---
title: "Discussion on \"Scraping LinkedIn with Python in 2026: Profiles, Companies, and Jobs\" | Hashnode"
description: "Discussion on \"Scraping LinkedIn with Python in 2026: Profiles, Companies, and Jobs\". Practical Python scraping for LinkedIn profiles, companies, and jobs using curl_cffi, structured data, and the public jobs endpoint."
url: "https://hashnode.com/posts/scraping-linkedin-with-python-in-2026-profiles-companies-and-jobs-mt86yytc/6a8d204852998c9c0426bf8c"
---

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# [Scraping LinkedIn with Python in 2026: Profiles, Companies, and Jobs](https://blog.zyvop.com/scraping-linkedin-with-python-in-2026-profiles-companies-and-jobs-mt86yytc?utm_source=hashnode&utm_medium=feed)

Practical Python scraping for LinkedIn profiles, companies, and jobs using curl\_cffi, structured data, and the public jobs endpoint.

Aug 25 · 12 min read · blog.zyvop.com  
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## Written by

[ZyVOP](https://hashnode.com/@zyvop)

@zyvop · Delhi, India

  
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## Responses 2

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[OnlineProxy](https://hashnode.com/@onlineproxyio)

[Aug 25](https://hashnode.com/posts/scraping-linkedin-with-python-in-2026-profiles-companies-and-jobs-mt86yytc/6a8d204852998c9c0426bf8c/comment/6a8da82e86591ed0bb862ebe)

I'd patch the worksfor unwrapping first, it should handle list or dict or absent same as the company scraper already does for numberofemployees. Bigger problem honestly is get\_html treating anything outside 302, 429, 999 as success, a login or consent page comes back 200 and looks totally fine until you check for an actual name or title. And don't use headline for that check, tons of real profiles just leave it blank. Same deal on jobs, the count string breaks on stuff like 1234+ jobs formatting, so instead of patching the regex I'd just stop paginating once a page quits adding new normalized urls. Also you're dumping blocked, parse\_miss, and not\_a\_profile into one bucket, split those out because they're different failures and you want to see which one spikes when errors go up

[ZyVOP](https://hashnode.com/@zyvop)

[Aug 26](https://hashnode.com/posts/scraping-linkedin-with-python-in-2026-profiles-companies-and-jobs-mt86yytc/6a8d204852998c9c0426bf8c/comment/6a8da82e86591ed0bb862ebe#reply-6a8e7c9cc26476a9e6ab6c33)

Good catches. The `worksFor` normalization and response validation are definitely worth fixing. I also agree that pagination should be driven by newly discovered normalized URLs rather than trusting the displayed job count. Splitting `blocked`, `parse_miss`, and `not_a_profile` will make the failure metrics much more useful too. I’ll patch these in the next revision. Thanks for the detailed review.