aw0007/AFrican-Educational-Data-Analysis-PASSEC-

# PASEC Data Visualization: Exploring Educational Test Scores by Gender and Country This repository contains two Python scripts for analyzing and visualizing educational test score data from the **PASEC 2019** dataset. The analysis focuses on gender and country-level variations in mathematics and reading test scores for Grade 6 students. --- ## Repository Structure - **Data Folder**: - Contains the input dataset: `PASEC2019_GRADE6_TREAT.dta`. - **Scripts Folder**: - `Box plot.py`: Generates box plots to compare test scores by gender and country. - `Distribution.py`: Produces KDE (Kernel Density Estimate) plots and faceted visualizations for detailed score distributions. - **Results Folder**: - Contains the output figures and visualizations generated by the scripts. --- ## Tools and Libraries Used - **Python**: - `pandas`: For data preprocessing and analysis. - `matplotlib`: For creating plots and visualizations. - `seaborn`: For advanced data visualization. - `os`: For managing file directories. - `Pillow (PIL)`: For resizing and optimizing output images. --- ## Key Features and Outputs ### 1. **Box plot.py** This script generates box plots to visualize the distribution of test scores by country and gender for both mathematics and reading. #### Key Outputs: - **Box Plots**: - `BoxPlot_MATHS_PV5.png`: Mathematics scores by country and gender. - `BoxPlot_LECT_PV5.png`: Reading scores by country and gender. - **Combined Box Plot**: - `Math_Lecture_PV5_Combined_Signature.png`: Combined box plots for mathematics and reading scores with a shared legend. #### Key Features: - Custom legends displaying percentages of male and female students. - Rotated x-axis labels for better readability of country names. - Inclusion of author signature and software details in the visualizations. --- ### 2. **Distribution.py** This script focuses on visualizing score distributions using KDE plots and faceted grids, providing insights into overall and country-specific trends …