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
,
and
statements implement traditional control flow constructs.
specifies exception handlers and/or cleanup code for a group of statements, while the
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
clause a following
clause would belong:
iftest1:iftest2:print(x)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
calls are executed:
ifx<y<z:print(x);print(y);print(z)Summarizing:
compound_stmt:
|
|
|
|
|
|
|
|
|
|
suite:
NEWLINE | NEWLINE INDENT
+ DEDENT statement:
NEWLINE |
stmt_list:
(";"
)* [";"] 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
’ problem is solved in Python by requiring nested
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
statement is used for conditional execution:
if_stmt: "if"
":"
("elif"
":"
)* ["else"":"
] It selects exactly one of the suites by evaluating the expressions one by one until one is found to be true (see section
for the definition of true and false); then that suite is executed (and no other part of the
statement is executed or evaluated). If all expressions are false, the suite of the
clause, if present, is executed.
8.2. The while statement
The
statement is used for repeated execution as long as an expression is true:
while_stmt: "while"
":"
["else"":"
] 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
statement executed in the first suite terminates the loop without executing the else clause’s suite. A
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
statement is used to iterate over the elements of a sequence (such as a string, tuple or list) or other iterable object:
for_stmt: "for"
"in"
":"
["else"":"
] The
expression is evaluated once; it should yield an
object. An
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
), 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
statement executed in the first suite terminates the loop without executing the else clause’s suite. A
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:
foriinrange(10):print(i)i=5# this will not affect the for-loop# because i will be overwritten with the next# index in the rangeNames 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
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:
try_stmt:
|
|
try1_stmt: "try"":"
("except" [
["as"
]] ":"
)+ ["else"":"
] ["finally"":"
] try2_stmt: "try"":"
("except""*"
["as"
] ":"
)+ ["else"":"
] ["finally"":"
] try3_stmt: "try"":"
"finally"":"
Additional information on exceptions can be found in section
, and information on using the
statement to generate exceptions may be found in section
.
Changed in version 3.14: Support for optionally dropping grouping parentheses when using multiple exception types. See
.
8.4.1. except clause
The except clause(s) specify one or more exception handlers. When no exception occurs in the
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, the expression must evaluate to an exception type or a tuple of exception types. Parentheses can be dropped if multiple exception types are provided and the as clause is not used. The raised exception matches an except clause whose expression evaluates to the class or a
of the exception object, or to a tuple that contains such a class.
If no except clause matches the exception, the search for an exception handler continues in the surrounding code and on the invocation stack.
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
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
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 astarget, it is cleared at the end of the except clause. This is as if:
exceptEasN:foowas translated to:
exceptEasN:try:foofinally:delNThis 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
module, where it can be accessed from within the body of the except clause by calling
. When leaving an exception handler, the exception stored in the sys module is reset to its previous value:
>>> print(sys.exception())None>>> try:... raiseTypeError... except:... print(repr(sys.exception()))... try:... raiseValueError... except:... print(repr(sys.exception()))... print(repr(sys.exception()))...TypeError()ValueError()TypeError()>>> print(sys.exception())None8.4.2. except* clause
The except* clause(s) specify one or more handlers for groups of exceptions (
instances). A
statement can have either
or except* clauses, but not both. The exception type for matching is mandatory in the case of except*, so except*: is a syntax error. The type is interpreted as in the case of except, but matching is performed on the exceptions contained in the group that is being handled. A
is raised if a matching type is a subclass of BaseExceptionGroup, because that would have ambiguous semantics.
When an exception group is raised in the try block, each except* clause splits (see
) it into the subgroups of matching and non-matching exceptions. If the matching subgroup is not empty, it becomes the handled exception (the value returned from
) and assigned to the target of the except* clause (if there is one). Then, the body of the except* clause executes. If the non-matching subgroup is not empty, it is processed by the next except* in the same manner. This continues until all exceptions in the group have been matched, or the last except* clause has run.
