code: def test_ColumnSelector_with_dataframe_drop_axis():
      iris = datasets.load_iris()
      df_in = pd.DataFrame(iris.data, columns=iris.feature_names)
      X1_out = ColumnSelector(
          cols=("petal length (cm)",), drop_axis=True
      ).transform(df_in)
      assert X1_out.shape == (150,)
  
      X1_out = ColumnSelector(
          cols=("petal length (cm)",), drop_axis=True
      ).transform(df_in)
      assert X1_out.shape == (150,)
  
      X1_out = ColumnSelector(cols="petal length (cm)").transform(df_in)
      assert X1_out.shape == (150, 1)
  
      X1_out = ColumnSelector(cols=("petal length (cm)",)).transform(
          df_in
      )
      assert X1_out.shape == (150, 1)
  
file_path: mlxtend/mlxtend/feature_selection/tests/test_column_selector.py
imports: import numpy
  import pandas
  from packaging.version import Version
  from sklearn import __version__
  from sklearn import datasets
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import GridSearchCV
  from sklearn.pipeline import make_pipeline
  from mlxtend.feature_selection import ColumnSelector
label_desc: Use image node_id 6 for calling a global function with example usage: test_ColumnSelector_with_dataframe_drop_axis() without return types