Introduction

In this post, I'll briefly explain a project I undertook to analyze Spotify tracks data. The goal was to explore song popularity, the relationship between track duration and popularity, and the impact of explicit content on popularity.

Step 1: Importing Libraries and Loading Data

I imported necessary libraries, loaded the dataset into a pandas DataFrame called df, and performed basic preprocessing.

Step 2: Data Preprocessing

I converted date columns to datetime objects, calculated track age in days, and removed duplicate track names.

Step 3: Exploratory Data Analysis

I performed various analyses, such as plotting the distribution of track popularity and analysing the relationship between track duration and popularity.

Step 4: Calculating Individual Artist Popularity

I created a DataFrame to store the popularity of each artist, dividing the track popularity by the number of artists in a track, and then summing the popularity for each artist.

Step 5: Visualising the Results

I visualised the top 20 most popular artists and explored the impact of explicit content on track popularity.

Conclusion

In this project, I analysed various aspects of Spotify tracks, including popularity, duration, and explicit content. By following these steps, you can perform similar analyses on your own datasets and uncover interesting insights.