I rewrite a matlab code in python but they result different outputs

the matlab code and the output i was expecting (gauss elimination method)

my code in python:

import numpy as np


A = np.array([
    [1,2,-1,1],
    [-1,4,3,1],
    [2,1,1,1]])

n = rows = len(A)
col = len(A[0])


for i in range(n):
    A[i,:] = A[i,:] / A[i,i]
    for j in range(n):
        if i==j:
            pass
        else:
            A[j,:] = A[j,:] - (A[i,:] * A[j,i])


print(A)

the output i got:

[[1 0 0 1]
 [0 1 0 0]
 [0 0 1 0]]

Answer

Your problem is related to casting. Without info, numpy cast your matrix to integer numbers, so when you divide, the result is not a float. For example 2 / 6 = 0 and not 0.33333. If you put

A = np.array([
    [1,2,-1,1],
    [-1,4,3,1],
    [2,1,1,1]], dtype=float)

your result will be

[[1. 0.  0. 0.33333333]
[0.  1.  0. 0.33333333]
[0.  0.  1. 0.]]

In matlab there is not this problem because your starting matrix is already casted to floating point numbers.