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Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
3 | 5MB | csv zip | 5 years ago | 5 years ago | PDDL-1.0 | IMF |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
indicators | 47kB | csv (47kB) , json (49kB) | ||
values | 13MB | csv (13MB) , json (27MB) | ||
imf-weo_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 5MB | zip (5MB) |
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This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
id | 1 | string | |
title | 2 | string | |
description | 3 | string | |
units | 4 | string | |
scale | 5 | string |
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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 | |
Indicator | 2 | string | |
Year | 3 | integer | |
Value | 4 | string |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/core/imf-weo
data info core/imf-weo
tree core/imf-weo
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/imf-weo/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/core/imf-weo/r/0.csv
curl -L https://datahub.io/core/imf-weo/r/1.csv
curl -L https://datahub.io/core/imf-weo/r/2.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/imf-weo/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/imf-weo/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/imf-weo/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/imf-weo/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)
}
}
})()
IMF World Economic Outlook (WEO) database. The IMF World Economic Outlook is a twice-yearly survey by IMF staff that presents IMF staff economists’ analyses of global economic developments during the near and medium term. Associated with the report is the World Economic Outlook Database, a country-level dataset of major macro-economic variables (GDP, Unemployment, Debt etc). It is the data from that database which is provided here.
The source database is made of annual values for each country on 45 indicators since 1980. In addition the database includes the IMF projects approximately 6 years into the future.
We extract this data and normalize into 2 files:
data/indicators.csv
- the list of indicatorsdata/values.csv
- set of values for each indicator, country, year tuple.Note the XLS files actual turn out to be tsv files!
Code to extract the data from the source WEO Database is in the scripts
directory.
This Data Package is licensed by its maintainers under the Public Domain Dedication and License (PDDL).
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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