Estatísticas da força de trabalho, taxas de participação da força de trabalho e população civil não institucional por idade, gênero, raça e grupos étnicos nos Estados Unidos.
Este conjunto de dados foi originado dos dados (nacionais) de CES (Estatísticas atuais de emprego) publicados pela BLS (Secretaria de Estatísticas Trabalhistas) dos EUA. Examine as Informações de vinculação e direitos autorais e Avisos importantes do site para ver os termos e condições relacionados ao uso deste conjunto de dados.
Local de armazenamento
Este conjunto de dados está armazenado na região Leste dos EUA do Azure. É recomendável alocar recursos de computação no Leste dos EUA para afinidade.
Conjuntos de dados relacionados
- Emprego, horas e ganhos nacionais nos EUA
- Emprego, horas e ganhos estaduais nos EUA
- Estatísticas de desemprego por local nos EUA
Avisos
A MICROSOFT FORNECE O AZURE OPEN DATASETS NO ESTADO EM QUE SE ENCONTRA. A MICROSOFT NÃO OFERECE GARANTIAS OU COBERTURAS, EXPRESSAS OU IMPLÍCITAS, EM RELAÇÃO AO USO DOS CONJUNTOS DE DADOS. ATÉ O LIMITE PERMITIDO PELA LEGISLAÇÃO LOCAL, A MICROSOFT SE EXIME DE TODA A RESPONSABILIDADE POR DANOS OU PERDAS, INCLUSIVE DIRETOS, CONSEQUENTES, ESPECIAIS, INDIRETOS, ACIDENTAIS OU PUNITIVOS, RESULTANTES DO USO DOS CONJUNTOS DE DADOS.
Esse conjunto de dados é fornecido de acordo com os termos originais com que a Microsoft recebeu os dados de origem. O conjunto de dados pode incluir dados originados da Microsoft.
Access
Available in | When to use |
---|---|
Azure Notebooks | Quickly explore the dataset with Jupyter notebooks hosted on Azure or your local machine. |
Azure Databricks | Use this when you need the scale of an Azure managed Spark cluster to process the dataset. |
Azure Synapse | Use this when you need the scale of an Azure managed Spark cluster to process the dataset. |
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 |
Name | Data type | Unique | Values (sample) | Description |
---|---|---|---|---|
absn_code | int | 4 | 3 4 |
|
activity_code | int | 7 | 8 3 |
|
ages_code | int | 35 | 10 17 |
|
born_code | int | 3 | 2 1 |
|
cert_code | int | 5 | 3 1 |
|
chld_code | int | 6 | 5 3 |
|
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 2 |
|
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 | 64 63 |
|
rwns_code | int | 17 | 10 1 |
|
seasonal | string | 2 | U S |
|
seek_code | int | 2 | 1 | |
series_id | string | 45,478 | LNU00000000 LNU01000000 |
|
series_title | string | 34,264 | (Unadj) Unemployment Rate (unadj) EMPL. LEVEL Agriculture Female |
|
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 |
Azure Notebooks
# This is a package in preview.
from azureml.opendatasets import UsLaborLFS
labor = UsLaborLFS()
labor_df = labor.to_pandas_dataframe()
labor_df.info()
# 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
Azure Databricks
# 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 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
# 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'))