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Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
2 | 156kB | csv zip | 5 years ago | 2 years ago | Open Data Commons Public Domain Dedication and License v1.0 | SIX Interbank Clearing Ltd (on behalf of ISO) |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
codes-all | 17kB | csv (17kB) , json (63kB) | ||
currency-codes_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 25kB | zip (25kB) |
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This is a preview version. There might be more data in the original version.
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WithdrawalDate | 6 | string | Date currency withdrawn (values can be ranges or months |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/core/currency-codes
data info core/currency-codes
tree core/currency-codes
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/currency-codes/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/core/currency-codes/r/0.csv
curl -L https://datahub.io/core/currency-codes/r/1.zip
If you are using R here's how to get the data you want quickly loaded:
install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")
json_file <- 'https://datahub.io/core/currency-codes/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)
}
}
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 = 'https://datahub.io/core/currency-codes/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/currency-codes/datapackage.json')
# print list of all resources:
print(package.resource_names)
# print processed tabular data (if exists any)
for resource in package.resources:
if resource.descriptor['datahub']['type'] == 'derived/csv':
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/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 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)
}
}
})()
List of currencies and their 3 digit codes as defined by ISO 4217. The data provided here is the consolidation of Table A.1 “Current currency & funds code list” and Table A.3 “Historic denominations”.
Note that the ISO page offers pay-for PDFs but also links to http://www.currency-iso.org/en/home/tables.html which does provide them in machine readable form freely.
The data provided (see data/codes.csv) in this data package provides a consolidated list of currency (and funds) codes by combining these two separate tables:
The script requires recode package to be istalled. Install it by running:
sudo apt install recode
Run the following script to download and convert the data from XML to CSV:
cd scripts/
./runall.sh
The raw XML files are stored in ./archive
. The cleaned data are
./data/codes-all.csv
.
The current tables have a published date of 28 March 2014 (as indicated in the XML files).
Placing in the Public Domain under the Public Domain Dedication and License. The original site states no restriction on use and the data is small and completely factual.
Notifications of data updates and schema changes
Warranty / guaranteed updates
Workflow integration (e.g. Python packages, NPM packages)
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