Purchasing Power Parity Conversion Factor For GDP


Files Size Format Created Updated License Source
2 446kB csv zip 2 weeks ago John Snow Labs Standard License John Snow Labs World Bank, International Comparison Program (ICP) Database

Data Files


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

Field information

Field Name Order Type (Format) Description
Country_Name 1 string Name of the country
Country_Code 2 string ISO 3166-1 alpha-2 country code. Country codes are short alphabetic or numeric geographical codes (geocodes) developed to represent countries and dependent areas, for use in data processing and communications
Region_Name 3 string Name of the region
Income_Group 4 string Country's income group
Special_Notes 5 string Special notes, if any.
Year_1990 6 number PPP rates in 1990
Year_1991 7 number PPP rates in 1991
Year_1992 8 number PPP rates in 1992
Year_1993 9 number PPP rates in 1993
Year_1994 10 number PPP rates in 1994
Year_1995 11 number PPP rates in 1995
Year_1996 12 number PPP rates in 1996
Year_1997 13 number PPP rates in 1997
Year_1998 14 number PPP rates in 1998
Year_1999 15 number PPP rates in 1999
Year_2000 16 number PPP rates in 2000
Year_2001 17 number PPP rates in 2001
Year_2002 18 number PPP rates in 2002
Year_2003 19 number PPP rates in 2003
Year_2004 20 number PPP rates in 2004
Year_2005 21 number PPP rates in 2005
Year_2006 22 number PPP rates in 2006
Year_2007 23 number PPP rates in 2007
Year_2008 24 number PPP rates in 2008
Year_2009 25 number PPP rates in 2009
Year_2010 26 number PPP rates in 2010
Year_2011 27 number PPP rates in 2011
Year_2012 28 number PPP rates in 2012
Year_2013 29 number PPP rates in 2013
Year_2014 30 number PPP rates in 2014
Year_2015 31 number PPP rates in 2015
Year_2016 32 number PPP rates in 2016


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

Read me

Import into your tool

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


json_file <- "http://datahub.io/JohnSnowLabs/purchasing-power-parity-conversion-factor-for-gdp/datapackage.json"
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# access csv file by the index starting from 1
path_to_file = json_data$resources$path[1][1]
data <- read.csv(url(path_to_file))

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

pip install datapackage
pip install jsontableschema-pandas

To get the data run following code:

import datapackage

data_url = "http://datahub.io/JohnSnowLabs/purchasing-power-parity-conversion-factor-for-gdp/datapackage.json"

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# data frames available (corresponding to data files in original dataset)

# you can access datasets inside storage, e.g. the first one:

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('http://datahub.io/JohnSnowLabs/purchasing-power-parity-conversion-factor-for-gdp/datapackage.json')

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

data = package.resources[0].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 = 'http://datahub.io/JohnSnowLabs/purchasing-power-parity-conversion-factor-for-gdp/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 the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await file.stream()
  // entire file as a buffer (be careful with large files!)
  const buffer = await file.buffer

Install the datapackage library created specially for Ruby language using gem:

gem install datapackage

Now get the dataset and read the data:

require 'datapackage'

path = 'http://datahub.io/JohnSnowLabs/purchasing-power-parity-conversion-factor-for-gdp/datapackage.json'

package = DataPackage::Package.new(path)
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data = resource.read
puts data