Travis E. Oliphant wrote: > to behave as you described in your previous post: where M[i] returned a > 1-d array
My thoughts on this: As Konrad suggested, row vectors and column vectors are different beasts ,and both need to be easily and intuitively available. M[i] returning a 1-d array breaks this -- that's what raw numpy arrays do, and I like it, but it's not so natural for linear algebra. If we really want to support matrixes, then no, M[i] should not return a 1-d array -- what is a 1-d array mean in the matrix/linear algebra context? It makes me think that M[i] should not even be possible, as you would always want one of: row vector: M[i,:] column vector: M[:,i] element: M[i,j] I do like the idea of a row/column vectors being different objects than matrices, then you could naturally index the elements from them. If you really want a 1-d array, you can always do: M.A[i] What if you want to naturally iterate through all the rows, or all the columns? what about: for row in M.rows for column in M.columns M.rows and M.columns would be iterators. -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception [EMAIL PROTECTED] _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion