London underground performance

london

Files Size Format Created Updated License Source
3 184kB csv zip 2 years ago 2 years ago
London underground performance report - this dataset was scraped from LondonData website. Data The data is given in two files key-trends, lost-customers-hours. Preparation You will need Python 3.6 or greater and dataflows library to run the script To update the data run the process script read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
key-trends 21kB csv (21kB) , json (33kB)
lost-customers-hours 6kB csv (6kB) , json (7kB)
underground-performance_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 30kB zip (30kB)

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

Field information

Field Name Order Type (Format) Description
Period and Financial year 1 string (default)
Reporting Period 2 integer (default)
Days in period 3 integer (default)
Period ending 4 datetime (%Y-%m-%d %H:%M:%S)
Month 5 datetime (%Y-%m-%d %H:%M:%S)
Number of Lost Customer Hours 6 number (default)
Operated Kms (Peak and Off Peak) 7 number (default)
% of Scheduled Operated 8 number (default)
Excess Journey Time (mins) 9 number (default)

lost-customers-hours  

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

Field information

Field Name Order Type (Format) Description
Financial Year 1 string (default)
Period 1 2 number (default)
Period 2 3 number (default)
Period 3 4 number (default)
Period 4 5 number (default)
Period 5 6 number (default)
Period 6 7 number (default)
Period 7 8 number (default)
Period 8 9 any (default)
Period 9 10 any (default)
Period 10 11 any (default)
Period 11 12 any (default)
Period 12 13 any (default)
Period 13 14 any (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

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

# Get resources

curl -L https://datahub.io/london/underground-performance/r/0.csv

curl -L https://datahub.io/london/underground-performance/r/1.csv

curl -L https://datahub.io/london/underground-performance/r/2.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/underground-performance/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/underground-performance/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/underground-performance/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/underground-performance/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

London underground performance report - this dataset was scraped from LondonData website.

Data

The data is given in two files key-trends, lost-customers-hours.

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-data.py:

Licence

Open goverment 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