Typing (numpy.typing)


Some of the types in this module rely on features only present in the standard library in Python 3.8 and greater. If you want to use these types in earlier versions of Python, you should install the typing-extensions package.

Large parts of the NumPy API have PEP-484-style type annotations. In addition, the following type aliases are available for users.

  • typing.ArrayLike: objects that can be converted to arrays

  • typing.DtypeLike: objects that can be converted to dtypes

Roughly speaking, typing.ArrayLike is “objects that can be used as inputs to np.array” and typing.DtypeLike is “objects that can be used as inputs to np.dtype”.

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.


The ArrayLike type tries to avoid creating object arrays. For example,

>>> np.array(x**2 for x in range(10))
array(<generator object <genexpr> at 0x10c004cd0>, 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:

>>> np.array(x**2 for x in range(10))  # type: ignore

or explicitly type the array like object as Any:

>>> from typing import Any
>>> array_like: Any = (x**2 for x in range(10))
>>> np.array(array_like)
array(<generator object <genexpr> at 0x1192741d0>, dtype=object)


It’s possible to mutate the dtype of an array at runtime. For example, the following code is valid:

>>> 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 insted use the numpy.ndarray.view method to create a view of the array with a different dtype.


The DTypeLike type tries to avoid creation of dtype objects using dictionary of fields like below:

>>> 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 : https://numpy.org/devdocs/reference/arrays.dtypes.html


class numpy.typing.NBitBase[source]

An object representing numpy.number precision during static type checking.

Used exclusively for the purpose static type checking, NBitBase represents the base of a hierachieral set of subclasses. Each subsequent subclass is herein used for representing a lower level of precision, e.g. 64Bit > 32Bit > 16Bit.


Below is a typical usage example: NBitBase is herein used for annotating a function that takes a float and integer of arbitrary precision as arguments and returns a new float of whichever precision is largest (e.g. np.float16 + np.int64 -> np.float64).

>>> from typing import TypeVar, TYPE_CHECKING
>>> import numpy as np
>>> import numpy.typing as npt

>>> T = TypeVar("T", bound=npt.NBitBase)

>>> def add(a: "np.floating[T]", b: "np.integer[T]") -> "np.floating[T]":
...     return a + b

>>> a = np.float16()
>>> b = np.int64()
>>> out = add(a, b)

...     reveal_locals()
...     # note: Revealed local types are:
...     # note:     a: numpy.floating[numpy.typing._16Bit*]
...     # note:     b: numpy.signedinteger[numpy.typing._64Bit*]
...     # note:     out: numpy.floating[numpy.typing._64Bit*]