As you will note, since most of the functions work on rows, the matrix in question has been transposed. From: numpy-discussion-bounces@scipy.org [mailto:numpy-discussion-bounces@scipy.org] On Behalf Of Mark Janikas Sent: Friday, August 26, 2011 10:11 AM To: 'Discussion of Numerical Python' Subject: [Numpy-discussion] Identifying Colinear Columns of a Matrix Hello All, I am trying to identify columns of a matrix that are perfectly collinear. It is not that difficult to identify when two columns are identical are have zero variance, but I do not know how to ID when the culprit is of a higher order. i.e. columns 1 + 2 + 3 = column 4. NUM.corrcoef(matrix.T) will return NaNs when the matrix is singular, and LA.cond(matrix.T) will provide a very large condition number.... But they do not tell me which columns are causing the problem. For example: zt = numpy. array([[ 1. , 1. , 1. , 1. , 1. ], [ 0.25, 0.1 , 0.2 , 0.25, 0.5 ], [ 0.75, 0.9 , 0.8 , 0.75, 0.5 ], [ 3. , 8. , 0. , 5. , 0. ]]) How can I identify that columns 0,1,2 are the issue because: column 1 + column 2 = column 0? Any input would be greatly appreciated. Thanks much, MJ