NumPy is now able to detect the SIMD instructions made available by the CPU and optimize for them. NumPy 1.20 also enables multi-platform SIMD compiler optimizations. The new numpy.typing module contains the new type aliases and can be imported at runtime: from numpy. dtype ( )Īlthough this is valid Numpy code, the type checker will complain about it, since its usage is discouraged. The DTypeLike type tries to avoid creation of dtype objects using dictionary of fields like below: x = np.

The two new types empower the type checker to recognize inefficient patterns and warn the users. A data type object ( numpy.dtype) specifies the content of the fixed-size block of memory corresponding to an array item and includes in particular information about the item data type (e.g., integer, float), size of the data, byte order ( little-endian or big-endian), and more. The DTypeLike is used for objects that can be converted to dtypes. The ArrayLike type is used for objects that can be converted to arrays. Even without annotations for shapes or dtypes, it would be valuable to have near-complete annotations for NumPy, the project at the bottom of the scientific stack.ĭevelopers can additionally use new types - ArrayLike and DTypeLike. For example, off-hand I know that at least SciPy, pandas, and xarray have at least part of their APIs type annotated. Over the past few years, using type annotations has become increasingly popular, even in the scientific Python stack. We still supported Python 2, which meant that we would need to use awkward comments for type annotations. When we started numpy-stubs a few years ago, putting type annotations in NumPy itself seemed premature. One contributor explained the rationale behind the move as follows: The NumPy library code is now annotated with type information, a move facilitated by NumPy no longer supporting Python 2. NumPy’s documentation additionally sees significant improvements. A wider use of SIMD (Single Instruction, Multiple Data) instructions increases the execution speed of universal functions ( ufunc). Developers can now use type annotations for NumPy functions. The newly released NumPy 1.20 features performance and documentation improvements.

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