PEP 622 – Structural Pattern Matching | peps.python.org (2024)

Patterns and shapes

The pattern syntax builds on Python’s existing syntax for sequenceunpacking (e.g., a, b = value).

A match statement compares a value (the subject)to several different shapes (the patterns) until a shape fits.Each pattern describes the type and structure of the accepted valuesas well as the variables where to capture its contents.

Patterns can specify the shape to be:

• a sequence to be unpacked, as already mentioned
• a mapping with specific keys
• an instance of a given class with (optionally) specific attributes
• a specific value
• a wildcard

Patterns can be composed in several ways.

Syntax

Syntactically, a match statement contains:

• a subject expression
• one or more case clauses

Each case clause specifies:

• a pattern (the overall shape to be matched)
• an optional “guard” (a condition to be checked if the pattern matches)
• a code block to be executed if the case clause is selected

Motivation

The rest of the PEP:

• motivates why we believe pattern matching makes a good addition to Python
• explains our design choices
• contains a precise syntactic and runtime specification
• gives guidance for static type checkers (and one small addition to the typing module)
• discusses the main objections and alternatives that have beenbrought up during extensive discussion of the proposal, both withinthe group of authors and in the python-dev community

Finally, we discuss some possible extensions that might be consideredin the future, once the community has ample experience with thecurrently proposed syntax and semantics.

Pattern, a new syntactic construct, and destructuring

A new syntactic construct called pattern is introduced in thisPEP. Syntactically, patterns look like a subset of expressions.The following are examples of patterns:

• [first, second, *rest]
• Point2d(x, 0)
• {"name": "Bruce", "age": age}
• 42

The above expressions may look like examples of object constructionwith a constructor which takes some values as parameters andbuilds an object from those components.

When viewed as a pattern, the above patterns mean the inverse operation ofconstruction, which we call destructuring. Destructuring takes a subject valueand extracts its components.

The syntactic similarity between object construction and destructuring isintentional. It also follows the existingPythonic style of contexts which makes assignment targets (write contexts) looklike expressions (read contexts).

Pattern matching never creates objects. This is in the same way that[a, b] = my_list doesn’t create anew [a, b] list, nor reads the values of a and b.

Matching process

During this matching process,the structure of the pattern may not fit the subject, and matching fails.

For example, matching the pattern Point2d(x, 0) to the subjectPoint2d(3, 0) successfully matches. The match also bindsthe pattern’s free variable x to the subject’s value 3.

As another example, if the subject is [3, 0], the match failsbecause the subject’s type list is not the pattern’s Point2d.

As a third example, if the subject isPoint2d(3, 7), the match fails because thesubject’s second coordinate 7 is not the same as the pattern’s 0.

The match statement tries to match a single subject to each of thepatterns in its case clauses. At the firstsuccessful match to a pattern in a case clause:

• the variables in the pattern are assigned, and
• a corresponding block is executed.

Each case clause can also specify an optional boolean condition,known as a guard.

Let’s look at a more detailed example of a match statement. Thematch statement is used within a function to define the buildingof 3D points. In this example, the function can accept as input any ofthe following: tuple with 2 elements, tuple with 3 elements, anexisting Point2d object or an existing Point3d object:

def make_point_3d(pt): match pt: case (x, y): return Point3d(x, y, 0) case (x, y, z): return Point3d(x, y, z) case Point2d(x, y): return Point3d(x, y, 0) case Point3d(_, _, _): return pt case _: raise TypeError("not a point we support")

Without pattern matching, this function’s implementation would require severalisinstance() checks, one or two len() calls, and a moreconvoluted control flow. The match example version and the traditionalPython version without match translate into similar code under the hood.With familiarity of pattern matching, a user reading this function using matchwill likely find this version clearer than the traditional approach.

Patterns

The pattern is a new syntactic construct, that could be considered a loosegeneralization of assignment targets. The key properties of a pattern are whattypes and shapes of subjects it accepts, what variables it captures and howit extracts them from the subject. For example, the pattern [a, b] matchesonly sequences of exactly 2 elements, extracting the first element into aand the second one into b.

This PEP defines several types of patterns. These are certainly not theonly possible ones, so the design decision was made to choose a subset offunctionality that is useful now but conservative. More patterns can be addedlater as this feature gets more widespread use. See the rejected ideasand deferred ideas sections for more details.

The patterns listed here are described in more detail below, but summarizedtogether in this section for simplicity:

• A literal pattern is useful to filter constant values in a structure.It looks like a Python literal (including some values like True,False and None). It only matches objects equal to the literal, andnever binds.
• A capture pattern looks like x and is equivalent to an identicalassignment target: it always matches and binds the variablewith the given (simple) name.
• The wildcard pattern is a single underscore: _. It always matches,but does not capture any variable (which prevents interference with otheruses for _ and allows for some optimizations).
• A constant value pattern works like the literal but for certain namedconstants. Note that it must be a qualified (dotted) name, given the possibleambiguity with a capture pattern. It looks like Color.RED andonly matches values equal to the corresponding value. It never binds.
• A sequence pattern looks like [a, *rest, b] and is similar toa list unpacking. An important difference is that the elements nestedwithin it can be any kind of patterns, not just names or sequences.It matches only sequences of appropriate length, as long as all the sub-patternsalso match. It makes all the bindings of its sub-patterns.
• A mapping pattern looks like {"user": u, "emails": [*es]}. It matchesmappings with at least the set of provided keys, and if all thesub-patterns match their corresponding values. It binds whatever thesub-patterns bind while matching with the values corresponding to the keys.Adding **rest at the end of the pattern to capture extra items is allowed.
• A class pattern is similar to the above but matches attributes insteadof keys. It looks like datetime.date(year=y, day=d). It matchesinstances of the given type, having at least the specifiedattributes, as long as the attributes match with the correspondingsub-patterns. It binds whatever the sub-patterns bind when matching with thevalues ofthe given attributes. An optional protocol also allows matching positionalarguments.
• An OR pattern looks like [*x] | {"elems": [*x]}. It matches if anyof its sub-patterns match. It uses the binding for the leftmost patternthat matched.
• A walrus pattern looks like d := datetime(year=2020, month=m). Itmatches onlyif its sub-pattern also matches. It binds whatever the sub-pattern match does, andalso binds the named variable to the entire object.

