SSA Extra Help With Medicare Prescription Drug Plan Cost 2010-2015


Files Size Format Created Updated License Source
2 0B csv zip 6 months ago John Snow Labs Standard License johnsnowlabs Social Security Administration

Data Files

File Description Size Last changed Download
ssa-extra-help-with-medicare-prescription-drug-plan-cost-2010-2015-csv 12kB csv (12kB) , json (40kB)
datapackage_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 15kB zip (15kB)


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

Field information

Field Name Order Type (Format) Description
Fiscal_Year 1 date (%Y-%m-%d) Year of Medicare prescription drug plan cost
State 2 string Geographic Area Indicates the state (or one other location which is the District of Columbia and is shown in the table as DC) provided as the applicant’s address. Locations other than the fifty states and DC are shown as “no zip available”
Decisions_Made 3 number Indicates the number of decisions made on Extra Help With Medicare Prescription Drug Plan Cost applications by the Social Security Administration.
Eligible 4 number Indicates the number of applicants found eligible after submitting an application for Extra Help With Medicare Prescription Drug Plan Cost.
Percentage_Eligible 5 number Eligible divided by Decision made expressed as a percentage. This is the percentage of applicants eligible for Extra Help With Medicare Prescription Drug Plan Cost after filing an application with Social Security.

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Download CLI tool and use it with the datahub almost like you use git with the github:

data get
data info JohnSnowLabs/ssa-extra-help-with-medicare-prescription-drug-plan-cost-2010-2015
tree JohnSnowLabs/ssa-extra-help-with-medicare-prescription-drug-plan-cost-2010-2015
# 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