Pop Songs Analytics
I really love documentaries! Last weekend I was watching a Netflix documentary called This is Pop, because it was Analytics Contest time and I thought: Why not creating a pop song analytics with InterSystems Iris?
The first challenge was the database. I found on Data World project a CSV file with the Billboard hot 100 songs from 2000 to 2018, created by "Michael Tauberg" @typhon, that fits perfectly.
Which genres were most popular between 2000 and 2018?
Which artists had more songs on Billboard?
Which year had more dance songs?
So let's analyze the data set, with a help of csvgen imported the CSV file.
The data set contains:
Title — Title of the song
Artist — Name of the Artist
Energy — The energy of the Song — higher the value, more energetic
Danceability — higher the value, easier it is to dance to the song
Loudness..dB.. — higher the value, louder the song.
Liveness — higher the value, more likely the song is a live recording.
Valence. — higher the value, more positive mood for the song.
Duration_ms. — The duration of the song in miliseconds.
Acousticness.. higher the value, more acoustic the song
Speechiness. — higher the value, more spoken word the song contains
Lyrics — Song lyric.
Genre — Musical Genre of Song.
On the CSV file the Genre is an array like this [u'dance pop', u'hip pop', u'pop', u'pop rap', u'rap']
My idea was to create a table for Genre and another table to solve the N:N relationship. A simple script on data populates this tables.
After that, just connect the Power BI on InterSystems Iris (here a step-by-step how to do that).
Next step: cool infographics.
A bar chart to show the count of artists and a line chart for the average duration by year.
A pie chart with the most common genres, for my surprise, contemporary country was the most popular genre.
Has pop music gotten louder over the years? To answer that I use a Scatter plot with the average loudness by songs.
The Pop Songs become less or more danceable?
On the second page a bar chart shows how danceability changed by the years and a relation between energy versus acousticness.
If you liked the idea, please consider voting for the pop-songs-analytics
Special thanks to @Henrique Dias for the nice chat and support.