There's definitely a conversion cost. For strings, Python apparently caches the UTF-8 encoded string, so if you _repeatedly_ transfer it to Rust I suspect (but haven't checked) that the cost is much lower.
In general I suspect it's the usual "NumPy arrays are fast, everything else you better be getting a sufficiently large boost from the low-level code to justify conversion".
For the thing I prototyped in Rust, it was wrapping the `ahocorasick` crate which was in fact faster than `pyahocorasick` which is written in C or Cython or something. Both have similar conversion costs, probably, so it came down to "for lots of data the Rust version was faster".
Nice! Reach out if there are any problems or if you need something exposed in the API. Looking at the pyahocorasick issue tracker, there are a number of features/bugs that your wrapper package would resolve. :)
NumPy also support conversions without copying. One thing I haven't found good way to bridge between Python is the pandas.DataFrame, it seems to be quite Python focused object and iterating through DataFrame is particularly slow.
In general I suspect it's the usual "NumPy arrays are fast, everything else you better be getting a sufficiently large boost from the low-level code to justify conversion".
For the thing I prototyped in Rust, it was wrapping the `ahocorasick` crate which was in fact faster than `pyahocorasick` which is written in C or Cython or something. Both have similar conversion costs, probably, so it came down to "for lots of data the Rust version was faster".