Make `to_numpy` conversions consistent with NumPy `dtype`

Moved discussion in #462 into this separate issue. Other PythonCall issues related to NumPy `dtype` and Numpy arrays that may be relevant: - #319 - #439 - #441 - #486 <details> <summary>My system details (click to expand)</summary> ### Julia ```julia julia> versioninfo() Julia Version 1.12.1 Commit ba1e628ee49 (2025-10-17 13:02 UTC) Build Info: Official https://julialang.org release Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 48 × AMD EPYC 7V13 64-Core Processor WORD_SIZE: 64 LLVM: libLLVM-18.1.7 (ORCJIT, znver3) GC: Built with stock GC Threads: 1 default, 1 interactive, 1 GC (on 48 virtual cores) julia> Pkg.status() Status `~/temp/Project.toml` [992eb4ea] CondaPkg v0.2.33 [6099a3de] PythonCall v0.9.28 ``` ### Python Environment - python 3.13.9 - numpy 2.3.4 </details> I noticed a difference in how the PythonCall [`to_numpy()`](https://github.com/JuliaPy/PythonCall.jl/blob/71666ca0a30eafee2456a191fa2490cdd0b0ed22/src/JlWrap/array.jl#L374) function and NumPy treat property values of `dtype` as follows: ```julia julia> using PythonCall julia> Py(rand(10)).to_numpy().dtype Python: dtype('float64') julia> Py([[1,2,3], [4,5,6]]).to_numpy().dtype Python: dtype('O') ``` I would expect the latter to be `dtype('int64')` to match Python: ```python >>> import numpy >>> a = numpy.array([[1,2,3], [4,5,6]]) >>> a.dtype dtype('int64') ``` While Julia does provide `Matrix{Int64}` and `to_numpy()` outputs `dtype('int64')` as I would expect ```julia julia> [1 2 3; 4 5 6] |> typeof Matrix{Int64} (alias for Array{Int64, 2}) ``` ```python julia> Py([1 2 3; 4 5 6]).to_numpy().dtype Python: dtype('int64') ``` I am working on a project where I need to use PythonCall to deal with `Vector{Vector{Int64}}` instances and changing into `Matrix{Int64}` is not an option. In Julia, using PythonCall `to_numpy()`, it is clear that `dtype('int64')` is only output for `Vector{Int64}` not `Vector{Vector{Int64}}` nor `Vector{Vector{Vector{Int64}}}` regardless of how many levels of nesting: ```julia julia> Py([1, 2, 3]).to_numpy().dtype Python: dtype('int64') julia> Py([[1,2,3], [4,5,6]]).to_numpy().dtype Python: dtype('O') julia> Py([[[1,2,3], [4,5,6]], [[7,8,9], [10,11,12]]]).to_numpy().dtype Python: dtype('O') ``` Whereas in Python, `dtype('int64')` is output for all the above: ```python >>> numpy.array([1, 2, 3]).dtype dtype('int64') >>> numpy.array([[1,2,3], [4,5,6]]).dtype dtype('int64') >>> numpy.array([[[1,2,3], [4,5,6]], [[7,8,9], [10,11,12]]]).dtype dtype('int64') ``` Thus the value of the property `.dtype` in NumPy is defined based on the innermost elements in the array, whereas in Julia the value of `.dtype` upon using `to_numpy()` is not based on the innermost elements (i.e. 1, 2, 3, etc) but on the whole structure containing them (i.e `Vector{Int64}` for the 2nd array, and `Vector{Vector{Int64}}` for the 3rd array). I was expecting the same behaviour from Python's NumPy and the conversions from `to_numpy()` given by PythonCall, but it turns out the conversion does not agree with NumPy on `dtype` property values.