Как сделать цикл в цикле python

python combine 'while loop' with 'for loop' to iterate through some data

I am trying to combine a while loop with a for loop to iterate through some list but I am getting infinite loops. My code:

This code results in an endless loop. I want an output when the condition lo+ l[i] is greater than 40; it should stop looping and print the final lo output as result. I tried every method of indentation of the print line, but could not get what I wanted. Thanks in advance.

4 Answers 4

You get infinite loop because you wrote the infinite loop. You’ve probably thought that the break statement will somehow «magically» know that you don’t want to end just the for loop, but also the while loop. But break will always break only one loop — the innermost one. So that means that your code actually does this:

If you want to end both loops, you have to do it explicitly — for example, you can use a boolean variable:

You don’t need the external infinite loop and you don’t have to manage the index yourself (see enumerate in the documentation).

I changed the condition if value not < 3: to if value >= 3: for improved readability.

Your break cancels the inner loop

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Python циклы

Важной частью структурного программированию помимо операторов условий считаются циклы. Они помогают автоматизировать последовательные задачи в программе, а именно: повторить выполнение определенных участков кода. Такая необходимость возникает достаточно часто, когда нужно сделать что-нибудь много раз, тем самым, циклы упрощают эту задачу.

Понятие циклов

В нашей жизни мы достаточно часто сталкиваемся с цикличными задачами, к ним можно отнести любые списки, будь-то: продукты, задачи на день, запланированные экзамены. И действительно, когда мы приходим в магазин, то покупаем все, что есть в нашем списке, не останавливаясь, пока не сделаем этого.

В программировании циклы позволяют повторять некоторое действие в зависимости от соблюдения заданного условия. Таким образом организуется исполнение многократной последовательности инструкций.

Есть еще несколько важных понятий, которые нужно знать:

• Телом цикла называется та последовательность кода, которую нужно выполнить несколько раз.
• Единоразовое выполнение – это итерация.

Python позволяет также создавать вложенные циклы. Так, сначала программа запустит внешний и в первой его итерации перейдет во вложенный. Затем она снова вернется к началу внешнего и снова вызовет внутренний. Это будет происходить до тех пор, пока последовательность не завершится или не прервется. Такие циклы полезны в том случае, если нужно перебрать определенное количество элементов в списке.

В Python есть только два цикла: for и while. Первый используется по большей мере в тех случаях, когда нужно написать многоуровневую программу с множеством условий.

Цикл «for»

Цикл for в Python 3 выполняет написанный код повторно согласно введенной переменной или счетчику. Он используется только тогда, когда необходимо совершить перебор элементов заранее известное число раз. Что это значит? У нас имеется список, сначала из него берется первый элемент, потом – второй и так далее, но с каждым из них совершается действие, которое указано в теле for. Примерно это выглядит так:

For может содержать данные разных типов: цифры, слова и пр. Рассмотрим пример:

На экране после запуска появится такая запись:

Для упрощения часто используется функция range(), или диапазон. В циклах она указывает на необходимое количество повторов последовательности, уточняя, какие именно элементы из списка for, нам необходимы в данный момент. В скобках может быть указано от одного до трех чисел:

• одно указывает на то, что нужно проверить все числа от 0 и до него;
• два говорят о том, что перебрать нужно все числа, находящиеся между ними;
• три числа сгенерируют список от первого до второго, но с шагом, равным третьей цифре.

Рассмотрим пример. Теоретически вы можете записать так:

Но это слишком затратно, особенно если чисел слишком много, поэтому лучше сделать так, используя указанный выше range():

И в первом, и во втором случае на экране появится такая последовательность:

Цикл «while»

While с английского языка переводится, как «до тех пор, как». Это достаточно универсальный цикл, он немного похож на условие if, но его код выполняется не один раз. Его условие записывается до тела цикла. После того, как оно выполнено первый раз, программа возвращается к заголовку и снова повторяет все действия. Этот процесс заканчивается тогда, когда условие цикла больше не может быть соблюдено, иными словами перестает быть истинным. Тем, что заранее неизвестно количество проверок, он отличается от предыдущего цикла for. Кстати, еще его называют циклом с предусловием.

Запись цикла while в Python выглядит так:

Приведем пример использования этого цикла:

Здесь переменной присваивается значение 0, после чего начинается цикл, в котором проверяется условие, чтобы число было меньше 6. В теле цикла также содержится две инструкции: первая выводит само число на экран, а вторая увеличивает его значение на два. Цикл, таким образом, выполняется, пока условие продолжает быть истинным. На экране перед вами появится следующая последовательность чисел:

Обычно в ней есть смысл, только если указана инструкция break, но программа работает и без последней. Посмотрим на код:

Переменная – 3, задается условие, что пока она меньше 7, нужно выводить ее и выражение «меньше 7», затем прибавлять к ней 1. В тех случаях, когда она уже становится равной 7, то в ход пойдет условие, указанное в else, и на экране появится, что переменная не меньше 7. В результате выполнения этого кода мы увидим:

Инструкции break и continue

Оператор break используется для выхода из цикла Python — прерывает его досрочно. Так, если во время выполнения кода, программа натыкается на break, то она сразу прекращает цикл и выходит из него, минуя else. Это необходимо, например, если при выполнении инструкций была обнаружена ошибка, и дальнейшая работа бессмысленна. Посмотрим на пример его применения:

Его реализация будет выглядеть так:

После этого выполнение программы будет прервано.

