Что это в питоне

Встроенные функции Python: какие нужно знать и на какие не стоит тратить время

В Python существуют десятки встроенных функций и классов, сотни инструментов, входящих в стандартную библиотеку Python, и тысячи сторонних библиотек на PyPI. Держать всё в голове начинающему программисту нереально. В статье расскажем про стандартные встроенные функции Python: какие используются часто, а какие вам, вероятно, не пригодятся никогда.

Чтобы разобраться, на какие функции стоит обратить внимание, их следует разделить на группы:

• общеизвестные: почти все новички используют эти функции и довольно часто;
• неочевидные для новичков: об этих функциях полезно знать, но их легко пропустить, когда вы новичок в Python;
• понадобятся позже: об этих встроенных функциях полезно помнить, чтобы найти их потом, когда/если они понадобятся;
• можно изучить когда-нибудь: это может пригодиться, но только при определённых обстоятельствах;
• скорее всего, они вам не нужны: они вряд ли понадобятся, если вы не занимаетесь чем-то достаточно специализированным.

Встроенные функции в первых двух категориях являются основными. Они в конечном итоге будут нужны почти всем начинающим программистам на Python. Встроенные модули в следующих двух категориях являются специализированными, но потребности в них будут варьироваться в зависимости от вашей специализации. Категория 5 — это скрытые встроенные функции. Они очень полезны, когда в них есть необходимость, но многим программистам Python они, вероятно, никогда не понадобятся.

Общеизвестные функции

Если вы уже писали код на Python, эти модули должны быть вам знакомы.

print

Вряд ли найдётся разработчик, даже начинающий, который не знает эту функцию вывода. Как минимум реализация «Hello, World!» требует использования данного метода. Но вы можете не знать о различных аргументах, которые можно передавать в print .

В Python нет синтаксиса вроде my_list.length() или my_string.length , вместо этого используются поначалу непривычные конструкции len(my_list) и len(my_string) .

Нравится вам такая реализация или нет, другой альтернативы не предусмотрено, поэтому к ней нужно привыкнуть.

К сожалению, в отличие от многих других языков программирования, в Python нельзя объединять строки и числа.

Python отказывается приводить целое число 3 к типу строка, поэтому нужно сделать это самостоятельно, используя встроенную функцию str (технически это класс, но с целью уменьшить количество ненужной информации будем принимать все методы за функции).

Если нужно пользовательский ввод преобразовать в integer , эта функция незаменима. Она может преобразовывать строки в целые числа.

Эту функцию также можно использовать для отсечения дробной части у числа с плавающей точкой.

Обратите внимание, если нужно обрезать дробную часть при делении, оператор « // » более уместен (с отрицательными числами это работает иначе).

float

Если строка, которую надо конвертировать в число, не является целым числом, здесь поможет метод float .

Float также можно использовать для преобразования целых чисел в числа с плавающей запятой.

В Python 2 такое преобразование необходимо, но в Python 3 целочисленное деление больше не является чем-то особенным (если вы специально не используете оператор « // »). Поэтому больше не нужно использовать float для этой цели, теперь float(x)/y можно легко заменить на x/y .

Эта функция может очень облегчить задачу, если вы хотите составить список из итераций цикла.

При работе со списком метод copy позволяет создать его копию.

Если вы не знаете, с какими элементами работаете, функция list является более общим способом перебора элементов и их копирования.

Также можно использовать списковое включение, но делать это не рекомендуется.

Обратите внимание, когда вы хотите создать пустой список, следует использовать буквальный синтаксис списка (« [ ] »).

Использование « [ ] » считается более идиоматическим, так как эти скобки на самом деле выглядят как список Python.

tuple

Эта функция во многом похожа на функцию list , за исключением того, что вместо списков она создает кортежи.

Если вы пытаетесь создать хешируемую коллекцию (например, ключ словаря), стоит отдать предпочтению кортежу вместо списка.

Эта функция создаёт новый словарь.

Подобно спискам и кортежам, dict эквивалентна проходу по массиву пар «ключ-значение» и созданию из них словаря.

Дан список кортежей, по два элемента в каждом.

Выведем его на экран с помощью цикла.

То же самое, но с использованием dict .

Функция dict может принимать 2 типа аргументов:

• другой словарь: в этом случае этот словарь будет скопирован;
• список кортежей «ключ-значение»: в этом случае из этих слов будет создан новый словарь.

Поэтому следующий код также будет работать.

Функция dict также может принимать ключевые слова в качестве аргументов для создания словаря со строковыми ключами.

Но рекомендуется всё же использовать литералы вместо ключевых слов.

Такой синтаксис более гибок и немного быстрее. Но самое главное он более чётко передаёт факт того, что вы создаёте именно словарь.

Как в случае со списком и кортежем, пустой словарь следует создавать с использованием буквального синтаксиса (« < >»).

Использование « < >» более идиоматично и эффективно с точки зрения использования процессора. Обычно для создания словарей используются фигурные скобки, dict встречается гораздо реже.

Функция set создаёт новый набор. Она принимает итерации из хешируемых значений (строк, чисел или других неизменяемых типов) и возвращает set .

Создать пустой набор с « < >» нельзя (фигурные скобки создают пустой словарь). Поэтому функция set — лучший способ создать пустой набор.

Можно использовать и другой синтаксис.

Такой способ имеет недостаток — он сбивает с толку (он основан на редко используемой функции оператора * ), поэтому он не рекомендуется.

Python: “[:]” Notation Explained with Examples!

In this article let us learn about the commonly used “[:]” notation in Python, and learn how we can wield the power of this notation in our programs.

For those of you in a hurry, here is the short version of the answer.

The Short Version of The Answer

Python’s “[:]” notation, officially known as Slice Notation is used to extract the desired portion/slice from a given sequence.

Example#1: Slice Notation and Lists

(On a side note, if you are looking for an example with the syntax x[1:] or similar syntax, scroll down to the section “The variants of using Slice Notation” below)

Let us see how the code in the above example works.

In line-2, a portion/slice of this list is extracted using the slice notation x[2:5:1].

In Slice Notation, the desired slice can be specified with the help of 3 parameters namely start_index, stop_index, and step_length. In this example, the values of these 3 parameters are,

start_index = 2,
stop_index = 5, and
step_length = 1

The image below illustrates this concept

Below are the 7 steps of code execution.

1. python starts at the start_index, which is index-2,
2. python collects the value at index-2, which is x[2] = ‘c’ (Python’s list index starts from 0 as shown in the illustration above)
3. python takes a step with step_length of 1 and collects the value at the next index which is index-3, x[3] = ‘d’
4. python takes another step with the step_length of 1 and collects the value at index-4, x[4] = ‘e’
5. Then python takes the last step with step_length of 1, reaches index 5,
6. Since stop_index = 5, python stops and returns the items collected thus far as a sequence
7. The returned list is then printed out by the interpreter

In other words, python starts collecting values from the given sequence from start_index, moves through the list by incrementing the index using the step_length and stops once reaching the stop_index and returns the collected values!

Here is another example of the usage of Slice Notation ([:]), this time on strings

Example#2: Slice Notation ([:]) and Strings

Here we have set the start_index to be 2 and step_length to be 1. Note how we have skipped the stop_index. If a required parameter is not provided by us, then Python will set the parameter with default values.

Since we did not provide stop_index, and the default value for stop index is the length of the sequence, its value is assigned to be 5 (the length of the string1 sequence can also be obtained using the expression len(string1))

The logic of execution is similar to that of Example#1.

Don’t worry if you haven’t understood everything just yet as this is just the short version of the answer.

Remember, our brain is just like any other muscle in our body. We need to train our brains with concepts to achieve mastery and understanding, just like we would train our muscles in the gym!

I have divided the article into 2 levels, Level#1 is all about practicing all possible variants of this notation, so that you will understand what each variant mean, and you will be able to read these notations intuitively, to a degree that even if someone wakes you up in the middle of the night and asks you to read some code, you would be able to understand the meaning of this notation with a single glance!

Level#2 is all about getting a deeper level of understanding, there we learn about how Python has implemented the [:]/slice notation internally and the common scenarios/use-cases where the power of this notation can be wielded.

I hope by the end of this article you will be able to master this concept of Slice Notations in Python!

Alright! Let’s begin!

The More Informative Version of The Answer

If you prefer videos to articles, here is a short video I have made for you on this topic! (I am a newbie YouTuber so go easy on me if the quality is not matching your expectations!)

