Reducing Assessments or Reclassifying Property


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

Data Files

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


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.

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Use data with the almost like you use git with the github. Here are installation instructions.

data get
tree JohnSnowLabs/reducing-assessments-or-reclassifying-property
# Get a list of dataset's resources
curl -L -s | grep path

# Get resources

curl -L

curl -L

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

install.packages("jsonlite", repos="")

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

# get list of all resources:

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))

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 = ''

# 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('')

# print list of all resources:

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

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 list of all resources:
  for (const id in dataset.resources) {
  // 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
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data