§2024-12-13

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Conda is an open-source, cross-platform, language-agnostic package manager and environment management system. It was originally developed to solve package management challenges faced by Python data scientists, and today is a popular package manager for Python and R. At first, Anaconda Python distribution was developed by Anaconda Inc.; later, it was spun out as a separate package, released under the BSD license. The Conda package and environment manager is included in all versions of Anaconda, Miniconda, and Anaconda Repository. Conda is a NumFOCUS affiliated project.

Anaconda is an open source data science and artificial intelligence distribution platform for Python and R programming languages. Developed by Anaconda, Inc., an American company founded in 2012, the platform is used to develop and manage data science and AI projects. In 2024, Anaconda Inc. has about 300 employees and 45 million users.

Miniconda is a free, miniature installation of Anaconda Distribution that includes only conda, Python, the packages they both depend on, and a small number of other useful packages.

Summary:

• Conda: The package and environment manager.

• Anaconda: A large distribution of Python and Conda with many pre-installed packages for data science and scientific computing.

• Miniconda: A minimal version of Anaconda that only installs Conda and its basic dependencies, allowing for more flexibility and smaller installation size.

• Should I use Anaconda Distribution or Miniconda?

Both the Anaconda Distribution and Miniconda installers include the conda package and environment manager, but how you plan to use the software will determine which installer you want to choose.

| Description | Anaconda Distribution | Miniconda | | :---------------- | :-------------------- | :-------- | | Created and published by Anaconda | Yes | Yes | | Has conda | Yes | Yes | | Has Anaconda Navigator | Yes | No | | # of packages | 300+ | < 70 | | Install space required | ~4.4 GB | ~480 MB |

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Conda, Anaconda, and Miniconda are closely related but distinct tools in the Python and data science ecosystems. They are used to manage packages, dependencies, and environments, but they differ in their scope and size.

1. Conda

What is Conda?

Conda is an open-source package management system and environment management system. It can manage Python and non-Python libraries and dependencies (e.g., R, Julia, etc.). Conda helps you install, update, and uninstall software packages and create isolated environments for different projects.

Features of Conda:

• Cross-language support: Conda is not limited to Python; it also manages other programming languages like R, Ruby, Lua, and others.
• Package and environment management: It allows you to manage libraries and their dependencies, as well as create isolated environments to avoid version conflicts. -Platform-independent: Conda works on Windows, macOS, and Linux, and it simplifies dependency management across different systems.

Basic use:

• Install packages: conda install
• Create an environment: conda create --name myenv
• Activate an environment: conda activate myenv
• Update packages: conda update
• List installed packages: conda list

2. Anaconda

What is Anaconda?

• Anaconda is a full distribution of Python and R for scientific computing and data science. It comes bundled with Conda and a collection of pre-installed libraries and tools commonly used for data science, machine learning, and scientific computing (e.g., NumPy, SciPy, Pandas, Matplotlib, Jupyter, etc.).

Features of Anaconda:

• Pre-installed libraries: Anaconda ships with over 1,500 open-source data science packages, including those for scientific computing, machine learning, data visualization, etc.
• Conda package manager: Anaconda includes Conda, so you can manage environments and packages easily.
• Jupyter Notebooks: It comes with Jupyter pre-installed, which is a popular tool for creating and sharing live code, equations, visualizations, etc.
• GUI (Anaconda Navigator): Anaconda includes a graphical user interface (GUI) called Anaconda Navigator, which allows users to manage environments and packages without needing to use the command line.

Size:

• Anaconda is quite large in size (about 3 GB or more), because it includes many libraries and tools out of the box.

Ideal for:

• Users who want an all-in-one solution for data science and machine learning.
• Beginners or those who prefer a GUI and want many tools pre-installed.

Basic use:

• Install Anaconda: You download the installer for your operating system from the Anaconda website.
• After installation, you can use the Anaconda Navigator GUI or the conda command-line interface to manage environments and packages.

3. Miniconda

• What is Miniconda?

  • Miniconda is a minimal, lightweight version of Anaconda. It is a small installer (around 40 MB) that installs only Conda, Python, and a few essential packages. With Miniconda, you can still create environments and install packages, but it does not come with the large number of pre-installed libraries that Anaconda includes.

-Features of Miniconda:

- Smaller size: Miniconda has a much smaller download size compared to Anaconda.
    - Flexibility: With Miniconda, you start with a minimal environment, and you can install only the packages you need, making it more lightweight and customizable.
- Uses Conda: Like Anaconda, Miniconda also uses Conda as the package and environment manager.

• Ideal for:

  • Users who want a lightweight starting point and prefer to install only the specific libraries they need.
  • Advanced users or those who want more control over which packages are installed and avoid the bloat of unnecessary libraries.
  • Users with limited bandwidth or disk space.

• Basic use:

  • After installing Miniconda, you can use conda to create environments and install packages just like with Anaconda, but you'll start with a much smaller base set of libraries.

• Key Differences:

| Feature | Conda | Anaconda | Miniconda | | :------ | :-------- | :----------- | :--------- | | Size | Depends on installed packages | ~3 GB (with many pre-installed libraries) | ~40 MB (minimal, only Conda and Python) | | Pre-installed libraries | None (just a package manager) | ~1,500 libraries (data science/ML focus) | None (you install what you need) | | GUI | None (command-line only) | Anaconda Navigator (GUI) | None (no GUI) | | Use case | Advanced users who need package/environment management | Data scientists or beginners who need an all-in-one solution | Users who want a minimal setup and more control | | Installation | Install Conda separately | Full installation of Conda + tools/libraries | Minimal installation of Conda only |

Which One Should You Choose? |

• Anaconda: Choose if you’re a beginner or if you want an easy-to-use, comprehensive package for data science, with a lot of pre-installed tools.
• Miniconda: Choose if you need a lightweight, minimal installation, and prefer to install only the packages you need for your projects.
• Conda: This is the underlying tool that powers both Anaconda and Miniconda, so if you're using either of those, you're using Conda. If you just want to manage environments and packages without the extra tools, you can use Conda standalone, too.

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