[Numpy-discussion] using reducing functions without eliminating dimensions?
Charles R Harris
Thu Apr 9 01:02:51 CDT 2009
On Tue, Apr 7, 2009 at 12:44 PM, Dan Lenski <firstname.lastname@example.org> wrote:
> Hi all,
> I often want to use some kind of dimension-reducing function (like min(),
> max(), sum(), mean()) on an array without actually removing the last
> dimension, so that I can then do operations broadcasting the reduced
> array back to the size of the full array. Full example:
> >> table.shape
> (47, 1814)
> >> table.min(axis=1).shape
> >> table - table.min(axis=1)
> ValueError: shape mismatch: objects cannot be broadcast to a single
> >> table - table.min(axis=1)[:, newaxis]
> I have to resort to ugly code with lots of stuff like "... axis=1)[:,
> Is there any way to get the reducing functions to leave a size-1 dummy
> dimension in place, to make this easier?
Not at the moment. There was talk a while back of adding a keyword for this
option, it would certainly make things easier for some common uses. It might
be worth starting that conversation up again.
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the Numpy-discussion