School Progress Reports 2006-2007


Files Size Format Created Updated License Source
2 912kB csv zip 2 weeks ago John Snow Labs Standard License johnsnowlabs Data City of New York

Data Files

File Description Size Last changed Download Other formats
school-progress-reports-2006-2007-csv [csv] 135kB school-progress-reports-2006-2007-csv [csv] school-progress-reports-2006-2007-csv [json] (506kB)
datapackage_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 136kB datapackage_zip [zip]


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

Field information

Field Name Order Type (Format) Description
DBN 1 string A unique identification number for database record for saving school progress report school wise.
District 2 integer School district code
School 3 string School Name or field
School_Support_Organization 4 string Organization that supports schools
Progress_Report_Type 5 string
School_Level 6 string
Peer_Index 7 number Peer indices are calculated differently depending on School Level
Grade 8 string Students Grades in school
Overall_Score 9 number Overall progress/score
Environment_Category_Score 10 number Score of School Environment Score
Performance_Category_Score 11 number Performance Category Score
Progress_Category_Score 12 number Progress Category Score
Additional_Credit 13 number Additional Credit taken
Quality_Review_Score 14 string Quality Review Score


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

Read me

Import into your tool

If you are using R here's how to get the data you want quickly loaded:


json_file <- ""
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# access csv file by the index starting from 1
path_to_file = json_data$resources[[1]]$path
data <- read.csv(url(path_to_file))

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

pip install datapackage
pip install jsontableschema-pandas

To get the data run following code:

import datapackage

data_url = ""

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

# data frames available (corresponding to data files in original dataset)

# you can access datasets inside storage, e.g. the first one:

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('')

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

data = package.resources[0].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 = ''

// We're using self-invoking function here as we want to use async-await syntax:
(async () => {
  const dataset = await Dataset.load(path)

  // Get the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await
  // entire file as a buffer (be careful with large files!)
  const buffer = await file.buffer

Install the datapackage library created specially for Ruby language using gem:

gem install datapackage

Now get the dataset and read the data:

require 'datapackage'

path = ''

package =
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data =
puts data