[Numpy-discussion] Slicing, sum, etc. reduces rank of array?
Fri Sep 24 21:31:48 CDT 2010
On Fri, Sep 24, 2010 at 10:23 PM, Benjamin Root <firstname.lastname@example.org> wrote:
> On Fri, Sep 24, 2010 at 8:56 PM, George <email@example.com> wrote:
>> I couldn't find an answer to my newbie question, so I'm posting it here.
>> I have:
>> Via broadcasting, I know that
>> Being a recent convert from MATLAB, I expected the same result from
>> assuming b[:,0] would be the column vector [,].
>> Unfortunately, I was wrong. b[:,0] is apparently a 1-rank array of shape
>> This causes a*b[:,0] to evaluate as
>> a*numpy.array([[5,7]])=numpy.array([[5,14],[15,28]]) instead of
>> To get the result I desire, the only way I've been able to come up with is
>> to "coerce" b[:,0] into a column vector. Is there an easier way to do
>> without having to do the reshape?
>> I find similar things happen when I use other operations (e.g. "sum") that
>> 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...).
np.atleast_2d() leaves you with a row vector
my preferred generic version since I found it,, has been
special solution for given axis
b[:,0:1] slice instead of index
b[:,0][:,None] add axis back in
I managed to get used to it, finally, but None is almost the most
frequent index in numpy (maybe 3rd place)
> I will leave the more elegant solutions to others to give.
> Ben Root
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