As a part of analysing Emily’s allergic rhinitis we want to test whether using the London Underground (notoriously dirty!) increases the likelihood of sneezing. The “black snot” phenomenon is well known to Londoners, possibly the particulates (from oil and metal) cause irritation. You can get updates via our allergic rhinitis analysis mailing list (very very low volume).
Transport for London lets us download a log of journeys – either as a CSV file (just dates and costs, no details) or a PDF file (containing full details of the journey and time). It would be much nicer if they made the data available in a cleanly-formatted open format (e.g. at least a CSV, preferably as HDF5).
The goal is to take the detail-rich PDFs and to build a DataFrame like:
from is_train to
2016-01-30 Bus Journey, Route 46 False
2016-01-28 Kentish Town True Leicester Square
2016-01-28 Old Street True Kentish Town
2016-01-28 Leicester Square True Old Street
2016-01-27 Angel True Kentish Town
Using textract (see these Python 3.4 install notes, I also use pdftotext) and a very hacky parser (written this evening, it really is a stateful-messy-hack <sorry>) I can parse a single PDF or a folder to build a Pandas DataFrame of journeys. You’ll find London Oyster PDF to DataFrame Parser here. The output is an HDF5 which can be loaded by Python into Pandas (or R or Matlab or whatever).
Ian is a Chief Interim Data Scientist via his Mor Consulting. Sign-up for Data Science tutorials in London and to hear about his data science thoughts and jobs. He lives in London, is walked by his high energy Springer Spaniel and is a consumer of fine coffees.