Pure functions

Consider trying to add type hints to a function movie_string:

def movie_string(movie: Movie) -> str:
    if movie.get("year") is None:
        return movie["name"]
    else:
        return f'{movie["name"]} ({movie["year"]})'

We could define this Movie type using a TypedDict:

from typing import NotRequired, TypedDict

class Movie(TypedDict):
    name: str
    year: NotRequired[int | None]

But suppose we have another type where year is required:

class MovieRecord(TypedDict):
    name: str
    year: int

Attempting to pass a MovieRecord into movie_string results in the error (using mypy):

Argument 1 to "movie_string" has incompatible type "MovieRecord"; expected "Movie"

This particular use case should be type-safe, but the type checker correctly stops the user from passing a MovieRecord into a Movie parameter in the general case, because the Movie class has mutator methods that could potentially allow the function to break the type constraints in MovieRecord (e.g. with movie["year"] = None or del movie["year"]). The problem disappears if we don’t have mutator methods in Movie. This could be achieved by defining an immutable interface using a PEP 544 Protocol:

from typing import Literal, Protocol, overload

class Movie(Protocol):
    @overload
    def get(self, key: Literal["name"]) -> str: ...

    @overload
    def get(self, key: Literal["year"]) -> int | None: ...

    @overload
    def __getitem__(self, key: Literal["name"]) -> str: ...

    @overload
    def __getitem__(self, key: Literal["year"]) -> int | None: ...

This is very repetitive, easy to get wrong, and is still missing important method definitions like __contains__() and keys().

Updating nested dicts

The structural typing of TypedDict is supposed to permit writing update functions that only constrain the types of items they modify:

class HasTimestamp(TypedDict):
    timestamp: float

class Logs(TypedDict):
    timestamp: float
    loglines: list[str]

def update_timestamp(d: HasTimestamp) -> None:
    d["timestamp"] = now()

def add_logline(logs: Logs, logline: str) -> None:
    logs["loglines"].append(logline)
    update_timestamp(logs)  # Accepted by type checker

However, this no longer works once you start nesting dictionaries:

class HasTimestampedMetadata(TypedDict):
    metadata: HasTimestamp

class UserAudit(TypedDict):
    name: str
    metadata: Logs

def update_metadata_timestamp(d: HasTimestampedMetadata) -> None:
    d["metadata"]["timestamp"] = now()

def rename_user(d: UserAudit, name: str) -> None:
    d["name"] = name
    update_metadata_timestamp(d)  # Type check error: "metadata" is not of type HasTimestamp

This looks like an error, but is simply due to the (unwanted) ability to overwrite the metadata item held by the HasTimestampedMetadata instance with a different HasTimestamp instance, that may no longer be a Logs instance.

It is possible to work around this issue with generics (as of Python 3.11), but it is very complicated, requiring a type parameter for every nested dict.

typing.ReadOnly type qualifier

The typing.ReadOnly type qualifier is used to indicate that an item declared in a TypedDict definition may not be mutated (added, modified, or removed):

from typing import ReadOnly

class Band(TypedDict):
    name: str
    members: ReadOnly[list[str]]

blur: Band = {"name": "blur", "members": []}
blur["name"] = "Blur"  # OK: "name" is not read-only
blur["members"] = ["Damon Albarn"]  # Type check error: "members" is read-only
blur["members"].append("Damon Albarn")  # OK: list is mutable

Alternative functional syntax

The alternative functional syntax for TypedDict also supports the new type qualifier:

Band = TypedDict("Band", {"name": str, "members": ReadOnly[list[str]]})

Interaction with other special types

ReadOnly[] can be used with Required[], NotRequired[] and Annotated[], in any nesting order:

class Movie(TypedDict):
    title: ReadOnly[Required[str]]  # OK
    year: ReadOnly[NotRequired[Annotated[int, ValueRange(-9999, 9999)]]]  # OK

class Movie(TypedDict):
    title: Required[ReadOnly[str]]  # OK
    year: Annotated[NotRequired[ReadOnly[int]], ValueRange(-9999, 9999)]  # OK

This is consistent with the behavior introduced in PEP 655.

