don't. The 'matrix'
subclass doesn't, and numpy functions like 'np.sum' intentionally
drop/ignore the keepdims argument when called with an ndarray subclass
as first argument.
This means you can't always use ndarray subclasses as 'drop in'
replacement for ndarrays if the code uses keepdims (even indirectly),
and it means code that deals with keepdims (eg np.sum and more) has to
detect ndarray subclasses and drop keepdims even if the subclass
supports it (since there is no good way to detect support). It seems to
me that if we are going to use inheritance, subclass methods should keep
the signature of the parent class methods. What does the list think?
---- Details: ----
This problem comes up in a PR I'm working on (#5706) to add the keepdims
arg to masked array methods. In order to support masked matrices (which
a lot of unit tests check), I would have to detect and drop the keepdims
arg to avoid an exception. This would be solved if the matrix class
supported keepdims (plus an update to np.sum). Similarly,
`np.sum(mymaskedarray, keepdims=True)` does not respect keepdims, but it
could work if all subclasses supported keepdims.
I do not foresee immediate problems with adding keepdims to the matrix
methods, except that it would be an unused argument. Modifying `np.sum`
to always pass on the keepdims arg is trickier, since it would break any
code that tried to np.sum a subclass that doesn't support keepdims, eg
pandas.DataFrame. **kwargs tricks might work. But if it's permissible I
think it would be better to require subclasses to support all the
keyword args ndarray supports.
Allan
