IMO IMDG Classification Codes

core

Files Size Format Created Updated License Source
2 29kB csv zip 5 days ago ODC-PDDL-1.0 IMO Gov.uk
Official IMDG Codes for use in transport of dangerous goods as described by the IMO Data Source of the information is from the IMO and Gov.uk: http://www.imo.org/blast/mainframe.asp?topic_id=158 https://www.gov.uk/guidance/moving-dangerous-goods#the-classification-of-dangerous-goods Requests for read more
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Data Files

File Description Size Last changed Download
data 1kB csv (1kB) , json (3kB)
imo-imdg-codes_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 5kB zip (5kB)

data  

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

Field information

Field Name Order Type (Format) Description
un class 1 number A Single digit to describe the class of goods
dangerous goods 2 string Group of Hazard
division 3 number A defined set of divisions of the main class
classification 4 string Descriptive explanation of the division or classification

imo-imdg-codes_zip  

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

Read me

Official IMDG Codes for use in transport of dangerous goods as described by the IMO

Data

Source of the information is from the IMO and Gov.uk: http://www.imo.org/blast/mainframe.asp?topic_id=158 https://www.gov.uk/guidance/moving-dangerous-goods#the-classification-of-dangerous-goods

Requests for addition to the codes should be made to the IMO directly

License

This data is made available under the Public Domain Dedication and License version v1.0 whose full text can be found at http://opendatacommons.org/licenses/pddl/ - See more at: http://opendatacommons.org/guide/#sthash.97PSVxmh.dpuf

Import into your tool

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite")
library("jsonlite")

json_file <- 'https://datahub.io/core/imo-imdg-codes/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)
  }
}

In order to work with Data Packages in Pandas you need to install the Frictionless Data data package library and the pandas extension:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/core/imo-imdg-codes/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/core/imo-imdg-codes/datapackage.json')

# get list of all resources:
resources = package.descriptor['resources']
resourceList = [resources[x]['name'] for x in range(0, len(resources))]
print(resourceList)

# print all tabular data(if exists any)
resources = package.resources
for resource in resources:
    if resource.tabular:
        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/core/imo-imdg-codes/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