If during the process of installing libraries (like pandas or scikit-learn) System Python breaks (and believe me, shit happens), the probabilities of being in trouble (complicated fix / fresh OS install) are high, that's one of the reasons virtual environments exist and are so popular. That's the reason we want to preserve this Python installation clean and working perfectly. For example, if you open a file explorer, it may use System Python under the hood to list files and folders. This Python installation is called System Python, and it means that this executable is used by our Operating System to do many things. Python is just an executable binary in our system. If you get a better understanding of virtual environments we have accomplished our goal.Īs you can see, the output of this command says that Python is already installed in our system, specifically in the location /usr/bin/python3. The goal of this post is to end this madness once for all.
You would be surprised how many excellent professionals, even with 5+ years of experience still struggle with chaotic, corrupted, and barely usable Python installations because of that. We also tried other alternatives and always go back to conda because it is the only, let's say, full featured solution on the market.īased on my experience of more than 6 years doing Data Science, conda (and virtual environments in general) is a tool that is often not well understood.
We use it for both development and production purposes and we strongly believe that conda stands out from other alternatives like virtualenv, poetry, pyenv or pipenv. This post is about conda, the tool we use to install and manage Python and its libraries in our systems. This is the first post of the WhiteBox toolkit series, where we will tell you more about the tools we use in our everyday job, in high detail. There are two types of Data Scientists, those who took the time to master conda and those who don't (and cry at the corners because of that).
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