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:
if test1: if test2: 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 print()
calls are
executed:
if x < y < z: print(x); print(y); print(z)
Summarizing:
compound_stmt ::=if_stmt
|while_stmt
|for_stmt
|try_stmt
|with_stmt
|match_stmt
|funcdef
|classdef
|async_with_stmt
|async_for_stmt
|async_funcdef
suite ::=stmt_list
NEWLINE | NEWLINE INDENTstatement
+ DEDENT statement ::=stmt_list
NEWLINE |compound_stmt
stmt_list ::=simple_stmt
(";"simple_stmt
)* [";"]
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:
if_stmt ::= "if"assignment_expression
":"suite
("elif"assignment_expression
":"suite
)* ["else" ":"suite
]
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:
while_stmt ::= "while"assignment_expression
":"suite
["else" ":"suite
]
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:
for_stmt ::= "for"target_list
"in"starred_list
":"suite
["else" ":"suite
]
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:
for i in range(10):
print(i)
i = 5 # this will not affect the for-loop
# because i will be overwritten with the next
# index in the range
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:
try_stmt ::=try1_stmt
|try2_stmt
|try3_stmt
try1_stmt ::= "try" ":"suite
("except" [expression
["as"identifier
]] ":"suite
)+ ["else" ":"suite
] ["finally" ":"suite
] try2_stmt ::= "try" ":"suite
("except" "*"expression
["as"identifier
] ":"suite
)+ ["else" ":"suite
] ["finally" ":"suite
] try3_stmt ::= "try" ":"suite
"finally" ":"suite
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, the
expression must evaluate to an exception type or a tuple of exception types.
The raised exception matches an except
clause whose expression evaluates
to the class or a non-virtual base class 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. [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
except E as N:
foo
was translated to
except E as N:
try:
foo
finally:
del N
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:
>>> print(sys.exception())
None
>>> try:
... raise TypeError
... except:
... print(repr(sys.exception()))
... try:
... raise ValueError
... except:
... print(repr(sys.exception()))
... print(repr(sys.exception()))
...
TypeError()
ValueError()
TypeError()
>>> print(sys.exception())
None
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.
>>> try:
... raise ExceptionGroup("eg",
... [ValueError(1), TypeError(2), OSError(3), OSError(4)])
... except* TypeError as e:
... print(f'caught {type(e)} with nested {e.exceptions}')
... except* OSError as e:
... 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 "<stdin>", line 2, in <module>
| ExceptionGroup: eg
+-+---------------- 1 ----------------
| ValueError: 1
+------------------------------------
Any remaining exceptions that were not handled by any except*
clause are re-raised at the end, along with all exceptions that were
raised from within the except*
clauses. If this list contains
more than one exception to reraise, they are combined into an exception
group.
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.
>>> try:
... raise BlockingIOError
... except* BlockingIOError as e:
... print(repr(e))
...
ExceptionGroup('', (BlockingIOError()))
An except*
clause must have a matching expression; it cannot be except*:
.
Furthermore, this expression cannot contain exception group types, because that would
have ambiguous semantics.
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:
>>> def f():
... try:
... 1/0
... finally:
... return 42
...
>>> f()
42
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:
>>> def foo():
... try:
... return 'try'
... finally:
... return 'finally'
...
>>> foo()
'finally'
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.
with_stmt ::= "with" ( "("with_stmt_contents
","? ")" |with_stmt_contents
) ":"suite
with_stmt_contents ::=with_item
(","with_item
)* with_item ::=expression
["as"target
]
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.Note
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, threeNone
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 thewith
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:
with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
enter = type(manager).__enter__
exit = type(manager).__exit__
value = enter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not exit(manager, *sys.exc_info()):
raise
finally:
if not hit_except:
exit(manager, None, None, None)
With more than one item, the context managers are processed as if multiple
with
statements were nested:
with A() as a, B() as b:
SUITE
is semantically equivalent to:
with A() as a:
with B() as b:
SUITE
You can also write multi-item context managers in multiple lines if the items are surrounded by parentheses. For example:
with (
A() as a,
B() as b,
):
SUITE
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.
