Numba can compile in “no Python” mode only with a subset of Python. Eg classes is limited and is still experimental. Also I think string manipulation is slow but the doc has details.
If you want to specify the type (for example for aot or just because you want to make it clear) then the call signature is less flexible.
In short, pick any random Python library, you’d find there are very few places you can jit accelerate something effectively. It is for numeric.
Even for numerical code, it is more like writing C functions than say C++ (with classes etc).
But it does makes accelerating vectorized code very easy. Even if you have a function that uses Numpy, it is likely you can speed it up using Numba with a decorator only.
But when it doesn’t work, it might often be not very clear why you can’t until you get some experience.
If you want to specify the type (for example for aot or just because you want to make it clear) then the call signature is less flexible.
In short, pick any random Python library, you’d find there are very few places you can jit accelerate something effectively. It is for numeric.
Even for numerical code, it is more like writing C functions than say C++ (with classes etc).
But it does makes accelerating vectorized code very easy. Even if you have a function that uses Numpy, it is likely you can speed it up using Numba with a decorator only.
But when it doesn’t work, it might often be not very clear why you can’t until you get some experience.