plotting

lifelines.plotting.add_at_risk_counts(*fitters, **kwargs)

Add counts showing how many individuals were at risk at each time point in survival/hazard plots.

Parameters:fitters – One or several fitters, for example KaplanMeierFitter, NelsonAalenFitter, etc…
Returns:The axes which was used.
Return type:ax

Examples

>>> # First train some fitters and plot them
>>> fig = plt.figure()
>>> ax = plt.subplot(111)
>>>
>>> f1 = KaplanMeierFitter()
>>> f1.fit(data)
>>> f1.plot(ax=ax)
>>>
>>> f2 = KaplanMeierFitter()
>>> f2.fit(data)
>>> f2.plot(ax=ax)
>>>
>>> # There are equivalent
>>> add_at_risk_counts(f1, f2)
>>> add_at_risk_counts(f1, f2, ax=ax, fig=fig)
>>>
>>> # This overrides the labels
>>> add_at_risk_counts(f1, f2, labels=['fitter one', 'fitter two'])
>>>
>>> # This hides the labels
>>> add_at_risk_counts(f1, f2, labels=None)
lifelines.plotting.plot_lifetimes(durations, event_observed=None, entry=None, left_truncated=False, sort_by_duration=True, event_observed_color='#A60628', event_censored_color='#348ABD', ax=None, **kwargs)

Returns a lifetime plot, see examples: https://lifelines.readthedocs.io/en/latest/Survival%20Analysis%20intro.html#Censoring

Parameters:
  • durations ((n,) numpy array or pd.Series) – duration subject was observed for.
  • event_observed ((n,) numpy array or pd.Series) – array of booleans: True if event observed, else False.
  • entry ((n,) numpy array or pd.Series) – offsetting the births away from t=0. This could be from left-truncation, or delayed entry into study.
  • left_truncated (boolean) – if entry is provided, and the data is left-truncated, this will display additional information in the plot to reflect this.
  • sort_by_duration (boolean) – sort by the duration vector
  • event_observed_color (str) – default: “#A60628”
  • event_censored_color (str) – default: “#348ABD”
Returns:

Return type:

ax

Examples

>>> from lifelines.datasets import load_waltons
>>> from lifelines.plotting import plot_lifetimes
>>> T, E = load_waltons()["T"], load_waltons()["E"]
>>> ax = plot_lifetimes(T.loc[:50], event_observed=E.loc[:50])
lifelines.plotting.qq_plot(model, ax=None, **plot_kwargs)

Produces a quantile-quantile plot of the empirical CDF against the fitted parametric CDF. Large deviances away from the line y=x can invalidate a model (though we expect some natural deviance in the tails).

Parameters:
  • model (obj) – A fitted lifelines univariate parametric model, like WeibullFitter
  • plot_kwargs – kwargs for the plot.
Returns:

The axes which was used.

Return type:

ax

Examples

>>> from lifelines import *
>>> from lifelines.plotting import qq_plot
>>> from lifelines.datasets import load_rossi
>>> df = load_rossi()
>>> wf = WeibullFitter().fit(df['week'], df['arrest'])
>>> qq_plot(wf)
lifelines.plotting.cdf_plot(model, timeline=None, ax=None, **plot_kwargs)