Class I Rail Freight Fuel Consumption and Travel


Files Size Format Created Updated License Source
2 59kB 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_Locomotives_In_Use 2 integer Number of locomotive in use in Class I rail freight
Number_Of_Freight_Cars_In_Use 3 integer Number of freight cars in use Class I rail freight
Million_Miles_Travelled_By_Freight_Train 4 integer Distance travelled by freight train in million miles
Million_Miles_Made_By_Locomotive_Unit 5 integer Distance travelled by locomotive units in million miles
Million_Miles_Made_By_Freight_Car 6 integer Distance travelled by freight cars in million miles
Average_Miles_Travelled_Per_Gallon_By_Freight_Trains 7 number Average distance travelled per gallon of fuel by freight train
Average_Miles_Travelled_Per_Gallon_By_Freight_Cars 8 number Average distance travelled per gallon of fuel by freight cars
Fuel_Consumed_In_Million_Gallons_Excluding_Passenger_And_Work_Trains 9 integer Fuel consumed in Class I rail freight excluding passenger and work trains in million gallons
Revenue_On_Miles_Per_Gallon_Of_Fuel_Consumed 10 integer Revenue genenrated per gallon of fuel consumed in Class I rail freight
Average_Miles_Travelled_Per_Locomotive_In_Thousands 11 number Average miles travelled per locomotive in Class I rail freight in thousands
Average_Fuel_Consumed_Per_Locomotive_In_Thousand_Gallons 12 number Average fuel consumed per locomotive in Class I rail freight in thousands gallons


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