It depends: we are heavily invested in making both numpy and pandas faster, so next releases will improve that. That said, you are quite likely to find your program slower (but it really depends on what you do). It's a good time to experiment, but definitely not to fully switch just yet

If you have for loops there are opportunities for the JIT to speed them up. It very much depends how close those loops are to pure python, if they are than PyPy should be able to help. Many code constructs do look like large for loops. The JIT will trace them (which requires memory to record the code paths) and after about 1000 times through will convert the python AST to assembler (with checks to bail out if assumptions are violated). So the question you ask is very specific to your workflow, and is best answered by trying it out