Python engineers have made it possible so that we can use even the negative indices with our Slice notation. Let us see an example of how to do this!

Variant 9:

• start_index: provided
• stop_index: skipped
• step_length: skipped

As you can see in the image above, start_index = –2, which points to ‘d’, stop_index, and step_length got their default values of 5 and 1 respectively and hence we got the above result!

Variants to traverse the sequence in Reverse Direction

We can also use the slice notation to traverse in reverse direction from the end of the sequence to the beginning of the sequence if needed by choosing step_length < 0

In other words, we must use the decrement/negative value for the “step_length” parameter to iterate through the list in the reverse direction.

Defaults for negative step_lengths:

If we use negative step_length the defaults for start_index and stop_index get different values as shown below.

• start_index: -1
• stop_index: – (len(list_name) + 1)

The pseudo-code below shows the C-equivalent for-loop for negative step sizes

Since a negative step_length parameter is a must for doing reverse iterations, we only have 4 variants with reverse traversing as shown below.

Variant#10:

• start_index: provided
• stop_index: provided
• step_length: provided (must for negative step_lengths)

As you can see in the illustration above, start_index = -1, stop_index = -5 and we step through the list in reverse direction, 1 item at a time as step_length = -1 and get the above output!

Variant#11:

• start_index: provided
• stop_index: skipped(default)
• step_length: provided (must for negative step_lengths)

Here start_index = -2, stop_index = -6 (the default value of – (len(list_name) + 1) )and we step through the list 1 item at a time as *step_length = *-1* and get the above output!

Variant#12:

• start_index: skipped(default)
• stop_index: provided
• step_length: provided (must for negative step_lengths)

Here start_index = -1 (the default value), stop_index = -3 and we step through the list 1 item at a time as step_length = -1 and get the above output!

Variant#13:

• start_index: skipped(default)
• stop_index: skipped(default)
• step_length: provided (must for negative step_lengths)

Here start_index = -1 (the default value), stop_index = -6 (the default value of – (len(list_name) + 1)) and we step through the list 2 indices at a time as step_length = -2 and get the above output!

This ends the variants section of this article!

To understand the real power of this Slice notation better, let us have a bit of fun with a 2-minute coding challenge!

Coding Challenge:

Clue: use the negative list index in python!

Take a couple of minutes to think this through and then scroll down for the answer!

If you came up with the correct answer as shown below, then congratulations! You have successfully completed Level#1!

Answer:

As you can see, this is a very powerful notation, by “powerful” I mean, with very little code you can get so much accomplished!

If your thirst for knowledge has not been quenched yet, read on for Level#2, where we try and understand how the slice notation is actually implemented in Python and the situations where we can use them in!

How does the “[:]” slice notation work?

If you have made it till here, I am sure you have a bright future ahead of you as a Python developer! Let’s continue our quest in Level#2, I promise this one won’t be as hard as Level#1!

Consider the code below.

The Python interpreter takes the above code that we entered and will translate it into something like this

where slice() is an inbuilt Python function that works with Sequence datatypes. This function when invoked with a sequence object, accepts our 3 parameters (start_index, stop_index, and step_length) as input and returns another sequence as output.

As shown in the example above, the slice() function should be invoked only inside the square brackets ‘[]’

This function is designed to accept a variable number of parameters so that if you skip a parameter, python will assign the default value.

The default value of each parameter, as we have seen above, depends upon the sign and availability of the step_length parameter

Internally, as we have seen earlier in this article the for-loop logic is used to get us the sequence returned after slicing!

Let us take some examples and play around with the slice() function!

slice() function Example#1

As you can see, slice() function gave us the same results as the Slice Notation!

Let us see another example where we invoke the slice() function with 2 arguments, instead of 3

slice() function Example#2

Again we got the same results!

For the next example let us see what happens when we invoke slice with just one argument

Looks like slice() uses the argument supplied as stop_index and uses the defaults for the other 2!

Let us verify this theory by having a quick look at the documentation of the slice() function by typing in the following line on the python interpreter

The first few lines of the output will be something like below

As you can see, in lines 4 and 5, if you just give one argument slice() uses that as stop_index, so our theory was indeed correct!

But say we wish to specify only the start_index or the step_length parameter, how can we do that?

Simple, we can use the 3 argument version of the slice() function (which is slice(start, stop[, step])) and provide None to fill in the values where we wish to use the default values!

The example below illustrates this.

Yay! We managed to get things working just the way we wanted to! If we can do something with the Slice Notation, we can do the same with the slice() function!

Where can we use the slice notation?

We can use this notation in a variety of situations, let us have a look at 3 most-common use-cases!

Use-case#1: Copy Portion of a sequence

Say we have a string and we wish to extract a sub-string, we can do so using Slice Notation as shown below.

Use-case#2: Iterate through portion of a sequence

Say you wish to iterate through a portion of a list, we can do so using Slice Notation as shown below.

Use-case#3: Create a copy to play with

List methods like pop() will alter our list, but since slice notation creates a copy. We can play with that slice in any way we want without worrying about altering the contents of the original sequence.

We can use the copy() method of Sequences to do the same, but that will copy over the entire list, which we might not want in situations where the list has tens of thousands of entries and we are only interested in a portion of those.

The above 3 are only the most common use-cases. Just use your imagination while you code, and I am sure you will come up with lots of scenarios where this notation will prove to be useful in your code!

And with that, we have reached the end of Level#2!

As I promised, Level-#2 was not so hard right!

Hopefully, now you have some good insight into the inner working of the slice notation and scenarios where this notation might prove to be useful!

To summarize this article let us have a one last look at the important points covered in this article.

Points to Remember

• The official name of this “[:]” notation is slice notation
• The 3 important parameters are: [start_index:stop_index:step_length]
• stop_index is not included and python starts counting from index-0
• We can choose to skip providing some of these 3 parameters and the python interpreter will use the default values for the skipped ones.
• The default values of these parameters (for step_length > 0) are
  start_index= 0
  stop_index= length of the list, len(list_name)
  step_length= 1
• The default values of these parameters for step_length < 0 are start_index= -1 and stop_index= -(seq_length + 1)
• you can use slice notation with any sequence in Python like strings, lists, tuples, bytes, and byte arrays.
• the inbuilt slice() function does the same job when invoked within the square brackets []
• slice notation can be used to extract slices, iterate through portions, and for playing with lists in a safe manner without making any changes to the original.

Alright, with that I will stop!
Hope you enjoyed reading this article as much I did writing it!
Feel free to share this article with your friends and colleagues!

Основы языка программирования Python за 10 минут

На сайте Poromenos’ Stuff была
опубликована статья, в которой, в сжатой форме,
рассказывают об основах языка Python. Я предлагаю вам перевод этой статьи. Перевод не дословный. Я постарался подробнее объяснить некоторые моменты, которые могут быть непонятны.

Если вы собрались изучать язык Python, но не можете найти подходящего руководства, то эта
статья вам очень пригодится! За короткое время, вы сможете познакомиться с
основами языка Python. Хотя эта статья часто опирается
на то, что вы уже имеете опыт программирования, но, я надеюсь, даже новичкам
этот материал будет полезен. Внимательно прочитайте каждый параграф. В связи с
сжатостью материала, некоторые темы рассмотрены поверхностно, но содержат весь
необходимый метриал.

Основные свойства

Python не требует явного объявления переменных, является регистро-зависим (переменная var не эквивалентна переменной Var или VAR — это три разные переменные) объектно-ориентированным языком.

Синтаксис

Во первых стоит отметить интересную особенность Python. Он не содержит операторных скобок (begin..end в pascal или <..>в Си), вместо этого блоки выделяются отступами: пробелами или табуляцией, а вход в блок из операторов осуществляется двоеточием. Однострочные комментарии начинаются со знака фунта «#», многострочные — начинаются и заканчиваются тремя двойными кавычками «»»»».
Чтобы присвоить значение пременной используется знак «=», а для сравнения —
«==». Для увеличения значения переменной, или добавления к строке используется оператор «+=», а для уменьшения — «-=». Все эти операции могут взаимодействовать с большинством типов, в том числе со строками. Например

Структуры данных

Python содержит такие структуры данных как списки (lists), кортежи (tuples) и словари (dictionaries). Списки — похожи на одномерные массивы (но вы можете использовать Список включающий списки — многомерный массив), кортежи — неизменяемые списки, словари — тоже списки, но индексы могут быть любого типа, а не только числовыми. «Массивы» в Python могут содержать данные любого типа, то есть в одном массиве может могут находиться числовые, строковые и другие типы данных. Массивы начинаются с индекса 0, а последний элемент можно получить по индексу -1 Вы можете присваивать переменным функции и использовать их соответственно.

