Best practices for turning jupyter notebooks into python scripts

Question

Jupyter (iPython) notebook is deservedly known as a good tool for prototyping the code and doing all kinds of machine learning stuff interactively. But when I use it, I inevitab

Answer 1

Life saver: as you're writing your notebooks, incrementally refactor your code into functions, writing some minimal assert tests and docstrings.

After that, refactoring from notebook to script is natural. Not only that, but it makes your life easier when writing long notebooks, even if you have no plans to turn them into anything else.

Basic example of a cell's content with "minimal" tests and docstrings:

def zip_count(f):
    """Given zip filename, returns number of files inside.

    str -> int"""
    from contextlib import closing
    with closing(zipfile.ZipFile(f)) as archive:
        num_files = len(archive.infolist())
    return num_files

zip_filename = 'data/myfile.zip'

# Make sure `myfile` always has three files
assert zip_count(zip_filename) == 3
# And total zip size is under 2 MB
assert os.path.getsize(zip_filename) / 1024**2 < 2

print(zip_count(zip_filename))

Once you've exported it to bare .py files, your code will probably not be structured into classes yet. But it is worth the effort to have refactored your notebook to the point where it has a set of documented functions, each with a set of simple assert statements that can easily be moved into tests.py for testing with pytest, unittest, or what have you. If it makes sense, bundling these functions into methods for your classes is dead-easy after that.

If all goes well, all you need to do after that is to write your if __name__ == '__main__': and its "hooks": if you're writing script to be called by the terminal you'll want to handle command-line arguments, if you're writing a module you'll want to think about its API with the __init__.py file, etc.

It all depends on what the intended use case is, of course: there's quite a difference between converting a notebook to a small script vs. turning it into a full-fledged module or package.

Here's a few ideas for a notebook-to-script workflow:

1. Export the Jupyter Notebook to Python file (.py) through the GUI.
2. Remove the "helper" lines that don't do the actual work: print statements, plots, etc.
3. If need be, bundle your logic into classes. The only extra refactoring work required should be to write your class docstrings and attributes.
4. Write your script's entryways with if __name__ == '__main__'.
5. Separate your assert statements for each of your functions/methods, and flesh out a minimal test suite in tests.py.