Другая инструкция, которая может менять цикл, — это continue. Если она указана внутри кода, то все оставшиеся инструкции до конца цикла пропускаются и начинается следующая итерация.

В целом, не следует слишком увлекаться использованием этих инструкций.

Цикл с постусловием

Во многих других языках программирования существуют циклы с постусловием, обычно они описываются так:

• repeat [тут исполняемый код] until [условия продолжения];
• do [тут исполняемый код] while [условия продолжения];

Цикл while может стать циклом с постусловием, тогда его примерный вид будет таким:

Так получается, что сначала дается тело цикла, а потом задается условие.

Бесконечные циклы

Бесконечными циклами в программировании называются те, в которых условие выхода их них не выполняется.

Цикл while становится бесконечным, когда его условие не может быть ложным. Например, при помощи него можно реализовать программу «Часы», которая бесконечно отображает время.

Примером реализации в Python бесконечного цикла будет такой код:

Очевидно, что заданная переменная всегда останется цифрой 3, поскольку никак не задано ее увеличение, поэтому на экране просто будет появляться слово «Привет».

Зачастую цикл не должен быть бесконечным, поскольку это причина неустойчивой работы программы. Для того чтобы выйти из него, нужно нажать комбинацию клавиш: CTRL+C. При этом программы, из которых нет выхода, также существуют. К ним относятся: операционные системы, прошивки микроконтроллеров.

Вложенные циклы

Рассмотрим вложенные циклы в Python. Для реализации можно использовать как вложенные for, так и while.

Про них мы уже писали выше. Здесь же хочется привести пару примеров использования. Они очень часто используются при обработке двухмерных списков.

Вот пример создания двумерного списка и вывода его на экран с помощью print.

Нельзя с помощью одного break в Python выйти сразу из двух циклов. В этом случае нужно во внешнем цикле создать условие следующим образом.

Здесь приведен пример использования в Python цикла в цикле. Оба бесконечны. Все будет выполняться бесконечно, до тех пор, пока функция doSomething не вернет True. После этого поочередно сработают break во внешнем и во внутреннем циклах.

Заключение

Как правило, команды в коде выполняются последовательно: одна за другой. Именно поэтому, когда есть необходимость выполнить тело кода несколько раз, используются циклы. Циклы для Python очень значимы, именно они делают повторение простым, логичным и очень понятным.

8. Compound statements¶

Compound statements contain (groups of) other statements; they affect or control the execution of those other statements in some way. In general, compound statements span multiple lines, although in simple incarnations a whole compound statement may be contained in one line.

The if , while and for statements implement traditional control flow constructs. try specifies exception handlers and/or cleanup code for a group of statements, while the with statement allows the execution of initialization and finalization code around a block of code. Function and class definitions are also syntactically compound statements.

A compound statement consists of one or more ‘clauses.’ A clause consists of a header and a ‘suite.’ The clause headers of a particular compound statement are all at the same indentation level. Each clause header begins with a uniquely identifying keyword and ends with a colon. A suite is a group of statements controlled by a clause. A suite can be one or more semicolon-separated simple statements on the same line as the header, following the header’s colon, or it can be one or more indented statements on subsequent lines. Only the latter form of a suite can contain nested compound statements; the following is illegal, mostly because it wouldn’t be clear to which if clause a following else clause would belong:

Also note that the semicolon binds tighter than the colon in this context, so that in the following example, either all or none of the print() calls are executed:

Note that statements always end in a NEWLINE possibly followed by a DEDENT . Also note that optional continuation clauses always begin with a keyword that cannot start a statement, thus there are no ambiguities (the ‘dangling else ’ problem is solved in Python by requiring nested if statements to be indented).

The formatting of the grammar rules in the following sections places each clause on a separate line for clarity.

8.1. The if statement¶

The if statement is used for conditional execution:

It selects exactly one of the suites by evaluating the expressions one by one until one is found to be true (see section Boolean operations for the definition of true and false); then that suite is executed (and no other part of the if statement is executed or evaluated). If all expressions are false, the suite of the else clause, if present, is executed.

8.2. The while statement¶

The while statement is used for repeated execution as long as an expression is true:

This repeatedly tests the expression and, if it is true, executes the first suite; if the expression is false (which may be the first time it is tested) the suite of the else clause, if present, is executed and the loop terminates.

A break statement executed in the first suite terminates the loop without executing the else clause’s suite. A continue statement executed in the first suite skips the rest of the suite and goes back to testing the expression.

8.3. The for statement¶

The for statement is used to iterate over the elements of a sequence (such as a string, tuple or list) or other iterable object:

The starred_list expression is evaluated once; it should yield an iterable object. An iterator is created for that iterable. The first item provided by the iterator is then assigned to the target list using the standard rules for assignments (see Assignment statements ), and the suite is executed. This repeats for each item provided by the iterator. When the iterator is exhausted, the suite in the else clause, if present, is executed, and the loop terminates.

