Energy prices

112,942 observations across 27 US energy price series from FRED: daily WTI and Brent crude, Henry Hub natural gas, New York Harbor and Gulf Coast gasoline, heating oil, ultra-low-sulfur diesel (New York, Gulf Coast, Los Angeles), Gulf Coast jet fuel and Mont Belvieu propane spot prices; weekly and monthly crude and gas; weekly and monthly EIA retail gasoline (regular, midgrade, premium, all grades, conventional) and diesel pump prices; and BLS average retail electricity and piped-gas prices. Long format with series name, frequency and unit on every row, full history from FRED as of 2026-09-06.

117,409 country-fuel-month observations of net electricity generation in gigawatt-hours for 23 fuel categories and 42 geographies (the EU aggregate, every EU member state, Iceland, Norway, Switzerland, the UK, the Western Balkans, Türkiye, Moldova, Georgia and Ukraine), 2008 to 2026: total net generation; combustible fuels with their renewable and non-renewable parts and the coal and manufactured gases, natural gas and oil and petroleum products lines; nuclear fuels and other fuels n.e.c.; renewables and biofuels with hydro (pure, mixed and pumped), geothermal, wind (onshore and offshore), solar (thermal and photovoltaic) and other renewable energies; and the fossil energy and other fuels n.e.c. aggregates. Long format with the SIEC fuel code and label on every row, the reference month in period and its first day in date. The fuel codes overlap (TOTAL, CF, RA000, RA100, RA300 and RA400 are aggregates of the finer codes), so filter by fuel_code rather than summing across it; the six main series (total, combustible fuels, nuclear and other fuels, hydro, geothermal and wind) run from 2008 and the finer breakdown from 2017, and Eurostat's estimated and provisional flags are not carried. From the Eurostat dissemination API as of 2026-09-06.

5,191 entity-year observations of primary energy supply per person in kilowatt-hours from fossil fuels, nuclear and renewables, plus their total where all three are reported, for 79 countries and 11 regions, income groups and the world (aggregates carry OWID codes such as OWID_WRL), 1800 to 2025, from Our World in Data's per-capita-energy-source-stacked grapher export as of 2026-09-06. Country series start in 1965 with the Energy Institute Statistical Review; the World series reaches back to 1800 on Vaclav Smil's historical estimates. Years are also given as a 1 January date.

23,377 country-year rows and 130 columns covering primary energy consumption and its mix (coal, oil, gas, nuclear, hydro, wind, solar, other renewables), electricity generation by source, per-capita and per-GDP intensities, energy imports and carbon intensity of electricity, from Our World in Data's energy dataset as of 2026-09-05.

4,342 country-half-year observations of the average electricity price paid by household consumers in Eurostat's consumption band DC (2,500 to 4,999 kWh a year, the reference band for EU price comparisons), in euro per kilowatt-hour at three tax levels (excluding taxes and levies, excluding VAT and other recoverable taxes and levies, and all taxes and levies included), for 43 geographies (EU and euro-area aggregates, every EU member state, Iceland, Liechtenstein, Norway, the UK and candidate and neighbouring countries), 2007 to 2025, from the Eurostat dissemination API as of 2026-09-06. Long format with the tax level code and label on every row, the original half-year (e.g. 2025-S1) in period and the first day of the half-year in date.

Ten years of daily OHLCV across all 40 commodity contracts indexed by FMP — 109,218 rows in a single gzipped CSV. Covers energy (WTI/Brent crude, natural gas, gasoline, heating oil), precious & base metals (gold, silver, platinum, palladium, copper, aluminum), grains & oilseeds (wheat, corn, soybeans, rice, oats, soybean meal & oil), softs (sugar, coffee, cocoa, cotton, orange juice, lumber), livestock (live cattle, lean hogs, feeder cattle, class III milk), Treasury futures (2y, 5y, 10y, 30y), fed funds, US Dollar Index, and index futures (E-mini S&P, NASDAQ-100, mini Dow, Russell 2000). Each row tags a category field for fast filtering. Read with pandas.readcsv(path, compression='gzip', parsedates=['date']). Useful for: commodity-curve research, inflation-hedge backtests, cross-asset correlations, macro-regime detection, and rates/futures basis analysis.