Compute C's `%` using Python's `%`?

Some ways would be:

def mod_c0(a, b):
    if b < 0:
        b = -b
    return -1 * (-a % b) if a < 0 else a % b

def mod_c1(a, b):
    return (-1 if a < 0 else 1) * ((a if a > 0 else -a) % (b if b > 0 else -b))

def mod_c2(a, b):
    return (-1 if a < 0 else 1) * (abs(a) % abs(b))

def mod_c3(a, b):
    r = a % b
    return (r - b) if (a < 0) != (b < 0) and r != 0 else r

def mod_c4(a, b):
    r = a % b
    return (r - b) if (a * b < 0) and r != 0 else r

def mod_c5(a, b):
    return a % (-b if a ^ b < 0 else b)

def mod_c6(a, b):
    a_xor_b = a ^ b
    n = a_xor_b.bit_length()
    x = a_xor_b >> n
    return a % (b * (x | 1))

def mod_c7(a, b):
    a_xor_b = a ^ b
    n = a_xor_b.bit_length()
    x = a_xor_b >> n
    return a % ((-b & x) | (b & ~x))

def mod_c8(a, b):
    q, r = divmod(a, b)
    if (a >= 0) != (b >= 0) and r:
        q += 1
    return a - q * b

def mod_c9(a, b):
    if a >= 0:
        if b >= 0:
            return a % b
        else:
            return a % -b
    else:
        if b >= 0:
            return -(-a % b)
        else:
            return a % b

which all work as expected, e.g.:

print(mod_c0(31, -3))
# 1

Essentially, mod_c0() implements an optimized version of mod_c1() and mod_c2(), which are identical except that in mod_c1() the call to (relatively expensive) call to abs() is replaced by a ternary conditional operator with the same semantic.
Instead, mod_c3() and mod_c4() try to directly fix the a % b value for the cases where it is needed. The difference between the two is in how they detect opposite signs of the arguments: (a < 0) != (b != 0) versus a * b < 0.
The mod_c5() approach is inspired by @ArborealAnole’s answer, and essentially uses the bit-wise xor to handle the cases correctly, while mod_c6() and mod_c7() are the same as @ArborealAnole’s answer but using adaptive right shift with int.bit_length().
The mod_c8() approach uses a corrected definition of integer division to fix up the modulus value.
The mod_c9() method is inspired by @NeverGoodEnough’s answer, and essentially goes full conditional.

---

Covering all sign cases:

vals = (3, -3, 31, -31)
s = '{:<{n}}' * 4
n = 14
print(s.format('a', 'b', 'mod(a, b)', 'mod_c(a, b)', n=n))
print(s.format(*(('-' * (n - 1),) * 4), n=n))
for a, b in itertools.product(vals, repeat=2):
    print(s.format(a, b, mod(a, b), mod_c0(a, b), n=n))

a             b             mod(a, b)     mod_c(a, b)   
------------- ------------- ------------- ------------- 
3             3             0             0             
3             -3            0             0             
3             31            3             3             
3             -31           -28           3             
-3            3             0             0             
-3            -3            0             0             
-3            31            28            -3            
-3            -31           -3            -3            
31            3             1             1             
31            -3            -2            1             
31            31            0             0             
31            -31           0             0             
-31           3             2             -1            
-31           -3            -1            -1            
-31           31            0             0             
-31           -31           0             0             

---

A bit more tests and benchmarks:

import itertools


n = 100
l = [x for x in range(-n, n + 1)]
ll = [(a, b) for a, b in itertools.product(l, repeat=2) if b]


funcs = mod_c0, mod_c1, mod_c2, mod_c3, mod_c4, mod_c5, mod_c6, mod_c7, mod_c8, mod_c9
for func in funcs:
    correct = all(func(a, b) == funcs[0](a, b) for a, b in ll)
    print(f"{func.__name__}  correct:{correct}  ", end="")
    %timeit -n 8 -r 8 [func(a, b) for a, b in ll]
# mod_c0  correct:True  8 loops, best of 8: 9.67 ms per loop
# mod_c1  correct:True  8 loops, best of 8: 11.1 ms per loop
# mod_c2  correct:True  8 loops, best of 8: 12.3 ms per loop
# mod_c3  correct:True  8 loops, best of 8: 10.3 ms per loop
# mod_c4  correct:True  8 loops, best of 8: 10 ms per loop
# mod_c5  correct:True  8 loops, best of 8: 10.1 ms per loop
# mod_c6  correct:True  8 loops, best of 8: 17.1 ms per loop
# mod_c7  correct:True  8 loops, best of 8: 20.3 ms per loop
# mod_c8  correct:True  8 loops, best of 8: 15.8 ms per loop
# mod_c9  correct:True  8 loops, best of 8: 9.29 ms per loop

---

Perhaps there are better (shorter?, faster?) ways, given that the implementation of Python’s % using C’s % seems much simpler:

((a % b) + b) % b

---

To get some feeling on how the C-style % computation (mod_c*() functions from above) stands against the usual % or the operations required to get Python-style % from C:

def mod_py(a, b):
    return a % b

def mod_c2py(a, b):
    return ((a % b) + b) % b


%timeit [mod_py(a, b) for a, b in ll]
# 100 loops, best of 3: 5.85 ms per loop
%timeit [mod_c2py(a, b) for a, b in ll]
# 100 loops, best of 3: 7.84 ms per loop

Note of course that mod_c2py() is only useful to get a feeling of what performances we could expect from a mod_c() function.

---

(EDITED to fix some of the proposed methods and include some timings)

(EDITED-2 to add the mod_c5() solution)

(EDITED-3 to add the mod_c6() to mod_c9() solutions)