Timeit in Python with Examples

The timeit module in Python accurately measures the execution time of small code snippets, offering more consistent results than time.time() by avoiding background interference and disabling garbage collection. It’s ideal for comparing code performance, benchmarking and optimization and can be used via the command line, in scripts or within code. Example:

import timeit
a = "5+5"
print(timeit.timeit(a))

0.015736839000055625

Explanation: timeit.timeit(a) measures how long it takes to execute the expression "5+5" one million times by default.

Python timeit syntax

timeit.timeit(stmt, setup, timer, number)

Parameter:

• stmt: The code you want to time (as a string)
• setup: Setup code that runs before timing (as a string).
• timer: Timer function (default is fine).
• number: Number of times to execute stmt.

Returns: It returns the number of seconds it took to execute the code.

Python timeit examples

Example 1: We can pass multiple lines of code in a single string by separating them with semicolons (;). This is useful when timing short snippets of code using timeit.timeit().

import timeit

a = timeit.timeit(stmt='x=15; y=15; sum=x+y')
print(a)

0.03771957599974485

Explanation: timeit.timeit() measures the total time to execute a code snippet, here assigning values to x and y and calculating their sum, run 1,000,000 times by default using semicolon separated statements in the stmt parameter.

Example 2: We can also pass a function directly to timeit.timeit() without wrapping it in a string. This makes your code cleaner and avoids using the stmt and setup strings.

import timeit
from math import sqrt

def example():
    a = [sqrt(x) for x in range(100)]

print(timeit.timeit(example, number=10000))

0.11834059100010563

Explanation: timeit.timeit() measures the total time to run the example function 10,000 times, where each run generates a list of square roots from 0 to 99 using list comprehension.

Example 3: We can use timeit.repeat() to run the same timing test multiple times and get a list of results. This is useful for observing variability in execution time due to system load or randomness.

import timeit
from random import randint

def fun(lst, target):
    return target in lst

print(min(timeit.repeat(
    lambda: fun(list(range(10000)), randint(0, 10000)),
    repeat=3, number=10000
)))

Output

0.7208773000002111

Explanation: timeit.repeat() measures how long the fun function takes to check if a random target exists in a list of 10,000 elements. It runs this 10,000 times per repeat for 3 repeats .

Example 4: We manually measure the start and end time of a code block. This is helpful when you want more control over what you're timing.

import timeit
import random

def fun():
    return random.randint(100, 800)

t1 = timeit.default_timer()
fun()
t2 = timeit.default_timer()

print(t2-t1)

8.594999599154107e-06

Explanation: timeit.default_timer() records start and end times around the fun function and their difference gives the time taken for a single execution.

Example 5: We can also use the timeit module directly from the command line to quickly benchmark simple Python statements without writing a full script.

python -m timeit -s "import math" "math.sqrt(25)"

Output

Explanation: This command times how long math.sqrt(25) takes to run, importing math once with -s and uses timeit's default settings to report total execution time.