Data science projects often start as single Jupyter notebooks that grow into thousands of lines of unmaintainable code. Functions get duplicated across cells, imports become chaotic, and collaboration becomes impossible. Breaking code into modules and packages creates reusable components, improves organization, and enables team collaboration.
# From project root
uv sync --group chapter3- 01_basic_modules/ - Create and import modules
- 02_modular_design/ - Break monolithic code into modules
- 03_import_practices/ - Import best practices
- 04_main_blocks/ - Control code execution
- 05_circular_imports/ - Avoid circular dependencies
- 06_project_structure/ - Standardized project layout
Modular code enables data science teams to share utilities, maintain large codebases, and build production-ready systems from notebook prototypes.
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