Project Title

Global Coffee Trade

Project Description

A data story about coffee exports, price paid to grower's, imports and retail prices from 1990-2019.

You are very welcome to have a look at our presentation slide deck for deeper insight into our story on Global Coffee Trade.

Project3SlideDeck_CW_v2.pptx

Contributing Members

Cindy Wong & Sandra Botica

Students @ UWA 6 month Data Analytics Bootcamp November 2022- June 2023

Acknowledgments

Data sourced from the International Coffee Organization.

Historical Data on the Global Coffee Trade. https://www.ico.org/new_historical.asp

• Trade Statistics Data - Exports - Calendar Year (excel), Thousand 60kg bags.

• Trade Statistics Data - Imports - Calendar Year (excel), Thousand 60kg bags.

• Price Data - Prices to Growers - Annual Averages (excel), US cents/lb.

• Price Data - Retail Prices - Annual Averages (excel), US dollars/lb.

• Folder original_resources The 4 original excel files

• Folder cleaned_resources 4 files cleaned for the purpose of this project.

Technologies used

• Excel
• Python notebook
• Matplotlib
• QuickDBD
• PostgreSQL
• pgAdmin4
• Python Flask API (HTML/CSS/JavaScript)
• Plotly

Getting Started

1. <coffee_data.ipynb>

   Code that populates the Resources folder.

   This folder contains the csv's that feed our API endpoints and visualisations.

   <coffee_data.ipynb> has the code for summary statistics and plots.

   This notebook populates the images folder used for the slidedeck.

2. coffee folder.

   Open <app.py> and run.

   Copy the URL from your terminal and paste into browser.

   Welcome to our Coffee Data API

   You will find the list of Available Data API Endpoints that populate our visualisations.

   You will find a list of 4 Data Visualisations.

   You will also find a list of 3 Data Comparison plots.

3. In the folder you will find:

   An image of our database creation from QuickDBD. <ERD_image_coffee_database.png>

   The exported SQL from Quick DBD used in pgAdmin. <coffee_dataset.sql>

   static folder with JavaScript files.

   Inside static another folder called styles with the CSS file.

   templates folder with HTML files.

4. The data_exploration folder includes files used when planning and exploring this dataset and story. Take for example original app.py and accessing the database using PyMongo.

5. A report/writeup of this project can be found in the file <writeup.md>

Contact

• Feel free to contact Cindy or Sandra with any questions.
• Cindy Wong cwsynyi88@gmail.com
• Sandra Botica smbotica@gmail.com