Mailman 3 January 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 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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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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Explicit Type Aliases
by Shannon Zhu 17 Aug '22

17 Aug '22

Hi everyone, I’d like to continue the discussion from our last typing summit on explicit type aliases. A quick summary of the current state and the proposal -- Current state: ``` from typing import List x = List[int] # considered a type alias y : Type[List[int]] = List[int] # considered an expression z : Any # considered a type alias z : x = [] # fine z : y = [] # invalid type error ``` Proposal (with explicit aliases): ``` from typing import List, TypeAlias x = TypeAlias[List[int]] # considered a type alias y = List[int] # considered an expression z : Any # considered an expression z = TypeAlias[Any] # considered a type alias reveal_type(x) # Type[List[int]] reveal_type(y) # Any (return type of __getitem__) z : x = [] # fine z : y = [] # invalid type error ``` Some of the benefits we’re hoping to gain from explicit type aliases: * Clearly distinguish between an unannotated global assignment and a type alias, especially when parsing forward-referencing string annotations. * Avoid the confusing case of type aliases in which some part of the type is invalid or undefined. This would facilitate warnings on invalid types at the alias definition rather than later on when the alias is used. * Make valid and invalid types more intuitive to Python programmers by shifting from delineation of non-aliases with a meta annotation toward delineation of aliases with `TypeAlias`. * Remove special treatment of values annotated as `Any`, which currently break all type aliasing rules. This will also allow us to clean up some of the distinctions we maintain between unannotated values and explicit Anys. Note: We also considered denoting the alias as an annotation (e.g. `x: TypeAlias = int`) as an alternative to the syntax above. I’m interested to hear your thoughts and suggestions on making type aliasing explicit going forward – we’d like to start implementing a version of this in Pyre soon! Shannon

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Dataclass Transform draft PEP
by Erik De Bonte 21 Apr '22

21 Apr '22

I've created a draft PEP for the "Dataclass Transforms" concept that Eric Traut presented at the December 1st Typing SIG meetup. Feedback is welcome! https://github.com/debonte/peps/blob/dataclass_transforms/pep-9999.rst Thanks, Erik

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Proposal: typing.assert_type() and with typing.assert_error()
by Jelle Zijlstra 16 Mar '22

