You could use Numba, which is a NumPy-aware dynamic Python compiler using LLVM. The resulting implementation is pretty simple and clear:

import numpy
import numba


@numba.jit
def minmax(x):
    maximum = x[0]
    minimum = x[0]
    for i in x[1:]:
        if i > maximum:
            maximum = i
        elif i < minimum:
            minimum = i
    return (minimum, maximum)


numpy.random.seed(1)
x = numpy.random.rand(1000000)
print(minmax(x) == (x.min(), x.max()))

It should also be faster than a Numpy's min() & max() implementation. And all without having to write a single C/Fortran line of code.

Do your own performance tests, as it is always dependent on your architecture, your data, your package versions...