The match statement

A simplified, approximate grammar for the proposed syntax is:

...compound_statement: | if_stmt ... | match_stmtmatch_stmt: "match" expression ':' NEWLINE INDENT case_block+ DEDENTcase_block: "case" pattern [guard] ':' blockguard: 'if' expressionpattern: walrus_pattern | or_patternwalrus_pattern: NAME ':=' or_patternor_pattern: closed_pattern ('|' closed_pattern)*closed_pattern: | literal_pattern | capture_pattern | wildcard_pattern | constant_pattern | sequence_pattern | mapping_pattern | class_pattern

See Appendix A for the full, unabridged grammar.The simplified grammars in this section are there for helping the reader,not as a full specification.

We propose that the match operation should be a statement, not an expression.Although inmany languages it is an expression, being a statement better suits the generallogic of Python syntax. See rejected ideas for more discussion.The allowed patterns are described in detail below in the patterns subsection.

The match and case keywords are proposed to be soft keywords,so that they are recognized as keywords at the beginning of a matchstatement or case block respectively, but are allowed to be used inother places as variable or argument names.

The proposed indentation structure is as following:

match some_expression: case pattern_1: ... case pattern_2: ...

Here, some_expression represents the value that is being matched against,which will be referred to hereafter as the subject of the match.

Match semantics

The proposed large scale semantics for choosing the match is to choose the firstmatching pattern and execute the corresponding suite. The remaining patternsare not tried. If there are no matching patterns, the statement ‘fallsthrough’, and execution continues at the following statement.

Essentially this is equivalent to a chain of if ... elif ... elsestatements. Note that unlike for the previously proposed switch statement,the pre-computed dispatch dictionary semantics does not apply here.

There is no default or else case - instead the special wildcard_ can be used (see the section on capture_pattern)as a final ‘catch-all’ pattern.

Name bindings made during a successful pattern match outlive the executed suiteand can be used after the match statement. This follows the logic of otherPython statements that can bind names, such as for loop and withstatement. For example:

match shape: case Point(x, y): ... case Rectangle(x, y, _, _): ...print(x, y) # This works

During failed pattern matches, some sub-patterns may succeed. For example,while matching the value [0, 1, 2] with the pattern (0, x, 1), thesub-pattern x may succeed if the list elements are matched from left to right.The implementation may choose to either make persistent bindings for thosepartial matches or not. User code including a match statement should not relyon the bindings being made for a failed match, but also shouldn’t assume thatvariables are unchanged by a failed match. This part of the behavior isleft intentionally unspecified so different implementations can addoptimizations, and to prevent introducing semantic restrictions that couldlimit the extensibility of this feature.

Note that some pattern types below define more specific rules about whenthe binding is made.

Allowed patterns

We introduce the proposed syntax gradually. Here we start from the mainbuilding blocks. The following patterns are supported:

literal_pattern: | number | string | 'None' | 'True' | 'False'

match number: case 0: print("Nothing") case 1: print("Just one") case 2: print("A couple") case -1: print("One less than nothing") case 1-1j: print("Good luck with that...")

case True: ...case 1: ...

capture_pattern: NAME

match greeting: case "": print("Hello!") case name: print(f"Hi {name}!")

match greeting: case "": print("Hello!") case name: print(f"Hi {name}!")if name == "Santa": # <-- might raise UnboundLocalError ... # but works fine if greeting was not empty

match data: case [x, x]: # Error! ...

wildcard_pattern: "_"

match data: case [_, _]: print("Some pair") print(_) # Error!

constant_pattern: NAME ('.' NAME)+

from enum import Enumclass Sides(str, Enum): SPAM = "Spam" EGGS = "eggs" ...match entree[-1]: case Sides.SPAM: # Compares entree[-1] == Sides.SPAM. response = "Have you got anything without Spam?" case side: # Assigns side = entree[-1]. response = f"Well, could I have their Spam instead of the {side} then?"

sequence_pattern: | '[' [values_pattern] ']' | '(' [value_pattern ',' [values pattern]] ')'values_pattern: ','.value_pattern+ ','?value_pattern: '*' capture_pattern | pattern

match collection: case 1, [x, *others]: print("Got 1 and a nested sequence") case (1, x): print(f"Got 1 and {x}")

mapping_pattern: '{' [items_pattern] '}'items_pattern: ','.key_value_pattern+ ','?key_value_pattern: | (literal_pattern | constant_pattern) ':' or_pattern | '**' capture_pattern

import constantsmatch config: case {"route": route}: process_route(route) case {constants.DEFAULT_PORT: sub_config, **rest}: process_config(sub_config, rest)

class_pattern: | name_or_attr '(' ')' | name_or_attr '(' ','.pattern+ ','? ')' | name_or_attr '(' ','.keyword_pattern+ ','? ')' | name_or_attr '(' ','.pattern+ ',' ','.keyword_pattern+ ','? ')'keyword_pattern: NAME '=' or_pattern

match shape: case Point(x, y): ... case Rectangle(x0, y0, x1, y1, painted=True): ...

Combining multiple patterns (OR patterns)

Multiple alternative patterns can be combined into one using |. This meansthe whole pattern matches if at least one alternative matches.Alternatives are tried from left to right and have a short-circuit property,subsequent patterns are not tried if one matched. Examples:

match something: case 0 | 1 | 2: print("Small number") case [] | [_]: print("A short sequence") case str() | bytes(): print("Something string-like") case _: print("Something else")

The alternatives may bind variables, as long as each alternative bindsthe same set of variables (excluding _). For example:

match something: case 1 | x: # Error! ... case x | 1: # Error! ... case one := [1] | two := [2]: # Error! ... case Foo(arg=x) | Bar(arg=x): # Valid, both arms bind 'x' ... case [x] | x: # Valid, both arms bind 'x' ...

