BankScoop

BankScoop is a Python ETL (Extract, Transform, Load) project that scrapes the top global banks by market capitalization from Wikipedia (via Wayback Machine), converts the data into multiple currencies, stores it in a SQLite database, and visualizes the top 5 banks in a bar chart.

📁 Project Structure

BankScoop/
│
├── banks_project.py             # Main script
├── exchange_rate.csv            # Input file with exchange rates
├── data/
│   └── Largest_banks_data.csv   # Output: transformed CSV data
├── output/
│   └── top5_banks_chart.png     # Output: visualization chart
├── code_log.txt                 # Execution log
└── README.md                    # Project description and usage

🔧 Installation

To run the script:

1. Clone the repository

git clone https://github.com/yourusername/BankScoop.git
cd Bank_Scoop

2. Install required packages

pip install -r requirements.txt

Or manually:

pip install beautifulsoup4 requests pandas numpy matplotlib tabulate

💡 Tip: You can use a virtual environment:

python3 -m venv venv
source venv/bin/activate  # For Unix/macOS
venv\Scripts\activate     # For Windows

🚀 How to Run

python3 banks_project.py

This will:

• Scrape data from Wikipedia
• Apply exchange rates
• Save CSV to /data
• Insert into SQLite database
• Display and save visualization to /output

✅ Expected Output

The script logs the progress and prints a clean table like this:

📋 Top 10 Banks (Formatted Table):

╒═════════════════════════════════════════╤══════════════════╤══════════════════╤══════════════════╤══════════════════╕
│ Name                                    │   MC_USD_Billion │   MC_GBP_Billion │   MC_EUR_Billion │   MC_INR_Billion │
╞═════════════════════════════════════════╪══════════════════╪══════════════════╪══════════════════╪══════════════════╡
│ JPMorgan Chase                          │           432.92 │           346.34 │           402.62 │          35910.7 │
│ Bank of America                         │           231.52 │           185.22 │           215.31 │          19204.6 │
│ ...                                     │              ... │              ... │              ... │              ... │
╘═════════════════════════════════════════╧══════════════════╧══════════════════╧══════════════════╧══════════════════╛

It also saves a chart to output/top5_banks_chart.png like this:

📊 Top 5 Banks by Market Cap (USD)

And shows terminal logs and query results like the following:

📸 Formatted Table Screenshot

📸 SQL and Log Output

🧠 Skills Highlighted

• Python Web Scraping (BeautifulSoup)
• Data Transformation with Pandas & NumPy
• ETL Workflow Design
• SQLite Database Interaction
• Data Visualization with Matplotlib
• CLI Logging and Clean Output Formatting (tabulate)