Welcome to Web Scraping AtoZ, the ultimate guide to mastering web scraping. This repository is a comprehensive collection of best practices, techniques, and projects that cover a wide range of web scraping tasks using Python.
- Introduction
- Features
- Getting Started
- Projects and Notebooks
- Tools & Libraries
- Resources
- Contributing
- License
Web Scraping (also termed Screen Scraping, Web Data Extraction, Web Harvesting etc.) is the process of automating data extraction from websites. It's a crucial skill for data enthusiasts, researchers, and developers who need to gather large datasets from the web efficiently. This repo will help you learn web scraping techniques, including how to handle dynamic content, scrape different formats, and store data for analysis.
- 🤖 Automate web scraping tasks using Python.
- 🛠️ Practical examples and notebooks for different scraping scenarios.
- 🌐 Real-world projects covering static and dynamic content scraping.
- 📄 Learn how to scrape, parse, and store data from various formats (HTML, JSON, XML).
- 🔑 Best practices for ethical and legal web scraping.
To get started with Web Scraping AtoZ, you'll need a basic understanding of Python and web technologies like HTML and HTTP requests. Familiarity with libraries such as BeautifulSoup, Selenium, requests, and Scrapy is recommended but not required. The repo provides step-by-step instructions to help you set up your environment and start scraping.
Clone the repository and install the required libraries:
git clone https://github.com/sanikamal/web-scraping-atoz.git
cd web-scraping-atoz
pip install -r requirements.txt| Title | Description | Tools/Library | Link |
|---|---|---|---|
| Scraping Car Dealer Website | Demonstrates web scraping techniques to extract data from car dealer websites. Covers scraping a single page and multi-page | BeautifulSoup, Requests, Pandas, Web Scraping |
Notebook |
| Dealing with Multiple Pages | Shows how to scrape multiple pages of a website using pagination. Extracts data from tinydeal.com using Scrapy |
Scrapy, Web Scraping |
Project |
Feel free to explore these projects and notebooks to gain hands-on experience in web scraping and data extraction techniques.
This repo utilizes the following tools and libraries:
- BeautifulSoup: For parsing HTML and XML content.
- Selenium: For automating browsers to scrape dynamic content.
- Scrapy: A powerful framework for scraping large websites and handling complex scraping tasks.
- Requests: For sending HTTP requests and retrieving content.
- Pandas: For storing and analyzing the extracted data.
For additional tutorials, blog posts, research papers, data extraction, and ethical guidelines for web scraping, refer to the Resources file. Key topics include:
- Web scraping best practices and legal guidelines.
- Handling CAPTCHAs and anti-scraping measures.
- Scaling web scraping tasks using cloud services.
We welcome contributions to make this repo even more resourceful! If you have ideas for new scraping techniques, examples, or mini-projects, feel free to submit a pull request. You can also report bugs or suggest improvements by opening an issue.
This project is licensed under the MIT License. See the LICENSE file for more details.