Measuring Execution Times

Question

I was wondering which time.time() of from datetime import timedelta was the quickest and best way to find how long a programme had been running for example.

import time
start = time.time()
#do stuff
print(start - time.time())

or (although longer)

from datetime import datetime
from datetime import timedelta
start_time = datetime.now()
def millis():
 dt = datetime.now() - start_time
 ms = (dt.days * 24 * 60 * 60 + dt.seconds) * 1000 + dt.microseconds / 1000.0
 return ms
def tickscheck(start):
 x = 0
 count = 0
 while millis() - start < 1000:
 x = 4+5
 #counting up
 count = count + 1
 print("It Took " + str(count) + " Counts\nOver " + str(millis()- start) + "ticks")
running = True
while(running == True):
 tickscheck(millis())

• 200_success: What is your goal here — just to benchmark the Python interpreter on your machine?
• Oliver Perring: @200_success basically yes

Answer 1

For benchmarking process times in Python, you should probably use the timeit module.

It is designed specifically for this purpose and you can keep your code clean by running it from the command-line.

This example is from the docs:

$ python -m timeit '"-".join(str(n) for n in range(100))'

For example, if the process you want to benchmark is a function foo

$ python -m 'timeit --setup 'from my_module import foo' 'foo()'

The snippet given with the --setup flag sets up the environment in which you can run your test.

Check out the Python docs for more information

• Oliver Perring: thnx ill look into it

Answer 2

In Python 3.3 and later you can use one of

time.perf_counter()
Return the value (in fractional seconds) of a performance counter, i.e. a clock with the highest available resolution to measure a short duration. It does include time elapsed during sleep and is system-wide. The reference point of the returned value is undefined, so that only the difference between the results of consecutive calls is valid.

time.process_time()
Return the value (in fractional seconds) of the sum of the system and user CPU time of the current process. It does not include time elapsed during sleep. It is process-wide by definition. The reference point of the returned value is undefined, so that only the difference between the results of consecutive calls is valid.

Older Python versions offer timeit.default_timer(), which is now an alias of time.perf_counter().

The timeit module offers tools to get more reliable timings by running the code multiple times.