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How to Install Anaconda on Ubuntu 18.04 and 20.04?
Anaconda is an open-source distribution of Python and R programming languages used for data science, machine learning, and artificial intelligence projects. It comes with various pre-installed libraries and packages that are useful for scientific computing, data analysis, and data visualization. This tutorial covers the complete installation and setup process for Anaconda on Ubuntu 18.04 and 20.04.
Prerequisites
Before installing Anaconda on Ubuntu 18.04 or 20.04, you should have access to a terminal window or command-line interface with superuser privileges.
Step 1: Download Anaconda
Download the Anaconda installer directly from the command line using wget. First, navigate to your home directory and download the latest version
cd ~ wget https://repo.anaconda.com/archive/Anaconda3-2023.09-0-Linux-x86_64.sh
Alternatively, visit the official Anaconda website at https://www.anaconda.com/products/individual and download the Linux installer manually.
Step 2: Verify Download (Optional)
It's good practice to verify the integrity of the downloaded file using SHA-256 checksum
sha256sum Anaconda3-2023.09-0-Linux-x86_64.sh
Step 3: Install Anaconda
Make the installation file executable and run the installer
chmod +x Anaconda3-2023.09-0-Linux-x86_64.sh ./Anaconda3-2023.09-0-Linux-x86_64.sh
Note: Replace the filename with the actual version you downloaded.
Step 4: Follow Installation Wizard
The installation wizard will guide you through several prompts
License Agreement: Type
yesto accept the license termsInstallation Location: Press Enter to use the default location (
~/anaconda3)Initialize Anaconda: Type
yesto add Anaconda to your PATHVisual Studio Code: Type
noif you don't need VS Code
After installation, activate the changes
source ~/.bashrc
Step 5: Verify Installation
Test your Anaconda installation by checking the version and listing installed packages
conda --version conda list
You should see the Conda version and a list of pre-installed packages.
Environment Management
Creating and Managing Environments
Create isolated environments for different projects
# Create new environment conda create --name myproject python=3.9 # Activate environment conda activate myproject # Deactivate environment conda deactivate # List all environments conda env list # Remove environment conda env remove --name myproject
Exporting and Sharing Environments
Create reproducible environments by exporting dependency lists
# Export current environment conda env export > environment.yml # Create environment from file conda env create -f environment.yml
Package Management
Install and manage packages using both Conda and pip
# Install with conda (preferred) conda install numpy pandas matplotlib # Install with pip pip install scikit-learn # Update all packages conda update --all # Update specific package conda update numpy
Using Jupyter Notebook
Anaconda includes Jupyter Notebook for interactive data analysis. Launch it with
jupyter notebook
This opens a web-based interface in your default browser where you can create and run Python notebooks.
Updating and Maintenance
Keep your Anaconda installation current
# Update conda itself conda update conda # Update Anaconda distribution conda update anaconda # Clean package cache conda clean --all
Uninstalling Anaconda
To completely remove Anaconda from your system
# Remove installation directory rm -rf ~/anaconda3 # Remove conda initialization from ~/.bashrc # Edit ~/.bashrc and remove conda-related lines # Remove hidden conda files rm -rf ~/.conda rm -rf ~/.continuum
Common Commands Reference
| Command | Description |
|---|---|
conda info |
Display system information |
conda search package_name |
Search for packages |
conda install package_name |
Install a package |
conda remove package_name |
Remove a package |
conda env list |
List all environments |
Conclusion
Anaconda provides a comprehensive platform for Python data science with easy package management and environment isolation. Following this installation guide, you now have a fully functional Anaconda setup on Ubuntu with the ability to create isolated environments, manage packages, and use Jupyter notebooks for your data science projects.
