EEP 153 Spring 2024 - Project 02 (Team Casimir Funk)

Team Members

Jordan Taqi-Eddin - jgte29@berkeley.edu; GitHub: jgte29

Khadija Arslan - khadija2002@berkeley.edu; GitHub: KHADIJAARSLAN

Xiang Wu - xiangwu@berkeley.edu; GitHub: xiangwu08

Yingyin Li - yingyinli2001@berkeley.edu; GitHub: yingyinlii

Marc Bonnot - mpb0614@berkeley.edu; GitHub: marcpb0614

Jane Wang - janeyjwang@berkeley.edu; GitHub username: janeyjwang

Welcome

Welcome to the Project 02 GitHub Repository for Team Casimir Funk. Our code for the project is organized in the following format:

• master_notebook.ipynb: In this notebook, we compile all of the finished products of code for this project and have interactive widgets that allow a user to use various functions we developed for the project.
• dietary_references.ipynb: In this notebook, we create our dietary reference intakes functions.
• nutritional_content.ipynb: In this notebook, we create our nutritional content functions. Moreover, we add the nutritional data to our food prices dataset.
• food_price_vs_stores.ipynb: This is a supplementary notebook that has select visualizations comparing price data across stores in our analysis.
• food_versus_protein.ipynb: This is a supplementary notebook that visualizes the relationship between price and the protein richness of different food products.

How to Use

1. Option A: Access Our Repository Via DataHub (Highly Recommended)
   • You can access all of the code in our repository via DataHub using this link.
2. Option B: Clone the repository on your local device:

   
   git clone https://github.com/jgte29/team-casimir-funk-proj02.git

Notebook Specific Usage Notes:

• master_notebook.ipynb:
  
  1. API Key:
     • In order to access the USDA's database, you will need to sign up for an API Key, which you can do using this link. If you already have one from class, that one will work as well.
       Note: You must enter in your API Key as a string.

       
       api_key = 'my_USDA_API_Key_1234' # Enter your API key here

• nutritional_content.ipynb:
  
  1. fooddatacentral & pint Packages:
     • If you have never used the fooddatacentral & pint Packages, you will likely have to pip install them. We have provided the code to do so in the notebook. All you have to do is uncomment the code to download such packages. Recommended Alternatively, you can download the packages by copying the code below in a terminal.
     • For fooddatacentral:

       
       pip install fooddatacentral

     • For pint:

       
       pip install pint
       2.`price_master` Dataset:

• When originally going about our research, we utilized the functions handle_query_nc_calc & compile_ncs to query the USDA FoodData Central API and pull the requisite nutritional contents for our food products. However, doing so takes a considerable amount of time. Therefore, in order to speed up and faciliate the usage of this notebook, especially when using the widgets in the master notebook, we have provided and read in a .csv file called price_master.csv that contains all of the information we compiled from the API. If you wish, feel free to run the code that we used to create price_master.csv by uncommenting the code in the cell that reads in file.
  Warning: The code may take up to a couple of minutes to run, so re-comment the code after running it in order to avoid unintentionally re-running it.

  
  price_master = pd.read_csv('./data/price_master.csv', dtype=str)
  
  ### Only uncomment if using, otherwise, DO NOT UNCOMMENT! 
  ### Code takes a lot of time to run and do not want to 
  ### exceed API call rate limits
  # ncs_master = pd.DataFrame()
  # search_col = 'GTIN/UPC'
  # API_KEY = ... # Enter your own API Key
  # fp_arr = price_rf[search_col]
  
  # for i in range(len(fp_arr)):
  #     ncs_master = compile_ncs(ncs_master, fp_arr, i, API_KEY)
  
  # price_master = pd.concat([price_rf, ncs_master], axis = 1)
  # price_master = price_master.dropna().reset_index(drop = True)
  # price_master['Vitamin A, RAE'] = price_master['Vitamin A, RAE'] + price_master['Vitamin A, IU']*0.3
  # price_master = price_master.drop(columns = ['Vitamin A, IU'])