Scrape public available jobs on Linkedin using headless browser. For each job, the following fields are extracted:
job_id
,link
,apply_link
,title
,company
,place
,description
,description_html
,date
,seniority_level
,job_function
,employment_type
,industries
.
- Requirements
- Installation
- Usage
- Anonymous vs authenticated session
- Rate limiting
- Filters
- Company filter
- Logging
- License
- Chrome or Chromium
- Chromedriver
- Python >= 3.6
Install package:
pip install linkedin-jobs-scraper
import logging
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
# Change root logger level (default is WARN)
logging.basicConfig(level = logging.INFO)
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
headless=True, # Overrides headless mode only if chrome_options is None
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)
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:
- Login to LinkedIn using an account of your choice.
- Open Chrome developer tools:
- Go to tab
Application
, then from left panel selectStorage
->Cookies
->https://www.linkedin.com
. In the main view locate row with nameli_at
and copy content from the columnValue
.
- 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
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.
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],
)
)
)
It is also possible to filter by company using the public company jobs url on LinkedIn. To find this url you have to:
- Login to LinkedIn using an account of your choice.
- Go to the LinkedIn page of the company you are interested in (e.g. https://www.linkedin.com/company/google).
- Click on
jobs
from the left menu.
- Scroll down and locate
See all jobs
orSee jobs
button.
- Right click and copy link address (or navigate the link and copy it from the address bar).
- 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',
)
)
)
Package logger can be retrieved using namespace li:scraper
. Default level is INFO
.
It is possible to change logger level using environment variable LOG_LEVEL
or in code:
import logging
# Change root logger level (default is WARN)
logging.basicConfig(level = logging.DEBUG)
# Change package logger level
logging.getLogger('li:scraper').setLevel(logging.DEBUG)
# Optional: change level to other loggers
logging.getLogger('urllib3').setLevel(logging.WARN)
logging.getLogger('selenium').setLevel(logging.WARN)