Transforming Data in Python
In this edition of Learn with Lantana, we walk through simple Python transformations that help make raw healthcare data actually usable --from reshaping data to cleaning records and creating grouped summaries.
Simplify Public Health Data Analysis with Python
Public health datasets are chaotic enough, your workflow doesn’t need to be.
A few lines of Python can do in seconds what takes way too many clicks (and re-clicks) to get right manually, and it’s actually repeatable.
Understanding Python Data Types
Python also needs to know what kind of data it's working with. Check out our quick guide for python data types, because Integers, floats, strings, and booleans are not interchangeable. A mis-coded date or a numeric field read incorrectly as text can cause big issues!
Understanding Python Data Types
The way we structure data directly impacts how efficiently we can clean, analyze, and act on it. We break down Python data structures and how they support real-world public health workflows.
Why Public Health Needs Python
From transforming messy datasets into actionable insights to powering scalable pipelines that process billions of data points, Python is helping modernize how we approach public health challenges.