16 Mar '22

Don't worry, this is the last one for today, but probably the trickiest. These two functions are intended to help users who want to check how their API behaves with a type checker: they want to assert that some type is inferred correctly, or that some code that they want the type checker to catch is rejected. # Specification The signature of typing.assert_type() is: def assert_type(obj: T, typ: TypeForm[T], /) -> T: ... (Using TypeForm from David Foster's pending proposal.) At runtime, this simply returns the first argument unchanged. As with my proposed behavior for reveal_type(), this allows using the function within an expression. A static type checker will emit an error if it encounters a call to assert_type() and the inferred type for the obj argument is not exactly equal to the provided type. The type passed in as the `typ` argument is interpreted the same way the type checker would interpret a type annotation, just as with the first argument to the existing `typing.cast()` function. For example: def f(x: int): assert_type(x, int) # ok assert_type(x, str) # error: type of x is int, not str assert_type(x, Any) # error: type of x is int, not Any The signature of typing.assert_error() is: def assert_error() -> ContextManager[None]: ... At runtime, this returns a context manager that does nothing. At type checking time, the context asserts that an error is found within the block. A static type checker will handle a `with assert_error():` block as follows: - Type check the block as normal, but don't emit any errors to the user - If there were no errors, emit an error - If there were errors, continue as normal For example: with assert_error(): # ok 1 + "x" with assert_error(): # error 1 + 1 # Motivation There has been a longstanding need for a way to check that typed code is understood correctly by the type checker: - https://github.com/python/typeshed/issues/1339 - typeshed issue looking for a way to test typeshed stubs - https://github.com/python/typing/discussions/1030 - recent discussion looking for the same thing in user code - https://pypi.org/project/pytest-mypy-plugins/ provides a mypy-specific way to test inferred types - https://google.github.io/pytype/faq.html#can-i-find-out-what-pytype-thinks-… - pytype added an assert_type() function as proposed here based on user feedback Adding these two functions to the standard library with defined semantics will create a portable, well-documented way to test type checker behavior. This will be useful for typeshed and for any library author who needs to support multiple type checkers. # Use case As a motivating example, I'd like to go over a recent change I made to typeshed: https://github.com/python/typeshed/pull/6941. I wanted to make it so that you cannot call `dict.setdefault()` with one argument if the dict's value type does not include `None`, so I wrote some tricky overloads. With `assert_type()` and `assert_error()`, I could have written tests like these to make sure my stubs work with all type checkers: int_int: dict[int, int] = {} int_any: dict[int, Any] = {} int_optional: dict[int, int | None] = {} with assert_error(): int_int.setdefault(1) # should error because this may set the value to None assert_type(int_int.setdefault(1, 1), int) assert_type(int_any.setdefault(1), Any) assert_type(int_optional.setdefault(1), int | None) What I did instead was run some samples manually with mypy, but that of course doesn't guarantee that the sample continues to work. This example comes from typeshed, but the utility isn't limited to typeshed; it can help any user who wants to make sure the types they wrote behave as they expect. # Open issues The assert_error() context manager is unlike other existing special forms in the typing module, since no other context manager currently has special meaning to the type checker. I'd be happy to hear any alternative ideas for covering this use case. Here are two alternative approaches I considered: - A magical comment, like TypeScript's "// $ExpectError". But a magical comment is not as explicit, is harder to understand for users, and may cause problems with autoformatters. - A function call: perhaps `typing.assert_type(expr, typing.Error)` would assert that evaluating `expr` produces an error. But this wouldn't allow asserting that an error occurs in a statement as opposed to an expression. In https://github.com/python/typing/discussions/1030, we explored several variants for the behavior of assert_type(), such as allowing an explicit way to provide the type as a string. I think the behavior specified here is the most intuitive and consistent with the behavior of other typing constructs, but I'm happy to discuss this further.

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PEP 655: Marking individual TypedDict items as required or potentially-missing
by David Foster 23 Feb '22

