dask_ml.impute.SimpleImputer

class dask_ml.impute.SimpleImputer(*, missing_values=nan, strategy='mean', fill_value=None, verbose=0, copy=True, add_indicator=False)

Methods

fit(X[, y])	Fit the imputer on X.
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Impute all missing values in X.

__init__(*, missing_values=nan, strategy='mean', fill_value=None, verbose=0, copy=True, add_indicator=False)

Initialize self. See help(type(self)) for accurate signature.

fit(X, y=None)

Fit the imputer on X.

Parameters:

X : {array-like, sparse matrix}, shape (n_samples, n_features)

Input data, where n_samples is the number of samples and n_features is the number of features.

Returns:

self : SimpleImputer

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:

X : {array-like, sparse matrix, dataframe} of shape (n_samples, n_features)

y : ndarray of shape (n_samples,), default=None

Target values.

**fit_params : dict

Additional fit parameters.

Returns:

X_new : ndarray array of shape (n_samples, n_features_new)

Transformed array.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : bool, default=True

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

Returns:

params : mapping of string to any

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 pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params : dict

Estimator parameters.

Returns:

self : object

Estimator instance.

transform(X)

Impute all missing values in X.

Parameters:

X : {array-like, sparse matrix}, shape (n_samples, n_features)

The input data to complete.