## EventTable


Per-time risk-set tabulation (optionally within strata).


Usage

``` python
EventTable(
    time,
    n_risk,
    n_event,
    n_censor,
    strata=None,
)
```


Every array is aligned row-wise. When `strata` is not `None`, rows are grouped by stratum (each stratum's times are ascending). Counts are weighted when case weights are supplied, so they may be floats.


## Parameter Attributes


`time: Array`  

`n_risk: Array`  

`n_event: Array`  

`n_censor: Array`  

`strata: Array | None = None`  


## Examples

An [EventTable](EventTable.md#greenwood.EventTable) is produced by [event_table](event_table.md#greenwood.event_table). Build one from the bundled `lung` dataset and view it as a Polars frame with [to_frame](AFT.md#greenwood.AFT.to_frame).


``` python
import greenwood as gw

lung = gw.load_dataset("lung", backend="polars")
y = gw.Surv.right(lung["time"], event=(lung["status"] == 2))
et = gw.event_table(y)
et.to_frame(format="polars")
```


shape: (186, 4)

| time   | n_risk | n_event | n_censor |
|--------|--------|---------|----------|
| f64    | f64    | f64     | f64      |
| 5.0    | 228.0  | 1.0     | 0.0      |
| 11.0   | 227.0  | 3.0     | 0.0      |
| 12.0   | 224.0  | 1.0     | 0.0      |
| 13.0   | 223.0  | 2.0     | 0.0      |
| 15.0   | 221.0  | 1.0     | 0.0      |
| …      | …      | …       | …        |
| 840.0  | 5.0    | 0.0     | 1.0      |
| 883.0  | 4.0    | 1.0     | 0.0      |
| 965.0  | 3.0    | 0.0     | 1.0      |
| 1010.0 | 2.0    | 0.0     | 1.0      |
| 1022.0 | 1.0    | 0.0     | 1.0      |


## Methods

| Name | Description |
|----|----|
| [to_frame()](#to_frame) | Return the tabulation as a DataFrame. |

------------------------------------------------------------------------


#### to_frame()


Return the tabulation as a DataFrame.


Usage

``` python
to_frame(*, format=None)
```


Exports the event-table rows with one row per unique exit time and columns for the risk set, events, censorings, and optional strata labels.


##### Parameters


`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


`pandas.DataFrame, polars.DataFrame, or pyarrow.Table`  
A tidy table containing `time`, `n_risk`, `n_event`, `n_censor`, and optionally `strata`.


##### Raises


`ImportError`  
If the requested (or, when auto-detecting, any) DataFrame library is not installed.


##### Examples

Build an event table from the bundled `lung` dataset and convert it to a Polars frame for inspection or downstream analysis:


``` python
import greenwood as gw

lung = gw.load_dataset("lung", backend="polars")
y = gw.Surv.right(lung["time"], event=(lung["status"] == 2))
et = gw.event_table(y)
et.to_frame(format="polars")
```


shape: (186, 4)

| time   | n_risk | n_event | n_censor |
|--------|--------|---------|----------|
| f64    | f64    | f64     | f64      |
| 5.0    | 228.0  | 1.0     | 0.0      |
| 11.0   | 227.0  | 3.0     | 0.0      |
| 12.0   | 224.0  | 1.0     | 0.0      |
| 13.0   | 223.0  | 2.0     | 0.0      |
| 15.0   | 221.0  | 1.0     | 0.0      |
| …      | …      | …       | …        |
| 840.0  | 5.0    | 0.0     | 1.0      |
| 883.0  | 4.0    | 1.0     | 0.0      |
| 965.0  | 3.0    | 0.0     | 1.0      |
| 1010.0 | 2.0    | 0.0     | 1.0      |
| 1022.0 | 1.0    | 0.0     | 1.0      |


Request a different backend with `format=`:


``` python
et.to_frame(format="pandas")
```


|     | time   | n_risk | n_event | n_censor |
|-----|--------|--------|---------|----------|
| 0   | 5.0    | 228.0  | 1.0     | 0.0      |
| 1   | 11.0   | 227.0  | 3.0     | 0.0      |
| 2   | 12.0   | 224.0  | 1.0     | 0.0      |
| 3   | 13.0   | 223.0  | 2.0     | 0.0      |
| 4   | 15.0   | 221.0  | 1.0     | 0.0      |
| ... | ...    | ...    | ...     | ...      |
| 181 | 840.0  | 5.0    | 0.0     | 1.0      |
| 182 | 883.0  | 4.0    | 1.0     | 0.0      |
| 183 | 965.0  | 3.0    | 0.0     | 1.0      |
| 184 | 1010.0 | 2.0    | 0.0     | 1.0      |
| 185 | 1022.0 | 1.0    | 0.0     | 1.0      |

186 rows × 4 columns