timeit Module

The timeit module measures execution time of code snippets, useful for benchmarking and performance comparison. It handles timing overhead and runs code multiple times for accurate results.

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Basic Timing

Simple Benchmarking

import timeit

result = timeit.timeit("sum(range(100))", number=100000)
print(f"Execution time: {result:.4f}s")

Output:

Execution time: 0.1234s

Timer Class

import timeit

timer = timeit.Timer("x = 1 + 1")
result = timer.timeit(number=1000000)
print(f"1000000 iterations: {result:.4f}s")

Output:

1000000 iterations: 0.0234s

Setup Code

Initializing Before Timing

import timeit

setup = "lst = list(range(1000))"
time1 = timeit.timeit("5 in lst", setup=setup, number=100000)
print(f"Membership test: {time1:.4f}s")

Output:

Membership test: 0.0456s

Comparing Code Snippets

Which is Faster?

import timeit

concat_time = timeit.timeit(
    'x = "a" + "b" + "c"',
    number=1000000
)

format_time = timeit.timeit(
    'x = f"{"a"}{"b"}{"c"}"',
    number=1000000
)

print(f"Concatenation: {concat_time:.4f}s")
print(f"F-string: {format_time:.4f}s")

Output:

Concatenation: 0.0234s
F-string: 0.0198s

Repeat and Compare

Multiple Runs

import timeit

times = timeit.repeat(
    "sum(range(100))",
    number=10000,
    repeat=5
)

print(f"Times: {[f'{t:.4f}' for t in times]}")
print(f"Best: {min(times):.4f}s")
print(f"Worst: {max(times):.4f}s")

Output:

Times: ['0.1234', '0.1201', '0.1198', '0.1205', '0.1211']
Best: 0.1198s
Worst: 0.1234s

Real-World Example

Benchmarking List Operations

import timeit

operations = {
    'append': 'lst.append(100)',
    'insert_0': 'lst.insert(0, 100)',
    'extend': 'lst.extend([100, 101])'
}

setup = "lst = list(range(100))"

for name, code in operations.items():
    time = timeit.timeit(code, setup=setup, number=10000)
    print(f"{name}: {time:.4f}s")

Output:

append: 0.0012s
insert_0: 0.0456s
extend: 0.0045s