7. Interactive Computing

The real capabilities of Jupyter Lie in the fact that it supports interactive computing which is very useful in Data Science particularly.

Creating New View for Output

I’ll be using the Lorenz differential equations notebook from the official Jupyter Github page. After running a few cells, we get the interactive Lorenz attractor as the output. Sometimes when we have an interactive output, it gets kind of frustrating having to scroll up and down to the code that generates it. As a solution to this problem, Jupyter Lab gives us an option to break the output into a new tab and we have a kind of pseudo dashboard where we can use the sliders and change the parameters.

New View on the same file

Sometimes our notebook is too long and so we can have two views of the same(or different) notebooks in a single instance. This could be useful when we want to look at the top and bottom of the notebook at the same time.

Dragging / Dropping and Editing Cells between Notebooks

We know the cells can be dragged within a notebook. However, the cells can also be dragged across different Notebooks. Also, the changes in one Notebook is reflected into the other as well.

Simplifying the Code Documentation Process

It is rightly said that Code is read more often than it is written. Documentation is a very important aspect of programming and Jupyter Lab tends to make it easier. One of the problems that I really face when writing documentation in a markdown file is that I have to run the code in a different console to check if it is running perfectly and then include it in the file. Switching tabs, again and again, is annoying.

Jupyter Labs lets you combine the editor and console into a single view. So you can check your code and your documentation and preview the entire file at the same time.

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