mdnh/us-company-facts

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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