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\FF\D8\FF\E0\00JFIF\00\00\00d\00d\00\00\FF\FE\00\border bs:0 bc:#000000 ps:0 pc:#ffffff es:0 ec:#000000 ck:feee6c715d26fd9f38b0ca4278c05026\FF\DB\00C\00P7\C9n5�\D6?\BD�\9Ds\EBp\9F[`8m\B7)o\B5\E8\E6I\99\FE3]]A2\BA\8Cw\D6E\93\\DEv\C8\009\F2\F1NI?uc\\F5\EA\96k\xN<~buv\EA\C8\D7 \8B\84\CEcxI\BBg\AE\9E=\D6+n\EC\80\C8A\8C\AE\EB\CF\D5\DA\E9"2\A4\B9j5\EB\F3W\B63\96\B30Yu\DA\FC8\ED\DF\E7Ms\FB\F1\8E\B3\FA\EA\E8\E6(\883zs\F2_\8DFk\8Bh \00\8C\DCw\D3R\B5+6X\BA\B2\C4j\AB0\B4\FCMw\C2I\8E\9B\E3\A9~9u\FA\D3l\80\C8%p\EE\FDn2� \00$\FEj\C4e\A9\DB\~\95\A7\A5\80EK\BB\8DDsP\00@@AD'k\CF\E8\DB\D2(\80\9AK\D3\85\D6lb\F2\BA\8C*\80\00)\95 59\A3R:\F3\CE"\B6\80\88\00\00i1u4�\E9\F2�\A6\A2\FACM\93WMb*\E0*\00\00\00\00\00(\A8\80\00\00\80\00\00\00\00\00\00\00\00\00\FF\D9 C/// """ ============================ Typing (:mod:`numpy.typing`) ============================ .. versionadded:: 1.20 Large parts of the NumPy API have :pep:`484`-style type annotations. In addition a number of type aliases are available to users, most prominently the two below: - `ArrayLike`: objects that can be converted to arrays - `DTypeLike`: objects that can be converted to dtypes .. _typing-extensions: https://pypi.org/project/typing-extensions/ Mypy plugin ----------- .. versionadded:: 1.21 .. automodule:: numpy.typing.mypy_plugin .. currentmodule:: numpy.typing Differences from the runtime NumPy API -------------------------------------- NumPy is very flexible. Trying to describe the full range of possibilities statically would result in types that are not very helpful. For that reason, the typed NumPy API is often stricter than the runtime NumPy API. This section describes some notable differences. ArrayLike ~~~~~~~~~ The `ArrayLike` type tries to avoid creating object arrays. For example, .. code-block:: python >>> np.array(x**2 for x in range(10)) array( at ...>, dtype=object) is valid NumPy code which will create a 0-dimensional object array. Type checkers will complain about the above example when using the NumPy types however. If you really intended to do the above, then you can either use a ``# type: ignore`` comment: .. code-block:: python >>> np.array(x**2 for x in range(10)) # type: ignore or explicitly type the array like object as `~typing.Any`: .. code-block:: python >>> from typing import Any >>> array_like: Any = (x**2 for x in range(10)) >>> np.array(array_like) array( at ...>, dtype=object) ndarray ~~~~~~~ It's possible to mutate the dtype of an array at runtime. For example, the following code is valid: .. code-block:: python >>> x = np.array([1, 2]) >>> x.dtype = np.bool_ This sort of mutation is not allowed by the types. Users who want to write statically typed code should instead use the `numpy.ndarray.view` method to create a view of the array with a different dtype. DTypeLike ~~~~~~~~~ The `DTypeLike` type tries to avoid creation of dtype objects using dictionary of fields like below: .. code-block:: python >>> x = np.dtype({"field1": (float, 1), "field2": (int, 3)}) Although this is valid NumPy code, the type checker will complain about it, since its usage is discouraged. Please see : :ref:`Data type objects ` Number precision ~~~~~~~~~~~~~~~~ The precision of `numpy.number` subclasses is treated as a covariant generic parameter (see :class:`~NBitBase`), simplifying the annotating of processes involving precision-based casting. .. code-block:: python >>> from typing import TypeVar >>> import numpy as np >>> import numpy.typing as npt >>> T = TypeVar("T", bound=npt.NBitBase) >>> def func(a: "np.floating[T]", b: "np.floating[T]") -> "np.floating[T]": ... ... Consequently, the likes of `~numpy.float16`, `~numpy.float32` and `~numpy.float64` are still sub-types of `~numpy.floating`, but, contrary to runtime, they're not necessarily considered as sub-classes. Timedelta64 ~~~~~~~~~~~ The `~numpy.timedelta64` class is not considered a subclass of `~numpy.signedinteger`, the former only inheriting from `~numpy.generic` while static type checking. 0D arrays ~~~~~~~~~ During runtime numpy aggressively casts any passed