To remove in the future –– numpy.typing
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:
:None:None:`ArrayLike`: objects that can be converted to arrays
:None:None:`DTypeLike`: objects that can be converted to dtypes
<Unimplemented 'target' '.. _typing-extensions: https://pypi.org/project/typing-extensions/'>
.. automodule:: numpy.typing.mypy_plugin
.. currentmodule:: numpy.typing
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 :None:None:`ArrayLike` type tries to avoid creating object arrays. For example,
.. code-block:: python
>>> np.array(x**2 for x in range(10))
array(<generator object <genexpr> 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 :None:None:`~typing.Any`:
.. code-block:: python
>>> from typing import Any
>>> array_like: Any = (x**2 for x in range(10))
>>> np.array(array_like)
array(<generator object <genexpr> at ...>, dtype=object)
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 :None:None:`numpy.ndarray.view` method to create a view of the array with a different dtype.
The :None:None:`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 : Data type objects <arrays.dtypes>
The precision of numpy.number
subclasses is treated as a covariant generic parameter (see ~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.
The ~numpy.timedelta64
class is not considered a subclass of ~numpy.signedinteger
, the former only inheriting from ~numpy.generic
while static type checking.
During runtime numpy aggressively casts any passed 0D arrays into their corresponding ~numpy.generic
instance. Until the introduction of shape typing (see Pep 646) it is unfortunately not possible to make the necessary distinction between 0D and >0D arrays. While thus not strictly correct, all operations are that can potentially perform a 0D-array -> scalar cast are currently annotated as exclusively returning an ndarray
.
If it is known in advance that an operation _will_ perform a 0D-array -> scalar cast, then one can consider manually remedying the situation with either typing.cast
or a # type: ignore
comment.
The dtype of numpy.recarray
, and the numpy.rec
functions in general, can be specified in one of two ways:
Directly via the dtype
argument.
With up to five helper arguments that operate via numpy.format_parser
: formats
, names
, titles
, aligned
and byteorder
.
These two approaches are currently typed as being mutually exclusive, i.e. if dtype
is specified than one may not specify formats
. While this mutual exclusivity is not (strictly) enforced during runtime, combining both dtype specifiers can lead to unexpected or even downright buggy behavior.
.. data:: ArrayLike
('value', 'typing.Union[...]')
A `~typing.Union` representing objects that can be coerced
into an `~numpy.ndarray`.
Among others this includes the likes of:
* Scalars.
* (Nested) sequences.
* Objects implementing the `~class.__array__` protocol.
.. versionadded:: 1.20
.. admonition:: See Also
:term:`array_like`:
Any scalar or sequence that can be interpreted as an ndarray.
.. rubric:: Examples
.. code-block:: python
>>> import numpy as np
>>> import numpy.typing as npt
>>> def as_array(a: npt.ArrayLike) -> np.ndarray:
... return np.array(a)
.. data:: DTypeLike
('value', 'typing.Union[...]')
A `~typing.Union` representing objects that can be coerced
into a `~numpy.dtype`.
Among others this includes the likes of:
* :class:`type` objects.
* Character codes or the names of :class:`type` objects.
* Objects with the ``.dtype`` attribute.
.. versionadded:: 1.20
.. admonition:: See Also
:ref:`Specifying and constructing data types <arrays.dtypes.constructing>`
A comprehensive overview of all objects that can be coerced
into data types.
.. rubric:: Examples
.. code-block:: python
>>> import numpy as np
>>> import numpy.typing as npt
>>> def as_dtype(d: npt.DTypeLike) -> np.dtype:
... return np.dtype(d)
.. data:: NDArray
('value', 'numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]')
A :term:`generic <generic type>` version of
`np.ndarray[Any, np.dtype[+ScalarType]] <numpy.ndarray>`.
Can be used during runtime for typing arrays with a given dtype
and unspecified shape.
.. versionadded:: 1.21
.. rubric:: Examples
.. code-block:: python
>>> import numpy as np
>>> import numpy.typing as npt
>>> print(npt.NDArray)
numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]
>>> print(npt.NDArray[np.float64])
numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]
>>> NDArrayInt = npt.NDArray[np.int_]
>>> a: NDArrayInt = np.arange(10)
>>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]:
... return np.array(a)
.. autoclass:: numpy.typing.NBitBase
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