Timeit

Which code unit executes faster in a Python program? We answer this question with timeit, a module that benchmarks code fragments.

This module provides a standardized way to perform benchmarks. But usually focusing on higher-level concerns and code quality is a better use of your time.

Example

We must import timeit with the "import timeit" statement. This is required unless you use the command-line syntax. Here we time 2 string-creation expressions.

And We pass the statements in quoted strings to the timeit.timeit method. We increase the iterations by specifying a number argument.

Note The numbers here are too close to know for sure which is faster. We can use the repeat() method to receive better information.

import timeit

# The instructions being timed.
print('y' * 3)
print('y' + 'y' + 'y')

# Call timeit on the statements and print the time returned.
# ... Specify optional number of iterations.
print(timeit.timeit("x = 'y' * 3", number=10000000))
print(timeit.timeit("x = 'y' + 'y' + 'y'", number=10000000))yyy
yyy
0.2625868763293428
0.26622904456542135

Repeat

Repeat is the same as timeit except it benchmarks repeatedly: it calls timeit internally several times. The default repetition is 3.

Here We increase the number of iterations of the string-multiplying code shown in the previous example. We start to get repeatable data.

And It seems to indicate that adding three strings together is faster than multiplying one by 3.

import timeit

# Call repeat.
print(timeit.repeat("x = 'y' * 3", number=100000000, repeat=3))
print(timeit.repeat("x = 'y' + 'y' + 'y'", number=100000000, repeat=3))[2.7390200865497176, 2.7475431168207223, 2.7429300279022177]
[2.6369100279087014, 2.631240758828813, 2.6300020650299665]

Command-line

The timeit module can be invoked directly from the command-line. This avoids creating an entire new program file. Timeit returns usec (microseconds) in the output.

Tip You will need to be careful with quotation marks when using the command-line. You may need to escape them, depending on your system.

C:\Users\Sam>C:\Python33\python.exe -m timeit "x = \"y\" * 3"
10000000 loops, best of 3: 0.0273 usec per loop

Multiple statements

With timeit, we can use multiple statements—we separate them with a semicolon. This makes it easier to specify longer code fragments.

Note For longer code fragments, please use the setup argument and call a method. The next example demonstrates.

import timeit

# Use semicolon for multiple statements.
print(timeit.repeat("a = 2; a *= 2", number=100000000))
print(timeit.repeat("a = 1; a *= 4", number=100000000))[7.334341642836696, 7.333336683198793, 7.332224095625474]
[7.235993375046725, 7.247406798908553, 7.256258872415835]

Methods, setup

We can benchmark custom methods in timeit by specifying a setup argument. In this argument, please specify an import statement that indicates the methods you invoke.

Here We benchmark the a() method against the b() method. As expected, the a() method is faster—it does less.

import timeit

def a():
    return 1

def b():
    return sum([-1, 0, 1, 1])

# Test methods.
print(a())
print(b())

# Pass setup argument to call methods.
print(timeit.repeat("a()", setup="from __main__ import a"))
print(timeit.repeat("b()", setup="from __main__ import b"))1
1
[0.11886792269331777, 0.11894442929800975, 0.11940800745355873]
[0.5983422704501993, 0.6003713163771788, 0.6014057764431624]

Issues, `timeit`

The syntax for timeit calls is difficult—it requires semicolons, and Python does not. The syntax for invoking methods is cumbersome.

Also I found that the ordering of calls to timeit impacts the results. This makes it harder to trust the results of timeit.

It usually costs more time running micro-benchmarks that you will get back in increased speed. With timeit, we have another benchmarking option.