pandas.Categorical not preserved during DataFrame conversion and jl.convert fails

Hi and thanks for the great package!

While working with `juliacall` + `PythonCall.jl`, I ran into two issues related to pandas.Categorical handling.

(In all examples below, `jl`refers to Main from `juliacall`, i.e., f`rom juliacall import Main as jl`.)

**DataFrame conversion ignores Categorical columns**
When passing a `pandas.DataFrame` with categorical columns (i.e., `dtype='category'`), those columns are silently converted to `Int64` vectors in Julia (presumably the .codes). This results in CategoricalArray semantics being lost — so interactions in Julia formulas like `id & η1` are treated as numeric rather than generating dummy variables.

**`jl.convert()` can’t convert pandas.Categorical to any Julia type**
I tried using `jl.convert(CategoricalArray, col)` directly on a `pandas.Series` with categorical dtype, but got a `MethodError`. It appears `PythonCall` doesn’t yet support converting `pandas.Categorical` to any Julia-native type.

To work around this, I convert the column to `str` in Python (so it arrives as a `Vector{String}`), then manually wrap it in `categorical(...)` on the Julia side. This works, but it's not ideal for type fidelity or automatic translation.

Let me know if there's a cleaner workaround — or if you'd be open to a PR to improve automatic `CategoricalArray` support.

Thanks again!