How to access data using raw pointers ===================================== One of the advantages of the ndarray wrapper is that the same data can be used in both Python and C++ and changes can be made to reflect at both ends. The from_data method makes this possible. Like before, first get the necessary headers, setup the namespaces and initialize the Python runtime and numpy module:: #include #include namespace p = boost::python; namespace np = boost::python::numpy; int main(int argc, char **argv) { Py_Initialize(); np::initialize(); Create an array in C++ , and pass the pointer to it to the from_data method to create an ndarray:: int arr[] = {1,2,3,4,5}; np::ndarray py_array = np::from_data(arr, np::dtype::get_builtin(), p::make_tuple(5), p::make_tuple(sizeof(int)), p::object()); Print the source C++ array, as well as the ndarray, to check if they are the same:: std::cout << "C++ array :" << std::endl; for (int j=0;j<4;j++) { std::cout << arr[j] << ' '; } std::cout << std::endl << "Python ndarray :" << p::extract(p::str(py_array)) << std::endl; Now, change an element in the Python ndarray, and check if the value changed correspondingly in the source C++ array:: py_array[1] = 5 ; std::cout << "Is the change reflected in the C++ array used to create the ndarray ? " << std::endl; for (int j = 0; j < 5; j++) { std::cout << arr[j] << ' '; } Next, change an element of the source C++ array and see if it is reflected in the Python ndarray:: arr[2] = 8; std::cout << std::endl << "Is the change reflected in the Python ndarray ?" << std::endl << p::extract(p::str(py_array)) << std::endl; } As we can see, the changes are reflected across the ends. This happens because the from_data method passes the C++ array by reference to create the ndarray, and thus uses the same locations for storing data.