iamramzan/Largest-Banks

Dataset Summary

This dataset contains information about the largest banks globally, including their rank, name, and total assets (in US$ billion as of 2023). The data was scraped from Wikipedia's List of Largest Banks. It can be used for financial analysis, market research, and educational purposes.

Dataset Structure

Columns

• Rank: The rank of the bank based on total assets.
• Bank Name: The name of the bank.
• Total Assets (2023, US$ billion): The total assets of the bank in billions of US dollars as of 2023.

Example

Rank	Bank Name	Total Assets (2023, US$ billion)
1	Industrial & Commercial Bank of China (ICBC)	5,000
2	China Construction Bank	4,500

Source

The data was scraped from Wikipedia's List of Largest Banks using Python and Scrapy.

Usage

This dataset can be used for:

• Financial market research.
• Trend analysis in global banking.
• Educational purposes and data visualization.

Licensing

The data is publicly available under Wikipedia's Terms of Use.

Limitations

• The data may not reflect real-time changes as it was scraped from a static page.
• Possible inaccuracies due to updates or inconsistencies on the source page.

Acknowledgements

Thanks to Wikipedia and the contributors of the "List of Largest Banks" page.

Citation

If you use this dataset, please cite it as:

@misc{largestbanks2023,
  author = {Your Name or Organization},
  title = {Largest Banks Dataset},
  year = {2023},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/your-dataset-name}
}

Who are the source Data producers ?

The data is machine-generated (using web scraping) and subjected to human additional treatment.

below, I provide the script I created to scrape the data (as well as my additional treatment):

import scrapy

class LargestBanksSpider(scrapy.Spider):
    name = "largest_banks"
    start_urls = ["https://en.wikipedia.org/wiki/List_of_largest_banks"]

    def parse(self, response):
        # Locate the table containing the data
        table = response.xpath("//table[contains(@class, 'wikitable')]")

        # Extract rows from the table
        rows = table.xpath(".//tr")

        for row in rows[1:]:  # Skip the header row
            rank = row.xpath(".//td[1]//text()").get()
            bank_name = row.xpath(".//td[2]//a/text() | .//td[2]//text()")
            total_assets = row.xpath(".//td[3]//text()").get()

            # Extract all text nodes for bank name and join them
            bank_name = ''.join(bank_name.getall()).strip() if bank_name else None

            if rank and bank_name and total_assets:
                yield {
                    "Rank": rank.strip(),
                    "Bank Name": bank_name,
                    "Total Assets (2023, US$ billion)": total_assets.strip()
                }