MeanCenterer

MeanCenterer()

Column centering of pandas Dataframe.

Attributes

col_means: numpy.ndarray [n_columns] or pandas [n_columns] mean values for centering after fitting the MeanCenterer object.

Examples

For usage examples, please see https://jaisenbe58r.github.io/MLearner/user_guide/preprocessing/MeanCenterer/

adapted from https://github.com/rasbt/mlxtend/blob/master/mlxtend/preprocessing/mean_centering.py Author: Sebastian Raschka License: BSD 3 clause

Methods

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fit(X, y=None)

Gets the column means for mean centering.

Parameters

• X : {Dataframe}, shape = [n_samples, n_features]

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

Returns

self

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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 : 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.

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get_params(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.

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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.

Returns

self

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transform(X)

Centers a pandas.

Parameters

• X : {Dataframe}, shape = [n_samples, n_features]

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

Returns

• X_transform : {DAtaframe}, shape = [n_samples, n_features]

  A copy of the input Dataframe with the columns centered.