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CarolyneMomiji/Rainfall-Patterns-Across-the-Continent-Africa-Jan-May-2026

Domaine:

climate

Type de record:

datasetproject
Créateur:
Car
Hôte:
Rainfall Patterns Across the Continent Africa Jan-May 2026 # Rainfall Patterns Across the Continent of Africa (Jan–May 2026) ## Overview This project is a mini exploratory data analysis (EDA) of rainfall patterns across five African cities — Cairo, Casablanca, Lagos, Nairobi, and Johannesburg — covering the period from January to May 2026. The goal is to understand how rainfall varies by city, geography (coastal vs inland, North vs South hemisphere), and time, as a starting point for thinking about water availability and scarcity across the continent. ## Dataset - **Source**: Worldwide Weather 2026: Daily City Weather Data (downloaded from Kaggle) - **Underlying data provider**: Open-Meteo Historical Weather API - **Raw file**: `worldwide_weather_2026_to_2026-05-05.csv` - **Cleaned output**: `africa_rainfall_cleaned.csv` ## Project Structure ``` . ├── rainfall_patterns_across_the_continent_africa.py # Analysis script ├── Rainfall_Patterns_Across_the_Continent_Africa.pptx # Presentation deck ├── worldwide_weather_2026_to_2026-05-05.csv # Dataset from Kaggle ├── africa_rainfall_cleaned.csv # Cleaned dataset (generated) └── README.md # This file ``` ## What the Script Does ### 1. Data Loading Loads the raw weather CSV from Google Drive (via Colab). ### 2. Data Cleaning - Checks for duplicate dates and confirms each date corresponds to multiple locations (one row per city per date). - Converts the `date` column from string to datetime. - Checks for null values across all columns. - Filters the dataset down to cities in the `Africa` continent only. - Selects a focused subset of columns relevant to rainfall analysis: `city`, `country`, `continent`, `latitude`, `longitude`, `coastal_city`, `date`, `precipitation_sum`, `rain_sum`, `season`. ### 3. Feature Engineering - Adds a `hemisphere` column (`North` / `South`) derived from `latitude`, since Africa spans both hemispheres. - Converts `season` to a categorical type for memory efficiency. - Converts …

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