Data Viz

A Data Scientist Breaks Down All 10 Taylor Swift Albums

Taylor Swift’s music has pretty much been the soundtrack of my life. When she was waiting for her Romeo in Love Story, so was I. When she was ignoring the haters in Shake It Off, so was I. And when she was lying awake in the middle of the night in Anti-Hero, so was I.

As a data scientist, I’ve been wanting to analyze Taylor Swift’s songs for years. She’s known for her complex lyrics and Easter eggs sprinkled throughout her music and messages. Would I be able to uncover something new using data analysis? (Spoiler alert: yes!)

With her Eras Tour kicking off this month, I thought it’d be the perfect time to dig into her 10 studio albums in the medium that I know best — data visualizations.

1. Why The Eras Tour is in Such High Demand

Taylor Swift made national news when the demand for her upcoming tour overwhelmed Ticketmaster. We all know she’s a famous musician, but what made The Eras Tour especially popular? (Swipe through images)

Due to the pandemic, it’s been 5 years since Taylor last toured and she’s released 4 studio albums in the meantime. Plus, The Eras Tour covers all 10 studio albums – it’s basically a best-of tour!

2. Taylor Swift’s Sound Evolution Over Time

Taylor started out as a country singer, she transitioned to pop, and her sound keeps on changing with each new album. I wanted to see if I could quantify the evolution, so I used the Spotify API look at the sound characteristics of her music. (Swipe through images)

You can see that her earlier albums all cluster together, meaning that her sound was very similar on those albums. As she grows up, she’s become more experimental, with each album introducing new sounds and moods. I can’t wait to see where she goes next!

The technical details for those of you who are interested – I looked at the acousticness (instrumental vs electronic) and valence (positive sound vs negative sound) of all of her songs and averaged them for each album.

3. All the Colors in Taylor Swift’s Songs

Taylor is known for having extremely descriptive lyrics where she references specific people and dates, what she was wearing, how she was feeling and also lots of colors.

I loved this visualization capturing the timeline of wardrobe colors in Breaking Bad and wanted to create my own for TSwift. (Swipe through images)

The most interesting pattern that I found was that her earlier six albums mostly stuck to basic colors like red, gold, blue and black and her later albums, starting with Lover, started to explore more complex colors.

She goes from the song Red on her 4^th album to the song Maroon on her 10^th album, where her perception of love changes from “burning red” to a more mature “maroon”.

4. All the Swear Words in Taylor Swift’s Songs

When I was listening to Taylor’s latest album Midnights, one thing that stood out to me was the amount of swearing. That’s when I realized that she didn’t swear that much in her earlier albums.

The word damn was used in just one song in her debut album, Taylor Swift. No swear words appeared in her next two albums, Fearless and Speak Now. Compare that to Midnights, where half the songs are explicit.

I also wanted to call out the word sexy. While not a swear word, the only time she’s ever used the term sexy in all 10 albums is in the song Anti-Hero — “Sometimes, I feel like everybody is a sexy baby and I’m a monster on a hill”. I find it pretty impressive that she didn’t use the term at all during her first two decades as a songwriter!

5. The Last Line of Every Taylor Swift Album

I love the last song on every Taylor Swift album. The final song seems to capture her mental state upon completing the album and I feel resolved and content after listening to it.

When I looked at the last line of each of her albums, I was able to see her thought progression over time, from her teen years of hope, to her twenties learning to overcome hard times, to her thirties becoming more confident in herself.

The way the final lines capture her mindset over time is so fascinating and it’s one of those things where I can’t tell if it was done on purpose, or if it just sort of happened.

But I should know by now that with Taylor, everything is intentional. Like she said so herself, she is a mastermind.

More Taylor Swift Data Visualizations

These are just 5 of the 22 Taylor Swift data visualizations I created for this project.

For the Swifties:

To see all 22 data visualizations about her lyrics, collaborators and more, check out my Extended Version blog post.

Want to watch a video?

