Virtual environments in python

Creating virtual environments is essential for isolating dependencies and ensuring consistency across different projects. Here are the main methods and tools available, along with their pros, cons, and recommendations:

1. venv (Built-in Python Virtual Environment)

Overview:

venv is a lightweight virtual environment module included in Python (since Python 3.3). It allows you to create isolated environments without additional dependencies.

How to Use:

python -m venv myenv
source myenv/bin/activate  # On macOS/Linux
myenv\Scripts\activate     # On Windows

Pros:

✅ Built-in – No need to install anything extra.
✅ Lightweight – Minimal overhead compared to other tools.
✅ Works across all platforms.
✅ Good for simple projects.

Cons:

❌ No dependency management – You still need pip and requirements.txt.
❌ Not as feature-rich as other tools.
❌ No package isolation per project directory (requires manual activation).

Recommendation:

Use venv if you need a simple, lightweight solution and don’t need advanced dependency management.

2. virtualenv

Overview:

virtualenv is an older tool that offers more flexibility than venv and supports both Python 2 and 3.

How to Use:

pip install virtualenv
virtualenv myenv
source myenv/bin/activate  # On macOS/Linux
myenv\Scripts\activate     # On Windows

Pros:

✅ More features than venv – Supports multiple Python versions.
✅ Faster than venv due to optimized environment creation.
✅ Better isolation – No interference with global Python packages.

Cons:

❌ Requires installation – Unlike venv, it is not built into Python.
❌ Can be overkill for simple projects.

Recommendation:

Use virtualenv if you need better performance and more control over virtual environments, especially if working with different Python versions.

3. conda (For Python and Non-Python Dependencies)

Overview:

conda is a package and environment manager from Anaconda/Miniconda, mainly used for data science and machine learning.

How to Use:

conda create --name myenv python=3.9
conda activate myenv

Pros:

✅ Manages both Python and non-Python dependencies (C libraries, R, etc.).
✅ Precompiled packages – No need for pip install + gcc headaches.
✅ Supports multiple Python versions easily.

Cons:

❌ Larger disk usage – Conda environments can be quite large.
❌ Slower compared to venv/virtualenv due to additional features.
❌ Not as Python-centric – Designed more for data science use cases.

Recommendation:

Use conda if you work in data science, ML, or need non-Python dependencies (e.g., TensorFlow, NumPy with optimized C libraries).

4. pipenv (Dependency & Environment Management)

Overview:

pipenv combines pip and virtualenv into a single tool for easier dependency management.

How to Use:

pip install pipenv
pipenv install
pipenv shell

Pros:

✅ Manages dependencies in a single Pipfile (replaces requirements.txt).
✅ Automatically creates a virtual environment.
✅ Handles dependency resolution better than pip alone.

Cons:

❌ Slower than virtualenv/venv.
❌ Can be overkill for small projects.
❌ Not as widely adopted as other tools.

Recommendation:

Use pipenv if you need better dependency management but still want a virtual environment with pip.

5. Poetry (Best for Package Management & Development)

Overview:

Poetry is a modern tool designed for dependency management and packaging, making it easier to manage virtual environments and publish packages.

How to Use:

pip install poetry
poetry new myproject
cd myproject
poetry add numpy
poetry shell

Pros:

✅ Dependency resolution – Automatically manages and locks dependencies.
✅ Built-in virtual environment management.
✅ Ideal for Python package development.

Cons:

❌ More complex setup compared to venv/virtualenv.
❌ Not as widely adopted as pip and virtualenv.

Recommendation:

Use Poetry if you are developing a Python package or need structured dependency management.

Which One Should You Use? (Final Recommendations)

Best Choice Based on Use Case

🔹 For small Python projects → venv
🔹 For performance & multiple Python versions → virtualenv
🔹 For data science & ML → conda
🔹 For better dependency management → pipenv
🔹 For Python package development → Poetry

How to install?

1. venv (Built-in Python Virtual Environment)

✅ Already included in Python 3.3+
No need to install anything extra!

2. virtualenv (More Features than venv)

✅ Works for Python 2 and 3
✅ Supports multiple versions of Python

pip install virtualenv

3. conda (For Data Science & ML)

✅ Comes with Anaconda and Miniconda
✅ Works for Python and non-Python dependencies

Installation:

1️⃣ Install Anaconda (Full package with data science tools)
Download Anaconda

2️⃣ Install Miniconda (Lightweight version)
Download Miniconda

3️⃣ Check Installation:

4. pipenv (Dependency + Virtualenv Management)

✅ Combines pip and virtualenv
✅ Uses a Pipfile instead of requirements.txt

pip install pipenv

Jageshwar Tripathi