If you have to import a new module, just add it to the import block and re-run that block or the entire notebook. One of the nice things is you can run each block individually, so you can run your import block and setup code, then mess around with your experimental code re-running it as needed as you test things out. One use for Jupyter Notebooks that isn't talked about a lot is when you are testing out code and working with an API and want to have instant feedback.
#Install jupyter notebook in mac how to#
To cover how to use it would be far more than I can do in a single post. I am not going to go to in depth as this is more to introduce you to the fact that Jupyter Notebooks exists and what it can do. To something far more complicated involving interactive charting and complex math.
You might be doing something where you have a simple dataset where you want to chart it.
Python is frequently used for data analysis and machine learning, in this capacity you are frequently working with large datasets and visualization. Jupyter Notebooks is different than anything I have ever seen before, the closest thing I describe it to is an interactive shell with charting, visualization, interactive widgets, reporting, and so much more. I only have experience using it for Python and Machine Learning. While I only use Jupyter Notebooks with Python, I know it can be used with lots of languages and frameworks.
If you haven't heard of Jupyter Notebooks, this is going to be a huge game changer for you. This is probably my favorite tip to offer.
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