After all except* clauses execute, the group of unhandled exceptions is merged with any exceptions that were raised or re-raised from within except* clauses. This merged exception group propagates on:
>>> try:... raiseExceptionGroup("eg",... [ValueError(1),TypeError(2),OSError(3),OSError(4)])... except*TypeErrorase:... print(f'caught {type(e)} with nested {e.exceptions}')... except*OSErrorase:... print(f'caught {type(e)} with nested {e.exceptions}')...caught <class 'ExceptionGroup'> with nested (TypeError(2),)caught <class 'ExceptionGroup'> with nested (OSError(3), OSError(4)) + Exception Group Traceback (most recent call last): | File "<doctest default[0]>", line 2, in <module> | raise ExceptionGroup("eg", | [ValueError(1), TypeError(2), OSError(3), OSError(4)]) | ExceptionGroup: eg (1 sub-exception) +-+---------------- 1 ---------------- | ValueError: 1 +------------------------------------If the exception raised from the
block 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. This ensures that the type of the target e is consistently
:
>>> try:... raiseBlockingIOError... except*BlockingIOErrorase:... print(repr(e))...ExceptionGroup('', (BlockingIOError(),))
,
and
cannot appear in an except* clause.
8.4.3. else clause
The optional else clause is executed if the control flow leaves the
suite, no exception was raised, and no
,
, or
statement was executed. Exceptions in the else clause are not handled by the preceding
clauses.
8.4.4. finally clause
If finally is present, it specifies a ‘cleanup’ handler. The
clause is executed, including any
and
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
,
or
statement, the saved exception is discarded. For example, this function returns 42.
deff():try:1/0finally:return42The exception information is not available to the program during execution of the finally clause.
When a
,
or
statement is executed in the
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
statement executed. Since the finally clause always executes, a return statement executed in the finally clause will always be the last one executed. The following function returns ‘finally’.
deffoo():try:return'try'finally:return'finally'Changed in version 3.8: Prior to Python 3.8, a
statement was illegal in the finally clause due to a problem with the implementation.
8.5. The with statement
The
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
…
…
usage patterns to be encapsulated for convenient reuse.
with_stmt: "with" ( "("
","? ")" |
) ":"
with_stmt_contents:
(","
)* with_item:
["as"
] The execution of the
statement with one “item” proceeds as follows:
The context expression (the expression given in the
) is evaluated to obtain a context manager.
The context manager’s
is loaded for later use.
The context manager’s
is loaded for later use.
The context manager’s
method is invoked.
If a target was included in the
statement, the return value from
is assigned to it.
Note
The
statement guarantees that if the
method returns without an error, then
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
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
arguments are supplied.
If the suite was exited due to an exception, and the return value from the
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
statement.
If the suite was exited for any reason other than an exception, the return value from
is ignored, and execution proceeds at the normal location for the kind of exit that was taken.
The following code:
withEXPRESSIONasTARGET:SUITEis semantically equivalent to:
manager=(EXPRESSION)enter=manager.__enter__exit=manager.__exit__value=enter()hit_except=Falsetry:TARGET=valueSUITEexcept:hit_except=Trueifnotexit(*sys.exc_info()):raisefinally:ifnothit_except:exit(None,None,None)except that implicit
is used for
and
.
With more than one item, the context managers are processed as if multiple
statements were nested:
withA()asa,B()asb:SUITEis semantically equivalent to:
withA()asa:withB()asb:SUITEYou can also write multi-item context managers in multiple lines if the items are surrounded by parentheses. For example:
with(A()asa,B()asb,):SUITEChanged 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.
See also
- The “with” statementThe specification, background, and examples for the Python
statement.
8.6. The match statement
Added in version 3.10.
The match statement is used for pattern matching. Syntax:
match_stmt: 'match'
":" NEWLINE INDENT
+ DEDENT subject_expr:
"," [
[',']] |
case_block: 'case'
[
] ":"
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
.
See also
– Structural Pattern Matching: Specification
– 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
.
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.
Note
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:
>>> flag=False>>> match(100,200):... case(100,300):# Mismatch: 200 != 300... print('Case 1')... case(100,200)ifflag:# Successful match, but guard fails... print('Case 2')... case(100,y):# Matches and binds y to 200... print(f'Case 3, y: {y}')... case_:# Pattern not attempted... print('Case 4, I match anything!')...Case 3, y: 200In this case, ifflag is a guard. Read more about that in the next section.
