naukri-scraper 0.1.1

A tool for scraping job listings from Naukri.com

🚀 Naukri.com Job Scraper

A powerful Python package for scraping job listings from Naukri.com, allowing you to search for jobs by title and collect detailed information in a CSV format. Available as both a command-line tool and a Python library.

📋 Table of Contents

• Features
• Requirements
• Installation
• Usage
• Project Structure
• How It Works
• Sample Output
• Contributing
• Connect With Me
• License

✨ Features

• 🔍 Search for jobs by title on Naukri.com
• 📄 Export job listings to CSV files for easy analysis
• 📊 Extract comprehensive job details including:
  • Job title
  • Company name
  • Job description
  • Company logo URL
  • Experience requirements
  • Job location
  • Posted date
  • Salary information
  • Skills required
  • Job ID and URL
• 📱 User-friendly command-line interface
• 📃 Page navigation functionality

📦 Requirements

• Python 3.6+
• requests library
• Internet connection

💾 Installation

Using pip (recommended)

pip install naukri-scraper

From source

git clone https://github.com/pawankumar941394/naukri-scraper.git
cd naukri-scraper
pip install -e .

🚀 Usage

Method 1: Command Line Interface

# Interactive mode (will prompt for job title and page number)
naukri-scraper

# With command line arguments
naukri-scraper --title "Python Developer" --page 1

Method 2: Python Code - Interactive Mode

from naukri_scraper import scraper

# This will prompt for job title and page number
scraper.main()

Method 3: Python Code - Direct API

from naukri_scraper.scraper import scrape_jobs

# Define job search parameters
job_title = "Data Scientist"
page_number = 1

# Call the function to scrape jobs
result = scrape_jobs(job_title, page_number)

if result:
    job_count, csv_filename = result
    print(f"Found {job_count} jobs matching '{job_title}'")
    print(f"Results saved to: {csv_filename}")

Method 4: Advanced Usage with Data Analysis

import csv
import os
from naukri_scraper.scraper import scrape_jobs

# Get job data
job_title = "Machine Learning"
result = scrape_jobs(job_title, 1)

if result:
    job_count, csv_filename = result
    
    # Example: Analyze the data
    with open(csv_filename, 'r', encoding='utf-8') as f:
        reader = csv.DictReader(f)
        jobs = list(reader)
    
    # Get unique locations
    locations = set()
    for job in jobs:
        if 'Location' in job and job['Location'] != 'N/A':
            locations.add(job['Location'])
    
    print(f"Found jobs in {len(locations)} different locations")

📁 Package Structure

naukri_scraper/
├── __init__.py     # Package initialization
├── scraper.py      # Main scraping functionality
├── headers.py      # Contains headers configuration for HTTP requests
└── cookies.py      # Contains cookies configuration for HTTP requests

🔧 How It Works

1. The package sends an HTTP request to Naukri.com's API with your search query
2. It uses custom headers and cookies to simulate a browser session
3. The API response is parsed to extract job details
4. Job information is displayed in the terminal and saved to a CSV file
5. The CSV file can be further analyzed using Python's data analysis tools
6. You can search for multiple job titles by running the tool again

📊 Sample Output

The script generates a CSV file with the following columns:

• Title
• Company
• Job Description
• Logo URL
• Experience
• Location
• Posted Date
• Salary
• Job ID
• Job URL
• Skills

📈 Example Code

See EXAMPLES.md for more detailed examples of how to use this package in your projects.

💻 Complete Usage Guide

For a comprehensive guide on installation, usage, and advanced features, see USAGE_GUIDE.md.

👥 Contributing

Contributions are welcome! Feel free to submit pull requests or open issues to improve this project.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

🔗 Connect With Me

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Developed with ❤️ by Pawan Kumar