Do not mind installing each of the packages you want to use individually.Wish to use a set of packages curated and vetted for interoperability and usability.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.It provides installation for many popular editors: pycharm, VScode, Spyder, Rstudio, etc.Ĭhoose Anaconda or Miniconda? Choose Anaconda: This makes it a good distribution to use for beginners. Most of packages used for Data Science are provided by Anaconda. There is also a small, bootstrap version of Anaconda called Miniconda, which includes only conda, Python, the packages they depend on, and a small number of other packages. This package manager was spun out as a separate open-source package as it ended up being useful on its own and for things other than Python. Package versions in Anaconda are managed by the package management system conda. It is developed and maintained by Anaconda, Inc., which was founded by Peter Wang and Travis Oliphant in 2012. This provides a simpler control of virtual environments and allows moving and copying environments in different systems. Anaconda includes most of the data-science packages and it is consistent for Windows, Linux, and macOS. It aims to simplify package management and deployment process involved in Python and R. Anaconda is a distribution of the Python and R programming languages for computing (data science, machine learning applications, large-scale data processing, etc.).
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