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.
From version 3.11.4, when the entire ExceptionGroup is handled and only one exception is raised from an except* clause, this exception is no longer wrapped to form a new ExceptionGroup .
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 _ .
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
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 .
Оператор if-else
Очень часто в программах надо выполнить ряд инструкций только в том случае, если соблюдаются определённые условия. Для обработки таких ситуаций в языках программирования имеются операторы управления. Эти операторы используются для управления потоком выполнения программы — создания циклов, пропуска какой-либо инструкции, когда условие истинно, и т.д.
Оператор if
Синтаксис для if следующий:
После оператора if идёт условие, т.е. логическое выражение, которое возвращает True или False . Окончание оператора обозначается с помощью символа : . В строках далее идут инструкции, которые начинаются с отступа. Всю эту конструкцию обычно называют условным блоком или блоком if . Пример:
Оператор if-else
Блок else , следующий за блоком if , является опциональным и позволяет задать инструкции для случая, когда ни одно предыдущее условие не оказалось истинным.
Синтаксис для if-else :
Вложенный условный блок
Для сложных условных ‘цепочек’ можно использовать вложенные условные блоки:
Когда интерпретатор переходит к 3-ей строке к оператору if-else , проверяется условие. Если оно неверно, тогда переходит к блоку else и аналогично проверяет вложенные в блок else другие условные блоки. Стоит заметить, что каждый условный блок находится на разном уровне отступа.
Хотя вложенные операторы if-else и позволяют проверять несколько условий, их бывает сложно воспринимать визуально. Данный пример можно сделать более читаемым с помощью конструкции if-elif-else .
Оператор if-elif-else
Блок elif (сокращение от else и if), следующий за блоком if , является опциональным и позволяет определить дополнительный условный блок со своим набором инструкций. Конструкций elif может быть бесконечно много, но располагаются они только после основного блока if . В конце и только в конце можно при случае добавить блок else , если другие условия не выполняются.
Синтаксис для if-elif-else :
Тернарный оператор if-else
Блок if-else также может быть использован в качестве тернарного оператора. В таком блоке обязательно используется три операнда. В других языках программирования используется конструкция <условие> ? <выражение 1> : <выражение 2> .
Синтаксис тернарного оператора if-else :
Если условие верно, то выполняется первое выражение, иначе второе. Данная конструкция используется для более сложный логических выражений или для однострочной записи коротких выражений. Пример:
Второй пример является более сложным, так как тут используется целых 5 тернарных операторов в одном выражении. На практике так редко записывают подобное выражение.
Подсказка
Для многострочной записи однострочной записи выражения можно использовать символ \ (символ продолжения строки) в конце строки. Интерпретатор в данном случае будет считать, что строка кода не была окончена и перейдет к следующей:
Также можно использовать круглые скобки вокруг выражения. Аналогичная запись:
4. More Control Flow Tools¶
Besides the while statement just introduced, Python uses the usual flow control statements known from other languages, with some twists.
4.1. if Statements¶
Perhaps the most well-known statement type is the if statement. For example:
There can be zero or more elif parts, and the else part is optional. The keyword ‘ elif ’ is short for ‘else if’, and is useful to avoid excessive indentation. An if … elif … elif … sequence is a substitute for the switch or case statements found in other languages.
If you’re comparing the same value to several constants, or checking for specific types or attributes, you may also find the match statement useful. For more details see match Statements .
4.2. for Statements¶
The for statement in Python differs a bit from what you may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), Python’s for statement iterates over the items of any sequence (a list or a string), in the order that they appear in the sequence. For example (no pun intended):
Code that modifies a collection while iterating over that same collection can be tricky to get right. Instead, it is usually more straight-forward to loop over a copy of the collection or to create a new collection:
4.3. The range() Function¶
If you do need to iterate over a sequence of numbers, the built-in function range() comes in handy. It generates arithmetic progressions:
The given end point is never part of the generated sequence; range(10) generates 10 values, the legal indices for items of a sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the ‘step’):
To iterate over the indices of a sequence, you can combine range() and len() as follows:
In most such cases, however, it is convenient to use the enumerate() function, see Looping Techniques .