Guards

Each top-level pattern can be followed by a guard of the formif expression. A case clause succeeds if the pattern matches and the guardevaluates to a true value. For example:

match input: case [x, y] if x > MAX_INT and y > MAX_INT: print("Got a pair of large numbers") case x if x > MAX_INT: print("Got a large number") case [x, y] if x == y: print("Got equal items") case _: print("Not an outstanding input")

If evaluating a guard raises an exception, it is propagated onwards ratherthan fail the case clause. Names that appear in a pattern are bound before theguard succeeds. So this will work:

values = [0]match values: case [x] if x: ... # This is not executed case _: ...print(x) # This will print "0"

Note that guards are not allowed for nested patterns, so that [x if x > 0]is a SyntaxError and 1 | 2 if 3 | 4 will be parsed as(1 | 2) if (3 | 4).

Walrus patterns

It is often useful to match a sub-pattern and bind the correspondingvalue to a name. For example, it can be useful to write more efficientmatches, or simply to avoid repetition. To simplify such cases, any pattern(other than the walrus pattern itself) can be preceded by a name andthe walrus operator (:=). For example:

match get_shape(): case Line(start := Point(x, y), end) if start == end: print(f"Zero length line at {x}, {y}")

The name on the left of the walrus operator can be used in a guard, inthe match suite, or after the match statement. However, the name willonly be bound if the sub-pattern succeeds. Another example:

match group_shapes(): case [], [point := Point(x, y), *other]: print(f"Got {point} in the second group") process_coordinates(x, y) ...

Technically, most such examples can be rewritten using guards and/or nestedmatch statements, but this will be less readable and/or will produce lessefficient code. Essentially, most of the arguments in PEP 572 apply hereequally.

The wildcard _ is not a valid name here.

The Match Protocol

The equivalent of an isinstance call is used to decide whether anobject matches a given class pattern and to extract the correspondingattributes. Classes requiring different matching semantics (such asduck-typing) can do so by defining __instancecheck__ (apre-existing metaclass hook) or by using typing.Protocol.

The procedure is as following:

• The class object for Class in Class(<sub-patterns>) islooked up and isinstance(obj, Class) is called, where obj isthe value being matched. If false, the match fails.
• Otherwise, if any sub-patterns are given in the form of positionalor keyword arguments, these are matched from left to right, asfollows. The match fails as soon as a sub-pattern fails; if allsub-patterns succeed, the overall class pattern match succeeds.
• If there are match-by-position items and the class has a__match_args__ attribute, the item at position iis matched against the value looked up by attribute__match_args__[i]. For example, a pattern Point2d(5, 8),where Point2d.__match_args__ == ["x", "y"], is translated(approximately) into obj.x == 5 and obj.y == 8.
• If there are more positional items than the length of__match_args__, a TypeError is raised.
• If the __match_args__ attribute is absent on the matched class,and one or more positional item appears in a match,TypeError is also raised. We don’t fall back onusing __slots__ or __annotations__ – “In the face of ambiguity,refuse the temptation to guess.”
• If there are any match-by-keyword items the keywords are looked upas attributes on the subject. If the lookup succeeds the value ismatched against the corresponding sub-pattern. If the lookup fails,the match fails.

Such a protocol favors simplicity of implementation over flexibility andperformance. For other considered alternatives, see extended matching.

For the most commonly-matched built-in types (bool,bytearray, bytes, dict, float,frozenset, int, list, set, str, and tuple), asingle positional sub-pattern is allowed to be passed tothe call. Rather than being matched against any particular attributeon the subject, it is instead matched against the subject itself. Thiscreates behavior that is useful and intuitive for these objects:

• bool(False) matches False (but not 0).
• tuple((0, 1, 2)) matches (0, 1, 2) (but not [0, 1, 2]).
• int(i) matches any int and binds it to the name i.

Overlapping sub-patterns

Certain classes of overlapping matches are detected atruntime and will raise exceptions. In addition to basic checksdescribed in the previous subsection:

• The interpreter will check that two match items are not targeting the sameattribute, for example Point2d(1, 2, y=3) is an error.
• It will also check that a mapping pattern does not attempt to matchthe same key more than once.

Special attribute __match_args__

The __match_args__ attribute is always looked up on the typeobject named in the pattern. If present, it must be a list or tupleof strings naming the allowed positional arguments.

In deciding what names should be available for matching, therecommended practice is that class patterns should be the mirror ofconstruction; that is, the set of available names and their typesshould resemble the arguments to __init__().

Only match-by-name will work by default, and classes should define__match_args__ as a class attribute if they would like to supportmatch-by-position. Additionally, dataclasses and named tuples willsupport match-by-position out of the box. See below for more details.

Exceptions and side effects

While matching each case, the match statement may trigger execution of otherfunctions (for example __getitem__(), __len__() ora property). Almost every exception caused by those propagates outside of thematch statement normally. The only case where an exception is not propagated isan AttributeError raised while trying to lookup an attribute while matchingattributes of a Class Pattern; that case results in just a matching failure,and the rest of the statement proceeds normally.

The only side-effect carried on explicitly by the matching process is the binding ofnames. However, the process relies on attribute access,instance checks, len(), equality and item access on the subject and some ofits components. It also evaluates constant value patterns and the left side ofclass patterns. While none of those typically create any side-effects, some ofthese objects could. This proposal intentionally leaves out any specificationof what methods are called or how many times. User code relying on thatbehavior should be considered buggy.

The standard library

To facilitate the use of pattern matching, several changes will be made tothe standard library:

• Namedtuples and dataclasses will have auto-generated __match_args__.
• For dataclasses the order of attributes in the generated __match_args__will be the same as the order of corresponding arguments in the generated__init__() method. This includes the situations where attributes areinherited from a superclass.

