Your Top Spotify Listening Habits, Favorite Artists, and Song Recommendations in a Playlist🎧🎶

Generate some visualizations, stats, and a video and stats. Inspired by Spotify Wrapped.

Using Python to grab recently played songs from Spotify API and chart.js to display a SpotifyWrapped.

Create your own small but transparent Spotify Wrapped to view your nerdy music stats.

Year in review generator for Foobar2000's Discord Rich Presence component. Made in Python with Pillow.

A discount version of the popular Spotify Wrapped feature. This project covers the usage of the Spotify API, storing data in a Supabase postgresdb and visualizing the data.

Spotify Wrapped for your AI pair programming sessions. Analyze Claude, Codex, Cursor, and Gemini to see your coding year in review.

Personal Spotify Wrapped using Airflow, Django and Docker

The wrapped.py script is a standalone feature inspired by Spotify Wrapped, designed specifically for the babTomaMusic (btM) app. It provides users with a personalized summary of their music listening habits, offering insights into their most-played songs, total listening time, and more.

Statistics logger for audio players, right now only AIMP supported.

Personalized Spotify Wrapped-style video generation from listening data, currently in a rudimentary state.

Spotilytics is a tool that allows you to view your downloaded Spotify streaming history throughout the year, without the need to wait for Spotify Wrapped. It works on Windows with Python 3.10+ installed.

Behavioral analytics engine that processes raw user logs into "Year-in-Review" visual narratives (Spotify Wrapped style) using Matplotlib and Seaborn.

Your Mac usage wrapped • 100% local, 9 shareable slides, 12 themes

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Python project that retrieves and reports a user's Spotify usage, weekly highlights, and patterns while generating personalized recommendations using AI.

This Exploratory Data Analysis (EDA) was made with the intention of emulating the famous annual Spotify Wrapped, with additional information layers to make an in-depth analysis of my streaming history