CI Table Not Outputting For All Classes In Column

[sapaca]

For this code:

for name, grouped_df in df.groupby('mar'):
    kmf = KaplanMeierFitter(alpha=alpha)
    kmf.fit(durations=grouped_df['week'], event_observed=grouped_df['arrest'], label=name)

After running the fit above and executing this:

kmf.confidence_interval_

only 1 class is generated for the output table even though plotting will show the CI estimates (shaded area) for all classes in the column of interest. In the below output I would expect to see a 0_lower_0.95 and 0_upper_0.5 column as well.

	1_lower_0.95	1_upper_0.95
0.0	1.000000	1.000000
12.0	0.873516	0.997320
23.0	0.857428	0.990427
26.0	0.834689	0.981385
39.0	0.811287	0.970985
42.0	0.788081	0.959609
43.0	0.742813	0.934739
46.0	0.720761	0.921486
52.0	0.720761	0.921486

[CamDavidsonPilon]

I suspect it's because you are re-assigning the kmf variable, so the last iteration defines what kmf is (which is a KaplanMeierFitter trained only on data where mar=1).

I suggest breaking this into something like:

kmfs = {}
for name, grouped_df in df.groupby('mar'):
    kmf = KaplanMeierFitter(alpha=alpha)
    kmf.fit(durations=grouped_df['week'], event_observed=grouped_df['arrest'], label=name)
    kmfs[name] = kmf

And then you have access to all kfms:

print(kmfs[0]. confidence_interval_)
print(kmfs[1]. confidence_interval_)

[sapaca]

Perfect, thank you. That works for me, I appreciate it!