US Consumer Price Index industry

The Producer Price Index (PPI) is a measure of average change over time in the selling prices received by domestic producers for their output. The prices included in the PPI are from the first commercial transaction for products and services covered.

Note

Microsoft provides Azure Open Datasets on an “as is” basis. Microsoft makes no warranties, express or implied, guarantees or conditions with respect to your use of the datasets. To the extent permitted under your local law, Microsoft disclaims all liability for any damages or losses, including direct, consequential, special, indirect, incidental or punitive, resulting from your use of the datasets.

This dataset is provided under the original terms that Microsoft received source data. The dataset may include data sourced from Microsoft.

The Producer Price Index Revision-Current Series indexes reflect price movements for the net output of producers organized according to the North American Industry Classification System (NAICS). The PC dataset is compatible with a wide assortment of NAICS-based economic time series including: productivity, production, employment, wages, and earnings.

The PPI universe consists of the output of all industries in the goods-producing sectors of the U.S. economy- mining, manufacturing, agriculture, fishing, and forestry- as well as natural gas, electricity, construction, and goods competitive with those made in the producing sectors, such as waste and scrap materials. In addition, as of January 2011, the PPI program covered more than three-quarters of the service sector’s output, publishing data for selected industries in the following industry sectors: wholesale and retail trade; transportation and warehousing; information; finance and insurance; real estate brokering, rental, and leasing; professional, scientific, and technical services; administrative, support, and waste management services; health care and social assistance; and accommodation.

README containing file for detailed information about this dataset is available at original dataset location. Additional information is available in the FAQs.

This dataset is produced from the Producer Price Indexes data published by US Bureau of Labor Statistics (BLS). Review Linking and Copyright Information and Important Web Site Notices for the terms and conditions related to the use this dataset.

Storage location

This dataset is stored in the East US Azure region. Allocating compute resources in East US is recommended for affinity.

Related datasets

• US Consumer Price Index
• US Producer Price Index - Commodities

Columns

Name	Data type	Unique	Values (sample)	Description
footnote_codes	string	3	nan P	Identifies footnotes for the data series. Most values are null. See https://download.bls.gov/pub/time.series/pc/pc.footnote.
industry_code	string	1,064	221122 325412	NAICS code for the industry. See https://download.bls.gov/pub/time.series/pc/pc.industry for codes and names.
industry_name	string	842	Electric power distribution Pharmaceutical preparation manufacturing	Name corresponding to the code for the industry. See https://download.bls.gov/pub/time.series/pc/pc.industry for codes and names.
period	string	13	M06 M07	Identifies period for which data is observed. See https://download.bls.gov/pub/time.series/pc/pc.period for full list.
product_code	string	4,822	324121P 316---	Code identifying the product to which the data observation refers. See https://download.bls.gov/pub/time.series/pc/pc.product for mapping of industry codes, product codes, and product names.
product_name	string	3,313	Primary products Secondary products	Name of the product to which the data observation refers. See https://download.bls.gov/pub/time.series/pc/pc.product for mapping of industry codes, product codes, and product names.
seasonal	string	1	U	Code identifying whether the data are seasonally adjusted. S=Seasonally Adjusted; U=Unadjusted
series_id	string	4,822	PCU332323332323M PCU333415333415C	Code identifying the specific series. A time series refers to a set of data observed over an extended period of time over consistent time intervals. See https://download.bls.gov/pub/time.series/pc/pc.series for details of series such as code, name, start and end year, etc.
series_title	string	4,588	PPI industry data for Electric power distribution-West South Central, not seasonally adjusted PPI industry data for Electric power distribution-Pacific, not seasonally adjusted
value	float	7,658	100.0 100.4000015258789	Price index for item.
year	int	22	2015 2017	Identifies year of observation.

Preview

product_code	industry_code	series_id	year	period	value	footnote_codes	seasonal	series_title	industry_name	product_name
2123240	212324	PCU2123242123240	1998	M01	117	nan	U	PPI industry data for Kaolin and ball clay mining-Kaolin and ball clay, not seasonally adjusted	Kaolin and ball clay mining	Kaolin and ball clay
2123240	212324	PCU2123242123240	1998	M02	116.9	nan	U	PPI industry data for Kaolin and ball clay mining-Kaolin and ball clay, not seasonally adjusted	Kaolin and ball clay mining	Kaolin and ball clay
2123240	212324	PCU2123242123240	1998	M03	116.3	nan	U	PPI industry data for Kaolin and ball clay mining-Kaolin and ball clay, not seasonally adjusted	Kaolin and ball clay mining	Kaolin and ball clay
2123240	212324	PCU2123242123240	1998	M04	116	nan	U	PPI industry data for Kaolin and ball clay mining-Kaolin and ball clay, not seasonally adjusted	Kaolin and ball clay mining	Kaolin and ball clay
2123240	212324	PCU2123242123240	1998	M05	116.2	nan	U	PPI industry data for Kaolin and ball clay mining-Kaolin and ball clay, not seasonally adjusted	Kaolin and ball clay mining	Kaolin and ball clay
2123240	212324	PCU2123242123240	1998	M06	116.3	nan	U	PPI industry data for Kaolin and ball clay mining-Kaolin and ball clay, not seasonally adjusted	Kaolin and ball clay mining	Kaolin and ball clay
2123240	212324	PCU2123242123240	1998	M07	116.6	nan	U	PPI industry data for Kaolin and ball clay mining-Kaolin and ball clay, not seasonally adjusted	Kaolin and ball clay mining	Kaolin and ball clay

Data access

Azure Notebooks

azureml-opendatasets

# This is a package in preview.
from azureml.opendatasets import UsLaborPPIIndustry

labor = UsLaborPPIIndustry()
labor_df = labor.to_pandas_dataframe()

labor_df.info()

azure-storage

# 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

# Azure storage access info
azure_storage_account_name = "azureopendatastorage"
azure_storage_sas_token = r""
container_name = "laborstatisticscontainer"
folder_name = "ppi_industry/"

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)

# 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)

# you can add your filter at below
print('Loaded as a Pandas data frame: ')
df

pyspark

Sample not available for this platform/package combination.

Azure Databricks

azureml-opendatasets

# This is a package in preview.
from azureml.opendatasets import UsLaborPPIIndustry

labor = UsLaborPPIIndustry()
labor_df = labor.to_spark_dataframe()

display(labor_df.limit(5))

azure-storage

Sample not available for this platform/package combination.

pyspark

# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "laborstatisticscontainer"
blob_relative_path = "ppi_industry/"
blob_sas_token = r""

# 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)

# 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')

# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Azure Synapse

azureml-opendatasets

Sample not available for this platform/package combination.

azure-storage

Sample not available for this platform/package combination.

pyspark

# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "laborstatisticscontainer"
blob_relative_path = "ppi_industry/"
blob_sas_token = r""

# 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)

# 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')

# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Next steps

View the rest of the datasets in the Open Datasets catalog.