Reducing Assessments or Reclassifying Property


Files Size Format Created Updated License Source
2 3MB csv zip 2 weeks ago John Snow Labs Standard License John Snow Labs Data City of New York

Data Files

File Description Size Last changed Download Other formats
reducing-assessments-or-reclassifying-property-csv [csv] 579kB reducing-assessments-or-reclassifying-property-csv [csv] reducing-assessments-or-reclassifying-property-csv [json] (2MB)
reducing-assessments-or-reclassifying-property_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 574kB reducing-assessments-or-reclassifying-property_zip [zip]


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

Field information

Field Name Order Type (Format) Description
Borough_Code 1 integer Borough Code is the first digit of the parcel number system used to identify each unit of real estate in New York City for numerous city purposes. Values: 1-Manhattan (New York County), 2-Bronx (Bronx County), 3-Brooklyn (Kings County)
Block_Number 2 integer Block Number
Lot_Number 3 integer Lot Number
Tax_Year 4 date (%Y-%m-%d) Taxing Year
Owner_Name 5 string Owner Name
Property_Address 6 string Property Address
Granted_Reduction_Amount 7 integer Reduction Amount Granted
Tax_Class_Code 8 string Tax classes codes are used to pay a different share of property tax in New York City. Each class has a corresponding assessment value for the property. Values: Tax class 1 - 6% per year, no more than 20% over 5 years, Tax class 2 and 4 - 45%, Tax class 2a, 2b, 2c - 8% per year, no more than 30% over 5 years for building with 10 or less units.


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$resources$path[1][1]
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