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 arraysDTypeLike
: objects that can be converted to dtypes
Mypy plugin
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,
>>> 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:
>>> np.array(x**2 for x in range(10)) # type: ignore
or explicitly type the array like object as ~typing.Any
:
>>> 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)
ndarray
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 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:
>>> 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 <arrays.dtypes>`
Number precision
The precision of numpy.number
subclasses is treated as a covariant generic
parameter (see ~NBitBase
), simplifying the annotating of processes
involving precision-based casting.
>>> 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 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.
Record array dtypes
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.
API
Module | mypy |
A mypy plugin for managing a number of platform-specific annotations. Its functionality can be split into three distinct parts: |
Module | setup |
Undocumented |
Package | tests |
No package docstring; 1/4 module documented |
From __init__.py
:
Class |
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A type representing numpy.number precision during static type checking. |
Type Alias |
|
Undocumented |
Variable |
|
Undocumented |
Variable |
|
Undocumented |
Variable | test |
Undocumented |