8.6.2. Guards
guard: "if"
A guard (which is part of the case) must succeed for code inside the case block to execute. It takes the form:
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:
whose left-hand side is irrefutable
containing at least one irrefutable pattern
parenthesized irrefutable patterns
8.6.4. Patterns
Note
This section uses grammar notations beyond standard EBNF:
the notation SEP.RULE+ is shorthand for RULE(SEPRULE)*
the notation !RULE is shorthand for a negative lookahead assertion
The top-level syntax for patterns is:
patterns:
|
pattern:
|
closed_pattern: |
|
|
|
|
|
|
|
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:
or_pattern: "|".
+ Only the final subpattern may be
, 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
keyword against a subject. Syntax:
as_pattern:
"as"
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 PasNAME will match with P, and on success it will set NAME=<subject>.
8.6.4.3. Literal Patterns
A literal pattern corresponds to most
in Python. Syntax:
literal_pattern:
|
"+" NUMBER |
"-" NUMBER |
| "None" | "True" | "False"signed_number: ["-"] NUMBER The rule strings and the token NUMBER are defined in the
. Triple-quoted strings are supported. Raw strings and byte strings are supported.
and
are not supported.
The forms signed_number'+'NUMBER and signed_number'-'NUMBER are for expressing
; 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
operator is used.
8.6.4.4. Capture Patterns
A capture pattern binds the subject value to a name. Syntax:
capture_pattern: !'_' NAME A single underscore _ is not a capture pattern (this is what !'_' expresses). It is instead treated as a
.
In a given pattern, a given name can only be bound once. E.g. casex,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
; the name becomes a local variable in the closest containing function scope unless there’s an applicable
or
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:
wildcard_pattern: '_'_ is a
within any pattern, but only within patterns. It is an identifier, as usual, even within match subject expressions, guards, 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:
value_pattern:
attr:
"." NAME name_or_attr:
| NAME The dotted name in the pattern is looked up using standard Python
. 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
Note
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:
group_pattern: "("
")"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.
sequence_pattern: "[" [
] "]" | "(" [
] ")"open_sequence_pattern:
"," [
] maybe_sequence_pattern: ",".
+ ","? maybe_star_pattern:
|
star_pattern: "*" (
|
) There is no difference if parentheses or square brackets are used for sequence patterns (i.e. (...) vs [...] ).
Note
A single pattern enclosed in parentheses without a trailing comma (e.g. (3|4)) is a
. 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
, 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.
Note
The length of the subject sequence is obtained via
(i.e. via the
protocol). This length may be cached by the interpreter in a similar manner as
.
In simple terms [P1,P2,P3, … ,P<N>] matches only if all the following happens:
check <subject> is a sequence
len(subject)==<N>
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:
mapping_pattern: "{" [
] "}"items_pattern: ",".
+ ","? key_value_pattern: (
|
) ":"
|
double_star_pattern: "**"
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
. Two keys that otherwise have the same value will raise a
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
,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
is raised for duplicate literal values; or a
for named keys of the same value.
Note
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
or
.
In simple terms {KEY1:P1,KEY2:P2,...} matches only if all the following happens:
check <subject> is a mapping
KEY1in<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:
class_pattern:
"(" [
","?] ")"pattern_arguments:
[","
] |
positional_patterns: ",".
+ keyword_patterns: ",".
+ keyword_pattern: NAME "="
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
, raise
.
If the subject value is not an instance of name_or_attr (tested via
), 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:
The keyword is looked up as an attribute on the subject.
If this raises an exception other than
, the exception bubbles up.
If this raises
, 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.
If all keyword patterns succeed, the class pattern succeeds.
If any positional patterns are present, they are converted to keyword patterns using the
attribute on the class name_or_attr before matching:
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
is raised.
If there are more positional patterns than len(cls.__match_args__),
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
is raised.
If there are duplicate keywords,
is raised.
Once all positional patterns have been converted to keyword patterns, 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:
isinstance(<subject>,CLS)
convert P1 to a keyword pattern using CLS.__match_args__
For each keyword argument attr=P2:
hasattr(<subject>,"attr")
P2 matches <subject>.attr
… and so on for the corresponding keyword argument/pattern pair.
