Loading...
The US Department of Agriculture publishes datasets on crop production, food prices, nutrition, soil quality, agricultural trade, and rural economic indicators. Used in data engineering for agricultural analytics pipelines, food supply chain analysis, and building agri-business intelligence dashboards in Python.
The USDA NASS Quick Stats API and ERS APIs provide JSON/CSV access to agricultural statistics. The `nass` Python library simplifies NASS API access. Engineers query crop yield time-series and commodity prices, storing data in PostgreSQL for supply chain analytics.
USDA agricultural data trains AI models for crop yield prediction, supply chain optimization, and commodity price forecasting. RAG systems built on USDA reports enable AI agricultural advisors to answer 'How has US corn yield per acre changed over the last 50 years?' with official statistics.
# pip install requests pandas
import requests, pandas as pd
# USDA NASS crop production data
resp = requests.get(
"https://quickstats.nass.usda.gov/api/api_GET/",
params={
"key": "YOUR_API_KEY",
"commodity_desc": "CORN", "statisticcat_desc": "YIELD",
"year__GE": 2015, "unit_desc": "BU / ACRE",
"agg_level_desc": "NATIONAL", "format": "JSON"
}
)
df = pd.DataFrame(resp.json()["data"])[["year", "Value", "unit_desc"]]
print(df.sort_values("year"))Official dataset source
More datasets used by Python data engineers.
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.
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.
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.