んにちは、データ！: How I Anime-fied My Spreadsheet Obsession

I’ve always been a closeted data nerd. Long before I called myself a “data scientist,” I was just a girl with too many spreadsheets and too little shame.

Habit Tracker? Check.
IELTS Study Log? Obviously.
Secret Kiss List? I plead the fifth.
Anime Spreadsheet? Oh, Darling… it’s a masterpiece.

This project started as procrastination (avoiding adult responsibilities) and turned into a full-blown data viz obsession. Today, I’m exposing my anime -watching sins— how many hours I’ve wasted, my embarrassing genre preferences, and why I’m basically a drama magnet.

Spoiler: The data doesn’t lie. I’m the problem.

The Cold, Hard Numbers (AKA: My Intervention File)

Let’s start with the brutal truth—courtesy of my MyAnimeList (MAL) data:

Metric	Value	Translation
Total Anime	58	“Not that bad… right?”
Total Hours	1,288.9 (≈54 days)	“Enough to watch One Piece 1.3x”
Avg. Score	7.86	“I have taste, okay?”
Inuyasha	Rewatched 7 times	“…I have no defense.”

Fun Fact: If you stacked all those hours as actual sleep, I’d be well-rested for once. But no. Here we are.

Deep Dive: What My Anime Habits Say About Me

1. I’m 80% Drama, 20% Liar

The WordCloud of my most-watched genres says it all:

Key Findings:

Top Genres: Romance, Drama, Action (aka “I love pain”)
Guiltiest Pleasure: re-watch Inuyasha, Banana Fish, Your Lie in April, and Yuri!! on ice. Judge me.

Graphs That Expose My Soul

1. The “Sunburst of Shame”

I cannot find a way to emddeb this on WordPress!

Takeaway: I claim to love “deep, philosophical anime,” but 70% of my hours are shoujo romance.

2. The “Are New Anime Getting Worse?” Plot

Interactive scatter plot of anime scores vs. release year, filtered by genre

Spoiler: No. I’m just nostalgic.

Bonus: The “Anime Recommender” That Judges You

I built a simple recommender system based on:

MyAnimeList rating
Episode count (because commitment issues)

Try it yourself:

def recommend_anime(genre):  
    if genre == "Drama":  
        return "Just go watch *Your Lie in April* and cry, you masochist."  
    else:  
        return "Nice try. Stick to dramas."

(Code available on GitHub)

Final Confessions (And Lessons Learned)

Data Doesn’t Lie: I’m emotionally invested in 2D characters.
Procrastination = Discovery: This project taught me more Python than my actual coursework.
Next Time, Track Sleep Instead.

Data Analyst & Economist