23 Feb '22

The new syntax Required[...] and NotRequired[...] for marking individual keys of a TypedDict has now been formally proposed as a draft PEP. Please see the latest text at the bottom of this email, or online at: https://www.python.org/dev/peps/pep-0655/ Comments welcome. :) Meanwhile I'll proceed to implement this syntax in mypy. -- David Foster | Seattle, WA, USA Contributor to TypedDict support for mypy >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> PEP: 655 Title: Marking individual TypedDict items as required or potentially-missing Author: David Foster <david at dafoster.net> Sponsor: Guido van Rossum <guido at python.org> Discussions-To: typing-sig at python.org Status: Draft Type: Standards Track Content-Type: text/x-rst Requires: 604 Created: 30-Jan-2021 Python-Version: 3.10 Post-History: 31-Jan-2021, 11-Feb-2021, 20-Feb-2021, 26-Feb-2021 Abstract ======== `PEP 589 <https://www.python.org/dev/peps/pep-0589/>`__ defines syntax for declaring a TypedDict with all required keys and syntax for defining a TypedDict with `all potentially-missing keys <https://www.python.org/dev/peps/pep-0589/#totality>`__ however it does not provide any syntax to declare some keys as required and others as potentially-missing. This PEP introduces two new syntaxes: ``Required[...]`` which can be used on individual items of a TypedDict to mark them as required, and ``NotRequired[...]`` which can be used on individual items to mark them as potentially-missing. Motivation ========== It is not uncommon to want to define a TypedDict with some keys that are required and others that are potentially-missing. Currently the only way to define such a TypedDict is to declare one TypedDict with one value for ``total`` and then inherit it from another TypedDict with a different value for ``total``: :: class _MovieBase(TypedDict): # implicitly total=True title: str class Movie(_MovieBase, total=False): year: int Having to declare two different TypedDict types for this purpose is cumbersome. Rationale ========= One might think it unusual to propose syntax that prioritizes marking *required* keys rather than syntax for *potentially-missing* keys, as is customary in other languages like TypeScript: :: interface Movie { title: string; year?: number; // ? marks potentially-missing keys } The difficulty is that the best word for marking a potentially-missing key, ``Optional[...]``, is already used in Python for a completely different purpose: marking values that could be either of a particular type or ``None``. In particular the following does not work: :: class Movie(TypedDict): ... year: Optional[int] # means int|None, not potentially-missing! Attempting to use any synonym of “optional” to mark potentially-missing keys (like ``Missing[...]``) would be too similar to ``Optional[...]`` and be easy to confuse with it. Thus it was decided to focus on positive-form phrasing for required keys instead, which is straightforward to spell as ``Required[...]``. Nevertheless it is common for folks wanting to extend a regular (``total=True``) TypedDict to only want to add a small number of potentially-missing keys, which necessitates a way to mark keys that are *not* required and potentially-missing, and so we also allow the ``NotRequired[...]`` form for that case. Specification ============= The ``typing.Required`` type qualifier is used to indicate that a variable declared in a TypedDict definition is a required key: :: class Movie(TypedDict, total=False): title: Required[str] year: int Additionally the ``typing.NotRequired`` type qualifier is used to indicate that a variable declared in a TypedDict definition is a potentially-missing key: :: class Movie(TypedDict): # implicitly total=True title: str year: NotRequired[int] It is an error to use ``Required[...]`` or ``NotRequired[...]`` in any location that is not an item of a TypedDict. It is valid to use ``Required[...]`` and ``NotRequired[...]`` even for items where it is redundant, to enable additional explicitness if desired: :: class Movie(TypedDict): title: Required[str] # redundant year: NotRequired[int] Backwards Compatibility ======================= No backward incompatible changes are made by this PEP. How to Teach This ================= To define a TypedDict where most keys are required and some are potentially-missing, define a single TypedDict as normal and mark those few keys that are potentially-missing with ``NotRequired[...]``. To define a TypedDict where most keys are potentially-missing and a few are required, define a ``total=False`` TypedDict and mark those few keys that are required with ``Required[...]``. If some items accept ``None`` in addition to a regular value, it is recommended that the ``TYPE|None`` syntax be preferred over ``Optional[TYPE]`` for marking such item values, to avoid using ``Required[...]`` or ``NotRequired[...]`` alongside ``Optional[...]`` within the same TypedDict definition: Yes: :: from __future__ import annotations # for Python 3.7-3.9 class Dog(TypedDict): name: str owner: NotRequired[str|None] Avoid (unless Python 3.5-3.6): :: class Dog(TypedDict): name: str # ick; avoid using both Optional and NotRequired owner: NotRequired[Optional[str]] Reference Implementation ======================== The goal is to be able to make the following statement: The `mypy <http://www.mypy-lang.org/>`__ type checker supports ``Required`` and ``NotRequired``. A reference implementation of the runtime component is provided in the `typing_extensions <https://github.com/python/typing/tree/master/typing_extensions>`__ module. The mypy implementation is currently still being worked on. Rejected Ideas ============== Special syntax around the *key* of a TypedDict item --------------------------------------------------- :: class MyThing(TypedDict): opt1?: str # may not exist, but if exists, value is string opt2: Optional[str] # always exists, but may have null value or: :: class MyThing(TypedDict): Optional[opt1]: str # may not exist, but if exists, value is string opt2: Optional[str] # always exists, but may have null value These syntaxes would require Python grammar changes and it is not believed that marking TypedDict items as required or potentially-missing would meet the high bar required to make such grammar changes. Also, “let’s just not put funny syntax before the colon.” [1]_ Marking required or potentially-missing keys with an operator ------------------------------------------------------------- We could use unary ``+`` as shorthand to mark a required key, unary ``-`` to mark a potentially-missing key, or unary ``~`` to mark a key with opposite-of-normal totality: :: class MyThing(TypedDict, total=False): req1: +int # + means a required key, or Required[...] opt1: str req2: +float class MyThing(TypedDict): req1: int opt1: -str # - means a potentially-missing key, or NotRequired[...] req2: float class MyThing(TypedDict): req1: int opt1: ~str # ~ means a opposite-of-normal-totality key req2: float Such operators could be implemented on ``type`` via the ``__pos__``, ``__neg__`` and ``__invert__`` special methods without modifying the grammar. It was decided that it would be prudent to introduce longform syntax (i.e. ``Required[...]