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GaussSimple.py solves a system of linear equations of the form Ax = b. You enter the coefficient matrix A and the results vector b interactively in the console, and the program prints the solution vector x.
Despite its name, the program delegates the actual solving to numpy.linalg.solve rather than implementing Gaussian elimination steps manually.

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Prerequisites

You need NumPy installed: 
pip install numpy

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How to run

python GaussSimple.py
The program runs once (no loop) — it collects input, solves, and exits.

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Functions

Prompts you to fill every cell of the square coefficient matrix. T1 is the matrix size, and matriz is the pre-allocated NumPy zero matrix passed in from the main script.
def IngresarMatriz(T1, matriz):  # Only square matrices are supported
    for i in range(T1):
        for j in range(T1):
            matriz[i][j] = int(input(f"Valor en (fila: {i},columna: {j}): "))
    return matriz
Only square matrices are supported. Both the row loop and the column loop iterate T1 times, so rectangular inputs are not possible.
Prompts you to fill the results column vector b. res is a T × 1 NumPy zero matrix; each element corresponds to the right-hand side of one equation.
def IngresarRes(T1, res):  # Only square matrices are supported
    for i in range(T1):
        res[i][0] = int(input(f"Valor en (fila: {i},columna: {0}): "))
    return res
Prints any matrix or vector row by row. The *m unpacking removes brackets and commas for clean output. Works for both the coefficient matrix and the solution vector.
def MostrarMatriz(matriz):  # Works for any matrix shape (foreach-style)
    for m in matriz:
        print(*m)

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Main script flow

T = int(input("Ingrese el tamaño de la matriz: "))
matriz = np.zeros((T, T))   # coefficient matrix A
res = np.zeros((T, 1))      # results vector b

print("Ingreso de coeficientes: ")
IngresarMatriz(T, matriz)

print("Ingreso de resultados: ")
IngresarRes(T, res)

print("Coeficientes: ")
MostrarMatriz(matriz)

print("Resultados: ")
MostrarMatriz(res)

print("Solucion: ")
resultado = np.linalg.solve(matriz, res)   # Solves Ax = b
MostrarMatriz(resultado)

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Worked example: 2×2 system

Consider this system of two equations: 
2x + 3y = 8
5x +  y = 7
The coefficient matrix A and results vector b are: 
A = [[2, 3],    b = [[8],
     [5, 1]]         [7]]
1

Enter the matrix size

Ingrese el tamaño de la matriz: 2
2

Enter the coefficient matrix

Ingreso de coeficientes:
Valor en (fila: 0,columna: 0): 2
Valor en (fila: 0,columna: 1): 3
Valor en (fila: 1,columna: 0): 5
Valor en (fila: 1,columna: 1): 1
3

Enter the results vector

Ingreso de resultados:
Valor en (fila: 0,columna: 0): 8
Valor en (fila: 1,columna: 0): 7
4

Review the echo of your inputs

Coeficientes:
2.0 3.0
5.0 1.0

Resultados:
8.0
7.0
5

Read the solution

Solucion:
1.0
2.0
This means x = 1 and y = 2. You can verify: 2(1) + 3(2) = 8 ✓ and 5(1) + 1(2) = 7 ✓.

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Constraints and limitations

ConstraintDetail
Square matrices onlyBoth IngresarMatriz and IngresarRes iterate T1 times in both dimensions; non-square input is not supported
Integer input onlyValues are cast with int(input(...)) — decimal coefficients will raise a ValueError
Single runThere is no loop or menu; the program exits after printing the solution
NumPy requirednumpy must be installed; the program imports it as np at the top of the file
If the coefficient matrix is singular (i.e., the system has no unique solution), numpy.linalg.solve will raise a LinAlgError. The program does not catch this exception.

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