Members: Kim Sommer, Howard Mayorga, Stephanie Rivas & Ian Castro
How to run our app:
- Open the directory of the repository and activate the Python environment PythonData.
- In the main folder run 'python app.py'
- Once the server is running click on the link for the local server and the webpage will load and allow interaction with the visualization.
Project Description and Outline: Kobe Bryant and LeBron James are both considered NBA legends. They were both drafted out of high school and went on to win numerous championships for their respective teams. But did one live up to the high expectations placed on him more than the other? We will examine this question by comparing the statistics from the two players' first twenty years in the league. We want to see whether either player improved from year one to year 20, who improved more and in what areas (i.e., points scord, rebounds, assists) and whose overall stats were more impressive. We'll create an interactive chart showing the two players' stats per year. The user will be able to select which stat to view, and the line chart will update for both players on the same plot area. The user will be able to see the trajectory of each player's stats per year starting with each player's respective first year.
Our static charts will compare both players' total summary data for all 20 years in various areas such as points scored, rebounds, championships won, and more. Completely subjective to the project team's sensibilities, we'll decide whether the data show a more impressive career for Kobe or LeBron. And then we'll award the GOAT status to Jordan anyway, just because.
__Metrics: __
- Our visualizations will include a Python Flask–powered API, HTML/CSS, JavaScript, and at least one database (SQL, MongoDB, SQLite, etc.).
- Your project should fall into one of the below four tracks: ○ A custom “creative” D3.js project (i.e., a nonstandard graph or chart) ○ A combination of web scraping and Leaflet or Plotly ○ A dashboard page with multiple charts that update from the same data ○ A “thick” server that performs multiple manipulations on data in a database prior to visualization (must be approved)
- Our project will include at least one JS library that we did not cover.
- It must be powered by a data set with at least 100 records.
- The project must include some level of user-driven interaction (e.g., menus, dropdowns, textboxes).
- The final visualization should ideally include at least three views.
Datasets:
Rough Breakdown of Tasks:
- Decompose the problem.
- Examine and learn about each API & write request loops to pull appropriate data from each API.
- Run loops & Put data into DataFrame.
- Clean and merge data.
- Visualize and Analyze Data.
- Make Observations regarding the tendencies we see.
- Create Presentation and Summary Documents and make the Project accessible.
