Python import notes

Version 3.0 introduces a reproducible Python import workflow.

The goal is to turn the public CSV sample data into a local SQLite database and validate the import.

Added file

scripts/build_database.py

What the script does

The script:

• reads the public CSV files from data/public/
• validates the expected CSV columns
• checks primary key uniqueness
• checks public CSV data for obvious sensitive terms
• creates a local SQLite database from sql/schema.sql
• imports the CSV rows into SQLite
• validates foreign key integrity
• runs sql/example_queries.sql
• runs sql/data_quality_queries.sql and expects no result rows

Build command

Run from the repository root:

python3 scripts/build_database.py

Default output:

db/music_production_data_lab.sqlite

The db/ folder is ignored by Git. The generated SQLite database is a local build artifact and should not be committed.

Why this matters

This turns the project from static documentation into a reproducible data workflow:

CSV
-> Python validation
-> SQLite database
-> SQL analysis

This is relevant for data and process analysis because it demonstrates:

• import logic
• schema alignment
• primary key checks
• relationship validation
• data-quality checks
• reproducible local builds

Current limitation

Version 3.0 still uses only the Python standard library.

A later version can add pandas-based checks, richer logging, automated tests or a Streamlit explorer.