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A static type analyzer for Python code

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Performant type-checking for python.

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Quick Overview

Pytype is a static type analyzer for Python code, developed by Google. It aims to infer the types of variables, function parameters, and return values, and to detect type-related errors in Python programs.

Pros

  • Improved Code Quality: Pytype helps catch type-related errors early in the development process, leading to more robust and maintainable code.
  • Supports Type Annotations: Pytype can work with Python's type annotation syntax, allowing developers to take advantage of type-checking features.
  • Interoperability with Existing Tools: Pytype can be integrated with other Python development tools, such as IDEs and linters, to provide a seamless type-checking experience.
  • Supports Multiple Python Versions: Pytype is designed to work with a wide range of Python versions, from 2.7 to the latest 3.x releases.

Cons

  • Complexity: Implementing a robust type-checking system for a dynamic language like Python can be challenging, and Pytype's complexity may be a barrier for some users.
  • Performance Impact: Depending on the size and complexity of the codebase, running Pytype can add overhead to the development workflow, which may be a concern for some teams.
  • Limited Type Inference: While Pytype can infer types in many cases, it may not be able to handle all the nuances of Python's dynamic nature, leading to incomplete or inaccurate type information.
  • Adoption Challenges: Integrating Pytype into an existing codebase or development process may require significant effort, which could discourage some teams from adopting the tool.

Code Examples

Pytype is a code analysis tool, so it does not have any executable code examples. However, here's an example of how you might use Pytype to analyze a simple Python function:

def add_numbers(a: int, b: int) -> int:
    return a + b

To run Pytype on this code, you would use the following command:

pytype add_numbers.py

Pytype would then analyze the function and report any type-related issues it finds.

Getting Started

To get started with Pytype, follow these steps:

  1. Install Pytype using pip:

    pip install pytype
    
  2. Navigate to the directory containing the Python code you want to analyze.

  3. Run Pytype on the code:

    pytype path/to/your/code.py
    

    Pytype will analyze the code and report any type-related issues it finds.

  4. Optionally, you can configure Pytype by creating a configuration file (.pytype/config) in the root directory of your project. This allows you to customize the behavior of Pytype, such as specifying the Python version, ignoring certain files or directories, and more.

  5. Integrate Pytype into your development workflow, such as by running it as part of your continuous integration (CI) pipeline or configuring your IDE to use Pytype for type-checking.

For more detailed information on using Pytype, including advanced features and configuration options, please refer to the Pytype documentation.

Competitor Comparisons

13,105

Static Type Checker for Python

Pros of Pyright

  • Pyright is generally faster and more efficient than Pytype, with a focus on performance and scalability.
  • Pyright provides more comprehensive type checking and analysis, with support for a wider range of Python features and constructs.
  • Pyright has better integration with popular IDEs like Visual Studio Code, providing a seamless typing experience for developers.

Cons of Pyright

  • Pyright has a steeper learning curve compared to Pytype, with a more complex configuration and setup process.
  • Pyright's error messages can sometimes be less intuitive and user-friendly than Pytype's.
  • Pyright's community and ecosystem are not as large and well-established as Pytype's, which may limit the availability of third-party plugins and tools.

Code Comparison

Pytype:

def add(a: int, b: int) -> int:
    return a + b

Pyright:

def add(a: int, b: int) -> int:
    return a + b

In this simple example, both Pytype and Pyright provide the same type annotations and functionality. The main differences would be in the underlying type checking and analysis performed by each tool, which may become more apparent in more complex code scenarios.

18,182

Optional static typing for Python

Pros of mypy

  • mypy is a more mature and widely-used static type checker for Python, with a larger community and more extensive documentation.
  • mypy supports a wider range of Python features and constructs, including more advanced type annotations and type inference.
  • mypy has better integration with popular Python IDEs and tools, such as PyCharm and Visual Studio Code.

Cons of mypy

  • mypy can be more complex to configure and set up, especially for larger projects with more complex type annotations.
  • mypy may have a steeper learning curve for developers who are new to static type checking in Python.
  • mypy's type checking can be more strict and conservative, which may require more effort to satisfy in some cases.

Code Comparison

Here's a brief comparison of how the same code would be handled by mypy and pytype:

mypy:

def greet(name: str) -> str:
    return f"Hello, {name}!"

greet(42)  # Error: Argument 1 to "greet" has incompatible type "int"; expected "str"

pytype:

def greet(name):
    return f"Hello, {name}!"

greet(42)  # No error, pytype infers the type of `name` as `Any`

In this example, mypy correctly identifies the type mismatch between the expected str and the provided int argument, while pytype does not raise an error, as it infers the type of name as Any.

Performant type-checking for python.

Pros of Pyre-check

  • Pyre-check supports a wider range of Python versions, including Python 3.9 and 3.10, while Pytype currently only supports up to Python 3.8.
  • Pyre-check has a more active community with more contributors and a faster response to issues and pull requests.
  • Pyre-check provides a more user-friendly command-line interface with better error reporting and integration with popular IDEs like Visual Studio Code.

