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","."]
code: def __init__(
      self,
      columns: t.Union[Hashable, t.List[Hashable], None] = None,
      ignore_columns: t.Union[Hashable, t.List[Hashable], None] = None,
      max_features_to_show: int = 5,
      aggregation_method: t.Optional[str] = "max",
      n_samples: int = 100_000,
      random_state: int = 42,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.columns = columns
      self.ignore_columns = ignore_columns
      self.max_features_to_show = max_features_to_show
      self.aggregation_method = aggregation_method
      self.n_samples = n_samples
      self.random_state = random_state
  
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 1 to create a new PercentOfNulls object from inherited base classes: SingleDatasetCheck, ReduceFeatureMixin with example: obj = PercentOfNulls(columns, ignore_columns, max_features_to_show, aggregation_method, n_samples, random_state)