Вы можете использовать часть массива, задавая первый и последний индекс через двоеточие «:». В таком случае вы получите часть массива, от первого индекса до второго не включительно. Если не указан первый элемент, то отсчет начинается с начала массива, а если не указан последний — то масив считывается до последнего элемента. Отрицательные значения определяют положение элемента с конца. Например:

Строки

Строки в Python обособляются кавычками двойными «»» или одинарными «’». Внутри двойных ковычек могут присутствовать одинарные или наоборот. К примеру строка «Он сказал ‘привет’!» будет выведена на экран как «Он сказал ‘привет’!». Если нужно использовать строку из несколько строчек, то эту строку надо начинать и заканчивать тремя двойными кавычками «»»»». Вы можете подставить в шаблон строки элементы из кортежа или словаря. Знак процента «%» между строкой и кортежем, заменяет в строке символы «%s» на элемент кортежа. Словари позволяют вставлять в строку элемент под заданным индексом. Для этого надо использовать в строке конструкцию «%(индекс)s». В этом случае вместо «%(индекс)s» будет подставлено значение словаря под заданным индексом.

>>>print «Name: %s\nNumber: %s\nString: %s» % (myclass.name, 3 , 3 * «-» )
Name: Poromenos
Number: 3
String: —
strString = «»«Этот текст расположен
на нескольких строках»»»

Операторы

Операторы while, if, for составляют операторы перемещения. Здесь нет аналога оператора select, так что придется обходиться if. В операторе for происходит сравнение переменной и списка. Чтобы получить список цифр до числа <number> — используйте функцию range(<number>). Вот пример использования операторов

rangelist = range ( 10 ) #Получаем список из десяти цифр (от 0 до 9)
>>> print rangelist
[ 0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]
for number in rangelist: #Пока переменная number (которая каждый раз увеличивается на единицу) входит в список…
# Проверяем входит ли переменная
# numbers в кортеж чисел ( 3 , 4 , 7 , 9 )
if number in ( 3 , 4 , 7 , 9 ): #Если переменная number входит в кортеж (3, 4, 7, 9).
# Операция «break» обеспечивает
# выход из цикла в любой момент
break
else :
# «continue» осуществляет «прокрутку»
# цикла. Здесь это не требуется, так как после этой операции
# в любом случае программа переходит опять к обработке цикла
continue
else :
# «else» указывать необязательно. Условие выполняется
# если цикл не был прерван при помощи «break».
pass # Ничего не делать

if rangelist[ 1 ] == 2 :
print «The second item (lists are 0-based) is 2»
elif rangelist[ 1 ] == 3 :
print «The second item (lists are 0-based) is 3»
else :
print «Dunno»

while rangelist[ 1 ] == 1 :
pass

Функции

Для объявления функции служит ключевое слово «def». Аргументы функции задаются в скобках после названия функции. Можно задавать необязательные аргументы, присваивая им значение по умолчанию. Функции могут возвращать кортежи, в таком случае надо писать возвращаемые значения через запятую. Ключевое слово «lambda» служит для объявления элементарных функций .

# arg2 и arg3 — необязательые аргументы, принимают значение объявленное по умолчни,
# если не задать им другое значение при вызове функци.
def myfunction(arg1, arg2 = 100 , arg3 = «test» ):
return arg3, arg2, arg1
#Функция вызывается со значением первого аргумента — «Argument 1», второго — по умолчанию, и третьего — «Named argument» .
>>>ret1, ret2, ret3 = myfunction( «Argument 1» , arg3 = «Named argument» )
# ret1, ret2 и ret3 принимают значения «Named argument», 100, «Argument 1» соответственно
>>> print ret1, ret2, ret3
Named argument 100 Argument 1

# Следующая запись эквивалентна def f(x): return x + 1
functionvar = lambda x: x + 1
>>> print functionvar( 1 )
2

Классы

Язык Python ограничен в множественном наследовании в классах. Внутренние переменные и внутренние методы классов начинаются с двух знаков нижнего подчеркивания «__» (например «__myprivatevar»). Мы можем также присвоить значение переменной класса извне. Пример:

class Myclass:
common = 10
def __init__( self ):
self .myvariable = 3
def myfunction( self , arg1, arg2):
return self .myvariable

# Здесь мы объявили класс Myclass. Функция __init__ вызывается автоматически при инициализации классов.
>>> classinstance = Myclass() # Мы инициализировали класс и переменная myvariable приобрела значение 3 как заявлено в методе инициализации
>>> classinstance.myfunction( 1 , 2 ) #Метод myfunction класса Myclass возвращает значение переменной myvariable
3
# Переменная common объявлена во всех классах
>>> classinstance2 = Myclass()
>>> classinstance.common
10
>>> classinstance2.common
10
# Поэтому, если мы изменим ее значение в классе Myclass изменятся
# и ее значения в объектах, инициализированных классом Myclass
>>> Myclass.common = 30
>>> classinstance.common
30
>>> classinstance2.common
30
# А здесь мы не изменяем переменную класса. Вместо этого
# мы объявляем оную в объекте и присваиваем ей новое значение
>>> classinstance.common = 10
>>> classinstance.common
10
>>> classinstance2.common
30
>>> Myclass.common = 50
# Теперь изменение переменной класса не коснется
# переменных объектов этого класса
>>> classinstance.common
10
>>> classinstance2.common
50

# Следующий класс является наследником класса Myclass
# наследуя его свойства и методы, ктому же класс может
# наследоваться из нескольких классов, в этом случае запись
# такая: class Otherclass(Myclass1, Myclass2, MyclassN)
class Otherclass(Myclass):
def __init__( self , arg1):
self .myvariable = 3
print arg1

>>> classinstance = Otherclass( «hello» )
hello
>>> classinstance.myfunction( 1 , 2 )
3
# Этот класс не имеет совйтсва test, но мы можем
# объявить такую переменную для объекта. Причем
# tэта переменная будет членом только classinstance.
>>> classinstance.test = 10
>>> classinstance.test
10

Исключения

Исключения в Python имеют структуру try—except [exceptionname]:

def somefunction():
try :
# Деление на ноль вызывает ошибку
10 / 0
except ZeroDivisionError :
# Но программа не «Выполняет недопустимую операцию»
# А обрабатывает блок исключения соответствующий ошибке «ZeroDivisionError»
print «Oops, invalid.»

Импорт

Внешние библиотеки можно подключить процедурой «import [libname]», где [libname] — название подключаемой библиотеки. Вы так же можете использовать команду «from [libname] import [funcname]», чтобы вы могли использовать функцию [funcname] из библиотеки [libname]

import random #Импортируем библиотеку «random»
from time import clock #И заодно функцию «clock» из библиотеки «time»

randomint = random .randint( 1 , 100 )
>>> print randomint
64

Работа с файловой системой

Python имеет много встроенных библиотек. В этом примере мы попробуем сохранить в бинарном файле структуру списка, прочитать ее и сохраним строку в текстовом файле. Для преобразования структуры данных мы будем использовать стандартную библиотеку «pickle»

import pickle
mylist = [ «This» , «is» , 4 , 13327 ]
# Откроем файл C:\binary.dat для записи. Символ «r»
# предотвращает замену специальных сиволов (таких как \n, \t, \b и др.).
myfile = file (r «C:\binary.dat» , «w» )
pickle .dump(mylist, myfile)
myfile.close()

myfile = file (r «C:\text.txt» , «w» )
myfile.write( «This is a sample string» )
myfile.close()

myfile = file (r «C:\text.txt» )
>>> print myfile.read()
‘This is a sample string’
myfile.close()

# Открываем файл для чтения
myfile = file (r «C:\binary.dat» )
loadedlist = pickle .load(myfile)
myfile.close()
>>> print loadedlist
[ ‘This’ , ‘is’ , 4 , 13327 ]

Особенности

• Условия могут комбинироваться. 1 < a < 3 выполняется тогда, когда а больше 1, но меньше 3.
• Используйте операцию «del» чтобы очищать переменные или элементы массива.
• Python предлагает большие возможности для работы со списками. Вы можете использовать операторы объявлении структуры списка. Оператор for позволяет задавать элементы списка в определенной последовательности, а if — позволяет выбирать элементы по условию.

