ebubefrancis15/Nigeria-Weather-ETL-Analysis
Domain:
climateRecord type:
dataset
Creator:
ebu
Host:
ETL pipeline and weather analysis of Nigeria's 36 states and FCT using the OpenWeather API.
# Nigeria Weather ETL Pipeline & Analysis
## Project Overview
This project demonstrates an ETL (Extract, Transform, Load) pipeline for collecting and analysing weather data across Nigeria's 36 states and the Federal Capital Territory (FCT).
Weather data was collected using the OpenWeather API, processed and transformed using Python and Pandas, and saved as a structured CSV dataset for further analysis.
The project also examines weather patterns across Nigeria's six geopolitical zones.
---
## Project Objective
The objectives of this project are to:
- Extract weather data from the OpenWeather API.
- Collect weather information for Nigeria's 36 states and the FCT.
- Transform and clean the collected data using Python and Pandas.
- Map each state to its geopolitical zone.
- Analyse temperature, humidity, pressure and wind speed across the zones.
- Identify differences in weather conditions between geopolitical zones.
- Create meaningful insights from the processed dataset.
- Demonstrate an end-to-end ETL workflow.
---
## Data Source
Weather data was obtained using the OpenWeather API.
The API provides current weather information for specified locations.
**Data source:** OpenWeather API
---
## Technologies Used
- Python
- Pandas
- Requests
- Jupyter Notebook
- OpenWeather API
- Matplotlib
- GitHub
---
## ETL Pipeline
The project follows three major stages:
### 1. Extract
Weather data was extracted from the OpenWeather API for Nigeria's 36 states and the Federal Capital Territory.
The extracted information includes:
- State
- Geopolitical Zone
- Temperature
- Feels Like Temperature
- Humidity
- Atmospheric Pressure
- Wind Speed
- Weather Condition
### 2. Transform
The extracted data was processed using Python and Pandas.
Transformation activities included:
- Creating a structured dataset.
- Mapping states to geopolitical zones.
- Cleaning and formatting the data.
- Checking for missing values.
- Checking the dataset structure and data types.
- P …