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US Local Area Unemployment Statistics

labor statistics local area unemployment

地区失业统计 (LAUS) 计划为美国人口普查地区及分区、州、县、大都市区和许多城市提供月度和年度就业、失业和劳动力数据。

原始数据集位置提供了自述文件,其中包含介绍此数据集详细信息的文件。

此数据集来源于美国劳工统计局 (BLS) 发布的地区失业统计数据。 要了解与使用此数据集相关的条款和条件,请查看链接与版权信息以及重要网站声明

存储位置

此数据集存储在美国东部 Azure 区域。 建议将计算资源分配到美国东部地区,以实现相关性。

相关数据集

通知

Microsoft 以“原样”为基础提供 AZURE 开放数据集。 Microsoft 对数据集的使用不提供任何担保(明示或暗示)、保证或条件。 在当地法律允许的范围内,Microsoft 对使用数据集而导致的任何损害或损失不承担任何责任,包括直接、必然、特殊、间接、偶发或惩罚。

此数据集是根据 Microsoft 接收源数据的原始条款提供的。 数据集可能包含来自 Microsoft 的数据。

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 ST1800000000000
ST5600000000000

标识地理区域的代码。 请参阅 https://download.bls.gov/pub/time.series/la/la.area。

area_text string 8,238 District of Columbia
New Hampshire

地理区域的名称。 请参阅https://download.bls.gov/pub/time.series/la/la.area

area_type_code string 14 F
G

定义区域类型的唯一代码。 请参阅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

区域类型的名称。

footnote_codes string 5 nan
P
measure_code string 4 4
5

标识被度量元素的代码。 03:失业率,04:失业人数,05:就业人数,06:劳动力总数。 请参阅 https://download.bls.gov/pub/time.series/la/la.measure。

measure_text string 4 labor force
unemployment

被度量元素的名称。 请参阅https://download.bls.gov/pub/time.series/la/la.measure

period string 13 M07
M04

标识时间段,通常为月。 请参阅https://download.bls.gov/pub/time.series/la/la.period

seasonal string 2 U
S
series_id string 33,476 LASRD930000000000005
LASST250000000000003

标识系列的代码。 有关完整系列列表,请参阅 https://download.bls.gov/pub/time.series/la/la.series 。

series_title string 33,268 Unemployment: Harrisonburg city, VA (U)
Labor Force: Franklin city, VA (U)

标识系列的标题。 有关完整系列列表,请参阅 https://download.bls.gov/pub/time.series/la/la.series 。

srd_code string 53 48
23

州、区域或部门代码。

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

特定度量单位的值。

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'))