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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 describe(df, dtype=None):
 """ Print a description of a Pandas dataframe. Parameters df : Pandas.DataFrame dtype : dict Maps column names to types """

    if dtype is None:
        dtype = {}
    print('Number of datapoints: {datapoints}'.format(datapoints=len(df)))
    column_info, column_info_meta = _get_column_info(df, dtype)

    if len(column_info['int']) > 0:
        _describe_int(df, column_info)

    if len(column_info['float']) > 0:
        _describe_float(df, column_info)

    if len(column_info['category']) > 0:
        _describe_category(df, column_info, column_info_meta)

    if len(column_info['time']) > 0:
        _describe_time(df, column_info, column_info_meta)

    if len(column_info['other']) > 0:
        _describe_other(df, column_info, column_info_meta)

    column_types = {}
    for column_type, columns in column_info.items():
        for column_name in columns:
            if column_type == 'other':
                column_type = 'str'
            column_types[column_name] = column_type
    return column_types