Typing (numpy.typing
)¶
Warning
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 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¶
A mypy plugin is distributed in numpy.typing
for managing a number of
platform-specific annotations. Its function can be split into to parts:
Assigning the (platform-dependent) precisions of certain
number
subclasses, including the likes ofint_
,intp
andlonglong
. See the documentation on scalar types for a comprehensive overview of the affected classes. without the plugin the precision of all relevant classes will be inferred asAny
.Removing all extended-precision
number
subclasses that are unavailable for the platform in question. Most notable this includes the likes offloat128
andcomplex256
. Without the plugin all extended-precision types will, as far as mypy is concerned, be available to all platforms.
To enable the plugin, one must add it to their mypy configuration file:
[mypy]
plugins = numpy.typing.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 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 : Data type objects
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 float16
, float32
and
float64
are still sub-types of floating
, but, contrary to
runtime, they’re not necessarily considered as sub-classes.
Timedelta64¶
The timedelta64
class is not considered a subclass of signedinteger
,
the former only inheriting from generic
while static type checking.
0D arrays¶
During runtime numpy aggressively casts any passed 0D arrays into their
corresponding 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.
API¶
- numpy.typing.ArrayLike = typing.Union[...]¶
A
Union
representing objects that can be coerced into anndarray
.Among others this includes the likes of:
Scalars.
(Nested) sequences.
Objects implementing the __array__ protocol.
See Also
- array_like:
Any scalar or sequence that can be interpreted as an ndarray.
Examples
>>> import numpy as np >>> import numpy.typing as npt >>> def as_array(a: npt.ArrayLike) -> np.ndarray: ... return np.array(a)
- numpy.typing.DTypeLike = typing.Union[...]¶
A
Union
representing objects that can be coerced into adtype
.Among others this includes the likes of:
See Also
- Specifying and constructing data types
A comprehensive overview of all objects that can be coerced into data types.
Examples
>>> import numpy as np >>> import numpy.typing as npt >>> def as_dtype(d: npt.DTypeLike) -> np.dtype: ... return np.dtype(d)
- numpy.typing.NDArray = numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]][source]¶
A generic version of
np.ndarray[Any, np.dtype[+ScalarType]]
.Can be used during runtime for typing arrays with a given dtype and unspecified shape.
Examples
>>> 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)
- final 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 hierarchical set of subclasses. Each subsequent subclass is herein used for representing a lower level of precision, e.g.64Bit > 32Bit > 16Bit
.Examples
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 __future__ import annotations >>> from typing import TypeVar, Union, TYPE_CHECKING >>> import numpy as np >>> import numpy.typing as npt >>> T1 = TypeVar("T1", bound=npt.NBitBase) >>> T2 = TypeVar("T2", bound=npt.NBitBase) >>> def add(a: np.floating[T1], b: np.integer[T2]) -> np.floating[Union[T1, T2]]: ... return a + b >>> a = np.float16() >>> b = np.int64() >>> out = add(a, b) >>> if TYPE_CHECKING: ... 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*]