• Глобальные переменные объявляются вне функций и могут быть прочитанны без каких либо объявлений. Но если вам необходимо изменить значение глобальной переменной из функции, то вам необходимо объявить ее в начале функции ключевым словом «global», если вы этого не сделаете, то Python объявит переменную, доступную только для этой функции.

def myfunc():
# Выводит 5
print number

def anotherfunc():
# Это вызывает исключение, поскольку глобальная апеременная
# не была вызванна из функции. Python в этом случае создает
# одноименную переменную внутри этой функции и доступную
# только для операторов этой функции.
print number
number = 3

def yetanotherfunc():
global number
# И только из этой функции значение переменной изменяется.
number = 3

Эпилог

Разумеется в этой статье не описываются все возможности Python. Я надеюсь что эта статья поможет вам, если вы захотите и в дальнейшем изучать этот язык программирования.

6. Expressions¶

This chapter explains the meaning of the elements of expressions in Python.

Syntax Notes: In this and the following chapters, extended BNF notation will be used to describe syntax, not lexical analysis. When (one alternative of) a syntax rule has the form

and no semantics are given, the semantics of this form of name are the same as for othername .

6.1. Arithmetic conversions¶

When a description of an arithmetic operator below uses the phrase “the numeric arguments are converted to a common type”, this means that the operator implementation for built-in types works as follows:

If either argument is a complex number, the other is converted to complex;

otherwise, if either argument is a floating point number, the other is converted to floating point;

otherwise, both must be integers and no conversion is necessary.

Some additional rules apply for certain operators (e.g., a string as a left argument to the ‘%’ operator). Extensions must define their own conversion behavior.

6.2. Atoms¶

Atoms are the most basic elements of expressions. The simplest atoms are identifiers or literals. Forms enclosed in parentheses, brackets or braces are also categorized syntactically as atoms. The syntax for atoms is:

6.2.1. Identifiers (Names)¶

An identifier occurring as an atom is a name. See section Identifiers and keywords for lexical definition and section Naming and binding for documentation of naming and binding.

When the name is bound to an object, evaluation of the atom yields that object. When a name is not bound, an attempt to evaluate it raises a NameError exception.

Private name mangling: When an identifier that textually occurs in a class definition begins with two or more underscore characters and does not end in two or more underscores, it is considered a private name of that class. Private names are transformed to a longer form before code is generated for them. The transformation inserts the class name, with leading underscores removed and a single underscore inserted, in front of the name. For example, the identifier __spam occurring in a class named Ham will be transformed to _Ham__spam . This transformation is independent of the syntactical context in which the identifier is used. If the transformed name is extremely long (longer than 255 characters), implementation defined truncation may happen. If the class name consists only of underscores, no transformation is done.

6.2.2. Literals¶

Python supports string and bytes literals and various numeric literals:

Evaluation of a literal yields an object of the given type (string, bytes, integer, floating point number, complex number) with the given value. The value may be approximated in the case of floating point and imaginary (complex) literals. See section Literals for details.

All literals correspond to immutable data types, and hence the object’s identity is less important than its value. Multiple evaluations of literals with the same value (either the same occurrence in the program text or a different occurrence) may obtain the same object or a different object with the same value.

6.2.3. Parenthesized forms¶

A parenthesized form is an optional expression list enclosed in parentheses:

A parenthesized expression list yields whatever that expression list yields: if the list contains at least one comma, it yields a tuple; otherwise, it yields the single expression that makes up the expression list.

An empty pair of parentheses yields an empty tuple object. Since tuples are immutable, the same rules as for literals apply (i.e., two occurrences of the empty tuple may or may not yield the same object).

Note that tuples are not formed by the parentheses, but rather by use of the comma. The exception is the empty tuple, for which parentheses are required — allowing unparenthesized “nothing” in expressions would cause ambiguities and allow common typos to pass uncaught.

6.2.4. Displays for lists, sets and dictionaries¶

For constructing a list, a set or a dictionary Python provides special syntax called “displays”, each of them in two flavors:

either the container contents are listed explicitly, or

they are computed via a set of looping and filtering instructions, called a comprehension.

Common syntax elements for comprehensions are:

The comprehension consists of a single expression followed by at least one for clause and zero or more for or if clauses. In this case, the elements of the new container are those that would be produced by considering each of the for or if clauses a block, nesting from left to right, and evaluating the expression to produce an element each time the innermost block is reached.

However, aside from the iterable expression in the leftmost for clause, the comprehension is executed in a separate implicitly nested scope. This ensures that names assigned to in the target list don’t “leak” into the enclosing scope.

The iterable expression in the leftmost for clause is evaluated directly in the enclosing scope and then passed as an argument to the implicitly nested scope. Subsequent for clauses and any filter condition in the leftmost for clause cannot be evaluated in the enclosing scope as they may depend on the values obtained from the leftmost iterable. For example: [x*y for x in range(10) for y in range(x, x+10)] .

To ensure the comprehension always results in a container of the appropriate type, yield and yield from expressions are prohibited in the implicitly nested scope.

Since Python 3.6, in an async def function, an async for clause may be used to iterate over a asynchronous iterator . A comprehension in an async def function may consist of either a for or async for clause following the leading expression, may contain additional for or async for clauses, and may also use await expressions. If a comprehension contains either async for clauses or await expressions or other asynchronous comprehensions it is called an asynchronous comprehension. An asynchronous comprehension may suspend the execution of the coroutine function in which it appears. See also PEP 530.

New in version 3.6: Asynchronous comprehensions were introduced.

Changed in version 3.8: yield and yield from prohibited in the implicitly nested scope.

Changed in version 3.11: Asynchronous comprehensions are now allowed inside comprehensions in asynchronous functions. Outer comprehensions implicitly become asynchronous.

6.2.5. List displays¶

A list display is a possibly empty series of expressions enclosed in square brackets:

A list display yields a new list object, the contents being specified by either a list of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and placed into the list object in that order. When a comprehension is supplied, the list is constructed from the elements resulting from the comprehension.

6.2.6. Set displays¶

A set display is denoted by curly braces and distinguishable from dictionary displays by the lack of colons separating keys and values:

A set display yields a new mutable set object, the contents being specified by either a sequence of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and added to the set object. When a comprehension is supplied, the set is constructed from the elements resulting from the comprehension.

An empty set cannot be constructed with <> ; this literal constructs an empty dictionary.

6.2.7. Dictionary displays¶

A dictionary display is a possibly empty series of key/datum pairs enclosed in curly braces:

A dictionary display yields a new dictionary object.

If a comma-separated sequence of key/datum pairs is given, they are evaluated from left to right to define the entries of the dictionary: each key object is used as a key into the dictionary to store the corresponding datum. This means that you can specify the same key multiple times in the key/datum list, and the final dictionary’s value for that key will be the last one given.

A double asterisk ** denotes dictionary unpacking. Its operand must be a mapping . Each mapping item is added to the new dictionary. Later values replace values already set by earlier key/datum pairs and earlier dictionary unpackings.

New in version 3.5: Unpacking into dictionary displays, originally proposed by PEP 448.

A dict comprehension, in contrast to list and set comprehensions, needs two expressions separated with a colon followed by the usual “for” and “if” clauses. When the comprehension is run, the resulting key and value elements are inserted in the new dictionary in the order they are produced.

Restrictions on the types of the key values are listed earlier in section The standard type hierarchy . (To summarize, the key type should be hashable , which excludes all mutable objects.) Clashes between duplicate keys are not detected; the last datum (textually rightmost in the display) stored for a given key value prevails.

Changed in version 3.8: Prior to Python 3.8, in dict comprehensions, the evaluation order of key and value was not well-defined. In CPython, the value was evaluated before the key. Starting with 3.8, the key is evaluated before the value, as proposed by PEP 572.

6.2.8. Generator expressions¶

A generator expression is a compact generator notation in parentheses:

A generator expression yields a new generator object. Its syntax is the same as for comprehensions, except that it is enclosed in parentheses instead of brackets or curly braces.

Variables used in the generator expression are evaluated lazily when the __next__() method is called for the generator object (in the same fashion as normal generators). However, the iterable expression in the leftmost for clause is immediately evaluated, so that an error produced by it will be emitted at the point where the generator expression is defined, rather than at the point where the first value is retrieved. Subsequent for clauses and any filter condition in the leftmost for clause cannot be evaluated in the enclosing scope as they may depend on the values obtained from the leftmost iterable. For example: (x*y for x in range(10) for y in range(x, x+10)) .

