Build Django Website Data Scraper
Budget / Salary$250–750
TypeFreelance project
LocationRemote
Posted2 hours ago
I need a Python developer to craft a self-contained scraper inside a lightweight Django app for our client’s own website. Phase 1 is all about reliability and clarity, not bells and whistles.
Scope of work
• Create a fresh Django project with a single command (e.g., python manage.py scrape) that navigates the distributed pages of the site, gathers the required text-based content, and captures any embedded timestamps or IDs that help with later analysis.
• Persist every record directly to the default SQLite database through Django models—no external queues, no Celery.
• Make the scraper idempotent: re-running it should update or skip existing entries rather than duplicate them.
• Keep the code modular and well-commented so we can swap in PostgreSQL and add scheduling in the next phase.
• Deliver a concise README that covers environment setup, model definitions, and the command’s expected runtime.
Success looks like running one command and ending up with a clean, query-ready dataset our analysts can immediately explore.
If you have proven experience with Django, BeautifulSoup, Requests or similar libraries, and can meet these requirements quickly and cleanly, let’s talk timeline and hand-off details.
Scope of work
• Create a fresh Django project with a single command (e.g., python manage.py scrape) that navigates the distributed pages of the site, gathers the required text-based content, and captures any embedded timestamps or IDs that help with later analysis.
• Persist every record directly to the default SQLite database through Django models—no external queues, no Celery.
• Make the scraper idempotent: re-running it should update or skip existing entries rather than duplicate them.
• Keep the code modular and well-commented so we can swap in PostgreSQL and add scheduling in the next phase.
• Deliver a concise README that covers environment setup, model definitions, and the command’s expected runtime.
Success looks like running one command and ending up with a clean, query-ready dataset our analysts can immediately explore.
If you have proven experience with Django, BeautifulSoup, Requests or similar libraries, and can meet these requirements quickly and cleanly, let’s talk timeline and hand-off details.
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