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Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
2 | 3MB | csv zip | 5 years ago | 3 years ago | Open Data Commons Public Domain Dedication and License v1.0 | World Bank |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
population | 477kB | csv (477kB) , json (1MB) | ||
population_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 362kB | zip (362kB) |
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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 Name | 1 | string | |
Country Code | 2 | string | |
Year | 3 | year | |
Value | 4 | number |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/core/population
data info core/population
tree core/population
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/population/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/core/population/r/0.csv
curl -L https://datahub.io/core/population/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/population/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/population/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/population/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/population/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)
}
}
})()
Population figures for countries, regions (e.g. Asia) and the world. Data comes originally from World Bank and has been converted into standard CSV.
The data is sourced from this World Bank dataset which in turn lists as sources: (1) United Nations Population Division. World Population Prospects, (2) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (3) Census reports and other statistical publications from national statistical offices, (4) Eurostat: Demographic Statistics, (5) Secretariat of the Pacific Community: Statistics and Demography Programme, and (6) U.S. Census Bureau: International Database.
This Data Package is licensed by its maintainers under the Public Domain Dedication and License (PDDL).
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Warranty / guaranteed updates
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