Mailman 3 October 2022 - Typing-sig

Re: Typing Meetup
by S Pradeep Kumar 19 Sep '23

19 Sep '23

Updating the Typing Meetup date to Wednesday as there were some conflicts. Let me know if there are any major time conflicts. Date: *Wednesday, November 03, 2021.*Time: 10 am San Francisco time (UTC-7) / 5 pm London time (UTC) Duration: 1 hour Potential discussion topics: + Typing **kwargs using a TypedDict (Franek Magiera?, Pradeep) [1] + Remaining questions about Callable syntax (Steven) + Non-required Protocol fields (Sebastian Rittau?) [2] + Type support for sealed classes (Adrian Freund?) [3] + Safe, typed JSON validation + Type system documentation (Shannon?) If you're working on these or any other topics and would like to start a discussion at the meetup, reach out to me. If you have interesting topics you'd like to see discussed, share them too. Once we have topics, we can always start a discussion even if there's no proposer yet. We will probably have time for 3-4 topics in this session. On Fri, Oct 15, 2021 at 12:55 PM S Pradeep Kumar <gohanpra(a)gmail.com> wrote: >>> >>>> Hello all, >>>> >>>> We plan to hold a virtual Typing Meetup to discuss key typing-related >>>> issues in real-time. The idea is that we can make progress on important or >>>> controversial discussions, get early feedback on in-progress PEPs, and >>>> gauge interest in new proposals. >>>> >>>> A virtual meetup will be more accessible to people in other timezones >>>> than Bay Area in-person meetups. It should also help consolidate >>>> discussions that are often spread out across mailing lists or Github repos. >>>> The tentative plan is to have a monthly meetup, but we can change that as >>>> needed. >>>> >>>> Date: Monday, November 01, 2021. >>>> Time: 10 am San Francisco time (UTC-7) / 5 pm London time (UTC) >>>> Duration: 1 hour >>>> >>>> Time and date are flexible. I'll share the zoom link and agenda the >>>> week before. I'll also record the meetup and share afterwards. >>>> >>>> If you're interested in presenting a new proposal or your work on some >>>> existing proposal, please respond here. We can shoot for 15 minutes per >>>> presentation. I know there are quite a few interesting threads on >>>> typing-sig that haven't seen much activity in a while :) >>>> -- >>>> S Pradeep Kumar >>>> _______________________________________________ >>>> Typing-sig mailing list -- typing-sig(a)python.org >>>> To unsubscribe send an email to typing-sig-leave(a)python.org >>>> https://mail.python.org/mailman3/lists/typing-sig.python.org/ >>>> Member address: guido(a)python.org >>> >>> Links: [1] Typing **kwargs using TypedDict: https://mail.python.org/archives/list/typing-sig@python.org/thread/IUCXT2IQ… [2] Non-required Protocol fields: https://mail.python.org/archives/list/typing-sig@python.org/thread/P6FCGRJU… [3] Sealed classes: https://mail.python.org/archives/list/typing-sig@python.org/thread/QSCT2N4R… -- S Pradeep Kumar

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Type narrowing for TypeGuard in the negative case
by Eric Traut 04 Aug '23

04 Aug '23

Since the introduction of PEP 647 (User-defined Type Guards), we've received a steady stream of input from users saying that they don't like the limitation that type narrowing is applied only in the positive case and is not applied in the negative case. In general, type narrowing is not safe to perform if a user-defined type guard returns False, so I've stood by the original decision not to provide type narrowing in the negative case, but there are cases where such narrowing is safe and desirable. Here's a recent thread where this was discussed in some detail: https://github.com/python/typing/issues/996#issuecomment-1002716830 In this discussion, Ilya Kamen proposed a solution. The proposal is to extend the existing `TypeGuard` to support an optional second type argument. If present, the second argument indicates the narrowed type in the negative type narrowing situation. Here's a simple (admittedly contrived) example: ```python def is_str(val: str | int) -> TypeGuard[str, int]: return isinstance(val, str) def func(val: str | int): if is_str(val): reveal_type(val) # str else: reveal_type(val) # int ``` I've implemented this proposal in the latest published version of pyright (1.1.202) so folks can experiment with it and see if they like it. If there's general consensus that it's the right approach, I can file an amendment for the existing PEP 647 to include this functionality. It was trivial to add to pyright, so I'm optimistic that it would likewise be easy to add to the other type checkers. Another common request for PEP 647 is the desire for an "assert" form of TypeGuard — a way to indicate that a function performs runtime validation of a type, raising an exception if the type is incorrect. It occurred to me that this two-argument form of `TypeGuard` could also be used to handle this use case. The second argument would be specified as a `NoReturn`. I've provisionally implemented this in pyright as well (although it's not in the currently-published version — you'll need to wait until 1.1.203 is published). Here's what this would look like: ```python def validate_sequence(val: Sequence[_T | None]) -> TypeGuard[Sequence[_T], NoReturn]: """Verifies that the sequence contains no None values""" if len([x for x in val if x is not None]) > 0: raise Exception() return True def func(val: Sequence[int | None]): validate_sequence(val) reveal_type(val) # Sequence[int] ``` I'm interested in input on these proposals. -Eric -- Eric Traut Contributor to Pyright & Pylance Microsoft

