Bringing your own Python packages into Jupyter notebooks in Cloud Pak for Data

Photo by NASA on Unsplash

It’s been a while since I have written a technical blog post. In the past week, I have answered the same question more than once, and that is my trigger to go and write a blog post — so I can just point people to the right answer.

Many people who are new to data science or AI will want to learn about Python, and learn to code Python. One of the best ways to learn Python, is by using Jupyter notebooks, which allows you to run Python code in an interactive mode. This is a great way to learn Python, and is really useful for doing some data science projects and other simple projects since it makes it quite easy to run, debug, and share your code with other people.

One of the challenges with using Jupyter notebooks is setting up an environment for yourself to use. Keeping that environment relatively clean — so that when you share a notebook, you can be confident that it will run in someone else’s environment — can be a challenge. I find that using the shared and cloud-hosted environment provided by IBM Cloud Pak for Data will help with this challenge. Using the shared environment provided by IBM Cloud Pak for Data, allows multiple people to work on, and execute, the same notebook.