Get Total Nonfarm Payroll Employment for the United States from the Bureau of Labor Statistics (via FRED).

Nonfarm Payrolls is the headline monthly employment report and one of the most closely watched real-activity indicators in macroeconomics: it counts the number of paid US workers excluding farm employees, general government employees, private household employees and nonprofit organization employees. Sharp month-over-month changes are a core input to business-cycle dating (used directly by the NBER’s Business Cycle Dating Committee) and, through Okun’s Law, are closely tied to changes in the Unemployment Rate (see get_unemployment_rate).

The series is the monthly level of employment in thousands of persons, seasonally adjusted, so 156857 means 156.857 million jobs – not the monthly change that the headline “jobs added” number refers to. Use growth=True for that change.

Requires a free FRED API key, see the fred_api_key parameter of the Economics class.

See definition: https://fred.stlouisfed.org/series/PAYEMS

Also known as: NFP, nonfarm employment, the “jobs report”.

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Calculate the Nonfarm Payrolls in Python

The Nonfarm Payrolls is available in the Economics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_nonfarm_payrolls as shown below.

from financetoolkit import Economics

economics = Economics(start_date='2020-01-01', fred_api_key='FRED_API_KEY')

economics.get_nonfarm_payrolls()

Which returns:

Date United States
2026-02-01 158436
2026-03-01 158650
2026-04-01 158798
2026-05-01 158927
2026-06-01 158984

Parameters

get_nonfarm_payrolls accepts the following parameters:

  • rolling (int, optional): The rolling window size to use for smoothing the data (simple moving average). Defaults to None.
  • trailing (int, optional): The trailing window size to use for summing the data over trailing periods. Defaults to None.
  • growth (bool, optional): Whether to return the growth data or the actual data.
  • lag (int, optional): The number of periods to lag the data by.
  • standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
  • rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.

Related Jobs & Society

Unemployment Rates Labour Productivity Income Inequality Population Poverty Rate Initial Jobless Claims

The Economics module page introduces the module, and the sidebar lists all of its functions.

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