Datasets:

FinzData
/

macro-other

---
license: other
license_name: public-domain-source-us-government
pretty_name: "US Macro Data: Other"
language:
- en
tags:
- finance
- economics
- macroeconomics
- time-series
- fred
- other
- quant
- backtesting
size_categories:
- n<1K
configs:
- config_name: long
  data_files: data/long.parquet
  default: true
- config_name: wide_monthly
  data_files: data/wide_monthly.parquet
- config_name: wide_quarterly
  data_files: data/wide_quarterly.parquet
- config_name: wide_semiannual
  data_files: data/wide_semiannual.parquet
---
# US macro data: Other (60 series)

60 other series from US government sources, cleaned into one consistent format and ready for pandas.
This free package holds the **last 12 months** (2025-11-02 on) of each series' latest values. It is refreshed weekly.

```python
import pandas as pd
df = pd.read_parquet("hf://datasets/FinzData/macro-other/data/long.parquet")      # series_id, date, value
wide = pd.read_parquet("hf://datasets/FinzData/macro-other/data/wide_monthly.parquet")  # one column per series
```

or `datasets.load_dataset("FinzData/macro-other")`. Wide tables by frequency: `wide_monthly` (42 series), `wide_quarterly` (14 series), `wide_semiannual` (4 series). `series.csv` lists every series
with its title, units, frequency and how far back the full history goes.

## The full history, point in time, through one API
The FinzData API serves every series back to its first observation, **every revision** (what was known on any past
date, so backtests do not peek at later revisions), and **daily updates**, in the same format:

```python
import finzdata as fz                     # pip install finzdata
c = fz.Client()                           # free API key: https://api.finzdata.com/
c.macro("CUURX000SEHF01")                        # full history, latest values
c.macro("CUURX000SEHF01", vintage="first")       # as first released
c.macro("CUURX000SEHF01", as_of="2020-03-01")    # exactly what was known on that date
c.macro_series(category="other")          # the catalogue for this category
```

## Most-watched series in this package

| ID | Title | Units | Frequency | Full history from |
|---|---|---|---|---|
| `CUURX000SEHF01` | Consumer Price Index for All Urban Consumers: Electricity in Size Class B/C | Index Dec 1997=100 | Monthly | 1997 |
| `CUUS0100SEHF01` | Consumer Price Index for All Urban Consumers: Electricity in Northeast | Index 1982-1984=100 | Semiannual | 1984 |
| `CUUS0300SEHF01` | Consumer Price Index for All Urban Consumers: Electricity in South | Index 1982-1984=100 | Semiannual | 1984 |
| `CUUSA000SEHF01` | Consumer Price Index for All Urban Consumers: Electricity in Size Class A | Index Dec 1986=100 | Semiannual | 1987 |
| `CUUSX000SEHF01` | Consumer Price Index for All Urban Consumers: Electricity in Size Class B/C | Index Dec 1997=100 | Semiannual | 1998 |
| `IPN221112NQ` | Industrial Production: Utilities: Fossil Fuel Electric Power Generation (NAICS = 221112) | Index 2017=100 | Quarterly | 2001 |
| `IPN221112SQ` | Industrial Production: Utilities: Fossil Fuel Electric Power Generation (NAICS = 221112) | Index 2017=100 | Quarterly | 2001 |
| `IPN221113NQ` | Industrial Production: Utilities: Nuclear Electric Power Generation (NAICS = 221113) | Index 2017=100 | Quarterly | 1972 |
| `IPN221114T8SQ` | Industrial Production: Utilities: Renewables and Other Electric Power Generation (NAICS = 221114-8) | Index 2017=100 | Quarterly | 2001 |
| `IPN22112CNQ` | Industrial Production: Utilities: Commercial and Other Electricity Sales (NAICS = 22112pt.) | Index 2017=100 | Quarterly | 1972 |
| `IPN22112CSQ` | Industrial Production: Utilities: Commercial and Other Electricity Sales (NAICS = 22112pt.) | Index 2017=100 | Quarterly | 1972 |
| `IPN22112MSQ` | Industrial Production: Utilities: Industrial Electricity Sales (NAICS = 22112pt.) | Index 2017=100 | Quarterly | 1972 |
| `IPN22112RSQ` | Industrial Production: Utilities: Residential Electricity Sales (NAICS = 22112pt.) | Index 2017=100 | Quarterly | 1972 |
| `PCU221122221122413` | Producer Price Index by Industry: Electric Power Distribution: Residential Electric Power for East North Central Census Division | Index Dec 1990=100 | Monthly | 1990 |
| `PCU221122221122415` | Producer Price Index by Industry: Electric Power Distribution: Residential Electric Power for South Atlantic Census Division | Index Dec 1990=100 | Monthly | 1990 |
| `PRS31006022` | Manufacturing, Durable Goods Sector: Average Weekly Hours for All Workers | Percent Change at Annual Rate | Quarterly | 1987 |
| `PRS31006151` | Manufacturing, Durable Goods Sector: Real Hourly Compensation for All Workers | Percent Change from Quarter One Year Ago | Quarterly | 1988 |
| `PRS32006022` | Manufacturing, Nondurable Goods Sector: Average Weekly Hours for All Workers | Percent Change at Annual Rate | Quarterly | 1987 |
| `PRS32006041` | Manufacturing, Nondurable Goods Sector: Real Sectoral Output for All Workers | Percent Change from Quarter One Year Ago | Quarterly | 1988 |
| `PRS88003022` | Nonfinancial Corporations Sector: Average Weekly Hours for Employees | Percent Change at Annual Rate | Quarterly | 1947 |
| `REV2211TPSA` | Total Revenue for 2211: Electric Power Generation, Transmission and Distribution, Establishments Subject to Federal Income Tax | Percent Change | Quarterly | 2010 |
| `SMU01000004322110001` | All Employees: Transportation and Utilities: Electric Power Generation, Transmission and Distribution in Alabama | Thousands of Persons | Monthly | 1990 |
| `SMU01000004322110001SA` | All Employees: Transportation and Utilities: Electric Power Generation, Transmission and Distribution in Alabama | Thousands of Persons | Monthly | 1990 |
| `SMU06000004322110001` | All Employees: Transportation and Utilities: Electric Power Generation, Transmission and Distribution in California | Thousands of Persons | Monthly | 1990 |
| `SMU06000004322110001SA` | All Employees: Transportation and Utilities: Electric Power Generation, Transmission and Distribution in California | Thousands of Persons | Monthly | 1990 |

