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import requests
pip install beautifulsoup4
from bs4 import BeautifulSoup
import pandas as pd
def get_job_details(job_url):
response = requests.get(job_url)
if response.status_code == 200:
soup = BeautifulSoup(response.text, 'html.parser')
# Extract job description
description_elem = soup.find('div', {'class': 'show-more-less-html__markup'})
job_description = description_elem.text.strip() if description_elem else ''
# Extract skills
skills_elem = soup.find('div', {'class': 'job-criteria__list'})
skills = [skill.text.strip() for skill in skills_elem.find_all('span', {'class': 'job-criteria__text'})] if skills_elem else []
return job_description, skills
else:
return '', []
url = 'https://www.linkedin.com/jobs/search/?currentJobId=3834896045&f_WT=3%2C1%2C2&geoId=102713980&keywords=power%20bi%20developer&location=&origin=JOB_SEARCH_PAGE_KEYWORD_AUTOCOMPLETE&refresh=true&sortBy=R'
response = requests.get(url)
def scrap_data(url):
response = requests.get(url)
if response.status_code == 200:
# print("scrapping now")
soup = BeautifulSoup(response.text, 'html.parser')
job_listings = soup.find_all('div', {'class':'job-search-card'})
# Initialize lists to store job data
titles = []
companies = []
locations = []
links = []
descriptions = []
skills_list = []
for job in job_listings:
title = job.find('h3', {'class': 'base-search-card__title'}).text.strip()
company = job.find('a', {'class': 'hidden-nested-link'}).text.strip()
location = job.find('span', {'class': 'job-search-card__location'}).text.strip()
anchor_tag = job.find('a', class_='base-card__full-link')
href_link = anchor_tag['href']
# Fetch job details
job_description, skills = get_job_details(href_link)
# Append data to lists
titles.append(title)
companies.append(company)
locations.append(location)
links.append(href_link)
descriptions.append(job_description)
skills_list.append(', '.join(skills))
# Create DataFrame from lists
df = pd.DataFrame({
'Title': titles,
'Company': companies,
'Location': locations,
'Job Link': links,
'Job Description': descriptions,
'Skills': skills_list
})
# Save DataFrame to CSV
# df.to_csv('output/linkedinjobs.csv', index=False, encoding='utf-8')
# print("Job listings saved to linkedinjobs.csv")
return df
else:
print("Failed to fetch job listings.")
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