The parentheses can be omitted on calls with only one argument. See section Calls for details.

To avoid interfering with the expected operation of the generator expression itself, yield and yield from expressions are prohibited in the implicitly defined generator.

If a generator expression contains either async for clauses or await expressions it is called an asynchronous generator expression. An asynchronous generator expression returns a new asynchronous generator object, which is an asynchronous iterator (see Asynchronous Iterators ).

New in version 3.6: Asynchronous generator expressions were introduced.

Changed in version 3.7: Prior to Python 3.7, asynchronous generator expressions could only appear in async def coroutines. Starting with 3.7, any function can use asynchronous generator expressions.

Changed in version 3.8: yield and yield from prohibited in the implicitly nested scope.

6.2.9. Yield expressions¶

The yield expression is used when defining a generator function or an asynchronous generator function and thus can only be used in the body of a function definition. Using a yield expression in a function’s body causes that function to be a generator function, and using it in an async def function’s body causes that coroutine function to be an asynchronous generator function. For example:

Due to their side effects on the containing scope, yield expressions are not permitted as part of the implicitly defined scopes used to implement comprehensions and generator expressions.

Changed in version 3.8: Yield expressions prohibited in the implicitly nested scopes used to implement comprehensions and generator expressions.

Generator functions are described below, while asynchronous generator functions are described separately in section Asynchronous generator functions .

When a generator function is called, it returns an iterator known as a generator. That generator then controls the execution of the generator function. The execution starts when one of the generator’s methods is called. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to the generator’s caller, or None if expression_list is omitted. By suspended, we mean that all local state is retained, including the current bindings of local variables, the instruction pointer, the internal evaluation stack, and the state of any exception handling. When the execution is resumed by calling one of the generator’s methods, the function can proceed exactly as if the yield expression were just another external call. The value of the yield expression after resuming depends on the method which resumed the execution. If __next__() is used (typically via either a for or the next() builtin) then the result is None . Otherwise, if send() is used, then the result will be the value passed in to that method.

All of this makes generator functions quite similar to coroutines; they yield multiple times, they have more than one entry point and their execution can be suspended. The only difference is that a generator function cannot control where the execution should continue after it yields; the control is always transferred to the generator’s caller.

Yield expressions are allowed anywhere in a try construct. If the generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), the generator-iterator’s close() method will be called, allowing any pending finally clauses to execute.

When yield from <expr> is used, the supplied expression must be an iterable. The values produced by iterating that iterable are passed directly to the caller of the current generator’s methods. Any values passed in with send() and any exceptions passed in with throw() are passed to the underlying iterator if it has the appropriate methods. If this is not the case, then send() will raise AttributeError or TypeError , while throw() will just raise the passed in exception immediately.

When the underlying iterator is complete, the value attribute of the raised StopIteration instance becomes the value of the yield expression. It can be either set explicitly when raising StopIteration , or automatically when the subiterator is a generator (by returning a value from the subgenerator).

Changed in version 3.3: Added yield from <expr> to delegate control flow to a subiterator.

The parentheses may be omitted when the yield expression is the sole expression on the right hand side of an assignment statement.

The proposal for adding generators and the yield statement to Python.

PEP 342 — Coroutines via Enhanced Generators

The proposal to enhance the API and syntax of generators, making them usable as simple coroutines.

PEP 380 — Syntax for Delegating to a Subgenerator

The proposal to introduce the yield_from syntax, making delegation to subgenerators easy.

The proposal that expanded on PEP 492 by adding generator capabilities to coroutine functions.

6.2.9.1. Generator-iterator methods¶

This subsection describes the methods of a generator iterator. They can be used to control the execution of a generator function.

Note that calling any of the generator methods below when the generator is already executing raises a ValueError exception.

Starts the execution of a generator function or resumes it at the last executed yield expression. When a generator function is resumed with a __next__() method, the current yield expression always evaluates to None . The execution then continues to the next yield expression, where the generator is suspended again, and the value of the expression_list is returned to __next__() ’s caller. If the generator exits without yielding another value, a StopIteration exception is raised.

This method is normally called implicitly, e.g. by a for loop, or by the built-in next() function.

generator. send ( value ) ¶

Resumes the execution and “sends” a value into the generator function. The value argument becomes the result of the current yield expression. The send() method returns the next value yielded by the generator, or raises StopIteration if the generator exits without yielding another value. When send() is called to start the generator, it must be called with None as the argument, because there is no yield expression that could receive the value.

generator. throw ( value ) ¶ generator. throw ( type [ , value [ , traceback ] ] )

Raises an exception at the point where the generator was paused, and returns the next value yielded by the generator function. If the generator exits without yielding another value, a StopIteration exception is raised. If the generator function does not catch the passed-in exception, or raises a different exception, then that exception propagates to the caller.

In typical use, this is called with a single exception instance similar to the way the raise keyword is used.

For backwards compatibility, however, the second signature is supported, following a convention from older versions of Python. The type argument should be an exception class, and value should be an exception instance. If the value is not provided, the type constructor is called to get an instance. If traceback is provided, it is set on the exception, otherwise any existing __traceback__ attribute stored in value may be cleared.

Raises a GeneratorExit at the point where the generator function was paused. If the generator function then exits gracefully, is already closed, or raises GeneratorExit (by not catching the exception), close returns to its caller. If the generator yields a value, a RuntimeError is raised. If the generator raises any other exception, it is propagated to the caller. close() does nothing if the generator has already exited due to an exception or normal exit.

6.2.9.2. Examples¶

Here is a simple example that demonstrates the behavior of generators and generator functions:

For examples using yield from , see PEP 380: Syntax for Delegating to a Subgenerator in “What’s New in Python.”

6.2.9.3. Asynchronous generator functions¶

The presence of a yield expression in a function or method defined using async def further defines the function as an asynchronous generator function.

When an asynchronous generator function is called, it returns an asynchronous iterator known as an asynchronous generator object. That object then controls the execution of the generator function. An asynchronous generator object is typically used in an async for statement in a coroutine function analogously to how a generator object would be used in a for statement.

Calling one of the asynchronous generator’s methods returns an awaitable object, and the execution starts when this object is awaited on. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to the awaiting coroutine. As with a generator, suspension means that all local state is retained, including the current bindings of local variables, the instruction pointer, the internal evaluation stack, and the state of any exception handling. When the execution is resumed by awaiting on the next object returned by the asynchronous generator’s methods, the function can proceed exactly as if the yield expression were just another external call. The value of the yield expression after resuming depends on the method which resumed the execution. If __anext__() is used then the result is None . Otherwise, if asend() is used, then the result will be the value passed in to that method.

If an asynchronous generator happens to exit early by break , the caller task being cancelled, or other exceptions, the generator’s async cleanup code will run and possibly raise exceptions or access context variables in an unexpected context–perhaps after the lifetime of tasks it depends, or during the event loop shutdown when the async-generator garbage collection hook is called. To prevent this, the caller must explicitly close the async generator by calling aclose() method to finalize the generator and ultimately detach it from the event loop.

In an asynchronous generator function, yield expressions are allowed anywhere in a try construct. However, if an asynchronous generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), then a yield expression within a try construct could result in a failure to execute pending finally clauses. In this case, it is the responsibility of the event loop or scheduler running the asynchronous generator to call the asynchronous generator-iterator’s aclose() method and run the resulting coroutine object, thus allowing any pending finally clauses to execute.

To take care of finalization upon event loop termination, an event loop should define a finalizer function which takes an asynchronous generator-iterator and presumably calls aclose() and executes the coroutine. This finalizer may be registered by calling sys.set_asyncgen_hooks() . When first iterated over, an asynchronous generator-iterator will store the registered finalizer to be called upon finalization. For a reference example of a finalizer method see the implementation of asyncio.Loop.shutdown_asyncgens in Lib/asyncio/base_events.py.

The expression yield from <expr> is a syntax error when used in an asynchronous generator function.

6.2.9.4. Asynchronous generator-iterator methods¶

This subsection describes the methods of an asynchronous generator iterator, which are used to control the execution of a generator function.

coroutine agen. __anext__ ( ) ¶

Returns an awaitable which when run starts to execute the asynchronous generator or resumes it at the last executed yield expression. When an asynchronous generator function is resumed with an __anext__() method, the current yield expression always evaluates to None in the returned awaitable, which when run will continue to the next yield expression. The value of the expression_list of the yield expression is the value of the StopIteration exception raised by the completing coroutine. If the asynchronous generator exits without yielding another value, the awaitable instead raises a StopAsyncIteration exception, signalling that the asynchronous iteration has completed.