8.6. The match
statement¶
Added in version 3.10.
The match statement is used for pattern matching. Syntax:
match_stmt ::= 'match'subject_expr
":" NEWLINE INDENTcase_block
+ DEDENT subject_expr ::=star_named_expression
","star_named_expressions
? |named_expression
case_block ::= 'case'patterns
[guard
] ":"block
Note
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.
See also
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
insidecase_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) if flag: # 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: 200
In this case, if flag
is a guard. Read more about that in the next section.
8.6.2. Guards¶
guard ::= "if" named_expression
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, theguard
is not evaluated and the nextcase
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¶
Note
This section uses grammar notations beyond standard EBNF:
the notation
SEP.RULE+
is shorthand forRULE (SEP RULE)*
the notation
!RULE
is shorthand for a negative lookahead assertion
The top-level syntax for patterns
is:
patterns ::=open_sequence_pattern
|pattern
pattern ::=as_pattern
|or_pattern
closed_pattern ::= |literal_pattern
|capture_pattern
|wildcard_pattern
|value_pattern
|group_pattern
|sequence_pattern
|mapping_pattern
|class_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 ::= "|".closed_pattern
+
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:
as_pattern ::=or_pattern
"as"capture_pattern
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:
literal_pattern ::=signed_number
|signed_number
"+" NUMBER |signed_number
"-" NUMBER |strings
| "None" | "True" | "False" signed_number ::= ["-"] NUMBER
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. f-strings 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:
capture_pattern ::= !'_' NAME
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:
wildcard_pattern ::= '_'
_
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:
value_pattern ::=attr
attr ::=name_or_attr
"." NAME name_or_attr ::=attr
| NAME
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
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 ::= "(" 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 ::= "[" [maybe_sequence_pattern
] "]" | "(" [open_sequence_pattern
] ")" open_sequence_pattern ::=maybe_star_pattern
"," [maybe_sequence_pattern
] maybe_sequence_pattern ::= ",".maybe_star_pattern
+ ","? maybe_star_pattern ::=star_pattern
|pattern
star_pattern ::= "*" (capture_pattern
|wildcard_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 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
orbytearray
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
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 sequencelen(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
] "}" items_pattern ::= ",".key_value_pattern
+ ","? key_value_pattern ::= (literal_pattern
|value_pattern
) ":"pattern
|double_star_pattern
double_star_pattern ::= "**"capture_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 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 aValueError
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 __missing__()
or
__getitem__()
.
In simple terms {KEY1: P1, KEY2: P2, ... }
matches only if all the following
happens:
check
<subject>
is a mappingKEY1 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:
class_pattern ::=name_or_attr
"(" [pattern_arguments
","?] ")" pattern_arguments ::=positional_patterns
[","keyword_patterns
] |keyword_patterns
positional_patterns ::= ",".pattern
+ keyword_patterns ::= ",".keyword_pattern
+ keyword_pattern ::= NAME "="pattern
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 builtintype
, raiseTypeError
.If the subject value is not an instance of
name_or_attr
(tested viaisinstance()
), 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 classname_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 notTypeError
is raised.If there are duplicate keywords,
TypeError
is raised.
- II. 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 value0
, but not the value0.0
.
In simple terms CLS(P1, attr=P2)
matches only if the following happens:
isinstance(<subject>, CLS)
convert
P1
to a keyword pattern usingCLS.__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.
8.7. Function definitions¶
A function definition defines a user-defined function object (see section The standard type hierarchy):
funcdef ::= [decorators
] "def"funcname
[type_params
] "(" [parameter_list
] ")" ["->"expression
] ":"suite
decorators ::=decorator
+ decorator ::= "@"assignment_expression
NEWLINE parameter_list ::=defparameter
(","defparameter
)* "," "/" ["," [parameter_list_no_posonly
]] |parameter_list_no_posonly
parameter_list_no_posonly ::=defparameter
(","defparameter
)* ["," [parameter_list_starargs
]] |parameter_list_starargs
parameter_list_starargs ::= "*" [star_parameter
] (","defparameter
)* ["," ["**"parameter
[","]]] | "**"parameter
[","] parameter ::=identifier
[":"expression
] star_parameter ::=identifier
[":" ["*"]expression
] defparameter ::=parameter
["="expression
] funcname ::=identifier
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
@f1(arg)
@f2
def func(): pass
is roughly equivalent to
def func(): pass
func = 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
assignment_expression
. Previously, the grammar was
much more restrictive; see PEP 614 for details.
