Files
Autoparts-DB/vehicle_database
consultoria-as d213ec2df0 Add engine data enrichment script and populate tech specs
New script (enrich_engine_data.py):
- Parses engine names to extract displacement, cylinders, fuel type
- Estimates HP and torque based on engine characteristics
- Turbo engines get +35% power estimate
- Handles V6, V8, I4, diesel, electric, hybrid patterns

Results:
- 13,287 engines updated with technical data
- 99% coverage for displacement_cc
- 59% coverage for cylinders
- 99.9% coverage for fuel_type
- 95.7% coverage for power_hp and torque_nm

Example data:
- Chevrolet avg: 4945cc, 337HP, 7.6 cyl
- Toyota avg: 2767cc, 202HP, 6.5 cyl
- BMW avg: 3117cc, 262HP, 8.7 cyl

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-02-05 08:38:56 +00:00
..
2026-01-19 08:45:03 +00:00
2026-01-19 08:45:03 +00:00
2026-01-19 08:45:03 +00:00

Vehicle Database

A comprehensive database system for storing information about vehicle brands, models, years, and engines.

Overview

This project provides a structured database for vehicle information with the following entities:

  • Brands: Vehicle manufacturers with details like country of origin and founding year
  • Models: Vehicle models with body type, generation, and production years
  • Years: Calendar years for vehicle production
  • Engines: Engine specifications including displacement, cylinders, power, and fuel type
  • Model-Year-Engine: Junction table linking all entities with trim levels and specifications

Database Schema

The database uses SQLite and consists of the following tables:

brands

  • id: Primary key
  • name: Brand name (e.g., Toyota, Ford)
  • country: Country of origin
  • founded_year: Year the company was founded

engines

  • id: Primary key
  • name: Engine name
  • displacement_cc: Engine displacement in cubic centimeters
  • cylinders: Number of cylinders
  • fuel_type: Type of fuel (gasoline, diesel, electric, hybrid)
  • power_hp: Horsepower
  • torque_nm: Torque in Newton meters
  • engine_code: Manufacturer engine code

models

  • id: Primary key
  • brand_id: Foreign key to brands table
  • name: Model name (e.g., Camry, Civic)
  • body_type: Body style (sedan, SUV, truck, etc.)
  • generation: Model generation
  • production_start_year: Year production started
  • production_end_year: Year production ended (NULL if still in production)

years

  • id: Primary key
  • year: Calendar year

model_year_engine

  • id: Primary key
  • model_id: Foreign key to models table
  • year_id: Foreign key to years table
  • engine_id: Foreign key to engines table
  • trim_level: Trim level (e.g., base, luxury, sport)
  • drivetrain: Drive system (FWD, RWD, AWD, 4WD)
  • transmission: Transmission type (manual, automatic, CVT)

Setup

  1. Install Python 3.x if not already installed
  2. Clone or download this repository
  3. Run the database manager script:
cd vehicle_database
python scripts/database_manager.py

This will create the database, populate it with sample data, and run example queries.

Usage

The VehicleDatabaseManager class provides methods to:

  • Create and manage the database schema
  • Insert new brands, models, engines, and years
  • Query vehicle information
  • Link models, years, and engines with trim levels and specifications

Sample Queries

The script demonstrates several query patterns:

  • Get all brands
  • Get models for a specific brand
  • Search for specific vehicles by brand, model, year, or engine
  • Retrieve comprehensive vehicle information

Extending the Database

To add more data, you can:

  1. Use the provided Python API
  2. Directly execute SQL commands on the SQLite database
  3. Import data from CSV files using the provided structure

File Structure

vehicle_database/
├── sql/
│   └── schema.sql          # Database schema
├── scripts/
│   └── database_manager.py # Python database manager
├── data/                   # Directory for data files
└── README.md              # This file