code: def get_cars_data():
      """
      Load the cars dataset, split it into X and y, and then call the label encoder to get an integer y column.
  
      :return:
      """
  
      df = pd.read_csv("source_data/cars/car.data.txt")
      X = df.reindex(columns=[x for x in df.columns if x != "class"])
      y = df.reindex(columns=["class"])
      y = preprocessing.LabelEncoder().fit_transform(
          y.to_numpy().ravel()
      )
  
      mapping = [
          {
              "col": "buying",
              "mapping": [
                  ("vhigh", 0),
                  ("high", 1),
                  ("med", 2),
                  ("low", 3),
              ],
          },
          {
              "col": "maint",
              "mapping": [
                  ("vhigh", 0),
                  ("high", 1),
                  ("med", 2),
                  ("low", 3),
              ],
          },
          {
              "col": "doors",
              "mapping": [("2", 0), ("3", 1), ("4", 2), ("5more", 3)],
          },
          {
              "col": "persons",
              "mapping": [("2", 0), ("4", 1), ("more", 2)],
          },
          {
              "col": "lug_boot",
              "mapping": [("small", 0), ("med", 1), ("big", 2)],
          },
          {
              "col": "safety",
              "mapping": [("high", 0), ("med", 1), ("low", 2)],
          },
      ]
  
      return X, y, mapping
  
imports: import pandas
  from sklearn import preprocessing
label_desc: Use image node_id 1 for calling a global function with example usage: get_cars_data() and returns: X,  y,  mapping