Reputation:
Regards. To analyze Python code's performance, may the code below does it?
import time
to = time.clock(); x = [];
for i in range(0,4):
x.append(i*0.1);
tend = time.clock(); print(tend-to);
to = time.clock();
y = list(map(lambda x: x*0.1, list(range(0,4))));
tend = time.clock(); print(tend-to);
The timers show inconsistency. But sometimes, the result of the two timers also shows inconsistency (sometimes the first timer is faster, sometimes the second one is, although the first one tends to be faster). Some outputs :
4.631622925399206e-05
4.4898385501326854e-05
4.9624531343562917e-05
6.852911471254275e-05
5.0569760512011734e-05
4.867930217511418e-05
3.78091667379527e-05
2.5993802132341648e-05
My question pertain to the code above :
Thanks before. Regards, Arief
Upvotes: 1
Views: 552
Reputation: 1324
Wouldn't it be more elegant to wrap the code in a function and use a function decorator such as @function_timer()
? For example:
from timer import function_timer
@function_timer()
def my_function():
x = [];
for i in range(0,4):
x.append(i * 0.1);
my_function()
The terminal output would be a little like this:
Elapsed time: 7.79 microseconds for thread MY_FUNCTION
PS: In full disclosure, I'm the author of Timer for Python, a lightweight package that makes it easy to measure time and performance of code.
Upvotes: 0
Reputation: 603
From the official documentation:
>>> import timeit
>>> timeit.timeit('"-".join(str(n) for n in range(100))', number=10000)
0.3018611848820001
>>> timeit.timeit('"-".join([str(n) for n in range(100)])', number=10000)
0.2727368790656328
>>> timeit.timeit('"-".join(map(str, range(100)))', number=10000)
0.23702679807320237
In other words: if we strive to talk about a precision in execution time measurements - we restricted to iterate a simple function several thousands times to elicit all side effects.
Upvotes: -1