Star it if you like it!
- Install kaplanmeier from PyPI (recommended). kaplanmeier is compatible with Python 3.6+ and runs on Linux, MacOS X and Windows.
- Distributed under the MIT license.
- Create environment:
conda create -n env_KM python=3.6
conda activate env_KM
pip install matplotlib numpy pandas seaborn lifelinespip install kaplanmeier
import kaplanmeier as kmdf = km.example_data()
time_event=df['time']
censoring=df['Died']
labx=df['group']
# Compute survival
out=km.fit(time_event, censoring, labx)km.plot(out)
km.plot(out, cmap='Set1', cii_lines=None, cii_alpha=0.05)
km.plot(out, cmap='Set1', cii_lines='line', cii_alpha=0.05)
km.plot(out, cmap=[(1, 0, 1),(0, 1, 1)])
km.plot(out, cmap='Set2')
km.plot(out, cmap='Set2', methodtype='custom')
- df looks like this:
time Died group
0 485 0 1
1 526 1 2
2 588 1 2
3 997 0 1
4 426 1 1
.. ... ... ...
175 183 0 1
176 3196 0 1
177 457 1 2
178 2100 1 1
179 376 0 1
[180 rows x 3 columns]
Please cite kaplanmeier in your publications if this is useful for your research. Here is an example BibTeX entry:
@misc{erdogant2019kaplanmeier,
title={kaplanmeier},
author={Erdogan Taskesen},
year={2019},
howpublished={\url{https://github.com/erdogant/kaplanmeier}},
}
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