Scrapy Projects

This repository contains multiple Scrapy spider projects designed to scrape data from various retail websites. Each project has its own specific challenges and solutions, demonstrating the complexity of web scraping in different contexts.

Projects

1. Coop_Co Spider

Path: Coop_Co/Coop_Co/spiders/Data.py

Complexity: Handles dynamic web elements and multiple promotions within a single product listing.
Solution: Uses XPath to navigate and extract data, ensuring all relevant promotions are captured.
Challenge: Managing dynamic content and ensuring accurate extraction of nested promotional data.

2. SuperValu Spider

Path: SuperValu/SuperValu/spiders/Data.py

Complexity: Involves parsing XML responses and handling product ID duplication.
Solution: Utilizes BeautifulSoup for XML parsing and maintains a list to track processed product IDs.
Challenge: Ensuring no duplicate data is processed and managing complex XML structures.

3. Musgrave Spider

Path: Musgrave/Musgrave/spiders/products.py

Complexity: Requires authentication and handles pagination with large datasets.
Solution: Implements token-based authentication and processes paginated API responses efficiently.
Challenge: Managing session tokens and efficiently handling large volumes of data.

4. Parfetts Spider

Path: Parfetts/Parfetts/spiders/products.py

Complexity: Involves logging in with credentials and handling category-specific product data.
Solution: Uses JSON requests for logging in and retrieving category-specific products.
Challenge: Securing login credentials and accurately mapping product attributes to categories.

Key Features

Data Export: Each spider exports data to Excel or CSV formats for easy analysis.
Error Handling: Robust error handling mechanisms to ensure smooth execution and data integrity.
Logging: Detailed logging to track the scraping process and identify issues quickly.

Challenges and Solutions

Dynamic Content: Managed by using robust XPath/CSS selectors and handling JavaScript-rendered content.
Data Integrity: Ensured by tracking processed items and using structured data storage.
Authentication: Handled using session tokens and secure storage of credentials.

Getting Started

Clone the repository and install the required dependencies to start using the spiders:

git clone https://github.com/faisal-fida/Scrapy-Projects.git
cd Scrapy-Projects
pip install -r requirements.txt

Run a spider:

scrapy crawl Data

Each spider is configured to save the scraped data in the Output directory.

Conclusion

This repository showcases the versatility and complexity of web scraping using Scrapy. Each project addresses specific challenges with tailored solutions, providing robust and reliable data extraction capabilities.