class_docstr: Percent of 'Null' values in each column.
  
  Parameters
  ----------
  columns : Union[Hashable, List[Hashable]] , default: None
      List of columns to check, if none given checks
      all columns Except ignored ones.
  ignore_columns : Union[Hashable, List[Hashable]] , default: None
      List of columns to ignore, if none given checks
      based on columns variable.
  max_features_to_show : int , default: 5
      maximum features with to show, showing top features based on percent of nulls.
  aggregation_method: t.Optional[str], default: 'max'
      {feature_aggregation_method_argument:2*indent}
  n_samples : int , default: 100_000
      number of samples to use for this check.
  random_state : int, default: 42
      random seed for all check internals.
class_docstr_tok: ["Percent","of","'Null","'","values","in","each","column",".","Parameters","--","--","--","--","--","columns",":","Union","[","Hashable",",","List","[","Hashable","]","]",",","default",":","None","List","of","columns","to","check",",","if","none","given","checks","all","columns","Except","ignored","ones",".","ignore_columns",":","Union","[","Hashable",",","List","[","Hashable","]","]",",","default",":","None","List","of","columns","to","ignore",",","if","none","given","checks","based","on","columns","variable",".","max_features_to_show",":","int",",","default",":","5","maximum","features","with","to","show",",","showing","top","features","based","on","percent","of","nulls",".","aggregation_method",":","t.Optional","[","str","]",",","default",":","'max'","{","feature_aggregation_method_argument:2","*","indent","}","n_samples",":","int",",","default",":","100_000","number","of","samples","to","use","for","this","check",".","random_state",":","int",",","default",":","42","random","seed","for","all","check","internals","."]
docstr: Return an aggregated drift score based on aggregation method defined.
docstr_tok: ["Return","an","aggregated","drift","score","based","on","aggregation","method","defined","."]
code: def reduce_output(
      self, check_result: CheckResult
  ) -> t.Dict[str, float]:
      """Return an aggregated drift score based on aggregation method defined."""
      feature_importance = (
          check_result.value["Feature importance"]
          if "Feature importance" in check_result.value.columns
          else None
      )
      values = check_result.value["Percent of nulls in sample"]
      return self.feature_reduce(
          self.aggregation_method,
          values,
          feature_importance,
          "Null Ratio",
      )
  
file_path: deepchecks/deepchecks/tabular/checks/data_integrity/percent_of_nulls.py
imports: import typing
  import pandas
  import plotly.express
  from deepchecks.core.check_result import CheckResult
  from deepchecks.core.checks import DatasetKind
  from deepchecks.core.condition import ConditionCategory, ConditionResult
  from deepchecks.core.reduce_classes import ReduceFeatureMixin
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.tabular._shared_docs import docstrings
  from deepchecks.tabular.utils.messages import get_condition_passed_message
  from deepchecks.utils.strings import format_percent
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 3 for calling the PercentOfNulls obj's underlying member method code with example usage: obj.reduce_output(check_result) and returns: self