Datahub.io – Brent and WTI Spot Prices (Daily CSV)
- Rows
- 9,893
- Columns
- 2
Daily Europe Brent spot price FOB in USD per barrel, from May 1987 to present. Open Data Commons Public Domain license. Packaged from EIA open data.
AI analysis
Dataset Analysis: Brent Spot Oil Prices (Daily)
1. What this dataset represents and its value
This dataset tracks the daily Europe Brent spot price FOB (USD/barrel) from May 20, 1987 through the present, sourced via the Datahub.io "core/oil-prices" package (datahub.io/core/oil-prices/r/brent-daily.csv), which itself repackages official EIA (U.S. Energy Information Administration) open data. Two things are notable about the source: it's a derivative republication rather than a primary feed, so freshness depends on how often Datahub syncs with EIA's API — worth verifying against EIA directly if near-real-time accuracy matters — and the time coverage extending to 2026-05-18 suggests either forward-filled/placeholder rows or a rolling ingestion window that should be sanity-checked. With ~39 years of daily granularity, this is a strong backbone series for macroeconomic and energy-market correlation studies, since Brent is a global benchmark that influences inflation, transportation costs, currency movements (especially petrocurrencies), and equity sectors like energy and airlines.
2. Data quality observations
The dataset is clean by conventional null/type metrics: 0 null cells across both columns and 0 missing dates in the Date column (9,893 distinct values matching 9,893 rows, implying no duplicate timestamps at the row level). Duplicate-row detection is still pending Phase D Parquet recomputation, so that figure shouldn't be treated as confirmed zero yet — flag this as an open verification item before using the data in production correlation pipelines. One structural quality note: daily financial series like this typically exclude weekends/holidays, so gaps in the Date sequence are expected (market closures), not missing data — this should be documented so downstream users don't misinterpret non-contiguous dates as data loss.
3. Key column distributions
The Price column (Decimal) spans a wide range — min 9.1 to max 143.95 — reflecting the extreme volatility of oil markets across multiple boom/bust cycles (e.g., 2008 spike, 2020 COVID crash). The mean (51.16) sitting meaningfully above the median (46.09) combined with a positive skew (0.563) indicates a right-skewed distribution: prices spend more time in lower/moderate ranges with periodic upward price spikes pulling the mean higher. The interquartile range (Q1=19.54, Q3=74.78) is notably wide, spanning roughly a 4x multiple, underscoring how much regime-shifting occurs over the sample period rather than stable clustering around the mean. With only 5,493 distinct values across 9,893 rows, there's noticeable price repetition (flat/unchanged days), which is typical for spot prices during low-volatility periods but worth checking for stale-data artifacts (e.g., holiday carryover values).
4. Recommended join key
The Date column is the clear and only viable join key — it's fully populated, uniquely distinct per row, and typed as a proper Date. For cross-dataset correlation, standardize to daily (or resample to weekly/monthly) granularity depending on the join partner's native frequency, since many macro datasets (GDP, CPI) report monthly or quarterly and will require Brent to be aggregated (mean/close-of-period) rather than joined row-for-row.
5. Suggested pairings for correlation discovery
Strong candidates include: (a) WTI daily spot prices (likely a sibling file in the same Datahub package) to study the Brent-WTI spread as a proxy for regional supply/demand imbalances; (b) macro inflation indicators like CPI or PPI to explore oil's pass-through effect on headline inflation; (c) currency exchange rates, particularly USD index and petrocurrency pairs (CAD, RUB, NOK, MXN); (d) equity sector indices (energy sector ETFs, airline stock indices) for sensitivity analysis; (e) geopolitical event datasets (conflict indices, OPEC production data) to explain price shock timing; and (f) natural gas or coal price series to examine energy-substitution correlation dynamics.
Columns
- Date (date)
- Price (decimal)
