Spotify data via twitter 02/12/20
seen from Russia
seen from China
seen from China
seen from Russia
seen from Albania
seen from United States
seen from Indonesia
seen from United States

seen from Indonesia
seen from Yemen
seen from Yemen
seen from China
seen from Canada
seen from China

seen from United States
seen from United States
seen from Canada

seen from United States
seen from Malaysia
seen from United States
Spotify data via twitter 02/12/20
#1
Spotify Data: How much similar are the TOP 200 songs between countries?
Why a Spotify data extractor matters to music lovers
Spotify search extraction is all about pulling data straight from Spotify’s massive music and podcast library—without doing it manually. Ever wondered how to pull up super‑specific Spotify data without manually scrolling for hours? That’s where Spotify search extraction comes in. With the help of a Spotify extractor, you can search and collect data on playlists, songs, albums, artists—even podcasts—in just a few clicks. Instead of browsing and copying stuff one by one, a Spotify scraper helps you grab exactly what you’re looking for. And the best part? You do not need to code a thing.
Instant deep‑dive into personal habits
A Spotify data extractor pulls your full listening history, not just the highlights Spotify Wrapped shows. You can spot deep‑cut artists, recurring mood patterns, and forgotten obsessions that the official recap misses. Want to see which genre you favored in 2022 or which workout tempo dominates your gym playlist? The extractor dumps the data into a spreadsheet, letting you filter, sort, and chart without a single line of code.
Science‑backed playlist engineering
Extract audio features—danceability, energy, tempo, key—for any track or entire playlist. With those numbers in hand, you can scientifically engineer mixes: filter for high energy > 0.8 and tempo 120‑140 BPM for a cardio session, or low valence < 0.3 for a rainy‑day mood. The process is click‑and‑download; no programming required.
Verify and improve Spotify’s recommendations
Pull the recommendation lists Spotify generates for you, then compare them to your actual listening data. Spot where the algorithm hits the mark and where it misses, then tweak the suggested tracks based on real‑world feature gaps. This DIY audit turns the black‑box suggestion engine into a transparent, controllable discovery tool.
A musical time machine
Extract historical data year by year and chart how your favorite genres, artists, and audio signatures have evolved. Build a personal musical biography: “2019 — indie folk surge; 2021 — electronic takeover; 2023 — K‑pop explosion.” Shareable graphics, all generated from the extracted CSVs, let you relive and showcase your taste journey.
Niche‑genre cartography
Scrape niche genre playlists and track follower growth over weeks. Map micro‑genre popularity, spot rising indie scenes, and find emerging artists before they hit the mainstream. A few clicks give you a live dashboard of underground movements.
Hit‑song ingredients
Pull audio features from chart‑topping tracks and run a quick statistical peek. Loudness, energy, valence, and tempo trends emerge, giving you a recipe for “hit‑like” qualities you can apply to your own mixes or DJ sets.
Legal & ethical quick‑check
Keep requests under Spotify’s rate limits.
Pull only publicly visible data; avoid personal user info you don’t own.
Use extracted data for personal enjoyment or non‑commercial projects; respect Spotify’s terms of service.
Bottom line
A Spotify data extractor turns endless scrolling into instant insight. By pulling playlists, audio features, historical streams, and niche genre data with zero coding, music lovers can decode listening habits, engineer perfect mixes, verify recommendations, and explore new sounds at a pace the official app can’t match. The power of Spotify search extraction is now in the hands of anyone who loves music.
I am looking to transfer my Spotify information into a spreadsheet format for my own personal use, and I was looking through their privacy policy section and they have these clauses built in
Has anybody requested their data, and was it in an accessible format?
Lol
Spotify Update
as of Thursday, March 4th, Wonho’s Spotify numbers are:
Lose - 742,510
Devil - 229,151
Best Shot - 191,533
WENEED - 194,396
Ain’t About You - 316,686
Flash - 171,117
Lose (English) - 238,368
Outro: And - 136,482
Total = 2,220,243
SEND ME A NUMBER AND I WILL TELL YOU THE CORRESPONDING NUMBER ON MY TOP 100 SPOTIFY FOR 2020