US National Employment Hours and Earnings (美國全國的工時與工資)

發行項
09/04/2024

目前就業統計 (CES) 計劃會產生詳細的美國非農就業產業預估值、工作時數和工作者的薪資收入。

注意

Microsoft 依「現況」提供 Azure 開放資料集。針對　貴用戶對資料集的使用方式，Microsoft 不提供任何明示或默示的擔保、保證或條件。在　貴用戶當地法律允許的範圍內，針對因使用資料集而導致的任何直接性、衍生性、特殊性、間接性、附隨性或懲罰性損害或損失，Microsoft 概不承擔任何責任。

此資料集是根據 Microsoft 接收來源資料的原始條款所提供。資料集可能包含源自 Microsoft 的資料。

讀我檔案是包含此資料集詳細資訊的檔案，位於原始資料集位置。

此資料集的來源是美國勞工統計局 (BLS) 所發佈的目前就業統計資料 - CES (全國) 資料。如需此資料集相關的使用條款及條件，請參閱 Copyright Information (連結與著作權資訊) 及 Important Web Site Notices (重要網站聲明)。

儲存位置

此資料集儲存於美國東部 Azure 區域。建議您在美國東部配置計算資源，以確保同質性。

US State Employment Hours and Earnings (美國各州的工時與工資)
US Local Area Unemployment Statistics (美國各地區域的失業統計資料)
US Labor Force Statistics (美國勞動力統計資料)

資料行

名稱	資料類型	唯一	Values (sample)	描述
data_type_code	字串	37	1 10	請參閱https://download.bls.gov/pub/time.series/ce/ce.datatype
data_type_text	字串	37	ALL EMPLOYEES, THOUSANDS WOMEN EMPLOYEES, THOUSANDS	請參閱https://download.bls.gov/pub/time.series/ce/ce.datatype
footnote_codes	字串	2	nan P
industry_code	字串	902	30000000 32000000	所涵蓋的不同產業。請參閱https://download.bls.gov/pub/time.series/ce/ce.industry
industry_name	字串	895	Nondurable goods Durable goods	所涵蓋的不同產業。請參閱https://download.bls.gov/pub/time.series/ce/ce.industry
Period	字串	13	M03 M06	請參閱https://download.bls.gov/pub/time.series/ce/ce.period
seasonal	字串	2	U S
series_id	字串	26,021	CEU3100000008 CEU9091912001	資料集中提供的不同資料數列類型。請參閱https://download.bls.gov/pub/time.series/ce/ce.series
series_title	字串	25,685	All employees, thousands, durable goods, not seasonally adjusted All employees, thousands, nondurable goods, not seasonally adjusted	資料集中提供之不同資料數列類型的標題。請參閱https://download.bls.gov/pub/time.series/ce/ce.series
supersector_code	字串	22	31 60	較高層級的產業或部門分類。請參閱https://download.bls.gov/pub/time.series/ce/ce.supersector
supersector_name	字串	22	Durable Goods Professional and business services	較高層級的產業或部門分類。請參閱https://download.bls.gov/pub/time.series/ce/ce.supersector
value	float	572,372	38.5 38.400001525878906
year	int	81	2017 2012

預覽

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	下午 07:39	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	下午 07:39	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	下午 07:39	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	下午 07:39	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	下午 07:39	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	下午 07:39	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	下午 07:39	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	下午 07:39	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

資料存取

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

此平台/封裝組合沒有可用的樣本。

# 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'))

下一步

檢視開放資料集目錄中的其餘資料集。

共用方式為

US National Employment Hours and Earnings (美國全國的工時與工資)

儲存位置

資料行

預覽

資料存取

Azure Notebooks

Azure Databricks

Azure Synapse

下一步

意見反應

其他資源

共用方式為

US National Employment Hours and Earnings (美國全國的工時與工資)

儲存位置

相關資料集

資料行

預覽​​

資料存取

Azure Notebooks

Azure Databricks

Azure Synapse

下一步

意見反應

其他資源

預覽