On Fri, Sep 24, 2010 at 8:56 PM, George <[email protected]> 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 > > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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