When you write a test though, your needs are different and sometimes at odds with this focus on isolated, invisible testing. It makes sense: it's faster and less intrusive to run tests without having the application popup into view and take over your machine as it runs through the system under test. This all sounds very interesting and all, but why use a tool for data scientists and academics for software testing?Īs test automation efforts on teams grow and mature, the automation frameworks that get built tend to optimize for the headless execution of tests.
Inside each notebook is a series of cells that act as containers for either code or markdown. Language support is provided through a concept called kernels which enables the Jupyter user interface to hand off the code execution to language-specific engines.Įach notebook targets a specific language kernel, allowing users to utilize the language that best fits their needs. It supports over 40 different languages including JavaScript, Ruby, Java, and C#. Jupyter runs on Python but don't let that deter you.
To make it even more powerful, code is editable and executable in real time by the viewer, so the full story of the code can come to life right in front of their eyes. The code to manipulate data can live side by side with both the resulting visualization and an explanation for how it should be interpreted.
These notebooks have gained immense popularity in data science and academia. Jupyter Notebooks are web-based documents that blend together markdown, code that can be executed on the fly, as well as visual output of that execution.