Anaconda

Installation and Setup

Download the installer for Windows, macOS, or Linux from the official website and follow the guided setup. After installation, update Conda and verify the setup:

$ conda --version
$ conda update conda

You can also use the Anaconda Navigator if you don’t want to deal with the terminal:

$ anaconda-navigator

Key Features

• Manages packages and environments across Python and compiled libraries using Conda
• Installs many prebuilt binary packages for scientific computing without requiring a separate compiler toolchain
• Provides Anaconda Navigator for managing environments and packages and launching tools from a GUI, although Anaconda’s documentation now lists Navigator under legacy applications and points to the newer Anaconda Desktop as its current graphical interface
• Supports multiple package sources known as channels, including Anaconda’s default channels and community channels like conda-forge
• Captures reproducible environments via explicit specifications, such as environment.yml

Under Anaconda’s current Terms of Service, for-profit organizations with more than 200 employees or contractors must purchase a paid Business license to use Anaconda’s default channels, installers, and repository. Academic and non-profit research institutions may qualify for an exemption. The community conda-forge channel and packages hosted on anaconda.org remain free to use.

Usage

Create an isolated environment and install packages:

$ conda create -n venv python=3.14
$ conda activate venv
$ conda install numpy pandas

List packages and export the environment:

$ conda list
$ conda env export > environment.yml

Install from a specific channel:

$ conda install -c conda-forge jupyterlab

You can use pip inside a Conda environment when a package isn’t available on your chosen channels. However, you should use a single package resolver for a given environment to keep dependency metadata consistent.

Related Resources

Tutorial

Python Virtual Environments: A Primer

Create isolated project setups on all platforms, and gain a deep understanding of Python's virtual environments created with the venv module.

intermediate devops tools

For additional information on related topics, take a look at the following resources:

• Working With Python Virtual Environments (Course)
• NumPy Tutorial: Your First Steps Into Data Science in Python (Tutorial)
• Data Science With Python Core Skills (Learning Path)
• Spyder: Your IDE for Data Science Development in Python (Tutorial)
• Using Python's pip to Manage Your Projects' Dependencies (Tutorial)
• Python Virtual Environments: A Primer (Quiz)
• NumPy Tutorial: Your First Steps Into Data Science in Python (Quiz)
• Spyder: Your IDE for Data Science Development in Python (Quiz)
• A Beginner's Guide to pip (Course)
• Using Python's pip to Manage Your Projects' Dependencies (Quiz)