How to Measure Execution Time of Function in Python Easily

You can measure the execution time of a function in Python using the time module by recording the time before and after the function runs. Alternatively, use the timeit module for more precise timing, especially for small or fast functions.

Syntax

To measure execution time using the time module, you record the start time before the function call and the end time after it finishes. The difference gives the elapsed time.

Using timeit, you pass the function call as a string or a callable to measure its execution time accurately over multiple runs.

python

import time

start_time = time.time()
# call your function here
end_time = time.time()
elapsed_time = end_time - start_time

# Using timeit
import timeit
elapsed_time = timeit.timeit('your_function()', globals=globals(), number=1000)

Example

This example shows how to measure the time taken by a simple function that sums numbers from 1 to 1 million using both time and timeit modules.

python

import time
import timeit

def sum_numbers():
    total = 0
    for i in range(1, 1000001):
        total += i
    return total

# Using time module
start = time.time()
sum_numbers()
end = time.time()
print(f"Elapsed time using time module: {end - start:.6f} seconds")

# Using timeit module
elapsed = timeit.timeit('sum_numbers()', globals=globals(), number=10)
print(f"Elapsed time using timeit module (10 runs): {elapsed:.6f} seconds")

Output

Elapsed time using time module: 0.080123 seconds
Elapsed time using timeit module (10 runs): 0.750456 seconds

Common Pitfalls

• Using time.time() can be affected by system clock changes; prefer time.perf_counter() for higher precision and monotonic timing.
• Measuring very fast functions once may give inaccurate results; use timeit to run multiple iterations.
• Passing the function call as a string to timeit.timeit() requires careful use of globals=globals() to access the function.

python

import time

def fast_function():
    return sum(range(10))

# Wrong: single run with time.time() may be too fast to measure
start = time.time()
fast_function()
end = time.time()
print(f"Single run time: {end - start}")

# Right: use timeit for multiple runs
import timeit
elapsed = timeit.timeit('fast_function()', globals=globals(), number=10000)
print(f"Multiple runs time: {elapsed}")

Output

Single run time: 1.1920928955078125e-06
Multiple runs time: 0.004321

Quick Reference

Method	Usage	Best For
time.time()	Record start and end time around function call	Simple timing, less precise
time.perf_counter()	High precision timer for elapsed time	More accurate timing, monotonic clock
timeit.timeit()	Run function multiple times and average	Small or fast functions, benchmarking

Key Takeaways

Use time.perf_counter() for precise and reliable timing in Python.

For fast or small functions, use timeit to run multiple iterations for accuracy.

Avoid measuring execution time with a single run for very quick functions.

Pass globals=globals() to timeit.timeit() when timing functions defined in your script.

Subtract start time from end time to get elapsed execution time.