linkedin-jobs-scraper 1.0.4

Scrape public available jobs on Linkedin using headless browser

linkedin-jobs-scraper

Scrape public available jobs on Linkedin using headless browser. For each job, the following fields are extracted: link, apply_link, title, company, place, description, description_html, date, seniority_level, job_function, employment_type, industries.

Table of Contents

• Requirements
• Installation
• Usage
• Anonymous vs authenticated session
• Rate limiting
• Filters
• Company filter
• Logging
• License

Requirements

• Chrome or Chromium
• Chromedriver
• Python >= 3.6

Installation

Install package:

pip install linkedin-jobs-scraper

Usage

from linkedin_jobs_scraper import LinkedinScraper
from linkedin_jobs_scraper.events import Events, EventData
from linkedin_jobs_scraper.query import Query, QueryOptions, QueryFilters
from linkedin_jobs_scraper.filters import RelevanceFilters, TimeFilters, TypeFilters, ExperienceLevelFilters


def on_data(data: EventData):
    print('[ON_DATA]', data.title, data.company, data.date, data.link, len(data.description))


def on_error(error):
    print('[ON_ERROR]', error)


def on_end():
    print('[ON_END]')


scraper = LinkedinScraper(
    chrome_options=None,  # You can pass your custom Chrome options here
    max_workers=1,  # How many threads will be spawned to run queries concurrently (one Chrome driver for each thread)
    slow_mo=0.4,  # Slow down the scraper to avoid 'Too many requests (429)' errors
)

# Add event listeners
scraper.on(Events.DATA, on_data)
scraper.on(Events.ERROR, on_error)
scraper.on(Events.END, on_end)

queries = [
    Query(
        options=QueryOptions(
            optimize=True,  # Blocks requests for resources like images and stylesheet
            limit=27  # Limit the number of jobs to scrape
        )
    ),
    Query(
        query='Engineer',
        options=QueryOptions(
            locations=['United States'],
            optimize=False,
            limit=5,
            filters=QueryFilters(
                company_jobs_url='https://www.linkedin.com/jobs/search/?f_C=1441%2C17876832%2C791962%2C2374003%2C18950635%2C16140%2C10440912&geoId=92000000',  # Filter by companies
                relevance=RelevanceFilters.RECENT,
                time=TimeFilters.MONTH,
                type=[TypeFilters.FULL_TIME, TypeFilters.INTERNSHIP],
                experience=None,
            )
        )
    ),
]

scraper.run(queries)

Anonymous vs authenticated session

By default the scraper will run in anonymous mode (no authentication required). In some environments (e.g. AWS or Heroku) this may be not possible though. You may face the following error message:

Scraper failed to run in anonymous mode, authentication may be necessary for this environment.

In that case the only option available is to run using an authenticated session. These are the steps required:

1. Login to LinkedIn using an account of your choice.
2. Open Chrome developer tools:

3. Go to tab Application, then from left panel select Storage -> Cookies -> https://www.linkedin.com. In the main view locate row with name li_at and copy content from the column Value.

4. Set the environment variable LI_AT_COOKIE with the value obtained in step 3, then run your application as normal. Example:

LI_AT_COOKIE=<your li_at cookie value here> python your_app.py

Rate limiting

You may experience the following rate limiting warning during execution:

[429] Too many requests. You should probably increase scraper "slow_mo" value or reduce concurrency.

This means you are exceeding the number of requests per second allowed by the server (this is especially true when using authenticated sessions where the rate limits are much more strict). You can overcome this by:

• Trying a higher value for slow_mo parameter (this will slow down scraper execution).
• Reducing the value of max_workers to limit concurrency. I recommend to use no more than one worker in authenticated mode.

Filters

It is possible to customize queries with the following filters:

• RELEVANCE:
  • RELEVANT
  • RECENT
• TIME:
  • DAY
  • WEEK
  • MONTH
  • ANY
• TYPE:
  • FULL_TIME
  • PART_TIME
  • TEMPORARY
  • CONTRACT
• EXPERIENCE LEVEL:
  • INTERNSHIP
  • ENTRY_LEVEL
  • ASSOCIATE
  • MID_SENIOR
  • DIRECTOR

See the following example for more details:

from linkedin_jobs_scraper.query import Query, QueryOptions, QueryFilters
from linkedin_jobs_scraper.filters import RelevanceFilters, TimeFilters, TypeFilters, ExperienceLevelFilters


query = Query(
    query='Engineer',
    options=QueryOptions(
        locations=['United States'],
        optimize=False,
        limit=5,
        filters=QueryFilters(            
            relevance=RelevanceFilters.RECENT,
            time=TimeFilters.MONTH,
            type=[TypeFilters.FULL_TIME, TypeFilters.INTERNSHIP],
            experience=[ExperienceLevelFilters.INTERNSHIP, ExperienceLevelFilters.MID_SENIOR],
        )
    )
)

Company Filter

It is also possible to filter by company using the public company jobs url on LinkedIn. To find this url you have to:

1. Login to LinkedIn using an account of your choice.
2. Go to the LinkedIn page of the company you are interested in (e.g. https://www.linkedin.com/company/google).
3. Click on jobs from the left menu.

4. Scroll down and locate See all jobs or See jobs button.

5. Right click and copy link address (or navigate the link and copy it from the address bar).
6. Paste the link address in code as follows:

query = Query(    
    options=QueryOptions(        
        filters=QueryFilters(
            # Paste link below
            company_jobs_url='https://www.linkedin.com/jobs/search/?f_C=1441%2C17876832%2C791962%2C2374003%2C18950635%2C16140%2C10440912&geoId=92000000',        
        )
    )
)

Logging

By default the logging level is INFO. You can override it as usual:

import logging

logging.getLogger().setLevel(logging.DEBUG)

License

MIT License