This project involves scraping book data from an online bookstore, Books to Scrape, to collect information such as book titles, prices, availability, and ratings. The data is then processed and analyzed to provide insights into pricing, availability, and ratings trends.
- Book Scraper: The scraper extracts book information such as Title, Price, Availability, and Ratings.
Once the data has been scraped, the analysis can be performed in the Jupyter Notebook. The notebook demonstrates how to:
- Load the scraped data into a Pandas DataFrame.
- Perform basic data cleaning (e.g., converting price to numeric, handling missing values).
- Create visualizations Eeg, bar charts, pie chart to analyze the relationship between book ratings, prices, and availability.
- Web Scraping: Collects books' titles, prices, availability, and ratings from the online bookstore.
- Data Analysis: Allows you to explore and visualize the scraped data.
- Visualizations: Offers multiple types of plots to analyze pricing trends, rating distribution, and availability.
- Bar Chart: Shows the average price of books grouped by ratings.
- Pie Chart: Displays the proportion of books in stock versus out of stock.
- Books to Scrape: This website is a valuable resource for practicing web scraping.
- BeautifulSoup: For parsing HTML and extracting data from the website.
- Pandas: For data manipulation and analysis.
- Matplotlib: For generating visualizations from the data.