numpy · rgommers · Nov 24, 2023 · Nov 6, 2023 · Nov 8, 2023 · Nov 8, 2023
diff --git a/benchmarks/benchmarks/bench_ufunc.py b/benchmarks/benchmarks/bench_ufunc.py
@@ -423,7 +423,7 @@ def time_divide_scalar2_inplace(self, dtype):
 
 class CustomComparison(Benchmark):
     params = (np.int8,  np.int16,  np.int32,  np.int64, np.uint8, np.uint16,
-              np.uint32, np.uint64, np.float32, np.float64, np.bool_)
+              np.uint32, np.uint64, np.float32, np.float64, np.bool)
     param_names = ['dtype']
 
     def setup(self, dtype):

diff --git a/doc/release/upcoming_changes/25080.change.rst b/doc/release/upcoming_changes/25080.change.rst
@@ -0,0 +1,2 @@
+* Redefine `numpy.bool` as an alias for `numpy.bool_` (as opposed to the `bool`
+it was until NumPy 1.24) for Array API compatibility.
diff --git a/doc/source/reference/arrays.scalars.rst b/doc/source/reference/arrays.scalars.rst
@@ -265,6 +265,10 @@ Other types
    :members: __init__
    :exclude-members: __init__
 
+.. autoclass:: numpy.bool
+   :members: __init__
+   :exclude-members: __init__
+
 .. autoclass:: numpy.datetime64
    :members: __init__
    :exclude-members: __init__

diff --git a/doc/source/user/basics.types.rst b/doc/source/user/basics.types.rst
@@ -30,7 +30,7 @@ confusion with builtin python type names, such as `numpy.bool_`.
       - Actual C type
       - Description
 
-    * - `numpy.bool_`
+    * - `numpy.bool` or `numpy.bool_`
       - N/A
       - ``bool`` (defined in ``stdbool.h``)
       - Boolean (True or False) stored as a byte.
@@ -141,7 +141,7 @@ aliases are provided (See :ref:`sized-aliases`).
 NumPy numerical types are instances of ``dtype`` (data-type) objects, each
 having unique characteristics.  Once you have imported NumPy using
 ``>>> import numpy as np``
-the dtypes are available as ``np.bool_``, ``np.float32``, etc.
+the dtypes are available as ``np.bool``, ``np.float32``, etc.
 
 Advanced types, not listed above, are explored in
 section :ref:`structured_arrays`.
@@ -187,7 +187,7 @@ the type itself as a function. For example: ::
     array([0, 1, 2], dtype=int8)
 
 Note that, above, we use the *Python* float object as a dtype.  NumPy knows
-that ``int`` refers to ``np.int_``, ``bool`` means ``np.bool_``,
+that ``int`` refers to ``np.int_``, ``bool`` means ``np.bool``,
 that ``float`` is ``np.float64`` and ``complex`` is ``np.complex128``.
 The other data-types do not have Python equivalents.
 

diff --git a/numpy/__init__.py b/numpy/__init__.py
@@ -128,16 +128,17 @@
         array_str, asanyarray, asarray, ascontiguousarray, asfortranarray,
         atleast_1d, atleast_2d, atleast_3d, base_repr, binary_repr,
         bitwise_and, bitwise_count, bitwise_not, bitwise_or, bitwise_xor,
-        block, bool_, broadcast, busday_count, busday_offset, busdaycalendar,
-        byte, bytes_, can_cast, cbrt, cdouble, ceil, character, choose, clip,
-        clongdouble, complex128, complex64, complexfloating, compress,
-        concatenate, conj, conjugate, convolve, copysign, copyto, correlate,
-        cos, cosh, count_nonzero, cross, csingle, cumprod, cumproduct, cumsum,
-        datetime64, datetime_as_string, datetime_data, deg2rad, degrees,
-        diagonal, divide, divmod, dot, double, dtype, e, einsum, einsum_path,
-        empty, empty_like, equal, errstate, euler_gamma, exp, exp2, expm1,
-        fabs, finfo, flatiter, flatnonzero, flexible, float16, float32,
-        float64, float_power, floating, floor, floor_divide, fmax, fmin, fmod,
+        block, bool, bool_, broadcast, busday_count, busday_offset,
+        busdaycalendar, byte, bytes_, can_cast, cbrt, cdouble, ceil,
+        character, choose, clip, clongdouble, complex128, complex64,
+        complexfloating, compress, concatenate, conj, conjugate, convolve,
+        copysign, copyto, correlate, cos, cosh, count_nonzero, cross, csingle,
+        cumprod, cumproduct, cumsum, datetime64, datetime_as_string,
+        datetime_data, deg2rad, degrees, diagonal, divide, divmod, dot,
+        double, dtype, e, einsum, einsum_path, empty, empty_like, equal,
+        errstate, euler_gamma, exp, exp2, expm1, fabs, finfo, flatiter,
+        flatnonzero, flexible, float16, float32, float64, float_power,
+        floating, floor, floor_divide, fmax, fmin, fmod,
         format_float_positional, format_float_scientific, frexp, from_dlpack,
         frombuffer, fromfile, fromfunction, fromiter, frompyfunc, fromstring,
         full, full_like, gcd, generic, geomspace, get_printoptions,
@@ -266,7 +267,6 @@
 
     _type_info = [
         ("object", ""),  # The NumPy scalar only exists by name.
-        ("bool", _specific_msg.format("bool_")),
         ("float", _specific_msg.format("float64")),
         ("complex", _specific_msg.format("complex128")),
         ("str", _specific_msg.format("str_")),
@@ -283,7 +283,7 @@
     # probably wait for NumPy 1.26 or 2.0.
     # When defined, these should possibly not be added to `__all__` to avoid
     # import with `from numpy import *`.
-    __future_scalars__ = {"bool", "str", "bytes", "object"}
+    __future_scalars__ = {"str", "bytes", "object"}
 
     # now that numpy core module is imported, can initialize limits
     _core.getlimits._register_known_types()
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,2 @@
		* Redefine `numpy.bool` as an alias for `numpy.bool_` (as opposed to the `bool`
		it was until NumPy 1.24) for Array API compatibility.
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