numpy-typing-compat
NumPy version information that type-checkers understand
Overview
This package provides version-specific boolean constants that allow library authors to
write NumPy-version-dependent static type annotations. Similar to how you might use
if sys.version_info >= (3, 12): for Python version checks, numpy-typing-compat
enables static type-checkers to understand which NumPy version is being used and apply
appropriate type annotations.
Installation
Modern package managers such as uv will
automatically install the appropriate version of numpy-typing-compat that matches your
installed NumPy version, in order to satisfy the numpy dependency restrictions of
numpy-typing-compat.
For example, the numpy-typing-compat==2.1.* distribution specifies numpy>=2.1,<2.2
as a required dependency. Modern package managers will ensure that these dependency
restrictions are satisfied. That way, if you upgrade numpy from 2.1 to 2.3, e.g.
by running uv sync --upgrade, then uv will also automatically look for a version of
numpy-typing-compat that satisfies the new numpy version, which in this case
would be numpy-typing-compat==2.3.*.
Reference
array_api
Additionally, the package provides a numpy_typing_compat.array_api namespace that's a
re-export of the numpy.array_api module on numpy < 2.0, or the main numpy module
on numpy >= 2.0. Note that the numpy.array_api module was introduced in
numpy >= 1.23, so it isn't available in numpy-typing-compat==1.22.*.
long and ulong
NumPy 2.0 introduced the new long and ulong scalar types, which are not available in
numpy < 2.0, and instead went by the names int_ and uint (which in numpy >= 2.0
are aliases for intp and uintp).
If you need to support both NumPy versions, you can use the long and ulong types
from numpy_typing_compat, which on numpy < 2.0 are aliases for np.int_ and
np.uint, and on numpy >= 2.0 are re-exports of np.long and np.ulong.
StringDType
In NumPy 2.0, the numpy.dtypes.StringDType was introduced, but it wasn't until
NumPy 2.1 that it was also available in the numpy stubs. The
numpy_typing_compat.StringDType is a re-export of numpy.dtypes.StringDType on
numpy >= 2.1, and an alias of np.dtype[Never] on numpy < 2.1. This allows type
checkers to also accept StringDType as a valid type on numpy == 2.0.*.
ABCPolyBase
In NumPy 2.1, the numpy.polynomial._polybase.ABCPolyBase was made into a generic type,
and in NumPy 2.2 the type parameter was made optional. This can be problematic on
numpy==2.1.* if you also require support for numpy < 2.1.
To work around this, the numpy_typing_compat.ABCPolyBase is a type alias for
numpy.polynomial._polybase.ABCPolyBase[LiteralString | None] on numpy==2.1.*, and
a direct re-export of numpy.polynomial._polybase.ABCPolyBase otherwise. This way,
type checkers (when configured in strict mode) won't report an error on numpy==2.1.*
when using ABCPolyBase. Note that numpy_typing_compat.ABCPolyBase does not accept a
type parameter, even on numpy >= 2.1, for the sake of consistency.
LiteralTrue and LiteralFalse
In NumPy 2.2, the np.bool scalar type became a generic type that accepts a type
parameter of either True or False. For compatibility with numpy < 2.2, the
numpy_typing_compat.LiteralTrue and numpy_typing_compat.LiteralFalse types are
provided. These are type aliases for Literal[True] and Literal[False] on
numpy < 2.2, and Literal[True] | np.bool[Literal[True]] and
Literal[False] | np.bool[Literal[False]] on numpy >= 2.2, respectively.
Version constants
The following low-level boolean version constants are available:
| Constant | True when |
|---|---|
NUMPY_GE_1_22 |
numpy >= 1.22 |
NUMPY_GE_1_23 |
numpy >= 1.23 |
NUMPY_GE_1_25 |
numpy >= 1.25 |
NUMPY_GE_2_0 |
numpy >= 2.0 |
NUMPY_GE_2_1 |
numpy >= 2.1 |
NUMPY_GE_2_2 |
numpy >= 2.2 |
NUMPY_GE_2_3 |
numpy >= 2.3 |
Each constant is typed as Literal[True] or Literal[False] depending on your NumPy
version, so that static type-checkers are able to understand the NumPy version being
used.
2 maintainers
Release files for numpy-typing-compat 2.3.20250729
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