School Progress Reports 2006-2007

JohnSnowLabs

Files Size Format Created Updated License Source
2 936kB csv zip 1 month ago John Snow Labs Standard License John Snow Labs Data City of New York
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Data Files

File Description Size Last changed Download
school-progress-reports-2006-2007-csv 135kB csv (135kB) , json (506kB)
school-progress-reports-2006-2007_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 137kB zip (137kB)

school-progress-reports-2006-2007-csv  

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

school-progress-reports-2006-2007_zip  

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:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/JohnSnowLabs/school-progress-reports-2006-2007/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

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/JohnSnowLabs/school-progress-reports-2006-2007/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/JohnSnowLabs/school-progress-reports-2006-2007/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/JohnSnowLabs/school-progress-reports-2006-2007/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