Inheritance

Subclasses can redeclare read-only items as non-read-only, allowing them to be mutated:

class NamedDict(TypedDict):
    name: ReadOnly[str]

class Album(NamedDict):
    name: str
    year: int

album: Album = { "name": "Flood", "year": 1990 }
album["year"] = 1973
album["name"] = "Dark Side Of The Moon"  # OK: "name" is not read-only in Album

If a read-only item is not redeclared, it remains read-only:

class Album(NamedDict):
    year: int

album: Album = { "name": "Flood", "year": 1990 }
album["name"] = "Dark Side Of The Moon"  # Type check error: "name" is read-only in Album

Subclasses can narrow value types of read-only items:

class AlbumCollection(TypedDict):
    albums: ReadOnly[Collection[Album]]

class RecordShop(AlbumCollection):
    name: str
    albums: ReadOnly[list[Album]]  # OK: "albums" is read-only in AlbumCollection

Subclasses can require items that are read-only but not required in the superclass:

class OptionalName(TypedDict):
    name: ReadOnly[NotRequired[str]]

class RequiredName(OptionalName):
    name: ReadOnly[Required[str]]

d: RequiredName = {}  # Type check error: "name" required

Subclasses can combine these rules:

class OptionalIdent(TypedDict):
    ident: ReadOnly[NotRequired[str | int]]

class User(OptionalIdent):
    ident: str  # Required, mutable, and not an int

Note that these are just consequences of structural typing, but they are highlighted here as the behavior now differs from the rules specified in PEP 589.

Type consistency

This section updates the type consistency rules introduced in PEP 589 to cover the new feature in this PEP. In particular, any pair of types that do not use the new feature will be consistent under these new rules if (and only if) they were already consistent.

A TypedDict type A is consistent with TypedDict B if A is structurally compatible with B. This is true if and only if all of the following are satisfied:

• For each item in B, A has the corresponding key, unless the item in B is read-only, not required, and of top value type (ReadOnly[NotRequired[object]]).
• For each item in B, if A has the corresponding key, the corresponding value type in A is consistent with the value type in B.
• For each non-read-only item in B, its value type is consistent with the corresponding value type in A.
• For each required key in B, the corresponding key is required in A.
• For each non-required key in B, if the item is not read-only in B, the corresponding key is not required in A.

Discussion:

• All non-specified items in a TypedDict implicitly have value type ReadOnly[NotRequired[object]].
• Read-only items behave covariantly, as they cannot be mutated. This is similar to container types such as Sequence, and different from non-read-only items, which behave invariantly. Example:

  class A(TypedDict):
      x: ReadOnly[int | None]
  
  class B(TypedDict):
      x: int
  
  def f(a: A) -> None:
      print(a["x"] or 0)
  
  b: B = {"x": 1}
  f(b)  # Accepted by type checker
  

• A TypedDict type A with no explicit key 'x' is not consistent with a TypedDict type B with a non-required key 'x', since at runtime the key 'x' could be present and have an incompatible type (which may not be visible through A due to structural subtyping). The only exception to this rule is if the item in B is read-only, and the value type is of top type (object). For example:

  class A(TypedDict):
      x: int
  
  class B(TypedDict):
      x: int
      y: ReadOnly[NotRequired[object]]
  
  a: A = { "x": 1 }
  b: B = a  # Accepted by type checker
  

Update method

In addition to existing type checking rules, type checkers should error if a TypedDict with a read-only item is updated with another TypedDict that declares that key:

class A(TypedDict):
    x: ReadOnly[int]
    y: int

a1: A = { "x": 1, "y": 2 }
a2: A = { "x": 3, "y": 4 }
a1.update(a2)  # Type check error: "x" is read-only in A

Unless the declared value is of bottom type (Never):

class B(TypedDict):
    x: NotRequired[typing.Never]
    y: ReadOnly[int]

def update_a(a: A, b: B) -> None:
    a.update(b)  # Accepted by type checker: "x" cannot be set on b

Note: Nothing will ever match the Never type, so an item annotated with it must be absent.

