TFL Cycling DataSet - Part 116 Jul 2017 - about 1 min to read
I’m hoping this will be a reasonably accurate account of my play with the TfL Cycling DataSets.
I’m still forming my plan, however loosely I think I want to end up with a visualisation where the bike points are highlighted in over a time series as bikes are taken and returned.
Initially, I’m working on my Mac, but I have a Databricks community cluster that I’ve migrated some of the parts to.
Preparing my Local Env
As I said, I’m using my MacBook so I’m going to install a couple of things
To install spark, I use
brew install spark
Installing jupyter notebooks is done with
pip install jupyter
Getting some data
I took a single file from the S3 bucket to play with locally, for no particular reason I went with
aws s3 cp s3://cycling.data.tfl.gov.uk/usage-stats/01aJourneyDataExtract10Jan16-23Jan16.csv ~/datasets/cycling/.
Run the following commands to get your Jupyter Notebook up and running
export PYSPARK_DRIVER_PYTHON=jupyter export PYSPARK_DRIVER_PYTHON_OPTS='notebook' pyspark
Finally a quick test to see how it looks. In the
Jupyter notebook I can do
data = spark.read.csv('~/datasets/cycling/01aJourneyDataExtract10Jan16-23Jan16.csv', header=True, inferSchema=True) data.show()
This should show you 20 rows from the data set and we’re off.