class_docstr: DropMissingData() will delete rows containing missing values. It provides
  similar functionality to pandas.drop_na().
  
  It works for numerical and categorical variables. You can enter the list of
  variables for which missing values should be evaluated. Alternatively, the imputer
  will evaluate missing data in all variables in the dataframe.
  
  More details in the :ref:`User Guide <drop_missing_data>`.
  
  Parameters
  ----------
  variables: list, default=None
      The list of variables to consider for the imputation. If None, the imputer will
      evaluate missing data in all variables in the dataframe. Alternatively, the
      imputer will evaluate missing data only in the variables in the list.
  
      Note that if `missing_only=True` only variables with missing data in the train
      set will be considered to drop a row, which might be a subset of the indicated
      list.
  
  missing_only: bool, default=True
      If `True`, rows will be dropped when they show missing data in variables that
      had missing data during `fit()`. If `False`, rows will be dropped if there is
      missing data in any of the variables. This parameter only works when
      `threshold=None`, otherwise it is ignored.
  
  threshold: int or float, default=None
      Require that percentage of non-NA values in a row to keep it. If
      `threshold=1`, all variables need to have data to keep the row. If
      `threshold=0.5`, 50% of the variables need to have data to keep the row.
      If `threshold=0.01`, 10% of the variables need to have data to keep the row.
      If `thresh=None`, rows with NA in any of the variables will be dropped.
  
  Attributes
  ----------
  variables_:
      The variables for which missing data will be examined to decide if a row is
      dropped. The attribute `variables_` is different from the parameter `variables`
      when the latter is `None`, or when only a subset of the indicated variables
      show NA in the train set if `missing_only=True`.
  
  {feature_names_in_}
  
  {n_features_in_}
  
  Methods
  -------
  fit:
      Find the variables for which missing data should be evaluated.
  
  {fit_transform}
  
  return_na_data:
      Returns a dataframe with the rows that contain missing data.
  
  transform:
      Remove rows with missing data.
  
  Examples
  --------
  
  >>> import pandas as pd
  >>> import numpy as np
  >>> from feature_engine.imputation import DropMissingData
  >>> X = pd.DataFrame(dict(
  >>>        x1 = [np.nan,1,1,0,np.nan],
  >>>        x2 = ["a", np.nan, "b", np.nan, "a"],
  >>>        ))
  >>> dmd = DropMissingData()
  >>> dmd.fit(X)
  >>> dmd.transform(X)
      x1 x2
  2  1.0  b
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code: def _more_tags(self):
      tags_dict = _return_tags()
      tags_dict["allow_nan"] = True
      tags_dict["variables"] = "all"
      return tags_dict
  
file_path: feature_engine/feature_engine/imputation/drop_missing_data.py
imports: from typing import List, Optional, Union
  import pandas
  from feature_engine._docstrings.fit_attributes import _feature_names_in_docstring, _n_features_in_docstring
  from feature_engine._docstrings.methods import _fit_transform_docstring
  from feature_engine._docstrings.substitute import Substitution
  from feature_engine._variable_handling.init_parameter_checks import _check_init_parameter_variables
  from feature_engine._variable_handling.variable_type_selection import _find_all_variables
  from feature_engine.dataframe_checks import check_X
  from feature_engine.imputation.base_imputer import BaseImputer
  from feature_engine.tags import _return_tags
label_desc: Use image node_id 5 for calling the DropMissingData obj's underlying member method code with example usage: obj._more_tags() and returns: tags_dict