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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 normalize_etpinard_df(df='https://plot.ly/~etpinard/191.csv', columns='x y size text'.split(), category_col='category', possible_categories=['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']):
 """Reformat a dataframe in etpinard's format for use in plot functions and sklearn models"""

    possible_categories = ['Africa', 'Americas', 'Asia', 'Europe',
                           'Oceania'] if possible_categories is None else possible_categories
    df.columns = clean_columns(df.columns)
    df = pd.read_csv(df) if isinstance(df, str) else df
    columns = clean_columns(list(columns))
    df2 = pd.DataFrame(columns=columns)
    df2[category_col] = np.concatenate([np.array([categ] * len(df)) for categ in possible_categories])
    columns = zip(columns, [[clean_columns(categ + ', ' + column) for categ in possible_categories] for column in columns])
    for col, category_cols in columns:
        df2[col] = np.concatenate([df[label].values for label in category_cols])
    return df2