US Gas
Weekly US gas-fired power generation vs natural-gas storage draw - the burn-vs-storage reconciliation nat-gas traders use to nowcast the EIA storage print.
- Source
- derived
- Licence
- Redistributable open data
- Updates
- Weekly
- Data through
- 2026-07-27
- Quality
- QC clean · 0 failures
- Format
- Apache Parquet + dictionary
Safe to train on
Built only from a named, redistributable official source under a documented open licence - not scraped web data. No copyright grey zone, no personal data. Ships machine-readable Croissant metadata (ML Commons - loads in Hugging Face / Kaggle / Google), an AI training-licence manifest documenting source, licence and provenance for your model's data governance, and a machine-readable data dictionary (drop it into an agent / RAG prompt so the model knows every column) - all inspectable before you buy.
What's included
- Gas-fired generation (TWh/wk), gas in storage (Bcf), weekly storage change
- Hourly generation aggregated to weekly to match storage
- Source: U.S. EIA (public domain, derived)
Schema
| Column | Type | Description | Filled | Distinct |
|---|---|---|---|---|
| zone | str | Always US | 100.0% | 1 |
| ts | timestamp (UTC) | Week | 100.0% | 1,156 |
| variable | str | Metric | 100.0% | 3 |
| value | float | Value | 100.0% | 1,213 |
| unit | str | Unit (mixed) | 100.0% | 1 |
| res_min | int | Native resolution (weekly) | 100.0% | 1 |
Sample & preview
Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: derived (redistributable open data; attribution passes through - see our Licence).
Don't trust screenshots - drop the sample into your notebook right now.
import pandas as pd df = pd.read_parquet("us-gas-power-burn_sample.parquet") df.info() # typed columns, gap-aware, ready to join⬇ Download free Parquet sample or CSV for Excel
Coverage: 1 zones
US
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