A list of type parameters 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
__type_params__
attribute. See Generic functions for more.
Changed in version 3.12: Type parameter lists are new in Python 3.12.
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.:
def whats_on_the_telly(penguin=None):
if penguin is None:
penguin = []
penguin.append("property of the zoo")
return penguin
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
. (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. 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.
Changed in version 3.11: Parameters of the form “*identifier
” may have an annotation
“: *expression
”. See PEP 646.
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.
See also
- PEP 3107 - Function Annotations
The original specification for function annotations.
- PEP 484 - Type Hints
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.
- PEP 318 - Decorators for Functions and Methods
Function and method decorators were introduced. Class decorators were introduced in PEP 3129.
8.8. Class definitions¶
A class definition defines a class object (see section The standard type hierarchy):
classdef ::= [decorators
] "class"classname
[type_params
] [inheritance
] ":"suite
inheritance ::= "(" [argument_list
] ")" classname ::=identifier
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,
class Foo:
pass
is equivalent to
class Foo(object):
pass
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,
@f1(arg)
@f2
class Foo: pass
is roughly equivalent to
class Foo: pass
Foo = 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
assignment_expression
. Previously, the grammar was
much more restrictive; see PEP 614 for details.
A list of type parameters 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
__type_params__
attribute. See Generic classes 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. Descriptors
can be used to create instance variables with different implementation details.
See also
- PEP 3115 - Metaclasses in Python 3000
The proposal that changed the declaration of metaclasses to the current syntax, and the semantics for how classes with metaclasses are constructed.
- PEP 3129 - Class Decorators
The proposal that added class decorators. Function and method decorators were introduced in PEP 318.
8.9. Coroutines¶
Added in version 3.5.
8.9.1. Coroutine function definition¶
async_funcdef ::= [decorators
] "async" "def"funcname
"(" [parameter_list
] ")" ["->"expression
] ":"suite
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:
async def func(param1, param2):
do_stuff()
await some_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 async for
statement¶
async_for_stmt ::= "async" for_stmt
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:
async for TARGET in ITER:
SUITE
else:
SUITE2
Is semantically equivalent to:
iter = (ITER)
iter = type(iter).__aiter__(iter)
running = True
while running:
try:
TARGET = await type(iter).__anext__(iter)
except StopAsyncIteration:
running = False
else:
SUITE
else:
SUITE2
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¶
async_with_stmt ::= "async" with_stmt
An asynchronous context manager is a context manager that is able to suspend execution in its enter and exit methods.
The following code:
async with EXPRESSION as TARGET:
SUITE
is semantically equivalent to:
manager = (EXPRESSION)
aenter = type(manager).__aenter__
aexit = type(manager).__aexit__
value = await aenter(manager)
hit_except = False
try:
TARGET = value
SUITE
except:
hit_except = True
if not await aexit(manager, *sys.exc_info()):
raise
finally:
if not hit_except:
await aexit(manager, None, None, None)
See also __aenter__()
and __aexit__()
for details.
It is a SyntaxError
to use an async with
statement outside the
body of a coroutine function.
See also
- PEP 492 - Coroutines with async and await syntax
The proposal that made coroutines a proper standalone concept in Python, and added supporting syntax.
8.10. Type parameter lists¶
Added in version 3.12.
type_params ::= "["type_param
(","type_param
)* "]" type_param ::=typevar
|typevartuple
|paramspec
typevar ::=identifier
(":"expression
)? typevartuple ::= "*"identifier
paramspec ::= "**"identifier
Functions (including coroutines), classes and type aliases may contain a type parameter list:
def max[T](args: list[T]) -> T:
...
async def amax[T](args: list[T]) -> T:
...
class Bag[T]:
def __iter__(self) -> Iterator[T]:
...
def add(self, arg: T) -> None:
...
type ListOrSet[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 def func[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 annotation scope) that
wraps the creation of the generic object.
