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US Producer Price Index - Commodities

labor statistics ppi commodity

O PPI (Índice de Preços ao Produtor) é uma medida da média de alterações ao longo do tempo nos preços de venda recebidos por produtores domésticos por sua produção. Os preços do PPI são da primeira transação comercial de produtos e serviços cobertos.

Cerca de 10 mil PPIs para produtos e grupos de produtos individuais são liberadas a cada mês. Os PPIs são disponibilizados de acordo com a produção de quase todas as indústrias nos setores de bens da economia dos EUA – mineração, manufatura, agricultura, pescaria e silvicultura – bem como gás natural, eletricidade, construção e bens que competem com os do setor de produção, como resíduos e sucata. O programa PPI cobre aproximadamente 72% da produção do setor de serviços, conforme medido pela receita apresentada no Censo Econômico de 2007. Os dados incluem indústrias nos seguintes setores: comércio em atacado e varejo; transportes e armazenagem; informações; finanças e seguros; corretagem imobiliária, aluguel e arrendamento; serviços técnicos, científicos e profissionais; serviços administrativos, de suporte e gerenciamento de resíduos; saúde e assistência social e hospedagem.

O LEIAME contendo um arquivo com informações detalhadas sobre este conjunto de dados está disponível no local do conjunto de dados original. Confira mais informações disponíveis nas Perguntas frequentes.

Este conjunto de dados foi produzido com base nos dados dos Índices de preços ao produtor, publicados pela BLS (Secretaria de Estatísticas Trabalhistas) dos EUA. Examine as Informações de vinculação e direitos autorais e Avisos importantes do site para ver os termos e condições relacionados ao uso deste conjunto de dados.

Local de armazenamento

Este conjunto de dados está armazenado na região Leste dos EUA do Azure. É recomendável alocar recursos de computação no Leste dos EUA para afinidade.

Conjuntos de dados relacionados

Avisos

A MICROSOFT FORNECE O AZURE OPEN DATASETS NO ESTADO EM QUE SE ENCONTRA. A MICROSOFT NÃO OFERECE GARANTIAS OU COBERTURAS, EXPRESSAS OU IMPLÍCITAS, EM RELAÇÃO AO USO DOS CONJUNTOS DE DADOS. ATÉ O LIMITE PERMITIDO PELA LEGISLAÇÃO LOCAL, A MICROSOFT SE EXIME DE TODA A RESPONSABILIDADE POR DANOS OU PERDAS, INCLUSIVE DIRETOS, CONSEQUENTES, ESPECIAIS, INDIRETOS, ACIDENTAIS OU PUNITIVOS, RESULTANTES DO USO DOS CONJUNTOS DE DADOS.

Esse conjunto de dados é fornecido de acordo com os termos originais com que a Microsoft recebeu os dados de origem. O conjunto de dados pode incluir dados originados da Microsoft.

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

Identifica notas de rodapé da série de dados. A maioria dos valores é nula. Consulte https://download.bls.gov/pub/time.series/wp/wp.footnote.

group_code string 56 02
01

Código que identifica um grande grupo de commodities coberto pelo índice. Confira https://download.bls.gov/pub/time.series/wp/wp.group para ver os códigos e os nomes dos grupos.

group_name string 56 Processed foods and feeds
Farm products

Nome do principal grupo de commodities coberto pelo índice. Confira https://download.bls.gov/pub/time.series/wp/wp.group para ver os códigos e os nomes dos grupos.

item_code string 2,949 1
11

Identifica o item a que pertencem as observações de dados. Confira https://download.bls.gov/pub/time.series/wp/wp.item para ver os códigos e os nomes dos itens.

item_name string 3,410 Warehousing, storage, and related services
Permanent placement services

Nomes completos dos itens. Confira https://download.bls.gov/pub/time.series/wp/wp.item para ver os códigos e os nomes dos itens.

period string 13 M06
M07

Identifica o período em que os dados foram observados. Confira https://download.bls.gov/pub/time.series/wp/wp.period para ver uma lista dos valores do período.

seasonal string 2 U
S

Código que identifica se os dados são ajustados sazonalmente. S = ajustados sazonalmente; U = não ajustados

series_id string 5,458 WPU101
WPU051

Código que identifica a série específica. Uma série temporal é um conjunto de dados observados durante um período de tempo estendido em intervalos de tempo constantes. Confira https://download.bls.gov/pub/time.series/wp/wp.series para ver detalhes da série, como o código, o nome, o ano de início e término, etc.

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

Título da série específica. Uma série temporal é um conjunto de dados observados durante um período de tempo estendido em intervalos de tempo constantes. Confira https://download.bls.gov/pub/time.series/wp/wp.series para ver detalhes da série, como a ID, o nome, o ano de início e término, etc.

value float 6,788 100.0
99.0999984741211

Índice de preços do item.

year int 26 2018
2017

Identifica o ano de observação.

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'))