In addition, a systematic effort will be put into going throughexisting standard library classes and adding __match_args__ whereit looks beneficial.

Exhaustiveness checks

From a reliability perspective, experience shows that missing a case whendealing with a set of possible data values leads to hard to debug issues,thus forcing people to add safety asserts like this:

def get_first(data: Union[int, list[int]]) -> int: if isinstance(data, list) and data: return data[0] elif isinstance(data, int): return data else: assert False, "should never get here"

PEP 484 specifies that static type checkers should support exhaustiveness inconditional checks with respect to enum values. PEP 586 later generalized thisrequirement to literal types.

This PEP further generalizes this requirement toarbitrary patterns. A typical situation where this applies is matching anexpression with a union type:

def classify(val: Union[int, Tuple[int, int], List[int]]) -> str: match val: case [x, y] if x > 0 and y > 0: return f"A pair of {x} and {y}" case [x, *other]: return f"A sequence starting with {x}" case int(): return f"Some integer" # Type-checking error: some cases unhandled.

The exhaustiveness checks should also apply where both pattern matchingand enum values are combined:

from enum import Enumfrom typing import Unionclass Level(Enum): BASIC = 1 ADVANCED = 2 PRO = 3class User: name: str level: Levelclass Admin: name: straccount: Union[User, Admin]match account: case Admin(name=name) | User(name=name, level=Level.PRO): ... case User(level=Level.ADVANCED): ... # Type-checking error: basic user unhandled

Obviously, no Matchable protocol (in terms of PEP 544) is needed, sinceevery class is matchable and therefore is subject to the checks specifiedabove.

Sealed classes as algebraic data types

Quite often it is desirable to apply exhaustiveness to a set of classes withoutdefining ad-hoc union types, which is itself fragile if a class is missing inthe union definition. A design pattern where a group of record-like classes iscombined into a union is popular in other languages that support patternmatching and is known under a name of algebraic data types.

We propose to add a special decorator class @sealed to the typingmodule, that will have no effect at runtime, but will indicate to statictype checkers that all subclasses (direct and indirect) of this class shouldbe defined in the same module as the base class.

The idea is that since all subclasses are known, the type checker can treatthe sealed base class as a union of all its subclasses. Together withdataclasses this allows a clean and safe support of algebraic data typesin Python. Consider this example:

from dataclasses import dataclassfrom typing import sealed@sealedclass Node: ...class Expression(Node): ...class Statement(Node): ...@dataclassclass Name(Expression): name: str@dataclassclass Operation(Expression): left: Expression op: str right: Expression@dataclassclass Assignment(Statement): target: str value: Expression@dataclassclass Print(Statement): value: Expression

With such definition, a type checker can safely treat Node asUnion[Name, Operation, Assignment, Print], and also safely treat e.g.Expression as Union[Name, Operation]. So this will result in a typechecking error in the below snippet, because Name is not handled (and typechecker can give a useful error message):

def dump(node: Node) -> str: match node: case Assignment(target, value): return f"{target} = {dump(value)}" case Print(value): return f"print({dump(value)})" case Operation(left, op, right): return f"({dump(left)} {op} {dump(right)})"

Type erasure

Class patterns are subject to runtime type erasure. Namely, although onecan define a type alias IntQueue = Queue[int] so that a pattern likeIntQueue() is syntactically valid, type checkers should reject such amatch:

queue: Union[Queue[int], Queue[str]]match queue: case IntQueue(): # Type-checking error here ...

Note that the above snippet actually fails at runtime with the currentimplementation of generic classes in the typing module, as well aswith builtin generic classes in the recently accepted PEP 585, becausethey prohibit isinstance checks.

To clarify, generic classes are not prohibited in general from participatingin pattern matching, just that their type parameters can’t be explicitlyspecified. It is still fine if sub-patterns or literals bind the typevariables. For example:

from typing import Generic, TypeVar, UnionT = TypeVar('T')class Result(Generic[T]): first: T other: list[T]result: Union[Result[int], Result[str]]match result: case Result(first=int()): ... # Type of result is Result[int] here case Result(other=["foo", "bar", *rest]): ... # Type of result is Result[str] here

Note about constants

The fact that a capture pattern is always an assignment target may create unwantedconsequences when a user by mistake tries to “match” a value againsta constant instead of using the constant value pattern. As a result, atruntime such a match will always succeed and moreover override the value ofthe constant. It is important therefore that static type checkers warn aboutsuch situations. For example:

from typing import FinalMAX_INT: Final = 2 ** 64value = 0match value: case MAX_INT: # Type-checking error here: cannot assign to final name print("Got big number") case _: print("Something else")

Note that the CPython reference implementation also generates aSyntaxWarning message for this case.

Precise type checking of star matches

Type checkers should perform precise type checking of star items in patternmatching giving them either a heterogeneous list[T] type, ora TypedDict type as specified by PEP 589. For example:

stuff: Tuple[int, str, str, float]match stuff: case a, *b, 0.5: # Here a is int and b is list[str] ...

Don’t do this, pattern matching is hard to learn

In our opinion, the proposed pattern matching is not more difficult thanadding isinstance() and getattr() to iterable unpacking. Also, webelieve the proposed syntax significantly improves readability for a widerange of code patterns, by allowing to express what one wants to do, ratherthan how to do it. We hope the few real code snippets we included in the PEPabove illustrate this comparison well enough. For more real code examplesand their translations see Ref. [1].

Don’t do this, use existing method dispatching mechanisms

We recognize that some of the use cases for the match statement overlapwith what can be done with traditional object-oriented programming (OOP) designtechniques using class inheritance. The ability to choose alternatebehaviors based on testing the runtime type of a match subject mighteven seem heretical to strict OOP purists.

However, Python has always been a language that embraces a variety ofprogramming styles and paradigms. Classic Python design idioms such as“duck”-typing go beyond the traditional OOP model.