All 60 series: `series.csv`. Browse them at https://finzdata.com/macro/category/other.

## More from FinzData
- Every macro category: https://huggingface.co/FinzData · index on GitHub: https://github.com/FinzData/macro-data
- Survivorship-free US stock prices since 2000, COT positioning, 13F holdings, insider trades, short interest
- Pricing (history bundles from $195 once): https://finzdata.com/pricing
- Python client (open source): https://github.com/FinzData/finzdata-python

Source: U.S. government agencies via FRED/ALFRED (Federal Reserve Bank of St. Louis). Compiled by FinzData.
Data for research, not investment advice.

	---
	license: other
	license_name: public-domain-source-us-government
	pretty_name: "US Macro Data: Other"
	language:
	- en
	tags:
	- finance
	- economics
	- macroeconomics
	- time-series
	- fred
	- other
	- quant
	- backtesting
	size_categories:
	- n<1K
	configs:
	- config_name: long
	data_files: data/long.parquet
	default: true
	- config_name: wide_monthly
	data_files: data/wide_monthly.parquet
	- config_name: wide_quarterly
	data_files: data/wide_quarterly.parquet
	- config_name: wide_semiannual
	data_files: data/wide_semiannual.parquet
	---

	# US macro data: Other (60 series)

	60 other series from US government sources, cleaned into one consistent format and ready for pandas.
	This free package holds the last 12 months (2025-11-02 on) of each series' latest values. It is refreshed weekly.

	```python
	import pandas as pd
	df = pd.read_parquet("hf://datasets/FinzData/macro-other/data/long.parquet") # series_id, date, value
	wide = pd.read_parquet("hf://datasets/FinzData/macro-other/data/wide_monthly.parquet") # one column per series
	```

	or `datasets.load_dataset("FinzData/macro-other")`. Wide tables by frequency: `wide_monthly` (42 series), `wide_quarterly` (14 series), `wide_semiannual` (4 series). `series.csv` lists every series
	with its title, units, frequency and how far back the full history goes.

	## The full history, point in time, through one API
	The FinzData API serves every series back to its first observation, every revision (what was known on any past
	date, so backtests do not peek at later revisions), and daily updates, in the same format:

	```python
	import finzdata as fz # pip install finzdata
	c = fz.Client() # free API key: https://api.finzdata.com/
	c.macro("CUURX000SEHF01") # full history, latest values
	c.macro("CUURX000SEHF01", vintage="first") # as first released
	c.macro("CUURX000SEHF01", as_of="2020-03-01") # exactly what was known on that date
	c.macro_series(category="other") # the catalogue for this category
	```

