from __future__ import division
matrix = [[1,1,1],
         [1,-1,2],
         [2,-1,-1]]
l = [6,
     5,
    -3]
def gauss(a,b):
 for k in range(len(a)-1):
   for i in range(k+1,len(a)):
     t = a[i][k]/a[k][k]
     for j in range(k+1,len(a)):
       a[i][j] -= a[i][k]*t
       b[i] -= b[k]*t
 # printing out results
   for i in range(len(a)):
     print(a[i],b[i])
gauss(matrix,l)

>>> from __future__ import print_function
>>> 
>>> print(1, 2, 3, sep='x')
1x2x3
>>>

# Copyright (c) Isaac Evans 2011
# All rights reserved.
def myGauss(m):
    # eliminate columns
    for col in range(len(m[0])):
        for row in range(col+1, len(m)):
            r = [(rowValue * (-(m[row][col] / m[col][col]))) for rowValue in m[col]]
            m[row] = [sum(pair) for pair in zip(m[row], r)]
    # now backsolve by substitution
    ans = []
    m.reverse() # makes it easier to backsolve
    for sol in range(len(m)):
            if sol == 0:
                ans.append(m[sol][-1] / m[sol][-2])
            else:
                inner = 0
                # substitute in all known coefficients
                for x in range(sol):
                    inner += (ans[x]*m[sol][-2-x])
                # the equation is now reduced to ax + b = c form
                # solve with (c - b) / a
                ans.append((m[sol][-1]-inner)/m[sol][-sol-2])
    ans.reverse()
    return ans

template <class TReal>
void matr_com_lux<TReal>::dcmp(void)
{
int i,j,k;
 for(k=0;k<Dim-1;k++)
 {
  for(i=k+1;i<Dim;i++)
  {
   d_alg(lu[k+k*Dim]==0.,"singularyty in dcmp");
   lu[i+k*Dim]/=lu[k+k*Dim];
   for(j=k+1;j<Dim;j++)
   {
    lu[i+j*Dim]-=lu[i+k*Dim]*lu[k+j*Dim];
   }
  }
 }
}

#define TINY 1.0e-20
template <class TReal>
void matr_com_luo<TReal>::dcmp(void)
{
int i,imax,j,k;
TReal aamax,dum,sum,d,*vv;
vv=new TReal[Dim];
 d=1.;
 for(i=0;i<Dim;i++)
 {
  aamax=0.;
  for(j=0;j<Dim;j++){if(fabs(lu[i*Dim+j])>aamax)aamax=fabs(lu[i*Dim+j]);}
  d_alg(aamax==0.,"singularyty in dcmp");
  vv[i]=TReal(1.)/aamax;
 }
 for(j=0;j<Dim;j++)
 {
  for(i=0;i<j;i++)
  {
   sum=lu[i*Dim+j];
   for(k=0;k<i;k++){sum-=lu[i*Dim+k]*lu[k*Dim+j];}
   lu[i*Dim+j]=sum;
  }
  aamax=0.;
  for(i=j;i<Dim;i++)
  {
   sum=lu[i*Dim+j];
   for(k=0;k<j;k++){sum-=lu[i*Dim+k]*lu[k*Dim+j];}
   lu[i*Dim+j]=sum;
   dum=vv[i]*fabs(sum);
   if(dum>=aamax){imax=i;aamax=dum;}
  }
  if(j!=imax)
  {
   for(k=0;k<Dim;k++)
   {
    dum=lu[imax*Dim+k];
    lu[imax*Dim+k]=lu[j*Dim+k];
    lu[j*Dim+k]=dum;
   }
   d=-d;
   vv[imax]=vv[j];
  }
  indx[j]=imax;
  if(lu[j*Dim+j]==TReal(0.))lu[j*Dim+j]=TReal(TINY);
  if(j!=Dim)
  {
   dum=TReal(1.)/lu[j*Dim+j];
   for(i=j+1;i<Dim;i++){lu[i*Dim+j]=lu[i*Dim+j]*dum;}
  }
 }
delete [] vv;
}
#undef TINY

Python 2.7.3 (default, Apr 10 2012, 23:31:26) [MSC v.1500 32 bit (Intel)] on wi32
Type "help", "copyright", "credits" or "license" for more information.
>>> print 'qwe'
qwe
>>> print('qwe')
qwe
>>> type(print)
  File "<stdin>", line 1
    type(print)
             ^
SyntaxError: invalid syntax
>>>
>>> from __future__ import print_function
>>> print('qwe')
qwe
>>> type(print)
<type 'builtin_function_or_method'>
>>>

matrix = [[1,1,1,6],
          [1,-1,2,5],
          [2,-1,-1,-3]]
def guass(a):
	x = [a[i][-1] for i in range(len(a))]
	for k in range(1,len(a)-1):
		for j in range(k,len(a)-1):
			m = a[j][k-1]/a[k-1][k-1]
			for i in range(len(a)-1):
				a[j][i] -= m*a[k-1][i]
			x[j] -= m*x[k-1]
	for i in range(len(a)-1,-1,-1):
		for j in range(i+1,len(a)-1):
			x[i] -= a[i][j]*x[j]
			x[i] = x[i]/a[i][i]
	print(x)