Support more of abstract array interface

[MilesCranmer]

Currently similar will return an array of a different type:

from juliacall import Main as jl
import numpy as np

x = np.random.randn(5)
y = jl.similar(x)

print(jl.typeof(x))
# PyArray{Float64, 1, true, true, Float64}
print(jl.typeof(y))
# Vector{Float64}

This can introduce some type instabilities in libraries due to the assumption that container type is preserved by similar.

I guess we just need the "optional methods" from here: https://docs.julialang.org/en/v1/manual/interfaces/#man-interface-array

[marcosdiezgarcia]

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:

>>> import numpy
>>> a = numpy.array([[1,2,3], [4,5,6]])
>>> a.dtype
dtype('int64')

I am using the following:

• julia 1.12.1
• PythonCall 0.9.28
• python 3.13.9
• numpy 2.3.4