US Consumer Price Index and Inflation (CPI)

core

Files Size Format Created Updated License Source
2 45kB csv zip 1 week ago public_domain_dedication_and_license US Bureau of Labor Statistics
Consumer Price Index for All Urban Consumers (CPI-U) from U.S. Department Of Labor Bureau of Labor Statistics. This is a monthly time series from January 1913. Values are U.S. city averages for all items and 1982-84=100. Note that there are many price indices and this is only one of them (albeit read more
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Data Files

File Description Size Last changed Download
cpiai 26kB csv (26kB) , json (68kB)
cpi-us_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 24kB zip (24kB)

cpiai  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
Date 1 date (%Y-%m-%d) Date (strictly the year and month)
Index 2 number Monthly Consumer Price Index. Values are U.S. city averages for all items and 1982-84=100.
Inflation 3 number Monthly Consumer Price Inflation. Values are for U.S. city averages for all items.

cpi-us_zip  

This is a preview version. There might be more data in the original version.

Read me

Consumer Price Index for All Urban Consumers (CPI-U) from U.S. Department Of Labor Bureau of Labor Statistics. This is a monthly time series from January 1913. Values are U.S. city averages for all items and 1982-84=100. Note that there are many price indices and this is only one of them (albeit the most standard and with the longest set of data).

Data

Data is sourced from ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt and normalized into a CSV.

License

This Data Package is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/

Import into your tool

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/core/cpi-us/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))
    print(data)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

In order to work with Data Packages in Pandas you need to install the Frictionless Data data package library and the pandas extension:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/core/cpi-us/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('https://datahub.io/core/cpi-us/datapackage.json')

# get list of all resources:
resources = package.descriptor['resources']
resourceList = [resources[x]['name'] for x in range(0, len(resources))]
print(resourceList)

# print all tabular data(if exists any)
resources = package.resources
for resource in resources:
    if resource.tabular:
        print(resource.read())

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'https://datahub.io/core/cpi-us/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()
Datapackage.json