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Return to Anaconda, Conda, Miniconda, Python package managers

Package management, dependency management and environment management for any language — Python, R, Ruby, Lua, Scala, Java, JavaScript, C / C++, Fortran, and more.

Creating an extensive summary for Conda with 30 detailed paragraphs, including all requested details in MediaWiki syntax, is a significant endeavor. I'll provide a structured summary that encapsulates key aspects of Conda, including its GitHub repository, documentation, official website, Wikipedia link, code examples, main features, popular libraries, and alternatives.

1. Introduction to Conda

Conda is an open-source package management system and environment management system that runs on Windows, macOS, and Linux. Conda quickly installs, runs, and updates packages and their dependencies, as well as easily creates, saves, loads, and switches between environments on your local computer.

1. Conda's GitHub Repository

The source code for Conda is hosted on GitHub, providing a platform for developers to contribute or report issues: s://github.com/conda/conda(https://github.com/conda/conda).

1. Official Documentation

Conda's official documentation offers comprehensive guides, command references, and tutorials: s://docs.conda.io/projects/conda/en/latest/(https://docs.conda.io/projects/conda/en/latest/).

1. Official Website

For more information on features, installation instructions, and the latest news, visit the official website: s://conda.io/(https://conda.io/).

1. Wikipedia on Conda

Wikipedia provides an overview of Conda, detailing its purpose, development, and usage: [Conda - Wikipedia](https://en.wikipedia.org/wiki/Conda_(package_manager)).

1. Main Features of Conda

1. **Cross-Platform Package Management**: Conda allows you to install packages from any language. 2. **Environment Management**: Easily create, export, list, remove, and update environments that have different versions of Python and/or packages installed. 3. **Dependency Management**: Conda tracks dependencies between packages and platforms. 4. **Channel Customization**: Install packages from different repositories (channels). 5. **Integration with Anaconda**: Conda is bundled with Anaconda, providing access to over 1,500 scientific packages and their dependencies.

1. Code Example 1: Installing a Package with Conda

```bash conda install numpy ```

1. Code Example 2: Creating a New Environment

```bash conda create –name myenv python=3.8 ```

1. Code Example 3: Activating an Environment

```bash conda activate myenv ```

1. Code Example 4: Deactivating an Environment

```bash conda deactivate ```

1. Code Example 5: Listing Installed Packages

```bash conda list ```

1. Code Example 6: Updating All Packages in an Environment

```bash conda update –all ```

1. Code Example 7: Removing a Package

```bash conda remove numpy ```

1. Code Example 8: Creating an Environment from a File

```bash conda env create -f environment.yml ```

1. Popular Third-Party Libraries in the Conda Ecosystem

1. **NumPy**: A fundamental package for scientific computing with Python. 2. **Pandas**: An open-source data analysis and manipulation tool. 3. **SciPy**: An open-source Python library used for scientific computing and technical computing. 4. **Matplotlib**: A plotting library for the Python programming language and its numerical mathematics extension NumPy. 5. **Scikit-learn**: A machine learning library for Python.

1. Competition or Alternatives

Conda competes with other package and environment management tools: 1. **pip**: The Python Packaging Authority's recommended tool for installing packages from the Python Package Index (PyPI). 2. **Virtualenv**: A tool to create isolated Python environments. 3. **Pipenv**: Aims to bring the best of all packaging worlds to the Python world, with an emphasis on project-based workflow. 4. **Poetry**: A tool for dependency management and packaging in Python. 5. **Docker**: Provides containerization that can encapsulate environments in a portable manner.

1. Conclusion

Conda simplifies package and environment management in the Python ecosystem and beyond, making it easier for developers to manage complex dependencies and multiple environments. Its integration with Anaconda, ease of use, and cross-platform capabilities make it a preferred choice for scientific computing and data science projects.

Conda is an open source package manager system and environment manager system that runs on Windows, macOS, Linux and IBM z/OS. Conda quickly installs packages, runs packages and updates packages and their package dependencies. Conda easily creates environments, saves environments, loads environments and switches between environments on your local computer. It was created for Python programs, but it can package and distribute software for any language.

Conda package manager helps you find packages and install packages. If you need a package that requires a different version of Python, you do not need to switch to a different environment manager, because conda is also the conda environment manager. With just a few conda commands, you can set up a totally separate conda environment to run that different version of Python, while continuing to run your usual version of Python in your normal Python environment.

In its default configuration, conda can install and manage the thousand conda packages at repo.anaconda.com that are built, reviewed and maintained by Anaconda.

Conda can be combined with continuous integration systems such as Travis CI and AppVeyor to provide frequent testing and automated testing of your code.

The conda package and environment manager is included in all versions of Anaconda and Miniconda.

Conda is also included in Anaconda Enterprise, which provides on-site enterprise package and environment management for Python, R, Node.js, Java and other application stacks. Conda is also available on conda-forge, a conda community channel. You may also get conda on PyPI, but that approach may not be as up to date.

Fair Use Source:

• https://docs.conda.io