US Labor Force Statistics

Labor Force Statistics labor force, labor force participation rates, and the civilian noninstitutional population by age, gender, race, and ethnic groups. in the United States.

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.

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.

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 National Employment Hours and Earnings
• US State Employment Hours and Earnings
• US Local Area Unemployment Statistics

Columns

Name	Data type	Unique	Values (sample)
absn_code	int	4	3 4
activity_code	int	7	8 3
ages_code	int	35	10 17
born_code	int	3	1 2
cert_code	int	5	4 3
chld_code	int	6	2 5
class_code	int	14	2 1
disa_code	int	3	2 1
duration_code	int	11	18 6
education_code	int	9	40 19
entr_code	int	3	1 2
expr_code	int	3	1 2
footnote_codes	string	7	nan 4.0
hheader_code	int	2	1
hour_code	int	13	1 16
indy_code	int	323	368 169
jdes_code	int	3	1 2
lfst_code	int	33	20 30
look_code	int	7	1 6
mari_code	int	5	2 1
mjhs_code	int	6	1 5
occupation_code	int	566	8999 4999
orig_code	int	14	1 2
pcts_code	int	23	5 8
period	string	18	M07 M06
periodicity_code	string	3	M Q
race_code	int	14	1 3
rjnw_code	int	9	1 3
rnlf_code	int	11	63 64
rwns_code	int	17	10 1
seasonal	string	2	U S
seek_code	int	2	1
series_id	string	45,478	LNU01300000 LNU02034560
series_title	string	34,264	(Unadj) Employment Level - Agriculture and Related Industries (Unadj) Civilian Labor Force Level
sexs_code	int	3	1 2
tdat_code	int	6	1 4
value	float	121,742	3.0 4.0
vets_code	int	8	25 1
wkst_code	int	7	1 4
year	int	80	2018 2017

Preview

series_id	year	period	value	footnote_codes	lfst_code	periodicity_code	series_title	absn_code	activity_code	ages_code	cert_code	class_code	duration_code	education_code	entr_code	expr_code	hheader_code	hour_code	indy_code	jdes_code	look_code	mari_code	mjhs_code	occupation_code	orig_code	pcts_code	race_code	rjnw_code	rnlf_code	rwns_code	seek_code	sexs_code	tdat_code	vets_code	wkst_code	born_code	chld_code	disa_code	seasonal
LNS11000031Q	1972	Q01	4300	nan	10	Q	(Seas) Civilian Labor Force Level - 20 yrs. & over, Black or African American Men	0	0	17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	S
LNS11000031Q	1972	Q02	4370	nan	10	Q	(Seas) Civilian Labor Force Level - 20 yrs. & over, Black or African American Men	0	0	17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	S
LNS11000031Q	1972	Q03	4397	nan	10	Q	(Seas) Civilian Labor Force Level - 20 yrs. & over, Black or African American Men	0	0	17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	S
LNS11000031Q	1972	Q04	4381	nan	10	Q	(Seas) Civilian Labor Force Level - 20 yrs. & over, Black or African American Men	0	0	17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	S
LNS11000031Q	1973	Q01	4408	nan	10	Q	(Seas) Civilian Labor Force Level - 20 yrs. & over, Black or African American Men	0	0	17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	S
LNS11000031Q	1973	Q02	4445	nan	10	Q	(Seas) Civilian Labor Force Level - 20 yrs. & over, Black or African American Men	0	0	17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	S
LNS11000031Q	1973	Q03	4477	nan	10	Q	(Seas) Civilian Labor Force Level - 20 yrs. & over, Black or African American Men	0	0	17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	S
LNS11000031Q	1973	Q04	4523	nan	10	Q	(Seas) Civilian Labor Force Level - 20 yrs. & over, Black or African American Men	0	0	17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	S
LNS11000031Q	1974	Q01	4574	nan	10	Q	(Seas) Civilian Labor Force Level - 20 yrs. & over, Black or African American Men	0	0	17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	S
LNS11000031Q	1974	Q02	4538	nan	10	Q	(Seas) Civilian Labor Force Level - 20 yrs. & over, Black or African American Men	0	0	17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	1	0	0	0	0	0	0	S

Data access

Azure Notebooks

azureml-opendatasets

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

labor = UsLaborLFS()
labor_df = labor.to_pandas_dataframe()

labor_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 = "lfs/"

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 UsLaborLFS

labor = UsLaborLFS()
labor_df = labor.to_spark_dataframe()

display(labor_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 = "lfs/"
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 = "lfs/"
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.