Blog
Python tutorials and analysis guides for our government datasets — from loading raw data to building production ML models.
Learn how to load and analyze 49 years of US fatal crash data from NHTSA FARS using Python and pandas. Discover fatality trends, DUI patterns, and geospatial hotspots.
Use 2.2M NHTSA vehicle complaint narratives to build an NLP defect detection model in Python. Cluster complaint text, predict recall likelihood, and profile high-risk makes.
Join six verified NTSB tables, use explicit coordinate quality flags, and count recorded events without confusing them with exposure-adjusted accident rates.
Load the verified EPA TRI 2022–2023 snapshot with Python. Preserve units, distinguish Form A zeroes, and join facility attribute versions without duplicating reports.
Load and analyze 20+ years of New Jersey traffic crash records using Python and pandas. Discover injury hotspots, seasonal patterns, and road-type risk factors in NJ's public crash dataset.
Use Python to analyze 400K+ NHTSA vehicle recall campaigns. Discover the brands, components, and model years most affected by safety recalls from 1966 to today.
Analyze 14M+ CFPB consumer financial complaints with Python. Discover which products generate the most complaints, which companies top the list, and how NLP unlocks the free-text narratives.
Analyze all 84 monthly BTS reporting-carrier files with explicit arrival-delay denominators and preserved cancellation/diversion outcomes.
Explore 75 years of US storm damage data with Python. Analyze property and crop losses by event type, decade, and state using the NOAA Storm Events Database — 2M+ events from 1950 to present.
Analyze 4M+ OSHA establishment injury records with Python. Benchmark DART and TCIR rates by industry, track year-over-year safety trends, and identify the highest-risk sectors using the OSHA ITA dataset.
Explore 341K+ FAA wildlife strike reports covering 1990–2025 with Python. Identify the riskiest airports, most dangerous species, and costliest strike phases using the cleaned ClarityStorm FAA Wildlife Strikes dataset.
Inspect 2,725,989 redacted NFIP v3 records while preserving missing payments and geographic limitations.
Analyze 4,173,884 USDA crop insurance summary records with corrected financial fields, exact decimal amounts, and explicit source limitations in Python.
Explore 18 years of county-level death rates across 3,100+ US counties. Identify geographic mortality disparities and compute population-adjusted trends in Python.