I understand `pandas` is widely used in finance and quantitative trading, but it does not seem to be the best fit especially when you want your research code to be quickly ported to production.

We found `numpy` and `jax` to be a good trade-off between "too high level to optimize" and "too low level to understand". Therefore in our hedge fund we just build data structures and helper functions on top of them. The downside of the above combination is on sparse data, for which we call wrapped c++/rust code in python.