Metadata-Version: 2.1
Name: lifelines
Version: 0.19.5
Summary: Survival analysis in Python, including Kaplan Meier, Nelson Aalen and regression
Home-page: https://github.com/CamDavidsonPilon/lifelines
Author: Cameron Davidson-Pilon
Author-email: cam.davidson.pilon@gmail.com
License: MIT
Keywords: survival analysis statistics data analysis
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
License-File: LICENSE

![](http://i.imgur.com/EOowdSD.png)

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[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 this survival analysis. For example:
- SaaS providers are interested in measuring customer lifetimes, or time to first behaviors
- 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
- analyzing [Godwin's law](https://raw.githubusercontent.com/lukashalim/GODWIN/master/Kaplan-Meier-Godwin.png) 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.

## Installation:

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.

##### Installation Issues?

See the common [problems/solutions for installing lifelines](https://github.com/CamDavidsonPilon/lifelines/issues?utf8=%E2%9C%93&q=label%3Ainstallation+).

#### Running the tests

You can optionally run the test suite after install with

    py.test


## 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, API, and syntax, please check out the [Documentation and Tutorials page](http://lifelines.readthedocs.org/en/latest/index.html)

Example:
```python
from lifelines import KaplanMeierFitter

durations = [11, 74, 71, 76, 28, 92, 89, 48, 90, 39, 63, 36, 54, 64, 34, 73, 94, 37, 56, 76]
event_observed = [True, True, False, True, True, True, True, False, False, True, True,
                  True, True, True, True, True, False, True, False, True]

kmf = KaplanMeierFitter()
kmf.fit(durations, event_observed)
kmf.plot()
```

<img src="https://imgur.com/d4Gi5J0.png" width="600">

### Contacting & troubleshooting
 - There is a [Gitter](https://gitter.im/python-lifelines/) channel available.
 - 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).

### Roadmap
You can find the roadmap for lifelines [here](https://www.notion.so/camdp/6e2965207f564eb2a3e48b5937873c14?v=47edda47ab774ca2ac7532bb0c750559).

-------------------------------------------------------------------------------

### Development

#### Setting up a lifelines development environment

1. From the root directory of `lifelines` activate your [virtual environment](https://realpython.com/python-virtual-environments-a-primer/) (if you plan to use one).
2. Install the development requirements and [`pre-commit`](https://pre-commit.com) hooks. If you are on Mac, Linux, or [Windows `WSL`](https://docs.microsoft.com/en-us/windows/wsl/faq) you can use the provided [`Makefile`](https://github.com/CamDavidsonPilon/lifelines/blob/master/Makefile). Just type `make` into the console and you're ready to start developing.

#### Formatting

`lifelines` uses the [`black`](https://github.com/ambv/black) python formatter.
There are 3 different ways to format your code.
1. Use the [`Makefile`](https://github.com/CamDavidsonPilon/lifelines/blob/master/Makefile).
   * `make format`
2. Call `black` directly and pass the correct line length.
   * `black . -l 120`
3. Have you code formatted automatically during commit with the `pre-commit` hook.
   * stage and commit your unformatted changes: `git commit -m "your_commit_message"`
   * Code that needs to be formatted will "fail" the commit hooks and be formatted for you.
   * Stage the newly formatted python code: `git add *.py`
   * Recall your original commit command and commit again: `git commit -m "your_commit_message"`

-------------------------------------------------------------------------------

### Citing lifelines

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

 [![DOI](https://zenodo.org/badge/12420595.svg)](https://zenodo.org/badge/latestdoi/12420595)
