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.values if x != 'class'])
    y = df.reindex(columns=['class'])
    y = preprocessing.LabelEncoder().fit_transform(y.values.reshape(-1, ))

    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
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.values if x != 'class'])
    y = df.reindex(columns=['class'])
    y = preprocessing.LabelEncoder().fit_transform(y.values.reshape(-1, ))

    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
def get_cars_data ( ) : df = pd . read_csv ( 'source_data/cars/car.data.txt' ) X = df . reindex ( columns = [ x for x in df . columns . values if x != 'class' ] ) y = df . reindex ( columns = [ 'class' ] ) y = preprocessing . LabelEncoder ( ) . fit_transform ( y . values . reshape ( - 1 , ) ) 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
Load the cars dataset, split it into X and y, and then call the label encoder to get an integer y column.

    :return:
Load the cars dataset split it into X and y and then call the label encoder to get an integer y column .