Certificated Air Carrier Fuel Consumption And Travel


Files Size Format Created Updated License Source
2 53kB csv zip 2 weeks ago John Snow Labs Standard License John Snow Labs Bureau of Transportation Statistics

Data Files


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

Field information

Field Name Order Type (Format) Description
Year 1 date (%Y-%m-%d) Year of data
Number_Of_Aircraft 2 integer Number of Aircraft operating under 14 CFR 121 and 14 CFR 135
Average_Miles_Flown_Per_Aircraft_In_Thousands 3 integer Average of distance flown by aircraft in domestic and international operations in thousands
Aircraft_Miles_In_Millions_Domestic_Operations 4 integer Distance flown by aircraft in domestic operations in million miles
Aircraft_Miles_In_Millions_International_Operations 5 integer Distance flown by aircraft in international operations in million miles
Fuel_Consumption_In_Million_Gallons_In_Domestic_Operations 6 integer Fuel consumption by aircraft in domestic operations in million gallons
Fuel_Consumption_In_Million_Gallons_In_International_Operations 7 integer Fuel consumption by aircraft in international operations in million gallons
Aircraft_Miles_Flown_Per_Gallon_Domestic_Operations 8 number Rate of fuel consumption in domestic operations in miles per gallon
Aircraft_Miles_Flown_Per_Gallon_International_Operations 9 number Rate of fuel consumption in international operations in miles per gallon


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 <- "http://datahub.io/JohnSnowLabs/certificated-air-carrier-fuel-consumption-and-travel/datapackage.json"
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 = "http://datahub.io/JohnSnowLabs/certificated-air-carrier-fuel-consumption-and-travel/datapackage.json"

# 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('http://datahub.io/JohnSnowLabs/certificated-air-carrier-fuel-consumption-and-travel/datapackage.json')

# 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 = 'http://datahub.io/JohnSnowLabs/certificated-air-carrier-fuel-consumption-and-travel/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 the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await file.stream()
  // 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 = 'http://datahub.io/JohnSnowLabs/certificated-air-carrier-fuel-consumption-and-travel/datapackage.json'

package = DataPackage::Package.new(path)
# So package variable contains metadata. You can see it:
puts package

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