Originally developed for data science applications written in Python, R, and Julia, Jupyter Notebook is useful in all kinds of ways for all kinds of projects: Data visualizations. Most people have ...

This repo contains various Python Jupyter notebooks I have created to experiment and learn with the core libraries essential for working with data in Python and work through exercises, assignments, ...

Jupyter Notebook is a powerful tool, but it can be supercharged with the right extensions. From productivity boosters like Nbextensions and IPyWidgets to specialized tools like Nbgrader and ...

You can add option -j to the command if you want to control the number of parallel processes that generate the notebook. For example -j 8 uses 8 processes. The include_dir setting in config.yml ...

The problem with notebooks is that they're much better for experimental data science work than they are for production data engineering work. That's my own opinion, of course. But I stand by it.

JupyterLab, the next-generation user interface for Jupyter Notebooks, provides an extensible environment for data science, scientific computing, and machine learning. Jupyter Notebooks allows users to ...

On its own, the Jupyter Notebook/JupyterLab container has enough features to satisfy your note-taking and coding needs, but its utility jumps to the next level once you integrate it with other ...