Jupyter code is draft code. Draft code rarely makes into clean code, because it requires extra tedious effort. Most of us are too overworked to have the time or energy to perform it.
Alternatively one can write exploratory code as unit tests and run them inside an IDE. When the exploration is done, three quarters of the tedious cleanup effort is already done. There are already unit tests. There are already APIs exercised by the unit tests. The code is in much better shape.
The missing two bits:
* No charting support. To the best of my knowledge, neither PyCharm nor VSCode have an output console with HTML support.
* Caching. Some operations take a lot of time. At the extreme, fast.ai style development, where some cells train a model for hours/days. Re-running such code from scratch on every test run is impractical. An opportunity for a library with explicit save/load/cache/checkpoint support.
Alternatively one can write exploratory code as unit tests and run them inside an IDE. When the exploration is done, three quarters of the tedious cleanup effort is already done. There are already unit tests. There are already APIs exercised by the unit tests. The code is in much better shape.
The missing two bits:
* No charting support. To the best of my knowledge, neither PyCharm nor VSCode have an output console with HTML support.
* Caching. Some operations take a lot of time. At the extreme, fast.ai style development, where some cells train a model for hours/days. Re-running such code from scratch on every test run is impractical. An opportunity for a library with explicit save/load/cache/checkpoint support.