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.
Read more in the User Guide.
 Parameters
 feature_rangetuple (min, max), default=(0, 1)
Desired range of transformed data.
 copybool, default=True
Set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array).
 Attributes
 min_ndarray of shape (n_features,)
Per feature adjustment for minimum. Equivalent to
min  X.min(axis=0) * self.scale_
 scale_ndarray of shape (n_features,)
Per feature relative scaling of the data. Equivalent to
(max  min) / (X.max(axis=0)  X.min(axis=0))
New in version 0.17: scale_ attribute.
 data_min_ndarray of shape (n_features,)
Per feature minimum seen in the data
New in version 0.17: data_min_
 data_max_ndarray of shape (n_features,)
Per feature maximum seen in the data
New in version 0.17: data_max_
 data_range_ndarray of shape (n_features,)
Per feature range
(data_max_  data_min_)
seen in the dataNew in version 0.17: data_range_
 n_samples_seen_int
The number of samples processed by the estimator. It will be reset on new calls to fit, but increments across
partial_fit
calls.
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.
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
 Xarraylike of shape (n_samples, n_features)
The data used to compute the perfeature 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{arraylike, 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
 Xarraylike of shape (n_samples, n_features)
Input data that will be transformed. It cannot be sparse.
 Returns
 Xtarraylike 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 ofn_samples
or because X is read from a continuous stream. Parameters
 Xarraylike 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
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.