Country, Regional and World GDP (Gross Domestic Product)

core

Files Size Format Created Updated License Source
2 479kB csv zip 1 week ago PDDL-1.0 World Bank and OECD
Country, regional and world GDP in current US Dollars ($). Regional means collections of countries e.g. Europe & Central Asia. Data The data is sourced from the World Bank (specifically this dataset) which in turn lists as sources: *World Bank national accounts data, and OECD National Accounts read more
Download

Data Files

File Description Size Last changed Download
gdp 444kB csv (444kB) , json (1MB)
gdp_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 455kB zip (455kB)

gdp  

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
Country Code 2 string
Year 3 year
Value 4 number GDP in current USD

gdp_zip  

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

Read me

Country, regional and world GDP in current US Dollars ($). Regional means collections of countries e.g. Europe & Central Asia.

Data

The data is sourced from the World Bank (specifically this dataset) which in turn lists as sources: World Bank national accounts data, and OECD National Accounts data files.

Note that there are a variety of different GDP indicators on offer from the World Bank including:

Preparation

Process is recorded and automated in python script:

scripts/process.py

Automation

Up-to-date (auto-updates every year) gdp dataset could be found on the datahub.io: https://datahub.io/core/gdp

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/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

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/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/core/gdp/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/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