This project demonstrates web scraping and data visualization using Python. The code scrapes book data (including image URL, rating, title, and price) from the "Books to Scrape" website and organizes it into a pandas DataFrame. After collecting the data, several visualizations are created to explore the distribution of ratings, prices, and title lengths, as well as the relationship between ratings and prices. The project also computes descriptive statistics for book prices grouped by rating.
- Web scraping of book data
- Data organization using pandas
- Visualization of ratings, prices, and title lengths
- Descriptive statistics grouped by rating
- Clone the repository.
- Install required dependencies.
- Run the Jupyter notebook to execute the scraping and visualization steps.
- Python
- BeautifulSoup
- pandas
- matplotlib
- seaborn
This repository is a practical example of combining web scraping with data analysis and visualization in Python, making it a useful reference for learning or teaching these skills.