`timeit`

The Python timeit module provides tools to measure the execution time of small code snippets. It’s useful for performance testing and benchmarking.

Here’s a quick example:

>>> import timeit

>>> timeit.timeit('"-".join(str(n) for n in range(100))', number=10000)
0.3422808000000001

Key Features

Measures the execution time of small code snippets
Provides repeatable and accurate timing results
Supports setup code for benchmarking context
Minimizes external influences for precise measurements
Works from the command line or within Python scripts
Enables micro-benchmarking for performance-critical code

Frequently Used Classes and Functions

Object	Type	Description
`timeit.timeit()`	Function	Times the execution of a single statement
`timeit.repeat()`	Function	Repeatedly times the execution to get multiple samples
`timeit.Timer`	Class	Provides a class-based interface for timing code

Examples

Measure the execution time of a code snippet:

>>> timeit.timeit("sum(range(100))", number=1000)
0.005625799999999981

Use repeat() to get multiple timing samples:

>>> timeit.repeat("sum(range(100))", repeat=5, number=1000)
[
    0.005487599999999985,
    0.005484900000000004,
    0.005458199999999995,
    0.005459500000000017,
    0.005463200000000028
]

Common Use Cases

Measuring the execution time of small code snippets
Comparing the performance of different code implementations
Optimizing code by identifying bottlenecks
Validating the impact of refactoring or optimization
Evaluating standard vs third-party implementation speed
Timing examples in tutorials or documentation

Real-World Example

Say that you want to compare the performance of two different ways to create a list of squares:

>>> def squares_1():
...     return [x**2 for x in range(1000)]
...

>>> def squares_2():
...     return list(map(lambda x: x**2, range(1000)))
...

>>> timeit.timeit(squares_1, number=10000)
0.6023182999999999
>>> timeit.timeit(squares_2, number=10000)
0.7064311999999999

In this example, you time two approaches for creating a list of squares. The results show that squares_1(), which uses a list comprehension, is faster than squares_2(), which uses map() and a lambda function.

Tutorial

Profiling in Python: How to Find Performance Bottlenecks

In this tutorial, you'll learn how to profile your Python programs using numerous tools available in the standard library, third-party libraries, as well as a powerful tool foreign to Python. Along the way, you'll learn what profiling is and cover a few related concepts.

intermediate tools

For additional information on related topics, take a look at the following resources: