': (['1'] * 100000 + ['X'] * 100000 +
    ...                          ['1'] * 100000),
    ...                    'b': ['b'] * 300000})  # doctest: +SKIP
    >>> df.to_csv('test.csv', index=False)  # doctest: +SKIP
    >>> df2 = pd.read_csv('test.csv')  # doctest: +SKIP
    ... # DtypeWarning: Columns (0) have mixed types

    Important to notice that ``df2`` will contain both `str` and `int` for the
    same input, '1'.

    >>> df2.iloc[262140, 0]  # doctest: +SKIP
    '1'
    >>> type(df2.iloc[262140, 0])  # doctest: +SKIP
    <class 'str'>
    >>> df2.iloc[262150, 0]  # doctest: +SKIP
    1
    >>> type(df2.iloc[262150, 0])  # doctest: +SKIP
    <class 'int'>

    One way to solve this issue is using the `dtype` parameter in the
    `read_csv` and `read_table` functions to explicit the conversion:

    >>> df2 = pd.read_csv('test.csv', sep=',', dtype={'a': str})  # doctest: +SKIP

    No warning was issued.
    Nr