A strange thing happens if you just print a range:
In many ways the object returned by range() behaves as if it is a list, but in fact it isn’t. It is an object which returns the successive items of the desired sequence when you iterate over it, but it doesn’t really make the list, thus saving space.
We say such an object is iterable , that is, suitable as a target for functions and constructs that expect something from which they can obtain successive items until the supply is exhausted. We have seen that the for statement is such a construct, while an example of a function that takes an iterable is sum() :
Later we will see more functions that return iterables and take iterables as arguments. In chapter Data Structures , we will discuss in more detail about list() .
4.4. break and continue Statements, and else Clauses on Loops¶
The break statement, like in C, breaks out of the innermost enclosing for or while loop.
Loop statements may have an else clause; it is executed when the loop terminates through exhaustion of the iterable (with for ) or when the condition becomes false (with while ), but not when the loop is terminated by a break statement. This is exemplified by the following loop, which searches for prime numbers:
(Yes, this is the correct code. Look closely: the else clause belongs to the for loop, not the if statement.)
When used with a loop, the else clause has more in common with the else clause of a try statement than it does with that of if statements: a try statement’s else clause runs when no exception occurs, and a loop’s else clause runs when no break occurs. For more on the try statement and exceptions, see Handling Exceptions .
The continue statement, also borrowed from C, continues with the next iteration of the loop:
4.5. pass Statements¶
The pass statement does nothing. It can be used when a statement is required syntactically but the program requires no action. For example:
This is commonly used for creating minimal classes:
Another place pass can be used is as a place-holder for a function or conditional body when you are working on new code, allowing you to keep thinking at a more abstract level. The pass is silently ignored:
4.6. match Statements¶
A match statement takes an expression and compares its value to successive patterns given as one or more case blocks. This is superficially similar to a switch statement in C, Java or JavaScript (and many other languages), but it’s more similar to pattern matching in languages like Rust or Haskell. Only the first pattern that matches gets executed and it can also extract components (sequence elements or object attributes) from the value into variables.
The simplest form compares a subject value against one or more literals:
Note the last block: the “variable name” _ acts as a wildcard and never fails to match. If no case matches, none of the branches is executed.
You can combine several literals in a single pattern using | (“or”):
Patterns can look like unpacking assignments, and can be used to bind variables:
Study that one carefully! The first pattern has two literals, and can be thought of as an extension of the literal pattern shown above. But the next two patterns combine a literal and a variable, and the variable binds a value from the subject ( point ). The fourth pattern captures two values, which makes it conceptually similar to the unpacking assignment (x, y) = point .
If you are using classes to structure your data you can use the class name followed by an argument list resembling a constructor, but with the ability to capture attributes into variables:
You can use positional parameters with some builtin classes that provide an ordering for their attributes (e.g. dataclasses). You can also define a specific position for attributes in patterns by setting the __match_args__ special attribute in your classes. If it’s set to (“x”, “y”), the following patterns are all equivalent (and all bind the y attribute to the var variable):
A recommended way to read patterns is to look at them as an extended form of what you would put on the left of an assignment, to understand which variables would be set to what. Only the standalone names (like var above) are assigned to by a match statement. Dotted names (like foo.bar ), attribute names (the x= and y= above) or class names (recognized by the “(…)” next to them like Point above) are never assigned to.
Patterns can be arbitrarily nested. For example, if we have a short list of points, we could match it like this:
We can add an if clause to a pattern, known as a “guard”. If the guard is false, match goes on to try the next case block. Note that value capture happens before the guard is evaluated:
Several other key features of this statement:
Like unpacking assignments, tuple and list patterns have exactly the same meaning and actually match arbitrary sequences. An important exception is that they don’t match iterators or strings.
Sequence patterns support extended unpacking: [x, y, *rest] and (x, y, *rest) work similar to unpacking assignments. The name after * may also be _ , so (x, y, *_) matches a sequence of at least two items without binding the remaining items.
Mapping patterns: <"bandwidth": b, "latency": l>captures the "bandwidth" and "latency" values from a dictionary. Unlike sequence patterns, extra keys are ignored. An unpacking like **rest is also supported. (But **_ would be redundant, so it is not allowed.)
