remove zero lines 2-D numpy array

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]]) 

Answer 2

Because the data are not equal zero exactly, we need set a threshold value for zero such as 1e-6, use numpy.all with axis=1 to check the rows are zeros or not. Use numpy.where and numpy.diff to get the split positions, and call numpy.split to split the array into a list of arrays.

import numpy as np [q,r] = np.linalg.qr(np.array([1,0,0,0,1,1,1,1,1]).reshape(3,3)) mask = np.all(np.abs(r) < 1e-6, axis=1) pos = np.where(np.diff(mask))[0] + 1 result = np.split(r, pos)