A break statement executed in the first suite terminates the loop without executing the else clause’s suite. A continue statement executed in the first suite skips the rest of the suite and continues with the next item, or with the else clause if there is no next item.

The for-loop makes assignments to the variables in the target list. This overwrites all previous assignments to those variables including those made in the suite of the for-loop:

Names in the target list are not deleted when the loop is finished, but if the sequence is empty, they will not have been assigned to at all by the loop. Hint: the built-in type range() represents immutable arithmetic sequences of integers. For instance, iterating range(3) successively yields 0, 1, and then 2.

Changed in version 3.11: Starred elements are now allowed in the expression list.

8.4. The try statement¶

The try statement specifies exception handlers and/or cleanup code for a group of statements:

Additional information on exceptions can be found in section Exceptions , and information on using the raise statement to generate exceptions may be found in section The raise statement .

8.4.1. except clause¶

The except clause(s) specify one or more exception handlers. When no exception occurs in the try clause, no exception handler is executed. When an exception occurs in the try suite, a search for an exception handler is started. This search inspects the except clauses in turn until one is found that matches the exception. An expression-less except clause, if present, must be last; it matches any exception. For an except clause with an expression, that expression is evaluated, and the clause matches the exception if the resulting object is “compatible” with the exception. An object is compatible with an exception if the object is the class or a non-virtual base class of the exception object, or a tuple containing an item that is the class or a non-virtual base class of the exception object.

If no except clause matches the exception, the search for an exception handler continues in the surrounding code and on the invocation stack. 1

If the evaluation of an expression in the header of an except clause raises an exception, the original search for a handler is canceled and a search starts for the new exception in the surrounding code and on the call stack (it is treated as if the entire try statement raised the exception).

When a matching except clause is found, the exception is assigned to the target specified after the as keyword in that except clause, if present, and the except clause’s suite is executed. All except clauses must have an executable block. When the end of this block is reached, execution continues normally after the entire try statement. (This means that if two nested handlers exist for the same exception, and the exception occurs in the try clause of the inner handler, the outer handler will not handle the exception.)

When an exception has been assigned using as target , it is cleared at the end of the except clause. This is as if

was translated to

This means the exception must be assigned to a different name to be able to refer to it after the except clause. Exceptions are cleared because with the traceback attached to them, they form a reference cycle with the stack frame, keeping all locals in that frame alive until the next garbage collection occurs.

Before an except clause’s suite is executed, the exception is stored in the sys module, where it can be accessed from within the body of the except clause by calling sys.exception() . When leaving an exception handler, the exception stored in the sys module is reset to its previous value:

8.4.2. except* clause¶

The except* clause(s) are used for handling ExceptionGroup s. The exception type for matching is interpreted as in the case of except , but in the case of exception groups we can have partial matches when the type matches some of the exceptions in the group. This means that multiple except* clauses can execute, each handling part of the exception group. Each clause executes at most once and handles an exception group of all matching exceptions. Each exception in the group is handled by at most one except* clause, the first that matches it.

Any remaining exceptions that were not handled by any except* clause are re-raised at the end, combined into an exception group along with all exceptions that were raised from within except* clauses.

If the raised exception is not an exception group and its type matches one of the except* clauses, it is caught and wrapped by an exception group with an empty message string.

An except* clause must have a matching type, and this type cannot be a subclass of BaseExceptionGroup . It is not possible to mix except and except* in the same try . break , continue and return cannot appear in an except* clause.

8.4.3. else clause¶

The optional else clause is executed if the control flow leaves the try suite, no exception was raised, and no return , continue , or break statement was executed. Exceptions in the else clause are not handled by the preceding except clauses.

8.4.4. finally clause¶

If finally is present, it specifies a ‘cleanup’ handler. The try clause is executed, including any except and else clauses. If an exception occurs in any of the clauses and is not handled, the exception is temporarily saved. The finally clause is executed. If there is a saved exception it is re-raised at the end of the finally clause. If the finally clause raises another exception, the saved exception is set as the context of the new exception. If the finally clause executes a return , break or continue statement, the saved exception is discarded:

The exception information is not available to the program during execution of the finally clause.

When a return , break or continue statement is executed in the try suite of a try … finally statement, the finally clause is also executed ‘on the way out.’

The return value of a function is determined by the last return statement executed. Since the finally clause always executes, a return statement executed in the finally clause will always be the last one executed:

Changed in version 3.8: Prior to Python 3.8, a continue statement was illegal in the finally clause due to a problem with the implementation.

8.5. The with statement¶

The with statement is used to wrap the execution of a block with methods defined by a context manager (see section With Statement Context Managers ). This allows common try … except … finally usage patterns to be encapsulated for convenient reuse.

The execution of the with statement with one “item” proceeds as follows:

The context expression (the expression given in the with_item ) is evaluated to obtain a context manager.

The context manager’s __enter__() is loaded for later use.

The context manager’s __exit__() is loaded for later use.

