Quote Scraper - Web Scraping Project

A Python-based web scraper for collecting quotes and author data from quotes.toscrape.com. This tool efficiently scrapes multiple pages, parses quote and author details, saves the results, analyzes them, and presents insights using a GUI.

🔧 Features

• Web Scraping:

  • Extracts quotes, authors, tags, and author profile links.
  • Follows pagination across all quote pages.
  • Respects robots.txt with delay and user-agent headers.

• Data Modeling:

  • Structured classes (Quote, Author) to hold scraped data.

• Data Analysis:

  • Finds most common tag.
  • Identifies most quoted author.
  • Calculates average number of tags per quote.

• Data Export:

  • Supports saving to both JSON and CSV.

• GUI Application:

  • Displays data in a table format.
  • Shows statistical analysis.
  • Visualizes top 10 tags using a bar graph.

📁 Project Structure

Python-Web-Scraping-Midterm-Project/
├── models/
│   └── data_models.py         # Quote and Author data classes
├── scraper/
│   ├── collector.py           # Handles all scraping logic
│   └── parser.py              # Parses HTML using BeautifulSoup
├── utils/
│   ├── analyzer.py            # Analyzes scraped quote data
│   ├── file_handler.py        # JSON/CSV read and write helpers
│   └── gui.py                 # Tkinter-based GUI to display and visualize
├── main.py                    # Main script to run scraping and saving
├── requirements.txt
└── README.md

📦 Installation

Clone the repository and install dependencies:

git clone https://github.com/yourusername/quote-scraper.git
cd Python-Web-Scraping-Midterm-Project
pip install -r requirements.txt

🚀 Usage

To run the scraper and save data:

python main.py

To launch the GUI:

python utils/gui.py

📊 Output

Data will be saved to the output/ directory as:

• quotes.json: All quotes in JSON format

• quotes.csv: Quotes exported to CSV

🛠️ Technologies Used

• requests: For making HTTP requests

• BeautifulSoup4: For parsing HTML

• tkinter: For GUI interface

• matplotlib: For data visualization

• pandas: For handling and displaying tabular data

📌 Notes

• Rate-limiting is handled with a 1-second delay between page requests.