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The Bureau of Transportation Statistics publishes datasets on airline on-time performance, freight shipments, highway traffic, transit ridership, and transportation safety in the US. Used in data engineering for logistics analytics pipelines, transportation planning systems, and building mobility dashboards in Python.
BTS provides the Transtats database for aviation data and the Freight Analysis Framework (FAF) for freight data. Engineers use `pandas.read_csv()` on BTS download files or query the RITA/BTS API endpoints, processing large transportation files with chunked loading.
BTS transportation data trains AI models for freight demand forecasting, route optimization, and transportation safety analytics. RAG systems built on BTS statistics help AI logistics tools answer 'What is the most common freight route between Chicago and Los Angeles?' with official data.
# pip install pandas
import pandas as pd
# BTS On-Time Airline Data (pre-downloaded from transtats.bts.gov)
df = pd.read_csv("T_ONTIME_REPORTING.csv",
usecols=["YEAR", "MONTH", "CARRIER", "DEP_DELAY", "ARR_DELAY"])
airline_perf = df.groupby("CARRIER")[["DEP_DELAY", "ARR_DELAY"]].mean()
print(airline_perf.sort_values("DEP_DELAY").head(10))Official dataset source
More datasets used by Python data engineers.
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.
CDC WONDER provides access to US public health datasets including mortality records, natality data, cancer statistics, vaccination rates, and disease surveillance. Used in data engineering for public health analytics pipelines, epidemiological research systems, and building population health indicator dashboards in Python.
The Federal Reserve Bank of St. Louis FRED database provides over 800,000 economic time series from 100+ sources, including interest rates, inflation, GDP, and employment data. Widely used in financial and economic data pipelines via the fredapi Python library for loading macro data into analytical systems.