Subpatterns may be captured using the as keyword:
will capture the second element of the input as p2 (as long as the input is a sequence of two points)
Most literals are compared by equality, however the singletons True , False and None are compared by identity.
Patterns may use named constants. These must be dotted names to prevent them from being interpreted as capture variable:
For a more detailed explanation and additional examples, you can look into PEP 636 which is written in a tutorial format.
4.7. Defining Functions¶
We can create a function that writes the Fibonacci series to an arbitrary boundary:
The keyword def introduces a function definition. It must be followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented.
The first statement of the function body can optionally be a string literal; this string literal is the function’s documentation string, or docstring. (More about docstrings can be found in the section Documentation Strings .) There are tools which use docstrings to automatically produce online or printed documentation, or to let the user interactively browse through code; it’s good practice to include docstrings in code that you write, so make a habit of it.
The execution of a function introduces a new symbol table used for the local variables of the function. More precisely, all variable assignments in a function store the value in the local symbol table; whereas variable references first look in the local symbol table, then in the local symbol tables of enclosing functions, then in the global symbol table, and finally in the table of built-in names. Thus, global variables and variables of enclosing functions cannot be directly assigned a value within a function (unless, for global variables, named in a global statement, or, for variables of enclosing functions, named in a nonlocal statement), although they may be referenced.
The actual parameters (arguments) to a function call are introduced in the local symbol table of the called function when it is called; thus, arguments are passed using call by value (where the value is always an object reference, not the value of the object). 1 When a function calls another function, or calls itself recursively, a new local symbol table is created for that call.
A function definition associates the function name with the function object in the current symbol table. The interpreter recognizes the object pointed to by that name as a user-defined function. Other names can also point to that same function object and can also be used to access the function:
Coming from other languages, you might object that fib is not a function but a procedure since it doesn’t return a value. In fact, even functions without a return statement do return a value, albeit a rather boring one. This value is called None (it’s a built-in name). Writing the value None is normally suppressed by the interpreter if it would be the only value written. You can see it if you really want to using print() :
It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it:
This example, as usual, demonstrates some new Python features:
The return statement returns with a value from a function. return without an expression argument returns None . Falling off the end of a function also returns None .
The statement result.append(a) calls a method of the list object result . A method is a function that ‘belongs’ to an object and is named obj.methodname , where obj is some object (this may be an expression), and methodname is the name of a method that is defined by the object’s type. Different types define different methods. Methods of different types may have the same name without causing ambiguity. (It is possible to define your own object types and methods, using classes, see Classes ) The method append() shown in the example is defined for list objects; it adds a new element at the end of the list. In this example it is equivalent to result = result + [a] , but more efficient.
4.8. More on Defining Functions¶
It is also possible to define functions with a variable number of arguments. There are three forms, which can be combined.
4.8.1. Default Argument Values¶
The most useful form is to specify a default value for one or more arguments. This creates a function that can be called with fewer arguments than it is defined to allow. For example:
This function can be called in several ways:
giving only the mandatory argument: ask_ok(‘Do you really want to quit?’)
giving one of the optional arguments: ask_ok(‘OK to overwrite the file?’, 2)
or even giving all arguments: ask_ok(‘OK to overwrite the file?’, 2, ‘Come on, only yes or no!’)
This example also introduces the in keyword. This tests whether or not a sequence contains a certain value.
