Your listening, charted

Use with: build-web-app. Resume line when done: Built {project_name}, an app that parses and visualizes a full streaming-history export — live at {url}

Brief

Request your listening history export from your streaming service, parse the raw file, and build an app that charts what you played: top artists by month, the song you would not admit to, the drift from one genre to another. Working from a raw export, with its duplicate rows and odd fields, is the point of the exercise.

Personalize

• Your export. Request it from your own account (most services deliver in a few days; start the request first). Someone else's data or a sample file defeats the project.
• Your questions. Write three questions about your own listening before touching the data, then build the views that answer them.
• Your embarrassment threshold. The app can stay private or go live with the incriminating chart included. Decide, and own the choice either way.

Milestones

1. Request the export; while waiting, write your three questions and sketch the answer views.
2. Import: parse the raw file, count the records, and write down what cleaning it needed (duplicates, missing fields, weird encodings).
3. Structure into plays with artist, track, timestamp, and duration.
4. First view working: top artists or tracks over a chosen time range.
5. The remaining views that answer your three questions.
6. A written findings note in the app: what surprised you, plus what the export leaves out (skips, offline plays, other devices).

Done means

• App live (or running locally with the repo published) charting your own export
• The cleaning steps are documented in the README
• Charts label their time ranges; play counts match the cleaned data
• The findings note names at least one limitation of the export
• At least 8 commits across at least 5 distinct days, authored by you
• SESSIONS.md has an entry per session, written by you