Cons of Pyre-check

  • Pyre-check has a steeper learning curve and requires more configuration to set up, especially for larger projects.
  • Pyre-check's type inference is not as advanced as Pytype's, which can lead to more false positives or missed type errors.
  • Pyre-check's documentation is not as comprehensive as Pytype's, making it harder for new users to get started.

Code Comparison

Pytype:

def add(a: int, b: int) -> int:
    return a + b

Pyre-check:

def add(a: int, b: int) -> int:
    return a + b

The code for both Pytype and Pyre-check is very similar, as they both aim to provide type checking for Python code. The main differences would be in the configuration and usage of the tools, rather than the actual code they analyze.

5,286

It's not just a linter that annoys you!

Pros of Pylint

  • Pylint is a more mature and widely-used tool, with a larger community and more features.
  • Pylint supports a wider range of Python versions and can be used with a variety of IDEs and build systems.
  • Pylint provides more detailed and customizable linting rules, allowing for more fine-grained control over code quality.

Cons of Pylint

  • Pylint can be slower and more resource-intensive than Pytype, especially on larger codebases.
  • Pylint's type-checking capabilities are not as advanced as Pytype's, which is specifically designed for type analysis.
  • Pylint's configuration can be more complex and less intuitive than Pytype's, especially for beginners.

Code Comparison

Pylint:

def my_function(x: int) -> int:
    """
    Adds 1 to the input integer.
    
    Args:
        x (int): The input integer.
    
    Returns:
        int: The input integer plus 1.
    """
    return x + 1

Pytype:

def my_function(x: int) -> int:
    """
    Adds 1 to the input integer.
    
    Args:
        x (int): The input integer.
    
    Returns:
        int: The input integer plus 1.
    """
    return x + 1

As you can see, the code is nearly identical between the two tools, as they both focus on static code analysis and type checking. The main difference is in the underlying implementation and the specific features and capabilities of each tool.

A simple program which checks Python source files for errors

Pros of PyCQA/pyflakes

  • Pyflakes is a lightweight static code analysis tool that focuses on detecting common programming errors, such as unused variables, imports, and function calls.
  • It is highly efficient and can be integrated into various development workflows, including text editors and continuous integration pipelines.
  • Pyflakes has a simple and straightforward codebase, making it easy to understand and contribute to.

Cons of PyCQA/pyflakes

  • Pyflakes has a more limited scope compared to PyType, as it only focuses on detecting certain types of programming errors and does not provide type checking or type inference capabilities.
  • Pyflakes may not be as comprehensive as PyType in terms of the types of errors it can detect, especially for more complex Python code.

Code Comparison

PyType (google/pytype):

def example_function(x: int) -> int:
    return x + 1

Pyflakes (PyCQA/pyflakes):

def example_function(x):
    return x + 1

The key difference is that PyType uses type annotations to perform type checking, while Pyflakes does not require type annotations and instead focuses on detecting other types of programming errors.

3,428

flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code.

Pros of Flake8

  • Flake8 is a widely-used and well-established linter for Python, with a large community and extensive documentation.
  • It supports a wide range of Python versions, from 2.7 to 3.9, making it a versatile tool.
  • Flake8 can be easily integrated into various development workflows, such as CI/CD pipelines, text editors, and IDEs.

Cons of Flake8

  • Flake8 is primarily focused on code style and formatting, and may not provide the same level of type checking and static analysis as Pytype.
  • The configuration and customization options in Flake8 can be more complex compared to Pytype, which has a more streamlined setup process.
  • Flake8 may not be as effective at detecting certain types of errors or issues that Pytype is designed to catch.

Code Comparison

Flake8 (PyCQA/flake8):

def my_function(a, b):
    return a + b

Pytype (google/pytype):

from typing import Tuple

def my_function(a: int, b: int) -> int:
    return a + b

In this example, Pytype provides more explicit type annotations, which can help catch type-related errors during the development process.

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README

CI PyPI - Wheel

pytype - 🦆✔

Pytype checks and infers types for your Python code - without requiring type annotations. Pytype can:

  • Lint plain Python code, flagging common mistakes such as misspelled attribute names, incorrect function calls, and much more, even across file boundaries.
  • Enforce user-provided type annotations. While annotations are optional for pytype, it will check and apply them where present.
  • Generate type annotations in standalone files ("pyi files"), which can be merged back into the Python source with a provided merge-pyi tool.

Pytype is a static analyzer; it does not execute the code it runs on.

Thousands of projects at Google rely on pytype to keep their Python code well-typed and error-free.

For more information, check out the user guide, FAQ, or supported features.

How is pytype different from other type checkers?

  1. Pytype uses inference instead of gradual typing. This means it will infer types on code even when the code has no type hints on it. So it can detect issues with code like this, which other type checkers would miss:

    def f():
        return "PyCon"
    def g():
        return f() + 2019
    
    # pytype: line 4, in g: unsupported operand type(s) for +: 'str'
    # and 'int' [unsupported-operands]
    
  2. Pytype is lenient instead of strict. That means it allows all operations that succeed at runtime and don't contradict annotations. For instance, this code will pass as safe in pytype, but fail in other type checkers, which assign types to variables as soon as they are initialized:

    from typing import List
    def get_list() -> List[str]:
        lst = ["PyCon"]
        lst.append(2019)
        return [str(x) for x in lst]
    
    # mypy: line 4: error: Argument 1 to "append" of "list" has
    # incompatible type "int"; expected "str"
    

Also see the corresponding FAQ entry.

