MAINT: gracefully shuffle memoryviews

numpy/random/_generator.pyx

@@ -2,7 +2,7 @@
 #cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True, language_level=3
 import operator
 import warnings
-from collections.abc import MutableSequence
+from collections.abc import Sequence
 
 from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
 from cpython cimport (Py_INCREF, PyFloat_AsDouble)
@@ -4436,7 +4436,7 @@ cdef class Generator:
                     x[i] = buf
         else:
             # Untyped path.
-            if not isinstance(x, MutableSequence):
+            if not isinstance(x, Sequence):
                 # See gh-18206. We may decide to deprecate here in the future.
                 warnings.warn(
                     "`x` isn't a recognized object; `shuffle` is not guaranteed "

numpy/random/mtrand.pyx

@@ -2,7 +2,7 @@
 #cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True, language_level=3
 import operator
 import warnings
-from collections.abc import MutableSequence
+from collections.abc import Sequence
 
 import numpy as np
 
@@ -4473,7 +4473,7 @@ cdef class RandomState:
                     x[i] = buf
         else:
             # Untyped path.
-            if not isinstance(x, MutableSequence):
+            if not isinstance(x, Sequence):
                 # See gh-18206. We may decide to deprecate here in the future.
                 warnings.warn(
                     "`x` isn't a recognized object; `shuffle` is not guaranteed "

numpy/random/tests/test_random.py

@@ -510,6 +510,21 @@ def test_shuffle_masked(self):
             assert_equal(
                 sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))
 
+    def test_shuffle_memoryview(self):
+        # gh-18273
+        # allow graceful handling of memoryviews
+        # (treat the same as arrays)
+        np.random.seed(self.seed)
+        a = np.arange(5).data
+        np.random.shuffle(a)
+        assert_equal(np.asarray(a), [0, 1, 4, 3, 2])
+        rng = np.random.RandomState(self.seed)
+        rng.shuffle(a)
+        assert_equal(np.asarray(a), [0, 1, 2, 3, 4])
+        rng = np.random.default_rng(self.seed)
+        rng.shuffle(a)
+        assert_equal(np.asarray(a), [4, 1, 0, 3, 2])
+
     def test_beta(self):
         np.random.seed(self.seed)
         actual = np.random.beta(.1, .9, size=(3, 2))