| name | lifelines |
| description | Survival analysis in Python: Kaplan-Meier, Cox proportional hazard, Aalen additive, parametric models, and competing risks. Censored data handling for churn, clinical, and actuarial applications. |
| tags | ["survival-analysis","kaplan-meier","cox-model","actuarial","churn","statistics","zorai"] |
Overview
Lifelines is a survival analysis library for Python. It implements Kaplan-Meier, Cox Proportional Hazard, parametric models (Weibull, Log-Normal), and Aalen's additive model. Use it for time-to-event data in clinical trials, churn analysis, reliability engineering, and customer retention studies.
Installation
uv pip install lifelines
Kaplan-Meier Estimate
from lifelines import KaplanMeierFitter
import pandas as pd
T = pd.Series([5, 10, 15, 20, 25, 30])
E = pd.Series([1, 1, 0, 1, 0, 0])
kmf = KaplanMeierFitter()
kmf.fit(T, E)
kmf.plot_survival_function()
print(kmf.median_survival_time_)
Cox Proportional Hazard
from lifelines import CoxPHFitter
df = pd.DataFrame({
"duration": [5, 10, 15, 20, 25, 30],
"event": [1, 1, 0, 1, 0, 0],
"age": [45, 60, 55, 70, 50, 65],
"treatment": [1, 0, 1, 0, 1, 0],
})
cph = CoxPHFitter()
cph.fit(df, duration_col="duration", event_col="event")
cph.print_summary()
cph.plot_partial_effects_on_outcome("treatment", [0, 1])
Weibull Parametric Model
from lifelines import WeibullAFTFitter
wbf = WeibullAFTFitter()
wbf.fit(df, duration_col="duration", event_col="event")
wbf.print_summary()
References
