School Level Graduation Outcomes 2010-2011

JohnSnowLabs

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

Data Files

File Description Size Last changed Download
school-level-graduation-outcomes-2010-2011-csv 129kB csv (129kB) , json (432kB)
datapackage_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 68kB zip (68kB)

school-level-graduation-outcomes-2010-2011-csv  

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

Field information

Field Name Order Type (Format) Description
Data_Base_Number 1 string A unique identification number for database record for saving school progress report school wise.
School_Name 2 string Name of School
Cohort_Year 3 date (%Y-%m-%d) Cohort Year
Demographics 4 string Demographic variable distribution for each Cohort
Total_Cohort_Number 5 integer Total number of Cohort
Total_Grade_Number 6 integer Total number of Graduates
Number_Achieving_APM 7 integer Number of Achieving Aspirational Performance Measure
Cohort_Achieving_Percentage 8 number Percentage of Achieving Cohort
Graduate_Achieving_Percentage 9 number Percentage of Grads Achieving

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 https://datahub.io/JohnSnowLabs/school-level-graduation-outcomes-2010-2011
data info JohnSnowLabs/school-level-graduation-outcomes-2010-2011
tree JohnSnowLabs/school-level-graduation-outcomes-2010-2011
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/school-level-graduation-outcomes-2010-2011/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/school-level-graduation-outcomes-2010-2011/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/school-level-graduation-outcomes-2010-2011/r/1.zip

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-level-graduation-outcomes-2010-2011/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

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 = 'https://datahub.io/JohnSnowLabs/school-level-graduation-outcomes-2010-2011/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-level-graduation-outcomes-2010-2011/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        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-level-graduation-outcomes-2010-2011/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