Regents Exam Results


Files Size Format Created Updated License Source
2 0B csv zip 6 months ago johnsnowlabs Data City of New York

Data Files

File Description Size Last changed Download
regents-exam-results-csv 58kB csv (58kB) , json (356kB)
datapackage_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 75kB zip (75kB)


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

Field information

Field Name Order Type (Format) Description
Cohort_Year 1 date (%Y-%m-%d) Cohort Year
Cohort_Category 2 string Category of Cohort
Demographics 3 string Demographic variable distribution for each Cohort
Total_Number_of_Cohort 4 integer Total number of Cohort
Total_Number_of_Grads 5 integer Total number of Graduates
Percentage_of_Cohort_Total_Grads 6 number Total Graduates Percentage
Number_of_Cohort_Total_Grads 7 integer Total number of Graduates
Percentage_of_Cohort_Total_Regents 8 number Total Regents Percentage
Percentage_of_Grads_Total_Regents 9 number Total number of Graduates Percentage
Number_of_Grads_Total_Regents 10 integer Total number of Regents Graduates
Number_of_Cohort_Advanced_Regents 11 number Total number of Advanced Regents
Percentage_of_Grads_Advanced_Regents 12 number Advanced Regents graduates graduates
Number_of_Grads_Advanced_Regents 13 integer Total number of advanced gradustes regents
Percentage_of_Cohort_Regents_Advanced 14 number Advanced Regents Percentage
Percentage_of_Grads_Regents_Advanced 15 number Advanced Regents Graduates Percentage
Number_of_Grads_Regents_Advanced 16 integer Total number of Advanced Regetnts Graduates
Percentage_of_Cohort_Local 17 number Local Cohort Percentage
Percentage_of_Grads_Local 18 number Local Graduates Percentage
Number_of_Still_Enrolled 19 integer Total number of Enrolled students
Number_of_Cohort_Still_Enrolled 20 number Total number of Cohot Enrolled students
Dropped_Out_Number 21 integer Number of dropped out students
Percentage_of_Cohort_Dropped_Out 22 number Percentage of dropped cohort

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/regents-exam-results
tree JohnSnowLabs/regents-exam-results
# 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