> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-fix-nav-issues.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> Documentación de la función de ventana leadInFrame

# leadInFrame

Devuelve un valor evaluado en la fila situada `offset` filas después de la fila actual dentro del frame ordenado.

<Warning>
  El comportamiento de `leadInFrame` difiere del de la función de ventana estándar de SQL `lead`.
  La función de ventana `leadInFrame` de ClickHouse respeta el frame de la ventana.
  Para obtener un comportamiento idéntico al de `lead`, usa `ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING`.
</Warning>

**Sintaxis**

```sql theme={null}
leadInFrame(x[, offset[, default]])
  OVER ([[PARTITION BY grouping_column] [ORDER BY sorting_column]
        [ROWS or RANGE expression_to_bound_rows_withing_the_group]] | [window_name])
FROM table_name
WINDOW window_name as ([[PARTITION BY grouping_column] [ORDER BY sorting_column])
```

Para obtener más información sobre la sintaxis de las funciones de ventana, consulte: [Funciones de ventana - Sintaxis](/es/reference/functions/window-functions#syntax).

**Parámetros**

* `x` — Nombre de la columna.
* `offset` — Desplazamiento que se debe aplicar. [(U)Int\*](/es/reference/data-types/int-uint). (Opcional: `1` de forma predeterminada).
* `default` — Valor que se devuelve si la fila calculada supera los límites del marco de ventana. (Opcional: valor predeterminado del tipo de columna cuando se omite).

**Valor devuelto**

* valor evaluado en la fila situada a `offset` filas después de la fila actual dentro del marco ordenado.

**Ejemplo**

Este ejemplo examina [datos históricos](https://www.kaggle.com/datasets/sazidthe1/nobel-prize-data) de los ganadores del Premio Nobel y utiliza la función `leadInFrame` para devolver una lista de ganadores consecutivos en la categoría de física.

```sql title="Query" theme={null}
CREATE OR REPLACE VIEW nobel_prize_laureates
AS SELECT *
FROM file('nobel_laureates_data.csv');
```

```sql title="Query" theme={null}
SELECT
    fullName,
    leadInFrame(year, 1, year) OVER (PARTITION BY category ORDER BY year ASC
      ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
    ) AS year,
    category,
    motivation
FROM nobel_prize_laureates
WHERE category = 'physics'
ORDER BY year DESC
LIMIT 9
```

```response title="Response" theme={null}
   ┌─fullName─────────┬─year─┬─category─┬─motivation─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
1. │ Anne L Huillier  │ 2023 │ physics  │ for experimental methods that generate attosecond pulses of light for the study of electron dynamics in matter                     │
2. │ Pierre Agostini  │ 2023 │ physics  │ for experimental methods that generate attosecond pulses of light for the study of electron dynamics in matter                     │
3. │ Ferenc Krausz    │ 2023 │ physics  │ for experimental methods that generate attosecond pulses of light for the study of electron dynamics in matter                     │
4. │ Alain Aspect     │ 2022 │ physics  │ for experiments with entangled photons establishing the violation of Bell inequalities and  pioneering quantum information science │
5. │ Anton Zeilinger  │ 2022 │ physics  │ for experiments with entangled photons establishing the violation of Bell inequalities and  pioneering quantum information science │
6. │ John Clauser     │ 2022 │ physics  │ for experiments with entangled photons establishing the violation of Bell inequalities and  pioneering quantum information science │
7. │ Giorgio Parisi   │ 2021 │ physics  │ for the discovery of the interplay of disorder and fluctuations in physical systems from atomic to planetary scales                │
8. │ Klaus Hasselmann │ 2021 │ physics  │ for the physical modelling of Earths climate quantifying variability and reliably predicting global warming                        │
9. │ Syukuro Manabe   │ 2021 │ physics  │ for the physical modelling of Earths climate quantifying variability and reliably predicting global warming                        │
   └──────────────────┴──────┴──────────┴────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
```