Mypy Extensions

Common errors

Warnings

Install compatibility stale last tested: 2026-05-12

Imports

• override
  wrong

  from typing import override

  correct

  from mypy_extensions import override

  While `typing.override` exists in Python 3.12+, `mypy_extensions.override` provides support for earlier Python versions when using mypy.

• Required
  wrong

  from typing import Required

  correct

  from mypy_extensions import Required

  For `TypedDict` keys, `mypy_extensions.Required` provides support for mypy, especially for Python versions older than 3.11 where `typing.Required` was introduced.

• NotRequired
  wrong

  from typing import NotRequired

  correct

  from mypy_extensions import NotRequired

  Similar to `Required`, `mypy_extensions.NotRequired` provides support for `TypedDict` optional keys in mypy for Python versions older than 3.11.

Quickstart stale last tested: 2026-04-23

This quickstart demonstrates the use of the `override` decorator from `mypy_extensions`. Run `mypy your_file_name.py` to type-check this example. If `SpecificAnalyzer.analyze` did not correctly override `BaseAnalyzer.analyze`, mypy would report an error. Note that for Python 3.12+, `typing.override` should generally be preferred if `mypy-extensions` is not otherwise needed. The core functionality of `mypy-extensions` is to provide types for mypy to check, not to execute specific runtime logic itself.

from mypy_extensions import override
from typing import Dict, Any, List

class BaseAnalyzer:
    def analyze(self, data: Dict[str, Any]) -> List[str]:
        raise NotImplementedError

class SpecificAnalyzer(BaseAnalyzer):
    @override
    def analyze(self, data: Dict[str, Any]) -> List[str]:
        if "items" in data:
            return [str(item) for item in data["items"]]
        return []

# Example usage for mypy checking
def process_data(analyzer: BaseAnalyzer, input_data: Dict[str, Any]) -> List[str]:
    return analyzer.analyze(input_data)

if __name__ == "__main__":
    analyzer_instance = SpecificAnalyzer()
    results = process_data(analyzer_instance, {"items": [1, "two", 3.0]})
    print(f"Analysis results: {results}")

# To type-check this file:
# mypy your_file_name.py