![]() #Use julia in jupyter notebook tutorial code#Jupyter Notebooks are a tool for easily integrating text, code, and code output into a single document. (Note: Jupyter Notebooks used to be called IPython Notebooks before they expanded to support more languages, so if you see people talking about IPython Notebooks, just think of that as an early, Python-specific version of Jupyter Notebooks.) Jupyter Notebooks ¶ Jupyter was originally focused on unifying Julia, Python, and R, but it actually now supports dozens and dozens of different kernels including JavaScript, Go, Haskell, Matlab, Stata, bash, Scala, and so much more. In the Jupyter ecosystem, the program being used to actually run your analysis (i.e., Python, R) is referred to as a kernel. This makes it possible to create one interface (a text editor, a window where results are displayed, etc.) that can be used to run your analyses in any number of different programs. The idea of Jupyter is to separate the interface you are working with from the underlying programming language doing your analysis. ![]() R users, for example, often use RStudio, while Python users use Spyder, and Julia users use Juno.īut in recent years, an amazing effort has been underway to provide a single set of tools that work with nearly any underlying programming language: Jupyter (as in Ju (Julia) - py (Python) - te R (R)). Today, if you use more than one programming language for data science, you probably also use different programs to edit and interact with those programs. ![]()
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