How to Extract Linkedin Company Data Using Python?

LinkedIn is a professional career development platform. It is being used by millions of individuals to create their portfolios, showcase their skills, and make connections with office colleagues, friends, and family, and so on. LinkedIn is a powerful medium to apply for jobs, build a brand, research companies, and keep up with industry news.

Scraping LinkedIn company data will provide you bundle of valuable insights for improving lead generation, enhancing competitive analysis, and enhancing the recruitment process. If you are professionals, have a business in sales, market research firm, or recruitment consultancy, then this blog is for you. Here, you will be able to learn how to scrape LinkedIn company data using Python.

Why Extract LinkedIn Company Data?

Improving Decision-Making

Decisions, whether they are small or shrewd, are important for any entrepreneur. They can gather data from LinkedIn to make crucial decisions by examining it. This data can be about stakeholders and competitors can guide in investing more smartly.

Content Aggregation

Collecting content is very helpful for a market research firm, a publisher, and a news aggregator. Web scraping can be used effectively to present significant and informative content to your readers, which ultimately saves your time and money.

Marketing

Extracting LinkedIn company data can help you search for services or product-related data from LinkedIn for a diverse range of industries. Extracting data from this platform assists you in collecting information that can improve marketing strategies and connect with your desired clients.

Lead Generation

Web scraping is an essential tool for the sales team to generate targeted leads. Entrepreneurs can find out their potential clients and pull out their contact data by scraping websites, social media profiles, and forums. This means organizations can work productively to increase leads and conversion rates.

Why Use Python for Extracting Company Data From LinkedIn?

Python is a simple and easy-to-use tool for creating a Linkedin Data Scraper. It offers important libraries that can be used effectively for extracting data from any website, including LinkedIn. Developers use Python for a variety of reasons. If we compare Python with other languages, Perl and JavaScript, then Python is simple and easy to understand, as it is natively written in English.

Developers can just write Python code once and collect numerous data points from LinkedIn. This code can be iterated to get data from as many pages as the user wants. If someone performs this task manually, it will waste time and resources. Many times, the result may contain mistakes. Therefore, Python is a pivotal part of our discussion.

What Company Data Can You Extract from LinkedIn?

Company data that can be scraped from Linkedin is as follows:

• Company Name
• Funding Details
• Job Openings
• Industry
• Job Descriptions & Criteria
• Year Of Establishment
• Headquarters
• Employee Count

What Are the Most Effective Methods for Extracting Linkedin Company Data?

There are various methods for extracting LinkedIn company data:

Third-Party Scraping Tools

Many third-party tools are specifically available to extract company data from LinkedIn. These tools can automatically access the company's pages one by one and gather all the data. Some tools have a feature for collecting scraped data into extensive file formats such as CSV, Excel Spreadsheets, JSON, etc. Third-party tools have a user-friendly interface and can handle technical challenges like IP rotation to avoid being blocked and perform a seamless company data scraping process.

LinkedIn API

LinkedIn has its own API, especially for developers to access its data. If any developer wants to use this API, then they need to become a LinkedIn partner through LinkedIn’s Talent solution partnership, marketing developer program, etc. Once it is approved by the LinkedIn community, developers can utilize it to scrape data. Because this is the official method, you do not have to worry about your account being banned for pulling out data.

Custom Web Scraping

This method applies to people who have a deeper understanding of writing code in Python. If they want to extract desired data from LinkedIn, they can utilize Scrapy or BeautifulSoup to parse HTML tags. The primary benefit of this type of web scraper is that you can customize it based on the LinkedIn data you have to pull out.

Manual Data Extraction

This is one of the simplest methods to extract data from Linkedin. However, if you want to extract large datasets, then this method will not be worth it. This is mainly because you have to visit every LinkedIn page manually, copy the needed data, and paste it into an Excel sheet.

