review comments

numpy · seberg · Jan 8, 2026 · Jul 31, 2025 · Jul 31, 2025 · Jul 31, 2025
commit f739d546fd61978bbf94edc0c46a0a8d4c6b9ec2
diff --git a/doc/release/upcoming_changes/29492.deprecation.rst b/doc/release/upcoming_changes/29492.deprecation.rst
@@ -6,5 +6,6 @@ threads.  As an alternative, you can create a new view via
 `np.reshape` or `np.ndarray.reshape`. For example: ``x = np.arange(15); x = np.reshape(x, (3, 5))``.
 To ensure no copy is made from the data, one can use ``np.reshape(..., copy = False)``.

-Directly setting the shape on an array is possible with the private method `np.ndarray._set_shape`.
+Directly setting the shape on an array is discouraged, but possible with the
+private method `np.ndarray._set_shape`.

diff --git a/numpy/_core/src/multiarray/getset.c b/numpy/_core/src/multiarray/getset.c
@@ -114,7 +114,7 @@ array_shape_set(PyArrayObject *self, PyObject *val, void* NPY_UNUSED(ignored))
    /* Deprecated NumPy 2.4, 2025-07-29 */
    if (DEPRECATE("Setting the shape on a NumPy array has been deprecated"
                  " in NumPy 2.4.\nAs an alternative, you can create a new"
-                  " view using np.reshape."
+                  " view using np.reshape (with copy=False if needed)."
                 ) < 0 ) {
            return -1;
    }

diff --git a/numpy/_core/tests/test_umath.py b/numpy/_core/tests/test_umath.py
@@ -4818,18 +4818,14 @@ class BadArr1(np.ndarray):
        def __array_finalize__(self, obj):
            # The outer call reshapes to 3 dims, try to do a bad reshape.
            if self.ndim == 3:
-                with warnings.catch_warnings():  # gh-28901
-                    warnings.filterwarnings('ignore', category=DeprecationWarning)
-                    self.shape = self.shape + (1,)
+                self._set_shape(self.shape + (1,))

    class BadArr2(np.ndarray):
        def __array_finalize__(self, obj):
            if isinstance(obj, BadArr2):
                # outer inserts 1-sized dims. In that case disturb them.
                if self.shape[-1] == 1:
-                    with warnings.catch_warnings():  # gh-28901
-                        warnings.filterwarnings('ignore', category=DeprecationWarning)
-                        self.shape = self.shape[::-1]
+                    self._set_shape(self.shape[::-1])

    for cls in [BadArr1, BadArr2]:
        arr = np.ones((2, 3)).view(cls)

diff --git a/numpy/linalg/_linalg.py b/numpy/linalg/_linalg.py
@@ -352,8 +352,7 @@ def tensorsolve(a, b, axes=None):
    a = a.reshape(prod, prod)
    b = b.ravel()
    res = wrap(solve(a, b))
-    res = res.reshape(oldshape)
-    return res
+    return res.reshape(oldshape)


 def _solve_dispatcher(a, b):

diff --git a/numpy/ma/core.py b/numpy/ma/core.py
@@ -1118,8 +1118,7 @@ def reduce(self, target, axis=0, dtype=None):
        if t.shape == ():
            t = t.reshape(1)
            if m is not nomask:
-                m = make_mask(m, copy=True)
-                m = m.reshape((1,))
+                m = make_mask(m, copy=True).reshape((1,))

        if m is nomask:
            tr = self.f.reduce(t, axis)
@@ -2726,8 +2725,7 @@ def __getitem__(self, indx):
            _mask = self.maskiter.__getitem__(indx)
            if isinstance(_mask, ndarray):
                # set shape to match that of data; this is needed for matrices
-                _mask = _mask.reshape(result.shape)
-                result._mask = _mask
+                result._mask = _mask.reshape(result.shape)
            elif isinstance(_mask, np.void):
                return mvoid(result, mask=_mask, hardmask=self.ma._hardmask)
            elif _mask:  # Just a scalar, masked
@@ -4793,9 +4791,9 @@ def reshape(self, *s, **kwargs):

        Notes
        -----
-        The reshaping operation cannot guarantee that a copy will not be made,
-        to modify the shape in place, use ``a.shape = s``. Note: reshaping in
-        place is deprecated.
+        By default, the reshaping operation will make a copy if a view
+        with different strides is not possible. To ensure a view,
+        pass ``copy=False``.

        Examples
        --------

diff --git a/numpy/random/_generator.pyx b/numpy/random/_generator.pyx
@@ -3952,8 +3952,7 @@ cdef class Generator:
            _factor = u * np.sqrt(s)

        x = mean + x @ _factor.T
-        x = x.reshape(tuple(final_shape))
-        return x
+        return x.reshape(tuple(final_shape))

    def multinomial(self, object n, object pvals, size=None):
        """

diff --git a/numpy/random/mtrand.pyx b/numpy/random/mtrand.pyx
@@ -4265,8 +4265,7 @@ cdef class RandomState:

        x = np.dot(x, np.sqrt(s)[:, None] * v)
        x += mean
-        x = x.reshape(tuple(final_shape))
-        return x
+        return x.reshape(tuple(final_shape))

    def multinomial(self, long n, object pvals, size=None):
        """
-Original file line number
+Diff line change
@@ -6,5 +6,6 @@ threads.  As an alternative, you can create a new view via
     `np.reshape` or `np.ndarray.reshape`. For example: ``x = np.arange(15); x = np.reshape(x, (3, 5))``.
     To ensure no copy is made from the data, one can use ``np.reshape(..., copy = False)``.
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-    Directly setting the shape on an array is possible with the private method `np.ndarray._set_shape`.
+    Directly setting the shape on an array is discouraged, but possible with the
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+    private method `np.ndarray._set_shape`.