US National Employment Hours and Earnings

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.

README containing file for detailed information about this dataset is available at the original dataset location.

This dataset is sourced from Current Employment Statistics - CES (National) 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.

Related datasets

• US State Employment Hours and Earnings
• US Local Area Unemployment Statistics
• US Labor Force Statistics

Columns

Name	Data type	Unique	Values (sample)	Description
data_type_code	string	37	1 10	See https://download.bls.gov/pub/time.series/ce/ce.datatype
data_type_text	string	37	ALL EMPLOYEES, THOUSANDS WOMEN EMPLOYEES, THOUSANDS	See https://download.bls.gov/pub/time.series/ce/ce.datatype
footnote_codes	string	2	nan P
industry_code	string	902	30000000 32000000	Different industries covered. See https://download.bls.gov/pub/time.series/ce/ce.industry
industry_name	string	895	Nondurable goods Durable goods	Different industries covered. See https://download.bls.gov/pub/time.series/ce/ce.industry
period	string	13	M03 M06	See https://download.bls.gov/pub/time.series/ce/ce.period
seasonal	string	2	U S
series_id	string	26,021	CEU3100000008 CEU9091912001	Different types of data series available in the dataset. See https://download.bls.gov/pub/time.series/ce/ce.series
series_title	string	25,685	All employees, thousands, durable goods, not seasonally adjusted All employees, thousands, nondurable goods, not seasonally adjusted	Title of the different types of data series available in the dataset. See https://download.bls.gov/pub/time.series/ce/ce.series
supersector_code	string	22	31 60	Higher-level industry or sector classification. See https://download.bls.gov/pub/time.series/ce/ce.supersector
supersector_name	string	22	Durable Goods Professional and business services	Higher-level industry or sector classification. See https://download.bls.gov/pub/time.series/ce/ce.supersector
value	float	572,372	38.5 38.400001525878906
year	int	81	2017 2012

Preview

data_type_code	industry_code	supersector_code	series_id	year	period	value	footnote_codes	seasonal	series_title	supersector_name	industry_name	data_type_text
26	5000000	5	CES0500000026	1939	M04	52	nan	S	All employees, 3-month average change, seasonally adjusted, thousands, total private, seasonally adjusted	Total private	Total private	ALL EMPLOYEES, 3-MONTH AVERAGE CHANGE, SEASONALLY ADJUSTED, THOUSANDS
26	5000000	5	CES0500000026	1939	M05	65	nan	S	All employees, 3-month average change, seasonally adjusted, thousands, total private, seasonally adjusted	Total private	Total private	ALL EMPLOYEES, 3-MONTH AVERAGE CHANGE, SEASONALLY ADJUSTED, THOUSANDS
26	5000000	5	CES0500000026	1939	M06	74	nan	S	All employees, 3-month average change, seasonally adjusted, thousands, total private, seasonally adjusted	Total private	Total private	ALL EMPLOYEES, 3-MONTH AVERAGE CHANGE, SEASONALLY ADJUSTED, THOUSANDS
26	5000000	5	CES0500000026	1939	M07	103	nan	S	All employees, 3-month average change, seasonally adjusted, thousands, total private, seasonally adjusted	Total private	Total private	ALL EMPLOYEES, 3-MONTH AVERAGE CHANGE, SEASONALLY ADJUSTED, THOUSANDS
26	5000000	5	CES0500000026	1939	M08	108	nan	S	All employees, 3-month average change, seasonally adjusted, thousands, total private, seasonally adjusted	Total private	Total private	ALL EMPLOYEES, 3-MONTH AVERAGE CHANGE, SEASONALLY ADJUSTED, THOUSANDS
26	5000000	5	CES0500000026	1939	M09	152	nan	S	All employees, 3-month average change, seasonally adjusted, thousands, total private, seasonally adjusted	Total private	Total private	ALL EMPLOYEES, 3-MONTH AVERAGE CHANGE, SEASONALLY ADJUSTED, THOUSANDS
26	5000000	5	CES0500000026	1939	M10	307	nan	S	All employees, 3-month average change, seasonally adjusted, thousands, total private, seasonally adjusted	Total private	Total private	ALL EMPLOYEES, 3-MONTH AVERAGE CHANGE, SEASONALLY ADJUSTED, THOUSANDS
26	5000000	5	CES0500000026	1939	M11	248	nan	S	All employees, 3-month average change, seasonally adjusted, thousands, total private, seasonally adjusted	Total private	Total private	ALL EMPLOYEES, 3-MONTH AVERAGE CHANGE, SEASONALLY ADJUSTED, THOUSANDS

