This project is part of my Data Application Development module @ the National College of Ireland, delivered by Dr. Caton.
This application can be used by anyone, by registering your own Spotify application.
ADD TO SCRIPTS - your client_id, secret_id, playlist_ids, and user_id
#NodeSpotifyAPI
This is a simple credentials flow application used to obtain a users ID,
which can be used as a paramter in API calls.
Adopted from here -> https://developer.spotify.com/web-api/tutorial/
# Data Extraction & pre-processing
Scraper.py - pulls data from Spotifys API
jsonToCSV.py - converts API response from JSON to CSV
ms_to_mins.py - adds the song duration to the dataset in mins/secs format
# Data Trnasformation, analysis, and vizualisation
Spotify.R - Imports the csv files, performs operations on the datasets,
exports datasets to tab delimeted text files for mapReduce.
# mapReduce
count & top N -> song tempos
max -> song tempos
max -> song duration
# Knowledge
This project helped me undertand the core attributes to popular songs,
as well understand the attributes that are in my favourite songs and how the two datasets differ.
1. My personal top 100 tracks from 2017 on Spotify
2. The top 100 tracks streamed on Spotify in 2017.
Python 3
Node.js
Spotipy(pip install)
- Spotipy - Lightweight Python library for the Spotify Web API.
- Sam Quigley - SamQuigley
This project is licensed under the MIT License
- https://opendatascience.com/blog/a-machine-learning-deep-dive-into-my-spotify-data/
- https://medium.com/cuepoint/visualizing-hundreds-of-my-favorite-songs-on-spotify-fe50c94b8af3
- I was inspired to undertake this project as a music lover and a computer science student, I wanted to understand what are the attributes that are common among my favoutite songs and compare them to the top 100 songs of 2017. From this proejct I have more knowledge about my own music taste and the current charts.