Python users also have access to a host of useful functionality inherited from Jupyter’s ancestor project, IPython. For instance, when using Python, the docstring of an object can be displayed in a separate window simply by appending a question mark to the end of the object in question. Jupyter Notebooks provide several additional conveniences that can improve productivity. Instead, the data can be loaded once, and all subsequent development or analysis can simply access the in-memory representation for the duration of the session. This feature of notebook interfaces is particularly suited for working with data, where oftentimes the user would rather avoid repeatedly reading a large file or set of files into memory. Being able to repeatedly re-run one or more cells, rather than re-running an entire script or executing a test suite, narrows the feedback loop for the developer and effectively allows the user to save their progress within a single “run” of their program. A user can experiment with new libraries, easily modifying code until it runs, and then create a new cell to continue onward.

The notebook interface-which allows the user to run chunks of code stored in separate cells and share objects in memory across cells-is particularly effective when exploring novel problems or doing rapid prototyping. They are language and system architecture agnostic.They can vastly increase development speed, particularly when working with data or novel problems.Jupyter Notebooks boast three advantages that make them an appealing solution: Long a staple of scientific and mathematical computing software, it is only more recently that notebook interfaces have become commonly encountered outside of academia and niche industries.

Here, we will delve deeper into the question of why Jupyter Notebook is such a useful tool, and provide a brief hands-on demonstration of its capabilities. I n his post, “ Jupyter on Kubernetes: A DevOps Perspective ”, Scott Stuart discusses the growing profile of Jupyter Notebook as an analytical tool in science and some of the technical considerations around how we deploy and manage notebooks at SemanticBits.

Paul romer python jupyter notebook