metadata
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
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
# 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
top_10 = df.nlargest(10, 'market_cap')[['ticker', 'name', 'market_cap', 'sector']]
print(top_10)
Example: Company Size Analysis
# 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
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.
Citation
@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}
}
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