Building Financial Networks
I have been receiving requests to release the Python code I wrote to produce the financial network discussed in my previous post titled PSE Correlation-Based Network. Well, here it is! 🙂
PSE Correlation-Based Network
In this blog post, I explore the use of networks (minimum spanning trees), as presented by Bonanno et al. in 2003, to quantify relationships of assets listed in the Philippine Stock Exchange.
Hypothetical Taxi Movements in MMla
Metro Manila, Philippines. Generating and visualising a day’s worth of synthetic taxi data.
Mining, reading, manipulating, and plotting map data
Sharing some Python recipes I wrote for mining, reading, manipulating, and plotting map data.
How well do you know your province?
Let’s play a game! Can you guess which Philippine provinces these road networks belong to? The first one is easy peasy!
Python Packages
I was tasked to come up with a list of all the Python packages we need for this new server we are setting up. Sharing the list below, hoping you’ll find them useful.
The 2016 VP Race
We take a peek at the data on the controversial Philippine VP race that prompted many Pinoy academics to download, visualise, and analyse the 2016 election results in the wake of the allegations of systematic cheating