Generic functions, classes, and type aliases have a
__type_params__
attribute listing their type parameters.
Type parameters come in three kinds:
typing.TypeVar
, introduced by a plain name (e.g.,T
). Semantically, this represents a single type to a type checker.typing.TypeVarTuple
, introduced by a name prefixed with a single asterisk (e.g.,*Ts
). Semantically, this stands for a tuple of any number of types.typing.ParamSpec
, introduced by a name prefixed with two asterisks (e.g.,**P
). Semantically, this stands for the parameters of a callable.
typing.TypeVar
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.TypeVar
s 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 annotation scope.
typing.TypeVarTuple
s and typing.ParamSpec
s cannot have bounds
or constraints.
The following example indicates the full set of allowed type parameter declarations:
def overly_generic[
SimpleTypeVar,
TypeVarWithBound: int,
TypeVarWithConstraints: (str, bytes),
*SimpleTypeVarTuple,
**SimpleParamSpec,
](
a: SimpleTypeVar,
b: TypeVarWithBound,
c: Callable[SimpleParamSpec, TypeVarWithConstraints],
*d: SimpleTypeVarTuple,
): ...
8.10.1. Generic functions¶
Generic functions are declared as follows:
def func[T](arg: T): ...
This syntax is equivalent to:
annotation-def TYPE_PARAMS_OF_func():
T = typing.TypeVar("T")
def func(arg: T): ...
func.__type_params__ = (T,)
return func
func = TYPE_PARAMS_OF_func()
Here annotation-def
indicates an annotation scope,
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 typing
module, but creates an instance of
typing.TypeVar
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:
@decorator
def func[T: int, *Ts, **P](*args: *Ts, arg: Callable[P, T] = some_default):
...
Except for the lazy evaluation of the
TypeVar
bound, this is equivalent to:
DEFAULT_OF_arg = some_default
annotation-def TYPE_PARAMS_OF_func():
annotation-def BOUND_OF_T():
return int
# 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")
def func(*args: *Ts, arg: Callable[P, T] = DEFAULT_OF_arg):
...
func.__type_params__ = (T, Ts, P)
return func
func = 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:
class Bag[T]: ...
This syntax is equivalent to:
annotation-def TYPE_PARAMS_OF_Bag():
T = typing.TypeVar("T")
class Bag(typing.Generic[T]):
__type_params__ = (T,)
...
return Bag
Bag = TYPE_PARAMS_OF_Bag()
Here again annotation-def
(not a real keyword) indicates an
annotation scope, and the name
TYPE_PARAMS_OF_Bag
is not actually bound at runtime.
Generic classes implicitly inherit from typing.Generic
.
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:
@decorator
class Bag(Base[T], arg=T): ...
This is equivalent to:
annotation-def TYPE_PARAMS_OF_Bag():
T = typing.TypeVar("T")
class Bag(Base[T], typing.Generic[T], arg=T):
__type_params__ = (T,)
...
return Bag
Bag = decorator(TYPE_PARAMS_OF_Bag())
8.10.3. Generic type aliases¶
The type
statement can also be used to create a generic type alias:
type ListOrSet[T] = list[T] | set[T]
Except for the lazy evaluation of the value, this is equivalent to:
annotation-def TYPE_PARAMS_OF_ListOrSet():
T = typing.TypeVar("T")
annotation-def VALUE_OF_ListOrSet():
return list[T] | set[T]
# In reality, the value is lazily evaluated
return typing.TypeAliasType("ListOrSet", VALUE_OF_ListOrSet(), type_params=(T,))
ListOrSet = TYPE_PARAMS_OF_ListOrSet()
Here, annotation-def
(not a real keyword) indicates an
annotation scope. The capitalized names
like TYPE_PARAMS_OF_ListOrSet
are not actually bound at runtime.
Footnotes