fips 10-4 unlisted

core

Files Size Format Created Updated License Source
2 981kB csv zip 1 month ago Federal Information Processing Standards (FIPS 10-4 Codes and history)
List of FIPS (Federal Information Processing Standards) region codes. Data FIPS publication 10-4: COUNTRIES, DEPENDENCIES, AREAS OF SPECIAL SOVEREIGNTY, AND THEIR PRINCIPAL ADMINISTRATIVE DIVISIONS Comes from efele.net Source url: http://efele.net/maps/fips-10/data/fips-414.txt Output csv file: read more
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Data Files

File Description Size Last changed Download
data 131kB csv (131kB) , json (373kB)
region-codes_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 159kB zip (159kB)

data  

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

Field information

Field Name Order Type (Format) Description
region_code 1 string (default) FIPS 10-4 code
region_division 2 string (default) division name for the given country
region_name 3 string (default) name of the region

region-codes_zip  

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

Read me

List of FIPS (Federal Information Processing Standards) region codes.

Data

FIPS publication 10-4: COUNTRIES, DEPENDENCIES, AREAS OF SPECIAL SOVEREIGNTY, AND THEIR PRINCIPAL ADMINISTRATIVE DIVISIONS

Comes from efele.net

Source url: http://efele.net/maps/fips-10/data/fips-414.txt
Output csv file: data/data.csv

Data format

region code,region division,region name
AA00,country,ARUBA
AC00,country,ANTIGUA AND BARBUDA
AC01,dependency,Barbuda
AC03,parish,Saint George
  • region_code - FIPS 10-4 code
  • region_division - division name for the given country
  • region_name - name of the region

Preparation

If you want to update this data, you will need git and python3 installed to run processing script.

git clone https://github.com/datasets/administrative-codes-FIPS-10-4.git
cd administrative-codes-FIPS-10-4
python3 scripts/process.py

license

Author: eric.muller at efele.net

To the extent possible under law, Eric Muller has waived all copyright and related or neighboring rights to this page. This work is published from the United States. Note that this does not affect the rights other persons may have in those files. I am not qualified to determine whether such rights exist.

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 <- 'https://datahub.io/core/region-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)
  }
}

In order to work with Data Packages in Pandas you need to install the Frictionless Data data package library and the pandas extension:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/core/region-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/region-codes/datapackage.json')

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

# print all tabular data(if exists any)
resources = package.resources
for resource in resources:
    if resource.tabular:
        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/region-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)
    }
  }
})()
Datapackage.json