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Airport Codes

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core

Files Size Format Created Updated License Source
2 38kB csv zip 1 year ago 10 hours ago Open Data Commons Public Domain Dedication and License v1.0 Our Airports
The airport codes may refer to either IATA airport code, a three-letter code which is used in passenger reservation, ticketing and baggage-handling systems, or the ICAO airport code which is a four letter code used by ATC systems and for airports that do not have an IATA airport code (from read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
airport-codes 118B csv (118B)
airport-codes_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 3kB zip (3kB)

airport-codes  

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

Field information

Field Name Order Type (Format) Description
ident 1 string (default)
type 2 string (default)
name 3 string (default)
elevation_ft 4 string (default)
continent 5 string (default)
iso_country 6 string (default)
iso_region 7 string (default)
municipality 8 string (default)
gps_code 9 string (default)
iata_code 10 string (default)
local_code 11 string (default)
coordinates 12 string (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/core/airport-codes
data info core/airport-codes
tree core/airport-codes
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/airport-codes/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/core/airport-codes/r/0.csv

curl -L https://datahub.io/core/airport-codes/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/airport-codes/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/airport-codes/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/airport-codes/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/airport-codes/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)
    }
  }
})()

Read me

The airport codes may refer to either IATA airport code, a three-letter code which is used in passenger reservation, ticketing and baggage-handling systems, or the ICAO airport code which is a four letter code used by ATC systems and for airports that do not have an IATA airport code (from wikipedia).

Airport codes from around the world. Downloaded from public domain source http://ourairports.com/data/ who compiled this data from multiple different sources. This data is updated nightly.

Data

“data/airport-codes.csv” contains the list of all airport codes, the attributes are identified in datapackage description. Some of the columns contain attributes identifying airport locations, other codes (IATA, local if exist) that are relevant to identification of an airport.
Original source url is http://ourairports.com/data/airports.csv (stored in archive/data.csv)

Preparation

You will need Python 3.6 or greater and dataflows library to run the script

To update the data run the process script locally:

# Install dataflows
pip install dataflows

# Run the script
python airport_codes_flow.py

Several steps will be done to get the final data.

  • merge columns “latitude_deg” and “longitude_deg” into “coordinates”
  • remove columns: “id”, “scheduled_service”, “home_link”, “wikipedia_link”, “keywords”

Automation

Daily updated ‘Airport codes’ datapackage could be found on the datahub.io:
https://datahub.io/core/airport-codes

License

The source specifies that the data can be used as is without any warranty. Given size and factual nature of the data and its source from a US company would imagine this was public domain and as such have licensed the Data Package under the Public Domain Dedication and License (PDDL).


Keywords and keyphrases: airport codes, IATA airport code, ICAO airport code, Airport codes from around the world, airport locations.
Datapackage.json

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