The context manager’s __enter__() method is invoked.

If a target was included in the with statement, the return value from __enter__() is assigned to it.

The with statement guarantees that if the __enter__() method returns without an error, then __exit__() will always be called. Thus, if an error occurs during the assignment to the target list, it will be treated the same as an error occurring within the suite would be. See step 7 below.

The suite is executed.

The context manager’s __exit__() method is invoked. If an exception caused the suite to be exited, its type, value, and traceback are passed as arguments to __exit__() . Otherwise, three None arguments are supplied.

If the suite was exited due to an exception, and the return value from the __exit__() method was false, the exception is reraised. If the return value was true, the exception is suppressed, and execution continues with the statement following the with statement.

If the suite was exited for any reason other than an exception, the return value from __exit__() is ignored, and execution proceeds at the normal location for the kind of exit that was taken.

The following code:

is semantically equivalent to:

With more than one item, the context managers are processed as if multiple with statements were nested:

is semantically equivalent to:

You can also write multi-item context managers in multiple lines if the items are surrounded by parentheses. For example:

Changed in version 3.1: Support for multiple context expressions.

Changed in version 3.10: Support for using grouping parentheses to break the statement in multiple lines.

The specification, background, and examples for the Python with statement.

8.6. The match statement¶

New in version 3.10.

The match statement is used for pattern matching. Syntax:

This section uses single quotes to denote soft keywords .

Pattern matching takes a pattern as input (following case ) and a subject value (following match ). The pattern (which may contain subpatterns) is matched against the subject value. The outcomes are:

A match success or failure (also termed a pattern success or failure).

Possible binding of matched values to a name. The prerequisites for this are further discussed below.

The match and case keywords are soft keywords .

PEP 634 – Structural Pattern Matching: Specification

PEP 636 – Structural Pattern Matching: Tutorial

8.6.1. Overview¶

Here’s an overview of the logical flow of a match statement:

The subject expression subject_expr is evaluated and a resulting subject value obtained. If the subject expression contains a comma, a tuple is constructed using the standard rules .

Each pattern in a case_block is attempted to match with the subject value. The specific rules for success or failure are described below. The match attempt can also bind some or all of the standalone names within the pattern. The precise pattern binding rules vary per pattern type and are specified below. Name bindings made during a successful pattern match outlive the executed block and can be used after the match statement.

Note

During failed pattern matches, some subpatterns may succeed. Do not rely on bindings being made for a failed match. Conversely, do not rely on variables remaining unchanged after a failed match. The exact behavior is dependent on implementation and may vary. This is an intentional decision made to allow different implementations to add optimizations.

If the pattern succeeds, the corresponding guard (if present) is evaluated. In this case all name bindings are guaranteed to have happened.

If the guard evaluates as true or is missing, the block inside case_block is executed.

Otherwise, the next case_block is attempted as described above.

If there are no further case blocks, the match statement is completed.

Users should generally never rely on a pattern being evaluated. Depending on implementation, the interpreter may cache values or use other optimizations which skip repeated evaluations.

A sample match statement:

In this case, if flag is a guard. Read more about that in the next section.

8.6.2. Guards¶

A guard (which is part of the case ) must succeed for code inside the case block to execute. It takes the form: if followed by an expression.

The logical flow of a case block with a guard follows:

Check that the pattern in the case block succeeded. If the pattern failed, the guard is not evaluated and the next case block is checked.

If the pattern succeeded, evaluate the guard .

If the guard condition evaluates as true, the case block is selected.

If the guard condition evaluates as false, the case block is not selected.

If the guard raises an exception during evaluation, the exception bubbles up.

Guards are allowed to have side effects as they are expressions. Guard evaluation must proceed from the first to the last case block, one at a time, skipping case blocks whose pattern(s) don’t all succeed. (I.e., guard evaluation must happen in order.) Guard evaluation must stop once a case block is selected.

8.6.3. Irrefutable Case Blocks¶

An irrefutable case block is a match-all case block. A match statement may have at most one irrefutable case block, and it must be last.

A case block is considered irrefutable if it has no guard and its pattern is irrefutable. A pattern is considered irrefutable if we can prove from its syntax alone that it will always succeed. Only the following patterns are irrefutable:

AS Patterns whose left-hand side is irrefutable

OR Patterns containing at least one irrefutable pattern

parenthesized irrefutable patterns

8.6.4. Patterns¶

This section uses grammar notations beyond standard EBNF:

the notation SEP.RULE+ is shorthand for RULE (SEP RULE)*

the notation !RULE is shorthand for a negative lookahead assertion

The top-level syntax for patterns is:

The descriptions below will include a description “in simple terms” of what a pattern does for illustration purposes (credits to Raymond Hettinger for a document that inspired most of the descriptions). Note that these descriptions are purely for illustration purposes and may not reflect the underlying implementation. Furthermore, they do not cover all valid forms.

8.6.4.1. OR Patterns¶

An OR pattern is two or more patterns separated by vertical bars | . Syntax:

Only the final subpattern may be irrefutable , and each subpattern must bind the same set of names to avoid ambiguity.

