DataHub Natural Gas Prices Daily

Rows: 7,374
Columns: 2

Daily Henry Hub natural gas spot prices in USD per MMBtu. Energy commodity time-series.

AI analysis

DataHub Natural Gas Prices Daily — Dataset Analysis

1. Dataset Overview & Value for Correlation Studies This dataset captures the Henry Hub natural gas spot price, the primary benchmark for natural gas pricing in North America, recorded at daily frequency in USD per MMBtu. Sourced from [datahub.io/core/natural-gas](https://datahub.io/core/natural-gas/r/-/data/daily.csv), DataHub.io is a well-maintained open data publisher that curates core economic and commodity datasets, lending reasonable confidence to the series' provenance and continuity. With 7,374 daily observations, the timeline spans roughly 20+ years of trading history, making it exceptionally well-suited for long-horizon correlation studies, seasonal decomposition, and macro-economic event analysis. Its energy commodity nature positions it as a high-value anchor dataset for exploring relationships with weather, industrial output, electricity prices, and broader inflation indices.

2. Data Quality Observations Data quality here is near-pristine. Across both columns, there is only 1 null cell total, isolated entirely to the Price column — representing a 0.0% null rate that is essentially negligible and unlikely to distort any analysis. The Date column is fully complete with zero nulls. Duplicate row counts are pending final Parquet recomputation (Phase D), but given that Date shows 7,374 distinct values across exactly 7,374 rows, the dataset appears to be uniquely keyed on date with no duplicates. No type mismatches are flagged. The single missing price record can be safely handled via forward-fill or linear interpolation given the time-series context, requiring minimal cleaning effort before analysis.

3. Key Column Distributions The Price column tells a rich story about natural gas market volatility. The mean of $4.08/MMBtu versus a median of $3.36 reveals meaningful right-skew — confirmed by a skewness score of 2 — indicating that extreme price spikes pull the average well above the typical trading range. The interquartile range of $2.61 (Q1) to $5.03 (Q3) captures the bulk of "normal" market conditions, while the 284 statistical outliers (roughly 3.9% of records) almost certainly correspond to identifiable market events: the 2005 post-Katrina spike, the 2008 commodity supercycle, and the 2022 European energy crisis are likely candidates. The min of $1.05 and max of $30.72 span nearly a 30x range, underscoring why volatility modeling and regime-change detection would be particularly valuable analyses on this series. The standard deviation of $2.18 relative to a median of $3.36 further confirms that the distribution is fat-tailed and non-normal.

4. Recommended Join Key Columns The Date column is the natural and only join key, and it is ideal for this purpose — fully populated, entirely distinct, and typed correctly as a Date field. For cross-dataset joins, standardizing to YYYY-MM-DD ISO format is advisable to ensure compatibility. Analysts should be mindful that this is a U.S. trading-day calendar, meaning weekends and holidays will produce gaps; joining to datasets with continuous daily records (e.g., weather data) will require date spine alignment or gap-filling logic. Monthly or weekly aggregations derived from this column would also serve as reliable join keys for lower-frequency macro datasets.

5. Suggested Paired Datasets for Correlation Discovery Several dataset categories would pair powerfully with this series. Heating and Cooling Degree Days (HDD/CDD) weather data is the most direct pairing, as temperature is the primary demand driver for natural gas — expect strong seasonal correlation. U.S. Electricity spot prices (e.g., PJM or ERCOT day-ahead prices) would reveal fuel-switching dynamics between gas and coal generation. Crude oil prices (WTI or Brent daily) would test the historically debated oil-gas price relationship, which has decoupled significantly in the shale era. U.S. CPI or PPI energy sub-indices could explore natural gas as an inflation leading indicator. Finally, natural gas storage reports (EIA weekly inventory data) would be a natural operational pairing, allowing analysts to model the classic inventory-surprise-to-price-move relationship that traders actively exploit.

Columns

Date (date)
Price (decimal)