See also
– Structural Pattern Matching: Specification
– Structural Pattern Matching: Tutorial
8.7. Function definitions
A function definition defines a user-defined function object (see section
):
funcdef: [
] "def"
[
] "(" [
] ")" ["->"
] ":"
decorators:
+ decorator: "@"
NEWLINE parameter_list:
(","
)* ",""/" ["," [
]] |
parameter_list_no_posonly:
(","
)* ["," [
]] |
parameter_list_starargs: "*"
(","
)* ["," [
]] | "*" (","
)+ ["," [
]] |
parameter_star_kwargs: "**"
[","] parameter:
[":"
] star_parameter:
[":" ["*"]
] defparameter:
["="
] funcname:
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.
A function definition may be wrapped by one or more
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
@f1(arg)@f2deffunc():passis roughly equivalent to
deffunc():passfunc=f1(arg)(f2(func))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
. Previously, the grammar was much more restrictive; see
for details.
A list of
may be given in square brackets between the function’s name and the opening parenthesis for its parameter list. This indicates to static type checkers that the function is generic. At runtime, the type parameters can be retrieved from the function’s
attribute. See
for more.
Changed in version 3.12: Type parameter lists are new in Python 3.12.
When one or more
have the form parameter=expression, the function is said to have “default parameter values.” For a parameter with a default value, the corresponding
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, for example:
defwhats_on_the_telly(penguin=None):ifpenguinisNone:penguin=[]penguin.append("property of the zoo")returnpenguinFunction call semantics are described in more detail in section
. 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
for details.
Parameters may have an
of the form “:expression” following the parameter name. Any parameter may have an annotation, even those of the form *identifier or **identifier. (As a special case, parameters of the form *identifier may have an annotation “:*expression”.) 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. See
for more information on annotations.
Changed in version 3.11: Parameters of the form “*identifier” may have an annotation “:*expression”. See
.
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
. Note that the lambda expression is merely a shorthand for a simplified function definition; a function defined in a “
” 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
for details.
See also
- Function AnnotationsThe original specification for function annotations.
- Type HintsDefinition of a standard meaning for annotations: type hints.
- Syntax for Variable AnnotationsAbility to type hint variable declarations, including class variables and instance variables.
- Postponed Evaluation of AnnotationsSupport for forward references within annotations by preserving annotations in a string form at runtime instead of eager evaluation.
- Decorators for Functions and MethodsFunction and method decorators were introduced. Class decorators were introduced in
.
8.8. Class definitions
A class definition defines a class object (see section
):
classdef: [
] "class"
[
] [
] ":"
inheritance: "(" [
] ")"classname:
A class definition is an executable statement. The inheritance list usually gives a list of base classes (see
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
; hence,
classFoo:passis equivalent to
classFoo(object):passThe class’s suite is then executed in a new execution frame (see
), 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.
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
. 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
.
Classes can also be decorated: just like when decorating functions,
@f1(arg)@f2classFoo:passis roughly equivalent to
classFoo:passFoo=f1(arg)(f2(Foo))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
. Previously, the grammar was much more restrictive; see
for details.
A list of
may be given in square brackets immediately after the class’s name. This indicates to static type checkers that the class is generic. At runtime, the type parameters can be retrieved from the class’s
attribute. See
for more.
Changed in version 3.12: Type parameter lists are new in Python 3.12.
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.
can be used to create instance variables with different implementation details.
See also
- Metaclasses in Python 3000The proposal that changed the declaration of metaclasses to the current syntax, and the semantics for how classes with metaclasses are constructed.
- Class DecoratorsThe proposal that added class decorators. Function and method decorators were introduced in
.
8.9. Coroutines
Added in version 3.5.
8.9.1. Coroutine function definition
async_funcdef: [
] "async""def"
"(" [
] ")" ["->"
] ":"
Execution of Python coroutines can be suspended and resumed at many points (see
).
expressions,
and
can only be used in the body of a coroutine function.
Functions defined with asyncdef syntax are always coroutine functions, even if they do not contain await or async keywords.
It is a
to use a yieldfrom expression inside the body of a coroutine function.
An example of a coroutine function:
asyncdeffunc(param1,param2):do_stuff()awaitsome_coroutine()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 asyncfor statement
async_for_stmt: "async"
An
provides an __aiter__ method that directly returns an
, which can call asynchronous code in its __anext__ method.
The asyncfor statement allows convenient iteration over asynchronous iterables.
The following code:
asyncforTARGETinITER:SUITEelse:SUITE2Is semantically equivalent to:
iter=(ITER).__aiter__()running=Truewhilerunning:try:TARGET=awaititer.__anext__()exceptStopAsyncIteration:running=Falseelse:SUITEelse:SUITE2except that implicit
is used for
and
.
