US Consumer Price Index

The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.

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.

README containing file for detailed information about this dataset is available at original dataset location.

This dataset is produced from the Consumer Price Index data, which is published by US Bureau of Labor Statistics (BLS). Review Linking and Copyright Information and Important Web Site Notices for the terms and conditions.

Storage location

This dataset is stored in the East US Azure region. We recommend locating compute resources in East US for affinity.

Related datasets

• US Producer Price Index - Industry
• US Producer Price Index - Commodities

Columns

Name	Data type	Unique	Values (sample)	Description
area_code	string	70	0000 0300	Unique code used to identify a specific geographic area. Full area codes found here: http://download.bls.gov/pub/time.series/cu/cu.area
area_name	string	69	U.S. city average South	Name of specific geographic area. See https://download.bls.gov/pub/time.series/cu/cu.area for all area names and codes.
footnote_codes	string	3	nan U	Identifies footnote for the data series. Most values are null.
item_code	string	515	SA0E SAF11	Identifies item for which data observations pertain. See https://download.bls.gov/pub/time.series/cu/cu.item for all item names and codes.
item_name	string	515	Energy Food at home	Full names of items. See https://download.bls.gov/pub/time.series/cu/cu.txt for item names and codes.
period	string	16	S01 S02	Identifies period for which data is observed. Format: M01-M13 or S01-S03 (M=Monthly, M13=Annual Avg, S=Semi-Annually). Ex: M06=June. See https://download.bls.gov/pub/time.series/cu/cu.period for period names and codes.
periodicity_code	string	3	R S	Frequency of data observation. S=Semi-Annual; R=Regular.
seasonal	string	1,043	U S	Code identifying whether the data is seasonally adjusted. S=Seasonally Adjusted; U=Unadjusted.
series_id	string	16,683	CWURS400SA0E CWUR0100SA0E	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 (that is, monthly, quarterly, semi-annually, annually). BLS time series data are typically produced at monthly intervals and represent data ranging from a specific consumer item in a specific geographical area whose price is gathered monthly to a category of worker in a specific industry whose employment rate is being recorded monthly, and so on. For more information, see https://download.bls.gov/pub/time.series/cu/cu.txt
series_title	string	8,336	Alcoholic drinks in U.S. city average, all urban consumers, not seasonally adjusted Transportation in Los Angeles-Long Beach-Anaheim, CA, all urban consumers, not seasonally adjusted	Series name of the corresponding series_id. See https://download.bls.gov/pub/time.series/cu/cu.series for series ids and names.
value	float	310,603	100.0 101.0999984741211	Price index for item.
year	int	25	2018 2017	Identifies year of observation.

Preview

area_code	item_code	series_id	year	period	value	footnote_codes	seasonal	periodicity_code	series_title	item_name	area_name
S49E	SEHF01	CUURS49ESEHF01	2017	M12	279.974	nan	U	R	Electricity in San Diego-Carlsbad, CA, all urban consumers, not seasonally adjusted	Electricity	San Diego-Carlsbad, CA
S49E	SEHF01	CUURS49ESEHF01	2017	M12	279.974	nan	U	R	Electricity in San Diego-Carlsbad, CA, all urban consumers, not seasonally adjusted	Electricity	San Diego-Carlsbad, CA
S49E	SEHF01	CUURS49ESEHF01	2017	M12	279.974	nan	U	R	Electricity in San Diego-Carlsbad, CA, all urban consumers, not seasonally adjusted	Electricity	San Diego-Carlsbad, CA
S49E	SEHF01	CUURS49ESEHF01	2017	M12	279.974	nan	U	R	Electricity in San Diego-Carlsbad, CA, all urban consumers, not seasonally adjusted	Electricity	San Diego-Carlsbad, CA
S49E	SEHF01	CUURS49ESEHF01	2017	M12	279.974	nan	U	R	Electricity in San Diego-Carlsbad, CA, all urban consumers, not seasonally adjusted	Electricity	San Diego-Carlsbad, CA
S49E	SEHF01	CUURS49ESEHF01	2017	M12	279.974	nan	U	R	Electricity in San Diego-Carlsbad, CA, all urban consumers, not seasonally adjusted	Electricity	San Diego-Carlsbad, CA

Data access

Azure Notebooks

azureml-opendatasets

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

usLaborCPI = UsLaborCPI()
usLaborCPI_df = usLaborCPI.to_pandas_dataframe()

usLaborCPI_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 = "cpi/"

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 UsLaborCPI

usLaborCPI = UsLaborCPI()
usLaborCPI_df = usLaborCPI.to_spark_dataframe()

display(usLaborCPI_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 = "cpi/"
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 = "cpi/"
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.