WhatsApp is one of the most popular messaging apps in the world, with over 2 billion active users. It’s no surprise that many people want to analyse their WhatsApp data to gain insights into their conversations, including myself! In this post, I’ll detail how I used Python and BigQuery to analyze my WhatsApp data, covering everything from parsing the raw TXT export to visualizing the data. Let’s get started!

A while ago I made the decision to log every film I watched so that I could one day analyse this data in some way. With a year’s worth of films I thought it could be interesting to also enrich this with more information about the films using all of the data available from IMDB. Was it worth it? Let’s find out…

Site speed metrics can be obtained using the Lighthouse auditing tool, which can be run within Google Chrome. Metrics like this have started to become used in SEO ranking, so naturally if efforts are being made to improve site speed, it makes sense to measure and report on it. When tasked with creating a dashboard displaying Lighthouse metrics over time, what better way to display the current score for each metric than in the very same gauges Lighthouse itself uses in its reports?