It is a
to use an asyncfor statement outside the body of a coroutine function.
8.9.3. The asyncwith statement
async_with_stmt: "async"
An
is a
that is able to suspend execution in its enter and exit methods.
The following code:
asyncwithEXPRESSIONasTARGET:SUITEis semantically equivalent to:
manager=(EXPRESSION)aenter=manager.__aenter__aexit=manager.__aexit__value=awaitaenter()hit_except=Falsetry:TARGET=valueSUITEexcept:hit_except=Trueifnotawaitaexit(*sys.exc_info()):raisefinally:ifnothit_except:awaitaexit(None,None,None)except that implicit
is used for
and
.
It is a
to use an asyncwith statement outside the body of a coroutine function.
See also
- Coroutines with async and await syntaxThe proposal that made coroutines a proper standalone concept in Python, and added supporting syntax.
8.10. Type parameter lists
Added in version 3.12.
Changed in version 3.13: Support for default values was added (see
).
type_params: "["
(","
)* "]"type_param:
|
|
typevar:
(":"
)? ("="
)? typevartuple: "*"
("="
)? paramspec: "**"
("="
)?
(including
),
and
may contain a type parameter list:
defmax[T](args:list[T])->T:...asyncdefamax[T](args:list[T])->T:...classBag[T]:def__iter__(self)->Iterator[T]:...defadd(self,arg:T)->None:...typeListOrSet[T]=list[T]|set[T]Semantically, this indicates that the function, class, or type alias is generic over a type variable. This information is primarily used by static type checkers, and at runtime, generic objects behave much like their non-generic counterparts.
Type parameters are declared in square brackets ([]) immediately after the name of the function, class, or type alias. The type parameters are accessible within the scope of the generic object, but not elsewhere. Thus, after a declaration deffunc[T]():pass, the name T is not available in the module scope. Below, the semantics of generic objects are described with more precision. The scope of type parameters is modeled with a special function (technically, an
) that wraps the creation of the generic object.
Generic functions, classes, and type aliases have a
attribute listing their type parameters.
Type parameters come in three kinds:
, introduced by a plain name (e.g., T). Semantically, this represents a single type to a type checker.
, introduced by a name prefixed with a single asterisk (e.g., *Ts). Semantically, this stands for a tuple of any number of types.
, introduced by a name prefixed with two asterisks (e.g., **P). Semantically, this stands for the parameters of a callable.
declarations can define bounds and constraints with a colon (:) followed by an expression. A single expression after the colon indicates a bound (e.g. T:int). Semantically, this means that the typing.TypeVar can only represent types that are a subtype of this bound. A parenthesized tuple of expressions after the colon indicates a set of constraints (e.g. T:(str,bytes)). Each member of the tuple should be a type (again, this is not enforced at runtime). Constrained type variables can only take on one of the types in the list of constraints.
For typing.TypeVars declared using the type parameter list syntax, the bound and constraints are not evaluated when the generic object is created, but only when the value is explicitly accessed through the attributes __bound__ and __constraints__. To accomplish this, the bounds or constraints are evaluated in a separate
.
s and
s cannot have bounds or constraints.
All three flavors of type parameters can also have a default value, which is used when the type parameter is not explicitly provided. This is added by appending a single equals sign (=) followed by an expression. Like the bounds and constraints of type variables, the default value is not evaluated when the object is created, but only when the type parameter’s __default__ attribute is accessed. To this end, the default value is evaluated in a separate
. If no default value is specified for a type parameter, the __default__ attribute is set to the special sentinel object
.
The following example indicates the full set of allowed type parameter declarations:
defoverly_generic[SimpleTypeVar,TypeVarWithDefault=int,TypeVarWithBound:int,TypeVarWithConstraints:(str,bytes),*SimpleTypeVarTuple=(int,float),**SimpleParamSpec=(str,bytearray),](a:SimpleTypeVar,b:TypeVarWithDefault,c:TypeVarWithBound,d:Callable[SimpleParamSpec,TypeVarWithConstraints],*e:SimpleTypeVarTuple,):...8.10.1. Generic functions
Generic functions are declared as follows:
deffunc[T](arg:T):...This syntax is equivalent to:
annotation-defTYPE_PARAMS_OF_func():T=typing.TypeVar("T")deffunc(arg:T):...func.__type_params__=(T,)returnfuncfunc=TYPE_PARAMS_OF_func()Here annotation-def indicates an
, which is not actually bound to any name at runtime. (One other liberty is taken in the translation: the syntax does not go through attribute access on the
module, but creates an instance of
directly.)
