Ore e guadagni per il settore dell'occupazione nazionale negli Stati Uniti

Articolo
04/16/2024

Il programma Current Employment Statistics (CES) genera stime dettagliate per il settore dell'occupazione non agricola con informazioni su ore e guadagni dei lavoratori stipendiati negli Stati Uniti.

Nota

Microsoft fornisce set di dati aperti di Azure "così come sono". Microsoft non offre alcuna garanzia o condizione esplicita o implicita relativamente all'uso dei set di dati da parte dell'utente. Nella misura massima consentita dalle leggi locali, Microsoft non riconosce alcuna responsabilità relativamente a danni o perdite commerciali, inclusi i danni diretti, consequenziali, speciali, indiretti, incidentali o punitivi derivanti dall'uso dei set di dati da parte dell'utente.

Questo set di dati viene fornito in conformità con le condizioni originali in base alle quali Microsoft ha ricevuto i dati di origine. Il set di dati potrebbe includere dati provenienti da Microsoft.

Il file README contenente il file che include informazioni dettagliate per questo set di dati è disponibile nella posizione del set di dati originale.

Il set di dati viene generato dai dati del programma Current Employment Statistics - CES (National) pubblicati da US Bureau of Labor Statistics (BLS). Per informazioni sui termini e sulle condizioni per l'utilizzo di questo set di dati, vedi le informazioni sui collegamenti e sul copyright e le informative importanti sul sito Web.

Posizione di archiviazione

Questo set di dati è archiviato nell'area Stati Uniti orientali di Azure. L'allocazione delle risorse di calcolo nell'area Stati Uniti orientali è consigliata per motivi di affinità.

Colonne

Nome	Tipo di dati	Unica	Valori (esempio)	Descrizione
data_type_code	string	37	1 10	Vedere https://download.bls.gov/pub/time.series/ce/ce.datatype
data_type_text	string	37	ALL EMPLOYEES, THOUSANDS WOMEN EMPLOYEES, THOUSANDS	Vedere https://download.bls.gov/pub/time.series/ce/ce.datatype
footnote_codes	string	2	nan P
industry_code	string	902	30000000 32000000	Settori diversi coperti. Vedere https://download.bls.gov/pub/time.series/ce/ce.industry
industry_name	string	895	Nondurable goods Durable goods	Settori diversi coperti. Vedere https://download.bls.gov/pub/time.series/ce/ce.industry
period	string	13	M03 M06	Vedere https://download.bls.gov/pub/time.series/ce/ce.period
seasonal	string	2	U S
series_id	string	26,021	CEU3100000008 CEU9091912001	Tipi diversi di serie di dati disponibili nel set di dati. Vedere 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	Titolo dei diversi tipi di serie di dati disponibili nel set di dati. Vedere https://download.bls.gov/pub/time.series/ce/ce.series
supersector_code	string	22	31 60	Classificazione di livello superiore dell'industria o del settore. Vedere https://download.bls.gov/pub/time.series/ce/ce.supersector
supersector_name	string	22	Durable Goods Professional and business services	Classificazione di livello superiore dell'industria o del settore. Vedere https://download.bls.gov/pub/time.series/ce/ce.supersector
value	float	572,372	38.5 38.400001525878906
year	int	81	2017 2012

Anteprima

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

Accesso ai dati

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

Campione non disponibile per questa combinazione di piattaforma/pacchetto.

# 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'))

Passaggi successivi

Visualizzare il resto dei set di dati nel catalogo di set di dati aperti.