lifelines.datasets package

Submodules

lifelines.datasets.dfcv_dataset module

Module contents

lifelines.datasets.load_canadian_senators(**kwargs)

A history of Canadian senators in office.

Size: (933,10) .. rubric:: Example

Name Abbott, John Joseph Caldwell Political Affiliation at Appointment Liberal-Conservative Province / Territory Quebec Appointed on the advice of Macdonald, John Alexander Term (yyyy.mm.dd) 1887.05.12 - 1893.10.30 (Death) start_date 1887-05-12 00:00:00 end_date 1893-10-30 00:00:00 reason Death diff_days 2363 observed True

lifelines.datasets.load_dataset(filename, **kwargs)

Load a dataset from lifelines.datasets

Parameters:
  • filename (string) – for example “larynx.csv”
  • usecols (list) – list of columns in file to use
Returns:

output

Return type:

DataFrame

lifelines.datasets.load_dd(**kwargs)

Classification of political regimes as democracy and dictatorship. Classification of democracies as parliamentary, semi-presidential (mixed) and presidential. Classification of dictatorships as military, civilian and royal. Coverage: 202 countries, from 1946 or year of independence to 2008.

Cheibub, José Antonio, Jennifer Gandhi, and James Raymond Vreeland. 2010. “Democracy and Dictatorship Revisited.” Public Choice, vol. 143, no. 2-1, pp. 67-101.

Size: (1808, 12) .. rubric:: Example

ctryname Afghanistan cowcode2 700 politycode 700 un_region_name Southern Asia un_continent_name Asia ehead Mohammad Zahir Shah leaderspellreg Mohammad Zahir Shah.Afghanistan.1946.1952.Mona… democracy Non-democracy regime Monarchy start_year 1946 duration 7 observed 1

lifelines.datasets.load_dfcv()

A toy example of a time dependent dataset. From http://www.math.ucsd.edu/~rxu/math284/slect7.pdf

Size: (14, 6) .. rubric:: Example

start group z stop id event

0 0 1.0 0 3.0 1 True 1 0 1.0 0 5.0 2 False 2 0 1.0 1 5.0 3 True 3 0 1.0 0 6.0 4 True

lifelines.datasets.load_g3(**kwargs)

Size: (17,7) .. rubric:: Example

no. 1 age 41 sex Female histology Grade3 group RIT event True time 53

lifelines.datasets.load_gbsg2(**kwargs)

A data frame containing the observations from the GBSG2 study of 686 women.

  1. Sauerbrei and P. Royston (1999). Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials. Journal of the Royal Statistics Society Series A, Volume 162(1), 71–94
  1. Schumacher, G. Basert, H. Bojar, K. Huebner, M. Olschewski, W. Sauerbrei, C. Schmoor, C. Beyerle, R.L.A. Neumann and H.F. Rauschecker for the German Breast Cancer Study Group (1994), Randomized 2 × 2 trial evaluating hormonal treatment and the duration of chemotherapy in node- positive breast cancer patients. Journal of Clinical Oncology, 12, 2086–2093

Size: (686,10) .. rubric:: Example

horTh yes age 56 menostat Post tsize 12 tgrade II pnodes 7 progrec 61 estrec 77 time 2018 cens 1

lifelines.datasets.load_holly_molly_polly(**kwargs)

From https://stat.ethz.ch/education/semesters/ss2011/seminar/contents/presentation_10.pdf Used as a toy example for CoxPH in recurrent SA.

ID Status Stratum Start(days) Stop(days) tx T

0 M 1 1 0 100 1 100 1 M 1 2 100 105 1 5 2 H 1 1 0 30 0 30 3 H 1 2 30 50 0 20 4 P 1 1 0 20 0 20

lifelines.datasets.load_kidney_transplant(**kwargs)

Size: (863,6) .. rubric:: Example

time 5 death 0 age 51 black_male 0 white_male 1 black_female 0

lifelines.datasets.load_larynx(**kwargs)

Size: (89,6) .. rubric:: Example

time age death Stage II Stage III Stage IV

0 0.6 77 1 0 0 0 1 1.3 53 1 0 0 0 2 2.4 45 1 0 0 0 3 2.5 57 0 0 0 0 4 3.2 58 1 0 0 0
lifelines.datasets.load_lcd(**kwargs)

Size: (104,3) .. rubric:: Example

C T group

0 0 1 alluvial_fan 1 0 1 alluvial_fan 2 0 1 alluvial_fan 3 0 1 alluvial_fan 4 1 1 alluvial_fan
lifelines.datasets.load_leukemia(**kwargs)

Leukemia dataset. From http://web1.sph.emory.edu/dkleinb/allDatasets/surv2datasets/anderson.dat Size: (42,5) .. rubric:: Example

t status sex logWBC Rx

0 35 0 1 1.45 0 1 34 0 1 1.47 0 2 32 0 1 2.20 0 3 32 0 1 2.53 0 4 25 0 1 1.78 0
lifelines.datasets.load_lung(**kwargs)

Size: (288,10) .. rubric:: Example

inst 3 time 306 status 2 age 74 sex 1 ph.ecog 1 ph.karno 90 pat.karno 100 meal.cal 1175 wt.loss NaN

lifelines.datasets.load_lymphoma(**kwargs)

From https://www.statsdirect.com/help/content/survival_analysis/logrank.htm

Size: (80, 3)

