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US Local Area Unemployment Statistics

labor statistics local area unemployment

Local Area Unemployment Statistics (LAUS) プログラムでは、米国の国勢調査地域/区分、州、郡、大都市圏、および多数の都市の雇用、失業、労働力に関する月次および年次データを生成します。

このデータセットの詳細情報が含まれた README ファイルは、データセットの元の場所で入手できます。

このデータセットは、米国労働統計局 (BLS) によって公開されている Local Area Unemployment Statistics (地域別失業統計) のデータをソースとしています。 このデータセットの使用に関する諸条件については、「Linking and Copyright Information (リンクおよび著作権情報)」と「Important Web Site Notices (Web サイトに関する重要な通知)」を確認してください。

保存先

このデータセットは、米国東部 Azure リージョンに保存されています。 アフィニティのために、米国東部でコンピューティング リソースを割り当てることをお勧めします。

関連データセット

通知

Microsoft は、Azure オープン データセットを “現状有姿” で提供します。 Microsoft は、データセットの使用に関して、明示または黙示を問わず、いかなる保証も行わないものとし、条件を定めることもありません。 現地の法律の下で認められている範囲内で、Microsoft は、データセットの使用に起因する、直接的、派生的、特別、間接的、偶発的、または懲罰的なものを含めたいかなる損害または損失に対しても一切の責任を負わないものとします。

このデータセットは、Microsoft がソース データを受け取った元の条件に基づいて提供されます。 データセットには、Microsoft が提供するデータが含まれている場合があります。

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

地理的地域を識別するコード。 以下を参照してください。https://download.bls.gov/pub/time.series/la/la.area

area_text string 8,238 District of Columbia
Missouri

地理的地域の名前。 「https://download.bls.gov/pub/time.series/la/la.area」を参照してください。

area_type_code string 14 F
G

地域の種類を識別する一意のコード。 「https://download.bls.gov/pub/time.series/la/la.area_type」を参照してください。

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

地域の種類の名前。

footnote_codes string 5 nan
P
measure_code string 4 4
3

測定された要素を識別するコード。 03: 失業率、04: 失業、05: 雇用、06: 労働力。 以下を参照してください。https://download.bls.gov/pub/time.series/la/la.measure

measure_text string 4 unemployment rate
unemployment

測定された要素の名前。 「https://download.bls.gov/pub/time.series/la/la.measure」を参照してください。

period string 13 M07
M05

期間を識別 (通常は月)。 「https://download.bls.gov/pub/time.series/la/la.period」を参照してください。

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

系列を識別するコード。 系列の完全なリストについては、 https://download.bls.gov/pub/time.series/la/la.series をご覧ください。

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

系列を識別するタイトル。 系列の完全なリストについては、 https://download.bls.gov/pub/time.series/la/la.series をご覧ください。

srd_code string 53 48
23

州、地域、または区域コード。

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

特定の測定の値。

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'))