US Firearm Background Checks (FBI NICS)
The FBI's National Instant Criminal Background Check System (NICS) monthly firearm background-check counts for every U.S. state and territory, back to the system's launch in November 1998 - one of the most-watched high-frequency proxies for U.S. retail firearm demand and consumer sentiment. Parsed directly from the FBI's official monthly PDF (U.S. public domain), reconciled row-by-row against the published grand totals, and delivered as one tidy Parquet panel: 50 states + DC + Guam, Puerto Rico, the U.S. Virgin Islands and the Northern Mariana Islands, ~330 months per state. Note the FBI's own caveat: a background check is not a one-to-one firearm sale.
- Source
- NICS
- Licence
- Redistributable open data
- Updates
- Monthly
- Data through
- 2026-06-01
- Quality
- QC clean · 0 failures
- Format
- Apache Parquet + dictionary
Safe to train on
Built only from a named, redistributable official source under a documented open licence - not scraped web data. No copyright grey zone, no personal data. Ships machine-readable Croissant metadata (ML Commons - loads in Hugging Face / Kaggle / Google), an AI training-licence manifest documenting source, licence and provenance for your model's data governance, and a machine-readable data dictionary (drop it into an agent / RAG prompt so the model knows every column) - all inspectable before you buy.
What's included
- Official FBI NICS counts - U.S. public domain, fully redistributable (parsed from the FBI PDF, not a 3rd-party CSV)
- Monthly, every U.S. state + DC + 4 territories, November 1998 to present
- The headline retail firearm-demand / consumer-sentiment proxy used across economics & finance
- Row-by-row reconciled to the FBI's printed grand totals; self-refreshing monthly
- One row per state x month; joins cleanly to macro / retail / sentiment series
- Source: FBI National Instant Criminal Background Check System (NICS) - public domain
Schema
| Column | Type | Description | Filled | Distinct |
|---|---|---|---|---|
| zone | str | 2-letter US state / territory code (AL..WY, DC, GU, PR, VI, MP) | 100.0% | 55 |
| ts | timestamp (UTC) | First of the month | 100.0% | 332 |
| checks | int | Number of NICS firearm background checks initiated that month | 100.0% | 14,695 |
| unit | str | Unit (checks) | 100.0% | 1 |
| res_min | int | Native resolution (43200 = monthly) | 100.0% | 1 |
Sample & preview
Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: NICS (redistributable open data; attribution passes through - see our Licence).
Don't trust screenshots - drop the sample into your notebook right now.
import pandas as pd df = pd.read_parquet("us-firearm-background-checks_sample.parquet") df.info() # typed columns, gap-aware, ready to join⬇ Download free Parquet sample or CSV for Excel
Coverage: 55 zones
AK · AL · AR · AZ · CA · CO · CT · DC · DE · FL · GA · GU · HI · IA · ID · IL · IN · KS · KY · LA · MA · MD · ME · MI · MN · MO · MP · MS · MT · NC · ND · NE · NH · NJ · NM · NV · NY · OH · OK · OR · PA · PR · RI · SC · SD · TN · TX · UT · VA · VI · VT · WA · WI · WV · WY
Related datasets
Commitments of Traders (COT) Historical Archive
Weekly CFTC Commitments of Traders positioning across every futures market - open interest, commercial (hedger) and non-commercial (speculator) long/short, in contracts. The raw CFTC drops are messy flat files; this is one clean, continuous, per-market Parquet.
COT Extreme Crowding Alerts Database
Every instance where speculators reached an extreme in a futures market - net-positioning z-score beyond ±2 vs its 3-year baseline. The crowded-trade reversal watchlist, pre-filtered from the full COT history so you don't have to compute it.
CFTC Disaggregated COT
The CFTC Disaggregated Commitments of Traders report - the trader-category breakdown professionals actually buy COT for. Weekly long and short positions for Producer/Merchant (hedgers), Swap Dealers, Managed Money (CTAs/funds) and Other Reportables across every physical-commodity futures market (energy, metals, grains, softs), plus total open interest, in contracts. The raw CFTC drops are messy flat files; this is one clean, continuous, per-market Parquet back to the report's 2006 inception.