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(self, X[, y]) Fit the imputer on X.
fit_transform(self, X[, y]) Fit to data, then transform it.
get_params(self[, deep]) Get parameters for this estimator.
set_params(self, \*\*params) Set the parameters of this estimator.
transform(self, X) Impute all missing values in X.
__init__(self, 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(self, 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(self, 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 : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns:
X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters:
deep : boolean, optional

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(self, **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.

Returns:
self
transform(self, X)

Impute all missing values in X.

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

The input data to complete.