hn-whoshiring-analysis

Analysis of HackerNews Who's Hiring posts using Interactive Python

Related blog post

Quick steps on how to use

1. git clone git@github.com:iblaine/hn-whoshiring-analysis.git
2. cd hn-whoshiring-analysis
3. python -m venv env
4. source env/bin/activate
5. pip install -r requirements.txt
6. code hn_hiring_analysis.py
7. click "Run Cell"

What does this notebook do?

• Load HackerNews Who is hiring posts from Jan 2013 to July 2021
• Parse the data, add to a dict stored as hn_metrics.json
• Denormalize data into a dataframe, make the data available for analysis

Notes

• Every month has about 1,000 items
• The data is not structured very well, which limits a lot of analysis

Example of data produced by this analysis...

Walkthrough...

Sample data, in case you want a quick look at what may be available

[
   "2021-06-01"
]{
   "company_names":{
      "Tourmaline Labs":1,
      "Dr. Bill / RBC Ventures":1,
      "Flowdash":1,
      "Koddi":1,
      "HVN":1,
      "Sparrow":1
   },
   "locations":{
      "Senior Back End and Mobile":1,
      "Senior Software Engineers, Tech Leads, Engineering Managers, Product Designers":1,
      "Software Developer":1,
      "San Francisco Bay Area":1,
      "Frontend Tech Lead, Full-Stack, Backend":1
   },
   "position_types":{
      "ML & Backend engineers":1,
      "Remote (EU) or Onsite (Berlin, Germany)":1,
      "Full-time, REMOTE in US":1,
      "Australia, Remote":1,
      "NYC":3,
      "REMOTE - US ONLY":1,
      "React, TypeScript, Tailwind, C#, React Native - both local and remote (Lancaster, PA":1,
      "Sarasota, FL":1
   },
   "position_names":{
      "<a href=\"https://vertexvis.com\" rel=\"nofollow\">https://vertexvis.com</a>":1,
      "ONSITE (post-covid)":2,
      "Software Developer":1,
      "Portland or Remote":1,
      "<a href=\"http://synth.earth\" rel=\"nofollow\">http://synth.earth</a>":1,
      "\\"
   },
   "search_results":{
      "data engineer":{
         "cnt_total":91,
         "cnt_unique":72
      },
      "software engineer":{
         "cnt_total":626,
         "cnt_unique":369
      },
      "full stack":{
         "cnt_total":174,
         "cnt_unique":131
      },
      "fullstack":{
         "cnt_total":77,
         "cnt_unique":59
      }
   }
}