I would retain my virtual env pip environment. Interestingly, a lot of my Windows friends use their Conda REPL like a terminal, so the value is certainly there for them.īut with the ease of use on Windows with Anaconda, what is to come of the situation on operating systems where we already have a package manager and a terminal to push commands through? Well, for me personally, I much more prefer Jupyter SH, and hopefully I can justify myself in my pros and cons to this approach.Īlthough we all-in-all lose our Conda Environment, we retain any virtual environment we utilize when we launch from the terminal, so if I were to $ - source env/bin/activateĪnd then run Jupyter Notebook, $ (env) - jupyter notebook With that in mind, Anaconda certainly is a great tool for Data Scientists, with extension applications like VSCode, Spark managers, and much more all easily implemented into the navigator to work inside of your Conda terminal, it’s easy to see why this is a common choice among Windows developers. For someone that uses a lot of APIs and assorted packages, this, of course, is a problem.
#Anaconda vs anaconda navigator install#
Therefore, it is possible through only a pretty challenging loophole to install traditional Python packages that haven’t been published to Conda. However, a significant disadvantage of Conda is the lack of the regular package index. Additionally, Anaconda comes with the “ Conda” package manager, which isn’t quite as expansive as the regular Python Package Index.Īs a result of these features, to an inexperienced soul, the startup learning curve of Anaconda Navigator isn’t as fierce as setting up docker images and virtual environments to run Jupyter out of. Of course, this can be seen as a huge advantage to using Anaconda over standalone Jupyter.
At the roots of Anaconda is environment virtualization.
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