Как измерить время выполнения скрипта Python
Допустим, вы хотите знать время выполнения следующего кода Python:
Есть несколько способов измерить время, необходимое для выполнения скрипта Python, но вот лучший способ сделать это, и я объясню почему:
В консоли получим: 0.01137321546
Это вывод, который я получаю на своем Macbook Pro. Итак, это более или менее 1/100 секунды.
Как работает вышеуказанный скрипт
Строка 1: мы импортируем модуль timeit . Строка 3: мы создаем переменную. В этой переменной мы храним код, который хотим протестировать. Этот код должен идти внутри тройных кавычек. Итак, тестовый код предоставляется в виде строки. Строка 10: мы вызываем функцию time.timeit() . Функция timeit() получает тестовый код в качестве аргумента, выполняет его и записывает время выполнения. Чтобы получить точное время, я приказал timeit() выполнить 100 циклов. Поэтому мне пришлось разделить вывод на 100, чтобы получить время выполнения только для одного цикла. Строка 11: мы просто распечатываем время выполнения. Результат — время выполнения в секундах.
Почему timeit() — лучший способ измерить время выполнения кода Python?
1. timeit() автоматически будет использовать time.clock() или time.time() для вас в фоновом режиме, в зависимости от того, какая операционная система вам нужна для получения наиболее точных результатов.
2. timeit() отключает сборщик мусора, который может исказить результаты.
3. timeit() повторяет тест много раз (в нашем случае 100 раз), чтобы минимизировать влияние других задач, выполняемых в вашей операционной системе.
Упражнение:
Кстати, код, который мы тестировали выше, строит список путем умножения элементов другого списка. Я могу достичь того же результата, используя range :
Если вам больше нечем заняться и попробуйте выполнить упражнение, попробуйте измерить время выполнения приведенного выше кода с помощью timeit() .
Наконец, совет: закрывайте тяжелые программы, которые запускаются на вашем компьютере, когда вы выполняете такие тесты, чтобы вы получили еще более точные результаты, которые не зависят от тяжелых задач процессора.
Следим за временем, необходимым на выполнение вашего кода на Python
Допустим, вам необходимо узнать, сколько времени занимает выполнение той или иной функции. Используя модуль time, вы можете рассчитать это время.
import time startTime = time.time() # время начала замера # здесь пишем код, время которого необходимо измерить endTime = time.time() #время конца замера totalTime = endTime — startTime #вычисляем затраченное время print(«Время, затраченное на выполнение данного кода widget_text awac-wrapper»>
Английский для программистов
Наш телеграм канал с тестами по английскому языку для программистов. Английский это часть карьеры программиста. Поэтому полезно заняться им уже сейчас
timeit — Measure execution time of small code snippets¶
This module provides a simple way to time small bits of Python code. It has both a Command-Line Interface as well as a callable one. It avoids a number of common traps for measuring execution times. See also Tim Peters’ introduction to the “Algorithms” chapter in the second edition of Python Cookbook, published by O’Reilly.
Basic Examples¶
The following example shows how the Command-Line Interface can be used to compare three different expressions:
This can be achieved from the Python Interface with:
A callable can also be passed from the Python Interface :
Note however that timeit() will automatically determine the number of repetitions only when the command-line interface is used. In the Examples section you can find more advanced examples.
Python Interface¶
The module defines three convenience functions and a public class:
timeit. timeit ( stmt=’pass’, setup=’pass’, timer=<default timer>, number=1000000, globals=None ) ¶
Create a Timer instance with the given statement, setup code and timer function and run its timeit() method with number executions. The optional globals argument specifies a namespace in which to execute the code.
Changed in version 3.5: The optional globals parameter was added.
Create a Timer instance with the given statement, setup code and timer function and run its repeat() method with the given repeat count and number executions. The optional globals argument specifies a namespace in which to execute the code.
Changed in version 3.5: The optional globals parameter was added.
Changed in version 3.7: Default value of repeat changed from 3 to 5.
The default timer, which is always time.perf_counter() .
Changed in version 3.3: time.perf_counter() is now the default timer.
Class for timing execution speed of small code snippets.
The constructor takes a statement to be timed, an additional statement used for setup, and a timer function. Both statements default to ‘pass’ ; the timer function is platform-dependent (see the module doc string). stmt and setup may also contain multiple statements separated by ; or newlines, as long as they don’t contain multi-line string literals. The statement will by default be executed within timeit’s namespace; this behavior can be controlled by passing a namespace to globals.
