Code performance analysis¶

Execution time¶

Unscientific way → time module: One sample¶

Do not forget to configure your logger to the DEBUG level

from functools import wraps
from time import time

def timing(f):
    @wraps(f)
    def wrap(*args, **kw):
        ts = time()
        result = f(*args, **kw)
        te = time()
        _logger.debug('func:%r took: %2.4fs args:[%r, %r] ', f.__name__, te-ts, args, kw)
        return result
    return wrap

@timing
def my_func(a,b,c):
     d = []
     return a + b + c

Scientific way → timeit module¶

It runs a function multiple times to statisitically determine the execution time

Memory¶

To analyze the dynamic usage of memory within a function use the memory_profiler python package. It will give you a line-by-line report of memory usage.

from memory_profiler import profile

@profile
def my_func(a,b,c):
     d = [b + (c * i) for i in range(len(a))]
     return d