London life expectancy

london

Files Size Format Created Updated License Source
4 674kB csv zip 2 years ago 2 years ago UK Open Government Licence
This dataset was scraped from London data website. Life expectancy at birth and age 65 by sex. Data for 2000-2002 to 2008-2010 revised on 24 July 2013. Local authorities based on boundaries as of 2010. England and Wales figures - non-resident deaths included. Figures given for 3 combined years to read more
Download Developers

Data Files

Download files in this dataset

File Description Size Last changed Download
male-life-expectancy 41kB csv (41kB) , json (115kB)
female-life-expectancy 41kB csv (41kB) , json (115kB)
life-expectancy-at-65 47kB csv (47kB) , json (31kB)
life-expectancy_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 51kB zip (51kB)

male-life-expectancy  

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

Field information

Field Name Order Type (Format) Description
New code 1 string (default)
Area code 2 any (default)
Local Authority 3 string (default)
Year 4 any (default)
Value 5 any (default)

female-life-expectancy  

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

Field information

Field Name Order Type (Format) Description
New code 1 string (default)
Area code 2 any (default)
Local Authority 3 string (default)
Year 4 any (default)
Value 5 any (default)

life-expectancy-at-65  

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

Field information

Field Name Order Type (Format) Description
Area code 1 string (default)
Local Authority 2 string (default)
Men 2000-2002 3 number (default)
Men 2001-2003 4 number (default)
Men 2002-2004 5 number (default)
Men 2003-2005 6 number (default)
Men 2004-2006 7 number (default)
Men 2005-2007 8 number (default)
Men 2006-2008 9 number (default)
Men 2007-2009 10 number (default)
Men 2008-2010 11 number (default)
Men 2009-2011 12 number (default)
Men 2010-2012 13 number (default)
Men 2011-2013 14 any (default)
Men 2012-2014 15 any (default)
Women 2000-2002 16 number (default)
Women 2001-2003 17 number (default)
Women 2002-2004 18 number (default)
Women 2003-2005 19 number (default)
Women 2004-2006 20 number (default)
Women 2005-2007 21 number (default)
Women 2006-2008 22 number (default)
Women 2007-2009 23 number (default)
Women 2008-2010 24 number (default)
Women 2009-2011 25 number (default)
Women 2010-2012 26 number (default)
Women 2011-2013 27 any (default)
Women 2012-2014 28 any (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/london/life-expectancy
data info london/life-expectancy
tree london/life-expectancy
# Get a list of dataset's resources
curl -L -s https://datahub.io/london/life-expectancy/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/london/life-expectancy/r/0.csv

curl -L https://datahub.io/london/life-expectancy/r/1.csv

curl -L https://datahub.io/london/life-expectancy/r/2.csv

curl -L https://datahub.io/london/life-expectancy/r/3.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/london/life-expectancy/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/london/life-expectancy/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/london/life-expectancy/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/london/life-expectancy/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)
    }
  }
})()

Read me

This dataset was scraped from London data website. Life expectancy at birth and age 65 by sex. Data for 2000-2002 to 2008-2010 revised on 24 July 2013. Local authorities based on boundaries as of 2010. England and Wales figures - non-resident deaths included. Figures given for 3 combined years to increase reliability at local levels.

Data

Dataset used for this scraping have been found on Life Expectancy at Birth and at Age 65, Borough.

Output data is located in data directory, it consists of three csv files:

  • male-life-expectancy.csv
  • female-life-expectancy.csv
  • life-expectancy-at-65.csv

Preparation

You will need Python 3.6 or greater and dataflows library to run the script

To update the data run the process script locally:

# Install dataflows
pip install dataflows

# Run the script
python london-life-expectancy.py

License

Open Government Licence

You are encouraged to use and re-use the Information that is available under this licence freely and flexibly, with only a few conditions. Using Information under this licence Use of copyright and database right material expressly made available under this licence (the ‘Information’) indicates your acceptance of the terms and conditions below. The Licensor grants you a worldwide, royalty-free, perpetual, non-exclusive licence to use the Information subject to the conditions below. This licence does not affect your freedom under fair dealing or fair use or any other copyright or database right exceptions and limitations.

You may find further information here

Datapackage.json