Steps to Extract LinkedIn Company Data Using Python

This is a complete blog post in which you will see how to use the Selenium and BeautifulSoup libraries for scraping data. To begin with, you have to install these libraries by entering the command mentioned below in the terminal:

pip install selenium pip install beautifulsoup4

Here, if you want to use selenium, then you need to install a web driver. You have to download and install the web drivers from Internet Explorer, Chrome, or Firefox. In this post, we'll use the Chrome web driver.

Now, you can perform the following steps to extract LinkedIn data:

Step 1: Logging in to LinkedIn

First of all, you will write code for login in HTML. For that, you need to initiate a web driver using Selenium and send a GET request to the URL and HTML file. This will help you to find out input tags and button tags that accept login credentials and the sign-in button.

from selenium import webdriver
from selenium.webdriver.common.by import By
from bs4 import BeautifulSoup
import time

driver = webdriver.Chrome("Enter-Location-Of-Your-Web-Driver")


driver.get("https://www.linkedin.com/login")

time.sleep(5)
username = driver.find_element(By.ID, "username")

username.send_keys("User_email")  

pword = driver.find_element(By.ID, "password")

pword.send_keys("User_pass")        

driver.find_element(By.XPATH, "//button[@type='submit']").click()

Step 2: Add Polite Delays

In the next step, you have to use short and random pauses between actions our code will perform for flawless execution.

import time, random

def jitter(a: float = 0.8, b: float = 1.8):
    """Polite randomized pause (seconds)."""
    time.sleep(random.uniform(a, b))

When your page is ready, you can use the jitter() function between clicks, requests, and navigation.

Step 3: Use WebDriverWait Over Fixed Sleep

You have to use WebDriverWait, which will wait for a specific condition.

from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

def wait_css_present(driver, css: str, timeout: int = 20):
    return WebDriverWait(driver, timeout).until(
        EC.presence_of_element_located((By.CSS_SELECTOR, css))
    )

def wait_css_visible(driver, css: str, timeout: int = 20):
    return WebDriverWait(driver, timeout).until(
        EC.visibility_of_element_located((By.CSS_SELECTOR, css))
    )

def click_when_clickable(driver, css: str, timeout: int = 15):
    el = WebDriverWait(driver, timeout).until(
        EC.element_to_be_clickable((By.CSS_SELECTOR, css))
    )
    el.click()
    return el

Step 4: Get HTML Tags and then parse with BeautifulSoup

Now, load the page and wait for the HTML tag. Once it is done, parse with BeautifulSoup.

from bs4 import BeautifulSoup
from drivers import make_chrome 
from waits import wait_css_present
from timing import jitter
def fetch_html(url: str, ready_css: str = "h1", headless: bool = True) -> str:
    d = make_chrome(headless=headless)      try:
        d.get(url)
        wait_css_present(d, ready_css, timeout=25)
        jitter()  
        return d.page_source
    finally:
        d.quit()
def parse_html_to_soup(html: str) -> BeautifulSoup:
    return BeautifulSoup(html, "lxml")

Step 5: Select Desired Fields

In this step, you will develop resilient selectors or fallbacks based on your webpage you are scraping. Press F12. It will open developer tools, prefer stable attributes.

from typing import Optional, Dict
from bs4 import BeautifulSoup
import re

def _first_text(soup: BeautifulSoup, selectors: list[str]) -> Optional[str]:
    for sel in selectors:
        el = soup.select_one(sel)
        if el:
            txt = el.get_text(" ", strip=True)
            if txt:
                return txt
    return None

def _first_href(soup: BeautifulSoup, selectors: list[str]) -> Optional[str]:
    for sel in selectors:
        el = soup.select_one(sel)
        if el and el.has_attr("href") and el["href"].strip():
            return el["href"].strip()
    return None

def _normalize_followers(txt: Optional[str]) -> Optional[int]:
    if not txt:
        return None
    m = re.search(r"([\d.,]+)\s*([kKmM])?", txt)
    if not m:
        return None
    num = float(m.group(1).replace(",", ""))
    suf = (m.group(2) or "").lower()
    if suf == "k": num *= 1_000
    if suf == "m": num *= 1_000_000
    return int(num)

