Internet Top Level Domain Names


Files Size Format Created Updated License Source
2 390kB csv zip 2 weeks ago John Snow Labs Standard License John Snow Labs Internet Assigned Numbers Authority (IANA)

Data Files

File Description Size Last changed Download Other formats
internet-top-level-domain-names-csv [csv] 70kB internet-top-level-domain-names-csv [csv] internet-top-level-domain-names-csv [json] (171kB)
internet-top-level-domain-names_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 65kB internet-top-level-domain-names_zip [zip]


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

Field information

Field Name Order Type (Format) Description
Domain_Name 1 string A domain name is an identification string that defines a realm of administrative autonomy, authority or control within the Internet. Domain names are formed by the rules and procedures of the Domain Name System (DNS). Any name registered in the DNS is a domain name.
Domain_Type 2 string
Sponsoring_Organization 3 string Company and/or brand name


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

Read me

Import into your tool

If you are using R here's how to get the data you want quickly loaded:


json_file <- ""
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# access csv file by the index starting from 1
path_to_file = json_data$resources$path[1][1]
data <- read.csv(url(path_to_file))

In order to work with Data Packages in Pandas you need to install the Frictionless Data data package library and the pandas extension:

pip install datapackage
pip install jsontableschema-pandas

To get the data run following code:

import datapackage

data_url = ""

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# data frames available (corresponding to data files in original dataset)

# you can access datasets inside storage, e.g. the first one:

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('')

# get list of resources:
resources = package.descriptor['resources']
resourceList = [resources[x]['name'] for x in range(0, len(resources))]

data = package.resources[0].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 = ''

// We're using self-invoking function here as we want to use async-await syntax:
(async () => {
  const dataset = await Dataset.load(path)

  // Get the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await
  // entire file as a buffer (be careful with large files!)
  const buffer = await file.buffer

Install the datapackage library created specially for Ruby language using gem:

gem install datapackage

Now get the dataset and read the data:

require 'datapackage'

path = ''

package =
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data =
puts data