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 uncertainity (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 measure political parties lifetimes, or relationships, or marriages
• Businesses are interested in what variables affect lifetime value

lifelines is a pure Python implementation of the best parts of survival analysis. We'd love to hear if you are using lifelines, please ping me at @cmrn_dp and let me know your thoughts on the library.

####Dependencies:

The usual Python data stack: Numpy, Scipy, Pandas (a modern version please), Matplotlib

You can install lifelines using

   pip install lifelines

Or getting the bleeding edge version with:

   pip install git+https://github.com/CamDavidsonPilon/lifelines.git

or upgrade with

   pip install --upgrade git+https://github.com/CamDavidsonPilon/lifelines.git

from the command line.

#fake data
#T are the durations, C are boolean censorships
T = np.random.exponential(1, size=500)
C = np.random.binomial(1, 0.5, size=500)

Non-parametrically fit the survival curve:

from lifelines.estimation import KaplanMeierFitter

kmf = KaplanMeierFitter()
kmf.fit(T, C) 
kmf.plot() #plot the curve with the confidence intervals
print kmf.survival_function_.head()
print kmf.confidence_interval_.head()

Non-parametrically fit the cumulative hazard curve:

from lifelines.estimation import  NelsonAalenFitter

naf = NelsonAalenFitter()
naf.fit(T, C) 
naf.plot()
print naf.cumulative_hazard_.head()

Compare two populations using the logrank test:

from lifelines.statistics import logrank_test

T2 = np.random.exponential(3, size=500)

summary, p_value, results = logrank_test(T, T2, alpha=0.95)
print s

If you are new to survival analysis, wondering why it is useful, or are interested in lifelines examples and syntax, I recommend running the IPython notebooks in the notebooks dir.

Alternatively, you can view them online

1. Introduction to survival analysis
2. Using lifelines on real data
3. Documentation (not fully complete)

classes, methods and data types. You can use the IPython notebook to view it, or view it online.

There are some IPython notebook files in the repo, and you can view them online here.

• Divorce data
• Gehan's survival dataset

The Feedback MIT License (FMIT)

Copyright (c) 2013, Cameron Davidson-Pilon

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

1. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

2. Person obtaining a copy must return feedback to the authors.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.