Airport Codes


Files Size Format Created Updated License Source
2 36MB csv zip 2 weeks ago John Snow Labs Standard License John Snow Labs OurAirports

Data Files

File Description Size Last changed Download Other formats
airport-codes-csv [csv] 6MB airport-codes-csv [csv] airport-codes-csv [json] (22MB)
airport-codes_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 7MB airport-codes_zip [zip]


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

Field information

Field Name Order Type (Format) Description
Airport_ID 1 integer The airport ID
Identifier 2 string Airport identifier
Type 3 string Airport type, airports can be small to large or dedicted to helicopters or other kinds of aircrafts
Name 4 string Airport name
Latitude 5 number Identifies the geographical location Latitude.
Longitude 6 number Identifies the geographical location Longitude.
Elevation_Ft 7 integer Identifies the geographical location elevation in Feet
Continent 8 string Continent of the airport
ISO_Country_Code 9 string ISO Country code of the airport
ISO_Region 10 string ISO Region code of the airport
Municipality 11 string Airport municipality
Is_Scheduled_Service 12 boolean Is the airport a scheduled service airport
GPS_Code 13 string Airport GPS code
IATA_Code 14 string Airport IATA code
Local_Code 15 string Airport local code
Home_Link 16 string Airport website
Wikipedia_Link 17 string Airport Wikipedia link
Keywords 18 string Airport keywords


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