Example sample transform on ISO 4217 Currency Codes

examples

Files Size Format Created Updated License Source
2 194kB 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
Download

Data Files

File Description Size Last changed Download Other formats
codes-all [csv] 17kB codes-all [csv] codes-all [json] (17kB)
example-sample-transform-on-currency-codes_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 23kB example-sample-transform-on-currency-codes_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WithdrawalDate 6 string Date currency withdrawn (values can be ranges or months

example-sample-transform-on-currency-codes_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

In order to use Data Package in R follow instructions below:

install.packages("devtools")
library(devtools)
install_github("hadley/readr")
install_github("ropenscilabs/jsonvalidate")
install_github("ropenscilabs/datapkg")

#Load client
library(datapkg)

#Get Data Package
datapackage <- datapkg_read("https://pkgstore.datahub.io/examples/example-sample-transform-on-currency-codes/latest")

#Package info
print(datapackage)

#Open actual data in RStudio Viewer
View(datapackage$data$"codes-all")
View(datapackage$data$"example-sample-transform-on-currency-codes_zip")

Tested with Python 3.5.2

To generate Pandas data frames based on JSON Table Schema descriptors we have to install jsontableschema-pandas plugin. To load resources from a data package as Pandas data frames use datapackage.push_datapackage function. Storage works as a container for Pandas data frames.

In order to work with Data Packages in Pandas you need to install our packages:

$ pip install datapackage
$ pip install jsontableschema-pandas

To get Data Package run following code:

import datapackage

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

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

# to see datasets in this package
storage.buckets

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

In order to work with Data Packages in Python you need to install our packages:

$ pip install datapackage

To get Data Package into your Python environment, run following code:

import datapackage

dp = datapackage.DataPackage('https://pkgstore.datahub.io/examples/example-sample-transform-on-currency-codes/latest/datapackage.json')

# see metadata
print(dp.descriptor)

# get list of csv files
csvList = [dp.resources[x].descriptor['name'] for x in range(0,len(dp.resources))]
print(csvList) # ["resource name", ...]

# access csv file by the index starting 0
print(dp.resources[0].data)

To use this dataset in JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use code snippet below:

  const {Dataset} = require('data.js')

  const path = 'https://pkgstore.datahub.io/examples/example-sample-transform-on-currency-codes/latest/datapackage.json'

  const dataset = Dataset.load(path)

  // get a data file in this dataset
  const file = dataset.resources[0]
  const data = file.stream()

In order to work with Data Packages in SQL you need to install our packages:

$ pip install datapackage
$ pip install jsontableschema-sql
$ pip install sqlalchemy

To import Data Package to your SQLite Database, run following code:

import datapackage
from sqlalchemy import create_engine

data_url = 'https://pkgstore.datahub.io/examples/example-sample-transform-on-currency-codes/latest/datapackage.json'
engine = create_engine('sqlite:///:memory:')

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

# to see datasets in this package
storage.buckets

# to execute sql command (assuming data is in "data" folder, name of resource is data and file name is data.csv)
storage._Storage__connection.execute('select * from data__data___data limit 1;').fetchall()

# description of the table columns
storage.describe('data__data___data')
Datapackage.json