[2022] CPSC 203 Programming, Problem Solving, and Algorithms - Project Billboard
Project 1 -- Billboard
DUE Thursday, Mar 10, 11:59p
Learning objectives:
- Use pandas data frames as a fundamental structure
- Filter, project, reassemble data frames to extract information
- Report and visualize information using a variety of plots
In class we explored one technique for extracting data from a web page, and in this project you'll use something a little bit different. Rather than using data from Billboard, we will use a library called Spotipy, which makes accessing Spotify data very simple. This project uses Spotipy to not only get the rankings, but also audio features about these songs!
Part 0 -- Getting the Code
In the project repository you'll find the skeleton python files in which you should build your solutions.
Part 1 -- Getting started
In this project, we will use the (free) Spotify Web API. It provides a set of endpoints for us to query a wide variety of aggregated information about songs, albums, artists, playlists, and more! Here is a link to the complete Spotify API reference We encourage you to explore the API and maybe build a side project with it if you'd like!
To access the endpoints, you will need a Spotify developer account:
- Step 1. Go to Spotify developer page (https://developer.spotify.com/) and click on
Dashboard. Follow the instructions to sign in or to create an account. - Step 2. Once on the Dashboard page, choose
Create an appand give your project a name and a description. - Step 3. Navigate to the app dashboard, you should see your
Client IDandClient Secretright under the app's name and description. - Step 4. Copy your
Client IDandClient Secretover to the firstTODOinspot.py
All set! Now we can start querying from Spotify using Spotipy!
Part 2 -- Organizing the data
In this part of the project you will be completing the code that we began writing in spot.py. Throughout that file you will see comments labeled "TODO". In each of those locations, you should either complete our code, or completely write your own. The dataclasses you'll use can be found in a file called models.py.
Complete the following function:
def getPlaylist(id: str) -> List[Track]:which queries spotify and assembles the data into a list ofTrackobjects. As you write this code, you should scrutinize the results of the spotify queries. They are quite complex -- part of your task is to extract the essential (and much simpler) information.
Write the following two functions:
getGenres(t: Track) -> List[str]which takes in aTrackand produce a list of unique genres that the artists of this track belong to.doesGenreContains(t: Track, genre: str) -> boolwhich checks if the genres of aTrackcontains the key string specified
We will use these functions to assemble a list of Track into a DataFrame containing various information about the tracks.
Part 2 -- The most popular artist of the week
In this section, we would like to find out the most popular artist of the week. We could measure popularity by the total number of tracks that an artist has on the Billboard Hot 100 list.
Write the function:
artist_with_most_tracks(tracks: List[Track]) -> (Artist, int)which takes in a list of tracks and produce the artist and the number of tracks the artist has on the list.
Part 3 -- Visualizing the data
In this section, we would like to explore the audio features that each track has and how they are related to a track's genres. A list of what these audio features represent can be found here.
- Write a function called
danceability_plot(tracks: List[Track]) -> matplotlib.pyplotthat returns a scatter plot. The plot should display all the tracks with"danceability"as x-axis and"speechiness"as y-axis. Color the dots based on whether the track"is_rap". Label the axes of the plot and add a legend.
Note that we cannot autograde this function. Please call the function, and show your plot within the main function in spot.py.
