Project Organization for Small Python Projects¶

As soon as code grows beyond a single short script, structure starts to matter. Good organization makes later work on testing, version control, packaging, and collaboration much easier.

A simple starting structure¶

For a small Python project, a layout like this is often enough:

my-project/
├── data/
├── docs/
├── notebooks/
├── src/
│   └── my_project/
│       ├── __init__.py
│       └── analysis.py
├── tests/
├── README.md
└── pyproject.toml

Not every project needs every folder immediately, but the structure helps you separate concerns.

Useful distinctions¶

• Put reusable code in modules, not only in notebooks.
• Keep raw data separate from generated outputs.
• Use tests/ for checks, even if they begin very small.
• Use docs/ for explanations and learning material.

Naming and modularization¶

Clear names reduce confusion:

• choose descriptive file names,
• group related functions into one module,
• avoid putting everything into a single giant script,
• prefer a few focused modules over one "miscellaneous" file.

Notebooks, scripts, and packages¶

Each format has a role:

• notebooks are good for exploration and teaching,
• scripts are good for repeatable command-style workflows,
• packages are good for reusable logic shared across files or people.

A strong habit is to move stable logic out of notebooks and into Python modules. That keeps notebooks shorter and makes the useful parts easier to test and reuse.

Why this matters for later modules¶

The topics in later RECODE modules build on this foundation:

• Git works better when changes are organized cleanly.
• AI coding assistants are more helpful in structured projects.
• Packaging becomes much easier when code already has a sensible layout.

Project organization is therefore not an "advanced extra." It is part of making your work easier to maintain from the beginning.