Expect the column discrete entropy to be between a minimum value and a maximum value.
The Shannon entropy of a discrete probability distribution is given by

• \sum_{i=1}^{n} P(x_i) * \log(P(x_i))
  where P(x_i) is the probability of occurrence of value x_i.
  For observed data the P(x_i) are replaced by the empirical frequencies n_{x_i}/N

expect_column_discrete_entropy_to_be_between is a :func:column_aggregate_expectation <great_expectations.execution_engine.MetaExecutionEngine.column_aggregate_expectation>.

Args:

• column (str): The column name.
• min_value (float or None): The minimum value for the column standard deviation.
• max_value (float or None): The maximum value for the column standard deviation.

strict_min (boolean):

If True, the column standard deviation must be strictly larger than min_value, default=False

strict_max (boolean):

If True, the column standard deviation must be strictly smaller than max_value, default=False

base (float):

The base of the logarithm, default=2

Other Parameters:

• result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see :ref:result_format <result_format>.
• include_config (boolean): If True, then include the expectation config as part of the result object. For more detail, see :ref:include_config.
• catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see :ref:catch_exceptions.
• meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see :ref:meta.

Returns:

An ExpectationSuiteValidationResult
Exact fields vary depending on the values passed to :ref:result_format <result_format> and
:ref:include_config, :ref:catch_exceptions, and :ref:meta.

Notes:

• min_value and max_value are both inclusive unless strict_min or strict_max are set to True.
• If min_value is None, then max_value is treated as an upper bound
• If max_value is None, then min_value is treated as a lower bound