`` and ``NotRequired[...]``) before introducing any shortform syntax. Future PEPs may reconsider introducing this or other shortform syntax options. Marking absence of a value with a special constant -------------------------------------------------- We could introduce a new type-level constant which signals the absence of a value when used as a union member, similar to JavaScript’s ``undefined`` type, perhaps called ``Missing``: :: class MyThing(TypedDict): req1: int opt1: str|Missing req2: float Such a ``Missing`` constant could also be used for other scenarios such as the type of a variable which is only conditionally defined: :: class MyClass: attr: int|Missing def __init__(self, set_attr: bool) -> None: if set_attr: self.attr = 10 :: def foo(set_attr: bool) -> None: if set_attr: attr = 10 reveal_type(attr) # int|Missing Misalignment with how unions apply to values '''''''''''''''''''''''''''''''''''''''''''' However this use of ``...|Missing``, equivalent to ``Union[..., Missing]``, doesn’t align well with what a union normally means: ``Union[...]`` always describes the type of a *value* that is present. By contrast missingness or non-totality is a property of a *variable* instead. Current precedent for marking properties of a variable include ``Final[...]`` and ``ClassVar[...]``, which the proposal for ``Required[...]`` is aligned with. Misalignment with how unions are subdivided ''''''''''''''''''''''''''''''''''''''''''' Furthermore the use of ``Union[..., Missing]`` doesn’t align with the usual ways that union values are broken down: Normally you can eliminate components of a union type using ``isinstance`` checks: :: class Packet: data: Union[str, bytes] def send_data(packet: Packet) -> None: if isinstance(packet.data, str): reveal_type(packet.data) # str packet_bytes = packet.data.encode('utf-8') else: reveal_type(packet.data) # bytes packet_bytes = packet.data socket.send(packet_bytes) However if we were to allow ``Union[..., Missing]`` you’d either have to eliminate the ``Missing`` case with ``hasattr`` for object attributes: :: class Packet: data: Union[str, Missing] def send_data(packet: Packet) -> None: if hasattr(packet, 'data'): reveal_type(packet.data) # str packet_bytes = packet.data.encode('utf-8') else: reveal_type(packet.data) # Missing? error? packet_bytes = b'' socket.send(packet_bytes) or a check against ``locals()`` for local variables: :: def send_data(packet_data: Optional[str]) -> None: packet_bytes: Union[str, Missing] if packet_data is not None: packet_bytes = packet.data.encode('utf-8') if 'packet_bytes' in locals(): reveal_type(packet_bytes) # bytes socket.send(packet_bytes) else: reveal_type(packet_bytes) # Missing? error? or a check via other means, such as against ``globals()`` for global variables: :: warning: Union[str, Missing] import sys if sys.version_info < (3, 6): warning = 'Your version of Python is unsupported!' if 'warning' in globals(): reveal_type(warning) # str print(warning) else: reveal_type(warning) # Missing? error? Weird and inconsistent. ``Missing`` is not really a value at all; it’s an absence of definition and such an absence should be treated specially. Difficult to implement '''''''''''''''''''''' Eric Traut from the Pyright type checker team has stated that implementing a ``Union[..., Missing]``-style syntax would be difficult. [2]_ Introduces a second null-like value into Python ''''''''''''''''''''''''''''''''''''''''''''''' Defining a new ``Missing`` type-level constant would be very close to introducing a new ``Missing`` value-level constant at runtime, creating a second null-like runtime value in addition to ``None``. Having two different null-like constants in Python (``None`` and ``Missing``) would be confusing. Many newcomers to JavaScript already have difficulty distinguishing between its analogous constants ``null`` and ``undefined``. Replace Optional with Nullable. Repurpose Optional to mean “optional item”. --------------------------------------------------------------------------- ``Optional[...]`` is too ubiquitous to deprecate. Although use of it *may* fade over time in favor of the ``T|None`` syntax specified by `PEP 604 <https://www.python.org/dev/peps/pep-0604/>`__. Change Optional to mean “optional item” in certain contexts instead of “nullable” --------------------------------------------------------------------------------- Consider the use of a special flag on a TypedDict definition to alter the interpretation of ``Optional`` inside the TypedDict to mean “optional item” rather than its usual meaning of “nullable”: :: class MyThing(TypedDict, optional_as_missing=True): req1: int opt1: Optional[str] or: :: class MyThing(TypedDict, optional_as_nullable=False): req1: int opt1: Optional[str] This would add more confusion for users because it would mean that in *some* contexts the meaning of ``Optional[...]`` is different than in other contexts, and it would be easy to overlook the flag. Various synonyms for “potentially-missing item” ----------------------------------------------- - Omittable – too easy to confuse with optional - OptionalItem, OptionalKey – two words; too easy to confuse with optional - MayExist, MissingOk – two words - Droppable – too similar to Rust’s ``Drop``, which means something different - Potential – too vague - Open – sounds like applies to an entire structure rather then to an item - Excludable - Checked References ========== .. [1] https://mail.python.org/archives/list/typing-sig@python.org/message/4I3GPIW… .. [2] https://mail.python.org/archives/list/typing-sig@python.org/message/S2VJSVG… Copyright ========= This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End: <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<

