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US Producer Price Index - Commodities

labor statistics ppi commodity

PPI(생산자 물가 지수)는 국내 생산자가 얻는 산출물 판매 가격의 시간에 따른 평균 변동을 측정한 값입니다. PPI에 포함된 가격은 해당 제품 및 서비스의 첫 번째 상거래에서 나온 것입니다.

매월 개별 제품 및 제품 그룹에 대해 약 10,000개의 PPI가 릴리스됩니다. PPI는 미국 경제의 제품 생산 부문에 속한 거의 모든 산업(광업, 제조업, 농업, 어업, 임업, 천연가스업, 전기업, 건설업뿐 아니라 생산 부문에서 제조된 제품의 경쟁 제품(예: 폐기물) 재활용업)의 산출물로 구성됩니다. 2007 Economic Census에서 보고된 수익 기준으로 측정된 바에 따르면 PPI 프로그램은 약 72%의 서비스 부문 산출물에 적용됩니다. 데이터에는 도매 및 소매업, 운송 및 창고업, 정보, 금융 및 보험, 부동산 중개, 임대 및 리스, 전문, 과학 및 기술 서비스, 행정, 지원 및 폐기물 관리 서비스, 건강 관리 및 사회적 지원, 숙박업 등의 부문에 속한 산업이 포함됩니다.

이 데이터 세트에 대한 자세한 정보의 파일을 포함하는 추가 정보원본 데이터 세트 위치에서 확인할 수 있습니다. 추가 정보는 FAQ에서 확인할 수 있습니다.

이 데이터 세트는 미국 BLS(노동 통계국)에서 게시한 생산자 물가 지수 데이터에서 생성됩니다. 이 데이터 세트 사용과 관련된 사용 약관은 연결 및 저작권 정보중요한 웹 사이트 고지 사항을 검토하세요.

스토리지 위치

이 데이터 세트는 미국 동부 Azure 지역에 저장됩니다. 선호도를 위해 미국 동부에 컴퓨팅 리소스를 할당하는 것이 좋습니다.

관련 데이터 세트

알림

Microsoft는 Azure Open Datasets를 “있는 그대로” 제공합니다. Microsoft는 귀하의 데이터 세트 사용과 관련하여 어떠한 명시적이거나 묵시적인 보증, 보장 또는 조건을 제공하지 않습니다. 귀하가 거주하는 지역의 법규가 허용하는 범위 내에서 Microsoft는 귀하의 데이터 세트 사용으로 인해 발생하는 일체의 직접적, 결과적, 특별, 간접적, 부수적 또는 징벌적 손해 또는 손실을 비롯한 모든 손해 또는 손실에 대한 모든 책임을 부인합니다.

이 데이터 세트는 Microsoft가 원본 데이터를 받은 원래 사용 약관에 따라 제공됩니다. 데이터 세트에는 Microsoft가 제공한 데이터가 포함될 수 있습니다.

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

item_code group_code series_id year period value footnote_codes seasonal series_title group_name item_name
120922 05 WPU05120922 2008 M06 100 nan U PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted Fuels and related products and power Prepared bituminous coal underground mine, mechanically crushed/screened/sized only
120922 05 WPU05120922 2008 M07 104.6 nan U PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted Fuels and related products and power Prepared bituminous coal underground mine, mechanically crushed/screened/sized only
120922 05 WPU05120922 2008 M08 104.4 nan U PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted Fuels and related products and power Prepared bituminous coal underground mine, mechanically crushed/screened/sized only
120922 05 WPU05120922 2008 M09 98.3 nan U PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted Fuels and related products and power Prepared bituminous coal underground mine, mechanically crushed/screened/sized only
120922 05 WPU05120922 2008 M10 101.5 nan U PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted Fuels and related products and power Prepared bituminous coal underground mine, mechanically crushed/screened/sized only
120922 05 WPU05120922 2008 M11 95.2 nan U PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted Fuels and related products and power Prepared bituminous coal underground mine, mechanically crushed/screened/sized only
120922 05 WPU05120922 2008 M12 96.7 nan U PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted Fuels and related products and power Prepared bituminous coal underground mine, mechanically crushed/screened/sized only
120922 05 WPU05120922 2009 M01 104.2 nan U PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted Fuels and related products and power Prepared bituminous coal underground mine, mechanically crushed/screened/sized only
120922 05 WPU05120922 2009 M02 113.2 nan U PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted Fuels and related products and power Prepared bituminous coal underground mine, mechanically crushed/screened/sized only
120922 05 WPU05120922 2009 M03 121 nan U PPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjusted Fuels and related products and power Prepared bituminous coal underground mine, mechanically crushed/screened/sized only
Name Data type Unique Values (sample) Description
footnote_codes string 3 nan
P

데이터 계열의 각주를 식별합니다. 대부분 값은 null입니다. https://download.bls.gov/pub/time.series/wp/wp.footnote을 참조하세요.

