Skip to content

leandrodotec/lifelines

 
 

Repository files navigation

PyPI version Anaconda-Server Badge Build Status Coverage Status DOI

What is survival analysis and why should I learn it? Survival analysis was originally developed and applied heavily by the actuarial and medical community. Its purpose was to answer why do events occur now versus later under uncertainty (where events might refer to deaths, disease remission, etc.). This is great for researchers who are interested in measuring lifetimes: they can answer questions like what factors might influence deaths?

But outside of medicine and actuarial science, there are many other interesting and exciting applications of survival analysis. For example:

  • SaaS providers are interested in measuring subscriber lifetimes, or time to some first action
  • inventory stock out is a censoring event for true "demand" of a good.
  • sociologists are interested in measuring political parties' lifetimes, or relationships, or marriages
  • A/B tests to determine how long it takes different groups to perform an action.

lifelines is a pure Python implementation of the best parts of survival analysis.

Documentation and intro to survival analysis

If you are new to survival analysis, wondering why it is useful, or are interested in lifelines examples, API, and syntax, please read the Documentation and Tutorials page

Contact

Roadmap

You can find the roadmap for lifelines here.

Development

See our Contributing guidelines.

Citing lifelines

You can use this badge below to generate a DOI and reference text for the latest related version of lifelines:

DOI

About

Survival analysis in Python

Resources

License

Code of conduct

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 82.2%
  • Jupyter Notebook 16.9%
  • Other 0.9%