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Any way to express that a dict is expected to have a key(s) but don't care about other keys?
by Brett Cannon 20 Jun '23

20 Jun '23

Looking at TypedDict it seems that it does not like the idea of superfluous keys existing in a dict. In my case I have a REST call returning JSON for an error and all I'm guaranteed is to get an "error" key. But there may be extra keys that provide more details about the specific error. Unfortunately I don't know the exhaustive list of keys so I can't use a TypedDict with `totality == False` to try to be complete. I can't think of any way to somehow say "require/expect this one key, but other keys are OK". Am I missing anything that would let me do this?

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Runtime Access to Type Parameters
by Saul Shanabrook 07 Jun '23

07 Jun '23

I would like to propose being able to access, at runtime, the type parameters in classmethods and in constructors. I wrote up a draft PEP on this to capture the thinking here: https://github.com/saulshanabrook/peps/blob/master/pep-9999.md It seems easier to add them to classmethods, so even that addition alone would be very welcome! It could be added to Guido's existing draft PR for pep 585, by making a small change in the getattr function (https://github.com/python/cpython/pull/18239/files#diff-a34ae826f869897e56e…), to first do a `__getattribute__` and check if the result is a descriptor (has a `__get__` attribute), and if so passing the generic class itself in as the class, instead of the origin class. I wrote up a little example of that in the PEP. To make it available in the constructor, I proposed adding a global function `get_parameters` to `typing` which returns the parameters in the constructor. I also provide a little mock implementation of that. Note that work is only for custom classes which inherit from `typing.Generic` not for any built in collections.

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Extending published stubs
by Irv Lustig 26 Apr '23

26 Apr '23

I'm the leader of the pandas-stubs project. In pandas, users can extend the library by creating their own accessor via `(a)pd.api.extensions.register_dataframe_accessor` (see https://pandas.pydata.org/docs/reference/api/pandas.api.extensions.register…). So for a `DataFrame`, if you do `(a)pd.api.extensions.register_dataframe_accessor("xyz")`, you now have `DataFrame.xyz` returning the class you annotated. Now suppose you want to create a stub for your new accessor, that would extend the existing pandas-stubs. AFAIK, there isn't a way to do something like this in a stub: ``` from pandas import DataFrame @typing.extend class DataFrame: def xyz(self) -> MyXyzClass: ... ``` The idea here is that there is a class (in this case, `DataFrame`) that already has stubs, and you want to add a method to it. Has this idea been discussed anywhere?

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Support Algebraic Data Types with @sealed
by John Hagen 20 Mar '23

20 Mar '23

The original (superseded) PEP 622 proposed adding a @sealed decorator so that users could define algebraic data types (ADTs) that static type checkers could verify are `match`ed exhaustively: - https://peps.python.org/pep-0622/#sealed-classes-as-algebraic-data-types There is a somewhat fairly-upvoted question about ADTs in Python, indicating interest in this area: - https://stackoverflow.com/questions/16258553 @sealed would be very valuable towards making ADT usage within Python more type-safe. Is there any historical discussion about why @sealed was dropped from the final pattern matching PEP? What would be the next steps needed to push this forward? Would writing a new PEP be the next step?

