In addition, Anaconda is a great package and dependency manager. Besides Spyder, it also includes the core Python language and comes with more than 100 pre-installed packages. That means that Anaconda provides you with everything you need for data science in Python “out-of-the-box”. The great thing about Spyder is also that it is part of the Anaconda distribution. So I went with Sypder (and I’ve done so well with it that I didn’t even had to try P圜harm or another IDE yet). Still, although Jupyter Notebook is often recommended as an easy-to-use tool for Python beginners, I had my problems with it as its structure differs somewhat from R-Studio, e.g. Spyder, P圜harm, and Jupyter Notebook all belong to the most popular IDEs for Python and no matter for which one you decide, it won’t be a bad choice. 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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