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US State Employment Hours and Earnings

  • Статия

The Current Employment Statistics (CES) program produces detailed industry estimates of nonfarm employment, hours, and earnings of workers on payrolls in the United States.

Note

Microsoft provides Azure Open Datasets on an “as is” basis. Microsoft makes no warranties, express or implied, guarantees or conditions with respect to your use of the datasets. To the extent permitted under your local law, Microsoft disclaims all liability for any damages or losses, including direct, consequential, special, indirect, incidental or punitive, resulting from your use of the datasets.

This dataset is provided under the original terms that Microsoft received source data. The dataset may include data sourced from Microsoft.

This dataset is sourced from State and Metro Area Employment, Hours & Earnings data published by US Bureau of Labor Statistics (BLS). Review Linking and Copyright Information and Important Web Site Notices for the terms and conditions related to the use this dataset.

Storage location

This dataset is stored in the East US Azure region. Allocating compute resources in East US is recommended for affinity.

Columns

Name Data type Unique Values (sample)
area_code string 446 0 31084
area_name string 446 Statewide Los Angeles-Long Beach-Glendale, CA Metropolitan Division
data_type_code string 9 1 3
data_type_text string 9 All Employees, In Thousands Average Weekly Hours of All Employees
footnote_codes string 3 nan P
industry_code string 343 0 5000000
industry_name string 343 Total Nonfarm Total Private
period string 13 M04 M05
seasonal string 2 U S
series_id string 23,853 SMU12000000000000001 SMU36000000000000001
state_code string 53 6 48
state_name string 53 California Texas
supersector_code string 22 90 60
supersector_name string 22 Government Professional and Business Services
value float 132,565 0.30000001192092896 0.10000000149011612
year int 81 2014 2018

Preview

area_code state_code data_type_code industry_code supersector_code series_id year period value footnote_codes seasonal supersector_name industry_name data_type_text state_name area_name
13460 41 26 0 0 SMS41134600000000026 1990 M04 0.2 nan S Total Nonfarm Total Nonfarm All Employees, 3-month average change, In Thousands, seasonally adjusted Oregon Bend-Redmond, OR
13460 41 26 0 0 SMS41134600000000026 1990 M05 0.2 nan S Total Nonfarm Total Nonfarm All Employees, 3-month average change, In Thousands, seasonally adjusted Oregon Bend-Redmond, OR
13460 41 26 0 0 SMS41134600000000026 1990 M06 0.1 nan S Total Nonfarm Total Nonfarm All Employees, 3-month average change, In Thousands, seasonally adjusted Oregon Bend-Redmond, OR
13460 41 26 0 0 SMS41134600000000026 1990 M07 0.1 nan S Total Nonfarm Total Nonfarm All Employees, 3-month average change, In Thousands, seasonally adjusted Oregon Bend-Redmond, OR
13460 41 26 0 0 SMS41134600000000026 1990 M08 0.2 nan S Total Nonfarm Total Nonfarm All Employees, 3-month average change, In Thousands, seasonally adjusted Oregon Bend-Redmond, OR
13460 41 26 0 0 SMS41134600000000026 1990 M09 0.2 nan S Total Nonfarm Total Nonfarm All Employees, 3-month average change, In Thousands, seasonally adjusted Oregon Bend-Redmond, OR
13460 41 26 0 0 SMS41134600000000026 1990 M10 0.1 nan S Total Nonfarm Total Nonfarm All Employees, 3-month average change, In Thousands, seasonally adjusted Oregon Bend-Redmond, OR

Data access

Azure Notebooks

# This is a package in preview.
from azureml.opendatasets import UsLaborEHEState

usLaborEHEState = UsLaborEHEState()
usLaborEHEState_df = usLaborEHEState.to_pandas_dataframe()

usLaborEHEState_df.info()

Azure Databricks

# This is a package in preview.
from azureml.opendatasets import UsLaborEHEState

usLaborEHEState = UsLaborEHEState()
usLaborEHEState_df = usLaborEHEState.to_spark_dataframe()

display(usLaborEHEState_df.limit(5))

Azure Synapse

Sample not available for this platform/package combination.

Next steps

View the rest of the datasets in the Open Datasets catalog.