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US Producer Price Index - Commodities

labor statistics ppi commodity

Der Erzeugerpreisindex (EPI) ist ein Maß für die durchschnittliche zeitliche Veränderung der Verkaufspreise, die inländische Erzeuger für ihre Leistungen erzielen. Die im EPI enthaltenen Preise stammen aus der ersten kommerziellen Transaktion für die erfassten Produkte und Dienstleistungen.

Jeden Monat werden etwa 10.000 EPIs für einzelne Produkte und Produktgruppen veröffentlicht. EPIs stehen für die Leistung fast aller Industrien in den warenproduzierenden Sektoren der US-Wirtschaft (Bergbau, Produktion, Landwirtschaft, Fischerei und Forstwirtschaft) zur Verfügung sowie für Erdgas, Elektrizität, Baugewerbe und Güter, die mit denen der produzierenden Industrie konkurrieren, wie Abfälle und Ausschussmaterialien. Das EPI-Programm deckt etwa 72 Prozent der Leistung des Dienstleistungssektors ab, gemessen am Umsatz der Wirtschaftszählung von 2007. Die Daten umfassen Branchen in den folgenden Sektoren: Groß- und Einzelhandel; Transport und Lagerung; Informationsbereich; Finanzen und Versicherungen; Immobilienvermittlung, -vermietung und -Leasing; professionelle, wissenschaftliche und technische Dienstleistungen; Verwaltungs-, Unterstützungs- und Entsorgungsdienstleistungen; Gesundheitswesen und Sozialhilfe sowie Beherbergung.

README mit detaillierten Informationen zu diesem Dataset finden Sie am Speicherort des ursprünglichen Datasets. Es sind zusätzliche Informationen in den FAQs verfügbar.

Dieses Dataset wird aus den vom US Bureau of Labor Statistics (BLS) veröffentlichten Daten der Erzeugerpreisindizes erzeugt. Lesen Sie die Informationen zu Verknüpfungen und Copyright und wichtige Hinweise zur Website, um mehr über die Bestimmungen für die Nutzung dieses Datasets zu erfahren.

Speicherort

Dieses Dataset wird in der Azure-Region „USA, Osten“ gespeichert. Aus Gründen der Affinität wird die Zuweisung von Computeressourcen in der Region „USA, Osten“ empfohlen.

Zugehörige Datasets

Benachrichtigungen

MICROSOFT STELLT DATASETS DER PLATTFORM AZURE OPEN DATASETS AUF EINER „AS IS“-BASIS (D. H. OHNE MÄNGELGEWÄHR) ZUR VERFÜGUNG. MICROSOFT ÜBERNIMMT WEDER AUSDRÜCKLICH NOCH STILLSCHWEIGEND DIE GEWÄHRLEISTUNG FÜR IHRE NUTZUNG DER DATENSÄTZE UND SICHERT KEINERLEI GARANTIEN ODER BEDINGUNGEN ZU. SOWEIT NACH ÖRTLICH ANWENDBAREM RECHT ZULÄSSIG, LEHNT MICROSOFT JEGLICHE HAFTUNG FÜR SCHÄDEN ODER VERLUSTE AB. DIES SCHLIEßT DIREKTE, INDIREKTE, BESONDERE ODER ZUFÄLLIGE SCHÄDEN ODER VERLUSTE SOWIE FOLGE- UND STRAFSCHÄDEN UND DAMIT VERBUNDENE VERLUSTE EIN.

Für die Bereitstellung dieses Datasets gelten die ursprünglichen Nutzungsbedingungen, unter denen Microsoft die Quelldaten bezogen hat. Das Dataset kann Daten von Microsoft enthalten.

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

Bezeichnet Fußnoten für die Datenreihe. Die meisten Werte sind NULL. Siehe https://download.bls.gov/pub/time.series/wp/wp.footnote.

group_code string 56 02
01

Code, der die wichtigsten Warengruppen nach Index angibt. Die Codes und Namen von Gruppen finden Sie unter https://download.bls.gov/pub/time.series/wp/wp.group.

group_name string 56 Processed foods and feeds
Farm products

Name der wichtigsten Warengruppen nach Index. Die Codes und Namen von Gruppen finden Sie unter https://download.bls.gov/pub/time.series/wp/wp.group.

item_code string 2,949 1
11

Identifiziert den Artikel, auf den die erfassten Daten zutreffen. Die Codes und Namen des Artikels finden Sie unter https://download.bls.gov/pub/time.series/wp/wp.item.

item_name string 3,410 Warehousing, storage, and related services
Security guard services

Vollständige Namen der Artikel. Die Codes und Namen des Artikels finden Sie unter https://download.bls.gov/pub/time.series/wp/wp.item.

period string 13 M06
M07

Identifiziert den Zeitraum, über den Daten beobachtet werden. Eine Liste von Werten für den Zeitraum finden Sie unter https://download.bls.gov/pub/time.series/wp/wp.period.

seasonal string 2 U
S

Code, der angibt, ob die Daten saisonabhängig angepasst sind. S=Seasonally Adjusted (saisonabhängig angepasst); U=Unadjusted (nicht angepasst)

series_id string 5,458 WPU601
WPU011

Code, der die spezifische Reihen angibt. Eine Zeitreihe umfasst Daten, die über einen längeren Zeitraum in konsistenten Zeitintervallen erfasst werden. Informationen zu Reihen wie Code, Name, Start- und Endjahr usw. finden Sie unter https://download.bls.gov/pub/time.series/wp/wp.series.

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

Titel der spezifischen Reihe. Eine Zeitreihe umfasst Daten, die über einen längeren Zeitraum in konsistenten Zeitintervallen erfasst werden. Informationen zu Reihen wie ID, Name, Start- und Endjahr usw. finden Sie unter https://download.bls.gov/pub/time.series/wp/wp.series.

value float 6,788 100.0
99.0999984741211

Preisindex für Artikel.

year int 26 2018
2017

Jahr, in dem die Überwachung stattgefunden hat.

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'))