Mailman 3 March 2023 - 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 19 Jun '23

19 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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Inline syntax for TypedDict
by Никита Соболев 14 Jun '23

14 Jun '23

Hi everyone! ## Problem Dicts are fundamental to Python programming. And today I found myself writing this piece of code (again): ```python def process_user(user_dict: User) -> ModifiedUser: ... class User(TypedDict): ... class ModifiedUser(User): ... ``` While in reality it was as simple as: `{'user_id': int}` -> `{'user_id': int, 'external_id': int}` What really bothers me is that I have to either: 1. Create a new whole class for it 2. Create a new type variable in a form of `User = TypedDict('User', {...})` I think that it hurts the readability quite a lot, because the definition of this type might not be near with a function that uses it. Another problem is that due to the nature of `TypedDict`s there are quite a lot of them in a regular web application, because of json. The biggest problem (for me personally) is that you cannot simply nest TypedDict. So, if you have a nested dict that you want to use, like `{'user': {'email': str}}`? You have to define *two* classes. And the final touch: it looks like many TypedDict are just used in one place. They are just like throw-away definitions. And don't forget that classes cannot have all keys in their body: `return`, `@1`, etc are all not allowed. ## Proposal To solve all the problems above, I propose adding "inline" syntax for typed dicts: ```python def get_user_data(arg: {'user': {'email': str}}) ->{'email': str}: return user['user'] ``` It has great potential: 0. Grammar already allows that with no changes from our side 1. `Required` and `NotRequired` can be used to enforce the presence of keys and values 2. Dicts can be nested with ease, just like in my example. Even multiline nesting is supported: ```python >>> def multiline() -> { ... 'user': { ... 'email': str, ... }, ... }: ... ... ... ``` 3. The intent is pretty clear: any user familiar with dicts would be able to read and understand this code 4. Other languages already have this feature, TS example: https://www.typescriptlang.org/play?#code/GYVwdgxgLglg9mABAcwKZQPogM6oE4YAm… 5. Existing tooling continues to work, see this example: ```python >>> import typing >>> def get_user_data(arg: {'user': {'email': str}}) ->{'email': str}: ... ... >>> get_user_data.__annotations__ {'arg': {'user': {'email': <class 'str'>}}, 'return': {'email': <class 'str'>}} >>> typing.get_type_hints(get_user_data) {'arg': {'user': {'email': <class 'str'>}}, 'return': {'email': <class 'str'>}} ``` And: ```python >>> import typing >>> class Some: ... a: {'x': int} = {'x': 1} ... def method(self, arg: {'x': int}) -> {'x': int, 'y': int}: ... return {'x': 1, 'y': 2} ... >>> Some.__annotations__ {'a': {'x': <class 'int'>}} >>> Some.method.__annotations__ {'arg': {'x': <class 'int'>}, 'return': {'x': <class 'int'>, 'y': <class 'int'>}} >>> typing.get_type_hints(Some) {'a': {'x': <class 'int'>}} >>> typing.get_type_hints(Some.method) {'arg': {'x': <class 'int'>}, 'return': {'x': <class 'int'>, 'y': <class 'int'>}} ``` 6. It is just a regular dict! No new things to create :) ## Extra We can also discuss the variable syntax change from: ```python X = TypedDict('X', {'a': int}) ``` To: ```python X: TypeAlias = {'a': int} ``` Because again, it is simpler: no need to duplicate the name and values can be nested. ## Problems The most important problem I see is that it is just a syntax to solve a problem with an existing solution. But, I think it is a good addition overall. ## Typecheckers From what I know, it would not be too hard to support this in mypy. We would need to add new logic to generate names from context (instead of an explicit name). Nesting needs to be tested, we might have some troubles here. I cannot speak for other typecheckers. ## Feedback This is a very rough idea for now. But, what do you think? Is that a problem you face in your projects? I would be glad to hear the feedback from the Python's typing community :) Best, Nikita Sobolev

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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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Opened and changed typing issues week 2023-W12
by Typing Bot 26 Mar '23

26 Mar '23

The following is an overview of all issues and pull requests in the typing repository on GitHub with the label 'topic: feature' that were opened or updated last week, excluding closed issues. --------------------------------------------------- The following issues and pull requests were updated last week: #213 Introduce an Intersection opened by @ilevkivskyi https://github.com/python/typing/issues/213 #688 advanced SimpleNamespace typing opened by @itajaja https://github.com/python/typing/issues/688 #1371 Proposal: Programmatically create types opened by @NeilGirdhar https://github.com/python/typing/issues/1371 --------------------------------------------------- All issues and pull requests with the label 'topic: feature' can be viewed under the following URL: https://github.com/python/typing/issues?q=label%3A%22topic%3A+feature%22

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Reminder
by Shalok Shalom 23 Mar '23

23 Mar '23

Hi there. I send an email to this mailing list several days ago, and it seems like the admins of this list are either busy, or it got lost. Can you confirm, its one of those two? Thanks

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How can I annotate class variables and instance variables with different types?
by Chihiro Sakai 23 Mar '23

23 Mar '23

I want means to annotate class variables and instance variables with distinct types. I'm using the well-known ORM library SQLAlchemy. With entity declaration, class variables are representations of table columns, while instance variables are the actual values of each record. Thus, because of this behaviour, I cannot write fully-typed code. For example, with the class declaration shown below: ``` class Entity(Base): __tablename__ = 'entity' column_1 = Column('column_1', BIGINT(unsigned=True), primary_key=True, autoincrement=True) ``` The actual type of the class variable `Entity.column_1` is `Column`, but on the other hand, the instance variable `Entity().column_1` is `int`. As far as I know, today, we cannot annotate variables like `Entity.column_1` with correct types, but I want to achieve those codes fully typed.

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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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