Company directories
Extract agreed business fields from accessible directories and company listings when collection is permitted.
PYTHON · WEB SCRAPING · DATA EXTRACTION
Web scraping & data extraction
Collect structured information from permitted web sources instead of copying records manually. Each project begins with a review of source access, required fields, data quality and expected output.
What can be collected
Product catalogues
Prepare structured product names, categories, attributes and other agreed fields from permitted sources.
Publicly available tables
Convert accessible web tables and structured listings into consistent datasets.
Repeated data updates
Design repeatable collection and validation workflows when source conditions and update frequency allow them.
How a scraping project works
01
Review sources
Check the source URLs, access conditions, available APIs and required permissions.
02
Define fields
Agree on the data columns, record identifiers and handling of missing values.
03
Extract and validate
Collect permitted records, normalize fields and flag duplicates or incomplete entries.
04
Export and review
Prepare the agreed CSV or Excel output and summarize extraction limitations.
Example: extracting a structured catalogue
Illustrative example with fictional products. No live website was scraped for this demonstration.
Source listing
| Source listing | Price |
|---|---|
| Item A — Tools — Code T-101 | €12.50 |
| Item B — Supplies — Code S-204 | €8.00 |
Structured output
| Code | Name | Category | Price |
|---|---|---|---|
| T-101 | Item A | Tools | 12.50 |
| S-204 | Item B | Supplies | 8.00 |
The example illustrates field extraction and normalization; actual availability depends on each permitted source.
Access, quality checks and deliverables
Before development, we confirm whether automated access is allowed, whether a documented API is preferable, which fields are needed and how incomplete or changed pages should be handled. Authentication requirements, rate limits and applicable restrictions are reviewed before collection.
Depending on scope, deliverables may include a Python extraction script, CSV or XLSX files, field-validation results and run instructions. Data completeness and repeatability depend on source availability and permitted access.
Discuss a data extraction task
Source URLs, permissions, expected record counts, sample output, update frequency, acceptance criteria, schedule and price are agreed before implementation.
Discuss a web scraping project