To measure the execution time of the first statement, use the timeit() method. The repeat() and autorange() methods are convenience methods to call timeit() multiple times.
The execution time of setup is excluded from the overall timed execution run.
The stmt and setup parameters can also take objects that are callable without arguments. This will embed calls to them in a timer function that will then be executed by timeit() . Note that the timing overhead is a little larger in this case because of the extra function calls.
Changed in version 3.5: The optional globals parameter was added.
Time number executions of the main statement. This executes the setup statement once, and then returns the time it takes to execute the main statement a number of times, measured in seconds as a float. The argument is the number of times through the loop, defaulting to one million. The main statement, the setup statement and the timer function to be used are passed to the constructor.
By default, timeit() temporarily turns off garbage collection during the timing. The advantage of this approach is that it makes independent timings more comparable. The disadvantage is that GC may be an important component of the performance of the function being measured. If so, GC can be re-enabled as the first statement in the setup string. For example:
Automatically determine how many times to call timeit() .
This is a convenience function that calls timeit() repeatedly so that the total time >= 0.2 second, returning the eventual (number of loops, time taken for that number of loops). It calls timeit() with increasing numbers from the sequence 1, 2, 5, 10, 20, 50, … until the time taken is at least 0.2 second.
If callback is given and is not None , it will be called after each trial with two arguments: callback(number, time_taken) .
New in version 3.6.
Call timeit() a few times.
This is a convenience function that calls the timeit() repeatedly, returning a list of results. The first argument specifies how many times to call timeit() . The second argument specifies the number argument for timeit() .
It’s tempting to calculate mean and standard deviation from the result vector and report these. However, this is not very useful. In a typical case, the lowest value gives a lower bound for how fast your machine can run the given code snippet; higher values in the result vector are typically not caused by variability in Python’s speed, but by other processes interfering with your timing accuracy. So the min() of the result is probably the only number you should be interested in. After that, you should look at the entire vector and apply common sense rather than statistics.
Changed in version 3.7: Default value of repeat changed from 3 to 5.
Helper to print a traceback from the timed code.
The advantage over the standard traceback is that source lines in the compiled template will be displayed. The optional file argument directs where the traceback is sent; it defaults to sys.stderr .
Command-Line Interface¶
When called as a program from the command line, the following form is used:
Where the following options are understood:
how many times to execute ‘statement’
how many times to repeat the timer (default 5)
statement to be executed once initially (default pass )
measure process time, not wallclock time, using time.process_time() instead of time.perf_counter() , which is the default
New in version 3.3.
specify a time unit for timer output; can select nsec, usec, msec, or sec
New in version 3.5.
print raw timing results; repeat for more digits precision
print a short usage message and exit
A multi-line statement may be given by specifying each line as a separate statement argument; indented lines are possible by enclosing an argument in quotes and using leading spaces. Multiple -s options are treated similarly.
If -n is not given, a suitable number of loops is calculated by trying increasing numbers from the sequence 1, 2, 5, 10, 20, 50, … until the total time is at least 0.2 seconds.
default_timer() measurements can be affected by other programs running on the same machine, so the best thing to do when accurate timing is necessary is to repeat the timing a few times and use the best time. The -r option is good for this; the default of 5 repetitions is probably enough in most cases. You can use time.process_time() to measure CPU time.
There is a certain baseline overhead associated with executing a pass statement. The code here doesn’t try to hide it, but you should be aware of it. The baseline overhead can be measured by invoking the program without arguments, and it might differ between Python versions.
Examples¶
It is possible to provide a setup statement that is executed only once at the beginning:
In the output, there are three fields. The loop count, which tells you how many times the statement body was run per timing loop repetition. The repetition count (‘best of 5’) which tells you how many times the timing loop was repeated, and finally the time the statement body took on average within the best repetition of the timing loop. That is, the time the fastest repetition took divided by the loop count.
The same can be done using the Timer class and its methods:
The following examples show how to time expressions that contain multiple lines. Here we compare the cost of using hasattr() vs. try / except to test for missing and present object attributes:
To give the timeit module access to functions you define, you can pass a setup parameter which contains an import statement:
Another option is to pass globals() to the globals parameter, which will cause the code to be executed within your current global namespace. This can be more convenient than individually specifying imports: