International Chamber of Commerce Incoterms

core

Files Size Format Created Updated License Source
2 25kB csv zip 5 days ago ODC-PDDL-1.0 International Chamber of Commerce Gov.uk
International Commercial Terms (‘Incoterms’) are internationally recognised standard trade terms used in sales contracts. They’re used to make sure buyer and seller know: who is responsible for the cost of transporting the goods, including insurance, taxes and duties where the goods should read more
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Data Files

File Description Size Last changed Download
data 3kB csv (3kB) , json (3kB)
icc-incoterms_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 6kB zip (6kB)

data  

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

Field information

Field Name Order Type (Format) Description
Term 1 string A 3 Digit code representing the incoterm
Name 2 string
Description 3 string

icc-incoterms_zip  

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

Read me

International Commercial Terms (‘Incoterms’) are internationally recognised standard trade terms used in sales contracts. They’re used to make sure buyer and seller know:

  • who is responsible for the cost of transporting the goods, including insurance, taxes and duties
  • where the goods should be picked up from and transported to
  • who is responsible for the goods at each step during transportation

Data

The current set of Incoterms is Incoterms 2010. A copy of the full terms is available from http://www.iccwbo.org/products-and-services/trade-facilitation/incoterms-2010/

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/icc-incoterms/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/icc-incoterms/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/icc-incoterms/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/icc-incoterms/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