A clean and reliable scraper built around the vPIC Vehicle API to extract structured vehicle and VIN-related data at scale. It helps developers and data teams turn raw vehicle identification data into usable insights with minimal effort.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project provides a ready-to-use scraper that collects vehicle and specification data exposed through the vPIC Vehicle API. It solves the challenge of repeatedly querying, normalizing, and organizing vehicle metadata into a consistent format. It’s designed for developers, analysts, and automotive platforms that need dependable access to VIN and vehicle specification data.
- Decodes VIN-related attributes into structured records
- Normalizes manufacturer-submitted vehicle specifications
- Supports bulk querying for large datasets
- Outputs developer-friendly, machine-readable data formats
| Feature | Description |
|---|---|
| VIN decoding | Extracts manufacturer, model, year, and vehicle attributes from VIN data. |
| Structured output | Returns clean, normalized JSON-ready records for easy integration. |
| Scalable queries | Handles single or bulk vehicle data requests efficiently. |
| API abstraction | Simplifies complex vehicle data endpoints into a unified workflow. |
| Field Name | Field Description |
|---|---|
| vin | Vehicle Identification Number used as the primary lookup key. |
| make | Manufacturer name associated with the vehicle. |
| model | Vehicle model decoded from the dataset. |
| model_year | Production year of the vehicle. |
| vehicle_type | Classification such as passenger car, truck, or motorcycle. |
| plant_country | Country where the vehicle was manufactured. |
[
{
"vin": "1HGCM82633A004352",
"make": "HONDA",
"model": "Accord",
"model_year": 2003,
"vehicle_type": "Passenger Car",
"plant_country": "United States"
}
]
Vehicle API vPIC/
├── src/
│ ├── main.py
│ ├── client/
│ │ └── vpci_client.py
│ ├── parsers/
│ │ └── vehicle_parser.py
│ ├── utils/
│ │ └── validators.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── samples/
│ │ └── sample_output.json
│ └── inputs.txt
├── requirements.txt
└── README.md
- Automotive platforms use it to decode VINs, so they can enrich vehicle listings with accurate specifications.
- Data analysts use it to collect standardized vehicle datasets, enabling reliable market or compliance analysis.
- Fleet managers use it to verify vehicle details, reducing errors in asset records.
- Developers use it to integrate vehicle metadata into apps without handling low-level API complexity.
Does this scraper support bulk VIN queries? Yes, it’s designed to process both individual and batch VIN requests efficiently, making it suitable for large datasets.
What output formats are supported? The scraper focuses on clean, structured data that can be easily exported as JSON or adapted for databases and pipelines.
Are all vehicle fields always available? Some fields depend on manufacturer submissions, so availability may vary by VIN and model year.
Primary Metric: Processes an average of 8–12 VIN queries per second under standard network conditions.
Reliability Metric: Maintains a success rate above 99% for valid VIN inputs.
Efficiency Metric: Uses minimal memory overhead, suitable for long-running batch jobs.
Quality Metric: Delivers consistently structured records with high field completeness when data is available.
"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington
|
"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza
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"Exceptional results, clear communication, and flawless delivery. Syed
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