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US Local Area Unemployment Statistics

labor statistics local area unemployment

Le programme LAUS (de statistiques sur le chômage au niveau local) produit des données mensuelles et annuelles sur l’emploi, le chômage et la population active pour les régions et divisions de recensement, les États, les comtés, les régions métropolitaines et de nombreuses villes des États-Unis.

Le fichier README contenant des informations détaillées sur ce jeu de données est disponible à l’emplacement d’origine du jeu de données.

Ce jeu de données provient des données Local Area Unemployment Statistics publiées par le Bureau of Labor Statistics (BLS) aux États-Unis. Consultez les informations de liaison et de copyright et les avis importants de site web pour connaître les conditions générales relatives à l’utilisation de ce jeu de données.

Emplacement de stockage

Ce jeu de données est stocké dans la région Azure USA Est. L’allocation de ressources de calcul dans la région USA Est est recommandée à des fins d’affinité.

Jeux de données associés

Remarques

MICROSOFT FOURNIT AZURE OPEN DATASETS “EN L’ÉTAT”. MICROSOFT N’OFFRE AUCUNE GARANTIE, EXPRESSE OU IMPLICITE, DE GARANTIE NI DE CONDITIONS RELATIVES À VOTRE UTILISATION DES JEUX DE DONNÉES. DANS LA MESURE AUTORISÉE PAR VOTRE DROIT LOCAL, MICROSOFT DÉCLINE TOUTE RESPONSABILITÉ POUR TOUT DOMMAGE OU PERTES, Y COMPRIS LES DIRECTIVES, CONSEQUENTIELLES, SPÉCIALES, INDIRECTES OU PUNITIVES, RÉSULTANT DE VOTRE UTILISATION DES JEUX DE DONNÉES.

Ce jeu de données est fourni selon les conditions initiales par lesquelles Microsoft a reçu les données sources. Le jeu de données peut inclure des données provenant de Microsoft.

Access

Available inWhen 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

area_code area_type_code srd_code measure_code series_id year period value footnote_codes seasonal series_title measure_text srd_text areatype_text area_text
CA3653200000000 E 36 3 LAUCA365320000000003 2000 M01 4.7 nan U Unemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U) unemployment rate New York Combined areas Syracuse-Auburn, NY Combined Statistical Area
CA3653200000000 E 36 3 LAUCA365320000000003 2000 M02 4.7 nan U Unemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U) unemployment rate New York Combined areas Syracuse-Auburn, NY Combined Statistical Area
CA3653200000000 E 36 3 LAUCA365320000000003 2000 M03 4.2 nan U Unemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U) unemployment rate New York Combined areas Syracuse-Auburn, NY Combined Statistical Area
CA3653200000000 E 36 3 LAUCA365320000000003 2000 M04 3.6 nan U Unemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U) unemployment rate New York Combined areas Syracuse-Auburn, NY Combined Statistical Area
CA3653200000000 E 36 3 LAUCA365320000000003 2000 M05 3.6 nan U Unemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U) unemployment rate New York Combined areas Syracuse-Auburn, NY Combined Statistical Area
CA3653200000000 E 36 3 LAUCA365320000000003 2000 M06 3.6 nan U Unemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U) unemployment rate New York Combined areas Syracuse-Auburn, NY Combined Statistical Area
CA3653200000000 E 36 3 LAUCA365320000000003 2000 M07 3.6 nan U Unemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U) unemployment rate New York Combined areas Syracuse-Auburn, NY Combined Statistical Area
CA3653200000000 E 36 3 LAUCA365320000000003 2000 M08 3.5 nan U Unemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U) unemployment rate New York Combined areas Syracuse-Auburn, NY Combined Statistical Area
CA3653200000000 E 36 3 LAUCA365320000000003 2000 M09 3.5 nan U Unemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U) unemployment rate New York Combined areas Syracuse-Auburn, NY Combined Statistical Area
CA3653200000000 E 36 3 LAUCA365320000000003 2000 M10 3.3 nan U Unemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U) unemployment rate New York Combined areas Syracuse-Auburn, NY Combined Statistical Area
Name Data type Unique Values (sample) Description
area_code string 8,290 ST0500000000000
RD8800000000000

Code identifiant la zone géographique. Consultez https://download.bls.gov/pub/time.series/la/la.area.

area_text string 8,238 District of Columbia
Missouri

Nom de la zone géographique. Consultez https://download.bls.gov/pub/time.series/la/la.area

area_type_code string 14 F
G

Code unique définissant le type de zone. Consultez https://download.bls.gov/pub/time.series/la/la.area_type

areatype_text string 14 Counties and equivalents
Cities and towns above 25,000 population

