![]() Remember, the best tool is the one that helps you get your job done efficiently. Whether you choose Anaconda’s comprehensive package library or Miniconda’s minimalistic approach, both will serve as valuable tools in your data science toolkit. If you don’t want the hundreds of packages included with Anaconda, install Miniconda, a mini version of Anaconda that includes just conda, its dependencies, and Python. ![]() Your choice depends on your specific needs and preferences. Review the system requirements listed below before installing Anaconda Distribution. It’s perfect for experienced users who know exactly what packages they need and want to maintain a clean, clutter-free environment.īoth Anaconda and Miniconda are powerful tools for managing Python environments and packages. It’s ideal for beginners and those who want to explore different packages without worrying about manual installation.Ĭhoose Miniconda if you prefer a minimal, lightweight solution. The choice between Anaconda and Miniconda depends on your specific needs:Ĭhoose Anaconda if you want a comprehensive, out-of-the-box solution with a wide range of pre-installed packages. If you need to quickly set up a Python environment, Miniconda might be the way to go. Installation Timeĭue to its smaller size, Miniconda installs faster than Anaconda. On the other hand, Miniconda allows you to install only the packages you need, reducing unnecessary clutter in your environment. Pre-installed PackagesĪnaconda comes with over 1,500 pre-installed packages, making it a comprehensive solution for data science projects. For example, before using pip, a Python interpreter must be installed via a system package manager or by downloading and running an installer. Pip installs Python packages whereas conda installs packages which may contain software written in any language. If you’re working with limited disk space, Miniconda might be the better choice. This highlights a key difference between conda and pip. Disk SpaceĪnaconda requires around 3 GB of disk space for installation, while Miniconda only requires around 400 MB. Anaconda is larger and comes with a vast array of pre-installed packages, while Miniconda is smaller and only includes Conda and Python. The main difference between Anaconda and Miniconda lies in their size and the number of pre-installed packages. Miniconda is lightweight and quick to install, making it a great choice if you’re working with limited disk space or only need specific packages. # Installing a package in Miniconda conda install numpy
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