Скачать или смотреть bug numpyfrombuffer is missing type hints 20472

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Okay, let's dive into the `numpy.frombuffer` issue, specifically the "missing type hints" problem that surfaces in NumPy versions after a certain point (typically around NumPy 1.20, but it can vary depending on your setup). This is a common hurdle, and understanding how to work around it is crucial for robust NumPy-based code.

*Understanding the Problem: `numpy.frombuffer` and Type Hints*

The `numpy.frombuffer()` function is a powerful tool in NumPy. It allows you to create a NumPy array from a buffer-like object (e.g., a bytes string, a memoryview, etc.) without copying the data. This is incredibly efficient when you're dealing with large datasets or data that's already in a specific memory layout.

The problem, however, comes with type hints, especially in more recent versions of Python and NumPy, as well as the introduction of tools like MyPy for static type checking.

*What are Type Hints?* Type hints (or type annotations) are a way to specify the expected data types of variables, function arguments, and function return values in Python code. They are used by static analysis tools (like MyPy) to help you catch potential type errors before you run your code. This leads to more robust and maintainable programs.

*Why the Conflict?* Historically, `numpy.frombuffer`'s type annotations were somewhat incomplete or inaccurate in NumPy's `.pyi` type stubs file. When you pass a specific `dtype` to `numpy.frombuffer`, the return type is known exactly. However, the type hint for frombuffer wasn't always precise enough to reflect this. As a result, type checkers could complain that the return type was too general (e.g., `np.ndarray`) rather than a specific type (e.g., `np.ndarray[np.float64]`). This is the "missing type hints" issue, even though `frombuffer` itself works perfectly fine at runtime. The error message would often involve the number 20472, which could be a specific code associated with the type checking rule being violated.

*Example Scenario* ...

#badvalue #badvalue #badvalue