Keyword argument typing

PEP 692 introduced Unpack to annotate **kwargs with a TypedDict. Marking one or more of the items of a TypedDict used in this way as read-only will have no effect on the type signature of the method. However, it will prevent the item from being modified in the body of the function:

class Args(TypedDict):
    key1: int
    key2: str

class ReadOnlyArgs(TypedDict):
    key1: ReadOnly[int]
    key2: ReadOnly[str]

class Function(Protocol):
    def __call__(self, **kwargs: Unpack[Args]) -> None: ...

def impl(**kwargs: Unpack[ReadOnlyArgs]) -> None:
    kwargs["key1"] = 3  # Type check error: key1 is readonly

fn: Function = impl  # Accepted by type checker: function signatures are identical

Runtime behavior

TypedDict types will gain two new attributes, __readonly_keys__ and __mutable_keys__, which will be frozensets containing all read-only and non-read-only keys, respectively:

class Example(TypedDict):
    a: int
    b: ReadOnly[int]
    c: int
    d: ReadOnly[int]

assert Example.__readonly_keys__ == frozenset({'b', 'd'})
assert Example.__mutable_keys__ == frozenset({'a', 'c'})

typing.get_type_hints will strip out any ReadOnly type qualifiers, unless include_extras is True:

assert get_type_hints(Example)['b'] == int
assert get_type_hints(Example, include_extras=True)['b'] == ReadOnly[int]

typing.get_origin and typing.get_args will be updated to recognize ReadOnly:

assert get_origin(ReadOnly[int]) is ReadOnly
assert get_args(ReadOnly[int]) == (int,)

A TypedMapping protocol type

An earlier version of this PEP proposed a TypedMapping protocol type, behaving much like a read-only TypedDict but without the constraint that the runtime type be a dict. The behavior described in the current version of this PEP could then be obtained by inheriting a TypedDict from a TypedMapping. This has been set aside for now as more complex, without a strong use-case motivating the additional complexity.

A higher-order ReadOnly type

A generalized higher-order type could be added that removes mutator methods from its parameter, e.g. ReadOnly[MovieRecord]. For a TypedDict, this would be like adding ReadOnly to every item, including those declared in superclasses. This would naturally want to be defined for a wider set of types than just TypedDict subclasses, and also raises questions about whether and how it applies to nested types. We decided to keep the scope of this PEP narrower.

Calling the type Readonly

Read-only is generally hyphenated, and it appears to be common convention to put initial caps onto words separated by a dash when converting to CamelCase. This appears consistent with the definition of CamelCase on Wikipedia: CamelCase uppercases the first letter of each word. That said, Python examples or counter-examples, ideally from the core Python libraries, or better explicit guidance on the convention, would be greatly appreciated.

Reusing the Final annotation

The Final annotation prevents an attribute from being modified, like the proposed ReadOnly qualifier does for TypedDict items. However, it is also documented as preventing redefinition in subclasses too; from PEP 591:

The typing.Final type qualifier is used to indicate that a variable or attribute should not be reassigned, redefined, or overridden.

This does not fit with the intended use of ReadOnly. Rather than introduce confusion by having Final behave differently in different contexts, we chose to introduce a new qualifier.

A readonly flag

Earlier versions of this PEP introduced a boolean flag that would ensure all items in a TypedDict were read-only:

class Movie(TypedDict, readonly=True):
    name: str
    year: NotRequired[int | None]

movie: Movie = { "name": "A Clockwork Orange" }
movie["year"] = 1971  # Type check error: "year" is read-only

However, this led to confusion when inheritance was introduced:

class A(TypedDict):
    key1: int

class B(A, TypedDict, readonly=True):
    key2: int

b: B = { "key1": 1, "key2": 2 }
b["key1"] = 4  # Accepted by type checker: "key1" is not read-only

It would be reasonable for someone familiar with frozen (from dataclasses), on seeing just the definition of B, to assume that the whole type was read-only. On the other hand, it would be reasonable for someone familiar with total to assume that read-only only applies to the current type.

The original proposal attempted to eliminate this ambiguity by making it both a type check and a runtime error to define B in this way. This was still a source of surprise to people expecting it to work like total.

