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Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
2 | 201kB | csv zip | 6 years ago | 5 years ago | ODC-PDDL-1.0 | PPP conversion factor, GDP (LCU per international $). World Bank, International Comparison Program database. |
Download files in this dataset
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. | 171kB | zip (171kB) |
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This is a preview version. There might be more data in the original version.
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 $) |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/core/ppp
data info core/ppp
tree core/ppp
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/ppp/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/core/ppp/r/0.csv
curl -L https://datahub.io/core/ppp/r/1.zip
If you are using R here's how to get the data you want quickly loaded:
install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")
json_file <- 'https://datahub.io/core/ppp/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))
# get list of all resources:
print(json_data$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
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/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')
# 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/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)
}
}
})()
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 $).
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.*
Data preparation requires Python 2. Required external Python modules are listed in the requirements.txt
file in this directory.
Run the following script from this directory to download and process the data:
make data
The raw data are output to ./tmp
. The processed data are output to ./data
.
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/
Refer to the terms of use of the source dataset for any specific restrictions on using these data in a public or commercial product.
Notifications of data updates and schema changes
Warranty / guaranteed updates
Workflow integration (e.g. Python packages, NPM packages)
Customized data (e.g. you need different or additional data)
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