This method is normally called implicitly by a async for loop.

coroutine agen. asend ( value ) ¶

Returns an awaitable which when run resumes the execution of the asynchronous generator. As with the send() method for a generator, this “sends” a value into the asynchronous generator function, and the value argument becomes the result of the current yield expression. The awaitable returned by the asend() method will return the next value yielded by the generator as the value of the raised StopIteration , or raises StopAsyncIteration if the asynchronous generator exits without yielding another value. When asend() is called to start the asynchronous generator, it must be called with None as the argument, because there is no yield expression that could receive the value.

coroutine agen. athrow ( type [ , value [ , traceback ] ] ) ¶

Returns an awaitable that raises an exception of type type at the point where the asynchronous generator was paused, and returns the next value yielded by the generator function as the value of the raised StopIteration exception. If the asynchronous generator exits without yielding another value, a StopAsyncIteration exception is raised by the awaitable. If the generator function does not catch the passed-in exception, or raises a different exception, then when the awaitable is run that exception propagates to the caller of the awaitable.

coroutine agen. aclose ( ) ¶

Returns an awaitable that when run will throw a GeneratorExit into the asynchronous generator function at the point where it was paused. If the asynchronous generator function then exits gracefully, is already closed, or raises GeneratorExit (by not catching the exception), then the returned awaitable will raise a StopIteration exception. Any further awaitables returned by subsequent calls to the asynchronous generator will raise a StopAsyncIteration exception. If the asynchronous generator yields a value, a RuntimeError is raised by the awaitable. If the asynchronous generator raises any other exception, it is propagated to the caller of the awaitable. If the asynchronous generator has already exited due to an exception or normal exit, then further calls to aclose() will return an awaitable that does nothing.

6.3. Primaries¶

Primaries represent the most tightly bound operations of the language. Their syntax is:

6.3.1. Attribute references¶

An attribute reference is a primary followed by a period and a name:

The primary must evaluate to an object of a type that supports attribute references, which most objects do. This object is then asked to produce the attribute whose name is the identifier. This production can be customized by overriding the __getattr__() method. If this attribute is not available, the exception AttributeError is raised. Otherwise, the type and value of the object produced is determined by the object. Multiple evaluations of the same attribute reference may yield different objects.

6.3.2. Subscriptions¶

The subscription of an instance of a container class will generally select an element from the container. The subscription of a generic class will generally return a GenericAlias object.

When an object is subscripted, the interpreter will evaluate the primary and the expression list.

The primary must evaluate to an object that supports subscription. An object may support subscription through defining one or both of __getitem__() and __class_getitem__() . When the primary is subscripted, the evaluated result of the expression list will be passed to one of these methods. For more details on when __class_getitem__ is called instead of __getitem__ , see __class_getitem__ versus __getitem__ .

If the expression list contains at least one comma, it will evaluate to a tuple containing the items of the expression list. Otherwise, the expression list will evaluate to the value of the list’s sole member.

For built-in objects, there are two types of objects that support subscription via __getitem__() :

Mappings. If the primary is a mapping , the expression list must evaluate to an object whose value is one of the keys of the mapping, and the subscription selects the value in the mapping that corresponds to that key. An example of a builtin mapping class is the dict class.

Sequences. If the primary is a sequence , the expression list must evaluate to an int or a slice (as discussed in the following section). Examples of builtin sequence classes include the str , list and tuple classes.

The formal syntax makes no special provision for negative indices in sequences . However, built-in sequences all provide a __getitem__() method that interprets negative indices by adding the length of the sequence to the index so that, for example, x[-1] selects the last item of x . The resulting value must be a nonnegative integer less than the number of items in the sequence, and the subscription selects the item whose index is that value (counting from zero). Since the support for negative indices and slicing occurs in the object’s __getitem__() method, subclasses overriding this method will need to explicitly add that support.

A string is a special kind of sequence whose items are characters. A character is not a separate data type but a string of exactly one character.

6.3.3. Slicings¶

A slicing selects a range of items in a sequence object (e.g., a string, tuple or list). Slicings may be used as expressions or as targets in assignment or del statements. The syntax for a slicing:

There is ambiguity in the formal syntax here: anything that looks like an expression list also looks like a slice list, so any subscription can be interpreted as a slicing. Rather than further complicating the syntax, this is disambiguated by defining that in this case the interpretation as a subscription takes priority over the interpretation as a slicing (this is the case if the slice list contains no proper slice).

The semantics for a slicing are as follows. The primary is indexed (using the same __getitem__() method as normal subscription) with a key that is constructed from the slice list, as follows. If the slice list contains at least one comma, the key is a tuple containing the conversion of the slice items; otherwise, the conversion of the lone slice item is the key. The conversion of a slice item that is an expression is that expression. The conversion of a proper slice is a slice object (see section The standard type hierarchy ) whose start , stop and step attributes are the values of the expressions given as lower bound, upper bound and stride, respectively, substituting None for missing expressions.

6.3.4. Calls¶

A call calls a callable object (e.g., a function ) with a possibly empty series of arguments :

An optional trailing comma may be present after the positional and keyword arguments but does not affect the semantics.

The primary must evaluate to a callable object (user-defined functions, built-in functions, methods of built-in objects, class objects, methods of class instances, and all objects having a __call__() method are callable). All argument expressions are evaluated before the call is attempted. Please refer to section Function definitions for the syntax of formal parameter lists.

If keyword arguments are present, they are first converted to positional arguments, as follows. First, a list of unfilled slots is created for the formal parameters. If there are N positional arguments, they are placed in the first N slots. Next, for each keyword argument, the identifier is used to determine the corresponding slot (if the identifier is the same as the first formal parameter name, the first slot is used, and so on). If the slot is already filled, a TypeError exception is raised. Otherwise, the argument is placed in the slot, filling it (even if the expression is None , it fills the slot). When all arguments have been processed, the slots that are still unfilled are filled with the corresponding default value from the function definition. (Default values are calculated, once, when the function is defined; thus, a mutable object such as a list or dictionary used as default value will be shared by all calls that don’t specify an argument value for the corresponding slot; this should usually be avoided.) If there are any unfilled slots for which no default value is specified, a TypeError exception is raised. Otherwise, the list of filled slots is used as the argument list for the call.

CPython implementation detail: An implementation may provide built-in functions whose positional parameters do not have names, even if they are ‘named’ for the purpose of documentation, and which therefore cannot be supplied by keyword. In CPython, this is the case for functions implemented in C that use PyArg_ParseTuple() to parse their arguments.

If there are more positional arguments than there are formal parameter slots, a TypeError exception is raised, unless a formal parameter using the syntax *identifier is present; in this case, that formal parameter receives a tuple containing the excess positional arguments (or an empty tuple if there were no excess positional arguments).

If any keyword argument does not correspond to a formal parameter name, a TypeError exception is raised, unless a formal parameter using the syntax **identifier is present; in this case, that formal parameter receives a dictionary containing the excess keyword arguments (using the keywords as keys and the argument values as corresponding values), or a (new) empty dictionary if there were no excess keyword arguments.

If the syntax *expression appears in the function call, expression must evaluate to an iterable . Elements from these iterables are treated as if they were additional positional arguments. For the call f(x1, x2, *y, x3, x4) , if y evaluates to a sequence y1, …, yM, this is equivalent to a call with M+4 positional arguments x1, x2, y1, …, yM, x3, x4.

A consequence of this is that although the *expression syntax may appear after explicit keyword arguments, it is processed before the keyword arguments (and any **expression arguments – see below). So:

It is unusual for both keyword arguments and the *expression syntax to be used in the same call, so in practice this confusion does not often arise.

If the syntax **expression appears in the function call, expression must evaluate to a mapping , the contents of which are treated as additional keyword arguments. If a parameter matching a key has already been given a value (by an explicit keyword argument, or from another unpacking), a TypeError exception is raised.

When **expression is used, each key in this mapping must be a string. Each value from the mapping is assigned to the first formal parameter eligible for keyword assignment whose name is equal to the key. A key need not be a Python identifier (e.g. "max-temp °F" is acceptable, although it will not match any formal parameter that could be declared). If there is no match to a formal parameter the key-value pair is collected by the ** parameter, if there is one, or if there is not, a TypeError exception is raised.

