This is a Sudoku Solver that uses backtracking algorithm to solve the puzzle.

Input the puzzle from command line.

import numpy as np

problem = []

for x in range(9):

i = input()

l = [int(v) for v in i]

problem.append(l)

np_problem = np.array(problem)

fixed_coordinates = [] # first getting the coordinates where fixed numbers are present

empty_coordinates = []

for i , sub_array in enumerate(problem) :

temp = [[i , c] for c , sub_element in enumerate(sub_array) if sub_element > 0]

temp2 = [[i , j] for j , sub_element2 in enumerate(sub_array) if sub_element2 == 0]

for z in temp : fixed_coordinates.append(z)

for w in temp2 : empty_coordinates.append(w)

l , m , r = [0 , 3 , 6] , [1 , 4 , 7] , [2 , 5 , 8]

avoid_dict = {idx : [] for idx in list(range(0 , len(empty_coordinates)))}

def generate_bounds(r , c) -> list:

lower_bound_c = c if c in l else c - 1 if c in m else c - 2

upper_bound_c = c + 3 if c in l else c + 2 if c in m else c + 1

lower_bound_r = r if r in l else r - 1 if r in m else r - 2

upper_bound_r = r + 3 if r in l else r + 2 if r in m else r + 1

return [lower_bound_c , upper_bound_c , lower_bound_r , upper_bound_r]

def backtrack(return_coordinates) :

n_r , n_c = empty_coordinates[empty_coordinates.index(return_coordinates) - 1] # getting back element coordinates

while [n_r , n_c] != empty_coordinates[empty_coordinates.index(return_coordinates) + 1]:

if np_problem[n_r , n_c] != 0 :

avoid_dict[empty_coordinates.index([n_r , n_c])].append(np_problem[n_r , n_c])

fix_flag = False

r , c = n_r , n_c

for num in range(1 , 10) :

l_b_c , u_b_c , l_b_r , u_b_r = generate_bounds(r , c)

if all([num not in np_problem[l_b_r : u_b_r , l_b_c : u_b_c] , num not in np_problem[r , :] , num not in np_problem[: , c]]) :

if num not in avoid_dict.get(empty_coordinates.index([n_r , n_c])) :

np_problem[n_r , n_c] , fix_flag = num , True

break

if fix_flag : n_r , n_c = empty_coordinates[empty_coordinates.index([n_r , n_c]) + 1]

if not fix_flag :

np_problem[n_r , n_c] = 0

avoid_dict[empty_coordinates.index([n_r , n_c])].clear()

n_r , n_c = empty_coordinates[empty_coordinates.index([n_r , n_c]) - 1]

for r in range(9) :

for c in range(9) :

if [r , c] not in fixed_coordinates :

fix_flag = False

for num in range(1 , 10) :

l_b_c , u_b_c , l_b_r , u_b_r = generate_bounds(r , c)

if all([num not in np_problem[l_b_r : u_b_r , l_b_c : u_b_c] , num not in np_problem[r , :] , num not in np_problem[: , c]]) :

np_problem[r , c] , fix_flag = num , True

break

if not fix_flag : backtrack([r , c])

print(np_problem)