[](https://badge.fury.io/py/lifelines)
[](https://conda.anaconda.org/conda-forge)
[](https://zenodo.org/badge/latestdoi/12420595)
[What is survival analysis and why should I learn it?](http://lifelines.readthedocs.org/en/latest/Survival%20Analysis%20intro.html)
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](http://lifelines.readthedocs.org/en/latest/index.html)
## Contact
- Start a conversation in our [Discussions room](https://github.com/CamDavidsonPilon/lifelines/discussions).
- Some users have posted common questions at [stats.stackexchange.com](https://stats.stackexchange.com/search?tab=votes&q=%22lifelines%22%20is%3aquestion).
- Creating an issue in the [Github repository](https://github.com/camdavidsonpilon/lifelines).
## Development
See our [Contributing](https://github.com/CamDavidsonPilon/lifelines/blob/master/.github/CONTRIBUTING.md) guidelines.
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