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PEP 698 typing.override
by Steven Troxler 02 Feb '23

02 Feb '23

A few weeks back we merged PEP 698 for `typing.override` as a draft PEP, with no significant changes since the typing-sig discussion in [1] One of the things we decided for the initial proposal was to avoid any runtime behavior; we considered setting `__override__` but decided not to because (a) it is hard to get the ordering of decorators right for runtime behavior, it depends on which decorators are descriptor- versus wrapper-function-based (b) some objects have fixed slots and therefore cannot have `__override__` set at all, there's no way to produce runtime behavior in these cases I think we should reconsider for two reasons: (a) The author of the overrides library got back to us and specifically requested this [2], because he believes there's potential for libraries to make runtime use of @override, for example automatically applying docstrings from parent classes to child classes which could be useful for notebook users (b) We do indeed set a `__final__` attribute in the @final decorator [3], and after some more thought I think consistency is important My current opinion is that we should make a best-effort attempt to set the attribute (as we do in `(a)typing.final`) and document edge cases that can impact runtime behavior, while ignoring those edge cases in static checkers. Are there any objections before I start working on these changes? ------------------- [1] Previous thread about typing.override proposal: https://mail.python.org/archives/list/typing-sig@python.org/message/7JDW2PK… [2] Discussion in peps repo regarding `__override__` https://github.com/python/peps/pull/2830 [3] @typing.final logic today: https://github.com/python/cpython/blob/main/Lib/typing.py#L2630-L2636

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Draft PEP for Improved Type Parameter Syntax
by Eric Traut 08 Nov '22

08 Nov '22

In last month's typing meetup, Sebastian presented some options for improving the syntax for type parameters. I subsequently posted some slides that explored a bunch of options. For reference, here's a link to those slides: https://docs.google.com/presentation/d/1aVHTaj8zGYAvM27uft1ktdthwEKO4KH4qaR…. In the interest of continuing to make progress on this topic, I've drafted a PEP that places some stakes in the ground. Here's a link to the PEP: https://github.com/erictraut/peps/blob/typeparams/pep-9999.rst. I'm interested in feedback. Since there appears to be broad interest in addressing this problem, perhaps we can use the next typing meetup to continue this discussion. -- Eric Traut Contributor to Pyright & Pylance Microsoft

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Defaults for TypeVar-likes
by James H-B 03 Nov '22

03 Nov '22

I've been toying with the idea of writting a PEP for this long requested feature (https://github.com/python/typing/issues/307). I've got a preliminary draft of it with the motivation and a specification for the feature. In short features like ```py T = TypeVar("T", default=int) # This means that if no type is specified T = int @dataclass class Box(Generic[T]): value: T | None = None reveal_type(Box()) # type is Box[int] reveal_type(Box(value="Hello World!")) # type is Box[str] ``` would be supported as would ```py YieldT = TypeVar("YieldT") SendT = TypeVar("SendT", default=None) ReturnT = TypeVar("ReturnT", default=None) class Generator(Generic[YieldT, SendT, ReturnT]): ... Generator[int] == Generator[int, None] == Generator[int, None, None] ``` Feedback and any questions on this would be very appreciated. https://gist.github.com/Gobot1234/8c9bfe8eb88f5ad42bf69b6f118033a7 Thanks, James H-B.

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Readonly TypedDicts
by Alice Purcell 02 Nov '22

02 Nov '22

I would like to propose adding a boolean `readonly` parameter to TypedDict, which would remove all mutate operations from the resulting type. # Specification ```python class Request(TypedDict, total=False, readonly=True): some_field: string | None request: Request = { "some_field": "foo" } request["some_field"] = None # error del request["some_field"] # error ``` I suggest making it illegal to have a subclass that does not have the same readonly value as its superclass(es), as on the one hand, it would be natural to expect the following to be equivalent: ```python # Declare some_field explicitly class MutableRequest(TypedDict, total=False): some_field: string | None # Inherit some_field from Request class MutableRequest(Request, readonly=False): pass ```python But on the other hand, by analogy with total we would expect only the fields explicitly declared on the class to be affected by the readonly flag. I'm not aware of a usecase for the latter, and the ambiguity between the two possible interpretations would probably be a source of issues. # Use case We are making increasing use of TypedDict to type hint code that uses dictionaries to hold requests to and responses from services (both client and server side). However, we run into problems whenever any constraint is loosened, e.g. passing data from a response where a field is guaranteed to a request where it is optional: ```python class Response(TypedDict): some_field: string class Request(TypedDict, total=False): some_field: string | None class Server1: def fetch_data() -> Response: ... class Server2: def accept_data(request: Request): ... def my_code(server1: Server1, server2: Server2): data: Response = server1.fetch_data() server2.accept_data(data) # error: Response and Request are incompatible ```python This last line is not type-safe, as there are mutation operations on Request that aren't valid on Response. If we could remove those mutation operations from the Request type, then it would be a valid subtype of Response. This problem seems quite common, e.g. https://github.com/python/mypy/pull/12142. I am thinking of making a PEP (Pablo Galindo has agreed to sponsor). What does everyone think? Is the proposal sound and implementable? --- Alice Bloomberg

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