u/Exotic-Finish-5400

Hey everyone,
I love end-of-year music recaps, but I always felt like Apple Music Replay barely scratched the surface. I wanted to see exactly when my tastes shifted, what songs I abandoned, and how my listening habits changed over time.

So, I spent the last few months building Coda—a local analytics dashboard that treats my Apple Music listening history like a financial stock market.

How I built it:
You request your data export via Apple's Privacy portal. Coda takes your raw massive CSV files, cleans up the messy metadata, and builds a local SQLite database on your machine to visualize your habits.

I built some really fun features into it:

• Sleep Data Filtering: It detects overnight autoplay sessions and filters them out so your data stays perfectly accurate.
• Sonic DNA: It uses local machine learning models to analyze the actual audio of your top tracks (extracting BPM, energy, danceability) and plots your "Sonic DNA" on a scatter chart.
• Market-Style Charts: It uses OHLC Candlestick charts to show listening volume over time, and Sankey flow diagrams to show how your listening flows from Genres down to Artists.
• Ghost Tracks: It identifies songs you used to have on heavy rotation but completely abandoned months ago.

What's next:
Right now, this is just a local Python/React project running on my machine. Because it processes your raw Apple Music export locally, your data stays completely private.

I'm currently doing the heavy lifting to package this into a polished Mac/Windows desktop app. Because it runs entirely on your machine and doesn't harvest your data to pay for cloud servers, I plan to release it for a small one-time fee (absolutely no subscriptions).

I threw up a quick waitlist if you want to be notified when the beta is ready. I'll put the link in the comments! Let me know what you guys think of the UI.

Hey data nerds,
I wanted to see what would happen if I treated my personal Apple Music listening history like financial market data. I built a local pipeline to process my Apple Privacy Export and visualize it.

The Data Pipeline:
Apple's export gives you a massive Play Activity.csv and Library Tracks.json. I wrote a Python pipeline to clean the strings, extract featured artists, deduplicate rapid play logs, and dump it into a normalized SQLite database. I also wrote a heuristic algorithm to detect and filter out "sleep listening" (8-hour overnight autoplay sessions) so the data isn't skewed.

The Visualizations:

• OHLC Candlesticks: Instead of bar charts, I bucketed listening minutes into Daily/Weekly/Monthly Open-High-Low-Close candles. It perfectly visualizes the "volatility" of my listening habits for specific artists.
• Sankey Diagrams: I mapped the flow of listening volume (in minutes) from broad Genres, branching out into specific Artists, and then down into Albums.
• Scatter Plots (Sonic DNA): I ran my top tracks through local TensorFlow audio models to extract continuous features (Energy, Valence/Mood, Danceability) and plotted them to find clusters in my taste.

Right now this is a local Python/React dashboard, but I'm packaging it into a desktop app so others can run their own CSVs through it.

I'll drop a link to a video showing the interactive charts in the comments. Would love to hear what other visualizations you'd apply to this dataset!

Hey everyone,

Like a lot of people here, I love Apple Music Replay, but I always find myself wanting to dig deeper into my data than just a top 100 playlist at the end of the year. I wanted to see exactly when my tastes shifted, what songs I abandoned, and how my listening habits changed over time.

So, I spent the last few months building Coda—a local analytics dashboard that treats my Apple Music listening history like a financial stock market.

How it works with Apple Music:
Apple allows you to request a full export of your data via their Privacy portal. Coda takes your raw Apple Music Play Activity.csv and Library Tracks.json files, cleans up the messy metadata (merging deluxe editions, extracting featured artists), and builds a local SQLite database on your machine to visualize your exact listening habits.

Instead of just showing play counts, I wanted to build some genuinely deep analytics. Here are a few of the coolest features I got working:

• Sleep Data Filtering: Have you ever fallen asleep listening to music and completely ruined your Replay stats because a playlist ran for 8 hours? I built a heuristic algorithm (and an Apple Health XML importer) that detects overnight autoplay sessions and filters them out of your charts so your data stays perfectly accurate.
• Sonic DNA & Audio Analysis: I integrated TensorFlow and Essentia into the backend. The app actually analyzes the audio of your top tracks to extract BPM, energy, danceability, and valence (mood), and plots your library on a scatter chart so you can see your exact "Sonic DNA."
• Interactive Previews: Because it's linked to the Apple Music catalog, you can double-click any data point, candlestick, or chart node to instantly play a 30-second audio preview of that track.
• Market-Style Charts: Instead of basic bar charts, it uses OHLC Candlestick charts to show listening volume over time. It also uses Sankey flow diagrams to show exactly how your listening flows from broad Genres down to specific Artists and Albums.
• Library Health & Ghost Tracks: It tracks "Volatility" (songs you skip the most) and "Ghost Tracks" (songs you used to have on heavy rotation but completely abandoned months ago).

Try it out:
Right now, this is just a local Python/React project running on my own machine. Because it processes your raw Apple Music export locally, your data stays completely private (no uploading your massive CSVs to a random cloud server).

I'm currently doing the heavy lifting to package this into a polished, easy-to-install Mac/Windows desktop app. Because it runs entirely on your machine and doesn't harvest your data to pay for cloud servers, I plan to release it for a small one-time fee (absolutely no subscriptions).

I threw up a quick waitlist if you want to be notified when the beta is ready (I'll send out an early-bird discount to anyone on the list), link in comments.

Would love to hear what other stats or charts you guys would want to see from your Apple Music data! Let me know what you think.