One of the essential characteristics of a matrix is that it be rectangular. This is neither spelt out or checked currently. The Doc description refers to a class: - *class *numpy.matrix[source] <http://github.com/numpy/numpy/blob/v1.9.1/numpy/matrixlib/defmatrix.py#L206> Returns a matrix from an array-like object, or from a string of data. A matrix is a specialized 2-D array that retains its 2-D nature through operations. It has certain special operators, such as * (matrix multiplication) and ** (matrix power). This illustrates a failure, which is reported later in the calculation: A2= np.matrix([[1, 2, -2], [-3, -1, 4], [4, 2 -6]]) Here 2 - 6 is treated as an expression. Wikipedia offers: In mathematics <http://en.wikipedia.org/wiki/Mathematics>, a *matrix* (plural *matrices*) is a rectangular <http://en.wikipedia.org/wiki/Rectangle> *array <http://en.wiktionary.org/wiki/array>*[1] <http://en.wikipedia.org/wiki/Matrix_%28mathematics%29#cite_note-1> of numbers <http://en.wikipedia.org/wiki/Number>, symbols <http://en.wikipedia.org/wiki/Symbol_%28formal%29>, or expressions <http://en.wikipedia.org/wiki/Expression_%28mathematics%29>, arranged in *rows <http://en.wiktionary.org/wiki/row>* and *columns <http://en.wiktionary.org/wiki/column>*.[2] <http://en.wikipedia.org/wiki/Matrix_%28mathematics%29#cite_note-2>[3] <http://en.wikipedia.org/wiki/Matrix_%28mathematics%29#cite_note-3> The individual items in a matrix are called its *elements* or *entries*. An example of a matrix with 2 rows and 3 columns is [image: \begin{bmatrix}1 & 9 & -13 \\20 & 5 & -6 \end{bmatrix}.]In the Numpy context, the symbols or expressions need to be evaluable. Colin W.