class_docstr: Fill the missing values in continuous columns.
class_docstr_tok: ["Fill","the","missing","values","in","continuous","columns","."]
docstr: Fill missing values in `self.cont_names` like in `apply_train`.
docstr_tok: ["Fill","missing","values","in","`","self.cont_names","`","like","in","`","apply_train","`","."]
code: def apply_test(self, df):
      "Fill missing values in `self.cont_names` like in `apply_train`."
      for name in self.cont_names:
          if name in self.na_dict:
              if self.add_col:
                  df[name + "_na"] = pd.isnull(df[name])
                  if name + "_na" not in self.cat_names:
                      self.cat_names.append(name + "_na")
              df[name] = df[name].fillna(self.filler_dict[name])
          elif pd.isnull(df[name]).sum() != 0:
              warnings.warn(
                  f"""There are nan values in field {name} but there were none in the training set.
                  Filled with {self.fill_strategy}."""
              )
              df[name] = df[name].fillna(self.filler_dict[name])
  
imports: from ..None import utils
  from ..imports import *
  from ..preprocessor import Preprocessor
  from numbers import Number
  from types import SimpleNamespace
  from typing import Any, AnyStr, Callable, Collection, Dict, Hashable, Iterator, List, Mapping, NewType, Optional, Sequence, Tuple, TypeVar, Union
  from pandas.api.types import is_categorical_dtype, is_numeric_dtype
label_desc: Use image node_id 3 for calling the FillMissing obj's underlying member method code with example usage: obj.apply_test(df) without return types