Airport Codes

JohnSnowLabs

Files Size Format Created Updated License Source
2 36MB csv zip 3 months ago John Snow Labs Standard License John Snow Labs OurAirports
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Data Files

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

airport-codes-csv  

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

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Use data with the datahub.io almost like you use git with the github. Here are installation instructions.

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

# Get resources

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

curl -L https://datahub.io/JohnSnowLabs/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/JohnSnowLabs/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/JohnSnowLabs/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/JohnSnowLabs/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/JohnSnowLabs/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)
    }
  }
})()
Datapackage.json