Chidera-07/Week7-AnaystLab-Africa

# Weather Data ETL Pipeline ## Project Overview This project demonstrates the development of a simple **ETL (Extract, Transform, Load) pipeline** using Python. The pipeline collects current weather data for ten major African cities from a weather API, transforms the raw data into a clean and structured dataset, saves the processed data as a CSV file, and performs basic exploratory analysis through visualizations. The project focuses on comparing: * Temperature across cities * Humidity levels across cities * The frequency of different weather conditions The final dataset contains weather information for **10 cities**. --- ## Data Source The data was extracted from the **OpenWeatherMap API** using Python's `requests` library. The cities included in the analysis are: * Lagos * Accra * Kigali * Johannesburg * Nairobi * Cairo * Casablanca * Port Louis * Gaborone * Tunisia The following weather attributes were collected: | Column | Description | | ----------- | ------------------------------------ | | City | Name of the city | | Temperature | Current temperature in °C | | Humidity | Current humidity percentage | | Condition | Current weather description | | Wind_Speed | Wind speed in metres per second | | Date_Time | Date and time the data was collected | The API returned the weather data in JSON format, which was then converted into a structured Pandas DataFrame. --- ## ETL Process ### 1. Extract Weather data was extracted from the OpenWeatherMap API using the `requests` library. For each city, the API request retrieved: * City name * Temperature * Humidity * Weather condition * Wind speed * Date and time of observation The data was collected using a Python function called `get_weather_data()`. The API was configured to return temperature values in **Celsius** using metric units. --- …