class_docstr: Pickier version of MetricFrame.
  
  This holds the internal result from a disaggregated metric
  computation, and provides `apply_grouping()` (to cover min
  and max), `difference()` and `ratio()` methods.
  
  The main difference to the results computed by MetricFrame
  is that no account is made of whether the user supplied
  a bare function or a dictionary. Hence the results are
  always Series or DataFrame.
  
  Parameters
  ----------
  overall: Series or DataFrame
      The metric function(s) computed on the entire dataset, split by
      control features if supplied
  by_group: Series or DataFrame
      The metric function(s) computed on each subgroup identified by
      the sensitive and control features
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docstr: Construct an object.
docstr_tok: ["Construct","an","object","."]
code: def __init__(
      self,
      overall: Union[pd.Series, pd.DataFrame],
      by_group: pd.DataFrame,
  ):
      """Construct an object."""
      self._overall = overall
      assert isinstance(by_group, pd.DataFrame)
      self._by_group = by_group
  
file_path: fairlearn/fairlearn/metrics/_disaggregated_result.py
imports: import logging
  from typing import Dict, List, Optional, Union
  import numpy
  import pandas
  from ._annotated_metric_function import AnnotatedMetricFunction
label_desc: Use image node_id 1 to create a new DisaggregatedResult object  with example: obj = DisaggregatedResult(overall, by_group)