## viz.get_risk_table_frame()


Return a tidy frame of the number at risk per stratum at each of `times`.


Usage

``` python
viz.get_risk_table_frame(
    km,
    times=None,
    *,
    format=None,
)
```


## Parameters


`km: KaplanMeier`  
A fitted [KaplanMeier](KaplanMeier.md#greenwood.KaplanMeier) estimator.

`times: Any = None`  
Query times for the numbers-at-risk table. Defaults to an automatic grid.

`format: str | None = None`  
Output format: `None` (default), `"pandas"`, `"polars"`, or `"pyarrow"`. When `None`, a backend is auto-detected (Polars, then Pandas, then PyArrow).


## Returns


`DataFrame`  
A tidy frame with columns `strata`, `time`, and `n_risk` (one row per stratum per time point).


## Examples

Fit a stratified Kaplan-Meier estimator on the bundled `lung` dataset, then tabulate the number of subjects still at risk in each group at a chosen set of times. This returns the numbers as a tidy frame (one row per stratum and time).


``` python
import greenwood as gw

# Load data and fit a stratified Kaplan-Meier estimator
lung = gw.load_dataset("lung", backend="polars")
y = gw.Surv.right(lung["time"], event=(lung["status"] == 2))
km = gw.KaplanMeier().fit(y, by=lung["sex"])

# Get the numbers at risk as a tidy Polars DataFrame
gw.get_risk_table_frame(km, times=[0, 250, 500, 750, 1000], format="polars")
```


shape: (10, 3)

| strata | time   | n_risk |
|--------|--------|--------|
| str    | f64    | f64    |
| "1"    | 0.0    | 138.0  |
| "1"    | 250.0  | 62.0   |
| "1"    | 500.0  | 20.0   |
| "1"    | 750.0  | 7.0    |
| "1"    | 1000.0 | 2.0    |
| "2"    | 0.0    | 90.0   |
| "2"    | 250.0  | 53.0   |
| "2"    | 500.0  | 21.0   |
| "2"    | 750.0  | 3.0    |
| "2"    | 1000.0 | 0.0    |