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Re: Thoughts on a NotType?
by Anton Agestam 22 Feb '22

22 Feb '22

(Hoping I figured out how to reply in the thread now... ) Yes, sorry for the cryptic message but it looks like you got it right. The example isn't impressive but just like Steven is saying the value comes in larger applications. In a web application for instance only the parsing layer will have to deal with the runtime validation of the type. The business logic and database layers can use static type checking to make sure only the narrowed value is allowed. So it's kind of like a NewType plus an "instantiation predicate" (I'm failing to find the correct term, nothing is actually instantiated). I thought it's relevant here since I imagine a NotType would also have to rely a lot on runtime narrowing. If a downstream API takes Sequence & Not[str] and we have a Sequence available, we'd have to narrow it with something like assert not isinstance(..., str).

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PyCon 2022 Typing Summit Interest Form
by Shannon Zhu 14 Feb '22

14 Feb '22

Hi everyone, PyCon 2022 is just a few months out, and Alfonso and I are looking forward to organizing another Typing Summit this year! We plan to be in-person in Salt Lake City during PyCon US 2022, on Thursday April 28th from 1-5pm. For those who haven’t been before, the summit is a 3-4 hour event organized around a series of presentation and discussion topics related to the type system or type checkers. Some topics are presentation heavy, some have just a few slides and then focus most of the time on group discussions of details or related questions. You can find a recording with time stamps of last year’s (virtual) typing summit here<https://pyvideo.org/pycon-us-2021/summit-typing.html>. Please fill out the PyCon 2022 Typing Summit Interest Form<https://docs.google.com/forms/d/e/1FAIpQLSdsgrGXrKZUXv9KMBwyYJkJ6L3CL_Os4Yg…> if you are interested in attending, so we can get a sense of expected size and potential presentation topics! Let me or Alfonso know if you have any questions! :) Shannon

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