![]() All files are saved with the extension of. ![]() It uses the Anaconda framework, which comes with some basic data science libraries like Pandas included, and you can set up different environments for working on various projects. This is the most common IDE choice you’ll see others use when trying to learn data science. ![]() Before I get into the benefits of Visual Studio Code, I’ll discuss some of the other IDEs I tried using and the various frustrations I ran into using them: Jupyter Notebooks That all changed when I found out how wonderful Visual Studio Code is. Compared to the robust data-science focused development of RStudio, Python’s options always left me with enough pain points with my workflow that I’d rather develop in R over Python when given the option. However, because Python was built as a general programming language first, I always found it frustrating to navigate the various clunky integrated development environments (also known as IDEs) that existed and were compatible with the language. It has flexibility for being used for both general programming purposes and data science (as opposed to R, which was built for statistical computing first and foremost). Python is seen by far across the industry as the most important programming language to know for doing data science.
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