![]() The best practices and tips that will help you to make your notebook an added value to any data science project!.A practical introduction to the components that were covered in the first section, complete with examples of Pandas DataFrames, an explanation on how to make your notebook documents magical, and answers to frequently asked questions, such as "How to toggle between Python 2 and 3?", and.An overview of the three most popular ways to run your notebooks: with the help of a Python distribution, with pip or in a Docker container,.The history of Jupyter Project to show how it's connected to IPython,.A basic overview of the Jupyter Notebook App and its components,.
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