Example sample transform on ISO 4217 Currency Codes

examples

Files Size Format Created Updated License Source
2 120kB csv zip 2 weeks ago SIX Interbank Clearing Ltd (on behalf of ISO)
This is an example dataset to demonstrate how data transforms works. In this example, we explain how to get a sample from a resource. We assume a publisher is already familiar with Data Packages and views specifications (views property in Data Package specifications). Getting sample data On the read more
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Data Files

File Description Size Last changed Download Other formats
codes-all [csv] 17kB codes-all [csv] codes-all [json] (63kB)
datapackage_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 24kB datapackage_zip [zip]

codes-all  

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

Field information

Field Name Order Type (Format) Description
Entity 1 string Country or region name
Currency 2 string Name of the currency
AlphabeticCode 3 string 3 digit alphabetic code for the currency
NumericCode 4 number 3 digit numeric code
MinorUnit 5 string
WithdrawalDate 6 string Date currency withdrawn (values can be ranges or months

datapackage_zip  

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

Read me

This is an example dataset to demonstrate how data transforms works. In this example, we explain how to get a sample from a resource. We assume a publisher is already familiar with Data Packages and views specifications (views property in Data Package specifications).

Getting sample data

On the top of this page, you can find a table that displays filtered data. Raw data is displayed in preview section. As you can see we are getting sample of 15 rows from the initial data. This is described in the view object of views property:

{
  "name": "sample-view",
  "specType": "table",
  "resources": [
    {
      "name": "codes-all",
      "transform": [
        {
          "type": "sample",
          "size": 15
        }
      ]
    }
  ]
}

where:

  • "specType": "table" - this way we define the view as a table (other options are "simple" (renders a graph and accepts Plotly spec) and "vega" (renders a graph and accepts Vega spec)).
  • "resources" property is an array of objects in this case, where publishers can define data transforms they want to apply.
  • "name" - name of the resource as a reference.
  • "transform" - array of transforms. Each transform is an object, which properties vary depending on transform type. Only common property is "type" that is used to specify transform type.

Transform properties for “sample”:

  • "type": "sample" - this way we define the transform to be a sample.
  • "size" - any integer that will be used as a size of a sample data.

Descriptor for this data package

This is the full datapackage.json of this dataset:

{
  "name": "example-sample-transform-on-currency-codes",
  "title": "Example sample transform on ISO 4217 Currency Codes",
  "licenses": [
    {
      "id": "odc-pddl",
      "label": "Open Data Commons Public Domain Dedication and Licence (PDDL)",
      "url": "http://opendatacommons.org/licenses/pddl/"
    }
  ],
  "keywords": [ "iso", "iso-4217", "currency", "codes" ],
  "homepage": "http://www.iso.org/iso/currency_codes",
  "sources": [{
    "name": "SIX Interbank Clearing Ltd (on behalf of ISO)",
    "email": "[email protected]"
  }],
  "maintainer": [{
    "name": "Rufus Pollock",
    "email": "[email protected]"
  }],
  "contributors": [
    {
      "name": "Rufus Pollock",
      "email": "[email protected]"
    },
    {
      "name": "Kristofer D. Kusano",
      "email": "[email protected]"
    }
  ],
  "resources": [
    {
      "name": "codes-all",
      "path": "data/codes-all.csv",
      "mimetype": "text/csv",
      "size": "16863",
      "schema": {
        "fields": [
          {
            "name": "Entity",
            "type": "string",
            "description": "Country or region name"
          },
          {
            "name": "Currency",
            "type": "string",
            "description": "Name of the currency"
          },
          {
            "name": "AlphabeticCode",
            "title": "Alphabetic Code",
            "type": "string",
            "description": "3 digit alphabetic code for the currency"
          },
          {
            "name": "NumericCode",
            "title": "Numeric Code",
            "type": "integer",
            "description": "3 digit numeric code"
          },
          {
            "name": "MinorUnit",
            "title": "Minor Unit",
            "type": "number",
            "description": ""
          },
          {
            "name": "WithdrawalDate",
            "title": "Withdrawal Date",
            "type": "string",
            "description": "Date currency withdrawn (values can be ranges or months"
          }
        ]
      }
    }
  ],
  "views": [
    {
      "name": "sample-view",
      "specType": "table",
      "resources": [
        {
          "name": "codes-all",
          "transform": [
            {
              "type": "sample",
              "size": 15
            }
          ]
        }
      ]
    }
  ]
}

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 <- "http://datahub.io/examples/example-sample-transform-on-currency-codes/datapackage.json"
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# access csv file by the index starting from 1
path_to_file = json_data$resources[[1]]$path
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:

pip install datapackage
pip install jsontableschema-pandas

To get the data run following code:

import datapackage

data_url = "http://datahub.io/examples/example-sample-transform-on-currency-codes/datapackage.json"

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# data frames available (corresponding to data files in original dataset)
storage.buckets

# you can access datasets inside storage, e.g. the first one:
storage[storage.buckets[0]]

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('http://datahub.io/examples/example-sample-transform-on-currency-codes/datapackage.json')

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

data = package.resources[0].read()
print(data)

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 = 'http://datahub.io/examples/example-sample-transform-on-currency-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 the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await file.stream()
  // entire file as a buffer (be careful with large files!)
  const buffer = await file.buffer
})()

Install the datapackage library created specially for Ruby language using gem:

gem install datapackage

Now get the dataset and read the data:

require 'datapackage'

path = 'http://datahub.io/examples/example-sample-transform-on-currency-codes/datapackage.json'

package = DataPackage::Package.new(path)
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data = resource.read
puts data
Datapackage.json