We believe that there are important use cases where the use of match resultsin a cleaner and more maintainable architecture. These use cases tend tobe characterized by a number of features:

• Algorithms which cut across traditional lines of data encapsulation. If analgorithm is processing heterogeneous elements of different types (such asevaluating or transforming an abstract syntax tree, or doing algebraicmanipulation of mathematical symbols), forcing the user to implementthe algorithm as individual methods on each element type results inlogic that is smeared across the entire codebase instead of being neatlylocalized in one place.
• Program architectures where the set of possible data types is relativelystable, but there is an ever-expanding set of operations to be performedon those data types. Doing this in a strict OOP fashion requires constantlyadding new methods to both the base class and subclasses to support the newmethods, “polluting” the base class with lots of very specialized methoddefinitions, and causing widespread disruption and churn in the code. Bycontrast, in a match-based dispatch, adding a new behavior merelyinvolves writing a new match statement.
• OOP also does not handle dispatching based on the shape of an object, suchas the length of a tuple, or the presence of an attribute – instead any suchdispatching decision must be encoded into the object’s type. Shape-baseddispatching is particularly interesting when it comes to handling “duck”-typedobjects.

Where OOP is clearly superior is in the opposite case: where the set of possibleoperations is relatively stable and well-defined, but there is an ever-growingset of data types to operate on. A classic example of this is UI widget toolkits,where there is a fixed set of interaction types (repaint, mouse click, keypress,and so on), but the set of widget types is constantly expanding as developersinvent new and creative user interaction styles. Adding a new kind of widgetis a simple matter of writing a new subclass, whereas with a match-based approachyou end up having to add a new case clause to many widespread match statements.We therefore don’t recommend using match in such a situation.

Allow more flexible assignment targets instead

There was an idea to instead just generalize the iterable unpacking to muchmore general assignment targets, instead of adding a new kind of statement.This concept is known in some other languages as “irrefutable matches”. Wedecided not to do this because inspection of real-life potential use casesshowed that in vast majority of cases destructuring is related to an ifcondition. Also many of those are grouped in a series of exclusive choices.

Make it an expression

In most other languages pattern matching is represented by an expression, notstatement. But making it an expression would be inconsistent with othersyntactic choices in Python. All decision making logic is expressed almostexclusively in statements, so we decided to not deviate from this.

Use a hard keyword

There were options to make match a hard keyword, or choose a differentkeyword. Although using a hard keyword would simplify life for simple-mindedsyntax highlighters, we decided not to use hard keyword for several reasons:

• Most importantly, the new parser doesn’t require us to do this. Unlike withasync that caused hardships with being a soft keyword for few releases,here we can make match a permanent soft keyword.
• match is so commonly used in existing code, that it would break almostevery existing program and will put a burden to fix code on many people whomay not even benefit from the new syntax.
• It is hard to find an alternative keyword that would not be commonly usedin existing programs as an identifier, and would still clearly reflect themeaning of the statement.

Use as or | instead of case for case clauses

The pattern matching proposed here is a combination of multi-branch controlflow (in line with switch in Algol-derived languages or cond in Lisp)and object-deconstruction as found in functional languages. While the proposedkeyword case highlights the multi-branch aspect, alternative keywords suchas as would equally be possible, highlighting the deconstruction aspect.as or with, for instance, also have the advantage of already beingkeywords in Python. However, since case as a keyword can only occur as aleading keyword inside a match statement, it is easy for a parser todistinguish between its use as a keyword or as a variable.

Other variants would use a symbol like | or =>, or go entirely withoutspecial marker.

Since Python is a statement-oriented language in the tradition of Algol, and aseach composite statement starts with an identifying keyword, case seemed tobe most in line with Python’s style and traditions.

Use a flat indentation scheme

There was an idea to use an alternative indentation scheme, for example whereevery case clause would not be indented with respect to the initial matchpart:

match expression:case pattern_1: ...case pattern_2: ...

The motivation is that although flat indentation saves some horizontal space,it may look awkward to an eye of a Python programmer, because everywhere elsecolon is followed by an indent. This will also complicate life forsimple-minded code editors. Finally, the horizontal space issue can bealleviated by allowing “half-indent” (i.e. two spaces instead of four) formatch statements.

In sample programs using match, written as part of the development of thisPEP, a noticeable improvement in code brevity is observed, more than making upfor the additional indentation level.

Another proposal considered was to use flat indentation but put theexpression on the line after match:, like this:

match: expressioncase pattern_1: ...case pattern_2: ...

This was ultimately rejected because the first block would be anovelty in Python’s grammar: a block whose only content is a singleexpression rather than a sequence of statements.

Alternatives for constant value pattern

This is probably the trickiest item. Matching against some pre-definedconstants is very common, but the dynamic nature of Python also makes itambiguous with capture patterns. Five other alternatives were considered:

• Use some implicit rules. For example, if a name was defined in the globalscope, then it refers to a constant, rather than representing acapture pattern:

  # Here, the name "spam" must be defined in the global scope (and# not shadowed locally). "side" must be local.match entree[-1]: case spam: ... # Compares entree[-1] == spam. case side: ... # Assigns side = entree[-1].

  This however can cause surprises and action at a distance if someonedefines an unrelated coinciding name before the match statement.

• Use a rule based on the case of a name. In particular, if the namestarts with a lowercase letter it would be a capture pattern, while ifit starts with uppercase it would refer to a constant:

  match entree[-1]: case SPAM: ... # Compares entree[-1] == SPAM. case side: ... # Assigns side = entree[-1].

  This works well with the recommendations for naming constants fromPEP 8. The main objection is that there’s no other part of corePython where the case of a name is semantically significant.In addition, Python allows identifiers to use different scripts,many of which (e.g. CJK) don’t have a case distinction.

• Use extra parentheses to indicate lookup semantics for a given name. Forexample:

  match entree[-1]: case (spam): ... # Compares entree[-1] == spam. case side: ... # Assigns side = entree[-1].

