def cols_to_cube_root_normalize(df, columns):
    """Convert data points to their normalized cube root value so all values are between 0-1 and return new columns of prefixed data. 
    
    Args: 
        df: Pandas dataframe.
        columns: List of columns to transform.

    Returns: 
        Original dataframe with additional prefixed columns.
    """
    for col in columns:
        df['cube_root_' + col] = (df[col] - df[col].min()) / (df[col].max() -
            df[col].min()) ** (1 / 3)
    return df
