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Contributing to typeshed

Welcome! typeshed is a community project that aims to work for a wide range of Python users and Python codebases. If you're trying a type checker on your Python code, your experience and what you can contribute are important to the project's success.

The contribution process at a glance

  1. Read the README.md file.
  2. Set up your environment to be able to run all tests. They should pass.
  3. Prepare your changes:
    • Small fixes and additions can be submitted directly as pull requests, but contact us before starting significant work.
    • Create your stubs conforming to the coding style.
    • Make sure your tests pass cleanly on mypy, pytype, and flake8.
    • Reformat your stubs with black and isort.
  4. Submit your changes by opening a pull request.
  5. You can expect a reply within a few days:
    • Diffs are merged when considered ready by the core team.
    • Feel free to ping the core team if your pull request goes without a reply for more than a few days.

For more details, read below.

Discussion

If you've run into behavior in the type checker that suggests the type stubs for a given library are incorrect or incomplete, we want to hear from you!

Our main forum for discussion is the project's GitHub issue tracker. This is the right place to start a discussion of any of the above or most any other topic concerning the project.

For less formal discussion, try the typing chat room on gitter.im. Some Mypy core developers are almost always present; feel free to find us there and we're happy to chat. Substantive technical discussion will be directed to the issue tracker.

Code of Conduct

Everyone participating in the typeshed community, and in particular in our issue tracker, pull requests, and IRC channel, is expected to treat other people with respect and more generally to follow the guidelines articulated in the Python Community Code of Conduct.

Submitting Changes

Even more excellent than a good bug report is a fix for a bug, or the implementation of a much-needed stub. We'd love to have your contributions.

We use the usual GitHub pull-request flow, which may be familiar to you if you've contributed to other projects on GitHub. For the mechanics, see Mypy's git and GitHub workflow help page, or GitHub's own documentation.

Anyone interested in type stubs may review your code. One of the core developers will merge your pull request when they think it's ready. For every pull request, we aim to promptly either merge it or say why it's not yet ready; if you go a few days without a reply, please feel free to ping the thread by adding a new comment.

To get your pull request merged sooner, you should explain why you are making the change. For example, you can point to a code sample that is processed incorrectly by a type checker. It is also helpful to add links to online documentation or to the implementation of the code you are changing.

Also, do not squash your commits after you have submitted a pull request, as this erases context during review. We will squash commits when the pull request is merged.

At present the core developers are (alphabetically):

  • David Fisher (@ddfisher)
  • Łukasz Langa (@ambv)
  • Jukka Lehtosalo (@JukkaL)
  • Ivan Levkivskyi (@ilevkivskyi)
  • Matthias Kramm (@matthiaskramm)
  • Greg Price (@gnprice)
  • Sebastian Rittau (@srittau)
  • Guido van Rossum (@gvanrossum)
  • Shantanu (@hauntsaninja)
  • Rune Tynan (@CraftSpider)
  • Jelle Zijlstra (@JelleZijlstra)

NOTE: the process for preparing and submitting changes also applies to core developers. This ensures high quality contributions and keeps everybody on the same page. Avoid direct pushes to the repository.

Preparing Changes

Before you begin

If your change will be a significant amount of work to write, we highly recommend starting by opening an issue laying out what you want to do. That lets a conversation happen early in case other contributors disagree with what you'd like to do or have ideas that will help you do it.

What to include

Stubs should include the complete interface (classes, functions, constants, etc.) of the module they cover, but it is not always clear exactly what is part of the interface.

The following should always be included:

  • All objects listed in the module's documentation.
  • All objects included in __all__ (if present).

Other objects may be included if they are being used in practice or if they are not prefixed with an underscore. This means that typeshed will generally accept contributions that add missing objects, even if they are undocumented. Undocumented objects should be marked with a comment of the form # undocumented. Example:

def list2cmdline(seq: Sequence[str]) -> str: ...  # undocumented

We accept such undocumented objects because omitting objects can confuse users. Users who see an error like "module X has no attribute Y" will not know whether the error appeared because their code had a bug or because the stub is wrong. Although it may also be helpful for a type checker to point out usage of private objects, we usually prefer false negatives (no errors for wrong code) over false positives (type errors for correct code). In addition, even for private objects a type checker can be helpful in pointing out that an incorrect type was used.

