Performance testing numpy subtract verses the interpreter

munkres-np-subtract.py

#!/usr/bin/python

import contextlib
import numpy
import time

@contextlib.contextmanager
def timer():
    start = time.time()
    yield
    finish = time.time()
    print('elapsed time: {}'.format(finish - start))


def using_interpreter():
    with timer():
        for _ in range(1000):
            mat = numpy.random.random((100,100))
            for row in mat:
                row -= numpy.min(row)


def using_numpy():
    with timer():
        for _ in range(1000):
            mat = numpy.random.random((100,100))
            mat = numpy.subtract(mat, numpy.min(mat, axis=1)[:,numpy.newaxis])


def main():
    using_interpreter()
    using_numpy()


if __name__ == "__main__":
    main()

The results that I get on my machine are,

jdowner@erebor:munkres-subtraction$ python munkres-np-subtract.py 
elapsed time: 0.401839971542
elapsed time: 0.119607925415

So using numpy is about 4 times faster that going through the interpreter.