US industrial production: mining (monthly)

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Monthly real output of US mining establishments (NAICS = 21, including oil and gas extraction), index 2017 = 100, seasonally adjusted, from the Federal Reserve Board's G.17 release via keyless FRED. The longest G.17 series in this pack (1919-01 ->) and the commodity-cycle pulse of industrial output. 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: Mining: Mining (NAICS = 21) [IPMINE], retrieved from FRED".

Source: US Manufacturing Intelligence Pack (FRED, keyless)
Rows: 1,292
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
1919-01-01	1919-01	United States	USA	23.571	—	—	—	60a16de2b18b6b14
1919-02-01	1919-02	United States	USA	20.822	-11.662	—	—	5e31320328358255
1919-03-01	1919-03	United States	USA	19.86	-4.621	—	—	6dee0fe3eb81f23b
1919-04-01	1919-04	United States	USA	21.234	6.92	—	—	2b6ba60169e3788f
1919-05-01	1919-05	United States	USA	22.128	4.207	—	—	cf2db1fb4d8a4b9b
1919-06-01	1919-06	United States	USA	22.746	2.795	—	—	7a98d5a952bf3c13
1919-07-01	1919-07	United States	USA	24.121	6.042	—	—	da9f721572fe4201
1919-08-01	1919-08	United States	USA	23.433	-2.849	—	—	236d5822ecf23718
1919-09-01	1919-09	United States	USA	25.289	7.918	—	—	655da44d7afc347f
1919-10-01	1919-10	United States	USA	26.045	2.989	—	—	4c178abd241488eb

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_mining_monthly" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/fred_manufacturing_pack/us_ip_mining_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_mining_monthly

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

Cite this snapshot

Pinned to snapshot 20261002T052828Z-25213f8821e3 and its content hash, so readers get exactly the data you used.

APA

US Manufacturing Intelligence Pack (FRED, keyless). (2026). US industrial production: mining (monthly) [Data set, snapshot 20261002T052828Z-25213f8821e3, sha256 25213f8821e3]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/en/datasets/fred_manufacturing_pack/us_ip_mining_monthly?snapshot=20261002T052828Z-25213f8821e3

BibTeX

@misc{dz_fred_manufacturing_pack_us_ip_mining_mon_25213f88,
  title        = {{US industrial production: mining (monthly)}},
  author       = {{US Manufacturing Intelligence Pack (FRED, keyless)}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/fred_manufacturing_pack/us_ip_mining_monthly?snapshot=20261002T052828Z-25213f8821e3}},
  note         = {Snapshot 20261002T052828Z-25213f8821e3, sha256 25213f8821e365b9abadd5399c685bc7639ce01e29af62b756ca712a47eb4d5b; accessed 2026-10-02}
}

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<iframe src="https://datazimuts.com/embed/chart?dataset=fred_manufacturing_pack%2Fus_ip_mining_monthly&lang=en&theme=auto&snapshot=20261002T052828Z-25213f8821e3&x=date&y=ip_index&agg=avg" title="US industrial production: mining (monthly)" width="100%" height="380" style="border:0" loading="lazy"></iframe>

US industrial production: mining (monthly)

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