CamDavidsonPilon/lifelines

Health v2 · maintenance only

Adoption not part of the score

2606 stars · 579 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

lifelines is a pure Python library for survival analysis, including Kaplan-Meier and Nelson-Aalen estimators and Cox regression models. It helps analyze time-to-event data with censored observations across medicine, reliability engineering, and business analytics.

Use cases

• estimate customer churn or subscriber lifetimes
• fit a Cox proportional hazards regression model
• plot Kaplan-Meier survival curves
• analyze time-to-event data with censored observations
• reliability analysis of equipment failure times
• compare survival between groups in an A/B test

When to choose

• you need survival analysis in pure Python with a pandas-friendly API
• you want Kaplan-Meier, Nelson-Aalen, and Cox regression in one library
• you're doing medical, actuarial, or SaaS lifetime analysis

When to avoid

• you need Bayesian survival models or heavy GPU-accelerated computation
• you need R's full survival ecosystem features
• your problem isn't time-to-event modeling

Facets

library · maturity stable

data-science math data-science healthcare analytics python survival-analysis kaplan-meier cox-regression reliability-analysis time-to-event censored-data statistics

Member repositories

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markdown · JSON · MCP: product_card(name="CamDavidsonPilon/lifelines")