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The US Federal Aviation Administration publishes datasets on aircraft registrations, pilot certifications, airport data, accident reports, and air traffic statistics. Used in data engineering for aviation analytics pipelines, safety analysis systems, and building aeronautical intelligence dashboards in Python.
The FAA provides downloadable CSV files for on-time performance data and the Aviation Safety Information Analysis and Sharing (ASIAS) system. Engineers use `pandas` to process flight performance data and join with airport metadata for delay pattern analysis.
FAA flight data trains AI models for delay prediction, disruption management, and route optimization. RAG systems built on FAA performance records can answer 'Which airlines have the best on-time performance at LAX?' with official aviation data, powering AI travel planning assistants.
# pip install pandas
import pandas as pd
# On-Time Performance data (Bureau of Transportation Statistics)
url = "https://transtats.bts.gov/PREZIP/On_Time_Reporting_Carrier_On_Time_Performance_1987_present_2024_1.zip"
# For smaller tests, use BTS pre-downloaded CSV:
df = pd.read_csv("On_Time_Performance_2024_1.csv",
usecols=["FlightDate", "Carrier", "DepDelay", "ArrDelay"])
print(df.groupby("Carrier")["DepDelay"].mean().sort_values(ascending=False).head(10))Official dataset source
More datasets used by Python data engineers.
The FEC provides access to campaign finance data, including information on political contributions, campaign expenditures, fundraising activities and financial disclosures filed by political candidates, parties and committees in the United States.
Data.gov hosts 300,000+ datasets from US federal agencies covering health, education, environment, agriculture, finance, and transportation. Used in data engineering for government analytics pipelines, public health research, geospatial analysis, and building civic data applications with Python.
Data.gov.uk provides datasets from UK central and local government covering crime, transport, planning, health, and environment. Used in data engineering for public sector analytics, policy research pipelines, geospatial visualisation, and building civic technology applications in Python.