I explain all 22 Taylor Swift data visualizations in my YouTube video.

A Data Scientist Breaks Down All 10 Taylor Swift Albums (The Extended Version)

Posted on March 1, 2023 by Alice Zhao Analysis Data Visualization Taylor Swift 6

I don’t know about you, but I’m feeling 22 Taylor Swift data visualizations.

This is the Extended Version post. If you want to read just the highlights:

Check out my shorter blog post

If you want to watch me talk about this post:

Check out my YouTube video

How Did I Do This Analysis?

1. Billboard Hits

The majority of the songs on her albums make it to the Billboard Top 100.

2. Demand for The Eras Tour

Fans have been waiting years for The Eras Tour and can’t wait to hear all her greatest hits.

3. Audience Growth

She keeps expanding her fan base. Somehow both me and my niece who’s 20 years younger than me are both Swifties!

4. Sound Evolution

Her sound keeps evolving and she’s getting more experimental over time.

5. Keys of Songs

She writes a song in almost every key on folklore!

My guess is she’s transitioned from writing songs mostly on the guitar to the piano.

Even though she writes a lot of sad songs, most of her songs are in major keys.

6. Seasons

Not a lot is going on for Taylor in the spring.

7. Days of the Week

She loves singing about Tuesday nights.

8. Complex Words

She hides complex words inside pop songs.

9. Swear Words

She’s been slowly adding swear words to her vocabulary over the years.

10. Never vs Always

As she gets older, she uses the term never less and always more.

11. Girl vs Woman

Her lyrics have transitioned from talking about girls to women, and boys to men.

12. Colors

The colors in her songs have gotten more complex over time.

13. Tour Openers

Ed Sheeran and Shawn Mendes both made it big after touring with Taylor.

14. Co-Writers

Taylor wrote all of Speak Now on her own, and had the most number of co-writers on reputation.

15. Top Hits with Main Co-Writers

She writes her best sad songs with Liz Rose, her angsty songs with Jack Antonoff and love songs on her own.

16. Up and Down Theme

Every song on Midnights references something up or down, just like the time midnight, where both hands of the clock are pointing up.

17. Song Connections

The words in one song lead to the next song in Midnights, like “haze” in the first song of the album to “hazy” in the second song of the album.

18. Album Titles From Song Lyrics

Starting with 1989, her album titles have come from prior song lyrics (except for folklore, which was a surprise album).

19. Song Lyrics From Albums Titles

She references her album titles in later songs, like when she talks about being a “fearless leader” in the song, The Man (Lover).

20. 3 Pen Types

She imagines what type of pen she is holding (fountain, quill or glitter gel) as she writes her songs.

21. Revenge Songs

She says she writes in the style of 3 pen types (see previous visualization), but I think there may be a 4th pen type for her revenge songs.

22. Last Line of Every Album

The last line of every album shows how she’s grown over time to becoming more confident in herself.

She can finally admit that she is a mastermind. I hope you enjoyed these 22 Taylor Swift data visualizations!

How Did I Do This Analysis?

These data visualizations may look simple, but they were the product of a lot of technical work behind the scenes!

I want to thank the following people for their technical resources:

I followed Cameron Watts‘s Extracting Song Data From the Spotify API Using Python tutorial on Medium to extract Taylor Swift’s song metadata using the Spotify API. He is a software engineer at Microsoft.
I borrowed heavily from Melanie Walsh‘s Introduction to Cultural Analytics & Python online textbook to extract Taylor Swift’s lyrics using the Genius API. She is a Professor at UW.
I did most of my data analysis using Excel and Python, but I double checked my work using Shayna Kothari’s awesome Taylor Swift Lyric Search app. She is a software engineer at Facebook.

I did most of my data analysis in Excel, my data extraction and text analysis in Python and my data visualizations in Keynote.

You can find spreadsheets containing Taylor Swift’s song lyrics and metadata, along with the Python code I used to get the data from the Genius API and Spotify API on Github.