GH-49644: [Python] Support converting list of multi-dimensional arrays to FixedShapeTensor

python/pyarrow/array.pxi

@@ -266,6 +266,24 @@ def array(object obj, type=None, mask=None, size=None, from_pandas=None,
     if type is not None and type.id == _Type_EXTENSION:
         extension_type = type
         type = type.storage_type
+        # GH-49644: when building a fixed_shape_tensor from a sequence of arrays,
+        # the converter only sees the flat storage type, so validate the
+        # tensor-specific constraints here where the type is still known.
+        if (isinstance(extension_type, FixedShapeTensorType)
+                and isinstance(obj, (list, tuple))):
+            if extension_type.permutation is not None:
+                raise NotImplementedError(
+                    "Converting a sequence of arrays to a fixed_shape_tensor with "
+                    "a permutation is not supported; use "
+                    "FixedShapeTensorArray.from_numpy_ndarray instead")
+            if np is not None:
+                expected_shape = tuple(extension_type.shape)
+                for element in obj:
+                    if (isinstance(element, np.ndarray) and element.ndim >= 2
+                            and tuple(element.shape) != expected_shape):
+                        raise ValueError(
+                            f"Cannot convert array of shape {element.shape} to a "
+                            f"fixed_shape_tensor of shape {expected_shape}")
 
     if from_pandas is None:
         c_from_pandas = False

python/pyarrow/src/arrow/python/python_to_arrow.cc

@@ -908,13 +908,32 @@ class PyListConverter : public ListConverter<T, PyConverter, PyConverterTrait> {
 
   Status AppendNdarray(PyObject* value) {
     PyArrayObject* ndarray = reinterpret_cast<PyArrayObject*>(value);
-    if (PyArray_NDIM(ndarray) != 1) {
-      return Status::Invalid("Can only convert 1-dimensional array values");
-    }
     if (PyArray_ISBYTESWAPPED(ndarray)) {
       // TODO
       return Status::NotImplemented("Byte-swapped arrays not supported");
     }
+    OwnedRef flattened;
+    if (PyArray_NDIM(ndarray) != 1) {
+      // GH-49644: a fixed-size list (e.g. fixed-shape-tensor storage) can be
+      // built from a multi-dimensional array, always flattened in C order
+      // regardless of the input's memory layout.
+      if (PyArray_NDIM(ndarray) < 2 || this->list_type_->id() != Type::FIXED_SIZE_LIST) {
+        return Status::Invalid(
+            "Can only convert 1-dimensional array values to a variable-sized list");
+      }
+      // Get an aligned, C-contiguous array (copying only if needed), then view
+      // it as 1-D so its values can be read directly in C order.
+      PyObject* contiguous =
+          PyArray_CheckFromAny(value, nullptr, /*min_depth=*/0, /*max_depth=*/0,
+                               NPY_ARRAY_C_CONTIGUOUS | NPY_ARRAY_ALIGNED, nullptr);
+      RETURN_IF_PYERROR();
+      flattened.reset(
+          PyArray_Ravel(reinterpret_cast<PyArrayObject*>(contiguous), NPY_CORDER));
+      Py_DECREF(contiguous);
+      RETURN_IF_PYERROR();
+      value = flattened.obj();
+      ndarray = reinterpret_cast<PyArrayObject*>(value);
+    }
     const int64_t size = PyArray_SIZE(ndarray);
     RETURN_NOT_OK(AppendTo(this->list_type_, size));
     RETURN_NOT_OK(this->list_builder_->ValidateOverflow(size));

python/pyarrow/tests/test_array.py

@@ -2924,6 +2924,36 @@ def test_array_from_invalid_dim_raises():
         pa.array(arr0d)
 
