FedComp Index Data
Pre-classified federal contractor datasets bundled for Python. Each contractor has a Posture Class (1-4) assigned by two-axis classification (total contract dollars x base contract count) from 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 classified contractors for a state
contractors = load_state("NV")
print(f"{len(contractors)} contractors classified")
# 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["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 |
Rank within the state (by total contract dollars) |
legal_name |
Registered legal name from SAM.gov |
uei |
Unique Entity Identifier |
cage |
CAGE code |
posture_class |
Posture Class: 1, 2, 3, or 4 (two-axis classification) |
total_dollars_5yr |
Total contract dollars (base + delivery orders) over trailing 5-year window |
base_contract_count |
Number of distinct base contracts |
award_count |
Total award count (including delivery/task orders) |
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 classification was computed |
Classification methodology (v1.1)
Two axes, no composite score, no weighted sum:
| Axis | Threshold | What it measures |
|---|---|---|
| Total contract dollars (5yr) | $5M | Volume of all obligated dollars (base + delivery orders) |
| Base contract count (5yr) | 3 | Frequency of distinct base contract wins |
| Posture Class | Volume | Frequency | Profile |
|---|---|---|---|
| Class 1 | $5M+ | 3+ contracts | Systematic winner |
| Class 2 | $5M+ | <3 contracts | Concentrated risk |
| Class 3 | <$5M | 3+ contracts | Growth pipeline |
| Class 4 | <$5M | <3 contracts | Entry level |
Full methodology: fedcompindex.org/methodology
Related packages
- fedcomp-index-scoring - classification 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 - classification engine
- fedcomp-index-data - pre-classified 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
- 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
Metadata
Release files for fedcomp-index-data 2026.4.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
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|---|---|---|---|
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|---|---|---|---|---|
| fedcomp_index_data-2026.4.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 78.0 kB
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