Purchasing Power Parity Conversion Factor For GDP

JohnSnowLabs

Files Size Format Created Updated License Source
2 446kB csv zip 3 months ago John Snow Labs Standard License John Snow Labs World Bank, International Comparison Program (ICP) Database
Download

Data Files

File Description Size Last changed Download
purchasing-power-parity-conversion-factor-for-gdp-csv 66kB csv (66kB) , json (196kB)
purchasing-power-parity-conversion-factor-for-gdp_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 76kB zip (76kB)

purchasing-power-parity-conversion-factor-for-gdp-csv  

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

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Use data with the datahub.io almost like you use git with the github. Here are installation instructions.

data get https://datahub.io/JohnSnowLabs/purchasing-power-parity-conversion-factor-for-gdp
tree JohnSnowLabs/purchasing-power-parity-conversion-factor-for-gdp
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/purchasing-power-parity-conversion-factor-for-gdp/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/purchasing-power-parity-conversion-factor-for-gdp/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/purchasing-power-parity-conversion-factor-for-gdp/r/1.zip

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/JohnSnowLabs/purchasing-power-parity-conversion-factor-for-gdp/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

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/JohnSnowLabs/purchasing-power-parity-conversion-factor-for-gdp/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/JohnSnowLabs/purchasing-power-parity-conversion-factor-for-gdp/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        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/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 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