group_code string 56 02
01

지수가 적용되는 주요 상품 그룹을 식별하는 코드입니다. 그룹 코드 및 이름은 https://download.bls.gov/pub/time.series/wp/wp.group 을 참조하세요.

group_name string 56 Processed foods and feeds
Farm products

지수가 적용되는 주요 상품 그룹의 이름입니다. 그룹 코드 및 이름은 https://download.bls.gov/pub/time.series/wp/wp.group 을 참조하세요.

item_code string 2,949 1
11

데이터 관찰이 적용되는 항목을 식별합니다. 항목 코드 및 이름은 https://download.bls.gov/pub/time.series/wp/wp.item 을 참조하세요.

item_name string 3,410 Warehousing, storage, and related services
Janitorial services

항목의 전체 이름입니다. 항목 코드 및 이름은 https://download.bls.gov/pub/time.series/wp/wp.item 을 참조하세요.

period string 13 M06
M07

데이터가 관찰된 기간을 식별합니다. 기간 값의 목록은 https://download.bls.gov/pub/time.series/wp/wp.period 를 참조하세요.

seasonal string 2 U
S

데이터가 계절에 따라 조정되었는지 여부를 식별하는 코드입니다. S=Seasonally Adjusted; U=Unadjusted

series_id string 5,458 WPU101
WPU601

특정 계열을 식별하는 코드입니다. 시계열은 일관된 시간 간격으로 긴 기간 동안 관찰된 데이터 세트를 의미합니다. 코드, 이름, 시작 및 종료 연도 등의 시리즈에 관한 자세한 내용은 https://download.bls.gov/pub/time.series/wp/wp.series 를 참조하세요.

series_title string 4,379 PPI Commodity data for Mining services, not seasonally adjusted
PPI Commodity data for Metal treatment services, not seasonally adjusted

특정 계열의 제목입니다. 시계열은 일관된 시간 간격으로 긴 기간 동안 관찰된 데이터 세트를 의미합니다. ID, 이름, 시작 및 종료 연도 등의 시리즈에 관한 자세한 내용은 https://download.bls.gov/pub/time.series/wp/wp.series 을 참조하세요.

value float 6,788 100.0
99.0999984741211

항목에 대한 가격 지수입니다.

year int 26 2018
2017

관찰 연도를 식별합니다.

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 UsLaborPPICommodity

labor = UsLaborPPICommodity()
labor_df = labor.to_pandas_dataframe()
ActivityStarted, to_pandas_dataframe
ActivityStarted, to_pandas_dataframe_in_worker
Looking for parquet files...
Reading them into Pandas dataframe...
Reading ppi_commodity/part-00000-tid-160579496407747812-077bf440-b39a-4520-9373-0a3f021dd59e-5654-1-c000.snappy.parquet under container laborstatisticscontainer
Done.
ActivityCompleted: Activity=to_pandas_dataframe_in_worker, HowEnded=Success, Duration=20409.23 [ms]
ActivityCompleted: Activity=to_pandas_dataframe, HowEnded=Success, Duration=20434.79 [ms]
In [2]:
labor_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6825676 entries, 0 to 6825675
Data columns (total 11 columns):
item_code         object
group_code        object
series_id         object
year              int32
period            object
value             float32
footnote_codes    object
seasonal          object
series_title      object
group_name        object
item_name         object
dtypes: float32(1), int32(1), object(9)
memory usage: 520.8+ MB
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 = "ppi_commodity/"
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 UsLaborPPICommodity

labor = UsLaborPPICommodity()
labor_df = labor.to_spark_dataframe()
ActivityStarted, to_spark_dataframe ActivityStarted, to_spark_dataframe_in_worker ActivityCompleted: Activity=to_spark_dataframe_in_worker, HowEnded=Success, Duration=2871.21 [ms] ActivityCompleted: Activity=to_spark_dataframe, HowEnded=Success, Duration=2875.06 [ms]
In [2]:
display(labor_df.limit(5))
item_codegroup_codeseries_idyearperiodvaluefootnote_codesseasonalseries_titlegroup_nameitem_name
12092205WPU05120922 2008M06100.0nanUPPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjustedFuels and related products and powerPrepared bituminous coal underground mine, mechanically crushed/screened/sized only
12092205WPU05120922 2008M07104.6nanUPPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjustedFuels and related products and powerPrepared bituminous coal underground mine, mechanically crushed/screened/sized only
12092205WPU05120922 2008M08104.4nanUPPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjustedFuels and related products and powerPrepared bituminous coal underground mine, mechanically crushed/screened/sized only
12092205WPU05120922 2008M0998.3nanUPPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjustedFuels and related products and powerPrepared bituminous coal underground mine, mechanically crushed/screened/sized only
12092205WPU05120922 2008M10101.5nanUPPI Commodity data for Fuels and related products and power-Prepared bituminous coal underground mine, mechanically crushed/screened/sized only, not seasonally adjustedFuels and related products and powerPrepared bituminous coal underground mine, mechanically crushed/screened/sized only
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "laborstatisticscontainer"
blob_relative_path = "ppi_commodity/"
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 = "ppi_commodity/"
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'))