Hawaii Public Electric Vehicle Charging Stations


Files Size Format Created Updated License Source
2 326kB csv zip 1 week ago John Snow Labs Standard License John Snow Labs Data City of Hawaii Home Lands

Data Files


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

Field information

Field Name Order Type (Format) Description
Station_ID 1 integer Identity of Vehicle Charging Station
Property_Business_Name 2 string Name of The Business Property
Street_Address 3 string Address of the Station
City 4 string Name 0f City
Zip_Code 5 integer Identify city and state for given code
Island 6 string Name of The Island
Charge_Fees 7 string Fees For Charging
Charger_Location 8 string Location of Charging Station
Hours_of_Operation 9 string Hours of Operation for Vehicle Charging
Number_of_Chargers 10 integer Total Number of Vehicle Charging Stations
Number_of_Ports 11 integer Total Number of Ports used for Charging
Charger_Level 12 integer Charging Station Level
Charger_Fee 13 string Fee for Charging Vehicle
Parking_Fee 14 string Vehicle Parking Fee
Manufacturers 15 string Station Manufacturers
Notes 16 string Notes For Vehicle Drivers
Latitude 17 number Latitude Location of Charging Station
Longitude 18 number Longitude Location of Charging Station


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 <- "http://datahub.io/JohnSnowLabs/hawaii-public-electric-vehicle-charging-stations/datapackage.json"
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 = "http://datahub.io/JohnSnowLabs/hawaii-public-electric-vehicle-charging-stations/datapackage.json"

# 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('http://datahub.io/JohnSnowLabs/hawaii-public-electric-vehicle-charging-stations/datapackage.json')

# 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 = 'http://datahub.io/JohnSnowLabs/hawaii-public-electric-vehicle-charging-stations/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 the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await file.stream()
  // 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 = 'http://datahub.io/JohnSnowLabs/hawaii-public-electric-vehicle-charging-stations/datapackage.json'

package = DataPackage::Package.new(path)
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data = resource.read
puts data