Metadata-Version: 2.1
Name: linkedin-jobs-scraper
Version: 1.0.5
Summary: Scrape public available jobs on Linkedin using headless browser
Home-page: https://github.com/spinlud/py-linkedin-jobs-scraper.git
Author: Ludovico Fabbri
Author-email: ludovico.fabbri@gmail.com
License: UNKNOWN
Description: # 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
        
        <!-- toc -->
        
        * [Requirements](#requirements)
        * [Installation](#installation)
        * [Usage](#usage)
        * [Anonymous vs authenticated session](#anonymous-vs-authenticated-session)
        * [Rate limiting](#rate-limiting)
        * [Filters](#filters)
        * [Company filter](#company-filter)
        * [Logging](#logging)
        * [License](#license)
        
        <!-- toc stop -->
        
        
        ## Requirements
        - [Chrome](https://www.google.com/intl/en_us/chrome/) or [Chromium](https://www.chromium.org/getting-involved/download-chromium)
        - [Chromedriver](https://chromedriver.chromium.org/)
        - Python >= 3.6
        
        
        ## Installation
        Install package:
        ```shell
        pip install linkedin-jobs-scraper
        ```
        
        
        ## Usage 
        ```python
        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:
        
        ![](https://github.com/spinlud/py-linkedin-jobs-scraper/raw/master/images/img3.png)
        
        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`.
        
        ![](https://github.com/spinlud/py-linkedin-jobs-scraper/raw/master/images/img4.png)
        
        4. Set the environment variable `LI_AT_COOKIE` with the value obtained in step 3, then run your application as normal.
        Example:
        
        ```shell script
        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:
        
        ```python
        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](https://www.linkedin.com/company/google)).
         3. Click on `jobs` from the left menu.
         
         ![](https://github.com/spinlud/py-linkedin-jobs-scraper/raw/master/images/img1.png)
        
         
         4. Scroll down and locate `See all jobs` or `See jobs` button.
         
         ![](https://github.com/spinlud/py-linkedin-jobs-scraper/raw/master/images/img2.png)
         
         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:
         
        ```python
        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:
        
        ```python
        import logging
        
        logging.getLogger().setLevel(logging.DEBUG)
        ```
        
        ## License
        [MIT License](http://en.wikipedia.org/wiki/MIT_License)
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
