## tidy()


Return a standardised term-level DataFrame for a fitted model.


Usage

``` python
tidy(
    model,
    **kwargs,
)
```


Produces one row per model term (coefficient, parameter, or stratum) with columns for the estimate, standard error, and confidence limits. This is the Python equivalent of R's `broom::tidy()`.


## Parameters


`model: object`  
A fitted Greenwood estimator (e.g., [KaplanMeier](KaplanMeier.md#greenwood.KaplanMeier), [CoxPH](CoxPH.md#greenwood.CoxPH), [AFT](AFT.md#greenwood.AFT), [Parametric](Parametric.md#greenwood.Parametric)).

`**kwargs: Any`  
Forwarded to the registered adapter. Common options include `format=` to choose the output backend (`"pandas"`, `"polars"`, or `"pyarrow"`).


## Returns


`DataFrame`  
A tidy, term-level summary of the fitted model.


## Examples

Tidy a fitted Cox model into a Polars DataFrame:


``` python
import greenwood as gw

# Load data, build a right-censored response, and fit a Cox model
lung = gw.load_dataset("lung", backend="polars")
y = gw.Surv.right(lung["time"], event=(lung["status"] == 2))
cox = gw.CoxPH().fit(y, covariates=lung[["age", "sex"]])

# Tidy the coefficients into a Polars DataFrame
gw.tidy(cox, format="polars")
```


shape: (2, 7)

| term  | estimate  | std_error | statistic | p_value  | conf_low  | conf_high |
|-------|-----------|-----------|-----------|----------|-----------|-----------|
| str   | f64       | f64       | f64       | f64      | f64       | f64       |
| "age" | 0.017045  | 0.009223  | 1.848078  | 0.064591 | -0.001032 | 0.035123  |
| "sex" | -0.513219 | 0.167458  | -3.06476  | 0.002178 | -0.84143  | -0.185007 |