Formal parameters using the syntax *identifier or **identifier cannot be used as positional argument slots or as keyword argument names.

Changed in version 3.5: Function calls accept any number of * and ** unpackings, positional arguments may follow iterable unpackings ( * ), and keyword arguments may follow dictionary unpackings ( ** ). Originally proposed by PEP 448.

A call always returns some value, possibly None , unless it raises an exception. How this value is computed depends on the type of the callable object.

a user-defined function:

The code block for the function is executed, passing it the argument list. The first thing the code block will do is bind the formal parameters to the arguments; this is described in section Function definitions . When the code block executes a return statement, this specifies the return value of the function call.

a built-in function or method:

The result is up to the interpreter; see Built-in Functions for the descriptions of built-in functions and methods.

A new instance of that class is returned.

a class instance method:

The corresponding user-defined function is called, with an argument list that is one longer than the argument list of the call: the instance becomes the first argument.

a class instance:

The class must define a __call__() method; the effect is then the same as if that method was called.

6.4. Await expression¶

Suspend the execution of coroutine on an awaitable object. Can only be used inside a coroutine function .

New in version 3.5.

6.5. The power operator¶

The power operator binds more tightly than unary operators on its left; it binds less tightly than unary operators on its right. The syntax is:

Thus, in an unparenthesized sequence of power and unary operators, the operators are evaluated from right to left (this does not constrain the evaluation order for the operands): -1**2 results in -1 .

The power operator has the same semantics as the built-in pow() function, when called with two arguments: it yields its left argument raised to the power of its right argument. The numeric arguments are first converted to a common type, and the result is of that type.

For int operands, the result has the same type as the operands unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered. For example, 10**2 returns 100 , but 10**-2 returns 0.01 .

Raising 0.0 to a negative power results in a ZeroDivisionError . Raising a negative number to a fractional power results in a complex number. (In earlier versions it raised a ValueError .)

This operation can be customized using the special __pow__() method.

6.6. Unary arithmetic and bitwise operations¶

All unary arithmetic and bitwise operations have the same priority:

The unary — (minus) operator yields the negation of its numeric argument; the operation can be overridden with the __neg__() special method.

The unary + (plus) operator yields its numeric argument unchanged; the operation can be overridden with the __pos__() special method.

(invert) operator yields the bitwise inversion of its integer argument. The bitwise inversion of x is defined as -(x+1) . It only applies to integral numbers or to custom objects that override the __invert__() special method.

In all three cases, if the argument does not have the proper type, a TypeError exception is raised.

6.7. Binary arithmetic operations¶

The binary arithmetic operations have the conventional priority levels. Note that some of these operations also apply to certain non-numeric types. Apart from the power operator, there are only two levels, one for multiplicative operators and one for additive operators:

The * (multiplication) operator yields the product of its arguments. The arguments must either both be numbers, or one argument must be an integer and the other must be a sequence. In the former case, the numbers are converted to a common type and then multiplied together. In the latter case, sequence repetition is performed; a negative repetition factor yields an empty sequence.

This operation can be customized using the special __mul__() and __rmul__() methods.

The @ (at) operator is intended to be used for matrix multiplication. No builtin Python types implement this operator.

New in version 3.5.

The / (division) and // (floor division) operators yield the quotient of their arguments. The numeric arguments are first converted to a common type. Division of integers yields a float, while floor division of integers results in an integer; the result is that of mathematical division with the ‘floor’ function applied to the result. Division by zero raises the ZeroDivisionError exception.

This operation can be customized using the special __truediv__() and __floordiv__() methods.

The % (modulo) operator yields the remainder from the division of the first argument by the second. The numeric arguments are first converted to a common type. A zero right argument raises the ZeroDivisionError exception. The arguments may be floating point numbers, e.g., 3.14%0.7 equals 0.34 (since 3.14 equals 4*0.7 + 0.34 .) The modulo operator always yields a result with the same sign as its second operand (or zero); the absolute value of the result is strictly smaller than the absolute value of the second operand 1.

The floor division and modulo operators are connected by the following identity: x == (x//y)*y + (x%y) . Floor division and modulo are also connected with the built-in function divmod() : divmod(x, y) == (x//y, x%y) . 2.

In addition to performing the modulo operation on numbers, the % operator is also overloaded by string objects to perform old-style string formatting (also known as interpolation). The syntax for string formatting is described in the Python Library Reference, section printf-style String Formatting .

The modulo operation can be customized using the special __mod__() method.

The floor division operator, the modulo operator, and the divmod() function are not defined for complex numbers. Instead, convert to a floating point number using the abs() function if appropriate.

The + (addition) operator yields the sum of its arguments. The arguments must either both be numbers or both be sequences of the same type. In the former case, the numbers are converted to a common type and then added together. In the latter case, the sequences are concatenated.

This operation can be customized using the special __add__() and __radd__() methods.

The — (subtraction) operator yields the difference of its arguments. The numeric arguments are first converted to a common type.

This operation can be customized using the special __sub__() method.

6.8. Shifting operations¶

The shifting operations have lower priority than the arithmetic operations:

These operators accept integers as arguments. They shift the first argument to the left or right by the number of bits given by the second argument.

This operation can be customized using the special __lshift__() and __rshift__() methods.

A right shift by n bits is defined as floor division by pow(2,n) . A left shift by n bits is defined as multiplication with pow(2,n) .

6.9. Binary bitwise operations¶

Each of the three bitwise operations has a different priority level:

The & operator yields the bitwise AND of its arguments, which must be integers or one of them must be a custom object overriding __and__() or __rand__() special methods.

The ^ operator yields the bitwise XOR (exclusive OR) of its arguments, which must be integers or one of them must be a custom object overriding __xor__() or __rxor__() special methods.

The | operator yields the bitwise (inclusive) OR of its arguments, which must be integers or one of them must be a custom object overriding __or__() or __ror__() special methods.

6.10. Comparisons¶

Unlike C, all comparison operations in Python have the same priority, which is lower than that of any arithmetic, shifting or bitwise operation. Also unlike C, expressions like a < b < c have the interpretation that is conventional in mathematics:

Comparisons yield boolean values: True or False . Custom rich comparison methods may return non-boolean values. In this case Python will call bool() on such value in boolean contexts.

Comparisons can be chained arbitrarily, e.g., x < y <= z is equivalent to x < y and y <= z , except that y is evaluated only once (but in both cases z is not evaluated at all when x < y is found to be false).

Formally, if a, b, c, …, y, z are expressions and op1, op2, …, opN are comparison operators, then a op1 b op2 c . y opN z is equivalent to a op1 b and b op2 c and . y opN z , except that each expression is evaluated at most once.

Note that a op1 b op2 c doesn’t imply any kind of comparison between a and c, so that, e.g., x < y > z is perfectly legal (though perhaps not pretty).

6.10.1. Value comparisons¶

The operators < , > , == , >= , <= , and != compare the values of two objects. The objects do not need to have the same type.

Chapter Objects, values and types states that objects have a value (in addition to type and identity). The value of an object is a rather abstract notion in Python: For example, there is no canonical access method for an object’s value. Also, there is no requirement that the value of an object should be constructed in a particular way, e.g. comprised of all its data attributes. Comparison operators implement a particular notion of what the value of an object is. One can think of them as defining the value of an object indirectly, by means of their comparison implementation.

Because all types are (direct or indirect) subtypes of object , they inherit the default comparison behavior from object . Types can customize their comparison behavior by implementing rich comparison methods like __lt__() , described in Basic customization .

The default behavior for equality comparison ( == and != ) is based on the identity of the objects. Hence, equality comparison of instances with the same identity results in equality, and equality comparison of instances with different identities results in inequality. A motivation for this default behavior is the desire that all objects should be reflexive (i.e. x is y implies x == y ).

A default order comparison ( < , > , <= , and >= ) is not provided; an attempt raises TypeError . A motivation for this default behavior is the lack of a similar invariant as for equality.

The behavior of the default equality comparison, that instances with different identities are always unequal, may be in contrast to what types will need that have a sensible definition of object value and value-based equality. Such types will need to customize their comparison behavior, and in fact, a number of built-in types have done that.

The following list describes the comparison behavior of the most important built-in types.

