[Numpy-discussion] Slicing, sum, etc. reduces rank of array?

[George]

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?