And to be sure, this is exactly why Jupyter is so powerful for experimentation, especially when doing data science. When you can run lines independently, alter them and re-run them, but keeping all the state of the previous lines, it's so much better than needing to re-run from the beginning. Of course, you can shoot yourself in the foot if some of those lines are like (i = i + 1) and you end up incrementing i by (# times you've run the code) rather than (# times it appears in the code). But if you name your variables sanely and treat them as const, you can easily avoid this.

The question at the core here is: is a debugger meant for experimentation to find the (often subtle) root cause of a defect, or is it meant to be a read-only window onto state during execution?