At what age are singer-songwriters most successful? I wondered this the other day when I heard an old Stevie Wonder song. My impression was that, like mathematicians, singer-songwriters peak in their mid-late 20s. But what does the data say?
In this Quick Success Data Science project, we’ll use Python, pandas, and the Seaborn plotting library to investigate this question. We’ll look at the careers of 16 prominent singer-songwriters with over 500 hits among them. We’ll also incorporate an attractive graphic known as the kernel density estimate plot into the analysis.
To determine when songwriters are most successful, we’ll need some guidelines. The plan is to examine:
- Singer-songwriters including those who work with co-writers.
- Singer-songwriters with decades-long careers.
- A diverse selection of singer-songwriters and musical genres.
- Singer-songwriters on the Billboard Hot 100 chart.
The Hot 100 is a weekly chart, published by Billboard magazine, that ranks the best-performing songs in the United States. The rankings are based on physical and digital sales, radio play, and online streaming. We’ll use it as a consistent and objective way to judge success.
We’ll use songs written by the following highly successful artists:
I’ve recorded the age of each artist at the time of each of their hits and saved it as a CSV file stored on this Gist. If they had multiple hits in the same year, their age entry was repeated for each hit. Here’s a glimpse at the top of the file:
Cross-referencing this information is tedious (ChatGPT refused to do it!). Consequently, a few hits written by these artists but performed by others may have been inadvertently excluded.
A kernel density estimate plot is a method — similar to a histogram — for visualizing the distribution of data points. While a histogram bins and counts observations, a KDE plot smooths the observations using a Gaussian kernel. This…