Question

I run a qr factorization in numpy which returns a list of ndarrays, namely Qand R:

>>> [q,r] = np.linalg.qr(np.array([1,0,0,0,1,1,1,1,1]).reshape(3,3))

R is a two-dimensional array, having pivoted zero-lines at the bottom (even proved for all examples in my test set):

>>> print r
[[ 1.41421356  0.70710678  0.70710678]
 [ 0.          1.22474487  1.22474487]
 [ 0.          0.          0.        ]]

. Now, I want to divide R in two matrices R_~:

[[ 1.41421356  0.70710678  0.70710678]
 [ 0.          1.22474487  1.22474487]]

and R_0:

[[ 0.          0.          0.        ]]

(extracting all zero-lines). It seems to be close to this solution: deleting rows in numpy array.

EDIT:
Even more interesting: np.linalg.qr() returns a n x n-matrix. Not, what I would have expected:

A := n x m
Q := n x m
R := n x m

Answer 1

Use np.all with an axis argument:

>>> r[np.all(r == 0, axis=1)]
array([[ 0.,  0.,  0.]])
>>> r[~np.all(r == 0, axis=1)]
array([[-1.41421356, -0.70710678, -0.70710678],
       [ 0.        , -1.22474487, -1.22474487]])