I will speed up python code using cython, pyo3 rust, or c extension

Slow Python code rarely needs a full rewrite. Usually one loop, one function, one hot path is responsible for most of the runtime and once identified, it can be replaced with a compiled native extension while the rest of your code stays untouched.

If you need to optimize Python code without breaking your architecture, my focus is strictly on measured Python performance optimization: profile first, optimize second. I locate the actual bottleneck using cProfile / py-spy, then rebuild that piece in Cython, PyO3 (Rust), or Pybind11 (C++) with before/after benchmarks so the gain is measured, not assumed.

What's included:

1) Profiling pass to identify the real bottleneck, not a guess

2) Native C/C++ or Rust extension (using Cython, PyO3, or Pybind11), matched to your stack

3) GIL bypass & multithreading for genuine multi-core CPU scaling

4) Before/after benchmarks with measured numbers

5) Clean, documented code - your working logic stays 100% intact

Well suited to: data pipelines, NumPy/Pandas processing, backend APIs under load, and any CPU-bound script where adding more servers isn't a real fix.

Not sure if your code can be optimized? Message me for a quick assessment