  This may be a viable option, but it can create some visual noise if usedoften. Also honestly it looks pretty unusual, especially in nested contexts.

  This also has the problem that we may want or need parentheses todisambiguate grouping in patterns, e.g. in Point(x, y=(y :=complex())).

• Introduce a special symbol, for example ., ?, $, or ^ toindicate that a given name is a value to be matched against, notto be assigned to. An earlier version of this proposal used aleading-dot rule:

  match entree[-1]: case .spam: ... # Compares entree[-1] == spam. case side: ... # Assigns side = entree[-1].

  While potentially useful, it introduces strange-looking new syntaxwithout making the pattern syntax any more expressive. Indeed,named constants can be made to work with the existing rules byconverting them to Enum types, or enclosing them in their ownnamespace (considered by the authors to be one honking great idea):

  match entree[-1]: case Sides.SPAM: ... # Compares entree[-1] == Sides.SPAM. case side: ... # Assigns side = entree[-1].

  If needed, the leading-dot rule (or a similar variant) could beadded back later with no backward-compatibility issues.

• There was also an idea to make lookup semantics the default, and require$ or ? to be used in capture patterns:

  match entree[-1]: case spam: ... # Compares entree[-1] == spam. case side?: ... # Assigns side = entree[-1].

  There are a few issues with this:

  • Capture patterns are more common in typical code, so it isundesirable to require special syntax for them.
  • The authors are not aware of any other language that adornscaptures in this way.
  • None of the proposed syntaxes have any precedent in Python;no other place in Python that binds names (e.g. import,def, for) uses special marker syntax.
  • It would break the syntactic parallels of the current grammar:

    match coords: case ($x, $y): return Point(x, y) # Why not "Point($x, $y)"?

In the end, these alternatives were rejected because of the mentioned drawbacks.

Disallow float literals in patterns

Because of the inexactness of floats, an early version of this proposaldid not allow floating-point constants to be used as match patterns. Partof the justification for this prohibition is that Rust does this.

However, during implementation, it was discovered that distinguishing betweenfloat values and other types required extra code in the VM that would slowmatches generally. Given that Python and Rust are very different languageswith different user bases and underlying philosophies, it was felt thatallowing float literals would not cause too much harm, and would be lesssurprising to users.

Range matching patterns

This would allow patterns such as 1...6. However, there are a host ofambiguities:

• Is the range open, half-open, or closed? (I.e. is 6 included in theabove example or not?)
• Does the range match a single number, or a range object?
• Range matching is often used for character ranges (‘a’…’z’) but thatwon’t work in Python since there’s no character data type, just strings.
• Range matching can be a significant performance optimization if you canpre-build a jump table, but that’s not generally possible in Python dueto the fact that names can be dynamically rebound.

Rather than creating a special-case syntax for ranges, it was decidedthat allowing custom pattern objects (InRange(0, 6)) would be more flexibleand less ambiguous; however those ideas have been postponed for the timebeing (See deferred ideas).

Use dispatch dict semantics for matches

Implementations for classic switch statement sometimes use a pre-computedhash table instead of a chained equality comparisons to gain some performance.In the context of match statement this is technically also possible formatches against literal patterns. However, having subtly different semanticsfor different kinds of patterns would be too surprising for potentiallymodest performance win.

We can still experiment with possible performance optimizations in thisdirection if they will not cause semantic differences.

Use continue and break in case clauses.

Another rejected proposal was to define new meanings for continueand break inside of match, which would have the following behavior:

• continue would exit the current case clause and continue matchingat the next case clause.
• break would exit the match statement.

However, there is a serious drawback to this proposal: if the match statementis nested inside of a loop, the meanings of continue and break are nowchanged. This may cause unexpected behavior during refactorings; also, anargument can be made that there are other means to get the same behavior (suchas using guard conditions), and that in practice it’s likely that the existingbehavior of continue and break are far more useful.

This proposal defines an OR-pattern (|) to match one of several alternates;why not also an AND-pattern (&)? Especially given that some other languages(F# for example) support this.

However, it’s not clear how useful this would be. The semantics for matchingdictionaries, objects and sequences already incorporates an implicit ‘and’: allattributes and elements mentioned must be present for the match to succeed. Guardconditions can also support many of the use cases that a hypothetical ‘and’operator would be used for.

In the end, it was decided that this would make the syntax more complex withoutadding a significant benefit.

Negative match patterns

A negation of a match pattern using the operator ! as a prefix would matchexactly if the pattern itself does not match. For instance, !(3 | 4)would match anything except 3 or 4.

This was rejected because there is documented evidence that this featureis rarely useful (in languages which support it) or used as double negation!! to control variable scopes and prevent variable bindings (which doesnot apply to Python). It can also be simulated using guard conditions.

Check exhaustiveness at runtime

The question is what to do if no case clause has a matching pattern, andthere is no default case. An earlier version of the proposal specified thatthe behavior in this case would be to throw an exception rather thansilently falling through.

The arguments back and forth were many, but in the end the EIBTI (ExplicitIs Better Than Implicit) argument won out: it’s better to have the programmerexplicitly throw an exception if that is the behavior they want.

For cases such as sealed classes and enums, where the patterns are all knownto be members of a discrete set, static checkers can warn about missingpatterns.

Type annotations for pattern variables

The proposal was to combine patterns with type annotations:

match x: case [a: int, b: str]: print(f"An int {a} and a string {b}:) case [a: int, b: int, c: int]: print(f"Three ints", a, b, c) ...

This idea has a lot of problems. For one, the colon can onlybe used inside of brackets or parens, otherwise the syntax becomesambiguous. And because Python disallows isinstance() checkson generic types, type annotations containing generics will notwork as expected.

