class sklearn.preprocessing.MinMaxScaler(feature_range=(0, 1), copy=True)
[source]
Transforms 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, i.e. 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: 


Attributes: 

See also
minmax_scale
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.
>>> from sklearn.preprocessing import MinMaxScaler >>> >>> data = [[1, 2], [0.5, 6], [0, 10], [1, 18]] >>> scaler = MinMaxScaler() >>> print(scaler.fit(data)) MinMaxScaler(copy=True, feature_range=(0, 1)) >>> 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. ]]
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)  Scaling features of X according to feature_range. 
__init__(feature_range=(0, 1), copy=True)
[source]
fit(X, y=None)
[source]
Compute the minimum and maximum to be used for later scaling.
Parameters: 


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: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

inverse_transform(X)
[source]
Undo the scaling of X according to feature_range.
Parameters: 


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: 


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.
Returns: 


transform(X)
[source]
Scaling features of X according to feature_range.
Parameters: 


sklearn.preprocessing.MinMaxScaler
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html