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US Local Area Unemployment Statistics

labor statistics local area unemployment

Yerel Bölge İşsizlik İstatistikleri (LAUS) programı; ABD’deki Sayım bölgeleri ile bölümleri, Eyaletler, vilayetler, metropol alanları ve birçok şehir için aylık ve yıllık istihdam, işsizlik ve işgücü verilerini üretir.

Bu veri kümesi hakkında ayrıntılı bilgilerin bulunduğu dosyayı içeren BENİOKU dosyasını orijinal veri kümesi konumunda bulabilirsiniz.

Bu veri kümesi ABD İşgücü İstatistikleri Bürosu (BLS) tarafından yayımlanan Yerel Bölge İşsizlik İstatistikleri verilerinden alınır. Bu veri kümesinin kullanımıyla ilgili hüküm ve koşullar için Bağlama ve Telif Hakkı Bilgileri ile Önemli Web Sitesi Bildirimleri’ni gözden geçirin.

Depolama Konumu

Bu veri kümesi Doğu ABD Azure bölgesinde depolanır. Benzeşim için Doğu ABD’deki işlem kaynaklarının ayrılması önerilir.

İlgili Veri Kümeleri

Bildirimler

MICROSOFT, AZURE AÇIK VERİ KÜMELERİNİ “OLDUĞU GİBİ” SAĞLAR. MICROSOFT, VERİ KÜMELERİNİ KULLANMANIZLA İLGİLİ AÇIK VEYA ÖRTÜLÜ HİÇBİR GARANTİ VEYA TAAHHÜTTE BULUNMAZ. YEREL KANUNLARINIZIN İZİN VERDİĞİ ÖLÇÜDE, MICROSOFT DOĞRUDAN, BAĞLI, ÖZEL, DOLAYLI, TESADÜFİ VEYA CEZA GEREKTİRENLER DE DAHİL OLMAK ÜZERE HERHANGİ BİR HASAR YA DA KAYIPLA İLGİLİ HİÇBİR SORUMLULUK KABUL ETMEZ.

Bu veri kümesi Microsoft’un kaynak verileri aldığı orijinal hükümler kapsamında sağlanır. Veri kümesi Microsoft’tan alınan verileri içerebilir.

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 ST0100000000000
ST7200000000000

Coğrafi alanı tanımlayan kod. Bkz. https://download.bls.gov/pub/time.series/la/la.area.

area_text string 8,238 District of Columbia
Minnesota

Coğrafi alanın adı. Bkz. https://download.bls.gov/pub/time.series/la/la.area

area_type_code string 14 F
G

Alan türünü tanımlayan benzersiz kod. Bkz. 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

Alan türünün adı.

footnote_codes string 5 nan
P
measure_code string 4 4
5

Ölçülen öğeyi tanımlayan kod. 03: işsizlik oranı, 04: işsizlik, 05: çalışma, 06: iş gücü. Bkz. https://download.bls.gov/pub/time.series/la/la.measure.

measure_text string 4 unemployment
labor force

Ölçülen öğenin adı. Bkz. https://download.bls.gov/pub/time.series/la/la.measure

period string 13 M07
M02

Genellikle ay olmak üzere dönemi tanımlar. Bkz. https://download.bls.gov/pub/time.series/la/la.period

seasonal string 2 U
S
series_id string 33,476 LASST130000000000006
LASST020000000000004

Seriyi tanımlayan kod. Serilerin tam listesi için bkz. https://download.bls.gov/pub/time.series/la/la.series

series_title string 33,268 Unemployment: Manassas city, VA (U)
Unemployment: Carson City, NV (U)

Seriyi tanımlayan başlık. Serilerin tam listesi için bkz. https://download.bls.gov/pub/time.series/la/la.series

srd_code string 53 48
23

Eyalet, bölge veya alan kodu.

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

Belirli ölçümün değeri.

year int 44 2009
2008

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'))