Business Directory Data Extraction
Budget: $250 – $750 USD
I need a reliable scraper built (or an existing one configured) to pull a complete data set from a public business directory. The target site lists companies across multiple categories and paginated results.
Scope
• Collect every available business name with its full street address.
• Capture any listed contact information—email addresses and phone numbers.
• Record the directory’s stated business category or service tags for each listing.
Delivery
• Provide the compiled results in a clean, de-duplicated Excel spreadsheet with separate columns for each field.
• Include a short “read-me” explaining any assumptions, filters, or rate-limiting tactics you used. A reusable script (Python preferred) is welcome but not mandatory as long as I receive the complete, accurate data.
Quality expectations
• No missing rows for entries that visibly display the required fields.
• Consistent formatting (e.g., one phone number style, split address components if practical).
• Respect the site’s robots.txt and deploy polite scraping practices—delays, rotating headers, etc.—to avoid service disruption.
If you have prior experience scraping business directories and can turn this around quickly while meeting the above criteria, I’d love to review your approach and timeline.
Scope
• Collect every available business name with its full street address.
• Capture any listed contact information—email addresses and phone numbers.
• Record the directory’s stated business category or service tags for each listing.
Delivery
• Provide the compiled results in a clean, de-duplicated Excel spreadsheet with separate columns for each field.
• Include a short “read-me” explaining any assumptions, filters, or rate-limiting tactics you used. A reusable script (Python preferred) is welcome but not mandatory as long as I receive the complete, accurate data.
Quality expectations
• No missing rows for entries that visibly display the required fields.
• Consistent formatting (e.g., one phone number style, split address components if practical).
• Respect the site’s robots.txt and deploy polite scraping practices—delays, rotating headers, etc.—to avoid service disruption.
If you have prior experience scraping business directories and can turn this around quickly while meeting the above criteria, I’d love to review your approach and timeline.
Related categories:
Python
Data Processing
Excel
Web Scraping
Data Mining
Data Extraction
Data Collection
Data Management