Allow *rest in class patterns

It was proposed to allow *rest in a class pattern, giving avariable to be bound to all positional arguments at once (similar toits use in unpacking assignments). It would provide some symmetrywith sequence patterns. But it might be confused with a feature toprovide the values for all positional arguments at once. And thereseems to be no practical need for it, so it was scrapped. (It couldeasily be added at a later stage if a need arises.)

Disallow _.a in constant value patterns

The first public draft said that the initial name in a constant valuepattern must not be _ because _ has a special meaning inpattern matching, so this would be invalid:

case _.a: ...

(However, a._ would be legal and load the attribute with name_ of the object a as usual.)

There was some pushback against this on python-dev (some people have alegitimate use for _ as an important global variable, esp. ini18n) and the only reason for this prohibition was to prevent someuser confusion. But it’s not the hill to die on.

Use some other token as wildcard

It has been proposed to use ... (i.e., the ellipsis token) or* (star) as a wildcard. However, both these look as if anarbitrary number of items is omitted:

case [a, ..., z]: ...case [a, *, z]: ...

Both look like the would match a sequence of at two or more items,capturing the first and last values.

In addition, if * were to be used as the wildcard character, wewould have to come up with some other way to capture the rest of asequence, currently spelled like this:

case [first, second, *rest]: ...

Using an ellipsis would also be more confusing in documentation andexamples, where ... is routinely used to indicate somethingobvious or irrelevant. (Yes, this would also be an argument againstthe other uses of ... in Python, but that water is already underthe bridge.)

Another proposal was to use ?. This could be acceptable, althoughit would require modifying the tokenizer.

Also, _ is already usedas a throwaway target in other contexts, and this use is prettysimilar. This example is from difflib.py in the stdlib:

for tag, _, _, j1, j2 in group: ...

Perhaps the most convincing argument is that _ is used as thewildcard in every other language we’ve looked at supporting patternmatching: C#, Elixir, Erlang, F#, Haskell, Mathematica, OCaml, Ruby,Rust, Scala, and Swift. Now, in general, we should not be concernedtoo much with what another language does, since Python is clearlydifferent from all these languages. However, if there is such anoverwhelming and strong consensus, Python should not go out of its wayto do something completely different – particularly given that _works well in Python and is already in use as a throwaway target.

Note that _ is not assigned to by patterns – this avoidsconflicts with the use of _ as a marker for translatable stringsand an alias for gettext.gettext, as recommended by thegettext module documentation.

Use some other syntax instead of | for OR patterns

A few alternatives to using | to separate the alternatives in ORpatterns have been proposed. Instead of:

case 401|403|404: print("Some HTTP error")

the following proposals have been fielded:

• Use a comma:

  case 401, 403, 404: print("Some HTTP error")

  This looks too much like a tuple – we would have to find adifferent way to spell tuples, and the construct would have to beparenthesized inside the argument list of a class pattern. Ingeneral, commas already have many different meanings in Python, weshouldn’t add more.

• Allow stacked cases:

  case 401:case 403:case 404: print("Some HTTP error")

  This is how this would be done in C, using its fall-throughsemantics for cases. However, we don’t want to mislead people intothinking that match/case uses fall-through semantics (whichare a common source of bugs in C). Also, this would be a novelindentation pattern, which might make it harder to support in IDEsand such (it would break the simple rule “add an indentation levelafter a line ending in a colon”). Finally, this wouldn’t supportOR patterns nested inside other patterns.

• Use case in followed by a comma-separated list:

  case in 401, 403, 404: print("Some HTTP error")

  This wouldn’t work for OR patterns nested inside other patterns,like:

  case Point(0|1, 0|1): print("A corner of the unit square")

• Use the or keyword:

  case 401 or 403 or 404: print("Some HTTP error")

  This could work, and the readability is not too different from using|. Some users expressed a preference for or because theyassociate | with bitwise OR. However:

  1. Many other languages that have pattern matching use | (thelist includes Elixir, Erlang, F#, Mathematica, OCaml, Ruby, Rust,and Scala).
  2. | is shorter, which may contribute to the readability ofnested patterns like Point(0|1, 0|1).
  3. Some people mistakenly believe that | has the wrong priority;but since patterns don’t support other operators it has the samepriority as in expressions.
  4. Python users use or very frequently, and may build animpression that it is strongly associated with Booleanshort-circuiting.
  5. | is used between alternatives in regular expressionsand in EBNF grammars (like Python’s own).
  6. | not just used for bitwise OR – it’s used for set unions,dict merging (PEP 584) and is being considered as analternative to typing.Union (PEP 604).
  7. | works better as a visual separator, especially betweenstrings. Compare:

     case "spam" or "eggs" or "cheese":

     to:

     case "spam" | "eggs" | "cheese":

Add an else clause

We decided not to add an else clause for several reasons.

• It is redundant, since we already have case _:
• There will forever be confusion about the indentation level of theelse: – should it align with the list of cases or with thematch keyword?
• Completionist arguments like “every other statement has one” arefalse – only those statements have an else clause where it addsnew functionality.

One-off syntax variant

While inspecting some code-bases that may benefit the most from the proposedsyntax, it was found that single clause matches would be used relatively often,mostly for various special-casing. In other languages this is supported inthe form of one-off matches. We proposed to support such one-off matches too:

if match value as pattern [and guard]: ...

or, alternatively, without the if:

match value as pattern [if guard]: ...

as equivalent to the following expansion:

match value: case pattern [if guard]: ...

To illustrate how this will benefit readability, consider this (slightlysimplified) snippet from real code:

if isinstance(node, CallExpr): if (isinstance(node.callee, NameExpr) and len(node.args) == 1 and isinstance(node.args[0], NameExpr)): call = node.callee.name arg = node.args[0].name ... # Continue special-casing 'call' and 'arg'... # Follow with common code

This can be rewritten in a more straightforward way as:

if match node as CallExpr(callee=NameExpr(name=call), args=[NameExpr(name=arg)]): ... # Continue special-casing 'call' and 'arg'... # Follow with common code

This one-off form would not allow elif match statements, as it was onlymeant to handle a single pattern case. It was intended to be special caseof a match statement, not a special case of an if statement:

if match value_1 as patter_1 [and guard_1]: ...elif match value_2 as pattern_2 [and guard_2]: # Not allowed ...elif match value_3 as pattern_3 [and guard_3]: # Not allowed ...else: # Also not allowed ...

