text: #!/user/bin/env/python
  '''
  Train test split a Data Frame
  '''
  from sklearn.model_selection import train_test_split
  
  class TrainTestSplit:
      '''
      Library:
      ------------------------------------
      from sklearn.model_selection import train_test_split
  
      Parameters:
      -------------------------------------
      feature - Takes in 1 or more dimension/column - X takes in a DataFrame
      target - Takes in 1 dimension/column - y takes in a Series
      test_size - Default is 20% or 0.2
      random_state - Default is 42
  
      Returns:
      --------------------------------------
      X_train, X_test, y_train, y_test
      '''
      
      def __init__(self, feature, target, test_size=0.2, random_state=42):
          self.feature = feature
          self.target = target
          self.test_size = test_size
          self.random_state = random_state
  
      def split(self):
          # Split to train and test data sets
          X_train, X_test, y_train, y_test = train_test_split(self.feature, self.target,     test_size=self.test_size, random_state=self.random_state)
  
          # Return X_train, X_test, y_train, y_test
          return X_train, X_test, y_train, y_test
  
  
  ##################  Old Code  ##############################
  
  #X = pd.DataFrame()
  #y = pd.Series()
  
  # def train_test(feature, target, test_size=0.2):
  #     '''
  #     Features(X) can be 1 or more dimension/column - X takes in a DataFrame
  #     Target(y) 1 dimension/column - y takes in a Series
  #     Returns X_train, X_test, y_train, y_test
  #     '''
  #     X_train, X_test, y_train, y_test = train_test_split(feature, target, test_size=test_size, random_state=42)
  #     return X_train, X_test, y_train, y_test
  # def train_val(feature, target):
  #     '''
  #     Features(X) can be 1 or more dimension/column - X_train takes in a DataFrame
  #     Target(y) 1 dimension/column - y_train takes in a Series
  #     Returns X_train, X_test, y_train, y_test
  #     '''
  #     X_train, X_val, y_train, y_val = train_test_split(feature, target, test_size=0.2, random_state=42)
  #     return X_train, X_val, y_train, y_val
  
  