Environment manager for Python

Why do you need an environment manager for Python?

• Version Control
• Ease of Deployment
• Avoiding Conflicts

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Python Virtual Environment (venv):

The venv module supports creating lightweight “virtual environments”.

# Create new environment
python -m venv myenv

# Activate environment
# On Windows:
myenv\Scripts\activate
# On Unix/MacOS:
source myenv/bin/activate

# Deactivate environment
deactivate

# Remove the virtual environment (delete the folder)
rm -rf myenv

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Anaconda

Anaconda provides package, dependency, and environment management for any language.

• Windows: setting system environment variables path
  • C:\ProgramData\anaconda3
  • C:\ProgramData\anaconda3\Library\bin
  • C:\ProgramData\anaconda3\Scripts
  • C:\ProgramData\anaconda3\Lib\site-packages

restart PC

Basic Commands

# Check Anaconda version
conda --version

# Update Anaconda
conda update conda
conda update anaconda

Disable Automatic Activation

To prevent Conda from being automatically activated as the default terminal environment when you open a terminal, you can run the following command in terminal:

conda config --set auto_activate_base false

Environment Management

# Create new environment
conda create --name myenv python=3.11

# Activate environment
conda activate myenv

# List all environment
conda env list

# Deactivate environment
conda deactivate

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

Package Management

# Install packages
conda install package_name

# Update a package
conda update package_name

# Remove a package
conda remove package_name

# List all installed packages
conda list

# Search for a package
conda search package_name

Environment Export and Import

# Export an environment
conda env export > environment.yml

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

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uv

uv is an extremely fast Python package and project manager, written in Rust.

The steps to use:

1. Install uv
2. Create a new project
3. Create a new environment in the project folder
4. Install the required packages
5. Add the package dependencies
6. Run program with uv

Install & Using uv

# Install uv
pip install uv

Project management

# Create new project
uv init new_project

cd new_project

# Create new environment
uv venv

# run a script
uv run hello.py

Python management

# Install multiple Python versions
uv python install 3.10 3.11 3.12

# Set a Python version in the current directory
uv python pin 3.11

# List all python version
uv python list

Install package

# Install packages (much faster than pip)
uv pip install numpy pandas

# Install from requirements.txt
uv pip install -r requirements.txt

Managing package dependencies

# Specify a version constraint
uv add 'package_name==2.31.0'

# Add a git dependency
uv add git+https://github.com/package_link

# remove a package
uv remove package_name

# upgrade a package
uv lock --upgrade-package package_name

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Summary

Feature	venv	conda	uv
Purpose	Basic virtual environments	Complete environment management	Fast package management
Speed	★★	★	★★★
Key Strength	Built into Python	Handles all dependencies	Ultra-fast installations
Package Source	PyPI only	Conda + PyPI	PyPI only
Multiple Python Versions	No	Yes	Yes
Main Use Case	Single Python projects	Data Science & Scientific Computing	Modern Python development

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