The default values are evaluated at the point of function definition in the defining scope, so that
Important warning: The default value is evaluated only once. This makes a difference when the default is a mutable object such as a list, dictionary, or instances of most classes. For example, the following function accumulates the arguments passed to it on subsequent calls:
This will print
If you don’t want the default to be shared between subsequent calls, you can write the function like this instead:
4.8.2. Keyword Arguments¶
Functions can also be called using keyword arguments of the form kwarg=value . For instance, the following function:
accepts one required argument ( voltage ) and three optional arguments ( state , action , and type ). This function can be called in any of the following ways:
but all the following calls would be invalid:
In a function call, keyword arguments must follow positional arguments. All the keyword arguments passed must match one of the arguments accepted by the function (e.g. actor is not a valid argument for the parrot function), and their order is not important. This also includes non-optional arguments (e.g. parrot(voltage=1000) is valid too). No argument may receive a value more than once. Here’s an example that fails due to this restriction:
When a final formal parameter of the form **name is present, it receives a dictionary (see Mapping Types — dict ) containing all keyword arguments except for those corresponding to a formal parameter. This may be combined with a formal parameter of the form *name (described in the next subsection) which receives a tuple containing the positional arguments beyond the formal parameter list. ( *name must occur before **name .) For example, if we define a function like this:
It could be called like this:
and of course it would print:
Note that the order in which the keyword arguments are printed is guaranteed to match the order in which they were provided in the function call.
4.8.3. Special parameters¶
By default, arguments may be passed to a Python function either by position or explicitly by keyword. For readability and performance, it makes sense to restrict the way arguments can be passed so that a developer need only look at the function definition to determine if items are passed by position, by position or keyword, or by keyword.
A function definition may look like:
where / and * are optional. If used, these symbols indicate the kind of parameter by how the arguments may be passed to the function: positional-only, positional-or-keyword, and keyword-only. Keyword parameters are also referred to as named parameters.
4.8.3.1. Positional-or-Keyword Arguments¶
If / and * are not present in the function definition, arguments may be passed to a function by position or by keyword.
4.8.3.2. Positional-Only Parameters¶
Looking at this in a bit more detail, it is possible to mark certain parameters as positional-only. If positional-only, the parameters’ order matters, and the parameters cannot be passed by keyword. Positional-only parameters are placed before a / (forward-slash). The / is used to logically separate the positional-only parameters from the rest of the parameters. If there is no / in the function definition, there are no positional-only parameters.
Parameters following the / may be positional-or-keyword or keyword-only.
4.8.3.3. Keyword-Only Arguments¶
To mark parameters as keyword-only, indicating the parameters must be passed by keyword argument, place an * in the arguments list just before the first keyword-only parameter.
4.8.3.4. Function Examples¶
Consider the following example function definitions paying close attention to the markers / and * :
The first function definition, standard_arg , the most familiar form, places no restrictions on the calling convention and arguments may be passed by position or keyword:
The second function pos_only_arg is restricted to only use positional parameters as there is a / in the function definition:
The third function kwd_only_args only allows keyword arguments as indicated by a * in the function definition:
And the last uses all three calling conventions in the same function definition:
Finally, consider this function definition which has a potential collision between the positional argument name and **kwds which has name as a key:
There is no possible call that will make it return True as the keyword ‘name’ will always bind to the first parameter. For example:
But using / (positional only arguments), it is possible since it allows name as a positional argument and ‘name’ as a key in the keyword arguments:
In other words, the names of positional-only parameters can be used in **kwds without ambiguity.
4.8.3.5. Recap¶
The use case will determine which parameters to use in the function definition:
Use positional-only if you want the name of the parameters to not be available to the user. This is useful when parameter names have no real meaning, if you want to enforce the order of the arguments when the function is called or if you need to take some positional parameters and arbitrary keywords.
Use keyword-only when names have meaning and the function definition is more understandable by being explicit with names or you want to prevent users relying on the position of the argument being passed.
For an API, use positional-only to prevent breaking API changes if the parameter’s name is modified in the future.
4.8.4. Arbitrary Argument Lists¶
Finally, the least frequently used option is to specify that a function can be called with an arbitrary number of arguments. These arguments will be wrapped up in a tuple (see Tuples and Sequences ). Before the variable number of arguments, zero or more normal arguments may occur.
Normally, these variadic arguments will be last in the list of formal parameters, because they scoop up all remaining input arguments that are passed to the function. Any formal parameters which occur after the *args parameter are ‘keyword-only’ arguments, meaning that they can only be used as keywords rather than positional arguments.
