code: def get_iris_data(target="species", use_str_target=False):
      """Get the Iris classification dataset, formatted as a DataFrame
  
      Parameters
      ----------
      target: String, default="species"
          What to name the column in `df` that contains the target output values
      use_str_target: Boolean, default=False
          If True, replace label-encoded target values with string labels
  
      Returns
      -------
      df: pandas.DataFrame
          This Iris dataset, with friendly column names
  
      Examples
      --------
      >>> get_iris_data(use_str_target=False).sample(n=5, random_state=32)
           sepal_length_(cm)  sepal_width_(cm)  petal_length_(cm)  petal_width_(cm)  species
      55                 5.7               2.8                4.5               1.3        1
      22                 4.6               3.6                1.0               0.2        0
      26                 5.0               3.4                1.6               0.4        0
      56                 6.3               3.3                4.7               1.6        1
      134                6.1               2.6                5.6               1.4        2
      >>> get_iris_data(use_str_target=True).sample(n=5, random_state=32)
           sepal_length_(cm)  sepal_width_(cm)  petal_length_(cm)  petal_width_(cm)     species
      55                 5.7               2.8                4.5               1.3  versicolor
      22                 4.6               3.6                1.0               0.2      setosa
      26                 5.0               3.4                1.6               0.4      setosa
      56                 6.3               3.3                4.7               1.6  versicolor
      134                6.1               2.6                5.6               1.4   virginica
      """
      data = load_iris()
      df = pd.DataFrame(
          data=data.data,
          columns=[_.replace(" ", "_") for _ in data.feature_names],
      )
      df[target] = data.target
  
      if use_str_target:
          # Replace label-encoded target values with string labels
          df.loc[:, target].replace(
              to_replace=dict(zip([0, 1, 2], data.target_names)),
              value=None,
              inplace=True,
          )
  
      return df
  
file_path: hyperparameter_hunter/hyperparameter_hunter/utils/learning_utils.py
imports: import pandas
  from sklearn.datasets import load_boston, load_breast_cancer, load_diabetes, load_iris, make_classification
label_desc: Use image node_id 5 for calling a global function with example usage: get_iris_data(target, use_str_target) and returns: df