Sophia-stack446/Week-7-wheather-ETL-pipeline-project-at-AnalystlabAfrica

Weather data ETL pipeline using OpenWeather API — extraction, transformation, and loading with Python and Pandas. AnalystLab Africa Internship, Week 7.” ***** Weather Data ETL Pipeline***** ## Project Overview A simple ETL (Extract, Transform, Load) pipeline built in Python that pulls real-time weather data from the OpenWeather API for multiple cities, cleans and structures it using Pandas, and stores it as a CSV file for analysis. Built as part of the AnalystLab Africa internship, Week 7 (Batch B). ## Data Source OpenWeather API — Current Weather Data endpoint. ## ETL Process - **Extract:** Connected to the OpenWeather API using an API key and pulled current weather data for three cities: Lagos, Benin City, and Abuja. - **Transform:** Parsed the raw JSON response into a structured Pandas DataFrame, extracting City, Temperature (°C), Humidity (%), Weather Condition, Wind Speed (m/s), and timestamp. - **Load:** Saved the cleaned dataset to `weather_data.csv` for future analysis. ## Tools Used - Python - Pandas - Requests - Google Colab ## Steps Taken 1. Generated an OpenWeather API key and tested connectivity. 2. Extracted current weather data for 3 Nigerian cities. 3. Transformed the raw JSON into a clean tabular dataset. 4. Saved the dataset as a CSV file. 5. Ran basic comparative analysis across cities. ## Key Findings - Abuja recorded the highest temperature among the three cities. - Benin City recorded the highest humidity. - Weather conditions varied across cities, reflecting typical regional weather diversity in Nigeria. ## Author Sophia Ogbeide — AnalystLab Africa Internship, Batch B