Top Level Domain Names

core

Files Size Format Created Updated License Source
2 61kB csv zip 5 days ago The Internet Assigned Numbers Authority (IANA)
This Data Package contains the delegation details of top-level domains Data The data is available on : http://www.iana.org/domains/root/db Preparation The data were copied manually from "The Internet Assigned Numbers Authority (IANA)" site, and then posted to Excel file and saved as CSV read more
Download

Data Files

File Description Size Last changed Download
top-level-domain-names.csv 84kB csv (84kB) , json (134kB)
top-level-domain-names_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 44kB zip (44kB)

top-level-domain-names.csv  

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

Field information

Field Name Order Type (Format) Description
Domain 1 string
Type 2 string
Sponsoring Organisation 3 string

top-level-domain-names_zip  

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

Read me

This Data Package contains the delegation details of top-level domains

Data

The data is available on : http://www.iana.org/domains/root/db

Preparation

The data were copied manually from “The Internet Assigned Numbers Authority (IANA)” site, and then posted to Excel file and saved as CSV file.

License

These data are 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")
library("jsonlite")

json_file <- 'https://datahub.io/core/top-level-domain-names/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)
  }
}

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/top-level-domain-names/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/top-level-domain-names/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/top-level-domain-names/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