This would defeat the purpose of one-off matches as a complement to exhaustivefull matches - it’s better and clearer to use a full match in this case.

Similarly, if not match would not be allowed, since match ... as ... is notan expression. Nor do we propose a while match construct present in some languageswith pattern matching, since although it may be handy, it will likely be usedrarely.

Other pattern-based constructions

Many other languages supporting pattern-matching use it as a basis for multiplelanguage constructs, including a matching operator, a generalized formof assignment, a filter for loops, a method for synchronizing communication,or specialized if statements. Some of these were mentioned in the discussionof the first draft. Another question asked was why this particular form (joiningbinding and conditional selection) was chosen while other forms were not.

Introducing more uses of patterns would be too bold and premature given theexperience we have using patterns, and would make this proposal toocomplicated. The statement as presented provides a form of the feature thatis sufficiently general to be useful while being self-contained, and withouthaving a massive impact on the syntax and semantics of the language as a whole.

After some experience with this feature, the community may have a betterfeeling for what other uses of pattern matching could be valuable in Python.

Algebraic matching of repeated names

A technique occasionally seen in functional languages like Erlang and Elixir isto use a match variable multiple times in the same pattern:

match value: case Point(x, x): print("Point is on a diagonal!")

The idea here is that the first appearance of x would bind the valueto the name, and subsequent occurrences would verify that the incomingvalue was equal to the value previously bound. If the value was not equal,the match would fail.

However, there are a number of subtleties involved with mixing load-storesemantics for capture patterns. For the moment, we decided to make repeateduse of names within the same pattern an error; we can always relax thisrestriction later without affecting backwards compatibility.

Note that you can use the same name more than once in alternate choices:

match value: case x | [x]: # etc.

Custom matching protocol

During the initial design discussions for this PEP, there were a lot of ideasthrown around about custom matchers. There were a couple of motivations forthis:

• Some classes might want to expose a different set of “matchable” namesthan the actual class properties.
• Some classes might have properties that are expensive to calculate, andtherefore shouldn’t be evaluated unless the match pattern actually neededaccess to them.
• There were ideas for exotic matchers such as IsInstance(),InRange(), RegexMatchingGroup() and so on.
• In order for built-in types and standard library classes to be ableto support matching in a reasonable and intuitive way, it was believedthat these types would need to implement special matching logic.

These customized match behaviors would be controlled by a special__match__ method on the class name. There were two competing variants:

• A ‘full-featured’ match protocol which would pass in not onlythe subject to be matched, but detailed information aboutwhich attributes the specified pattern was interested in.
• A simplified match protocol, which only passed in the subject value,and which returned a “proxy object” (which in most cases could bejust the subject) containing the matchable attributes.

Here’s an example of one version of the more complex protocol proposed:

match expr: case BinaryOp(left=Number(value=x), op=op, right=Number(value=y)): ...from types import PatternObjectBinaryOp.__match__( (), { "left": PatternObject(Number, (), {"value": ...}, -1, False), "op": ..., "right": PatternObject(Number, (), {"value": ...}, -1, False), }, -1, False,)

One drawback of this protocol is that the arguments to __match__would be expensive to construct, and could not be pre-computed due tothe fact that, because of the way names are bound, there are no realconstants in Python. It also meant that the __match__ method wouldhave to re-implement much of the logic of matching which would otherwisebe implemented in C code in the Python VM. As a result, this option wouldperform poorly compared to an equivalent if-statement.

The simpler protocol suffered from the fact that although it was moreperformant, it was much less flexible, and did not allow for many ofthe creative custom matchers that people were dreaming up.

Late in the design process, however, it was realized that the need fora custom matching protocol was much less than anticipated. Virtuallyall the realistic (as opposed to fanciful) uses cases brought up couldbe handled by the built-in matching behavior, although in a few casesan extra guard condition was required to get the desired effect.

Moreover, it turned out that none of the standard library classes reallyneeded any special matching support other than an appropriate__match_args__ property.

The decision to postpone this feature came with a realization that this isnot a one-way door; that a more flexible and customizable matching protocolcan be added later, especially as we gain more experience with real-worlduse cases and actual user needs.

The authors of this PEP expect that the match statement will evolveover time as usage patterns and idioms evolve, in a way similar to whatother “multi-stage” PEPs have done in the past. When this happens, theextended matching issue can be revisited.

Parameterized Matching Syntax

(Also known as “Class Instance Matchers”.)

This is another variant of the “custom match classes” idea that would allowdiverse kinds of custom matchers mentioned in the previous section – however,instead of using an extended matching protocol, it would be achieved byintroducing an additional pattern type with its own syntax. This pattern typewould accept two distinct sets of parameters: one set which consists of theactual parameters passed into the pattern object’s constructor, and anotherset representing the binding variables for the pattern.

The __match__ method of these objects could use the constructor parametervalues in deciding what was a valid match.

This would allow patterns such as InRange<0, 6>(value), which would matcha number in the range 0..6 and assign the matched value to ‘value’. Similarly,one could have a pattern which tests for the existence of a named group ina regular expression match result (different meaning of the word ‘match’).

Although there is some support for this idea, there was a lot of bikesheddingon the syntax (there are not a lot of attractive options available)and no clear consensus was reached, so it was decided that for now, thisfeature is not essential to the PEP.

Pattern Utility Library

Both of the previous ideas would be accompanied by a new Python standardlibrary module which would contain a rich set of useful matchers.However, it is not really possible to implement such a library withoutadopting one of the extended pattern proposals given in the previous sections,so this idea is also deferred.