.. _imlifelinesrst:

=========
lifelines
=========


.. only:: html

    **Links:** :download:`notebook <im_lifelines.ipynb>`, :downloadlink:`html <im_lifelines2html.html>`, :download:`PDF <im_lifelines.pdf>`, :download:`python <im_lifelines.py>`, :downloadlink:`slides <im_lifelines.slides.html>`, :githublink:`GitHub|_doc/notebooks/2016/pydata/im_lifelines.ipynb|*`


*lifelines* implements methods and algorithm for life insurance. As many
dedicated module, it contains custom graphs built on the top of
`matplotlib <http://matplotlib.org/>`__ for this module.

`documentation <http://lifelines.readthedocs.io/en/latest/>`__
`source <https://github.com/CamDavidsonPilon/lifelines>`__
`installation <https://pypi.python.org/pypi/lifelinesl>`__
`tutorial <http://lifelines.readthedocs.io/en/latest/Examples.html>`__

.. code:: ipython3

    from jyquickhelper import add_notebook_menu
    add_notebook_menu()






.. contents::
    :local:





.. code:: ipython3

    %matplotlib inline

.. code:: ipython3

    import matplotlib.pyplot as plt
    plt.style.use('ggplot')

example
-------

.. code:: ipython3

    from lifelines.plotting import plot_lifetimes
    from numpy.random import uniform, exponential
    from numpy import array, minimum
    import matplotlib.pyplot as plt
    
    N = 25
    current_time = 10
    actual_lifetimes = array([[exponential(12), exponential(2)][uniform()<0.5] for i in range(N)])
    observed_lifetimes = minimum(actual_lifetimes,current_time)
    observed = actual_lifetimes < current_time
    
    plt.xlim(0,25)
    plt.vlines(10,0,30,lw=2, linestyles="--")
    plt.xlabel('time')
    plt.title('Births and deaths of our population, at $t=10$')
    plot_lifetimes(observed_lifetimes, event_observed=observed)
    print("Observed lifetimes at time %d:\n"%(current_time), observed_lifetimes)



.. image:: im_lifelines_6_0.png


.. parsed-literal::
    Observed lifetimes at time 10:
     [ 10.           5.39173892   0.96070227   4.30409009  10.           0.16092116
       1.51180601  10.          10.           0.13168284   2.24095861  10.
       1.48363817   5.53642893   9.16920642   3.04028587   0.42805536
       7.51075415   1.18884195  10.           3.72807581   1.18750325
       7.0485026    0.08488696   2.29143555]


.. code:: ipython3

    import lifelines.datasets
    from lifelines import KaplanMeierFitter
    kmf = KaplanMeierFitter()
    
    data = lifelines.datasets.load_dd()
    T = data["duration"]
    C = data["observed"]
    kmf.fit(T, event_observed=C )
    
    kmf.plot()
    plt.title('Survival function of political regimes')




.. parsed-literal::
    Text(0.5,1,'Survival function of political regimes')




.. image:: im_lifelines_7_1.png