	## Most-watched series in this package

	\| ID \| Title \| Units \| Frequency \| Full history from \|
	\|---\|---\|---\|---\|---\|
	\| `CUURX000SEHF01` \| Consumer Price Index for All Urban Consumers: Electricity in Size Class B/C \| Index Dec 1997=100 \| Monthly \| 1997 \|
	\| `CUUS0100SEHF01` \| Consumer Price Index for All Urban Consumers: Electricity in Northeast \| Index 1982-1984=100 \| Semiannual \| 1984 \|
	\| `CUUS0300SEHF01` \| Consumer Price Index for All Urban Consumers: Electricity in South \| Index 1982-1984=100 \| Semiannual \| 1984 \|
	\| `CUUSA000SEHF01` \| Consumer Price Index for All Urban Consumers: Electricity in Size Class A \| Index Dec 1986=100 \| Semiannual \| 1987 \|
	\| `CUUSX000SEHF01` \| Consumer Price Index for All Urban Consumers: Electricity in Size Class B/C \| Index Dec 1997=100 \| Semiannual \| 1998 \|
	\| `IPN221112NQ` \| Industrial Production: Utilities: Fossil Fuel Electric Power Generation (NAICS = 221112) \| Index 2017=100 \| Quarterly \| 2001 \|
	\| `IPN221112SQ` \| Industrial Production: Utilities: Fossil Fuel Electric Power Generation (NAICS = 221112) \| Index 2017=100 \| Quarterly \| 2001 \|
	\| `IPN221113NQ` \| Industrial Production: Utilities: Nuclear Electric Power Generation (NAICS = 221113) \| Index 2017=100 \| Quarterly \| 1972 \|
	\| `IPN221114T8SQ` \| Industrial Production: Utilities: Renewables and Other Electric Power Generation (NAICS = 221114-8) \| Index 2017=100 \| Quarterly \| 2001 \|
	\| `IPN22112CNQ` \| Industrial Production: Utilities: Commercial and Other Electricity Sales (NAICS = 22112pt.) \| Index 2017=100 \| Quarterly \| 1972 \|
	\| `IPN22112CSQ` \| Industrial Production: Utilities: Commercial and Other Electricity Sales (NAICS = 22112pt.) \| Index 2017=100 \| Quarterly \| 1972 \|
	\| `IPN22112MSQ` \| Industrial Production: Utilities: Industrial Electricity Sales (NAICS = 22112pt.) \| Index 2017=100 \| Quarterly \| 1972 \|
	\| `IPN22112RSQ` \| Industrial Production: Utilities: Residential Electricity Sales (NAICS = 22112pt.) \| Index 2017=100 \| Quarterly \| 1972 \|
	\| `PCU221122221122413` \| Producer Price Index by Industry: Electric Power Distribution: Residential Electric Power for East North Central Census Division \| Index Dec 1990=100 \| Monthly \| 1990 \|
	\| `PCU221122221122415` \| Producer Price Index by Industry: Electric Power Distribution: Residential Electric Power for South Atlantic Census Division \| Index Dec 1990=100 \| Monthly \| 1990 \|
	\| `PRS31006022` \| Manufacturing, Durable Goods Sector: Average Weekly Hours for All Workers \| Percent Change at Annual Rate \| Quarterly \| 1987 \|
	\| `PRS31006151` \| Manufacturing, Durable Goods Sector: Real Hourly Compensation for All Workers \| Percent Change from Quarter One Year Ago \| Quarterly \| 1988 \|
	\| `PRS32006022` \| Manufacturing, Nondurable Goods Sector: Average Weekly Hours for All Workers \| Percent Change at Annual Rate \| Quarterly \| 1987 \|
	\| `PRS32006041` \| Manufacturing, Nondurable Goods Sector: Real Sectoral Output for All Workers \| Percent Change from Quarter One Year Ago \| Quarterly \| 1988 \|
	\| `PRS88003022` \| Nonfinancial Corporations Sector: Average Weekly Hours for Employees \| Percent Change at Annual Rate \| Quarterly \| 1947 \|
	\| `REV2211TPSA` \| Total Revenue for 2211: Electric Power Generation, Transmission and Distribution, Establishments Subject to Federal Income Tax \| Percent Change \| Quarterly \| 2010 \|
	\| `SMU01000004322110001` \| All Employees: Transportation and Utilities: Electric Power Generation, Transmission and Distribution in Alabama \| Thousands of Persons \| Monthly \| 1990 \|
	\| `SMU01000004322110001SA` \| All Employees: Transportation and Utilities: Electric Power Generation, Transmission and Distribution in Alabama \| Thousands of Persons \| Monthly \| 1990 \|
	\| `SMU06000004322110001` \| All Employees: Transportation and Utilities: Electric Power Generation, Transmission and Distribution in California \| Thousands of Persons \| Monthly \| 1990 \|
	\| `SMU06000004322110001SA` \| All Employees: Transportation and Utilities: Electric Power Generation, Transmission and Distribution in California \| Thousands of Persons \| Monthly \| 1990 \|

	All 60 series: `series.csv`. Browse them at https://finzdata.com/macro/category/other.

	## More from FinzData
	- Every macro category: https://huggingface.co/FinzData · index on GitHub: https://github.com/FinzData/macro-data
	- Survivorship-free US stock prices since 2000, COT positioning, 13F holdings, insider trades, short interest
	- Pricing (history bundles from $195 once): https://finzdata.com/pricing
	- Python client (open source): https://github.com/FinzData/finzdata-python

	Source: U.S. government agencies via FRED/ALFRED (Federal Reserve Bank of St. Louis). Compiled by FinzData.
	Data for research, not investment advice.