They’re good for allowing some projects to simulate having their own dedicated setup (computer). So that:

You don’t need to give the program admin permissions.
It’s especially good if you don’t think you’ll be using the program anywhere else.
Prevents conflicts
- say you like python3, but need python2 for a program, just install python2 in the environment
You can always close and open past environments.

Python Virtual Environments vs. Docker

A Python environment (like a virtual environment) and Docker both help manage dependencies, but they work at different levels:

🏠 Manages Python packages for a specific project.
🐍 Isolates dependencies inside Python (e.g., different versions of numpy, pandas).
🚫 Doesn’t handle system dependencies (e.g., database servers, different OS versions).
💻 Runs on the host machine.

📦 Encapsulates everything (OS, libraries, code, and dependencies).
🌍 Works across different machines without compatibility issues.
🏗️ Can run different versions of Python, databases, or even entire applications.
🚀 More powerful for deploying apps on servers.

👉 Python environments isolate dependencies at the Python level, while Docker isolates the entire system environment.

Pip & Venv vs. Conda Package Management

pip: Installs Python packages from PyPI. It resolves dependencies but doesn’t handle system libraries.
conda: Installs packages from Anaconda repositories. It manages both Python and system dependencies, making it more reliable for scientific computing.

Use pip for general Python packages and conda for scientific/ML packages with complex dependencies.

venv: Only manages Python environments; you install packages with pip. Doesn’t handle system dependencies.
conda: Manages both Python environments and system dependencies (e.g., CUDA, MKL). Works with conda packages instead of just pip.

Use venv for lightweight projects and conda for ML, data science, or complex dependencies.

conda create --name env_name python=3.10

conda activate env_name

conda deactivate