code: def _with_max_na_prop(df, max_prop):
      if max_prop is not None:
          na_prop = df.isna().sum(axis="index") / len(df)
          return na_prop[na_prop > max_prop].index.tolist()
      else:
          return []
  
imports: from copy import copy, deepcopy
  import abc
  import inspect
  import importlib
  import itertools
  from collections.abc import Mapping
  import pandas
  from decorator import decorator
  from sklearn_evaluation.exceptions import DataSelectorError
  from sklearn_evaluation.util import map_parameters_in_fn_call
  from sklearn_evaluation.table import Table
label_desc: Use image node_id 8 for calling a global function with example usage: _with_max_na_prop(df, max_prop) and returns: na_prop, list