Fast Actual/Expected Data Processing
Abstract
A common problem with mortality analyses on company or registry data is that the processing time on large datasets can be an impediment to the interactive process of the analysis. The following paper delineates an approach using the Polars dataframe library and the programming language Python to speed up the processing time considerably.
Fields of Study & Subjects
Insurance
Mortality
Demography
Risk Management
Computational Physics and Python Applications
Probability and Risk Models
Publication details
- Journal / source
- Journal of Insurance Medicine
- Volume
- 53
- Issue
- 1
- Pages
- 105–114
- Publication date
- 1 January 2026
- Language
- EN