Skip to main content

Survival analysis in Python, including Kaplan Meier, Nelson Aalen and regression

Project description

PyPI version Build Status Coverage Status Join the chat at https://gitter.im/python-lifelines/Lobby DOI Code style: black

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.

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.

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

Example:

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()

Contacting & troubleshooting

Roadmap

You can find the roadmap for lifelines here.


Development

Setting up a lifelines development environment

  1. From the root directory of lifelines activate your virtual environment (if you plan to use one).
  2. Install the development requirements and pre-commit hooks. If you are on Mac, Linux, or Windows WSL you can use the provided Makefile. Just type make into the console and you're ready to start developing.

Formatting

lifelines uses the black python formatter. There are 3 different ways to format your code.

  1. Use the 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

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

lifelines-0.16.3.tar.gz (218.0 kB view details)

Uploaded Source

Built Distributions

lifelines-0.16.3-py3-none-any.whl (243.0 kB view details)

Uploaded Python 3

lifelines-0.16.3-py2-none-any.whl (243.0 kB view details)

Uploaded Python 2

File details

Details for the file lifelines-0.16.3.tar.gz.

File metadata

  • Download URL: lifelines-0.16.3.tar.gz
  • Upload date:
  • Size: 218.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.21.0 setuptools/40.6.3 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.1

File hashes

Hashes for lifelines-0.16.3.tar.gz
Algorithm Hash digest
SHA256 24fe6c5d9eef9e9237d7a3a07e24cd4d5e67748a1baed14797d6131697cf207d
MD5 0e38ba59ca02bda3472c8fcfc94a297b
BLAKE2b-256 6a3b112ac2daa0b712bb6fb042ead551495e32e0687a87e9fcb3e2c4ccab9c7c

See more details on using hashes here.

File details

Details for the file lifelines-0.16.3-py3-none-any.whl.

File metadata

  • Download URL: lifelines-0.16.3-py3-none-any.whl
  • Upload date:
  • Size: 243.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.21.0 setuptools/40.6.3 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.1

File hashes

Hashes for lifelines-0.16.3-py3-none-any.whl
Algorithm Hash digest
SHA256 5f0d5968f21a59c9463b65496a0df00e13282aac499e917e3d122c4e8a5dabbb
MD5 b7e99725ae6da147a0311aaaac9ac2fd
BLAKE2b-256 cef29abfbd1995632c2b0f7167cf3fc5fce6ec7e8b8fb08ec5d5384983fbdc04

See more details on using hashes here.

File details

Details for the file lifelines-0.16.3-py2-none-any.whl.

File metadata

  • Download URL: lifelines-0.16.3-py2-none-any.whl
  • Upload date:
  • Size: 243.0 kB
  • Tags: Python 2
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.21.0 setuptools/40.6.3 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.1

File hashes

Hashes for lifelines-0.16.3-py2-none-any.whl
Algorithm Hash digest
SHA256 005bf9000bbdfa3ad6a4d41669a8470b24a7e58e0ca9af839cfacfb8aed344e7
MD5 af6afaaec522dc0929097be98232ea62
BLAKE2b-256 8341f7d08be99e753fbe20728f87aaa7165026f4d99b9ea3fcf9c0bd0757ebe3

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page