Demographic Statistics By Zip Code

JohnSnowLabs

Files Size Format Created Updated License Source
2 447kB csv zip 4 months ago John Snow Labs Standard License John Snow Labs Data City of New York
Download

Data Files

File Description Size Last changed Download
demographic-statistics-by-zip-code-csv 27kB csv (27kB) , json (327kB)
demographic-statistics-by-zip-code_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 22kB zip (22kB)

demographic-statistics-by-zip-code-csv  

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

Field information

Field Name Order Type (Format) Description
Jurisdiction_Name 1 integer Jurisdiction Name
Count_Participants 2 integer Count Participants
Count_Female 3 integer Number of Female
Percent_Female 4 number Female Percentage
Count_Male 5 integer Number of Female
Percent_Male 6 number Male Percentange
Count_Gender 7 integer Gender Count
Percent_Gender 8 number Gender Percentage
Count_Gender_Total 9 integer Gender Total
Percent_Gender_Total 10 number Gender Total
Count_Pacific_Islander 11 integer Pacific Islander
Percent_Pacific_Islander 12 number Pacific Islander
Count_Hispanic_Latino 13 integer Hispanic Latino
Percent_Hispanic_Latino 14 number Hispanic Latino
Count_American_Indian 15 integer American Indian
Percent_American_Indian 16 number American Indian
Count_Asian_Non_Hispanic 17 integer Asian Non Hispanic
Percent_Asian_Non_Hispanic 18 number Asian Non Hispanic
Count_White_Non_Hispanic 19 integer White Non Hispanic
Percent_White_Non_Hispanic 20 number White Non Hispanic
Count_Black_Non_Hispanic 21 integer Black Non Hispanic
Percent_Black_Non_Hispanic 22 number Black Non Hispanic
Count_Other_Ethnicity 23 integer Other Ethnicity
Percent_Other_Ethnicity 24 number Other Ethnicity
Count_Ethnicity 25 integer Ethnicity
Percent_Ethnicity 26 number Ethnicity
Count_Ethnicity_Total 27 integer Ethnicity Total
Percent_Ethnicity_Total 28 number Ethnicity Total
Count_Permanent_Resident_Alien 29 integer Permanent Resident Alien
Percent_Permanent_Resident_Alien 30 number Permanent Resident Alien
Count_Us_Citizen 31 integer Us Citizen
Percent_Us_Citizen 32 number Us Citizen
Count_Other_Citizen_Status 33 integer Other Citizen Status
Percent_Other_Citizen_Status 34 number Other Citizen Status
Count_Citizen_Status 35 integer Citizen Status
Percent_Citizen_Status 36 number Citizen Status
Count_Citizen_Status_Total 37 integer Citizen Status Total
Percent_Citizen_Status_Total 38 number Citizen Status Total
Count_Receives_Public_Assistance 39 integer Public Assistance
Percent_Receives_Public_Assistance 40 number Public Assistance
Count_Nreceives_Public_Assistance 41 integer Public Assistance
Percent_Nreceives_Public_Assistance 42 number Public Assistance
Count_Public_Assistance 43 integer Public Assistance
Percent_Public_Assistance 44 number Public Assistance
Count_Public_Assistance_Total 45 integer Public Assistance
Percent_Public_Assistance_Total 46 number Public Assistance

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/demographic-statistics-by-zip-code
data info JohnSnowLabs/demographic-statistics-by-zip-code
tree JohnSnowLabs/demographic-statistics-by-zip-code
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/demographic-statistics-by-zip-code/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/demographic-statistics-by-zip-code/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/demographic-statistics-by-zip-code/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/demographic-statistics-by-zip-code/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/demographic-statistics-by-zip-code/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/demographic-statistics-by-zip-code/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/demographic-statistics-by-zip-code/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