An OR pattern matches each of its subpatterns in turn to the subject value, until one succeeds. The OR pattern is then considered successful. Otherwise, if none of the subpatterns succeed, the OR pattern fails.

In simple terms, P1 | P2 | . will try to match P1 , if it fails it will try to match P2 , succeeding immediately if any succeeds, failing otherwise.

8.6.4.2. AS Patterns¶

An AS pattern matches an OR pattern on the left of the as keyword against a subject. Syntax:

If the OR pattern fails, the AS pattern fails. Otherwise, the AS pattern binds the subject to the name on the right of the as keyword and succeeds. capture_pattern cannot be a a _ .

In simple terms P as NAME will match with P , and on success it will set NAME = <subject> .

8.6.4.3. Literal Patterns¶

A literal pattern corresponds to most literals in Python. Syntax:

The rule strings and the token NUMBER are defined in the standard Python grammar . Triple-quoted strings are supported. Raw strings and byte strings are supported. Formatted string literals are not supported.

The forms signed_number ‘+’ NUMBER and signed_number ‘-‘ NUMBER are for expressing complex numbers ; they require a real number on the left and an imaginary number on the right. E.g. 3 + 4j .

In simple terms, LITERAL will succeed only if <subject> == LITERAL . For the singletons None , True and False , the is operator is used.

8.6.4.4. Capture Patterns¶

A capture pattern binds the subject value to a name. Syntax:

A single underscore _ is not a capture pattern (this is what !’_’ expresses). It is instead treated as a wildcard_pattern .

In a given pattern, a given name can only be bound once. E.g. case x, x: . is invalid while case [x] | x: . is allowed.

Capture patterns always succeed. The binding follows scoping rules established by the assignment expression operator in PEP 572; the name becomes a local variable in the closest containing function scope unless there’s an applicable global or nonlocal statement.

In simple terms NAME will always succeed and it will set NAME = <subject> .

8.6.4.5. Wildcard Patterns¶

A wildcard pattern always succeeds (matches anything) and binds no name. Syntax:

_ is a soft keyword within any pattern, but only within patterns. It is an identifier, as usual, even within match subject expressions, guard s, and case blocks.

In simple terms, _ will always succeed.

8.6.4.6. Value Patterns¶

A value pattern represents a named value in Python. Syntax:

The dotted name in the pattern is looked up using standard Python name resolution rules . The pattern succeeds if the value found compares equal to the subject value (using the == equality operator).

In simple terms NAME1.NAME2 will succeed only if <subject> == NAME1.NAME2

If the same value occurs multiple times in the same match statement, the interpreter may cache the first value found and reuse it rather than repeat the same lookup. This cache is strictly tied to a given execution of a given match statement.

8.6.4.7. Group Patterns¶

A group pattern allows users to add parentheses around patterns to emphasize the intended grouping. Otherwise, it has no additional syntax. Syntax:

In simple terms (P) has the same effect as P .

8.6.4.8. Sequence Patterns¶

A sequence pattern contains several subpatterns to be matched against sequence elements. The syntax is similar to the unpacking of a list or tuple.

There is no difference if parentheses or square brackets are used for sequence patterns (i.e. (. ) vs [. ] ).

A single pattern enclosed in parentheses without a trailing comma (e.g. (3 | 4) ) is a group pattern . While a single pattern enclosed in square brackets (e.g. [3 | 4] ) is still a sequence pattern.

At most one star subpattern may be in a sequence pattern. The star subpattern may occur in any position. If no star subpattern is present, the sequence pattern is a fixed-length sequence pattern; otherwise it is a variable-length sequence pattern.

The following is the logical flow for matching a sequence pattern against a subject value:

If the subject value is not a sequence 2, the sequence pattern fails.

If the subject value is an instance of str , bytes or bytearray the sequence pattern fails.

The subsequent steps depend on whether the sequence pattern is fixed or variable-length.

If the sequence pattern is fixed-length:

If the length of the subject sequence is not equal to the number of subpatterns, the sequence pattern fails

Subpatterns in the sequence pattern are matched to their corresponding items in the subject sequence from left to right. Matching stops as soon as a subpattern fails. If all subpatterns succeed in matching their corresponding item, the sequence pattern succeeds.

Otherwise, if the sequence pattern is variable-length:

If the length of the subject sequence is less than the number of non-star subpatterns, the sequence pattern fails.

The leading non-star subpatterns are matched to their corresponding items as for fixed-length sequences.

If the previous step succeeds, the star subpattern matches a list formed of the remaining subject items, excluding the remaining items corresponding to non-star subpatterns following the star subpattern.

Remaining non-star subpatterns are matched to their corresponding subject items, as for a fixed-length sequence.

The length of the subject sequence is obtained via len() (i.e. via the __len__() protocol). This length may be cached by the interpreter in a similar manner as value patterns .

In simple terms [P1, P2, P3, … , P<N>] matches only if all the following happens:

check <subject> is a sequence

P1 matches <subject>[0] (note that this match can also bind names)

P2 matches <subject>[1] (note that this match can also bind names)

… and so on for the corresponding pattern/element.

