Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- text-classification
+- translation
+- summarization
+- text-generation
+language:
+- en
+tags:
+- scrapy
+- pandas
+- datasets
+size_categories:
+- n<1K
+---
+## 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](https://en.wikipedia.org/wiki/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](https://en.wikipedia.org/wiki/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](https://foundation.wikimedia.org/wiki/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()
+                }