# Quickstart¶

## Kaplan-Meier and Nelson-Aalen¶

Let’s start by importing some data. We need the durations that individuals are observed for, and whether they “died” or not.

```
from lifelines.datasets import load_waltons
df = load_waltons() # returns a Pandas DataFrame
print df.head()
"""
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
"""
T = df['T']
E = df['E']
```

`T`

is an array of durations, `E`

is a either boolean or binary array representing whether the “death” was observed (alternatively an individual can be censored).

Note

By default, *lifelines* assumes all “deaths” are observed.

```
from lifelines import KaplanMeierFitter
kmf = KaplanMeierFitter()
kmf.fit(T, event_observed=E) # more succiently, kmf.fit(T,E)
```

After calling the `fit`

method, we have access to new properties like `survival_function_`

and methods like `plot()`

. The latter is a wrapper around Pandas internal plotting library.

```
kmf.survival_function_
kmf.median_
kmf.plot()
```

### Multiple groups¶

```
groups = df['group']
ix = (groups == 'miR-137')
kmf.fit(T[~ix], E[~ix], label='control')
ax = kmf.plot()
kmf.fit(T[ix], E[ix], label='miR-137')
kmf.plot(ax=ax)
```

Similar functionality exists for the `NelsonAalenFitter`

:

```
from lifelines import NelsonAalenFitter
naf = NelsonAalenFitter()
naf.fit(T, event_observed=E)
```

but instead of a `survival_function_`

being exposed, a `cumulative_hazard_`

is.

Note

Similar to Scikit-Learn, all statistically estimated quantities append an underscore to the property name.

## Getting Data in The Right Format¶

Often you’ll have data that looks like:

*start_time*, *end_time*

Lifelines has some utility functions to transform this dataset into durations and censorships:

```
from lifelines.utils import datetimes_to_durations
# start_times is a vector of datetime objects
# end_times is a vector of (possibly missing) datetime objects.
T, C = datetimes_to_durations(start_times, end_times, freq='h')
```

Alternatively, perhaps you are interested in viewing the survival table given some durations and censorship vectors.

```
from lifelines.utils import survival_table_from_events
table = survival_table_from_events(T, E)
print table.head()
"""
removed observed censored entrance at_risk
event_at
0 0 0 0 163 163
6 1 1 0 0 163
7 2 1 1 0 162
9 3 3 0 0 160
13 3 3 0 0 157
"""
```

## Survival Regression¶

While the above `KaplanMeierFitter`

and `NelsonAalenFitter`

are useful, they only give us an “average” view of the population. Often we have specific data at the individual level, either continuous or categorical, that we would like to use. For this, we turn to **survival regression**, specifically `AalenAdditiveFitter`

or `CoxPHFitter`

.

```
from lifelines.datasets import load_regression_dataset
regression_dataset = load_regression_dataset()
regression_dataset.head()
```

The input of the `fit`

method’s API on `AalenAdditiveFitter`

is different than above. All the data, including durations, censorships and covariates must be contained in **a Pandas DataFrame** (yes, it must be a DataFrame). The duration column and event occured column must be specified in the call to `fit`

.

```
from lifelines import AalenAdditiveFitter, CoxPHFitter
# Using Cox Proportional Hazards model
cf = CoxPHFitter()
cf.fit(regression_dataset, 'T', event_col='E')
cf.print_summary()
# Using Aalen's Additive model
aaf = AalenAdditiveFitter(fit_intercept=False)
aaf.fit(regression_dataset, 'T', event_col='E')
```

After fitting, you’ll have access to properties like `cumulative_hazards_`

and methods like `plot`

, `predict_cumulative_hazards`

, and `predict_survival_function`

. The latter two methods require an additional argument of individual covariates:

```
x = regression_dataset[regression_dataset.columns - ['E','T']]
aaf.predict_survival_function(x.ix[10:12]).plot() #get the unique survival functions of the first two subjects
```

Like the above estimators, there is also a built-in plotting method:

```
aaf.plot()
```