lifelines
A pure Python library for survival analysis, modeling time-to-event data with censoring.
What is lifelines?
Lifelines is a Python library for survival analysis, which models time-to-event data where some events may be censored. It helps answer questions about why events occur now versus later under uncertainty, such as measuring lifetimes or time to first actions. Originally developed for medical and actuarial applications, it is now used in SaaS, sociology, and A/B testing.
Data scientists, researchers, and analysts working with time-to-event data in fields like healthcare, SaaS, sociology, or inventory management who need to handle censored observations.
Lifelines provides a pure Python implementation of survival analysis with an intuitive API, comprehensive documentation, and support for various censoring types, making it easier to apply these methods outside traditional domains.
Overview
Survival analysis in Python
Use Cases
Best For
- Analyzing subscriber churn and lifetime value in SaaS applications
- Modeling patient survival times and treatment effects in medical research
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