Catastrophe Risk Modeling with Python is a practical guide to analyzing natural hazard risk, simulating losses, and evaluating insurance portfolio exposure using Python-based workflows.

The book introduces the core concepts behind catastrophe risk analysis, including hazard events, vulnerability assumptions, exposure data, loss distributions, exceedance probability, aggregate loss modeling, and portfolio-level risk measurement. Readers are guided through structured Python examples that demonstrate how catastrophe models can be built, tested, and interpreted for insurance, reinsurance, and risk analytics contexts.

Rather than treating catastrophe risk as a purely theoretical subject, this book focuses on applied modeling techniques that connect data, assumptions, and financial outcomes. Topics include event simulation, loss severity modeling, portfolio aggregation, geographic exposure analysis, uncertainty, scenario testing, and practical reporting for decision-making.

Designed for analysts, actuaries, insurance professionals, data scientists, and finance practitioners, this book provides a clear foundation for using Python to understand and model large-scale natural hazard risk.