Argus Auto Scraper collects detailed automobile listings from Argus.fr, transforming raw vehicle ads into structured, analytics-ready data. It helps automotive professionals, analysts, and lead-generation teams build reliable used-car datasets with rich vehicle and seller information.
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Argus Auto Scraper extracts comprehensive car-for-sale data from Argus.fr, covering both vehicle specifications and seller contact details. It solves the challenge of manually collecting and normalizing large volumes of automotive listings by delivering clean, structured outputs. This project is designed for automotive analysts, dealerships, data teams, and platforms that rely on up-to-date vehicle market data.
- Gathers detailed specifications for new and used vehicles
- Captures verified seller and dealership contact information
- Normalizes complex automotive attributes into structured fields
- Supports scalable analysis across regions, brands, and models
- Enables downstream analytics, reporting, and CRM integration
| Feature | Description |
|---|---|
| Vehicle Listing Extraction | Collects brand, model, trim, engine, gearbox, fuel type, mileage, and registration data. |
| Seller Information Capture | Extracts dealership name, email, phone number, address, and region. |
| Rich Equipment Details | Includes standard and optional equipment with categorized labels. |
| Pricing Intelligence | Captures list price, discounted price, VAT, and promotional data. |
| Structured Output | Delivers normalized JSON suitable for analytics and automation pipelines. |
| Field Name | Field Description |
|---|---|
| brand-label | Dealership or seller brand name. |
| brand-email | Seller or dealership email address. |
| brand-phone | Seller or dealership phone number. |
| vehicule-model-make | Vehicle manufacturer (e.g., Renault, Peugeot). |
| vehicule-model-model | Vehicle model name. |
| vehicule-model-year | Model year of the vehicle. |
| vehicule-mileage | Reported vehicle mileage. |
| vehicule-public-price-posted-incl-tax | Final public price including tax. |
| specifics-registration-card-mec-date | First registration date. |
| equipments | List of vehicle equipment and features. |
[
{
"vehicule-model-make": "RENAULT",
"vehicule-model-model": "Clio V",
"vehicule-model-year": "2023",
"vehicule-mileage": "12898",
"vehicule-public-price-posted-incl-tax": "15490",
"brand-label": "RENAULT CONCARNEAU",
"brand-email": "webvocon@bodemer.fr",
"brand-phone": "33297703578",
"vehicule-model-energy": "Essence",
"vehicule-model-gearbox-description": "Manuelle 6",
"vehicule-model-number-of-doors": "5",
"specifics-air-quality-certificate": "Crit'Air 1"
}
]
Argus Auto Scraper/
├── src/
│ ├── runner.py
│ ├── extractors/
│ │ ├── vehicle_parser.py
│ │ ├── seller_parser.py
│ │ └── equipment_parser.py
│ ├── outputs/
│ │ └── exporters.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── inputs.sample.json
│ └── sample_output.json
├── requirements.txt
└── README.md
- Automotive analysts use it to track pricing and feature trends, so they can understand market dynamics across France.
- Car dealerships use it to collect seller leads, so they can identify new sales opportunities faster.
- Data teams use it to enrich internal vehicle databases, so they can improve reporting and forecasting.
- Market researchers use it to monitor competitor inventories, so they can compare positioning and pricing strategies.
Does this scraper support both new and used vehicles? Yes, it extracts listings across vehicle classifications, including used and certified vehicles, with consistent data fields.
Can the output be integrated into existing systems? The structured JSON format is designed for easy ingestion into CRMs, analytics tools, and data warehouses.
Is regional filtering supported? Listings include region, city, and postal code fields, enabling precise geographic segmentation.
How complete is the equipment data? Both standard and optional equipment are captured when available, categorized by type for clarity.
Primary Metric: Processes several hundred vehicle listings per hour under typical conditions.
Reliability Metric: Maintains a high success rate on listing extraction with consistent field coverage.
Efficiency Metric: Optimized parsing minimizes redundant processing and reduces resource usage.
Quality Metric: Delivers high data completeness with normalized fields suitable for analytics and automation.
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