Datahub.io – WTI Daily Spot Price CSV
- Rows
- 10,163
- Columns
- 2
Daily WTI crude oil spot price in USD per barrel from February 1986 to present. Open Data Commons Public Domain license. Packaged from U.S. EIA open data API.
AI analysis
Analysis: WTI Daily Spot Price Dataset
1. Dataset Overview & Value
This dataset provides a long, continuous daily time series of West Texas Intermediate (WTI) crude oil spot prices spanning over 40 years (1986–2026), sourced from the U.S. Energy Information Administration (EIA) via their open data API and redistributed through datahub.io. The EIA is a highly authoritative, government-backed source, which lends strong credibility to this data — it's the benchmark reference series used throughout energy markets. The source URL (a .csv endpoint under datahub.io's "core/oil-prices" package) suggests this is a maintained, periodically-refreshed extract rather than a one-off snapshot, and the coverage extending into 2026 indicates the pipeline is actively pulling current data. With 10,163 daily observations, this dataset is exceptionally well-suited for correlation and time-series studies involving macroeconomic indicators, inflation, energy-sector equities, currency movements, and geopolitical event studies, given its long history and daily granularity.
2. Data Quality Observations
Data quality appears strong on the surface: there are zero null cells across both columns, and the Date column has 0 nulls with full distinct-value coverage (10,163 distinct dates for 10,163 rows), confirming no duplicate timestamps and a clean, unique daily index — ideal for join operations. However, two caveats temper full confidence: (a) duplicate-row detection is marked as pending Phase D Parquet recomputation, so exact-duplicate rows haven't yet been verified despite the unique date count; and (b) the sample data rows are empty pending pipeline wire-up, meaning no eyeball validation of actual record formatting, decimal precision, or edge cases (e.g., negative prices) has been performed yet. Notably, the Price column's minimum value of -36.98 is not a data-quality error but reflects the real April 2020 negative-pricing event (COVID-19 storage crisis) — analysts should retain this rather than treat it as an outlier to be scrubbed, though it should be flagged/documented for anyone doing log-transforms or ratio-based analysis, since log(negative) is undefined.
3. Key Column Distributions
The Price column shows a mean of 48.37 versus a median of 43.11, with the mean exceeding the median — consistent with the reported right-skew (skewness = 0.566), indicating a longer tail toward higher prices (driven by spikes like the 2008 peak near $145 and 2022 highs). The interquartile range (Q1 = 20.34, Q3 = 71.365) spans roughly $51, reflecting the massive regime changes oil has undergone (sub-$20 in the 1990s/2020 vs. $100+ in 2008/2011-2014). Standard deviation (σ = 29.54) is large relative to the mean, underscoring high volatility typical of commodity prices. With only 5,644 distinct price values against 10,163 rows, there's meaningful repetition (~44% repeated values), likely reflecting periods of price stability/consolidation or rounding to two decimals — worth checking whether this repetition clusters in specific eras (e.g., pre-1990s when prices were more stable).
4. Recommended Join Keys
The Date column is the clear and only viable join key — it's complete (0 nulls), fully unique (distinct count equals row count), and daily-grained, making it straightforward to merge with any other daily or lower-frequency time series (weekly/monthly data can be joined via date-truncation or forward-fill/backward-fill logic). Analysts should standardize timezone/format handling (the ISO 8601 T00:00:00 suffix) before joining against datasets with different date-time conventions.
5. Complementary Datasets for Correlation Discovery
Strong pairing candidates include: (a) Brent crude spot prices (also EIA-sourced) for spread analysis; (b) USD exchange rate indices (DXY) given oil's historical inverse dollar correlation; (c) U.S. CPI/inflation data, since energy prices are a major inflation driver; (d) equity indices for energy sector stocks (XLE, S&P 500 Energy) and airline/transport stocks (cost-sensitive to fuel); (e) natural gas spot prices for cross-commodity substitution effects; and (f) macroeconomic recession indicators (GDP, unemployment) to study oil-price shocks' relationship to economic downturns, particularly around 2008 and 2020 events already visible in this series.
Columns
- Date (date)
- Price (decimal)
