Fuel Consumption And Travel Different Types Of Vehicles

JohnSnowLabs

Files Size Format Created Updated License Source
2 126kB 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
fuel-consumption-and-travel-different-types-of-vehicles-csv 13kB csv (13kB) , json (71kB)
fuel-consumption-and-travel-different-types-of-vehicles_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 15kB zip (15kB)

fuel-consumption-and-travel-different-types-of-vehicles-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
Type_Of_Vehicle 2 string Data of different type vehicle which are divided to light duty vehicles & short wheel base, motorcycles, light duty vehicle & long wheel base, single unit 2 axle 6 tire or more truck, combination truck, bus
Number_Of_Vehicle_Registered_In_Thousand 3 integer Number of each type of vehicle registered in thousands
Vehicle_Miles_Travelled_In_Millions 4 integer Distance travelled by vehicle in million miles
Fuel_Consumed_In_Million_Gallons 5 integer Fuel consumed by each type of vehicle in million gallons
Average_Miles_Travelled_Per_Vehicle_In_Thousands 6 number Avergae distance travelled by vehicle in thousand miles
Average_Miles_Travelled_Per_Vehicle_In_Thousands_Per_Gallon 7 number Average distance travelled per vehicle per gallon in thousands
Average_Fuel_Consumed_Per_Vehicle_In_Gallons 8 integer Average fuel consumed per vehicle in gallons

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

# Get resources

curl -L https://datahub.io/JohnSnowLabs/fuel-consumption-and-travel-different-types-of-vehicles/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/fuel-consumption-and-travel-different-types-of-vehicles/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/fuel-consumption-and-travel-different-types-of-vehicles/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/fuel-consumption-and-travel-different-types-of-vehicles/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/fuel-consumption-and-travel-different-types-of-vehicles/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/fuel-consumption-and-travel-different-types-of-vehicles/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