[Numpy-discussion] Slicing, sum, etc. reduces rank of array?
Benjamin Root
ben.root@ou....
Fri Sep 24 21:23:26 CDT 2010
On Fri, Sep 24, 2010 at 8:56 PM, George <gburdell1@gmail.com> wrote:
> I couldn't find an answer to my newbie question, so I'm posting it here.
>
> I have:
>
> a=numpy.array([[1,2],[3,4]])
> b=numpy.array([[5,6],[7,8]])
>
> Via broadcasting, I know that
> a*[[5],[7]]=numpy.array([[5,10],[21,28]])
>
> Being a recent convert from MATLAB, I expected the same result from
> a*b[:,0],
> assuming b[:,0] would be the column vector [[5],[7]].
>
> Unfortunately, I was wrong. b[:,0] is apparently a 1-rank array of shape
> (2,).
> This causes a*b[:,0] to evaluate as
> a*numpy.array([[5,7]])=numpy.array([[5,14],[15,28]]) instead of
> a*numpy.array([[5],[7]])
>
> To get the result I desire, the only way I've been able to come up with is
>
> a*b[:,0].reshape(2,1)
>
> to "coerce" b[:,0] into a column vector. Is there an easier way to do this,
> without having to do the reshape?
>
> I find similar things happen when I use other operations (e.g. "sum") that
> also
> seem to reduce the array rank.
>
> For example, I would expect numpy.sum(b,1) to also be a "column vector,"
> but it
> also evaluates to a 1-rank array [11, 15] with shape (2,)
>
> Any thoughts, suggestions?
>
>
This has bitten me several times in the past. While there are some neat
tricks around this issue, the one sure-fire, blunt-object solution to the
problem is the np.atleast_2d() function. There is also a 1d and 3d variant
(although the 3d variant messes around a bit with the order of the axes...).
I will leave the more elegant solutions to others to give.
Ben Root
>
>
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