Given that no extra types could be expressed with the readonly flag, it has been removed from the proposal to avoid ambiguity and surprise.

Supporting type-checked removal of read-only qualifier via copy and other methods

An earlier version of this PEP mandated that code like the following be supported by type-checkers:

class A(TypedDict):
    x: ReadOnly[int]

class B(TypedDict):
    x: ReadOnly[str]

class C(TypedDict):
    x: int | str

def copy_and_modify(a: A) -> C:
    c: C = copy.copy(a)
    if not c['x']:
        c['x'] = "N/A"
    return c

def merge_and_modify(a: A, b: B) -> C:
    c: C = a | b
    if not c['x']:
        c['x'] = "N/A"
    return c

However, there is currently no way to express this in the typeshed, meaning type-checkers would be forced to special-case these functions. There is already a way to code these operations that mypy and pyright do support, though arguably this is less readable:

copied: C = { **a }
merged: C = { **a, **b }

While not as flexible as would be ideal, the current typeshed stubs are sound, and remain so if this PEP is accepted. Updating the typeshed would require new typing features, like a type constructor to express the type resulting from merging two or more dicts, and a type qualifier to indicate a returned value is not shared (so may have type constraints like read-only and invariance of generics loosened in specific ways), plus details of how type-checkers would be expected to interpret these features. These could be valuable additions to the language, but are outside the scope of this PEP.

Given this, we have deferred any update of the typeshed stubs.

Preventing unspecified keys in TypedDicts

Consider the following “type discrimination” code:

class A(TypedDict):
  foo: int

class B(TypedDict):
  bar: int

def get_field(d: A | B) -> int:
  if "foo" in d:
    return d["foo"]  # !!!
  else:
    return d["bar"]

This is a common idiom, and other languages like Typescript allow it. Technically, however, this code is unsound: B does not declare foo, but instances of B may still have the key present, and the associated value may be of any type:

class C(TypedDict):
  foo: str
  bar: int

c: C = { "foo": "hi", "bar" 3 }
b: B = c  # OK: C is structurally compatible with B
v = get_field(b)  # Returns a string at runtime, not an int!

mypy rejects the definition of get_field on the marked line with the error TypedDict "B" has no key "foo", which is a rather confusing error message, but is caused by this unsoundness.

One option for correcting this would be to explicitly prevent B from holding a foo:

class B(TypedDict):
  foo: NotRequired[Never]
  bar: int

b: B = c  # Type check error: key "foo" not allowed in B

However, this requires every possible key that might be used to discriminate on to be explicitly declared in every type, which is not generally feasible. A better option would be to have a way of preventing all unspecified keys from being included in B. mypy supports this using the @final decorator from PEP 591:

@final
class B(TypedDict):
  bar: int

The reasoning here is that this prevents C or any other type from being considered a “subclass” of B, so instances of B can now be relied on to never hold the key foo, even though it is not explicitly declared to be of bottom type.

With the introduction of read-only items, however, this reasoning would imply type-checkers should ban the following:

@final
class D(TypedDict):
  field: ReadOnly[Collection[str]]

@final
class E(TypedDict):
  field: list[str]

e: E = { "field": ["value1", "value2"] }
d: D = e  # Error?

The conceptual problem here is that TypedDicts are structural types: they cannot really be subclassed. As such, using @final on them is not well-defined; it is certainly not mentioned in PEP 591.

An earlier version of this PEP proposed resolving this by adding a new flag to TypedDict that would explicitly prevent other keys from being used, but not other kinds of structural compatibility:

class B(TypedDict, other_keys=Never):
  bar: int

b: B = c  # Type check error: key "foo" not allowed in B

However, during the process of drafting, the situation changed:

• pyright, which previously worked similarly to mypy in this type discrimination case, changed to allow the original example without error, despite the unsoundness, due to it being a common idiom
• mypy has an open issue to follow the lead of pyright and Typescript and permit the idiom as well
• a draft of PEP-728 was created that is a superset of the other_keys functionality

As such, there is less urgency to address this issue in this PEP, and it has been deferred to PEP-728.