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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def default(return_X_y=True):
 """credit default dataset Parameters return_X_y : bool, if True, returns a model-ready tuple of data (X, y) otherwise, returns a Pandas DataFrame Returns ------- model-ready tuple of data (X, y) OR Pandas DataFrame Notes ----- X contains the category of student or not, credit card balance, and income. y contains the outcome of default (0) or not (1). Source: https://vincentarelbundock.github.io/Rdatasets/doc/ISLR/Default.html """

    # y is binary
    # recommend LogisticGAM
    default = pd.read_csv(PATH + '/default.csv', index_col=0)
    if return_X_y:
        default = default.values
        default[:,0] = np.unique(default[:,0], return_inverse=True)[1]
        default[:,1] = np.unique(default[:,1], return_inverse=True)[1]
        X = default[:,1:]
        y = default[:,0]
        return _clean_X_y(X, y)
    return default