class_docstr: Calculates the entropy for a categorical column
  
  Description:
      Given a list of observations from a categorical
      column return the entropy of the distribution.
      NaN values can be treated as a category or
      dropped.
  
  Args:
      dropna (bool): Whether to consider NaN values as a separate category
          Defaults to False.
      base (float): The logarithmic base to use
          Defaults to e (natural logarithm)
  
  Examples:
      >>> pd_entropy = Entropy()
      >>> pd_entropy([1, 2, 3, 4])
      1.3862943611198906
code: def __init__(self, dropna=False, base=None):
      self.dropna = dropna
      self.base = base
  
imports: from scipy import stats
  from woodwork.column_schema import ColumnSchema
  from featuretools.primitives.base.aggregation_primitive_base import AggregationPrimitive
  from featuretools.utils.gen_utils import Library
label_desc: Use image node_id 1 to create a new Entropy object from inherited base classes: AggregationPrimitive with example: obj = Entropy(dropna, base)