Choosing the Right Python Environment on WSL: venv vs conda vs uv

Why Python works well in WSL

Installing dependencies : Windows often fails with missing headers, while WSL can resolve them with apt install.

Path handling : Windows uses backslashes and spaces; WSL uses clean forward slashes.

Script execution : Windows file‑association is messy; WSL runs scripts directly with python3 file.py.

Multiple versions : Windows requires manual PATH tweaks; WSL can switch versions easily with pyenv or package managers.

Production consistency : Windows differs from Linux servers; WSL is almost identical.

NumPy/Pandas performance : Faster on Linux‑compiled WSL.

Step 1: Install Python

Ubuntu comes with Python

# Ubuntu 24.04 includes Python 3.12+
python3 --version
# Install pip, venv, dev headers
sudo apt install -y python3-pip python3-venv python3-dev
# Upgrade pip
python3 -m pip install --upgrade pip
# Verify versions
python3 --version && pip --version

Important rule: never sudo pip install

# ❌ Wrong: pollutes system Python
sudo pip install requests
# ✅ Correct: use a virtual environment
python3 -m venv .venv
source .venv/bin/activate
pip install requests
deactivate

Three package managers comparison

Overview

pip + venv – official standard, lightweight, suitable for small projects and learning (⭐️⭐️⭐️⭐️).

uv – Rust‑based, extremely fast (10‑100×), auto‑creates virtual env, ideal for new projects (⭐️⭐️⭐️⭐️⭐️).

Miniconda – data‑science ecosystem, binary packages, easy multi‑Python switching, larger install size (⭐️⭐️⭐️⭐️).

Detailed comparison

pip + venv:
┌─────────────────────────────┐
│ Advantages                   │
│ • Official Python solution   │
│ • No extra installation      │
│ • Lightweight, per‑project   │
│ Disadvantages                │
│ • Slower (pip download)      │
│ • No Python version manager  │
│ • Large projects may stall    │
└─────────────────────────────┘

uv:
┌─────────────────────────────┐
│ Advantages                   │
│ • Extremely fast (10‑100×)   │
│ • Auto‑creates/manages env    │
│ • One‑command project init    │
│ • Built‑in Python version mgmt│
│ • Low resource usage (Rust)   │
│ Disadvantages                │
│ • Newer ecosystem            │
│ • Some edge‑case compatibility│
└─────────────────────────────┘

Miniconda / conda:
┌─────────────────────────────┐
│ Advantages                   │
│ • Full data‑science stack    │
│ • Binary packages (no compile)│
│ • Manages non‑Python packages│
│ • Easy multi‑Python switching│
│ Disadvantages                │
│ • Large installer (~500 MB) │
│ • Slower startup             │
│ • Possible pip conflicts     │
│ • License restrictions      │
└─────────────────────────────┘

Solution 1: uv – preferred for new projects

Installation

# One‑line install (official script)
curl -LsSf https://astral.sh/uv/install.sh | sh
# Reload shell or restart terminal
source ~/.bashrc
uv --version  # e.g., uv 0.x.x

Quick start

# Initialise a new project (creates pyproject.toml + env)
uv init my-project && cd my-project
# Add dependencies (installed into the env)
uv add requests fastapi uvicorn sqlalchemy
# Run the code
uv run main.py
# Add development‑only dependencies
uv add --dev pytest black ruff
# Run tests
uv run pytest
# List installed packages
uv pip list
# Export requirements for compatibility
uv pip freeze > requirements.txt
# Install from a requirements file
uv pip install -r requirements.txt
# Install a specific Python version
uv python install 3.11
uv run --python 3.11 main.py

Project structure example

my-project/
├── .venv/          # managed by uv
├── pyproject.toml  # project config & dependencies
├── main.py
└── README.md

[project]
name = "my-project"
version = "0.1.0"
requires-python = ">=3.10"
dependencies = [
    "requests>=2.31.0",
    "fastapi>=0.104.0",
    "uvicorn[standard]>=0.24.0",
]

Solution 2: pip + venv – traditional reliable

Create and use a virtual environment

# Create project directory
mkdir -p ~/projects/myapp && cd ~/projects/myapp
# Create virtual environment
python3 -m venv .venv
# Activate it
source .venv/bin/activate
# Upgrade packaging tools
pip install --upgrade pip setuptools wheel
# Install dependencies
pip install flask pandas numpy
# List packages
pip list
# Exit the environment
deactivate

Batch dependency management

# Export all packages
pip freeze > requirements.txt
# Install from file
pip install -r requirements.txt
# Export only direct dependencies
pip freeze --exclude-editable > requirements.txt
# Use pip-tools for precise control
pip install pip-tools
pip-compile requirements.in   # generates requirements.txt
pip-sync requirements.txt    # installs exactly

Auto‑activate virtual environment (optional)

# Install autoenv
pip install autoenv
# Add to bashrc
echo 'source $(python3 -m autoenv site)' >> ~/.bashrc
# Create .env in project to auto‑activate
echo 'source .venv/bin/activate' > ~/projects/myapp/.env
# Next cd into the directory will auto‑activate

Solution 3: Miniconda – data‑science choice

Install Miniconda

# Download latest Miniconda installer
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
# Run installer (accept defaults)
bash Miniconda3-latest-Linux-x86_64.sh
# Reload shell
source ~/.bashrc
conda --version

Common conda commands

# Create environment with specific Python version
conda create -n datascience python=3.11
conda activate datascience
# Install data‑science packages (conda handles binaries)
conda install numpy pandas matplotlib scikit-learn jupyter seaborn plotly
# Install packages not in conda via pip
pip install some-special-package
# List environments
conda env list
# Export / import environment
conda export > environment.yml
conda env create -f environment.yml
# Remove an environment
conda remove -n old-env --all
# Clean cache
conda clean --all

Jupyter Notebook setup

# Install Jupyter
conda install jupyter ipykernel
# Launch Lab (accessible from Windows browser)
jupyter lab --ip=0.0.0.0 --port=8888 --no-browser
# Open http://localhost:8888 and copy the token if prompted

Common Python packages quick reference

Web development

pip install fastapi uvicorn   # modern API framework
pip install flask            # classic lightweight framework
pip install django           # full‑stack framework
pip install httpie           # API testing tool (curl alternative)

Data science

pip install numpy pandas matplotlib
pip install seaborn plotly
pip install scikit-learn
pip install jupyterlab
pip install openpyxl xlrd

Development tools

pip install pytest pytest-cov
pip install black isort ruff
pip install mypy
pip install httpx
pip install rich
pip install python-dotenv

VS Code + Python + Remote‑WSL configuration

Install the following VS Code extensions: Python (ms-python.python), Pylance (ms-python.vscode-pylance), Black Formatter (ms-python.black-formatter), Jupyter (ms-toolsai.jupyter).

If VS Code does not auto‑detect the virtual environment, select the interpreter via “Python: Select Interpreter” and choose .venv/bin/python or the conda python path.

{
    "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python",
    "python.formatting.provider": "black",
    "python.linting.enabled": true,
    "python.testing.pytestEnabled": true,
    "[python]": {
        "editor.defaultFormatter": "ms-python.black-formatter",
        "editor.formatOnSave": true,
        "editor.codeActionsOnSave": {
            "source.organizeImports": "explicit"
        }
    }
}