consultoria-as
f395d67136
- Base de datos SQLite con información de vehículos - Dashboard web con Flask y Bootstrap - Scripts de web scraping para RockAuto - Interfaz CLI para consultas - Documentación completa del proyecto Incluye: - 12 marcas de vehículos - 10,923 modelos - 10,919 especificaciones de motores - 12,075 combinaciones modelo-año-motor
106 lines
3.1 KiB
Markdown
106 lines
3.1 KiB
Markdown
# Vehicle Database
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A comprehensive database system for storing information about vehicle brands, models, years, and engines.
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## Overview
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This project provides a structured database for vehicle information with the following entities:
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- **Brands**: Vehicle manufacturers with details like country of origin and founding year
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- **Models**: Vehicle models with body type, generation, and production years
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- **Years**: Calendar years for vehicle production
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- **Engines**: Engine specifications including displacement, cylinders, power, and fuel type
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- **Model-Year-Engine**: Junction table linking all entities with trim levels and specifications
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## Database Schema
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The database uses SQLite and consists of the following tables:
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### `brands`
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- `id`: Primary key
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- `name`: Brand name (e.g., Toyota, Ford)
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- `country`: Country of origin
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- `founded_year`: Year the company was founded
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### `engines`
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- `id`: Primary key
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- `name`: Engine name
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- `displacement_cc`: Engine displacement in cubic centimeters
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- `cylinders`: Number of cylinders
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- `fuel_type`: Type of fuel (gasoline, diesel, electric, hybrid)
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- `power_hp`: Horsepower
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- `torque_nm`: Torque in Newton meters
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- `engine_code`: Manufacturer engine code
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### `models`
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- `id`: Primary key
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- `brand_id`: Foreign key to brands table
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- `name`: Model name (e.g., Camry, Civic)
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- `body_type`: Body style (sedan, SUV, truck, etc.)
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- `generation`: Model generation
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- `production_start_year`: Year production started
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- `production_end_year`: Year production ended (NULL if still in production)
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### `years`
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- `id`: Primary key
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- `year`: Calendar year
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### `model_year_engine`
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- `id`: Primary key
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- `model_id`: Foreign key to models table
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- `year_id`: Foreign key to years table
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- `engine_id`: Foreign key to engines table
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- `trim_level`: Trim level (e.g., base, luxury, sport)
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- `drivetrain`: Drive system (FWD, RWD, AWD, 4WD)
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- `transmission`: Transmission type (manual, automatic, CVT)
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## Setup
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1. Install Python 3.x if not already installed
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2. Clone or download this repository
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3. Run the database manager script:
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```bash
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cd vehicle_database
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python scripts/database_manager.py
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```
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This will create the database, populate it with sample data, and run example queries.
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## Usage
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The `VehicleDatabaseManager` class provides methods to:
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- Create and manage the database schema
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- Insert new brands, models, engines, and years
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- Query vehicle information
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- Link models, years, and engines with trim levels and specifications
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## Sample Queries
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The script demonstrates several query patterns:
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- Get all brands
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- Get models for a specific brand
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- Search for specific vehicles by brand, model, year, or engine
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- Retrieve comprehensive vehicle information
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## Extending the Database
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To add more data, you can:
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1. Use the provided Python API
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2. Directly execute SQL commands on the SQLite database
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3. Import data from CSV files using the provided structure
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## File Structure
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```
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vehicle_database/
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├── sql/
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│ └── schema.sql # Database schema
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├── scripts/
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│ └── database_manager.py # Python database manager
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├── data/ # Directory for data files
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└── README.md # This file
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``` |