US industrial production: electric & gas utilities (monthly)

Commercial use OKFresh

The full snapshot isn't published yet — check back after the next refresh.Download sample CSV Download sample JSONSample rows only (up to 20) — not the complete dataset.

Monthly real output of US electric and gas utilities (NAICS = 2211,2), index 2017 = 100, seasonally adjusted, from the Federal Reserve Board's G.17 release via keyless FRED. Utilities output tracks weather-driven energy demand almost one-for-one — the physical-economy mirror of temperature extremes. Derived columns: month-over-month and year-over-year change, deviation from the trailing-12-month mean, and 12-month record-high/low flags. Caveats: seasonally adjusted; the latest month is preliminary and routinely revised. Board of Governors of the Federal Reserve System (US) — U.S. federal public domain; cite "Board of Governors of the Federal Reserve System (US), Industrial Production: Utilities: Electric and Gas Utilities (NAICS = 2211,2) [IPUTIL], retrieved from FRED".

Source: US Manufacturing Intelligence Pack (FRED, keyless)
Rows: 1,052
Columns: 11
Source cadence: Monthly
Last refreshed: Oct 2, 2026
Theme: economy

Column	Type	Description
date	string	Observation month as an ISO date (first of the month); G.17 publishes mid-month for the prior month.
year_month	string	Observation month as YYYY-MM — the join key for persona models (sales territories, shop orders, subscription cohorts all join on year_month).
country	string	Country name, 'United States' (all series are US national).
country_code	string	ISO 3166-1 alpha-3 code 'USA' — the join key for the 'Enrich with public data' feature.
ip_index	float	Industrial production index (2017 = 100): real output of the covered establishments, seasonally adjusted. The primary level series of this dataset. (unit: Index 2017=100, Seasonally Adjusted)
mom_pct	float	Month-over-month change of the level series, in percent. Null for the first observation — never back-filled. (unit: percent)
yoy_pct	float	Year-over-year change of the level series, in percent. Null for the first 12 observations — never back-filled. (unit: percent)
vs_12m_avg_pp	float	Deviation of the level series from its trailing-12-month mean, in index points for production series and percentage points for capacity utilization — the momentum-vs-trend gauge. (unit: index points)
record_high_12m_flag	integer	1 when the print equals the trailing-12-month maximum (inclusive, requires >= 12 observations), 0 otherwise.
record_low_12m_flag	integer	1 when the print equals the trailing-12-month minimum (inclusive, requires >= 12 observations), 0 otherwise.
row_hash	string	Deterministic 16-hex row identity (date + value) for change detection.

First 10 sample rows — a preview, not the complete dataset.

date	year_month	country	country_code	ip_index	mom_pct	yoy_pct	vs_12m_avg_pp	row_hash
1939-01-01	1939-01	United States	USA	3.328	—	—	—	4b2d8493cf9867ca
1939-02-01	1939-02	United States	USA	3.354	0.763	—	—	31bfde7460af0d8d
1939-03-01	1939-03	United States	USA	3.43	2.272	—	—	0154018744de3d13
1939-04-01	1939-04	United States	USA	3.455	0.741	—	—	a44388dbf8ab2763
1939-05-01	1939-05	United States	USA	3.481	0.738	—	—	da887fe7c29f4cb6
1939-06-01	1939-06	United States	USA	3.506	0.73	—	—	fff0975a3cc96d43
1939-07-01	1939-07	United States	USA	3.506	0	—	—	774eecc6ae716aa1
1939-08-01	1939-08	United States	USA	3.532	0.724	—	—	768599be1cf64b18
1939-09-01	1939-09	United States	USA	3.633	2.877	—	—	45e3f07d5ad8ccfc
1939-10-01	1939-10	United States	USA	3.633	0	—	—	e4476106dba7b66d

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/fred_manufacturing_pack/us_ip_electric_gas_utilities_monthly" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/fred_manufacturing_pack/us_ip_electric_gas_utilities_monthly").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/fred_manufacturing_pack/us_ip_electric_gas_utilities_monthly

Tip: fetch /llms.txt for the full machine-readable catalog.

Cite this snapshot

Pinned to snapshot 20261002T052830Z-0127f7a66c26 and its content hash, so readers get exactly the data you used.

APA

US Manufacturing Intelligence Pack (FRED, keyless). (2026). US industrial production: electric & gas utilities (monthly) [Data set, snapshot 20261002T052830Z-0127f7a66c26, sha256 0127f7a66c26]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/en/datasets/fred_manufacturing_pack/us_ip_electric_gas_utilities_monthly?snapshot=20261002T052830Z-0127f7a66c26

BibTeX

@misc{dz_fred_manufacturing_pack_us_ip_electric_g_0127f7a6,
  title        = {{US industrial production: electric \& gas utilities (monthly)}},
  author       = {{US Manufacturing Intelligence Pack (FRED, keyless)}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/fred_manufacturing_pack/us_ip_electric_gas_utilities_monthly?snapshot=20261002T052830Z-0127f7a66c26}},
  note         = {Snapshot 20261002T052830Z-0127f7a66c26, sha256 0127f7a66c2643dacef81725c573e7f696d423d961751f1b3c35d5de47dfafa9; accessed 2026-10-02}
}

Embed a table or a chart

Paste this into any page. The embed is pinned to the same snapshot, follows the reader's light or dark setting, and always shows the source, license and a link back.

Type

X axis

Value

Aggregate

Theme

HTML

<iframe src="https://datazimuts.com/embed/chart?dataset=fred_manufacturing_pack%2Fus_ip_electric_gas_utilities_monthly&lang=en&theme=auto&snapshot=20261002T052830Z-0127f7a66c26&x=date&y=ip_index&agg=avg" title="US industrial production: electric & gas utilities (monthly)" width="100%" height="380" style="border:0" loading="lazy"></iframe>

US industrial production: electric & gas utilities (monthly)

Follow this dataset

Cite this snapshot

Embed a table or a chart

Use your own AI key