Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
2 | 68kB | csv zip | 4 years ago | 4 years ago |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
unemployment-rate | 10kB | csv (10kB) , json (10kB) | ||
unemployment_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 14kB | zip (14kB) |
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
date | 1 | date (%Y-%m-%d) | |
unemployment_real_numbers | 2 | number (default) | |
unemployment_rate | 3 | number (default) |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/london/unemployment
data info london/unemployment
tree london/unemployment
# Get a list of dataset's resources
curl -L -s https://datahub.io/london/unemployment/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/london/unemployment/r/0.csv
curl -L https://datahub.io/london/unemployment/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/london/unemployment/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/unemployment/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/unemployment/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/unemployment/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)
}
}
})()
London unemployment rate - is an dataset scraped from LondonData website.
Is given in data folder. It represents total number of unemployed every month given in real numbers and in rates.
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-data.py
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