Survival analysis in Python, including Kaplan Meier, Nelson Aalen and regression
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 this lesser-known technique, for example: - SaaS providers are interested in measuring customer lifetimes, or time to first behaviours - sociologists are interested in measuring political parties’ lifetimes, or relationships, or marriages - analysing Godwin’s law in Reddit comments - 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. We’d love to hear if you are using lifelines, please leave an Issue and let us know your thoughts on the library.
The usual Python data stack: NumPy, SciPy, Pandas (a modern version please). Matplotlib is optional (as of 0.6.0+).
You can install lifelines using
pip install lifelines
Or getting the bleeding edge version with:
pip install --upgrade --no-deps git+https://github.com/CamDavidsonPilon/lifelines.git
from the command line.
See the common problems/solutions for installing lifelines.
Running the tests
You can optionally run the test suite after install with
lifelines Documentation and an intro to survival analysis
If you are new to survival analysis, wondering why it is useful, or are interested in lifelines examples and syntax, please check out the Documentation and Tutorials page
There is a Gitter channel available. The main author, Cam Davidson-Pilon, is also available for contact on email and twitter.