Data access

Azure Notebooks

azureml-opendatasets

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

usLaborEHENational = UsLaborEHENational()
usLaborEHENational_df = usLaborEHENational.to_pandas_dataframe()

usLaborEHENational_df.info()

azure-storage

# Pip install packages
import os, sys

!{sys.executable} -m pip install azure-storage-blob
!{sys.executable} -m pip install pyarrow
!{sys.executable} -m pip install pandas

# Azure storage access info
azure_storage_account_name = "azureopendatastorage"
azure_storage_sas_token = r""
container_name = "laborstatisticscontainer"
folder_name = "ehe_national/"

from azure.storage.blob import BlockBlobServicefrom azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient

if azure_storage_account_name is None or azure_storage_sas_token is None:
    raise Exception(
        "Provide your specific name and key for your Azure Storage account--see the Prerequisites section earlier.")

print('Looking for the first parquet under the folder ' +
      folder_name + ' in container "' + container_name + '"...')
container_url = f"https://{azure_storage_account_name}.blob.core.windows.net/"
blob_service_client = BlobServiceClient(
    container_url, azure_storage_sas_token if azure_storage_sas_token else None)

container_client = blob_service_client.get_container_client(container_name)
blobs = container_client.list_blobs(folder_name)
sorted_blobs = sorted(list(blobs), key=lambda e: e.name, reverse=True)
targetBlobName = ''
for blob in sorted_blobs:
    if blob.name.startswith(folder_name) and blob.name.endswith('.parquet'):
        targetBlobName = blob.name
        break

print('Target blob to download: ' + targetBlobName)
_, filename = os.path.split(targetBlobName)
blob_client = container_client.get_blob_client(targetBlobName)
with open(filename, 'wb') as local_file:
    blob_client.download_blob().download_to_stream(local_file)

# Read the parquet file into Pandas data frame
import pandas as pd

print('Reading the parquet file into Pandas data frame')
df = pd.read_parquet(filename)

# you can add your filter at below
print('Loaded as a Pandas data frame: ')
df

pyspark

Sample not available for this platform/package combination.

Azure Databricks

azureml-opendatasets

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

usLaborEHENational = UsLaborEHENational()
usLaborEHENational_df = usLaborEHENational.to_spark_dataframe()

display(usLaborEHENational_df.limit(5))

azure-storage

Sample not available for this platform/package combination.

pyspark

# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "laborstatisticscontainer"
blob_relative_path = "ehe_national/"
blob_sas_token = r""

# Allow SPARK to read from Blob remotely
wasbs_path = 'wasbs://%s@%s.blob.core.windows.net/%s' % (blob_container_name, blob_account_name, blob_relative_path)
spark.conf.set(
  'fs.azure.sas.%s.%s.blob.core.windows.net' % (blob_container_name, blob_account_name),
  blob_sas_token)
print('Remote blob path: ' + wasbs_path)

# SPARK read parquet, note that it won't load any data yet by now
df = spark.read.parquet(wasbs_path)
print('Register the DataFrame as a SQL temporary view: source')
df.createOrReplaceTempView('source')

# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Azure Synapse

azureml-opendatasets

Sample not available for this platform/package combination.

azure-storage

Sample not available for this platform/package combination.

pyspark

# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "laborstatisticscontainer"
blob_relative_path = "ehe_national/"
blob_sas_token = r""

# Allow SPARK to read from Blob remotely
wasbs_path = 'wasbs://%s@%s.blob.core.windows.net/%s' % (blob_container_name, blob_account_name, blob_relative_path)
spark.conf.set(
  'fs.azure.sas.%s.%s.blob.core.windows.net' % (blob_container_name, blob_account_name),
  blob_sas_token)
print('Remote blob path: ' + wasbs_path)

# SPARK read parquet, note that it won't load any data yet by now
df = spark.read.parquet(wasbs_path)
print('Register the DataFrame as a SQL temporary view: source')
df.createOrReplaceTempView('source')

# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Next steps

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