scrape_linkedin

Introduction

scrape_linkedin is a python package to scrape all details from public LinkedIn profiles, turning the data into structured json. You can scrape Companies and user profiles with this package.

Warning: LinkedIn has strong anti-scraping policies, they may blacklist ips making unauthenticated or unusual requests

Table of Contents

• scrape_linkedin
  • Introduction
  • Table of Contents
  • Installation
  • Install with pip
  • Install from source
  • Tests
  • Getting & Setting LI_AT
  • Getting LI_AT
  • Setting LI_AT
  • Examples
  • Usage
  • Command Line
  • Python Package
    • Profiles
    • Companies
    • config
  • Scraping in Parallel
  • Example
  • Configuration
  • Issues

Installation

Install with pip

Run pip install git+git://github.com/austinoboyle/scrape-linkedin-selenium.git

Install from source

git clone https://github.com/austinoboyle/scrape-linkedin-selenium.git

Run python setup.py install

Tests

Tests are (so far) only run on static html files. One of which is a linkedin profile, the other is just used to test some utility functions.

Getting & Setting LI_AT

Because of Linkedin's anti-scraping measures, you must make your selenium browser look like an actual user. To do this, you need to add the li_at cookie to the selenium session.

Getting LI_AT

1. Navigate to www.linkedin.com and log in
2. Open browser developer tools (Ctrl-Shift-I or right click -> inspect element)
3. Select the appropriate tab for your browser (Application on Chrome, Storage on Firefox)
4. Click the Cookies dropdown on the left-hand menu, and select the www.linkedin.com option
5. Find and copy the li_at value

Setting LI_AT

There are two ways to set your li_at cookie:

1. Set the LI_AT environment variable
   • $ export LI_AT=YOUR_LI_AT_VALUE
   • On Windows: C:/foo/bar> set LI_AT=YOUR_LI_AT_VALUE
2. Pass the cookie as a parameter to the Scraper object.

   >>> with ProfileScraper(cookie='YOUR_LI_AT_VALUE') as scraper:

A cookie value passed directly to the Scraper will override your environment variable if both are set.

Examples

See /examples

Usage

Command Line

scrape_linkedin comes with a command line argument module scrapeli created using click.

Note: CLI only works with Personal Profiles as of now.

Options:

• --url : Full Url of the profile you want to scrape
• --user: www.linkedin.com/in/USER
• --driver: choose Browser type to use (Chrome/Firefox), default: Chrome
• -a --attribute : return only a specific attribute (default: return all attributes)
• -i --input_file : Raw path to html file of the profile you want to scrape
• -o --output_file: Raw path to output file for structured json profile (just prints results by default)
• -h --help : Show this screen.

Examples:

• Get Austin O'Boyle's profile info: $ scrapeli --user=austinoboyle
• Get only the skills of Austin O'Boyle: $ scrapeli --user=austinoboyle -a skills
• Parse stored html profile and save json output: $ scrapeli -i /path/file.html -o output.json

Python Package

Profiles

Use ProfileScraper component to scrape profiles.

from scrape_linkedin import ProfileScraper

with ProfileScraper() as scraper:
    profile = scraper.scrape(user='austinoboyle')
print(profile.to_dict())

Profile - the class that has properties to access all information pulled from a profile. Also has a to_dict() method that returns all of the data as a dict

with open('profile.html', 'r') as profile_file:
    profile = Profile(profile_file.read())

print (profile.skills)
# [{...} ,{...}, ...]
print (profile.experiences)
# {jobs: [...], volunteering: [...],...}
print (profile.to_dict())
# {personal_info: {...}, experiences: {...}, ...}

Structure of the fields scraped

• personal_info
  • name
  • company
  • school
  • headline
  • followers
  • summary
  • websites
  • email
  • phone
  • connected
  • image
• skills
• experiences
  • volunteering
  • jobs
  • education
• interests
• accomplishments
  • publications
  • cerfifications
  • patents
  • courses
  • projects
  • honors
  • test scores
  • languages
  • organizations

Companies

Use CompanyScraper component to scrape companies.

from scrape_linkedin import CompanyScraper

with CompanyScraper() as scraper:
    company = scraper.scrape(company='facebook')
print(company.to_dict())

Company - the class that has properties to access all information pulled from a company profile. There will be three properties: overview, jobs, and life. Overview is the only one currently implemented.

with open('overview.html', 'r') as overview,
    open('jobs.html', 'r') as jobs,
    open('life.html', 'r') as life:
        company = Company(overview, jobs, life)

print (company.overview)
# {...}

Structure of the fields scraped

• overview
  • name
  • company_size
  • specialties
  • headquarters
  • founded
  • website
  • description
  • industry
  • num_employees
  • type
  • image
• jobs NOT YET IMPLEMENTED
• life NOT YET IMPLEMENTED

config

Pass these keyword arguments into the constructor of your Scraper to override default values. You may (for example) want to decrease/increase the timeout if your internet is very fast/slow.

• cookie {str}: li_at cookie value (overrides env variable)
  • default: None
• driver {selenium.webdriver}: driver type to use
  • default: selenium.webdriver.Chrome
• _driveroptions {dict}: kwargs to pass to driver constructor
  • default: {}
• _scrollpause {float}: time(s) to pause during scroll increments
  • default: 0.1
• _scrollincrement {int} num pixels to scroll down each time
  • default: 300
• timeout {float}: default time to wait for async content to load
  • default: 10

Scraping in Parallel

New in version 0.2: built in parallel scraping functionality. Note that the up-front cost of starting a browser session is high, so in order for this to be beneficial, you will want to be scraping many (> 15) profiles.

Example

from scrape_linkedin import scrape_in_parallel, CompanyScraper

companies = ['facebook', 'google', 'amazon', 'microsoft', ...]

#Scrape all companies, output to 'companies.json' file, use 4 browser instances
scrape_in_parallel(
    scraper_type=CompanyScraper,
    items=companies,
    output_file="companies.json",
    num_instances=4
)

Configuration

Parameters:

• _scrapertype {scrape_linkedin.Scraper}: Scraper to use
• items {list}: List of items to be scraped
• _outputfile {str}: path to output file
• _numinstances {int}: number of parallel instances of selenium to run
• _tempdir {str}: name of temporary directory to use to store data from intermediate steps
  • default: 'tmp_data'
• driver {selenium.webdriver}: driver to use for scraping
  • default: selenium.webdriver.Chrome
• _driveroptions {dict}: dict of keyword arguments to pass to the driver function.
  • default: scrape_linkedin.utils.HEADLESS_OPTIONS
• **kwargs {any}: extra keyword arguments to pass to the scraper_type constructor for each job

Issues

Report bugs and feature requests here.