function - Python functools partial efficiency

Question

I have been working with Python and I set up the following code situation:

import timeit

setting = """
import functools

def f(a,b,c):
    pass

g = functools.partial(f,c=3)    
h = functools.partial(f,b=5,c=3)   
i = functools.partial(f,a=4,b=5,c=3)
"""

print timeit.timeit('f(4,5,3)', setup = setting, number=100000)
print timeit.timeit('g(4,5)', setup = setting, number=100000)
print timeit.timeit('h(4)', setup = setting, number=100000)
print timeit.timeit('i()', setup = setting, number=100000)

I get the following as a result:

f: 0.181384086609
g: 0.39066195488
h: 0.425783157349
i: 0.391901016235

Why do the calls to the partial functions take longer? Is the partial function just forwarding the parameters to the original function or is it mapping the static arguments throughout? And also, is there a function in Python to return the body of a function filled in given that all the parameters are predefined, like with function i?

Answer 1

Why do the calls to the partial functions take longer?

The code with partial takes about two times longer because of the additional function call. Function calls are expensive:

Function call overhead in Python is relatively high, especially compared with the execution speed of a builtin function.

-

Is the partial function just forwarding the parameters to the original function or is it mapping the static arguments throughout?

As far as i know - yes, it just forwards the arguments to the original function.

-

And also, is there a function in Python to return the body of a function filled in given that all the parameters are predefined, like with function i?

No, i am not aware of such built-in function in Python. But i think it's possible to do what you want, as functions are objects which can be copied and modified.

Here is a prototype:

import timeit
import types


# http://stackoverflow.com/questions/6527633/how-can-i-make-a-deepcopy-of-a-function-in-python
def copy_func(f, name=None):
    return types.FunctionType(f.func_code, f.func_globals, name or f.func_name,
        f.func_defaults, f.func_closure)


def f(a, b, c):
    return a + b + c


i = copy_func(f, 'i')
i.func_defaults = (4, 5, 3)


print timeit.timeit('f(4,5,3)', setup = 'from __main__ import f', number=100000)
print timeit.timeit('i()', setup = 'from __main__ import i', number=100000)

which gives:

0.0257439613342
0.0221881866455