8.6.4.9. Mapping Patterns¶

A mapping pattern contains one or more key-value patterns. The syntax is similar to the construction of a dictionary. Syntax:

At most one double star pattern may be in a mapping pattern. The double star pattern must be the last subpattern in the mapping pattern.

Duplicate keys in mapping patterns are disallowed. Duplicate literal keys will raise a SyntaxError . Two keys that otherwise have the same value will raise a ValueError at runtime.

The following is the logical flow for matching a mapping pattern against a subject value:

If the subject value is not a mapping 3,the mapping pattern fails.

If every key given in the mapping pattern is present in the subject mapping, and the pattern for each key matches the corresponding item of the subject mapping, the mapping pattern succeeds.

If duplicate keys are detected in the mapping pattern, the pattern is considered invalid. A SyntaxError is raised for duplicate literal values; or a ValueError for named keys of the same value.

Key-value pairs are matched using the two-argument form of the mapping subject’s get() method. Matched key-value pairs must already be present in the mapping, and not created on-the-fly via __missing__() or __getitem__() .

In simple terms matches only if all the following happens:

check <subject> is a mapping

KEY1 in <subject>

P1 matches <subject>[KEY1]

… and so on for the corresponding KEY/pattern pair.

8.6.4.10. Class Patterns¶

A class pattern represents a class and its positional and keyword arguments (if any). Syntax:

The same keyword should not be repeated in class patterns.

The following is the logical flow for matching a class pattern against a subject value:

If name_or_attr is not an instance of the builtin type , raise TypeError .

If the subject value is not an instance of name_or_attr (tested via isinstance() ), the class pattern fails.

If no pattern arguments are present, the pattern succeeds. Otherwise, the subsequent steps depend on whether keyword or positional argument patterns are present.

For a number of built-in types (specified below), a single positional subpattern is accepted which will match the entire subject; for these types keyword patterns also work as for other types.

If only keyword patterns are present, they are processed as follows, one by one:

I. The keyword is looked up as an attribute on the subject.

• If this raises an exception other than AttributeError , the exception bubbles up.

• If this raises AttributeError , the class pattern has failed.

• Else, the subpattern associated with the keyword pattern is matched against the subject’s attribute value. If this fails, the class pattern fails; if this succeeds, the match proceeds to the next keyword.

II. If all keyword patterns succeed, the class pattern succeeds.

If any positional patterns are present, they are converted to keyword patterns using the __match_args__ attribute on the class name_or_attr before matching:

I. The equivalent of getattr(cls, "__match_args__", ()) is called.

• If this raises an exception, the exception bubbles up.

• If the returned value is not a tuple, the conversion fails and TypeError is raised.

• If there are more positional patterns than len(cls.__match_args__) , TypeError is raised.

• Otherwise, positional pattern i is converted to a keyword pattern using __match_args__[i] as the keyword. __match_args__[i] must be a string; if not TypeError is raised.

• If there are duplicate keywords, TypeError is raised.

the match proceeds as if there were only keyword patterns.

For the following built-in types the handling of positional subpatterns is different:

These classes accept a single positional argument, and the pattern there is matched against the whole object rather than an attribute. For example int(0|1) matches the value 0 , but not the value 0.0 .

In simple terms CLS(P1, attr=P2) matches only if the following happens:

convert P1 to a keyword pattern using CLS.__match_args__

P2 matches <subject>.attr

… and so on for the corresponding keyword argument/pattern pair.

PEP 634 – Structural Pattern Matching: Specification

PEP 636 – Structural Pattern Matching: Tutorial

8.7. Function definitions¶

A function definition defines a user-defined function object (see section The standard type hierarchy ):

A function definition is an executable statement. Its execution binds the function name in the current local namespace to a function object (a wrapper around the executable code for the function). This function object contains a reference to the current global namespace as the global namespace to be used when the function is called.

The function definition does not execute the function body; this gets executed only when the function is called. 4

A function definition may be wrapped by one or more decorator expressions. Decorator expressions are evaluated when the function is defined, in the scope that contains the function definition. The result must be a callable, which is invoked with the function object as the only argument. The returned value is bound to the function name instead of the function object. Multiple decorators are applied in nested fashion. For example, the following code

is roughly equivalent to

except that the original function is not temporarily bound to the name func .

Changed in version 3.9: Functions may be decorated with any valid assignment_expression . Previously, the grammar was much more restrictive; see PEP 614 for details.

When one or more parameters have the form parameter = expression, the function is said to have “default parameter values.” For a parameter with a default value, the corresponding argument may be omitted from a call, in which case the parameter’s default value is substituted. If a parameter has a default value, all following parameters up until the “ * ” must also have a default value — this is a syntactic restriction that is not expressed by the grammar.