Numbers of built-in numeric types ( Numeric Types — int, float, complex ) and of the standard library types fractions.Fraction and decimal.Decimal can be compared within and across their types, with the restriction that complex numbers do not support order comparison. Within the limits of the types involved, they compare mathematically (algorithmically) correct without loss of precision.

The not-a-number values float(‘NaN’) and decimal.Decimal(‘NaN’) are special. Any ordered comparison of a number to a not-a-number value is false. A counter-intuitive implication is that not-a-number values are not equal to themselves. For example, if x = float(‘NaN’) , 3 < x , x < 3 and x == x are all false, while x != x is true. This behavior is compliant with IEEE 754.

None and NotImplemented are singletons. PEP 8 advises that comparisons for singletons should always be done with is or is not , never the equality operators.

Binary sequences (instances of bytes or bytearray ) can be compared within and across their types. They compare lexicographically using the numeric values of their elements.

Strings (instances of str ) compare lexicographically using the numerical Unicode code points (the result of the built-in function ord() ) of their characters. 3

Strings and binary sequences cannot be directly compared.

Sequences (instances of tuple , list , or range ) can be compared only within each of their types, with the restriction that ranges do not support order comparison. Equality comparison across these types results in inequality, and ordering comparison across these types raises TypeError .

Sequences compare lexicographically using comparison of corresponding elements. The built-in containers typically assume identical objects are equal to themselves. That lets them bypass equality tests for identical objects to improve performance and to maintain their internal invariants.

Lexicographical comparison between built-in collections works as follows:

For two collections to compare equal, they must be of the same type, have the same length, and each pair of corresponding elements must compare equal (for example, [1,2] == (1,2) is false because the type is not the same).

Collections that support order comparison are ordered the same as their first unequal elements (for example, [1,2,x] <= [1,2,y] has the same value as x <= y ). If a corresponding element does not exist, the shorter collection is ordered first (for example, [1,2] < [1,2,3] is true).

Mappings (instances of dict ) compare equal if and only if they have equal (key, value) pairs. Equality comparison of the keys and values enforces reflexivity.

Order comparisons ( < , > , <= , and >= ) raise TypeError .

Sets (instances of set or frozenset ) can be compared within and across their types.

They define order comparison operators to mean subset and superset tests. Those relations do not define total orderings (for example, the two sets <1,2>and <2,3>are not equal, nor subsets of one another, nor supersets of one another). Accordingly, sets are not appropriate arguments for functions which depend on total ordering (for example, min() , max() , and sorted() produce undefined results given a list of sets as inputs).

Comparison of sets enforces reflexivity of its elements.

Most other built-in types have no comparison methods implemented, so they inherit the default comparison behavior.

User-defined classes that customize their comparison behavior should follow some consistency rules, if possible:

Equality comparison should be reflexive. In other words, identical objects should compare equal:

x is y implies x == y

Comparison should be symmetric. In other words, the following expressions should have the same result:

x == y and y == x

x != y and y != x

x < y and y > x

x <= y and y >= x

Comparison should be transitive. The following (non-exhaustive) examples illustrate that:

x > y and y > z implies x > z

x < y and y <= z implies x < z

Inverse comparison should result in the boolean negation. In other words, the following expressions should have the same result:

x == y and not x != y

x < y and not x >= y (for total ordering)

x > y and not x <= y (for total ordering)

The last two expressions apply to totally ordered collections (e.g. to sequences, but not to sets or mappings). See also the total_ordering() decorator.

The hash() result should be consistent with equality. Objects that are equal should either have the same hash value, or be marked as unhashable.

Python does not enforce these consistency rules. In fact, the not-a-number values are an example for not following these rules.

6.10.2. Membership test operations¶

The operators in and not in test for membership. x in s evaluates to True if x is a member of s, and False otherwise. x not in s returns the negation of x in s . All built-in sequences and set types support this as well as dictionary, for which in tests whether the dictionary has a given key. For container types such as list, tuple, set, frozenset, dict, or collections.deque, the expression x in y is equivalent to any(x is e or x == e for e in y) .

For the string and bytes types, x in y is True if and only if x is a substring of y. An equivalent test is y.find(x) != -1 . Empty strings are always considered to be a substring of any other string, so "" in "abc" will return True .

For user-defined classes which define the __contains__() method, x in y returns True if y.__contains__(x) returns a true value, and False otherwise.

For user-defined classes which do not define __contains__() but do define __iter__() , x in y is True if some value z , for which the expression x is z or x == z is true, is produced while iterating over y . If an exception is raised during the iteration, it is as if in raised that exception.

Lastly, the old-style iteration protocol is tried: if a class defines __getitem__() , x in y is True if and only if there is a non-negative integer index i such that x is y[i] or x == y[i] , and no lower integer index raises the IndexError exception. (If any other exception is raised, it is as if in raised that exception).

The operator not in is defined to have the inverse truth value of in .

6.10.3. Identity comparisons¶

The operators is and is not test for an object’s identity: x is y is true if and only if x and y are the same object. An Object’s identity is determined using the id() function. x is not y yields the inverse truth value. 4

6.11. Boolean operations¶

In the context of Boolean operations, and also when expressions are used by control flow statements, the following values are interpreted as false: False , None , numeric zero of all types, and empty strings and containers (including strings, tuples, lists, dictionaries, sets and frozensets). All other values are interpreted as true. User-defined objects can customize their truth value by providing a __bool__() method.

The operator not yields True if its argument is false, False otherwise.

The expression x and y first evaluates x; if x is false, its value is returned; otherwise, y is evaluated and the resulting value is returned.

The expression x or y first evaluates x; if x is true, its value is returned; otherwise, y is evaluated and the resulting value is returned.

Note that neither and nor or restrict the value and type they return to False and True , but rather return the last evaluated argument. This is sometimes useful, e.g., if s is a string that should be replaced by a default value if it is empty, the expression s or ‘foo’ yields the desired value. Because not has to create a new value, it returns a boolean value regardless of the type of its argument (for example, not ‘foo’ produces False rather than » .)

6.12. Assignment expressions¶

An assignment expression (sometimes also called a “named expression” or “walrus”) assigns an expression to an identifier , while also returning the value of the expression .

One common use case is when handling matched regular expressions:

Or, when processing a file stream in chunks:

Assignment expressions must be surrounded by parentheses when used as sub-expressions in slicing, conditional, lambda, keyword-argument, and comprehension-if expressions and in assert and with statements. In all other places where they can be used, parentheses are not required, including in if and while statements.

New in version 3.8: See PEP 572 for more details about assignment expressions.

6.13. Conditional expressions¶

Conditional expressions (sometimes called a “ternary operator”) have the lowest priority of all Python operations.

The expression x if C else y first evaluates the condition, C rather than x. If C is true, x is evaluated and its value is returned; otherwise, y is evaluated and its value is returned.

See PEP 308 for more details about conditional expressions.

6.14. Lambdas¶

Lambda expressions (sometimes called lambda forms) are used to create anonymous functions. The expression lambda parameters: expression yields a function object. The unnamed object behaves like a function object defined with:

See section Function definitions for the syntax of parameter lists. Note that functions created with lambda expressions cannot contain statements or annotations.

6.15. Expression lists¶

Except when part of a list or set display, an expression list containing at least one comma yields a tuple. The length of the tuple is the number of expressions in the list. The expressions are evaluated from left to right.

An asterisk * denotes iterable unpacking. Its operand must be an iterable . The iterable is expanded into a sequence of items, which are included in the new tuple, list, or set, at the site of the unpacking.

New in version 3.5: Iterable unpacking in expression lists, originally proposed by PEP 448.

The trailing comma is required only to create a single tuple (a.k.a. a singleton); it is optional in all other cases. A single expression without a trailing comma doesn’t create a tuple, but rather yields the value of that expression. (To create an empty tuple, use an empty pair of parentheses: () .)

6.16. Evaluation order¶

Python evaluates expressions from left to right. Notice that while evaluating an assignment, the right-hand side is evaluated before the left-hand side.

In the following lines, expressions will be evaluated in the arithmetic order of their suffixes:

6.17. Operator precedence¶

The following table summarizes the operator precedence in Python, from highest precedence (most binding) to lowest precedence (least binding). Operators in the same box have the same precedence. Unless the syntax is explicitly given, operators are binary. Operators in the same box group left to right (except for exponentiation and conditional expressions, which group from right to left).

Note that comparisons, membership tests, and identity tests, all have the same precedence and have a left-to-right chaining feature as described in the Comparisons section.

Binding or parenthesized expression, list display, dictionary display, set display