 
+@pytest.mark.numpy
+def test_fixed_size_list_from_multidim_ndarray():
+    # GH-49644: a fixed-size list can be built from multi-dimensional ndarray
+    # elements by flattening them in C order.
+    arr = pa.array([np.array([[1, 2, 3]], dtype=np.int64),
+                    np.array([[4, 5, 6]], dtype=np.int64)],
+                   type=pa.list_(pa.int64(), 3))
+    assert arr.type == pa.list_(pa.int64(), 3)
+    assert arr.to_pylist() == [[1, 2, 3], [4, 5, 6]]
+
+    # A non-trivial 2D shape confirms values are flattened in C (row-major) order
+    arr = pa.array([np.array([[1, 2], [3, 4]], dtype=np.int64)],
+                   type=pa.list_(pa.int64(), 4))
+    assert arr.to_pylist() == [[1, 2, 3, 4]]
+
+    # The flattened length must still match the fixed size
+    with pytest.raises(pa.lib.ArrowInvalid):
+        pa.array([np.array([[1, 2], [3, 4]], dtype=np.int64)],
+                 type=pa.list_(pa.int64(), 3))
+
+    # Variable-sized lists still require 1-dimensional values
+    with pytest.raises(pa.lib.ArrowInvalid, match="1-dimensional"):
+        pa.array([np.array([[1, 2, 3]], dtype=np.int64)],
+                 type=pa.list_(pa.int64()))
+
+    # 0-dimensional arrays are still rejected (not flattened to length 1)
+    with pytest.raises(pa.lib.ArrowInvalid, match="1-dimensional"):
+        pa.array([np.array(1, dtype=np.int64)], type=pa.list_(pa.int64(), 1))
+
+
 @pytest.mark.numpy
 def test_array_from_strided_bool():
     # ARROW-6325

python/pyarrow/tests/test_extension_type.py

@@ -1730,6 +1730,56 @@ def test_tensor_array_from_numpy(np_type_str):
         pa.FixedShapeTensorArray.from_numpy_ndarray(arr, dim_names=[0, 1])
 
 
+@pytest.mark.numpy
+@pytest.mark.parametrize("np_type_str", ("int8", "int64", "float32"))
+def test_tensor_array_from_list_of_ndarrays(np_type_str):
+    # GH-49644: build a fixed-shape-tensor array from a list of individual
+    # (multi-dimensional) ndarrays, not only from a single stacked ndarray.
+    np_dtype = np.dtype(np_type_str)
+    tensor_type = pa.fixed_shape_tensor(pa.from_numpy_dtype(np_dtype), (2, 3))
+
+    elements = [
+        np.arange(6, dtype=np_dtype).reshape(2, 3),
+        np.arange(6, 12, dtype=np_dtype).reshape(2, 3),
+    ]
+    result = pa.array(elements, type=tensor_type)
+    assert isinstance(result, pa.FixedShapeTensorArray)
+    assert result.type == tensor_type
+    assert len(result) == 2
+
+    # Must match the existing from_numpy_ndarray path on the same data
+    expected = pa.FixedShapeTensorArray.from_numpy_ndarray(np.stack(elements))
+    assert result.storage.equals(expected.storage)
+
+    # Each element round-trips back to the original ndarray (with its shape)
+    for scalar, original in zip(result, elements):
+        np.testing.assert_array_equal(scalar.to_numpy(), original)
+
+    # Higher-dimensional tensors work too
+    tensor_3d = pa.fixed_shape_tensor(pa.from_numpy_dtype(np_dtype), (2, 2, 3))
+    elements_3d = [np.arange(12, dtype=np_dtype).reshape(2, 2, 3)]
+    result_3d = pa.array(elements_3d, type=tensor_3d)
+    assert result_3d.type == tensor_3d
+    np.testing.assert_array_equal(result_3d[0].to_numpy(), elements_3d[0])
+
+    # None elements are allowed
+    result_with_null = pa.array([elements[0], None], type=tensor_type)
+    assert result_with_null.null_count == 1
+    assert result_with_null[1].as_py() is None
+
+    # A multi-dimensional element whose shape doesn't match the tensor shape is
+    # rejected, even when the total number of elements is the same (GH-49644).
+    with pytest.raises(ValueError, match="shape"):
+        pa.array([np.arange(6, dtype=np_dtype).reshape(3, 2)], type=tensor_type)
+
+    # Permuted tensor types can't be built from a sequence (the flatten would
+    # store the wrong layout), so they're rejected for now.
+    permuted_type = pa.fixed_shape_tensor(
+        pa.from_numpy_dtype(np_dtype), (2, 3), permutation=[1, 0])
+    with pytest.raises(NotImplementedError, match="permutation"):
+        pa.array(elements, type=permuted_type)
+
+
 @pytest.mark.numpy
 @pytest.mark.parametrize("tensor_type", (
     pa.fixed_shape_tensor(pa.int8(), [2, 2, 3]),