South african condom imports nometa


Files Size Format Created Updated License Source
2 859kB csv zip 1 year ago 1 year ago
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Data Files

Download files in this dataset

File Description Size Last changed Download
ksem-bfcs 142kB csv (142kB) , json (475kB)
south-african-condom-imports-nometa_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 39kB zip (39kB)


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

Field information

Field Name Order Type (Format) Description
districtofficecode 1 string (default)
districtofficename 2 string (default)
countryoforigin 3 string (default)
countryoforiginname 4 string (default)
countryofdestination 5 string (default)
countryofdestinationname 6 string (default)
tariff 7 integer (default)
statisticalunit 8 string (default)
transportcode 9 number (default)
transportcodedescription 10 string (default)
yearmonth 11 integer (default)
calendaryear 12 year (default)
section 13 integer (default)
sectionanddescription 14 string (default)
chapter 15 integer (default)
chapteranddescription 16 string (default)
tariffanddescription 17 string (default)
statisticalquantity 18 any (default)
customsvalue 19 integer (default)
worldregion 20 string (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get
data info pi/south-african-condom-imports-nometa
tree pi/south-african-condom-imports-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