don't understand matrix-multiplication should be reversed in python?

[Robert Kern]

On 2015-11-12 15:57, PythonDude wrote:
> Hi all,
>
> I've come around a webpage with python-tutorial/description for obtaining something and I'll solve this:
>
> R = p^T w
>
> where R is a vector and p^T is the transpose of another vector.
>
> ...
> p is a Nx1 column vector, so p^T turns into a 1xN row vector which can be multiplied with the
> Nx1 weight (column) vector w to give a scalar result. This is equivalent to the dot
> product used in the code. Keep in mind that Python has a reversed definition of
> rows and columns and the accurate NumPy version of the previous equation would
> be R = w * p.T
> ...
>
> (source: http://blog.quantopian.com/markowitz-portfolio-optimization-2/ )
>
> I don't understand this: "Keep in mind that Python has a reversed definition of
> rows and columns and the accurate NumPy version of the previous equation would
> be R = w * p.T"
>
> Not true for numpy, is it? This page: http://mathesaurus.sourceforge.net/matlab-numpy.html says it python and matlab looks quite similar...
>
> Anyone could please explain or elaborate on exactly this (quote): "Keep in mind that Python has a reversed definition of rows and columns"???

He's wrong, simply put. There is no "reversed definition of rows and columns". 
He simply instantiated the two vectors as row-vectors instead of column-vectors, 
which he could have easily done, so he had to flip the matrix expression.

-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
  that is made terrible by our own mad attempt to interpret it as though it had
  an underlying truth."
   -- Umberto Eco