numpy (matrix solver) - python vs. matlab

[someone]

Hi,

Notice cross-post, I hope you bear over with me for doing that (and I 
imagine that some of you also like python in the matlab-group like 
myself)...

------------------------------------------
Python vs. Matlab:
------------------------------------------

Python:
========
from numpy import matrix
from numpy import linalg
A = matrix( [[1,2,3],[11,12,13],[21,22,23]] )
print "A="
print A
print "A.I (inverse of A)="
print A.I

A.I (inverse of A)=
[[  2.81466387e+14  -5.62932774e+14   2.81466387e+14]
  [ -5.62932774e+14   1.12586555e+15  -5.62932774e+14]
  [  2.81466387e+14  -5.62932774e+14   2.81466387e+14]]


Matlab:
========
 >> A=[1 2 3; 11 12 13; 21 22 23]

A =

      1     2     3
     11    12    13
     21    22    23

 >> inv(A)
Warning: Matrix is close to singular or badly scaled.
          Results may be inaccurate. RCOND = 1.067522e-17.

ans =

    1.0e+15 *

     0.3002   -0.6005    0.3002
    -0.6005    1.2010   -0.6005
     0.3002   -0.6005    0.3002

------------------------------------------
Python vs. Matlab:
------------------------------------------

So Matlab at least warns about "Matrix is close to singular or badly 
scaled", which python (and I guess most other languages) does not...

Which is the most accurate/best, even for such a bad matrix? Is it 
possible to say something about that? Looks like python has a lot more 
digits but maybe that's just a random result... I mean.... Element 1,1 = 
2.81e14 in Python, but something like 3e14 in Matlab and so forth - 
there's a small difference in the results...

With python, I would also kindly ask about how to avoid this problem in 
the future, I mean, this maybe means that I have to check the condition 
number at all times before doing anything at all ? How to do that?

I hope you matlabticians like this topic, at least I myself find it 
interesting and many of you probably also program in some other language 
and then maybe you'll find this worthwhile to read about.