The Tool Desk
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Time a small snippet with timeit
Python’s standard library includes timeit, designed for timing small pieces of code. The command-line interface is a quick way to test an expression:
python -m timeit 'sum(range(100))'If you omit a loop count, the command chooses one automatically and repeats measurements by default. See the official timeit documentation for command-line options and details.
From Python, pass a zero-argument callable and choose how many times to run it:
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import timeit
elapsed_seconds = timeit.timeit(lambda: sum(range(100)), number=10_000)
print(elapsed_seconds)The returned value is the total duration for all requested executions, not the average for one execution. Divide it by number to calculate a per-call estimate. For repeated samples, use timeit.repeat():
import timeit
samples = timeit.repeat(lambda: sum(range(100)), number=10_000, repeat=5)
print(samples)
print(min(samples))Concurrent activity can make some samples slower. The minimum is often a useful indication of how quickly the machine can run the code under favorable conditions, but it does not promise typical application latency. Avoid treating a mean and standard deviation as automatically informative; inspect the samples in light of what you are trying to learn.
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Measure elapsed time for a larger operation
For a block of code or an operation whose elapsed duration you want to capture, record readings from time.perf_counter() before and after it, then subtract them. Only the difference is meaningful; the clock’s reference point is undefined.
import time
start = time.perf_counter()
run_my_operation()
elapsed_seconds = time.perf_counter() - start
print(f"{elapsed_seconds:.6f} seconds")perf_counter() measures elapsed time, including time spent sleeping. It is therefore suitable when the question is how long the operation took from start to finish.
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Choose CPU time when elapsed time is not the question
Use time.process_time() instead when you want the current process’s user and system CPU time. It excludes time spent sleeping, so it answers a different question from elapsed duration. To apply it to the same pattern, replace both calls to perf_counter() with process_time(). The Python time documentation describes these clocks.
Make the measurement match the real workload
Decide whether setup belongs in the timing
With timeit.Timer, setup code is excluded from the timed statement. Prepare inputs in setup if you want to isolate the operation itself. If real-world elapsed time should include preparation, put that work inside the measured callable instead.
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Account for garbage collection
timeit temporarily disables garbage collection by default. That can make isolated runs more comparable, but it may leave out work that matters when the real function allocates objects and triggers collection. If garbage collection is part of the workload you care about, re-enable it in the setup code.
Be cautious with tiny measurements
Timer overhead and activity from other programs can affect very short runs. Use repeated measurements to observe variation, and avoid treating tiny timing differences as decisive without considering interference and the workload being tested.
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Use a profiler to find a bottleneck
A single duration tells you how long a measured operation took; it does not identify which parts of a larger program account for that time. For a breakdown of execution time, use a profiler such as cProfile. Python’s profiling documentation explains the available profilers.
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