Example

Stage_group Time Censor

0 1 6 1 1 1 19 1 2 1 32 1 3 1 42 1 4 1 42 1

lifelines.datasets.load_panel_test(**kwargs)

Size: (28,5) .. rubric:: Example

id t E var1 var2

0 1 1 0 0.0 1 1 1 2 0 0.0 1 2 1 3 0 4.0 3 3 1 4 1 8.0 4 4 2 1 0 1.2 1
lifelines.datasets.load_psychiatric_patients(**kwargs)

Size: (26,4) .. rubric:: Example

Age T C sex

0 51 1 1 2 1 58 1 1 2 2 55 2 1 2 3 28 22 1 2 4 21 30 0 1
lifelines.datasets.load_recur(**kwargs)

From ftp://ftp.wiley.com/public/sci_tech_med/survival/, first published in “Applied Survival Analysis: Regression Modeling of Time to Event Data, Second Edition”

ID Subject Identification 1 - 400 AGE Age years TREAT Treatment Assignment 0 = New

1 = Old

TIME0 Day of Previous Episode Days TIME1 Day of New Episode Days

or censoring
CENSOR Indicator for Soreness 1 = Episode Occurred
Episode or Censoring at TIME1
0 = Censored

EVENT Soreness Episode Number 0 to at most 4

Size: (1296, 7) .. rubric:: Example

ID,AGE,TREAT,TIME0,TIME1,CENSOR,EVENT 1,43,0,9,56,1,3 1,43,0,56,88,1,4 1,43,0,0,6,1,1 1,43,0,6,9,1,2

lifelines.datasets.load_regression_dataset(**kwargs)

Artificial regression dataset. Useful since there are no ties in this dataset. Slightly edit in v0.15.0 to achieve this, however.

Size: (200,5) .. rubric:: Example

var1 var2 var3 T E

0 0.595170 1.143472 1.571079 14.785479 1 1 0.209325 0.184677 0.356980 7.336734 1 2 0.693919 0.071893 0.557960 5.271527 1 3 0.443804 1.364646 0.374221 11.684168 1 4 1.613324 0.125566 1.921325 7.637764 1
lifelines.datasets.load_rossi(**kwargs)

This data set is originally from Rossi et al. (1980), and is used as an example in Allison (1995). The data pertain to 432 convicts who were released from Maryland state prisons in the 1970s and who were followed up for one year after release. Half the released convicts were assigned at random to an experimental treatment in which they were given financial aid; half did not receive aid.

Rossi, P.H., R.A. Berk, and K.J. Lenihan (1980). Money, Work, and Crime: Some Experimental Results. New York: Academic Press. John Fox, Marilia Sa Carvalho (2012). The RcmdrPlugin.survival Package: Extending the R Commander Interface to Survival Analysis. Journal of Statistical Software, 49(7), 1-32.

Size: (432,9) .. rubric:: Example

week 20 arrest 1 fin 0 age 27 race 1 wexp 0 mar 0 paro 1 prio 3

lifelines.datasets.load_stanford_heart_transplants(**kwargs)

This is a classic dataset for survival regression with time varying covariates. The original dataset is from [1], and this dataset is from R’s survival library.

[1] J Crowley and M Hu. Covariance analysis of heart transplant survival data. J American
Statistical Assoc, 72:27–36, 1977.

Size: (172, 8) .. rubric:: Example

start stop event age year surgery transplant id

0 0.0 50.0 1 -17.155373 0.123203 0 0 1 1 0.0 6.0 1 3.835729 0.254620 0 0 2 2 0.0 1.0 0 6.297057 0.265572 0 0 3 3 1.0 16.0 1 6.297057 0.265572 0 1 3 4 0.0 36.0 0 -7.737166 0.490075 0 0 4
lifelines.datasets.load_static_test(**kwargs)

Size: (7,5) .. rubric:: Example

id t E var1 var2

0 1 4 1 -1 -1 1 2 3 1 -2 -2 2 3 3 0 -3 -3 3 4 4 1 -4 -4 4 5 2 1 -5 -5 5 6 0 1 -6 -6 6 7 2 1 -7 -7
lifelines.datasets.load_waltons(**kwargs)

Genotypes and number of days survived in Drosophila. Since we work with flies, we don’t need to worry about left-censoring. We know the birth date of all flies. We do have issues with accidentally killing some or if some escape. These would be right-censored as we do not actually observe their death due to “natural” causes.

Size: (163,3) .. rubric:: Example

T E group

0 6 1 miR-137 1 13 1 miR-137 2 13 1 miR-137 3 13 1 miR-137 4 19 1 miR-137
lifelines.datasets.multicenter_aids_cohort_study(**kwargs)

Originally in [1]

Siz: (78, 4)

AIDSY: date of AIDS diagnosis W: years from AIDS diagnosis to study entry T: years from AIDS diagnosis to minimum of death or censoring D: indicator of death during follow up

i AIDSY W T D 1 1990.425 4.575 7.575 0 2 1991.250 3.750 6.750 0 3 1992.014 2.986 5.986 0 4 1992.030 2.970 5.970 0 5 1992.072 2.928 5.928 0 6 1992.220 2.780 4.688 1

[1] Cole SR, Hudgens MG. Survival analysis in infectious disease research: describing events in time. AIDS. 2010;24(16):2423-31.