NumPytyyppisten

NumPytyyppisten is a colloquial Finnish term used to refer to the data types, or dtypes, that NumPy supports. The concept covers scalar dtypes such as integers, unsigned integers, floating-point numbers, booleans, and complex numbers, as well as specialized dtypes like datetime64, timedelta64, and the fixed-length string and Unicode dtypes. It also includes object and structured dtypes used for records and mixed data. In practice, NumPytyyppisten describes the set of types that define the storage and behavior of elements in NumPy arrays and in related typing constructs.

The term appears in Finnish programming communities and documentation as a shorthand for discussing type behavior, compatibility with Python types, and performance implications of different dtypes. It is not an official NumPy term, but is often used when talking about array creation, type promotion rules, and dtype-aware operations.

In modern code, NumPytyyppisten is closely connected to Python type hints that reference NumPy dtypes, such as those provided by the numpy.typing module. For example, a typed array can be annotated as an NDArray[np.float64] to indicate 64-bit floating-point elements, or a dtype like np.dtype('int32'). The numpy.typing module provides NDArray, DTypeLike, and related aliases to express these constraints in type checkers.

At runtime, NumPy enforces dtypes through array metadata, but static type checkers only approximate this behavior. The support in type checkers and stubs varies by version, so NumPytyyppisten annotations may require careful management of version compatibility and runtime checks.