# PreviousImputer¶

Imputes missing values by using the most recent value.

## Examples¶

>>> from river import preprocessing

>>> imputer = preprocessing.PreviousImputer()

>>> imputer = imputer.learn_one({'x': 1, 'y': 2})
>>> imputer.transform_one({'y': None})
{'y': 2}

>>> imputer.transform_one({'x': None})
{'x': 1}


## Methods¶

learn_one

Update with a set of features x.

A lot of transformers don't actually have to do anything during the learn_one step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the learn_one can override this method.

Parameters

• x (dict)

Returns

Transformer: self

transform_one

Transform a set of features x.

Parameters

• x (dict)

Returns

dict: The transformed values.