Default parameter values are evaluated from left to right when the function definition is executed. This means that the expression is evaluated once, when the function is defined, and that the same “pre-computed” value is used for each call. This is especially important to understand when a default parameter value is a mutable object, such as a list or a dictionary: if the function modifies the object (e.g. by appending an item to a list), the default parameter value is in effect modified. This is generally not what was intended. A way around this is to use None as the default, and explicitly test for it in the body of the function, e.g.:

Function call semantics are described in more detail in section Calls . A function call always assigns values to all parameters mentioned in the parameter list, either from positional arguments, from keyword arguments, or from default values. If the form “ *identifier ” is present, it is initialized to a tuple receiving any excess positional parameters, defaulting to the empty tuple. If the form “ **identifier ” is present, it is initialized to a new ordered mapping receiving any excess keyword arguments, defaulting to a new empty mapping of the same type. Parameters after “ * ” or “ *identifier ” are keyword-only parameters and may only be passed by keyword arguments. Parameters before “ / ” are positional-only parameters and may only be passed by positional arguments.

Changed in version 3.8: The / function parameter syntax may be used to indicate positional-only parameters. See PEP 570 for details.

Parameters may have an annotation of the form “ : expression ” following the parameter name. Any parameter may have an annotation, even those of the form *identifier or **identifier . Functions may have “return” annotation of the form “ -> expression ” after the parameter list. These annotations can be any valid Python expression. The presence of annotations does not change the semantics of a function. The annotation values are available as values of a dictionary keyed by the parameters’ names in the __annotations__ attribute of the function object. If the annotations import from __future__ is used, annotations are preserved as strings at runtime which enables postponed evaluation. Otherwise, they are evaluated when the function definition is executed. In this case annotations may be evaluated in a different order than they appear in the source code.

It is also possible to create anonymous functions (functions not bound to a name), for immediate use in expressions. This uses lambda expressions, described in section Lambdas . Note that the lambda expression is merely a shorthand for a simplified function definition; a function defined in a “ def ” statement can be passed around or assigned to another name just like a function defined by a lambda expression. The “ def ” form is actually more powerful since it allows the execution of multiple statements and annotations.

Programmer’s note: Functions are first-class objects. A “ def ” statement executed inside a function definition defines a local function that can be returned or passed around. Free variables used in the nested function can access the local variables of the function containing the def. See section Naming and binding for details.

The original specification for function annotations.

Definition of a standard meaning for annotations: type hints.

PEP 526 — Syntax for Variable Annotations

Ability to type hint variable declarations, including class variables and instance variables

PEP 563 — Postponed Evaluation of Annotations

Support for forward references within annotations by preserving annotations in a string form at runtime instead of eager evaluation.

8.8. Class definitions¶

A class definition defines a class object (see section The standard type hierarchy ):

A class definition is an executable statement. The inheritance list usually gives a list of base classes (see Metaclasses for more advanced uses), so each item in the list should evaluate to a class object which allows subclassing. Classes without an inheritance list inherit, by default, from the base class object ; hence,

is equivalent to

The class’s suite is then executed in a new execution frame (see Naming and binding ), using a newly created local namespace and the original global namespace. (Usually, the suite contains mostly function definitions.) When the class’s suite finishes execution, its execution frame is discarded but its local namespace is saved. 5 A class object is then created using the inheritance list for the base classes and the saved local namespace for the attribute dictionary. The class name is bound to this class object in the original local namespace.

The order in which attributes are defined in the class body is preserved in the new class’s __dict__ . Note that this is reliable only right after the class is created and only for classes that were defined using the definition syntax.

Class creation can be customized heavily using metaclasses .

Classes can also be decorated: just like when decorating functions,

is roughly equivalent to

The evaluation rules for the decorator expressions are the same as for function decorators. The result is then bound to the class name.

Changed in version 3.9: Classes may be decorated with any valid assignment_expression . Previously, the grammar was much more restrictive; see PEP 614 for details.

Programmer’s note: Variables defined in the class definition are class attributes; they are shared by instances. Instance attributes can be set in a method with self.name = value . Both class and instance attributes are accessible through the notation “ self.name ”, and an instance attribute hides a class attribute with the same name when accessed in this way. Class attributes can be used as defaults for instance attributes, but using mutable values there can lead to unexpected results. Descriptors can be used to create instance variables with different implementation details.

The proposal that changed the declaration of metaclasses to the current syntax, and the semantics for how classes with metaclasses are constructed.

The proposal that added class decorators. Function and method decorators were introduced in PEP 318.

8.9. Coroutines¶

New in version 3.5.

8.9.1. Coroutine function definition¶

Execution of Python coroutines can be suspended and resumed at many points (see coroutine ). await expressions, async for and async with can only be used in the body of a coroutine function.

Functions defined with async def syntax are always coroutine functions, even if they do not contain await or async keywords.

It is a SyntaxError to use a yield from expression inside the body of a coroutine function.

An example of a coroutine function:

Changed in version 3.7: await and async are now keywords; previously they were only treated as such inside the body of a coroutine function.

8.9.2. The async for statement¶

An asynchronous iterable provides an __aiter__ method that directly returns an asynchronous iterator , which can call asynchronous code in its __anext__ method.

The async for statement allows convenient iteration over asynchronous iterables.

The following code:

Is semantically equivalent to:

See also __aiter__() and __anext__() for details.

It is a SyntaxError to use an async for statement outside the body of a coroutine function.

