Purchasing power parity (PPP)

core

Files Size Format Created Updated License Source
2 224kB csv zip 1 week ago ODC-PDDL-1.0 PPP conversion factor, GDP (LCU per international $). World Bank, International Comparison Program database.
Purchasing power parity (PPP). Data are sourced from the World Bank, International Comparison Program database. One dataset is provided: PPP conversion factor, GDP (LCU per international $). Data Description > Purchasing power parity conversion factor is the number of units of a country's read more
Download

Data Files

File Description Size Last changed Download
ppp-gdp 163kB csv (163kB) , json (388kB)
ppp_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 196kB zip (196kB)

ppp-gdp  

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

Field information

Field Name Order Type (Format) Description
Country 1 string
Country ID 2 string ISO 3166-1 alpha-2 code
Year 3 year Relevant year
PPP 4 number PPP conversion factor, GDP (LCU per international $)

ppp_zip  

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

Read me

Purchasing power parity (PPP). Data are sourced from the World Bank, International Comparison Program database. One dataset is provided: PPP conversion factor, GDP (LCU per international $).

Data

Description

Purchasing power parity conversion factor is the number of units of a country’s currency required to buy the same amounts of goods and services in the domestic market as U.S. dollar would buy in the United States.*

Citations

  1. PPP conversion factor, GDP (LCU per international $). World Bank, International Comparison Program database.

Sources

Preparation

Requirements

Data preparation requires Python 2. Required external Python modules are listed in the requirements.txt file in this directory.

Processing

Run the following script from this directory to download and process the data:

make data

Resources

The raw data are output to ./tmp. The processed data are output to ./data.

License

ODC-PDDL-1.0

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/

Notes

Refer to the terms of use of the source dataset for any specific restrictions on using these data in a public or commercial product.

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/ppp/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/ppp/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/ppp/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/ppp/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