Working with Jupyter Notebooks

The iPython Notebook and its offshoots, Jupyter, Zeppelin, Spark, etc. are very useful for learning, data science, collaboration, data visualization, and instant information using a REPL (Read-Eval-Print-Loop) interface.  A REPL allows you to run and compile code line-by-line, and in the case of Spark and other Hadoop tools run code against a cluster of machines.

A good history of the iPython notebook from Fernando Perez, creator of iPython.

"We coded frantically in parallel: one of us wrote the kernel and the other the client, and we'd debug one of them while leaving the other running in the meantime.  It was the perfect blend of pair programming and simultaneous development, and in just two days we had a prototype of a python shell over zmq working."

As of this writing, Jupyter, the latest incarnation of iPython's notebook, has over 50 interpreters to parse and compile code within a notebook interface.

Further to the last blog post, search GitHub for Jupyter Notebooks on GitHub to see more examples.
filename:ipynb

https://github.com/search?l=jupyter-notebook&q=filename%3Aipynb&type=Code&utf8=%E2%9C%93

Here are some interesting examples on Github:

Parsing Apache Logs with Spark
Interactive C# Notebook
Predicting Airline Delays with Pig and Python

Binder is just one host of notebooks, an example using CERN's ROOT framework to run C++ in a browser.
http://app.mybinder.org/2191543109/notebooks/index.ipynb
http://app.mybinder.org/2191543109/notebooks/notebooks/ROOT_Example.ipynb