`sklearn.preprocessing`.MinMaxScaler¶

class sklearn.preprocessing.MinMaxScaler(feature_range=(0, 1), *, copy=True)[source]¶

Transform features by scaling each feature to a given range.

This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.

The transformation is given by:

X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
X_scaled = X_std * (max - min) + min

where min, max = feature_range.

This transformation is often used as an alternative to zero mean, unit variance scaling.

See also

minmax_scale: Equivalent function without the estimator API.

Notes

NaNs are treated as missing values: disregarded in fit, and maintained in transform.

For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.

Examples

>>> from sklearn.preprocessing import MinMaxScaler
>>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
>>> scaler = MinMaxScaler()
>>> print(scaler.fit(data))
MinMaxScaler()
>>> print(scaler.data_max_)
[ 1. 18.]
>>> print(scaler.transform(data))
[[0.   0.  ]
 [0.25 0.25]
 [0.5  0.5 ]
 [1.   1.  ]]
>>> print(scaler.transform([[2, 2]]))
[[1.5 0. ]]

Methods

`fit`(X[, y])	Compute the minimum and maximum to be used for later scaling.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(X)	Undo the scaling of X according to feature_range.
`partial_fit`(X[, y])	Online computation of min and max on X for later scaling.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Scale features of X according to feature_range.

__init__(feature_range=(0, 1), *, copy=True)[source]¶: Initialize self. See help(type(self)) for accurate signature.

fit(X, y=None)[source]¶

Compute the minimum and maximum to be used for later scaling.

Parameters

Xarray-like of shape (n_samples, n_features): The data used to compute the per-feature minimum and maximum used for later scaling along the features axis.
yNone: Ignored.

Returns

selfobject: Fitted scaler.

fit_transform(X, y=None, **fit_params)[source]¶

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)
yndarray of shape (n_samples,), default=None: Target values.
**fit_paramsdict: Additional fit parameters.

Returns

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_params(deep=True)[source]¶

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

paramsmapping of string to any: Parameter names mapped to their values.

inverse_transform(X)[source]¶

Undo the scaling of X according to feature_range.

Parameters

Xarray-like of shape (n_samples, n_features): Input data that will be transformed. It cannot be sparse.

Returns

Xtarray-like of shape (n_samples, n_features): Transformed data.

partial_fit(X, y=None)[source]¶

Online computation of min and max on X for later scaling.

All of X is processed as a single batch. This is intended for cases when fit is not feasible due to very large number of n_samples or because X is read from a continuous stream.

Parameters

Xarray-like of shape (n_samples, n_features): The data used to compute the mean and standard deviation used for later scaling along the features axis.
yNone: Ignored.

Returns

selfobject: Transformer instance.

set_params(**params)[source]¶

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