The annotations of generic functions are evaluated within the annotation scope used for declaring the type parameters, but the function’s defaults and decorators are not.
The following example illustrates the scoping rules for these cases, as well as for additional flavors of type parameters:
@decoratordeffunc[T:int,*Ts,**P](*args:*Ts,arg:Callable[P,T]=some_default):...Except for the
of the
bound, this is equivalent to:
DEFAULT_OF_arg=some_defaultannotation-defTYPE_PARAMS_OF_func():annotation-defBOUND_OF_T():returnint# In reality, BOUND_OF_T() is evaluated only on demand.T=typing.TypeVar("T",bound=BOUND_OF_T())Ts=typing.TypeVarTuple("Ts")P=typing.ParamSpec("P")deffunc(*args:*Ts,arg:Callable[P,T]=DEFAULT_OF_arg):...func.__type_params__=(T,Ts,P)returnfuncfunc=decorator(TYPE_PARAMS_OF_func())The capitalized names like DEFAULT_OF_arg are not actually bound at runtime.
8.10.2. Generic classes
Generic classes are declared as follows:
classBag[T]:...This syntax is equivalent to:
annotation-defTYPE_PARAMS_OF_Bag():T=typing.TypeVar("T")classBag(typing.Generic[T]):__type_params__=(T,)...returnBagBag=TYPE_PARAMS_OF_Bag()Here again annotation-def (not a real keyword) indicates an
, and the name TYPE_PARAMS_OF_Bag is not actually bound at runtime.
Generic classes implicitly inherit from
. The base classes and keyword arguments of generic classes are evaluated within the type scope for the type parameters, and decorators are evaluated outside that scope. This is illustrated by this example:
@decoratorclassBag(Base[T],arg=T):...This is equivalent to:
annotation-defTYPE_PARAMS_OF_Bag():T=typing.TypeVar("T")classBag(Base[T],typing.Generic[T],arg=T):__type_params__=(T,)...returnBagBag=decorator(TYPE_PARAMS_OF_Bag())8.10.3. Generic type aliases
The
statement can also be used to create a generic type alias:
typeListOrSet[T]=list[T]|set[T]Except for the
of the value, this is equivalent to:
annotation-defTYPE_PARAMS_OF_ListOrSet():T=typing.TypeVar("T")annotation-defVALUE_OF_ListOrSet():returnlist[T]|set[T]# In reality, the value is lazily evaluatedreturntyping.TypeAliasType("ListOrSet",VALUE_OF_ListOrSet(),type_params=(T,))ListOrSet=TYPE_PARAMS_OF_ListOrSet()Here, annotation-def (not a real keyword) indicates an
. The capitalized names like TYPE_PARAMS_OF_ListOrSet are not actually bound at runtime.
8.11. Annotations
Changed in version 3.14: Annotations are now lazily evaluated by default.
Variables and function parameters may carry
, created by adding a colon after the name, followed by an expression:
x:annotation=1deff(param:annotation):...Functions may also carry a return annotation following an arrow:
deff()->annotation:...Annotations are conventionally used for
, but this is not enforced by the language, and in general annotations may contain arbitrary expressions. The presence of annotations does not change the runtime semantics of the code, except if some mechanism is used that introspects and uses the annotations (such as
or
).
By default, annotations are lazily evaluated in an
. This means that they are not evaluated when the code containing the annotation is evaluated. Instead, the interpreter saves information that can be used to evaluate the annotation later if requested. The
module provides tools for evaluating annotations.
If the
from__future__importannotations is present, all annotations are instead stored as strings:
>>> from__future__importannotations>>> deff(param:annotation):...>>> f.__annotations__{'param': 'annotation'}This future statement will be deprecated and removed in a future version of Python, but not before Python 3.13 reaches its end of life (see
). When it is used, introspection tools like
annotationlib.get_annotations()
and
are less likely to be able to resolve annotations at runtime.
Footnotes