WTI and Brent Oil Prices Dataset
- Rows
- 485
- Columns
- 4
Daily real (CPI-adjusted) WTI crude oil prices with historical time series data.
AI analysis
Dataset Analysis: WTI and Brent Oil Prices
1. Dataset Overview and Value
This dataset captures daily real (CPI-adjusted) WTI crude oil prices across 483 observations, offering a rich time series suitable for macroeconomic and energy market research. The three core numeric columns — nominal WTI price, CPI index, and inflation-adjusted WTI price — together form a self-contained price deflation pipeline, meaning the dataset is not just a price series but also encodes the adjustment methodology directly. Sourced from [eco3min.fr](https://eco3min.fr/dataset/real-oil-price.csv), a French economics data portal, the dataset appears curated for analytical use rather than raw exchange feeds. While freshness cannot be confirmed without sample rows (pending Phase D wire-up), the 483 distinct dates suggest approximately 9–40 years of monthly or daily coverage, depending on frequency, making it valuable for long-horizon correlation studies spanning multiple economic cycles.
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2. Data Quality Observations
Data quality here is exceptionally clean by most measures. All four columns report zero null cells across all 483 rows — a total of 0 null cells out of 1,932 possible values — and no column carries a disproportionate missing-data burden. Duplicate row counts are flagged as pending Phase D Parquet recomputation, so that figure should be confirmed before treating the dataset as fully validated; however, the Date column showing 483 distinct values across 483 rows strongly implies no duplicate dates exist. The wtireal column is the only one flagged with 6 outliers, and wtinominal shows 461 distinct values versus 483 rows, suggesting approximately 22 repeated price values — likely a natural artifact of price stickiness or rounding rather than a data entry problem. No type mismatches are reported. Overall, this is a production-ready dataset with minimal remediation needed.
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3. Key Column Distributions
The most analytically interesting column is wtireal (inflation-adjusted price), which spans a dramatic range of $23.03 to $213.94 with a mean of $75.19 and median of $64.76 — the mean-median gap of ~$10 signals right skew, confirmed by a skewness of 0.986, the highest of any column. The six outliers likely correspond to known price spike events (e.g., 2008 oil shock or post-COVID demand surges). The interquartile range of $48.46 (Q1) to $96.32 (Q3) represents the "normal" operating band, with values above ~$130 being statistically exceptional. wtinominal tells a similar story with lower skew (0.552), ranging from $10.25 to $139.96 and a mean of $48.51, reflecting that inflation amplifies the apparent extremes in real terms. The cpiindex column (mean 201.68, σ 57.38) behaves as expected for a long-run price index — gently right-skewed (0.33), monotonically trending, and useful as a standalone inflation covariate in regression models.
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4. Recommended Join Key Columns
The Date column is the natural and only join key, confirmed by its designation as [Join Key] and its perfect cardinality (483 distinct values = 483 rows). For cross-dataset correlation, this column should be standardized to ISO 8601 format (YYYY-MM-DD) before joining, and analysts should confirm the time frequency (daily, weekly, monthly) once sample rows are available in Phase D. If joining against lower-frequency datasets (e.g., quarterly GDP), date aggregation via month-end or period-average values of wtireal is recommended to avoid frequency mismatch artifacts.
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5. Recommended Complementary Datasets for Correlation Discovery
| Suggested Dataset | Rationale | |---|---| | US/Global GDP or Industrial Production | Oil prices are a leading/lagging indicator of economic output; real price adjustments make comparisons more valid | | Inflation / CPI series (BLS or Eurostat) | The embedded cpiindex column can be cross-validated and extended for multi-country purchasing power analysis | | Natural Gas or Coal Prices | Energy substitution effects create strong inter-commodity correlations worth modeling | | USD Exchange Rate (DXY Index) | Oil is dollar-denominated; currency strength inversely correlates with nominal oil prices | | S&P 500 or Energy Sector ETFs | Equity market sensitivity to energy shocks is a classic finance research question | | OPEC Production Data or Rig Count (Baker Hughes) | Supply-side variables that mechanistically drive the price series |
Pairing this dataset with US CPI or Federal Reserve interest rate data would be particularly powerful, since cpiindex is already embedded — allowing analysts to isolate whether oil prices lead inflation or respond to it across different economic regimes.
Columns
- Date (date)
- wti_nominal (decimal)
- cpi_index (decimal)
- wti_real (decimal)
