Netflix Wrapped

If you are unfamiliar with Spotify Wrapped it’s an end-of-year review of listening habits on the platform. Despite having a wealth of data, Netflix does not give users any personalised insights into their watching habits so I decided to take on the project for myself and call it Netflix Wrapped.

Netflix Wrapped was written using Python and Matplotlib. Within a week I was able to download my usage data, clean the data and create 5 data visualisations. Through this project, I gained experience with creating:

• radial graphs
• dumbbell graphs
• radar graphs
• maps

Below I’ll take you through what I did to prepare the data and what findings emerged. See my Netflix Wrapped essay for an extended discussion on what transpired during this project.

🌈 Genres watched

Here I pulled the 27 genre categories used on the IMDB and categorised every title on my list into a primary genre and secondary genre where necessary. One title represents a unique TV show or Movie title. My top 5 favourite genres were Comedy, Documentary, Drama, Game-shows, with Thrillers, Crime, and Animation tied for fifth place.

🍿 Binge-watching

Key Stats

• In 2023 I watched 450 unique titles.
• 67 of these were once-off shows or movies and 383 were episodes in a series.
• Fastest watch time: 8 episodes in 1 day 🏁

It’s well-known that many video streaming platforms like Netflix and YouTube are built to keep you watching so you could say binge-watching is a key marker of success for these platforms. And let’s just say the house always wins. Binge-watching peaked in March for me, when I watched 3 different game shows from start to finish. In September my viewing peaked again with 55 titles watched.

A closer look at September revealed that 20 of the 55 were episodes of Sex Education. Based on the order the episode were watched it was clear that after watching the 8 episodes of the final season, I then watched 12 more episodes from previous seasons. This look the signs of withdrawal because the show was over. In my essay, I further discuss the pros and cons of the binge-watching strategy employed by Netflix.

✈️ Geographic spread

I have always considered my taste in entertainment to be very diverse and global but I wondered if the data would match up. I think TV and movies are great ways to explore new perspectives and places with relative ease. For this visualization I categorized my viewing history based on the language and/or filming location of each title. Here I wanted to see what titles I watched that originated outside of Hollywood and the USA. I have managed to watch several South African (also known as Mzansi), Brazilian, and Kenyan titles but most bizarrely 2023 had been the year of Australian content for me and I watched 6 titles from down under. “