Quickstart

To quickly get started with type-checking a file or directory, run the following, replacing file_or_directory with your input:

pip install pytype
pytype file_or_directory

To set up pytype on an entire package, add the following to a pyproject.toml file in the directory immediately above the package, replacing package_name with the package name:

[tool.pytype]
inputs = ['package_name']

Now you can run the no-argument command pytype to type-check the package. It's also easy to add pytype to your automated testing; see this example of a GitHub project that runs pytype on GitHub Actions.

Finally, pytype generates files of inferred type information, located by default in .pytype/pyi. You can use this information to type-annotate the corresponding source file:

merge-pyi -i <filepath>.py .pytype/pyi/<filename>.pyi

Requirements

You need a Python 3.8-3.11 interpreter to run pytype, as well as an interpreter in $PATH for the Python version of the code you're analyzing (supported: 3.8-3.11).

Platform support:

  • Pytype is currently developed and tested on Linux*, which is the main supported platform.
  • Installation on MacOSX requires OSX 10.7 or higher and Xcode v8 or higher**.
  • Windows is currently not supported unless you use WSL.

* On Alpine Linux, installation may fail due to issues with upstream dependencies. See the details of this issue for a possible fix.
** If the ninja dependency fails to install, make sure cmake is installed. See this issue for details.

Installing

Pytype can be installed via pip. Note that the installation requires wheel and setuptools. (If you're working in a virtualenv, these two packages should already be present.)

pip install pytype

Or from the source code on GitHub.

git clone --recurse-submodules https://github.com/google/pytype.git
cd pytype
pip install .

Instead of using --recurse-submodules, you could also have run

git submodule init
git submodule update

in the pytype directory. To edit the code and have your edits tracked live, replace the pip install command with:

pip install -e .

Installing on WSL

Follow the steps above, but make sure you have the correct libraries first:

sudo apt install build-essential python3-dev libpython3-dev

Usage

usage: pytype [options] input [input ...]

positional arguments:
  input                 file or directory to process

Common options:

  • -V, --python-version: Python version (major.minor) of the target code. Defaults to the version that pytype is running under.
  • -o, --output: The directory into which all pytype output goes, including generated .pyi files. Defaults to .pytype.
  • -d, --disable. Comma or space-separated list of error names to ignore. Detailed explanations of pytype's error names are in this doc. Defaults to empty.

For a full list of options, run pytype --help.

In addition to the above, you can direct pytype to use a custom typeshed installation instead of its own bundled copy by setting $TYPESHED_HOME.

Config File

For convenience, you can save your pytype configuration in a file. The config file can be a TOML-style file with a [tool.pytype] section (preferred) or an INI-style file with a [pytype] section. If an explicit config file is not supplied, pytype will look for a pytype section in the first pyproject.toml or setup.cfg file found by walking upwards from the current working directory.

Start off by generating a sample config file:

$ pytype --generate-config pytype.toml

Now customize the file based on your local setup, keeping only the sections you need. Directories may be relative to the location of the config file, which is useful if you want to check in the config file as part of your project.

For example, suppose you have the following directory structure and want to analyze package ~/repo1/foo, which depends on package ~/repo2/bar:

~/
├── repo1
│   └── foo
│       ├── __init__.py
│       └── file_to_check.py
└── repo2
    └── bar
        ├── __init__.py
        └── dependency.py

Here is the filled-in config file, which instructs pytype to type-check ~/repo1/foo as Python 3.9 code, look for packages in ~/repo1 and ~/repo2, and ignore attribute errors. Notice that the path to a package does not include the package itself.

$ cat ~/repo1/pytype.toml

# NOTE: All relative paths are relative to the location of this file.

[tool.pytype]

# Space-separated list of files or directories to process.
inputs = [
    'foo',
]

# Python version (major.minor) of the target code.
python_version = '3.9'

# Paths to source code directories, separated by ':'.
pythonpath = .:~/repo2

# Space-separated list of error names to ignore.
disable = [
    'attribute-error',
]

We could've discovered that ~/repo2 needed to be added to the pythonpath by running pytype's broken dependency checker:

$ pytype --config=~/repo1/pytype.toml ~/repo1/foo/*.py --unresolved

Unresolved dependencies:
  bar.dependency

Subtools

Pytype ships with a few scripts in addition to pytype itself:

  • annotate-ast, an in-progress type annotator for ASTs.
  • merge-pyi, for merging type information from a .pyi file into a Python file.
  • pytd-tool, a parser for .pyi files.
  • pytype-single, a debugging tool for pytype developers, which analyzes a single Python file assuming that .pyi files have already been generated for all of its dependencies.
  • pyxref, a cross-references generator.

2023 Roadmap

  • Typegraph rewrite for improved correctness and performance.
  • Basic Python 3.11 support.

License

Apache 2.0

Disclaimer

This is not an official Google product.