code: def get_splice_data():
      """
      Load the mushroom 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/splice/splice.csv")
      X = df.reindex(columns=[x for x in df.columns if x != "class"])
      X["dna"] = X["dna"].map(lambda x: list(str(x).strip()))
      for idx in range(60):
          X["dna_%d" % (idx,)] = X["dna"].map(lambda x: x[idx])
      del X["dna"]
  
      y = df.reindex(columns=["class"])
      y = preprocessing.LabelEncoder().fit_transform(
          y.to_numpy().ravel()
      )
  
      # this data is truly categorical, with no known concept of ordering
      mapping = None
  
      return X, y, mapping
  
imports: import pandas
  from sklearn import preprocessing
label_desc: Use image node_id 3 for calling a global function with example usage: get_splice_data() and returns: X,  y,  mapping