fedcomp-index-data 2026.3.4

Pre-scored federal contractor datasets with FedComp Index scores and posture class assignments. Updated monthly.

FedComp Index Data

Pre-scored federal contractor datasets bundled for Python. Each contractor has a FedComp Index score (0-100) and posture class assignment based on five years of USASpending award data.

Updated monthly with fresh data from the FedComp Index pipeline.

Install

pip install fedcomp-index-data

Usage

from fedcomp_index_data import load_state, lookup

# Load all scored contractors for a state
contractors = load_state("NV")
print(f"{len(contractors)} contractors scored")

# Filter by posture class
class_1 = [c for c in contractors if c["posture_class"] == "Class 1"]
print(f"{len(class_1)} Class 1 contractors")

# Look up a specific contractor by UEI
c = lookup("CGAKREGGN9J3")
print(c["legal_name"])      # FLEET VEHICLE SOURCE INC
print(c["fedcomp_index"])   # 81
print(c["posture_class"])   # Class 1

Available states

State	Contractors	Updated
Nevada (NV)	348	March 2026

More states are added as the FedComp Index expands coverage.

Data fields

Field	Description
rank	FedComp Index rank within the state
legal_name	Registered legal name from SAM.gov
uei	Unique Entity Identifier
cage	CAGE code
fedcomp_index	FedComp Index score (0-100)
posture_class	Posture class: Class 1 (60+), Class 2 (40-59), Class 3 (<40)
awards_5yr_total_usd	Total award dollars over trailing 5-year window
award_count	Number of distinct awards
active_contracts	Currently active contracts
last_award_date	Date of most recent award (YYYY-MM)
primary_naics	Primary NAICS code
top_agency	Most frequent awarding agency
city	Registered city
certifications	SBA certifications (pipe-separated)
state	State of registration
scored_date	Year-month this score was computed

FedComp Index scoring

Two drivers, no normalization:

Driver	Weight	How it works
Award volume	90%	log10 of total dollars won, mapped to 0-100
Award recency	10%	Last award date, bucketed by age

Posture classes are fixed thresholds:

• Class 1 - score 60+ (~$100M+ in awards)
• Class 2 - score 40-59 (~$5M-$100M)
• Class 3 - below 40

Full methodology: fedcompindex.org/wiki/methodology

Related packages

• fedcomp-index-scoring - scoring engine
• fedcomp-index - meta-package

Data sources

• USASpending.gov - award history
• SAM.gov - entity registration
• SBA.gov - certification verification

Also available on npm

npm install fedcomp-index-data

• fedcomp-index - meta-package
• fedcomp-index-scoring - scoring engine
• fedcomp-index-data - pre-scored datasets

Links

• Website: https://fedcompindex.org/
• Nevada Rankings: https://fedcompindex.org/nv/
• Methodology: https://fedcompindex.org/methodology/
• Tabularium: https://fedcompindex.org/tabularium/
• FAQ: https://fedcompindex.org/faq/
• Source: https://github.com/fedcompindex/FedCompIndex
• npm packages: https://github.com/fedcompindex/fedcomp-index-npm
• PyPI: https://pypi.org/project/fedcomp-index/
• PyPI (Scoring): https://pypi.org/project/fedcomp-index-scoring/
• PyPI (Data): https://pypi.org/project/fedcomp-index-data/
• HuggingFace: https://huggingface.co/datasets/npetro6/nevada-federal-contractors
• Kaggle: https://www.kaggle.com/datasets/npetro6/nevada-federal-contractors-fedcomp-index

License

MIT