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#The notebook script code
Inevitably, you will still need to step through individual functions line by line, but if you've decomposed your code into small methods, the amount of code you need to "re-write" in the notebook is very small.In a nursing home, resident Duke reads a romance story to an old woman who has senile dementia with memory loss. You need the reload function (or similar) to force python to recompile/execute the module code.
#The notebook script update
import mymoduleĬalling import again will not actually update your namespace. You can use the reload function in the importlib module to quickly refresh your imported modules with changes made to the source. You can call the "private" methods in your notebook for testing/debugging, but users of your module should know to ignore these methods. You can break larger "public" methods into smaller "private" methods named using a leading underscore (e.g. Keeping each method small is good practice in general, and is very helpful for testing the logic at each step using the notebook. In my experience, once I have created a module/package from my jupyter notebook, it is easier to maintain/develop the code outside of the notebook and import that module into the notebook for testing. I do not think there is a simple solution to the inconvenience you encounter (I have run into the same issues), but I will describe my practice (I'm not so bold as to proclaim it the "best" practice) and maybe it will be helpful in your use case. I've developed several packages by first hashing out the details and then eventually moving the polished product in to separate. I have used your same development workflow and recognize the value of being able to step through code using the jupyter notebook.
![the notebook script the notebook script](https://i.pinimg.com/originals/00/c0/89/00c089d05ecd5b71ab22fecf7a369323.jpg)
Yes, I know that I can import the class into the notebook, but then I lose the ability to look at intermediate results inside the class method via individual cells, which is what I do when it is a pure script. So what is the best practice between keeping a script code and code embedded within a class method in sync? Ideally, I'd like to be able to run a class method across cells, so I can check intermediate results. I fix the issue, but then I have to carefully make the change back in the regular Python class method code. I could go back to my notebook and go step-by-step through the various cells to find the issue. That Python class is used in a much larger application, so that is the real deliverable.īut then there was a bug for a certain data set in the implementation in the method, which also was in my script. I downloaded that working code into a Python ".py" file, and converted that script to be a method within a Python class in my program, that is instantiated with the input data, and provides the output as a result of that method. I wrote a long script that uses pandas inside a notebook, and have multiple cells which made the development easy, because I could check intermediate results within the notebook. I'm trying to figure out the best way to keep code in an Jupyter/iPython notebook and the same code inside of a class method in sync.