4.8.5. Unpacking Argument Lists¶
The reverse situation occurs when the arguments are already in a list or tuple but need to be unpacked for a function call requiring separate positional arguments. For instance, the built-in range() function expects separate start and stop arguments. If they are not available separately, write the function call with the * -operator to unpack the arguments out of a list or tuple:
In the same fashion, dictionaries can deliver keyword arguments with the ** -operator:
4.8.6. Lambda Expressions¶
Small anonymous functions can be created with the lambda keyword. This function returns the sum of its two arguments: lambda a, b: a+b . Lambda functions can be used wherever function objects are required. They are syntactically restricted to a single expression. Semantically, they are just syntactic sugar for a normal function definition. Like nested function definitions, lambda functions can reference variables from the containing scope:
The above example uses a lambda expression to return a function. Another use is to pass a small function as an argument:
4.8.7. Documentation Strings¶
Here are some conventions about the content and formatting of documentation strings.
The first line should always be a short, concise summary of the object’s purpose. For brevity, it should not explicitly state the object’s name or type, since these are available by other means (except if the name happens to be a verb describing a function’s operation). This line should begin with a capital letter and end with a period.
If there are more lines in the documentation string, the second line should be blank, visually separating the summary from the rest of the description. The following lines should be one or more paragraphs describing the object’s calling conventions, its side effects, etc.
The Python parser does not strip indentation from multi-line string literals in Python, so tools that process documentation have to strip indentation if desired. This is done using the following convention. The first non-blank line after the first line of the string determines the amount of indentation for the entire documentation string. (We can’t use the first line since it is generally adjacent to the string’s opening quotes so its indentation is not apparent in the string literal.) Whitespace “equivalent” to this indentation is then stripped from the start of all lines of the string. Lines that are indented less should not occur, but if they occur all their leading whitespace should be stripped. Equivalence of whitespace should be tested after expansion of tabs (to 8 spaces, normally).
Here is an example of a multi-line docstring:
4.8.8. Function Annotations¶
Function annotations are completely optional metadata information about the types used by user-defined functions (see PEP 3107 and PEP 484 for more information).
Annotations are stored in the __annotations__ attribute of the function as a dictionary and have no effect on any other part of the function. Parameter annotations are defined by a colon after the parameter name, followed by an expression evaluating to the value of the annotation. Return annotations are defined by a literal -> , followed by an expression, between the parameter list and the colon denoting the end of the def statement. The following example has a required argument, an optional argument, and the return value annotated:
4.9. Intermezzo: Coding Style¶
Now that you are about to write longer, more complex pieces of Python, it is a good time to talk about coding style. Most languages can be written (or more concise, formatted) in different styles; some are more readable than others. Making it easy for others to read your code is always a good idea, and adopting a nice coding style helps tremendously for that.
For Python, PEP 8 has emerged as the style guide that most projects adhere to; it promotes a very readable and eye-pleasing coding style. Every Python developer should read it at some point; here are the most important points extracted for you:
Use 4-space indentation, and no tabs.
4 spaces are a good compromise between small indentation (allows greater nesting depth) and large indentation (easier to read). Tabs introduce confusion, and are best left out.
Wrap lines so that they don’t exceed 79 characters.
This helps users with small displays and makes it possible to have several code files side-by-side on larger displays.
Use blank lines to separate functions and classes, and larger blocks of code inside functions.
When possible, put comments on a line of their own.
Use spaces around operators and after commas, but not directly inside bracketing constructs: a = f(1, 2) + g(3, 4) .
Name your classes and functions consistently; the convention is to use UpperCamelCase for classes and lowercase_with_underscores for functions and methods. Always use self as the name for the first method argument (see A First Look at Classes for more on classes and methods).
Don’t use fancy encodings if your code is meant to be used in international environments. Python’s default, UTF-8, or even plain ASCII work best in any case.
Likewise, don’t use non-ASCII characters in identifiers if there is only the slightest chance people speaking a different language will read or maintain the code.
Actually, call by object reference would be a better description, since if a mutable object is passed, the caller will see any changes the callee makes to it (items inserted into a list).
несколько условий if
Если необходимо выполнение всех условий:
Если необходимо выполнение хотя бы одного условия:
Чтобы проверить, содержится ли элемент во всех доступных коллекциях:
где к примеру: collections = b, c, d .
Чтобы проверить содержится ли элемент в любой из коллекций:
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