8.9.3. The async with statement¶

An asynchronous context manager is a context manager that is able to suspend execution in its enter and exit methods.

The following code:

is semantically equivalent to:

See also __aenter__() and __aexit__() for details.

It is a SyntaxError to use an async with statement outside the body of a coroutine function.

PEP 492 — Coroutines with async and await syntax

The proposal that made coroutines a proper standalone concept in Python, and added supporting syntax.

The exception is propagated to the invocation stack unless there is a finally clause which happens to raise another exception. That new exception causes the old one to be lost.

In pattern matching, a sequence is defined as one of the following:

• a class that inherits from collections.abc.Sequence

• a Python class that has been registered as collections.abc.Sequence

• a builtin class that has its (CPython) Py_TPFLAGS_SEQUENCE bit set

• a class that inherits from any of the above

The following standard library classes are sequences:

• array.array

• collections.deque

• list

• memoryview

• range

• tuple

Subject values of type str , bytes , and bytearray do not match sequence patterns.

In pattern matching, a mapping is defined as one of the following:

• a class that inherits from collections.abc.Mapping

• a Python class that has been registered as collections.abc.Mapping

• a builtin class that has its (CPython) Py_TPFLAGS_MAPPING bit set

• a class that inherits from any of the above

The standard library classes dict and types.MappingProxyType are mappings.

A string literal appearing as the first statement in the function body is transformed into the function’s __doc__ attribute and therefore the function’s docstring .

A string literal appearing as the first statement in the class body is transformed into the namespace’s __doc__ item and therefore the class’s docstring .

Вложенные циклы Python

Изучение

В языке программирования Python есть два типа циклов: цикл for и цикл while. Используя эти циклы, мы можем создавать в Python вложенные циклы. Вложенные циклы означают циклы внутри цикла. Например, цикл while внутри цикла for, цикл for внутри цикла for и т. д.

Вложенные циклы Python

Синтаксис вложенных циклов Python:

Outer_loop Expression:

Inner_loop Expression:

Statement inside inner_loop

Statement inside Outer_loop

Пример 1: базовый пример вложенных циклов Python

Time Complexity: O(n 2 )

Auxiliary Space: O(1)

Пример 2: Печать таблицы умножения с использованием вложенных циклов for в Python

Time Complexity: O(n 2 )

Auxiliary Space: O(1)

В приведенном выше примере мы берем внешний цикл for, работающий от 2 до 3, для таблицы умножения 2 и 3, а затем внутри этого цикла мы берем внутренний цикл for, который будет выполняться от 1 до 10, внутри которого мы печатаем умножение. table путем умножения каждого значения итерации внутреннего цикла на значение итерации внешнего цикла, как мы видим в приведенном ниже выводе.

Пример 3: Печать с использованием разных внутренних и внешних вложенных циклов

Time Complexity: O(n 2 )

Auxiliary Space: O(1)

В этом примере мы инициализируем два списка некоторыми строками. Сохраните размер list2 в ’list2_Size’, используя функцию len() и используя ее в цикле while в качестве счетчика. После этого запустите внешний цикл for для перебора list1, а внутри этого цикла запустите внутренний цикл while для перебора list2 с использованием индексации списка внутри, в котором мы печатаем каждое значение list2 для каждого значения list1.

Использование оператора break во вложенных циклах

Это тип оператора управления циклом. В цикле мы можем использовать оператор break для выхода из цикла. Когда мы используем оператор break в цикле, он пропускает оставшуюся часть итерации и завершает цикл. давайте разберемся на примере.

Time Complexity: O(n 2 )

Auxiliary Space: O(1)

Приведенный выше код такой же, как и в примере 2. В этом коде мы используем оператор break внутри внутреннего цикла с помощью оператора if. Внутри внутреннего цикла, если «i» становится равным «j», тогда внутренний цикл будет завершен и не выполнен, остальная часть итерации, как мы видим в выходной таблице 3, печатается до двух итераций, потому что в следующей итерации «i» становится равным «j», и цикл прерывается.

Использование оператора continue во вложенных циклах

Оператор continue также является оператором управления циклом. Это прямо противоположно оператору break. Оператор continue заставляет цикл перейти к следующей итерации цикла, тогда как оператор break прерывает цикл. Давайте разберемся с этим, используя код.

Time Complexity: O(n 2 )

Auxiliary Space: O(1)

В приведенном выше коде вместо оператора break мы используем оператор continue. Здесь, когда «i» становится равным «j» во внутреннем цикле, он пропускает остальную часть кода во внутреннем цикле и переходит к следующей итерации, как мы видим в выводе «2 * 2 = 4» и «3 * 3 = 9» не печатается, потому что в этот момент ‘i’ становится равным ‘j’.

Однострочные вложенные циклы с использованием list comprehension

Чтобы преобразовать многострочные вложенные циклы в одну строку, мы собираемся использовать генератор списков в Python. Понимание списка включает в себя скобки, состоящие из выражения, которое выполняется для каждого элемента, и цикла for для перебора каждого элемента в списке.

Синтаксис list comprehension:

newList = [ expression(element) for element in oldList if condition ]