Give PyArray<PyObject> another try.

Author: adamreichold

src/dtype.rs

@@ -179,8 +179,9 @@ impl DataType {
 ///
 /// A type `T` that implements this trait should be safe when managed in numpy array,
 /// thus implementing this trait is marked unsafe.
-/// For example, we don't support `PyObject` because of [an odd segfault](https://github.com/PyO3/rust-numpy/pull/143),
-/// although numpy itself supports it.
+/// This means that all data types except for `DataType::Object` are assumed to be trivially copyable.
+/// Furthermore, it is assumed that for `DataType::Object` the elements are pointers into the Python heap
+/// and that the corresponding `Clone` implemenation will never panic as it only increases the reference count.
 pub unsafe trait Element: Clone + Send {
     /// `DataType` corresponding to this type.
     const DATA_TYPE: DataType;
@@ -246,3 +247,10 @@ cfg_if! {
         impl_num_element!(u64, Uint64, NPY_ULONG, NPY_ULONGLONG);
     }
 }
+
+unsafe impl Element for PyObject {
+    const DATA_TYPE: DataType = DataType::Object;
+    fn is_same_type(dtype: &PyArrayDescr) -> bool {
+        dtype.get_typenum() == NPY_TYPES::NPY_OBJECT as i32
+    }
+}

tests/to_py.rs

@@ -169,3 +169,26 @@ fn forder_into_pyarray() {
         pyo3::py_run!(py, fmat_py, "assert fmat_py.flags['F_CONTIGUOUS']")
     })
 }
+
+#[test]
+fn to_pyarray_object_vec() {
+    use pyo3::{
+        types::{PyDict, PyString},
+        ToPyObject,
+    };
+    use std::cmp::Ordering;
+
+    pyo3::Python::with_gil(|py| {
+        let dict = PyDict::new(py);
+        let string = PyString::new(py, "Hello:)");
+        let vec = vec![dict.to_object(py), string.to_object(py)];
+        let arr = vec.to_pyarray(py).readonly();
+
+        for (a, b) in vec.iter().zip(arr.as_slice().unwrap().iter()) {
+            assert_eq!(
+                a.as_ref(py).compare(b).map_err(|e| e.print(py)).unwrap(),
+                Ordering::Equal
+            );
+        }
+    })
+}