code: def test_missing_values_param_functionality():
      df = {
          "Name": ["tom", "nick", "krish", "jack"],
          "City": ["London", "Manchester", "Liverpool", "Bristol"],
          "Age": [20, 21, 19, 18],
          "Marks": [0.9, 0.8, 0.7, 0.6],
          "dob": pd.date_range("2020-02-24", periods=4, freq="T"),
          "const_feat_num": [1, 1, 1, np.nan],
          "const_feat_cat": ["a", "a", "a", "a"],
          "quasi_feat_num": [1, 1, 1, 2],
          "quasi_feat_cat": ["a", "a", "a", np.nan],
      }
      df = pd.DataFrame(df)
  
      # test raises error if there is na
      transformer = DropConstantFeatures(missing_values="raise")
      with pytest.raises(ValueError):
          transformer.fit(df)
  
      # test ignores na
      transformer = DropConstantFeatures(missing_values="ignore").fit(
          df
      )
      constant = ["const_feat_num", "const_feat_cat", "quasi_feat_cat"]
      assert transformer.features_to_drop_ == constant
      pd.testing.assert_frame_equal(
          df.drop(constant, axis=1), transformer.transform(df)
      )
  
      # test includes na
      transformer = DropConstantFeatures(
          tol=0.7, missing_values="include"
      ).fit(df)
      qconstant = [
          "const_feat_num",
          "const_feat_cat",
          "quasi_feat_num",
          "quasi_feat_cat",
      ]
      assert transformer.features_to_drop_ == qconstant
      pd.testing.assert_frame_equal(
          df.drop(qconstant, axis=1), transformer.transform(df)
      )
  
file_path: feature_engine/tests/test_selection/test_drop_constant_features.py
imports: import numpy
  import pandas
  import pytest
  from feature_engine.selection import DropConstantFeatures
label_desc: Use image node_id 9 for calling a global function with example usage: test_missing_values_param_functionality() without return types