That said, if you have experience with another language, the Python in this article shouldn't be too cryptic and pandas should be interpretable. To get the most out of this tutorial you should be familiar with programming, specifically Python and pandas specifically. Although it is possible to use many different programming languages within Jupyter Notebooks, this article will focus on Python as it is the most common use case. The Jupyter project is the successor to the earlier IPython Notebook, which was first published as a prototype in 2010.
Best of all, as part of the open source Project Jupyter, they are completely free. The intuitive workflow promotes iterative and rapid development, making notebooks an increasingly popular choice at the heart of contemporary data science, analysis, and increasingly science at large. A notebook integrates code and its output into a single document that combines visualisations, narrative text, mathematical equations, and other rich media. The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects.
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