CSV,JSON
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Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
2 | 829kB | csv zip | 6 years ago | 5 years ago | Geonames |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
world-cities | 875kB | csv (875kB) , json (2MB) | ||
world-cities_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 792kB | zip (792kB) |
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This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
name | 1 | string | English name of the city |
country | 2 | string | Common name of the country, in english |
subcountry | 3 | string | Name of the major administrative area |
geonameid | 4 | integer | id from geonames |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/core/world-cities
data info core/world-cities
tree core/world-cities
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/world-cities/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/core/world-cities/r/0.csv
curl -L https://datahub.io/core/world-cities/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/world-cities/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/world-cities/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/world-cities/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/world-cities/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)
}
}
})()
List of major cities in the world
The data is extracted from geonames, a very exhaustive list of worldwide toponyms.
This datapackage only list cities above 15,000 inhabitants. Each city is associated with its
country and subcountry to reduce the number of ambiguities. Subcountry can be the name of a state (eg in
United Kingdom or the United States of America) or the major administrative section (eg ‘‘region’’ in France’’).
See admin1
field on geonames website for further info about subcountry.
Notice that :
N/A
.geonameid
is provided.You can run the script yourself to update the data and publish them to github : see scripts README
All data is licensed under the Creative Common Attribution License as is the original data from geonames. This means you have to credit geonames when using the data. And while no credit is formally required a link back or credit to Lexman and the Open Knowledge Foundation is much appreciated.
All source code is licensed under the MIT licence.
Notifications of data updates and schema changes
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