Nom du type de zone.

footnote_codes string 5 nan
P
measure_code string 4 4
3

Code identifiant l’élément mesuré. 03 : taux de chômage, 04 : chômage, 05 : emploi, 06 : main-d’œuvre. Consultez https://download.bls.gov/pub/time.series/la/la.measure.

measure_text string 4 unemployment rate
unemployment

Nom de l’élément mesuré. Consultez https://download.bls.gov/pub/time.series/la/la.measure

period string 13 M07
M05

Identifie la période, généralement le mois. Consultez https://download.bls.gov/pub/time.series/la/la.period

seasonal string 2 U
S
series_id string 33,476 LASST210000000000004
LASST040000000000006

Code identifiant la série. Voir https://download.bls.gov/pub/time.series/la/la.series pour la liste complète de séries.

series_title string 33,268 Unemployment Rate: Virginia Beach city, VA (U)
Employment: Fredericksburg city, VA (U)

Titre identifiant la série. Voir https://download.bls.gov/pub/time.series/la/la.series pour la liste complète de séries.

srd_code string 53 48
23

Code de division, État ou région.

srd_text string 53 Texas
Maine
value float 600,099 4.0
5.0

Valeur de la mesure spécifique.

year int 44 2008
2009

Select your preferred service:

Azure Notebooks

Azure Databricks

Azure Synapse

Azure Notebooks

Package: Language: Python Python
In [1]:
# This is a package in preview.
from azureml.opendatasets import UsLaborLAUS

usLaborLAUS = UsLaborLAUS()
usLaborLAUS_df = usLaborLAUS.to_pandas_dataframe()
Looking for parquet files... Reading them into Pandas dataframe... Reading laus/part-00000-tid-6506298405389763282-d1280c40-3980-4136-af49-5def25951a63-53767-c000.snappy.parquet under container laborstatisticscontainer Done.
In [2]:
usLaborLAUS_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 12289052 entries, 0 to 12289051 Data columns (total 15 columns): area_code object area_type_code object srd_code object measure_code object series_id object year int32 period object value float32 footnote_codes object seasonal object series_title object measure_text object srd_text object areatype_text object area_text object dtypes: float32(1), int32(1), object(13) memory usage: 1.3+ GB
In [3]:
 
In [1]:
# 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
In [2]:
# Azure storage access info
azure_storage_account_name = "azureopendatastorage"
azure_storage_sas_token = r""
container_name = "laborstatisticscontainer"
folder_name = "laus/"
In [3]:
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)
In [4]:
# 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)
In [5]:
# you can add your filter at below
print('Loaded as a Pandas data frame: ')
df
In [6]:
 

Azure Databricks

Package: Language: Python Python
In [1]:
# This is a package in preview.
from azureml.opendatasets import UsLaborLAUS

usLaborLAUS = UsLaborLAUS()
usLaborLAUS_df = usLaborLAUS.to_spark_dataframe()
In [2]:
display(usLaborLAUS_df.limit(5))
area_codearea_type_codesrd_codemeasure_codeseries_idyearperiodvaluefootnote_codesseasonalseries_titlemeasure_textsrd_textareatype_textarea_text
CA3653200000000E363LAUCA3653200000000032000M014.7nanUUnemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U)unemployment rateNew YorkCombined areasSyracuse-Auburn, NY Combined Statistical Area
CA3653200000000E363LAUCA3653200000000032000M024.7nanUUnemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U)unemployment rateNew YorkCombined areasSyracuse-Auburn, NY Combined Statistical Area
CA3653200000000E363LAUCA3653200000000032000M034.2nanUUnemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U)unemployment rateNew YorkCombined areasSyracuse-Auburn, NY Combined Statistical Area
CA3653200000000E363LAUCA3653200000000032000M043.6nanUUnemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U)unemployment rateNew YorkCombined areasSyracuse-Auburn, NY Combined Statistical Area
CA3653200000000E363LAUCA3653200000000032000M053.6nanUUnemployment Rate: Syracuse-Auburn, NY Combined Statistical Area (U)unemployment rateNew YorkCombined areasSyracuse-Auburn, NY Combined Statistical Area
In [3]:
 
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "laborstatisticscontainer"
blob_relative_path = "laus/"
blob_sas_token = r""
In [2]:
# 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)
In [3]:
# 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')
In [4]:
# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Azure Synapse

Package: Language: Python
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "laborstatisticscontainer"
blob_relative_path = "laus/"
blob_sas_token = r""
In [2]:
# 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)
In [3]:
# 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')
In [4]:
# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))