imranasalisu1/PASS-Capstone-Climate-Nigeria

This notebook combines the full pipeline in one place: Part 1 — Data Cleaning & Reshaping: reads the raw Meteorological_NIMET.xlsx (7 state sheets, each with 5 side-by-side blocks) and produces one tidy, clean dataset. Part 2 — Statistical Analysis & Visualization: works through every capstone objective (descriptive stats, annual trends, & Others # PASS-FELLOWSHIP-Capstone-Project-Climate-Variability-Analysis-in-Northern-Nigeria-2013-2022 This notebook combines the full pipeline in one place: Part 1 — Data Cleaning & Reshaping: reads the raw Meteorological_NIMET.xlsx (7 state sheets, each with 5 side-by-side blocks) and produces one tidy, clean dataset. Part 2 — Statistical Analysis & Visualization: works through every capstone objective (descriptive stats, annual trends, & Others. # Analysis of Climate Variability Across Northern Nigerian States (2013–2022) ## 1. Introduction Climate variability significantly influences agriculture, water resources, and public health, particularly in regions such as Northern Nigeria where livelihoods are highly climate-dependent. Understanding long-term changes in meteorological variables is essential for informed policy-making and sustainable development planning. This study investigates temporal and spatial patterns in key climatic variables across seven selected Northern Nigerian states (**Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto, and Zamfara**) over a ten-year period (2013–2022). --- ## 2. Objectives The objectives of this study are to: - Analyze trends in meteorological variables over time. - Compare climatic conditions across states. - Identify variability and anomalies in climate data. - Examine relationships among variables. - Provide evidence-based insights for decision-making. --- ## 3. Dataset Description The dataset consists of monthly meteorological observations from 2013 to 2022 collected across seven Northern Nigerian states. The primary variables analyzed include: - **Rainfall (mm):** Monthly total precipitation. - **Maximum & Minimum Temperature (°C):** Monthly thermal extremes. - **Relative Humidity (%):** Atmospheric moisture content. - **Wind Speed (m/s):** Monthly surface wind velocity. - **Timeframe:** 10-Year period (2013–2022) aggregated monthly. --- ## 4. Methodology ### 4.1 Data Cleaning The dataset underwent the fol …