Chocolate imports into south africa 2010 2017 nometa


Files Size Format Created Updated License Source
2 992kB csv zip 4 months ago 4 months ago
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Data Files

Download files in this dataset

File Description Size Last changed Download
qccd-zgyb 171kB csv (171kB) , json (537kB)
chocolate-imports-into-south-africa-2010-2017-nometa_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 49kB zip (49kB)


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

Field information

Field Name Order Type (Format) Description
tradetype 1 string (default)
districtofficecode 2 string (default)
districtofficename 3 string (default)
countryoforigin 4 string (default)
countryoforiginname 5 string (default)
countryofdestination 6 string (default)
countryofdestinationname 7 string (default)
tariff 8 integer (default)
statisticalunit 9 string (default)
transportcode 10 number (default)
transportcodedescription 11 string (default)
yearmonth 12 integer (default)
calendaryear 13 year (default)
tariffanddescription 14 string (default)
statisticalquantity 15 any (default)
customsvalue 16 integer (default)
worldregion 17 string (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get
data info pi/chocolate-imports-into-south-africa-2010-2017-nometa
tree pi/chocolate-imports-into-south-africa-2010-2017-nometa
# Get a list of dataset's resources
curl -L -s | grep path

# Get resources

curl -L

curl -L

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

install.packages("jsonlite", repos="")

json_file <- ''
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))

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 = ''

# 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('')

# print list of all resources:

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':

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 = ''

// 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) {
  // 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
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data