NYC Services Volunteer Opportunities


Files Size Format Created Updated License Source
2 1MB csv zip 2 weeks ago John Snow Labs Standard License johnsnowlabs City of New York

Data Files

File Description Size Last changed Download Other formats
nyc-services-volunteer-opportunities-csv [csv] 296kB nyc-services-volunteer-opportunities-csv [csv] nyc-services-volunteer-opportunities-csv [json] (548kB)
datapackage_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 295kB 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
Opportunity_Id 1 integer Opportunity Id
Content_Id 2 integer Content Id
Vol_Requests 3 integer Number of Requests
Event_Time 4 number Event Time
Title 5 string Title of Event
Hits 6 integer Number of Hits
Summary 7 string Summary of Event
Is_Priority 8 boolean Priority
Category_Id 9 integer Category Id
Category_Desc 10 string Category Description
Org_Title 11 string Organization Title
Org_Content_Id 12 string Race Ethnicity
Addresses_Count 13 integer Total Addresses
Locality 14 string Location
Region 15 string Region
Postal_Code 16 integer Postal Code
URL 17 string URL Address
Recurrence_Type 18 string Recurrence Type
Hours 19 integer Time in Hours
Created_Date 20 date (%Y-%m-%d) Date Created
Last_Modified_Date 21 date (%Y-%m-%d) Modified Date
Start_Date 22 date (%Y-%m-%d) Date Started
End_Date 23 date (%Y-%m-%d) Date Ended
Status 24 string Status of the case


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