Yordanos888/Ethiopia-Food-Price-Analysis--2020-2025-

# Ethiopia Food Price Analysis (2005–2025) ## Project Overview This analysis investigates the volatility of food prices in Ethiopia, with a specific focus on the 2024 "Great Float" (currency devaluation) and its impact on national food security. The study tracks the transition of the Ethiopian Birr's purchasing power. --- ## The Dataset - **Source:** Historical food price records for staples including Wheat, Teff, Maize, and Cooking Oil. - **Granularity:** National, Regional (`admin1`), and Zonal (`admin2`) levels. - **Temporal Scope:** 2005 to 2025 (with high-density data focusing on the 2020–2025 window). --- ## Feature Engineering To move beyond raw price tracking, the following features were engineered: 1. **Real Price (Inflation Adjusted):** Deflated nominal prices using the Consumer Price Index (CPI) to calculate the "Real Value" in 2015 constant Birr. 2. **Column Splitting:** To enhance the granularity of the dataset, the original commodity column was decomposed into two specialized features using the Python Pandas `.str.split`() method --- ## Key Analysis Modules 1. **The "Great Float" Impact (2024):** Visualized the immediate vertical price spike in imported and local staples following the July 2024 currency devaluation. 2. **2020 vs. 2025 Comparison:** A longitudinal study of price doubling and tripling effects over a 5-year period of high inflation. 3. **Regional Price Disparity:** Data was scarce to complete this analysis. 4. **The Cooking Oil Crisis:** 5. **Real Price vs. Nominal Price:** An analysis proving that while nominal prices skyrocketed, real prices remained relatively stable, indicating the crisis is primarily monetary (currency-driven). --- ## Python Implementation This analysis relied heavily on the **Pandas, Matplotlib & Seaborn** library, specifically leveraging: - **Vectorized Operations:** For efficient calculation of real prices. - **`groupby` and `pivottable` Aggregations:** To transition from noisy monthly data to cle …