European Union Emissions Trading System

JohnSnowLabs

Files Size Format Created Updated License Source
2 865kB csv zip 3 months ago John Snow Labs Standard License John Snow Labs European Union Emissions Trading System (EU ETS)
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Data Files

File Description Size Last changed Download
european-union-emissions-trading-system-csv 4MB csv (4MB) , json (10MB)
european-union-emissions-trading-system_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 832kB zip (832kB)

european-union-emissions-trading-system-csv  

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

Field information

Field Name Order Type (Format) Description
Country_Code 1 string Indicates the Codes for different countries.
Country 2 string Refers to the Name of the specific country out of 31 total countries which participated in European Union Emissions Trading System (EU ETS).
Main_Activity_Sector_Name 3 string Refers to the code of the specific Sector for which the data is gathered. The sectors include: Aviation, Combustion of Fuels, All Stationary Installations, Refining of Mineral Oil, All Industrial Installations (Excluding Combustion), Production of Coke, Production of Pig Iron or Steel, Production or Processing of Ferrous Matals, Production of Primary Aluminium, Production of Secondary Aluminium, Production or Processing of Non-Ferrous Metals, Production of Cement Clinker, Production of Lime or Calcination of Dolomite/Magnesite, Manufacture of Glass, Manufacture of Ceramics, Manufacture of Mineral Wool, Production or Processing of Gypsum or Plasterboard, Production of Pulp, Production of Paper or Cardboard, Production of Carbon Black, Production of Nitric Acid, Production of Adipic Acid, Production of Glyoxal and Glyoxylic Acid, Production of Ammonia, Production of Bulk Chemicals, Production of Hydrogen and Synthesis Gas, Production of Soda Ash and Sodium Bicarbonate, Capture of Greenhouse Gases under Directive 2009/31/EC, and Other Activity Opted-in under Art. 24.
ETS_Information 4 string It holds information regarding the Emissions Trading System (ETS). In this column, there are the categories mentioned next to the information in brackets to which the particular information belongs to or not. These categories include: European Union Allowance (EUA), European Union Aviation Allowance (EUAA), European Union Transaction Log (EUTL), Certified Emission Reductions (CERs), Emission Reduction Units (ERUs).
Data_Collection_Date 5 string Refers to the specific year or month of the year when the data is collected.
Measurement_Value 6 number Indicates the value of the measure used to analyze data.

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/european-union-emissions-trading-system
tree JohnSnowLabs/european-union-emissions-trading-system
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/european-union-emissions-trading-system/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/european-union-emissions-trading-system/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/european-union-emissions-trading-system/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/european-union-emissions-trading-system/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/european-union-emissions-trading-system/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/european-union-emissions-trading-system/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/european-union-emissions-trading-system/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