Dear NumPy developers, I've been working on a glue package that allows the Julia language (http://julialang.org/) to call Python routines easily https://github.com/stevengj/PyCall.jl and I'm using NumPy to pass multidimensional arrays between languages. Julia has the ability to call C functions directly (without writing C glue), and I've been exploiting this to write PyCall purely in Julia. (This is nice for a number of reasons; besides programming and linking convenience, it means that I can dynamically load different Python versions on the same machine, and don't need to recompile if e.g. NumPy is updated.) However, calling NumPy has been a challenge, because of NumPy's heavy reliance on macros in its C API. I wanted to make a couple of suggestions to keep in mind as you plan for NumPy 2.0: 1) Dynamically linking to NumPy's C API was challenging, to say the least. Assuming you stick with the PyArray_API lookup table of pointers, it would be much easier to call from other languages if you include e.g. a numpy.core.multiarray._ARRAY_API_NAMES variable in the Python module that is a list of strings giving the symbol names corresponding to the numpy.core.multiarray._ARRAY_API pointer. (Plus documentation, of course.) Currently I need to parse __multiarray_api.h to extract this information, which is somewhat hackish. 2) Please provide non-macro equivalents (exported in the _ARRAY_API symbol table or otherwise) of PyArray_NDIM etcetera to access PyArrayObject members. (e.g. call them PyArray_ndim etc. Note that inline functions are not enough, since they are not loadable dynamically.) Right now, the only ways[*] I can see to access this information are either to use C glue (which I want to avoid for the reasons above) or to call Python to access the __array_interface__ attribute (which is suboptimal from a performance standpoint). Thanks for all your efforts! Any feedback on PyCall would be welcome, too. --SGJ [*] A third way would be to parse ndarraytypes.h to extract the format of the PyArrayObject_fields structure, and use upcoming Julia support for accessing C struct types to read the fields. This is likely to require tracking NumPy releases carefully to avoid breakage, however, as well as involving some care with the PyObject_HEAD macro. PS. If you want to try out PyCall with NumPy, note that a patch to Julia is currently required for this to work: https://github.com/JuliaLang/julia/pull/2317