> ## Documentation Index
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> Use this file to discover all available pages before exploring further.

> Compute transformations on exogenous regressors

# Feature engineering | MLForecast

```python theme={null}
import numpy as np
import pandas as pd
from nbdev import show_doc

from mlforecast.lag_transforms import ExpandingMean
from mlforecast.utils import generate_daily_series
```

## Setup

```python theme={null}
rng = np.random.RandomState(0)
series = generate_daily_series(100, equal_ends=True)
starts_ends = series.groupby(
    'unique_id', observed=True, as_index=False
)['ds'].agg(['min', 'max'])
prices = []
for r in starts_ends.itertuples():
    dates = pd.date_range(r.min, r.max + 14 * pd.offsets.Day())
    df = pd.DataFrame({'ds': dates, 'price': rng.rand(dates.size)})
    df['unique_id'] = r.Index
    prices.append(df)
prices = pd.concat(prices)
prices['price2'] = prices['price'] * rng.rand(prices.shape[0])
prices.head()
```

|   | ds         | price    | unique\_id | price2   |
| - | ---------- | -------- | ---------- | -------- |
| 0 | 2000-10-05 | 0.548814 | 0          | 0.345011 |
| 1 | 2000-10-06 | 0.715189 | 0          | 0.445598 |
| 2 | 2000-10-07 | 0.602763 | 0          | 0.165147 |
| 3 | 2000-10-08 | 0.544883 | 0          | 0.041373 |
| 4 | 2000-10-09 | 0.423655 | 0          | 0.391577 |

***

### `transform_exog`

```python theme={null}
transform_exog(
    df,
    lags=None,
    lag_transforms=None,
    id_col="unique_id",
    time_col="ds",
    num_threads=1,
)
```

Compute lag features for dynamic exogenous regressors.

**Parameters:**

| Name             | Type                                          | Description                                                                                                 | Default                   |
| ---------------- | --------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | ------------------------- |
| `df`             | <code>pandas or polars DataFrame</code>       | Dataframe with ids, times and values for the exogenous regressors.                                          | *required*                |
| `lags`           | <code>list of int</code>                      | Lags of the target to use as features. Defaults to None.                                                    | <code>None</code>         |
| `lag_transforms` | <code>dict of int to list of functions</code> | Mapping of target lags to their transformations. Defaults to None.                                          | <code>None</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 timestep, its values can be timestamps or integers. Defaults to 'ds'.           | <code>'ds'</code>         |
| `num_threads`    | <code>[int](#int)</code>                      | Number of threads to use when computing the features. Use -1 to use all available CPU cores. Defaults to 1. | <code>1</code>            |

**Returns:**

| Type                                    | Description                                   |
| --------------------------------------- | --------------------------------------------- |
| <code>pandas or polars DataFrame</code> | Original DataFrame with the computed features |

```python theme={null}
transformed = transform_exog(
    prices,
    lags=[1, 2],
    lag_transforms={1: [ExpandingMean()]}
)
transformed.head()
```

|   | ds         | price    | unique\_id | price2   | price\_lag1 | price\_lag2 | price\_expanding\_mean\_lag1 | price2\_lag1 | price2\_lag2 | price2\_expanding\_mean\_lag1 |
| - | ---------- | -------- | ---------- | -------- | ----------- | ----------- | ---------------------------- | ------------ | ------------ | ----------------------------- |
| 0 | 2000-10-05 | 0.548814 | 0          | 0.345011 | NaN         | NaN         | NaN                          | NaN          | NaN          | NaN                           |
| 1 | 2000-10-06 | 0.715189 | 0          | 0.445598 | 0.548814    | NaN         | 0.548814                     | 0.345011     | NaN          | 0.345011                      |
| 2 | 2000-10-07 | 0.602763 | 0          | 0.165147 | 0.715189    | 0.548814    | 0.632001                     | 0.445598     | 0.345011     | 0.395304                      |
| 3 | 2000-10-08 | 0.544883 | 0          | 0.041373 | 0.602763    | 0.715189    | 0.622255                     | 0.165147     | 0.445598     | 0.318585                      |
| 4 | 2000-10-09 | 0.423655 | 0          | 0.391577 | 0.544883    | 0.602763    | 0.602912                     | 0.041373     | 0.165147     | 0.249282                      |

```python theme={null}
import polars as pl
```

```python theme={null}
prices_pl = pl.from_pandas(prices)
transformed_pl = transform_exog(
    prices_pl,
    lags=[1, 2],
    lag_transforms={1: [ExpandingMean()]},
    num_threads=2,
)
transformed_pl.head()
```

| ds                  | price    | unique\_id | price2   | price\_lag1 | price\_lag2 | price\_expanding\_mean\_lag1 | price2\_lag1 | price2\_lag2 | price2\_expanding\_mean\_lag1 |
| ------------------- | -------- | ---------- | -------- | ----------- | ----------- | ---------------------------- | ------------ | ------------ | ----------------------------- |
| datetime\[ns]       | f64      | i64        | f64      | f64         | f64         | f64                          | f64          | f64          | f64                           |
| 2000-10-05 00:00:00 | 0.548814 | 0          | 0.345011 | NaN         | NaN         | NaN                          | NaN          | NaN          | NaN                           |
| 2000-10-06 00:00:00 | 0.715189 | 0          | 0.445598 | 0.548814    | NaN         | 0.548814                     | 0.345011     | NaN          | 0.345011                      |
| 2000-10-07 00:00:00 | 0.602763 | 0          | 0.165147 | 0.715189    | 0.548814    | 0.632001                     | 0.445598     | 0.345011     | 0.395304                      |
| 2000-10-08 00:00:00 | 0.544883 | 0          | 0.041373 | 0.602763    | 0.715189    | 0.622255                     | 0.165147     | 0.445598     | 0.318585                      |
| 2000-10-09 00:00:00 | 0.423655 | 0          | 0.391577 | 0.544883    | 0.602763    | 0.602912                     | 0.041373     | 0.165147     | 0.249282                      |