Incomplete stubs

We accept partial stubs, especially for larger packages. These need to follow the following guidelines:

  • Included functions and methods must list all arguments, but the arguments can be left unannotated. Do not use Any to mark unannotated arguments or return values.
  • Partial classes must include a __getattr__() method marked with an # incomplete comment (see example below).
  • Partial modules (i.e. modules that are missing some or all classes, functions, or attributes) must include a top-level __getattr__() function marked with an # incomplete comment (see example below).
  • Partial packages (i.e. packages that are missing one or more sub-modules) must have a __init__.pyi stub that is marked as incomplete (see above). A better alternative is to create empty stubs for all sub-modules and mark them as incomplete individually.

Example of a partial module with a partial class Foo and a partially annotated function bar():

def __getattr__(name: str) -> Any: ...  # incomplete

class Foo:
    def __getattr__(self, name: str) -> Any: ...  # incomplete
    x: int
    y: str

def bar(x: str, y, *, z=...): ...

Using stubgen

Mypy includes a tool called stubgen that auto-generates stubs for Python and C modules using static analysis, Sphinx docs, and runtime introspection. It can be used to get a starting point for your stubs. Note that this generator is currently unable to determine most argument and return types and omits them or uses Any in their place. Fill out manually the types that you know.

Stub file coding style

Syntax example

The below is an excerpt from the types for the datetime module.

MAXYEAR: int
MINYEAR: int

class date:
    def __init__(self, year: int, month: int, day: int) -> None: ...
    @classmethod
    def fromtimestamp(cls, timestamp: float) -> date: ...
    @classmethod
    def today(cls) -> date: ...
    @classmethod
    def fromordinal(cls, ordinal: int) -> date: ...
    @property
    def year(self) -> int: ...
    def replace(self, year: int = ..., month: int = ..., day: int = ...) -> date: ...
    def ctime(self) -> str: ...
    def weekday(self) -> int: ...

Conventions

Stub files are like Python files and you should generally expect them to look the same. Your tools should be able to successfully treat them as regular Python files. However, there are a few important differences you should know about.

Style conventions for stub files are different from PEP 8. The general rule is that they should be as concise as possible. Specifically:

  • lines can be up to 130 characters long;
  • functions and methods that don't fit in one line should be split up with one argument per line;
  • all function bodies should be empty;
  • prefer ... over pass;
  • prefer ... on the same line as the class/function signature;
  • avoid vertical whitespace between consecutive module-level functions, names, or methods and fields within a single class;
  • use a single blank line between top-level class definitions, or none if the classes are very small;
  • do not use docstrings;
  • use variable annotations instead of type comments, even for stubs that target older versions of Python;
  • for arguments with a type and a default, use spaces around the =.

Stubs should be reformatted with the formatters black and isort before submission. These formatters are included in typeshed's requirements-tests-py3.txt file. A sample pre-commit file is included in the typeshed repository. Copy it to .git/hooks and adjust the path to your virtual environment's bin directory to automatically reformat stubs before commit.

Stub files should only contain information necessary for the type checker, and leave out unnecessary detail:

  • for arguments with a default, use ... instead of the actual default;
  • for arguments that default to None, use Optional[] explicitly (see below for details);
  • use float instead of Union[int, float].

Some further tips for good type hints:

  • avoid invariant collection types (List, Dict) in argument positions, in favor of covariant types like Mapping or Sequence;
  • avoid Union return types: python/mypy#1693;
  • in Python 2, whenever possible, use unicode if that's the only possible type, and Text if it can be either unicode or bytes;
  • use platform checks like if sys.platform == 'win32' to denote platform-dependent APIs.

Imports in stubs are considered private (not part of the exported API) unless:

  • they use the form from library import name as name (sic, using explicit as even if the name stays the same); or
  • they use the form from library import * which means all names from that library are exported.

When adding type hints, avoid using the Any type when possible. Reserve the use of Any for when:

  • the correct type cannot be expressed in the current type system; and
  • to avoid Union returns (see above).

Note that Any is not the correct type to use if you want to indicate that some function can accept literally anything: in those cases use object instead.

For arguments with type and a default value of None, PEP 484 prescribes that the type automatically becomes Optional. However we prefer explicit over implicit in this case, and require the explicit Optional[] around the type. The mypy tests enforce this (through the use of --no-implicit-optional) and the error looks like Incompatible types in assignment (expression has type None, variable has type "Blah") .

Stub files support forward references natively. In other words, the order of class declarations and type aliases does not matter in a stub file. You can also use the name of the class within its own body. Focus on making your stubs clear to the reader. Avoid using string literals in type annotations.

