Jupyter notebooks have gained immense popularity among data scientists and researchers for interactive computing and data visualization. Often, these notebooks contain snippets of code, markdown cells, and outputs like plots. However, sometimes you might need to convert your Jupyter notebook into a plain Python script for easier deployment or to use in different environments.

nbconvert is a built-in Jupyter tool that allows users to convert their notebook files (.ipynb) into various formats including Python files (.py). By converting a notebook to a Python script, you can streamline your code and remove the cell structures and outputs, leaving just the code and markdown in comment form.

For those wanting a pure code version of their work, this conversion process ensures that the code retains its functionality, minus the visual flair of Jupyter. This is especially useful when you want to share your work with others or deploy it in a different environment.

Steps to convert a Jupyter notebook to a Python file using nbconvert:

  1. Open the terminal.
  2. Install nbconvert if you don't already have it installed.
    $ pip install nbconvert
  3. Navigate to the directory containing your Jupyter notebook.
    $ cd /path/to/your/notebook_directory/
  4. Use nbconvert to convert the notebook to a Python file.
    $ jupyter nbconvert --to python YourNotebookName.ipynb
  5. Verify the conversion by checking for the .py file in the directory.
    $ ls | grep YourNotebookName.py

    You should now see a file named “YourNotebookName.py” in your directory, which is the converted Python script.

  6. Open the generated .py file to review the contents.
    $ cat YourNotebookName.py

    The converted script will contain your code and markdown cells will be transformed into Python comments.

  7. Run the generated Python script to ensure it functions correctly.
    $ python YourNotebookName.py
  8. Optionally, move or rename the converted file as required.
    $ mv YourNotebookName.py NewScriptName.py
  9. Implement any additional changes or optimizations to the script if necessary, to ensure seamless execution outside of the Jupyter environment.
  10. If you're migrating the Python script to another environment or sharing it, remember to include any dependencies or additional files.

With these steps, you can easily transition your work from the Jupyter notebook environment to a more traditional Python script, enabling broader applications and use-cases.

Discuss the article:

Comment anonymously. Login not required.