# CamDavidsonPilon/lifelines

Survival analysis in Python

Repository: https://github.com/CamDavidsonPilon/lifelines
Canonical: https://ross.abutalabs.com/products/lifelines
Language: Python
License: MIT
License Family: permissive
Topics: survival-analysis, python, statistics, data-science, maximum-likelihood, reliability-analysis, cox-regression
Last push: 2026-03-07T01:40:07+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 70, release rhythm 73, longevity 100
- inputs: {"age_days": 4754, "days_push": 180, "days_rel": 181, "gap_med": 28, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2606, forks 579 (observed 2026-08-28T04:07:04.087150+00:00)

## What it is
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
- artifact type: library
- maturity: stable
- function: data-science, math
- domain: data-science, healthcare, analytics
- platform: python
- tags: survival-analysis, kaplan-meier, cox-regression, reliability-analysis, time-to-event, censored-data, statistics

## Member repositories
- CamDavidsonPilon/lifelines (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:04.087150+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:20:57.657051+00:00, confidence not recorded.
  - readme: https://github.com/CamDavidsonPilon/lifelines (fetched 2026-08-28T04:07:04.087150+00:00, sha 4f23921b437e)
  - registry_pypi: https://pypi.org/pypi/lifelines/json (fetched 2026-08-29T10:03:56.043926+00:00, sha 574b7915ae01)
- Data as of 2026-08-30T08:39:29.467469+00:00.