https://github.com/invictusaman/glassdoor-webscraper

I designed a scraping 🕸️ tool to extract job posting data from Glassdoor. This scraping tool will return job title, company name, job id, location, salary, language and skills and many more.
https://github.com/invictusaman/glassdoor-webscraper

data-analyst data-collection glassdoor glassdoor-scraper python3 webscraping

Last synced: 3 days ago
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I designed a scraping 🕸️ tool to extract job posting data from Glassdoor. This scraping tool will return job title, company name, job id, location, salary, language and skills and many more.

• Host: GitHub
• URL: https://github.com/invictusaman/glassdoor-webscraper
• Owner: invictusaman
• License: mit
• Created: 2024-08-25T03:54:57.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-08-25T03:59:30.000Z (3 months ago)
• Last Synced: 2024-08-25T04:42:41.026Z (3 months ago)
• Topics: data-analyst, data-collection, glassdoor, glassdoor-scraper, python3, webscraping
• Language: Python
• Homepage:
• Size: 0 Bytes
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Glassdoor Scraper

I designed a scraping 🕸️ tool to extract job posting data from Glassdoor. This scraping tool will return job title, company name, job id, location, salary, language and skills and many more.

*It was easier to extract Glassdoor data compared to Indeed because job postings in Glassdoor are organized, properly labelled and glassdoor also provides estimated salary (if not present).*

**Thank you Glassdoor**




## Step 1: Install dependencies

Install required dependencies in your project folder.

```

pip install -r requirements.txt

```




## Step 2: Run Glassdoor_Scraper.py

Make sure you have Chrome ⬇️ latest version installed in your system. This step creates `scraped_glassdoor_job_file.csv` with all columns. *You can check the sample output in this repository itself, I extracted for Data Analyst Position in Canada.*




### Further Work:

Currently, only one web address can be processed during each run. Create a list of different addresses, and pass the index value; the tool should fetch each url one by one, and scrap accordingly, and create a final output or multiple outputs.

#### Follow my data-analyst journey: [Portfolio_Link](https://www.amanbhattarai.com)