It is lightweight, portable, simple to use and I also find pyenv has fewer ‘gotchas’ than conda. Personally, I prefer to use pyenvįor managing my python environments. There are alternative package managers for Python instead of Conda. 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 □ # 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. The environment construction method with Miniconda is summarized below.Conda is a very popular package manager for Python, particularly in the data science community. I first built the environment with Anaconda, but I couldn't grasp the contents, so I uninstalled it and rebuilt it with Miniconda.Īlthough Anaconda is standard and rich in tools, you end up having to look into the package when you write your own programs.I think it's important that you know what's in it. People who don't like installing unnecessary packages.People who want to know which package they are using.Those who want to start machine learning as soon as possible.People who don't care if there are unnecessary packages.People who do not want to have a hard time building an environment.Which one should build the environment Suitable for Anaconda Installation of python is easy, but necessary packages and execution environment are built individually using conda. The smallest configuration version of Anaconda. Graphical User Interface (GUI): Anaconda Navigator. Integrated Development Environment (IDE): Jupyter, JupyterLab, Spyder, RStudio.Package: numpy, pandas, Matplotlib, Scikit-learn, Tensorflow.If you install Anaconda, you will be able to use packages for scientific calculation and data science together with Python.It also includes "R", a programming language for data science alongside Python, and their comprehensive development environment.Roughly speaking, the following applications are installed. "Python + R language + conda + 1000 or more related packages + execution environment + etc. It's true that Anaconda makes it easy to build an environment, but it also has its disadvantages.Therefore, I compared the characteristics of Anaconda and Miniconda. When it comes to building a machine learning environment with python, many books and sites say that you should use Anaconda for the time being.
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