Austin Discarded Materials 2013 to 2015

JohnSnowLabs

Files Size Format Created Updated License Source
2 431kB csv zip 3 months ago John Snow Labs Standard License John Snow Labs Data City of Austin
Download

Data Files

File Description Size Last changed Download
austin-discarded-materials-2013-to-2015-csv 97kB csv (97kB) , json (272kB)
austin-discarded-materials-2013-to-2015_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 27kB zip (27kB)

austin-discarded-materials-2013-to-2015-csv  

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

Field information

Field Name Order Type (Format) Description
Fiscal_Year 1 date (%Y-%m-%d) Fiscal Year when Material was discarded
Quarter 2 string Quarter of Collection
Month 3 date (%Y-%m-%d) Month of Collection
Category 4 string The category (Recycling or waste)
Sub_Category 5 string Sub-category (recycled material, reused material and material sent to the landfill)
Material_Source_Donated_To 6 string Institution or organization where Material Source is donated.
Weight_in_Pounds 7 integer Weight of Materials in Pounds

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 datahub.io almost like you use git with the github. Here are installation instructions.

data get https://datahub.io/JohnSnowLabs/austin-discarded-materials-2013-to-2015
tree JohnSnowLabs/austin-discarded-materials-2013-to-2015
# Get a list of dataset's resources
curl -L -s https://datahub.io/JohnSnowLabs/austin-discarded-materials-2013-to-2015/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/JohnSnowLabs/austin-discarded-materials-2013-to-2015/r/0.csv

curl -L https://datahub.io/JohnSnowLabs/austin-discarded-materials-2013-to-2015/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/austin-discarded-materials-2013-to-2015/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/austin-discarded-materials-2013-to-2015/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/austin-discarded-materials-2013-to-2015/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/austin-discarded-materials-2013-to-2015/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