parameters: (y_name="price", train_fraction=0.7, seed=None)
return_statement: return (x_train, y_train), (x_test, y_test)
docstring: Get the imports85 data set.
  
    A description of the data is available at:
      https://archive.ics.uci.edu/ml/datasets/automobile
  
    The data itself can be found at:
      https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data
  
    Args:
      y_name: the column to return as the label.
      train_fraction: the fraction of the dataset to use for training.
      seed: The random seed to use when shuffling the data. `None` generates a
        unique shuffle every run.
    Returns:
      a pair of pairs where the first pair is the training data, and the second
      is the test data:
      `(x_train, y_train), (x_test, y_test) = get_imports85_dataset(...)`
      `x` contains a pandas DataFrame of features, while `y` contains the label
      array.
docstring_summary: Get the imports85 data set.
docstring_tokens: Get the imports85 data set .
function: def load_data(y_name="price", train_fraction=0.7, seed=None):
    """Get the imports85 data set.
  
    A description of the data is available at:
      https://archive.ics.uci.edu/ml/datasets/automobile
  
    The data itself can be found at:
      https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data
  
    Args:
      y_name: the column to return as the label.
      train_fraction: the fraction of the dataset to use for training.
      seed: The random seed to use when shuffling the data. `None` generates a
        unique shuffle every run.
    Returns:
      a pair of pairs where the first pair is the training data, and the second
      is the test data:
      `(x_train, y_train), (x_test, y_test) = get_imports85_dataset(...)`
      `x` contains a pandas DataFrame of features, while `y` contains the label
      array.
    """
    # Load the raw data columns.
    data = raw_dataframe()
  
    # Delete rows with unknowns
    data = data.dropna()
  
    # Shuffle the data
    np.random.seed(seed)
  
    # Split the data into train/test subsets.
    x_train = data.sample(frac=train_fraction, random_state=seed)
    x_test = data.drop(x_train.index)
  
    # Extract the label from the features dataframe.
    y_train = x_train.pop(y_name)
    y_test = x_test.pop(y_name)
  
    return (x_train, y_train), (x_test, y_test)
function_tokens: def load_data ( y_name = "price" , train_fraction = 0.7 , seed = None ) : # Load the raw data columns. data = raw_dataframe ( ) # Delete rows with unknowns data = data . dropna ( ) # Shuffle the data np . random . seed ( seed ) # Split the data into train/test subsets. x_train = data . sample ( frac = train_fraction , random_state = seed ) x_test = data . drop ( x_train . index ) # Extract the label from the features dataframe. y_train = x_train . pop ( y_name ) y_test = x_test . pop ( y_name ) return ( x_train , y_train ) , ( x_test , y_test )