Spotify Explorer is a data visualization dashboard app (built on Dash) displaying insights and trends on Spotify's music, artists and song types. This app is made for anyone who's keen in discovering what lies underneath a vast data collected on Spotify's platform, be they music enthusiasts or music business executives.
- The App
- Motivation and Purpose
- Dashboard description
- App Design
- If you want to help further develop the app
- Run the app locally via Docker
- Contributing
- App Contributors
- License
You can access the dashboard app here: Spotify Explorer
Our purpose for developing this app can be branched into 2 main goals: to help others discover more about the artists, genres they care about and to help the music industry solve business problems with data. As music enthusiasts ourselves, we wanted to help fellow music fans understand more about their favourite artists, genres and song types. Furthermore, the app would be beneficial for music industry professionals who want to make business decisions on which artists and genres to develop their business in.
Below are just a couple of potential problem-solving scenarios to achieve these objectives:
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Music listeners may want to dive more into new music with a specific genre. Our app can display top popular atists belonging to that genre and listeners can try out listening to those artists, thereby discovering new music they may like.
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Business executives can use our tool to find trends in popularity of artists over the past few years, and decide whether to pursue contract extension with specific artists (based on the artist's performance). If they're interested to zoom into any specific artist, the histogram plot would be able to tell them what the distribution of popularity looks like for that artist. For example, if they prefer to pursue an artist with less variability in popularity, they can infer that from the plot. They can also explore the relationship between music characteristics distribution and popularity classes to gain insights on the music track features influencing the popularity scoring from customers and act on them in the future music content collection.
The app contains 2 tabs that shows a total of four plots with sidebar widgets allowing users to control filtering options for the plots.
- Top artists with an average popularity score (filtered by Genre)
Here the user can select a particulargenreof music by using the drop down list for genre located on the left hand side of the dashboard. The plot shows the artist and their corresponding popularity score for the specific genre. - Popularity Trend of Specific Artists across time (filtered by Artist)
The user on choosing a specific artist from a different down drop list, will be provided with a line chart of the artist's average popularity score over the years. - Popularity Distribution of Specific Artists (filtered by Artist)
Based on the artist chosen from the previous drop down list, this plot will show the distribution of the Artist's popularity score. This helps the user identify the artist's popularity score across their songs. - Popular Song Characteristics (filtered by Genre)
Finally, a seperate drop down list for song characteristics help users choose from a list of attributes such asloudness,danceability, etc. The plot then shows the relationship of the chosen attribute withpopularitythrough a scatter plot.
- Fork the repository
- Set up conda environment as follows
conda env create -f spotify.yml
conda activate spotify-ex- To run the app locally, run the following command from the root of this repository
python src/app.py- Create an issue on this repo to inform the Team about the changes/improvements you want to make. See Contributing section below for more details.
If you wish to run our app using Docker, fork our repo and write the following commands in the root of this repo after loading up Docker Desktop:
docker-compose upFinally, open the app at this URL: http://localhost:8000/
Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
- Christopher Alexander
- Jennifer Hoang
- Thea Wenxin
- Michelle
spotify-explorer-py was created by Christopher Alexander, Jennifer Hoang, Thea Wenxin and Michelle Wang. It is licensed under the terms of the MIT License.
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