SELECTORS = {
    "name":         ["h1[data-test='company-name']", "header h1", "h1"],
    "about":        ["[data-test='about']", "section.about-section"],
    "website":      ["a[data-test='company-website']", "a[href^='http']"],
    "industry":     ["[data-test='industry']", ".industry", "dt:contains('Industry') + dd"],
    "size":         ["[data-test='company-size']", ".company-size"],
    "headquarters": ["[data-test='hq']", ".headquarters"],
    "founded":      ["[data-test='founded']", ".founded"],
    "specialties":  ["[data-test='specialties']", ".specialties"],
    "followers":    ["[data-test='followers']", ".followers"],
}

def extract_company_fields(soup: BeautifulSoup, source_url: str) -> Dict:
    data = {
        "name":         _first_text(soup, SELECTORS["name"]),
        "about":        _first_text(soup, SELECTORS["about"]),
        "website":      _first_href(soup, SELECTORS["website"]),
        "industry":     _first_text(soup, SELECTORS["industry"]),
        "size":         _first_text(soup, SELECTORS["size"]),
        "headquarters": _first_text(soup, SELECTORS["headquarters"]),
        "founded":      _first_text(soup, SELECTORS["founded"]),
        "specialties":  _first_text(soup, SELECTORS["specialties"]),
        "followers":    _normalize_followers(_first_text(soup, SELECTORS["followers"])),
        "source_url":   source_url,
    }
    return {k: v for k, v in data.items() if v is not None}

Step 6: Export Company Data into CSV

You can export company data into files such as Spreadsheets, JSON, and CSV files. CSV is a plain text format and easy to scan compared to JSON and Spreadsheets; we will stick to CSV only.

import json, csv
from typing import List, Dict

def save_json(records: List[Dict], path: str) -> None:
    with open(path, "w", encoding="utf-8") as f:
        json.dump(records, f, ensure_ascii=False, indent=2)

def save_csv(records: List[Dict], path: str) -> None:
    keys = set()
    for r in records:
        keys.update(r.keys())
    fieldnames = ["source_url"] + sorted(k for k in keys if k != "source_url")
    with open(path, "w", newline="", encoding="utf-8") as f:
        w = csv.DictWriter(f, fieldnames=fieldnames)
        w.writeheader()
        for r in records:
            w.writerow(r)

You have now extracted the company’s name, about, website, industry, size, headquarters, founded, specialties, and followers & imported them into a CSV file.

Important Use Cases of Linkedin Data Extraction

Scraping LinkedIn company data can be used for multiple purposes, as shown below:

• Competitor Analysis: You can gather competitor data, such as sizes, services, and activities, to gain useful insights. By performing competitor analysis, you can find out where your organization is lacking and make strategies accordingly.
• Market Research: By extracting organization information such as locations, industries, and headquarters, you can analyze trends and find new opportunities or expand your business.
• Lead Generation: Use of company updates and follower count enables you to find growing organizations that might have an interest in your products or services.
• Sales Intelligence: Scraping company data will help you identify potential customers by collecting their information about their headquarters, contact details, specialities, and more.

Conclusion

In this blog, we saw how to extract LinkedIn company data using Python’s libraries BeautifulSoup and Selenium. We knew why to use the Python language to extract LinkedIn company data. You have gained deeper knowledge about the company data fields you can scrape from LinkedIn, use cases of it, and various other ways to scrape LinkedIn company data.

At Scraping Intelligence, we help you extract publicly available LinkedIn company data. Our AI-powered web scraping services provided by us not only help you collect insights from LinkedIn, but also analyze them to provide comprehensive and actionable insights. You can reach out to us if you want to grow your business in a competitive market landscape.

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About the author

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Zoltan Bettenbuk

Zoltan Bettenbuk is the CTO of ScraperAPI - helping thousands of companies get access to the data they need. He’s a well-known expert in data processing and web scraping. With more than 15 years of experience in software development, product management, and leadership, Zoltan frequently publishes his insights on our blog as well as on Twitter and LinkedIn.