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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - feature-extraction
+language:
+  - en
+tags:
+  - finance
+  - stocks
+  - companies
+  - fundamentals
+  - market-data
+pretty_name: US Public Company Facts
+size_categories:
+  - n<1K
+---
+
+# US Public Company Facts Dataset
+
+A comprehensive dataset of **441 major US public companies** with fundamental company information including sector, industry, market cap, employee count, and more.
+
+## Dataset Description
+
+This dataset provides essential company metadata for major publicly traded US companies, useful for financial analysis, company classification, and as features for machine learning models.
+
+### Key Statistics
+
+| Metric | Value |
+|--------|-------|
+| Total Companies | 441 |
+| Sectors | 12 |
+| Industries | 58 |
+| Exchanges | NASDAQ, NYSE |
+| Total Market Cap | $60.7 trillion |
+| Total Employees | 27.2 million |
+
+### Sectors Covered
+
+- Information Technology
+- Financials
+- Healthcare
+- Consumer Discretionary
+- Consumer Staples
+- Energy
+- Industrials
+- Materials
+- Real Estate
+- Utilities
+- Communication Services
+
+## Column Descriptions
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `ticker` | string | Stock ticker symbol |
+| `name` | string | Full company name |
+| `cik` | string | SEC Central Index Key |
+| `sector` | string | GICS sector classification |
+| `industry` | string | GICS industry classification |
+| `category` | string | Security type (Common Stock, ADR, etc.) |
+| `exchange` | string | Primary exchange (NASDAQ/NYSE) |
+| `is_active` | bool | Whether the company is actively trading |
+| `listing_date` | date | IPO / listing date |
+| `location` | string | Headquarters location |
+| `market_cap` | float | Market capitalization in USD |
+| `number_of_employees` | int | Total employee count |
+| `sec_filings_url` | string | Link to SEC EDGAR filings |
+| `sic_code` | string | Standard Industrial Classification code |
+| `sic_industry` | string | SIC industry description |
+| `sic_sector` | string | SIC sector description |
+| `website_url` | string | Company website |
+| `weighted_average_shares` | float | Weighted average shares outstanding |
+
+## Usage
+
+### Load with Hugging Face Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mdnh/us-company-facts")
+df = dataset['train'].to_pandas()
+
+print(f"Total companies: {len(df)}")
+print(f"Sectors: {df['sector'].nunique()}")
+```
+
+### Example: Filter by Sector
+
+```python
+# Get all tech companies
+tech = df[df['sector'] == 'Information Technology']
+print(f"Tech companies: {len(tech)}")
+print(f"Total tech market cap: ${tech['market_cap'].sum()/1e12:.1f}T")
+```
+
+### Example: Top Companies by Market Cap
+
+```python
+top_10 = df.nlargest(10, 'market_cap')[['ticker', 'name', 'market_cap', 'sector']]
+print(top_10)
+```
+
+### Example: Company Size Analysis
+
+```python
+# Employees per billion market cap
+df['employees_per_bn'] = df['number_of_employees'] / (df['market_cap'] / 1e9)
+
+# Most efficient by this metric
+efficient = df.nsmallest(10, 'employees_per_bn')[['ticker', 'name', 'employees_per_bn']]
+print(efficient)
+```
+
+### Example: Sector Breakdown
+
+```python
+sector_stats = df.groupby('sector').agg({
+    'ticker': 'count',
+    'market_cap': 'sum',
+    'number_of_employees': 'sum'
+}).rename(columns={'ticker': 'companies'})
+
+print(sector_stats.sort_values('market_cap', ascending=False))
+```
+
+## Use Cases
+
+- **Company Classification**: Train models to classify companies by sector/industry
+- **Feature Engineering**: Use as features for stock prediction models
+- **Portfolio Analysis**: Analyze sector exposure and diversification
+- **Screening**: Filter companies by size, sector, or other criteria
+- **Research**: Study industry composition and market structure
+
+## License
+
+This dataset is released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).
+
+## Citation
+
+```bibtex
+@dataset{company_facts_2026,
+  author = {mdnh},
+  title = {US Public Company Facts Dataset},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/mdnh/us-company-facts}
+}
+```
+
+## Related Datasets
+
+- [US Stock Insider Trades](https://huggingface.co/datasets/mdnh/insider-trades-us-stocks) - Insider trading transactions
+- [Hourly Stock Data 2023](https://huggingface.co/datasets/mdnh/hourly-stock-data-2023) - OHLCV + technical indicators