Conda

Conda is a cross-platform package and environment manager for Python, R, and other languages. Install packages and manage isolated Python environments.

Installation

Linux/macOS

# Download Miniconda (lightweight, recommended)
curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
# or for macOS
curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh

# Install
bash Miniconda3-latest-Linux-x86_64.sh

# Or download Anaconda (full distribution with GUI)
# https://www.anaconda.com/download

Windows

# Download Miniconda installer
# https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe

# Run installer and follow prompts
# Or use Chocolatey
choco install miniconda3

# Or use Winget
winget install Anaconda.Miniconda3

Basic Commands

Command	Description
`conda --version`	Show conda version
`conda --help`	Display help information
`conda info`	Show system and environment info
`conda list`	List packages in current environment
`conda search [package]`	Search for packages
`conda install [package]`	Install package in current environment
`conda remove [package]`	Remove package from environment
`conda update [package]`	Update package to latest version
`conda clean --all`	Remove unused packages and cache

Creating & Managing Environments

# Create new environment with Python version
conda create -n myenv python=3.11

# Create environment from file
conda create --file environment.yml

# Create environment with multiple packages
conda create -n myenv python=3.10 numpy pandas matplotlib

# Activate environment
conda activate myenv

# Deactivate environment (back to base)
conda deactivate

# List all environments
conda env list

# Remove environment
conda remove --name myenv --all

# Clone environment
conda create --clone myenv --name myenv_backup

# Display environment info
conda info myenv

Package Management

Installing Packages

# Install package in current environment
conda install numpy

# Install specific version
conda install numpy=1.21.0

# Install multiple packages
conda install numpy pandas scikit-learn

# Install package from specific channel
conda install -c conda-forge numpy

# Install with build specification
conda install numpy=1.21.0=py39_0

# Install with pip in conda environment
conda install pip
pip install package-name

# Update all packages in environment
conda update --all

# Update specific package
conda update numpy

Removing Packages

# Remove package
conda remove numpy

# Remove multiple packages
conda remove numpy pandas scipy

# Remove unused packages
conda clean --all

# Remove specific unused package
conda clean --tempfiles

Channels & Repositories

# List channels (sources)
conda config --show channels

# Add channel (conda-forge is popular)
conda config --add channels conda-forge

# Remove channel
conda config --remove channels conda-forge

# Set channel priority
conda config --set channel_priority strict

# Show all configuration
conda config --show

# Search in specific channel
conda search -c conda-forge numpy

# Install from specific channel
conda install -c bioconda biopython

Popular Channels

# Use conda-forge (more packages, faster updates)
conda install -c conda-forge numpy

# Use defaults (official Anaconda channel)
conda install numpy

# Use bioconda (for bioinformatics)
conda install -c bioconda samtools

# Use pytorch (for machine learning)
conda install -c pytorch pytorch::pytorch torchvision

# Combine multiple channels
conda install -c conda-forge -c pytorch pytorch

Environment Files

Export Environment

# Export to environment.yml
conda env export > environment.yml

# Export without build numbers (more portable)
conda env export --no-builds > environment.yml

# Export with history
conda env export > environment.full.yml

Import Environment

# Create environment from file
conda env create -f environment.yml

# Create with specific name from file
conda env create -n myenv -f environment.yml

# Update existing environment
conda env update -f environment.yml --prune

# Prune removes packages not in file

Listing & Searching

# List installed packages
conda list

# List packages with version and build
conda list --explicit

# Show detailed package info
conda list numpy

# Search for package
conda search numpy

# Search for package with version
conda search "numpy=1.21"

# Show package info with dependencies
conda search --info numpy

# List updates available
conda list --outdated

# Search in all channels
conda search --all-channels numpy

Package Details

# Show package information
conda info numpy

# List dependencies of package
conda search --contains numpy

# Show package homepage
conda info --json numpy

# Export package list
conda list --export > requirements.txt

# View package dependencies
conda search --json numpy | grep '"depends"'

Virtual Environments Workflow

# Create development environment
conda create -n dev python=3.11 \
  numpy pandas scikit-learn jupyter matplotlib

# Activate environment
conda activate dev

# Install additional packages
conda install pytorch::pytorch -c pytorch

# List what's installed
conda list

# Deactivate environment
conda deactivate

# Remove when done
conda remove --name dev --all

Python Version Management

# Create environment with Python 3.9
conda create -n py39 python=3.9

# Create environment with Python 3.11
conda create -n py311 python=3.11

# Switch Python versions
conda activate py39
python --version
conda deactivate
conda activate py311
python --version

# Update Python in environment
conda install python=3.12

# Show available Python versions
conda search "python"

Conda Configuration

# Show current configuration
conda config --show

# Show all channels
conda config --show channels

# Show channel priority
conda config --show channel_priority

# Set auto-activate base environment
conda config --set auto_activate_base true

# Disable auto-activate base
conda config --set auto_activate_base false

# Set default Python version
conda config --set default_python 3.11

# Show configuration file location
conda config --show-sources

Troubleshooting

Solve Package Conflicts

# Check for conflicts
conda list --explicit

# Use strict channel priority (prevent conflicts)
conda config --set channel_priority strict

# Solve environment dependencies
conda install numpy --dry-run

# Use mamba for faster solves (if installed)
mamba install numpy

# Downgrade package if conflict
conda install numpy=1.20.0

# Clean up and retry
conda clean --all
conda install numpy

Fix Common Issues

# Environment not activating
conda init bash
source ~/.bashrc

# Packages not found
conda update conda
conda config --add channels conda-forge

# Permission denied
sudo chown -R $USER ~/anaconda3

# Remove corrupted environment
conda remove --name myenv --all --force

# Fix conda itself
conda update -n base -c defaults conda

# See what changed
conda env export > env_before.yml
# ... make changes ...
conda env update -f env_before.yml

Mamba (Optional: Faster Alternative)

# Install mamba in base environment
conda install -c conda-forge mamba

# Use mamba instead of conda (same syntax)
mamba create -n myenv python=3.11
mamba install numpy pandas
mamba env export > environment.yml

# Mamba is faster at dependency solving
mamba install pytorch::pytorch torchvision -c pytorch

Docker & Conda

# Create environment and export for Docker
conda env export --no-builds > environment.yml

# In Dockerfile
FROM continuumio/miniconda3
COPY environment.yml /tmp/
RUN conda env create -f /tmp/environment.yml
ENV PATH /opt/conda/envs/myenv/bin:$PATH

Useful Aliases

# Add to ~/.bashrc or ~/.zshrc
alias ca='conda activate'
alias cde='conda deactivate'
alias cl='conda list'
alias ce='conda env list'
alias cew='conda env export --no-builds > environment.yml'
alias cec='conda env create -f environment.yml'
alias ceu='conda env update -f environment.yml --prune'
alias cir='conda install -r requirements.txt'
alias cup='conda update --all'
alias ccc='conda clean --all'

Best Practices

Use conda-forge channel for latest packages and better compatibility
Create separate environments for different projects
Document dependencies in environment.yml files
Use specific versions for production environments
Regularly update packages with conda update --all
Pin major.minor versions in environment files
Use --no-builds when exporting for portability
Keep base environment minimal
Use mamba for faster dependency resolution on large projects
Version control your environment.yml files

Resources

conda --help - Conda help
conda create --help - Environment creation help
conda install --help - Installation help
Conda Documentation
Conda Cheat Sheet
Conda-Forge - Community package channel
Anaconda Distribution

Last updated: 2026-03-30