Let me therefore shed some light into the dark: While searching for the (to me) most suitable IDE, I stumbled across names like Spyder, P圜harm, Jupyter Notebook or even Anaconda (which is not an IDE, but I will come to this in a second) not knowing how they differ or how they relate to each other. For Python, however, the situation is somewhat different. I’ve rarely seen R code run in another IDE. ![]() (and what the heck is Anaconda?)Īn Integrated Development Environment (IDE) is a tool which helps you write, test, and debug code. So here are the first three hurdles I needed to take and what my learnings were.ġ. ![]() As I spent quite some time looking for an easy(!) to understand installation guide and figuring out how to properly get started with Python, I’d like to spare you some confusion. Even before I could enter and run code, I had to realize that there are some differences between both languages when it comes to the installation and setup you need to be aware of. At that point, I decided to break new ground and to discover what the opponent has to offer…Īlthough I have by now learned to appreciate the benefits of Python, my start was still rather bumpy. ![]() However, while I advanced as a data scientist, I eventually reached the point where R has no longer been the best possible option for me (e.g. It has been the language that sparked my enthusiasm for programming and data science and that’s why I’ve always positioned myself as an R advocate in the battle between Python and R. I started my data science journey with R. Troubles I had when switching from R to Python and which way worked for me
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