I also believe it is better practice to make sure your environment is as ‘lean’ as possible, containing only the packages your project directly depends on. Given installing a library is only a ‘pip install’ away, it is easy to use Miniconda and only install libraries as and when you need them. Anaconda is very bloated and contains many libraries which you are unlikely to use, especially not in a single project. I would always recommend using Miniconda.
Want fast access to Python and the conda commands and you wish to sort out the other programs later.Do not have time or disk space to install over 1,500 packages at once.Do not mind installing each of the packages you want to use individually.Wish to use a curated and vetted set of packages.Do not want to individually install each of the packages you want to use.Have the time and disk space – a few minutes and 3 GB.Like the convenience of having Python and over 1,500 scientific packages automatically installed at once.Provides the following helpful distinction: Which one should you choose? The Conda documentation In this post, I will demonstrate a useful short script for installing Miniconda directly from the command line on Linux and MacOS.įor the script 🚀 Miniconda vs Anaconda 🐍 #
How to only install linux on a mac full#
Has two main distributions: Anaconda (a full distribution with all the libraries of the PyData ecosystem pre-installed) and a bootstrap version called Miniconda which includes the conda package manager and the libraries it depends on only. Conda is a very popular package manager for Python, particularly in the data science community.
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