Occurrence Of Firearm Discharge

JohnSnowLabs

Files Size Format Created Updated License Source
2 24kB csv zip 4 months ago John Snow Labs Standard License John Snow Labs City of New York
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Data Files

File Description Size Last changed Download
occurrence-of-firearm-discharge-csv 426B csv (426B) , json (1kB)
occurrence-of-firearm-discharge_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 3kB zip (3kB)

occurrence-of-firearm-discharge-csv  

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

Field information

Field Name Order Type (Format) Description
Discharge_Detail 1 string Discharge Detail
Year_2002 2 integer 2002 Report
Year_2003 3 integer 2003 Report
Year_2004 4 integer 2004 Report
Year_2005 5 integer 2005 Report
Year_2006 6 integer 2006 Report
Year_2007 7 integer 2007 Report
Year_2008 8 integer 2008 Report
Year_2009 9 integer 2009 Report
Year_2010 10 integer 2010 Report
Year_2011 11 integer 2011 Report
Year_2012 12 integer 2012 Report

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/occurrence-of-firearm-discharge
data info JohnSnowLabs/occurrence-of-firearm-discharge
tree JohnSnowLabs/occurrence-of-firearm-discharge
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/occurrence-of-firearm-discharge/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/occurrence-of-firearm-discharge/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/occurrence-of-firearm-discharge/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/occurrence-of-firearm-discharge/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/occurrence-of-firearm-discharge/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/occurrence-of-firearm-discharge/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/occurrence-of-firearm-discharge/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