Function TensorToNdarray

python/pyarrow/src/arrow/python/numpy_convert.cc:229–277

}

Status TensorToNdarray(const std::shared_ptr<Tensor>& tensor, PyObject* base,
                       PyObject** out) {
  int type_num = 0;
  RETURN_NOT_OK(GetNumPyType(*tensor->type(), &type_num));
  PyArray_Descr* dtype = PyArray_DescrNewFromType(type_num);
  RETURN_IF_PYERROR();

  const int ndim = tensor->ndim();
  std::vector<npy_intp> npy_shape(ndim);
  std::vector<npy_intp> npy_strides(ndim);

  for (int i = 0; i < ndim; ++i) {
    npy_shape[i] = tensor->shape()[i];
    npy_strides[i] = tensor->strides()[i];
  }

  const void* immutable_data = nullptr;
  if (tensor->data()) {
    immutable_data = tensor->data()->data();
  }

  // Remove const =(
  void* mutable_data = const_cast<void*>(immutable_data);

  int array_flags = 0;
  if (tensor->is_row_major()) {
    array_flags |= NPY_ARRAY_C_CONTIGUOUS;
  }
  if (tensor->is_column_major()) {
    array_flags |= NPY_ARRAY_F_CONTIGUOUS;
  }
  if (tensor->is_mutable()) {
    array_flags |= NPY_ARRAY_WRITEABLE;
  }

  PyObject* result =
      PyArray_NewFromDescr(&PyArray_Type, dtype, ndim, npy_shape.data(),
                           npy_strides.data(), mutable_data, array_flags, nullptr);
  RETURN_IF_PYERROR();

  if (base == Py_None || base == nullptr) {
    base = py::wrap_tensor(tensor);
  } else {
    Py_XINCREF(base);
  }
  PyArray_SetBaseObject(reinterpret_cast<PyArrayObject*>(result), base);
  *out = result;
  return Status::OK();
}

// Wrap the dense data of a sparse tensor in a ndarray
static Status SparseTensorDataToNdarray(const SparseTensor& sparse_tensor,