How to time a function in Python
Jul 16, 2025 am 02:56 AM
There are three common methods to calculate the running time of Python code. 1. Use time module: Get the timestamp before and after the function is executed through time.time(), and the subtraction is time-consuming, suitable for fast testing but limited accuracy; 2. Use timeit module: suitable for precise measurement of small pieces of code, automatically run multiple times to average, avoid errors and disable garbage collection to reduce interference; 3. Use decorator: can batch manage the timing operations of multiple functions, improve code reusability and debugging efficiency. The selection method needs to be judged based on the scene: use time for simple tests, use timeit for accurate comparisons, and use decorator for unified timekeeping for multiple functions.
Want to know how long a piece of Python code has been running? The most direct way is to "count the time". With the right method, you can easily master the function execution time, help optimize performance or troubleshoot bottlenecks.
The easiest way to use the time module
If you just want to know how long a function has been running, time module is the most direct choice. Its time() function can return the current timestamp, and by recording it once before and after the function, subtraction can result in time.
import time
def my_function():
# Simulate time-consuming operation time.sleep(1)
start_time = time.time()
my_function()
end_time = time.time()
print(f"Time taken: {end_time - start_time:.4f} seconds")This method is suitable for quick testing, especially if you temporarily add a time point in a script. But note that it is not very suitable for measuring very short functions (such as microseconds) because the accuracy may not be enough.
More precise with timeit
The timeit module is a better choice when you need to measure the execution time of small pieces of code more accurately, especially for performance comparisons. It will automatically run the code multiple times and take the average value to avoid errors in a single run.
import timeit
def my_function():
sum([i**2 for i in range(100)])
# The default duration is 1 million times = timeit.timeit(my_function, number=1000)
print(f"Average time taken: {duration / 1000:.6f} seconds/time")- It disables garbage collection mechanism to reduce interference
- You can specify the number of runs (
number=) - It is especially useful when comparing different writing methods
Write a decorator to manage timing uniformly
If you have many functions that need to be timed, writing start and end one by one will be too repetitive. At this time, you can use a decorator to handle it:
import time
def timer(func):
def wrapper(*args, **kwargs):
start = time.time()
result = func(*args, **kwargs)
duration = time.time() - start
print(f"{func.__name__} time taken: {duration:.5f} seconds")
return result
Return wrapper
@timer
def test_func():
time.sleep(0.5)
test_func() In this way, all functions with @timer added will automatically output the execution time. Suitable for debugging multiple functions, modular tools and other scenarios.
Basically these methods. Which one to choose depends on the requirements: use time simply to use time, use timeit for accurate measurement, and use decorator for batch processing. What is not complicated but easy to ignore is: Don't forget the impact of the running environment, such as external factors such as disk IO and network requests, may also slow down your functions.
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