Horario laboral y salarios de ámbito nacional de Estados Unidos

Artículo
12/06/2023

El programa Current Employment Statistics (CES) realiza cálculos detallados sobre el empleo, el horario y los ingresos de los trabajadores en plantilla fuera del sector agrario en Estados Unidos.

Nota

Microsoft proporciona Azure Open Datasets "tal cual". Microsoft no ofrece ninguna garantía, expresa o implícita, ni condición con respecto al uso que usted haga de los conjuntos de datos. En la medida en la que lo permita su legislación local, Microsoft declina toda responsabilidad por posibles daños o pérdidas, incluidos los daños directos, consecuenciales, especiales, indirectos, incidentales o punitivos, que resulten de su uso de los conjuntos de datos.

Este conjunto de datos se proporciona bajo los términos originales con los que Microsoft recibió los datos de origen. El conjunto de datos puede incluir datos procedentes de Microsoft.

El archivo LÉAME que contiene el archivo de información detallada sobre este conjunto de datos está disponible en la ubicación original del conjunto de datos.

Este conjunto de datos se alimenta con los datos del programa Current Employment Statistics - CES (nacional) que publica la Oficina de Estadísticas Laborales (BLS, por sus siglas en inglés) de Estados Unidos. Revise la información sobre enlaces y copyright y los avisos importantes del sitio web para conocer los términos y condiciones relacionados con el uso de este conjunto de datos.

Ubicación de almacenamiento

Este conjunto de datos se almacena en la región Este de EE. UU. de Azure. Se recomienda asignar recursos de proceso de la misma región por afinidad.

Columnas

Nombre	Tipo de datos	Único	Valores (ejemplo)	Descripción
data_type_code	string	37	1 10	Consulta https://download.bls.gov/pub/time.series/ce/ce.datatype.
data_type_text	string	37	ALL EMPLOYEES, THOUSANDS WOMEN EMPLOYEES, THOUSANDS	Consulta https://download.bls.gov/pub/time.series/ce/ce.datatype.
footnote_codes	string	2	nan P
industry_code	string	902	30000000 32000000	Los diferentes sectores abarcados. Vea https://download.bls.gov/pub/time.series/ce/ce.industry.
industry_name	string	895	Nondurable goods Durable goods	Los diferentes sectores abarcados. Vea https://download.bls.gov/pub/time.series/ce/ce.industry.
period	string	13	M03 M06	Consulta https://download.bls.gov/pub/time.series/ce/ce.period.
seasonal (estacional)	string	2	EE. UU.
series_id	string	26,021	CEU3100000008 CEU9091912001	Los diferentes tipos de series de datos disponibles en el conjunto de datos. Vea 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	El título de los diferentes tipos de series de datos disponibles en el conjunto de datos. Vea https://download.bls.gov/pub/time.series/ce/ce.series.
supersector_code	string	22	31 60	La clasificación industrial o sectorial de mayor nivel. Consulta https://download.bls.gov/pub/time.series/ce/ce.supersector.
supersector_name	string	22	Durable Goods Professional and business services	La clasificación industrial o sectorial de mayor nivel. Consulta https://download.bls.gov/pub/time.series/ce/ce.supersector.
value	FLOAT	572,372	38.5 38.400001525878906
year	int	81	2017 2012

Vista previa

data_type_code	industry_code	supersector_code	series_id	year	period	value	footnote_codes	seasonal (estacional)	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

Acceso a datos

Azure Notebooks

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

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

usLaborEHENational_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 = "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

Azure Databricks

# 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 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

Ejemplo no disponible para esta combinación de plataforma y paquete.

# 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'))

Pasos siguientes

Consulte el resto de los conjuntos de datos en el catálogo de Open Datasets.