bitu-kebe/Ethiopian-Coffee-Export-Analysis

🇪🇹 Ethiopian Coffee Export Analysis 📌 Project Overview This project analyzes Ethiopian coffee export data using Python, Pandas, and Matplotlib. The purpose of the project is to explore coffee export quantities, revenue, prices, Ethiopian regions, destination countries, and coffee types. «Dataset Note: This is a practice dataset created for educational and portfolio purposes. The figures are not official Ethiopian government export statistics.» 🎯 Objectives The project aims to answer the following questions: - What is the total quantity of coffee exported? - What is the total export revenue? - What is the average coffee price per kilogram? - Which year had the highest export revenue? - Which Ethiopian region exported the most coffee? - Which destination country generated the most revenue? - Which coffee type generated the most revenue? - How did coffee export quantity change over time? - How does export quantity relate to revenue? 🛠️ Technologies Used - Python - Pandas - Matplotlib - CSV - Git & GitHub 📂 Project Structure Ethiopian-Coffee-Export-Analysis/ │ ├── ethiopian_coffee_exports.csv ├── coffee_analysis.py └── README.md 📊 Dataset The dataset contains information about Ethiopian coffee exports. Column| Description Year| Year of export Region| Ethiopian coffee-producing region CoffeeType| Type of coffee Destination| Country receiving the coffee Quantity_Tons| Export quantity in tons Price_Per_Kg| Coffee price per kilogram 🔍 Analysis Performed 1. Total Export Quantity The total quantity of coffee exported is calculated by adding all values in the "Quantity_Tons" column. 2. Export Revenue Revenue is calculated using: Revenue = Quantity × Price Because quantity is recorded in tons, it is first converted to kilograms: Revenue = Quantity_Tons × 1,000 × Price_Per_Kg 3. Yearly Revenue Analysis The project groups export records by year to identify changes in export revenue over time. 4. Regional Analysis Coffee export quantities and revenue are …