> ## Documentation Index
> Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Preprocessing

> Utilities for processing data before training/analysis

### `id_time_grid`

```python theme={null}
id_time_grid(df, freq, start='per_serie', end='global', id_col='unique_id', time_col='ds')
```

Generate all expected combiations of ids and times.

**Parameters:**

| Name       | Type                                                                                              | Description                                                                                                                                                                                                                                                         | Default                   |
| ---------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------- |
| `df`       | <code>pandas or polars DataFrame</code>                                                           | Input data                                                                                                                                                                                                                                                          | *required*                |
| `freq`     | <code>[str](#str) or [int](#int)</code>                                                           | Series' frequency                                                                                                                                                                                                                                                   | *required*                |
| `start`    | <code>([str](#str), [int](#int), [date](#datetime.date) or [datetime](#datetime.datetime))</code> | Initial timestamp for the series. \* 'per\_serie' uses each serie's first timestamp \* 'global' uses the first timestamp seen in the data \* Can also be a specific timestamp or integer, e.g. '2000-01-01', 2000 or datetime(2000, 1, 1) Defaults to "per\_serie". | <code>'per\_serie'</code> |
| `end`      | <code>([str](#str), [int](#int), [date](#datetime.date) or [datetime](#datetime.datetime))</code> | Initial timestamp for the series. \* 'per\_serie' uses each serie's last timestamp \* 'global' uses the last timestamp seen in the data \* Can also be a specific timestamp or integer, e.g. '2000-01-01', 2000 or datetime(2000, 1, 1) Defaults to "global".       | <code>'global'</code>     |
| `id_col`   | <code>[str](#str)</code>                                                                          | Column that identifies each serie. Defaults to 'unique\_id'.                                                                                                                                                                                                        | <code>'unique\_id'</code> |
| `time_col` | <code>[str](#str)</code>                                                                          | Column that identifies each timestamp. Defaults to 'ds'.                                                                                                                                                                                                            | <code>'ds'</code>         |

**Returns:**

| Type                                                | Description                                                        |
| --------------------------------------------------- | ------------------------------------------------------------------ |
| <code>[DFType](#utilsforecast.compat.DFType)</code> | pandas or polars DataFrame: Dataframe with expected ids and times. |

### `fill_gaps`

```python theme={null}
fill_gaps(df, freq, start='per_serie', end='global', id_col='unique_id', time_col='ds')
```

Enforce start and end datetimes for dataframe.

**Parameters:**

| Name       | Type                                                                                              | Description                                                                                                                                                                                                                                                         | Default                   |
| ---------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------- |
| `df`       | <code>pandas or polars DataFrame</code>                                                           | Input data                                                                                                                                                                                                                                                          | *required*                |
| `freq`     | <code>[str](#str) or [int](#int)</code>                                                           | Series' frequency                                                                                                                                                                                                                                                   | *required*                |
| `start`    | <code>([str](#str), [int](#int), [date](#datetime.date) or [datetime](#datetime.datetime))</code> | Initial timestamp for the series. \* 'per\_serie' uses each serie's first timestamp \* 'global' uses the first timestamp seen in the data \* Can also be a specific timestamp or integer, e.g. '2000-01-01', 2000 or datetime(2000, 1, 1) Defaults to "per\_serie". | <code>'per\_serie'</code> |
| `end`      | <code>([str](#str), [int](#int), [date](#datetime.date) or [datetime](#datetime.datetime))</code> | Initial timestamp for the series. \* 'per\_serie' uses each serie's last timestamp \* 'global' uses the last timestamp seen in the data \* Can also be a specific timestamp or integer, e.g. '2000-01-01', 2000 or datetime(2000, 1, 1) Defaults to "global".       | <code>'global'</code>     |
| `id_col`   | <code>[str](#str)</code>                                                                          | Column that identifies each serie. Defaults to 'unique\_id'.                                                                                                                                                                                                        | <code>'unique\_id'</code> |
| `time_col` | <code>[str](#str)</code>                                                                          | Column that identifies each timestamp. Defaults to 'ds'.                                                                                                                                                                                                            | <code>'ds'</code>         |

**Returns:**

| Type                                                | Description                                             |
| --------------------------------------------------- | ------------------------------------------------------- |
| <code>[DFType](#utilsforecast.compat.DFType)</code> | pandas or polars DataFrame: Dataframe with gaps filled. |