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# `sklearn.preprocessing`.MinMaxScaler

class sklearn.preprocessing.MinMaxScaler(feature_range=(0, 1), *, copy=True, clip=False)

  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.

  `MinMaxScaler` doesn’t reduce the effect of outliers, but it linearily scales them down into a fixed range, where the largest occuring data point corresponds to the maximum value and the smallest one corresponds to the minimum value. For an example visualization, refer to Compare MinMaxScaler with other scalers.

  Read more in the User Guide.

  Parameters:
    feature_range tuple (min, max), default=(0, 1)

      Desired range of transformed data.

    copy bool, default=True

      Set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array).

    clip bool, default=False

      Set to True to clip transformed values of held-out data to provided `feature range`.

      New in version 0.24.

  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 data

      New in version 0.17: data_range_

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    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.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

  See also

  `minmax_scale`

    Equivalent function without the estimator API.

  Notes

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

  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_feature_names_out`([input_features]) | Get output feature names for transformation.
  `get_metadata_routing`() | Get metadata routing of this object.
  `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_output`(*[, transform]) | Set output container.
  `set_params`(**params) | Set the parameters of this estimator.
  `transform`(X) | Scale features of X according to feature_range.

  fit(X, y=None)

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

    Parameters:
      X array-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.

      y None

        Ignored.

    Returns:
      self object

        Fitted scaler.

  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 array-like of shape (n_samples, n_features)

        Input samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs), default=None

        Target values (None for unsupervised transformations).

      **fit_params dict

        Additional fit parameters.

    Returns:
      X_new ndarray array of shape (n_samples, n_features_new)

        Transformed array.

  get_feature_names_out(input_features=None)

    Get output feature names for transformation.

    Parameters:
      input_features array-like of str or None, default=None

        Input features.

        - If `input_features` is `None`, then `feature_names_in_` is used as feature names in. If `feature_names_in_` is not defined, then the following input feature names are generated: `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.

        - If `input_features` is an array-like, then `input_features` must match `feature_names_in_` if `feature_names_in_` is defined.

    Returns:
      feature_names_out ndarray of str objects

        Same as input features.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  inverse_transform(X)

    Undo the scaling of X according to feature_range.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Input data that will be transformed. It cannot be sparse.

    Returns:
      Xt ndarray of shape (n_samples, n_features)

        Transformed data.

  partial_fit(X, y=None)

    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:
      X array-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.

      y None

        Ignored.

    Returns:
      self object

        Fitted scaler.

  set_output(*, transform=None)

    Set output container.

    See Introducing the set_output API for an example on how to use the API.

    Parameters:
      transform{“default”, “pandas”}, default=None

        Configure output of `transform` and `fit_transform`.

        - `"default"`: Default output format of a transformer

        - `"pandas"`: DataFrame output

        - `None`: Transform configuration is unchanged

    Returns:
      self estimator instance

        Estimator instance.

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `<component>__<parameter>` so that it’s possible to update each component of a nested object.

    Parameters:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  transform(X)

    Scale features of X according to feature_range.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Input data that will be transformed.

    Returns:
      Xt ndarray of shape (n_samples, n_features)

        Transformed data.

## Examples using `sklearn.preprocessing.MinMaxScaler`

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