![]() ![]() If any more experienced devs spot errors/mistakes in my writeup, please let me know I'm fairly novice as well :)Īnaconda usually comes with an old version of Python and a large number of older versions of data science libraries. The Spyder IDE has its haters too but I've used it on occasion for simple debugging. I find it pretty easy & straightforward to use. You've likely seen this: vs code's python extension.Īnother great tool is the Pycharm IDE. In ST4, there is a conda package to be found in the package manager then, it needs to be activated when starting each new instance of ST4 in order to build/run within ST4. I do almost all my work in Sublime Text, but from what I gather, their interaction with Conda is similar. I've even got a nice little addition to my zsh theme that shows the active conda environment right-justified in my terminal. Jupyter Lab is very easily and well managed by conda. That'll use the pip that is in your working environment, as opposed to the base environment. Also, sometimes you'll need a library or package that is only found via pip, but you're managing envs with conda: in these cases you'll need to install pip to that environment with conda install pip or conda install -c conda-forge pip and then, from within that environment, do python -m pip install. A few notes on that though I always end up googling the install syntax because it can differ depending on the specific library conda install -c conda-forge is the most common but not universal. Environment management is easy, and as u/synthphreak says, its package manager is generally nicer to use. ![]() Conda is the stripped-down version, it comes with only the most important/fundamental libraries you'll need to get basic scripts running. u/synthphreak mentioned it I recommend it.Īnaconda is stuffed to the gills with every concievable library you'd need, that's why it's so bloated and gargantuan. Conda is lightweight and manages library & package installs pretty well. I have several environments for several different projects that require totally different libraries. However, as my coding skill has increased (also not a dev), I've found Conda to be really valuable. You totally can manage Python libraries and run code without Conda or Anaconda. ![]() Relatedly, using conda to create virtual environments is a nicer experience than using venv or anything else I've triedĪnaconda ships with a huge number of libraries useful for scientific computing, so if that's ultimately what you're using Python for, it does save a lot of up-front headacheīut if using Anaconda it itself a source of headache for you, just keep it simple and stop using it until you understand why you might want it back in your life. The conda package manager is IMHO nicer and smarter than pip Given that Anaconda isn't required for this, the reasons one might still want to use it are that: First Python and pip, then basically whatever packages you want can be installed using pip. You can uninstall everything, then reinstall only what you need manually a la carte. I feel like past me wrote this post haha.Īll I want to do is run VS Code, Python and have the ability to pip install the libraries that I need. ![]()
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