class_docstr: Scales the data to a fixed range from 0 to 1.
  
  Under the hood a running min and a running peak to peak (max - min) are maintained.
  
  Attributes
  ----------
  min : dict
      Mapping between features and instances of `stats.Min`.
  max : dict
      Mapping between features and instances of `stats.Max`.
  
  Examples
  --------
  
  >>> import random
  >>> from river import preprocessing
  
  >>> random.seed(42)
  >>> X = [{'x': random.uniform(8, 12)} for _ in range(5)]
  >>> for x in X:
  ...     print(x)
  {'x': 10.557707}
  {'x': 8.100043}
  {'x': 9.100117}
  {'x': 8.892842}
  {'x': 10.945884}
  
  >>> scaler = preprocessing.MinMaxScaler()
  
  >>> for x in X:
  ...     scaler.learn_one(x)
  ...     print(scaler.transform_one(x))
  {'x': 0.0}
  {'x': 0.0}
  {'x': 0.406920}
  {'x': 0.322582}
  {'x': 1.0}
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code: def __init__(self):
      self.min = collections.defaultdict(stats.Min)
      self.max = collections.defaultdict(stats.Max)
  
imports: from __future__ import annotations
  import collections
  import functools
  import itertools
  import numbers
  import numpy
  import pandas
  from river import base, stats, utils
label_desc: Use image node_id 1 to create a new MinMaxScaler object from inherited base classes: base with example: obj = MinMaxScaler()