vivian-okoaze/analystlab-africa-week-7-task-data-pipelines-automation

This project is a student assignment that builds a simple weather data pipeline, the kind of tool that automatically pulls information from an online source, cleans it up, and saves it for later use. It fetches current weather details for several Nigerian cities plus London from the OpenWeather API, a free online service that reports things like temperature, humidity, and wind speed for a given location. The pipeline works in three steps. First it requests the weather data for each city over the internet, handling cases where a request fails or times out instead of crashing the whole program. Second it cleans that raw data using a Python library called Pandas, converting text values to proper numbers, fixing inconsistent capitalization and spacing, and dropping any incomplete records. Third it saves the cleaned data as a CSV file, which is a simple spreadsheet style format that can be opened in tools like Excel, Power BI, or Tableau for further analysis or charting. The README includes a sample of the cleaned data table and a short written analysis, noting for example that Kano recorded the highest temperature among the cities checked while Abuja had the lowest, and that southern cities like Lagos and Enugu had noticeably higher humidity than the drier north. The author also describes the steps they took, including setting up a free OpenWeather account and recording a demo video of the process. To run it, a user needs Python 3, the requests and pandas libraries, a free OpenWeather API key, and the Jupyter Notebook file included in the repository. This is a course assignment demonstrating a basic extract, transform, and load workflow rather than a production ready tool.