Certificated Air Carrier Fuel Consumption And Travel

JohnSnowLabs

Files Size Format Created Updated License Source
2 53kB csv zip 3 months ago John Snow Labs Standard License John Snow Labs Bureau of Transportation Statistics
Download

Data Files

File Description Size Last changed Download
certificated-air-carrier-fuel-consumption-and-travel-csv 2kB csv (2kB) , json (16kB)
certificated-air-carrier-fuel-consumption-and-travel_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 6kB zip (6kB)

certificated-air-carrier-fuel-consumption-and-travel-csv  

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

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/certificated-air-carrier-fuel-consumption-and-travel
tree JohnSnowLabs/certificated-air-carrier-fuel-consumption-and-travel
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/certificated-air-carrier-fuel-consumption-and-travel/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/certificated-air-carrier-fuel-consumption-and-travel/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/certificated-air-carrier-fuel-consumption-and-travel/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/certificated-air-carrier-fuel-consumption-and-travel/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/certificated-air-carrier-fuel-consumption-and-travel/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/certificated-air-carrier-fuel-consumption-and-travel/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/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 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