ArbitraryNumberImputer

class feature_engine.imputation.ArbitraryNumberImputer(arbitrary_number=999, variables=None, imputer_dict=None)

The ArbitraryNumberImputer() replaces missing data by an arbitrary value determined by the user. It works only with numerical variables.

You can impute all variables with the same number by defining the variables to impute in variables and the imputation number in arbitrary_number. Alternatively, you can pass a dictionary with the variable names and the numbers to use for their imputation in the imputer_dict parameter.

More details in the User Guide.

Parameters

arbitrary_number: int or float, default=999

The number to replace the missing data. This parameter is used only if imputer_dict is None.

variables: list, default=None

The list of variables to impute. If None, the imputer will select all numerical variables. This parameter is used only if imputer_dict is None.

imputer_dict: dict, default=None

The dictionary of variables and the arbitrary numbers for their imputation. If specified, it overrides the above parameters.

Attributes

imputer_dict_:

Dictionary with the values to replace missing data in each variable.

variables_:

The group of variables that will be transformed.

feature_names_in_:

List with the names of features seen during fit.

n_features_in_:

The number of features in the train set used in fit.

See also

feature_engine.imputation.EndTailImputer

Examples

>>> import pandas as pd
>>> import numpy as np
>>> from feature_engine.imputation import ArbitraryNumberImputer
>>> X = pd.DataFrame(dict(
>>>        x1 = [np.nan,1,1,0,np.nan],
>>>        x2 = ["a", np.nan, "b", np.nan, "a"],
>>>       ))
>>> ani = ArbitraryNumberImputer(arbitrary_number=-999)
>>> ani.fit(X)
>>> ani.transform(X)
      x1   x2
0 -999.0    a
1    1.0  NaN
2    1.0    b
3    0.0  NaN
4 -999.0    a

Methods

fit:	This transformer does not learn parameters.
fit_transform:	Fit to data, then transform it.
get_feature_names_out:	Get output feature names for transformation.
get_params:	Get parameters for this estimator.
set_params:	Set the parameters of this estimator.
transform:	Impute missing data.

fit(X, y=None)

This method does not learn any parameter.

Parameters

X: pandas dataframe of shape = [n_samples, n_features]

The training dataset.

y: None

y is not needed in this imputation. You can pass None or y.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)

Get output feature names for transformation. In other words, returns the variable names of transformed dataframe.

Parameters

input_featuresarray or list, default=None

This parameter exits only for compatibility with the Scikit-learn pipeline.

• If None, then feature_names_in_ is used as feature names in.

• If an array or list, then input_features must match feature_names_in_.

Returns

feature_names_out: list

Transformed feature names.

rtype

List[Union[str, int]] ..

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict

Parameter names mapped to their values.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.

transform(X)

Replace missing data with the learned parameters.

Parameters

X: pandas dataframe of shape = [n_samples, n_features]

The data to be transformed.

Returns

X_new: pandas dataframe of shape = [n_samples, n_features]

The dataframe without missing values in the selected variables.

rtype

DataFrame ..