Euro 5 Cars Emissions Traded On UK Market 2009-2015

JohnSnowLabs

Files Size Format Created Updated License Source
2 28MB csv zip 3 months ago John Snow Labs Standard License John Snow Labs Vehicle Certification Agency, UK
Download

Data Files

File Description Size Last changed Download
euro-5-cars-emissions-traded-on-uk-market-2009-2015-csv 3MB csv (3MB) , json (19MB)
euro-5-cars-emissions-traded-on-uk-market-2009-2015_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 2MB zip (2MB)

euro-5-cars-emissions-traded-on-uk-market-2009-2015-csv  

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

Field information

Field Name Order Type (Format) Description
Years_Released 1 date (%Y-%m-%d) The year UK Vehicle Certification Agency released the raport about emissions from new cars traded on UK Market
Car_Manufacturer 2 string The official car name manufacturer or importer
Car_Model 3 string The official brand under which a car manufacturer or importer is selling a specific type of car
Car_Description 4 string Specific characteristics of a car or cars belonging to a model sold by a car manufacturer or importer
Car_Transmission_Type 5 string The abbreviate description for a car model transmission types or types
Car_Engine_Capacity_In_Cubic_Centimeters 6 integer A car model engine capacity or capacities in cubic centimeters(cc)
Car_Fuel_Type 7 string The type of fuel or fuels a car model with specific characteristics is using
Urban_Fuel_Consumption_In_Metric_Units 8 number The urban fuel consumption in liers per 100 kilometres, at an ambient temperature of 20-30 Celsius degrees
Extra_Urban_Fuel_Consumption_In_Metric_Units 9 number The extra-urban fuel consumption in liers per 100 kilometres, at an ambient temperature of 20-30 Celsius degrees
Combined_Fuel_Consumption_In_Metric_Units 10 number The urban and extra-urban combined fuel consumption in liers per 100 kilometres, at an ambient temperature of 20-30 Celsius degrees
Urban_Fuel_Consumption_In_Imperial_Units 11 number The urban fuel consumption in miles per imperial gallon, at an ambient temperature of 20-30 Celsius degrees
Extra_Urban_Fuel_Consumption_In_Imperial_Units 12 number The extra-urban fuel consumption in miles per imperial gallon, at an ambient temperature of 20-30 Celsius degrees
Combined_Urban_Fuel_Consumption_In_Imperial_Units 13 number The urban and extra-urban combined fuel consumption in miles per imperial gallon, at an ambient temperature of 20-30 Celsius degrees
Fuel_Cost_Per_6000_Miles 14 number The estimated fuel cost in pounds at 6000 miles or halved from cost at 12000 miles
Electric_Energy_Consumption_Miles_Per_KWh 15 number Electricity consumption miles per kilowatt-hour
Wh_Per_Km 16 number Electricity consumption watt-hour per kilometre
Maximum_Range_Km 17 number The maximum number of kilometres a car can run using electrical power
Maximum_Range_Miles 18 number The maximum number of miles a car can run using electrical power
Electricity_Cost 19 number The estimated electricity cost in pounds
Total_Cost 20 number Ratio
Level_Of_External_Noise_Emitted_In_Decibels 21 number The estimated level of external noise in decibels, measured on the A scale (so it is more closely represented what is heard by the human ear), emitted by a car model with specific characteristics
CO2_Emissions_In_Grams_Per_Km 22 integer The estimated level of CO2 emissions, in grams per kilometer, released by a car model with specific characteristics
CO_Emmisions_In_Milligrams_Per_Km 23 number The estimated level of CO emissions, in milligrams per kilometer, released by a car model with specific characteristics
THC_Emissions_In_Milligrams_Per_Km 24 number The estimated level of hydrocarbons emissions, in milligrams per kilometer, released by a car model with specific characteristics
NOx_Emissions_In_Milligrams_Per_Km 25 number The estimated level of oxides of nitrogen (nitrogen dioxide - NO2 and nitric oxide - NO) emissions, in milligrams per kilometer, released by a car model with specific characteristics
THC_And_NOx_Emissions_In_Milligrams_Per_Km 26 number The estimated cumulative level of hydrocarbons and oxides of nitrogen (nitrogen dioxide - NO2 and nitric oxide - NO) emissions, in milligrams per kilometer, released by a car model with specific characteristics
Particulate_Matter_In_Milligrams_Per_Km 27 number The estimated level of particulate matter emissions, in milligrams per kilometer, released by a car model with specific characteristics

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/euro-5-cars-emissions-traded-on-uk-market-2009-2015
tree JohnSnowLabs/euro-5-cars-emissions-traded-on-uk-market-2009-2015
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/euro-5-cars-emissions-traded-on-uk-market-2009-2015/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/euro-5-cars-emissions-traded-on-uk-market-2009-2015/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/euro-5-cars-emissions-traded-on-uk-market-2009-2015/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/euro-5-cars-emissions-traded-on-uk-market-2009-2015/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/euro-5-cars-emissions-traded-on-uk-market-2009-2015/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/euro-5-cars-emissions-traded-on-uk-market-2009-2015/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/euro-5-cars-emissions-traded-on-uk-market-2009-2015/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