2024
Blood pressure by age, cleaning and analysis
Cleaning a clinical dataset full of entry errors, and analyzing peak blood pressure by age group.
Problem
The data mixed units, decimal commas and impossible values.
Data
A course-provided clinical dataset (`talla-peso-edad.csv`) with height, weight, sex, age and peak blood pressure. Not included in the repo.
Method
Unit normalization, filtering of impossible values, low/normal/high blood-pressure classification, and a cross-tab by age group.
Result
High blood pressure goes from about half of cases at ages 18–29 to nearly all cases after 70, with the sharpest jump between 40 and 59.
Stack
- Python
- pandas
- NumPy
- Seaborn
Context
Course: Professional Practicum I · 2024