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Fast Actual/Expected Data Processing.

Journal of Insurance Medicine (New York, N.Y.)

|February 25, 2026

Summary

Processing large mortality datasets for company or registry analysis can be slow. This study introduces a Python-based approach using the Polars library to significantly accelerate data processing times.

Area of Science:

Data Science
Computational Statistics
Bioinformatics

Background:

Mortality analyses often involve large datasets from company or registry sources.
Extended data processing times hinder interactive analysis and timely insights.
Efficient data handling is crucial for reproducible and scalable research.

Purpose of the Study:

To present a method for accelerating the processing of large datasets in mortality analyses.
To demonstrate the utility of the Polars dataframe library in Python for performance optimization.
To overcome the processing time bottleneck in interactive data analysis.

Main Methods:

Utilized the Polars dataframe library, a high-performance data manipulation tool.
Implemented Python programming for data processing workflows.

Keywords:

Mortality Polars Python actual to expected methodology pivot table

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Applied the approach to typical mortality analysis datasets.

Main Results:

Achieved considerable speed-up in data processing times compared to traditional methods.
Demonstrated the effectiveness of Polars for handling large-scale datasets.
Enabled a more interactive and efficient analytical process.

Conclusions:

The Polars library in Python offers a significant advantage for accelerating mortality data analysis.
This approach addresses a key challenge in working with large datasets.
Researchers can benefit from faster processing for more dynamic data exploration.