How to Set Up a Python Development Environment on Your Breeze

How to Set Up a Python Development Environment on Your Breeze

Python is one of the most versatile programming languages, used for web development, data science, automation, machine learning, and scripting. Setting up a proper Python development environment on your Breeze ensures isolated project dependencies, reproducible builds, and a smooth workflow. This guide covers everything from installing Python to managing virtual environments and deploying applications.

Installing Python

Most Breeze OS templates include Python 3, but you may want a newer version or multiple versions:

# Check existing Python version
python3 --version

# Install Python and essential tools on Ubuntu/Debian
sudo apt update
sudo apt install -y python3 python3-pip python3-venv python3-dev build-essential

# Install on AlmaLinux/Rocky
sudo dnf install -y python3 python3-pip python3-devel gcc

Installing Multiple Python Versions with pyenv

If your projects require different Python versions, use pyenv:

# Install pyenv dependencies
sudo apt install -y make libssl-dev zlib1g-dev libbz2-dev \
  libreadline-dev libsqlite3-dev wget curl llvm libncursesw5-dev \
  xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev

# Install pyenv
curl https://pyenv.run | bash

# Add to your shell profile (~/.bashrc or ~/.zshrc)
export PYENV_ROOT="$HOME/.pyenv"
export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init -)"

# Install and use a specific Python version
pyenv install 3.12.1
pyenv global 3.12.1
python --version  # Should show 3.12.1

Working with Virtual Environments

Virtual environments isolate project dependencies so that different projects can use different package versions without conflicts:

# Create a virtual environment
python3 -m venv /home/user/projects/myapp/venv

# Activate the virtual environment
source /home/user/projects/myapp/venv/bin/activate

# Your prompt changes to show the active environment
(venv) user@breeze:~$

# Install packages within the environment
pip install flask gunicorn sqlalchemy

# Freeze dependencies for reproducibility
pip freeze > requirements.txt

# Deactivate when done
deactivate

Managing Dependencies with pip

Best practices for dependency management:

# Always use a requirements file
pip install -r requirements.txt

# Pin exact versions for production
Flask==3.0.0
gunicorn==21.2.0
SQLAlchemy==2.0.23

# Separate development dependencies
pip install -r requirements-dev.txt  # pytest, flake8, black, etc.

# Upgrade pip itself regularly
pip install --upgrade pip

Using Poetry for Advanced Dependency Management

Poetry is a modern tool that handles dependency resolution, virtual environments, and packaging:

# Install Poetry
curl -sSL https://install.python-poetry.org | python3 -

# Create a new project
poetry new myproject
cd myproject

# Add dependencies
poetry add flask gunicorn sqlalchemy
poetry add --group dev pytest black flake8

# Install all dependencies
poetry install

# Run commands within the environment
poetry run python app.py
poetry run pytest

Setting Up a Flask Application

Create a basic Flask application structure:

myapp/
├── app/
│   ├── __init__.py
│   ├── routes.py
│   └── models.py
├── tests/
│   └── test_routes.py
├── requirements.txt
├── wsgi.py
└── .env

Running in Production with Gunicorn

Use Gunicorn as your WSGI server for production deployments:

# Install gunicorn in your virtual environment
pip install gunicorn

# Run with sensible defaults
gunicorn --workers 4 --bind 0.0.0.0:8000 wsgi:app

# Create a systemd service for automatic start
sudo tee /etc/systemd/system/myapp.service > /dev/null <<EOF
[Unit]
Description=My Python App
After=network.target

[Service]
User=www-data
Group=www-data
WorkingDirectory=/home/user/projects/myapp
Environment="PATH=/home/user/projects/myapp/venv/bin"
ExecStart=/home/user/projects/myapp/venv/bin/gunicorn --workers 4 --bind 127.0.0.1:8000 wsgi:app

[Install]
WantedBy=multi-user.target
EOF

sudo systemctl enable myapp
sudo systemctl start myapp

Development Workflow Tips

• Use .env files with python-dotenv for environment-specific configuration
• Set up pre-commit hooks with pre-commit to enforce code quality
• Use black for consistent code formatting and flake8 for linting
• Write tests with pytest and aim for meaningful coverage of critical paths
• Use logging instead of print() for application output
• Store secrets in environment variables, never in source code