Major cities of the word

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)
    }
  }
})()

The municipality of Chongqing, China, whose administrative jurisdictional area is around the size of Austria, has the largest population for a city proper. However, more than 70% of its residents live in rural areas.

1. ^ For urban/metropolitan areas that have more than one core city, the figure for their city proper should use either the most populous one (e.g. Dallas for Dallas–Fort Worth metroplex) or the best-known one (e.g. Manila for Metro Manila, instead of Quezon City).
2. ^ UN figures for each cities are varied between city proper, metropolitan area, and urban agglomeration.^[12]
3. ^ Figures taken from official census when possible
4. ^ Figures taken from official census when possible
5. ^ Includes large areas Tokyo, Kanagawa, Chiba and Saitama prefectures and smaller areas of Gunma, Tochigi and Ibaraki prefectures.
6. ^ Includes Faridabad, Ghaziabad, Noida, Gurgaon and Bahadurgarh urban areas and New Delhi.
7. ^ Includes Taicang
8. ^ Includes Francisco Morato
9. ^ Includes Kalyan and urban areas of Panvel, Bhiwandi and Vasai-Virar.
10. ^ Includes major cities of Kobe and Kyoto, collectively also known as Keihanshin area.
11. ^ Includes Bridgeport, New Haven, Trenton, Danbury, Waterbury and Twin Rivers urban areas.
12. ^ The continuous urbanization of Manila extends outward its Metro Manila into other neighbouring provinces such as Bulacan, Cavite, Laguna, Rizal, Pampanga and Batangas provinces.
13. ^ Includes the city of Foshan.
14. ^ Includes Tangerang, South Tangerang, Bogor, Bekasi and Karawang.
15. ^ Included in Guangzhou
16. ^ Includes the city of Fushun.
17. ^ Includes the city of Fort Worth, collectively known as Dallas–Fort Worth metroplex.
18. ^ Includes Pretoria
19. ^ Includes the city of Baltimore.

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