Type variables and aliases you introduce purely for legibility reasons should be prefixed with an underscore to make it obvious to the reader they are not part of the stubbed API.

When adding type annotations for context manager classes, annotate the return type of __exit__ as bool only if the context manager sometimes suppresses exceptions -- if it sometimes returns True at runtime. If the context manager never suppresses exceptions, have the return type be either None or Optional[bool]. If you are not sure whether exceptions are suppressed or not or if the context manager is meant to be subclassed, pick Optional[bool]. See python/mypy#7214 for more details.

A few guidelines for protocol names below. In cases that don't fall into any of those categories, use your best judgement.

  • Use plain names for protocols that represent a clear concept (e.g. Iterator, Container).
  • Use SupportsX for protocols that provide callable methods (e.g. SupportsInt, SupportsRead, SupportsReadSeek).
  • Use HasX for protocols that have readable and/or writable attributes or getter/setter methods (e.g. HasItems, HasFileno).

NOTE: there are stubs in this repository that don't conform to the style described above. Fixing them is a great starting point for new contributors.

Stub versioning

There are separate directories for stdlib and third_party stubs. Within those, there are separate directories for different versions of Python the stubs target.

The directory name indicates the major version of Python that a stub targets and optionally the lowest minor version, with the exception of the 2and3 directory which applies to both Python 2 and 3.

For example, stubs in the 3 directory will be applied to all versions of Python 3, though stubs in the 3.7 directory will only be applied to versions 3.7 and above. However, stubs in the 2 directory will not be applied to Python 3.

It is preferred to use a single stub in the more generic directory that conditionally targets specific versions when needed, as opposed to maintaining multiple stub files within more specific directories. Similarly, if the given library works on both Python 2 and Python 3, prefer to put your stubs in the 2and3 directory, unless the types are so different that the stubs become unreadable that way.

You can use checks like if sys.version_info >= (3, 8): to denote new functionality introduced in a given Python version or solve type differences. When doing so, only use one-tuples or two-tuples. This is because:

  • mypy doesn't support more fine-grained version checks; and more importantly

  • the micro versions of a Python release will change over time in your checking environment and the checker should return consistent results regardless of the micro version used.

Because of this, if a given functionality was introduced in, say, Python 3.7.4, your check:

  • should be expressed as if sys.version_info >= (3, 7):
  • should NOT be expressed as if sys.version_info >= (3, 7, 4):
  • should NOT be expressed as if sys.version_info >= (3, 8):

This makes the type checker assume the functionality was also available in 3.7.0 - 3.7.3, which while technically incorrect is relatively harmless. This is a strictly better compromise than using the latter two forms, which would generate false positive errors for correct use under Python 3.7.4.

Note: in its current implementation, typeshed cannot contain stubs for multiple versions of the same third-party library. Prefer to generate stubs for the latest version released on PyPI at the time of your stubbing.

What to do when a project's documentation and implementation disagree

Type stubs are meant to be external type annotations for a given library. While they are useful documentation in its own merit, they augment the project's concrete implementation, not the project's documentation. Whenever you find them disagreeing, model the type information after the actual implementation and file an issue on the project's tracker to fix their documentation.

Issue-tracker conventions

We aim to reply to all new issues promptly. We'll assign one or more labels to indicate we've triaged an issue, but most typeshed issues are relatively simple (stubs for a given module or package are missing, incomplete or incorrect) and we won't add noise to the tracker by labeling all of them. Please see the list of all labels for a detailed description of the labels we use.

Sometimes a PR can't make progress until some external issue is addressed. We indicate this by editing the subject to add a [WIP] prefix. (This should be removed before committing the issue once unblocked!)

Core developer guidelines

Core developers should follow these rules when processing pull requests:

  • Always wait for tests to pass before merging PRs.
  • Use "Squash and merge" to merge PRs.
  • Delete branches for merged PRs (by core devs pushing to the main repo).
  • Make sure commit messages to master are meaningful. For example, remove irrelevant intermediate commit messages.
  • If stubs for a new library are submitted, notify the library's maintainers.

When reviewing PRs, follow these guidelines:

  • Typing is hard. Try to be helpful and explain issues with the PR, especially to new contributors.
  • When reviewing auto-generated stubs, just scan for red flags and obvious errors. Leave possible manual improvements for separate PRs.
  • When reviewing large, hand-crafted PRs, you only need to look for red flags and general issues, and do a few spot checks.
  • Review smaller, hand-crafted PRs thoroughly.