Project-1
NU Bootcamp Project 1
Goal: Analyze the popularity of various artists and songs using the Spotify API and Spotify’s Chart data -What makes a song popular? Are there certain characteristics that correlate with it being popular? -Are there any correlations between the audio features of the most popular songs?
Terms to Know: Spotify keeps track of several different audio features for every song in their catalog including: popularity, tempo, energy, loudness, speechiness, danceability and valence -URI: Unique (Uniform) Resource Indicator that you can enter in the Spotify Desktop client’s search box to locate an artist, album, or track. -Popularity: Measure of a track’s popularity on a scale of 0-100, where 100 is most popular. Calculated based on total number of plays and how recent those plays are. -Tempo: An estimate of the track’s beats per minute -Energy: A value between 0.0-1.0 that measures how the intenseness and activity of the track -Loudness: The calculated loudness of track in decibels. The value is an average of the loudness over the entire track. -Speechiness: A measure of the presence of spoken words in a track ranging from 0.0-1.0. The closer a track is to 1.0, the more the track is exclusively speech. -Danceability: A measure of how suitable a track is for dancing based on various song elements. The scale is from 0.0-1.0 -Valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).
Scope: Using Spotify’s web API as well as the SpotiPy library, we analyzed the top ranked songs from several different artists including: -Beyonce -SZA -Bad Bunny -Adele -Morgan Wallen
Process Steps: -Pull the song data from Spotify API using SpotiPy library -Wrangle each artists’ data into a dataframe (beydata_df, szadata_df, etc…) -Merge these dataframes to get a master dataframe containing all of the song data -Order the master dataframe based on the ‘popularity’ score for each song -Create scatter plots for the master dataframe to get an idea of the “meta” trends -Break the master dataframe into tiers based on popularity rank and perform the same analysis -We broke the dataframe up into 4 roughly equal tiers using the following scores: -Top Tier: ‘popularity’ >= 86 -Second Tier: 80 <= ‘popularity’ < 86 -Third Tier: 75 <= ‘popularity’ < 80 -Bottom Tier: 0 <= ‘popularity’ < 75
Limitations: -We looked at a limited number of artists -We only looked at the top 9 songs from those artists -We only looked at a limited number of genres -These results may be different based on the genre (i.e. grunge music might have low valence and high popularity?) -‘Popularity’, as defined by spotify, has a time component to it - it’s a mixture of the total number of plays and how recently those plays occurred. This skews ‘popularity’ towards newer music, while older songs could have a disadvantage if they are no longer being played as much as they once were at their peak. -We could take a look at the same song datasets and sort them by raw number of plays to get a clearer perspective of a songs “true” popularity. Comparing spotify’s ‘popularity’ measurement to it’s raw number of plays could be enlightening and provide a baseline to help contextualize the song’s popularity
Future Work: -Looking at data from different markets and making comparisons between countries. -Analyzing larger set of data (more artists, more charts, etc…) -We have to manually input an artist’s URI into the initial code in order to pull their song data. If we created a list of artist URIs and cycled through them programmatically, we could analyze a larger dataset without as much manual labor (aka typing)