Learning resources and practical tips on how to use Jupyter notebooks for fun & profit.

Contents

• What is this?
• How to use the files in this repository?
• Links
• License

This is not a Jupyter notebook 101, but expects you to have a working Jupyter environment, and the basic experience of how to use it. Examples are tested on Linux, behaviour on other platforms might differ.

See the next section on what can be found here, including how-tos and complete scenarios for solving typical problems, using Pandas and other scientific Python libraries.

The notebooks are grouped into the following categories:

• setup – Jupyter and IPython setup / configuration.
• how-tos – Tips & tricks regarding notebooks and data manipulation.
• visualization – Data visualization how-tos.
• charts – Vocabulary of chart types done with at least one of the common frameworks.
• publish – How to publish your results.
• complete-scenarios – Practical examples from start to finish.
• data – Data files used in the example notebooks.

The notebooks state any special requirements you need to install to be able to successfully run them, in text or as a comment in the first code cell. Also, the requirements.txt file lists them explicitly, for use with Binder.

Consider the 1and1/debianized-jupyterhub project to get a fully working runtime environment on Debian-like platforms or in a Docker container, with all extensions already installed.

• The Jupyter Notebook documentation (Project Jupyter Homepage)
• Beyond Interactive: Notebook Innovation at Netflix · Part 2: Scheduling Notebooks at Netflix

• jhermann/til-about-data-science – Records of what I learned while exploring the waters of Data Science (using Python). Including a Jupyter wiki page.
• josephcslater/iPythonExamples – Examples of illustrative Jupyter notebooks for those trying to learn Python.
• jakevdp/PythonDataScienceHandbook – “Python Data Science Handbook” with the full text in Jupyter Notebooks.
• hangtwenty/dive-into-machine-learning – Dive into Machine learning with Jupyter notebooks and scikit-learn.
• jupyter4edu/jupyter-edu-book – A Handbook for teaching and learning with Jupyter.
• jvns/pandas-cookbook – Recipes for using Python's pandas library.
• guipsamora/pandas_exercises – Practice your pandas skills!
• chris1610/pbpython – Code, notebooks and examples from “Practical Business Python”.

This work is licensed under a Creative Commons Attribution-ShareAlike 2.0 Generic License.