Iterate over DataFrame rows as (index, Series) pairs.

        Yields
        ------
        index : label or tuple of label
            The index of the row. A tuple for a `MultiIndex`.
        data : Series
            The data of the row as a Series.

        it : generator
            A generator that iterates over the rows of the frame.

        See Also
        --------
        itertuples : Iterate over DataFrame rows as namedtuples of the values.
        iteritems : Iterate over (column name, Series) pairs.

        Notes
        -----

        1. Because ``iterrows`` returns a Series for each row,
           it does **not** preserve dtypes across the rows (dtypes are
           preserved across columns for DataFrames). For example,

           >>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
           >>> row = next(df.iterrows())[1]
           >>> row
           int      1.0
           float    1.5
           Name: 0, dtype: float64
           >>> print(row['int'].dtype)
           float64
           >>> print(df['int'].dtype)
           int64

           To preserve dtypes while iterating over the rows, it is better
           to use :meth:`itertuples` which returns namedtuples of the values
           and which is generally faster than ``iterrows``.

        2. You should **never modify** something you are iterating over.
           This is not guaranteed to work in all cases. Depending on the
           data types, the iterator returns a copy and not a view, and writing
           to it will have no effect.

def iterrows(self):
        """
        Iterate over DataFrame rows as (index, Series) pairs.

        Yields
        ------
        index : label or tuple of label
            The index of the row. A tuple for a `MultiIndex`.
        data : Series
            The data of the row as a Series.

        it : generator
            A generator that iterates over the rows of the frame.

        See Also
        --------
        itertuples : Iterate over DataFrame rows as namedtuples of the values.
        iteritems : Iterate over (column name, Series) pairs.

        Notes
        -----

        1. Because ``iterrows`` returns a Series for each row,
           it does **not** preserve dtypes across the rows (dtypes are
           preserved across columns for DataFrames). For example,

           >>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
           >>> row = next(df.iterrows())[1]
           >>> row
           int      1.0
           float    1.5
           Name: 0, dtype: float64
           >>> print(row['int'].dtype)
           float64
           >>> print(df['int'].dtype)
           int64

           To preserve dtypes while iterating over the rows, it is better
           to use :meth:`itertuples` which returns namedtuples of the values
           and which is generally faster than ``iterrows``.

        2. You should **never modify** something you are iterating over.
           This is not guaranteed to work in all cases. Depending on the
           data types, the iterator returns a copy and not a view, and writing
           to it will have no effect.
        """
        columns = self.columns
        klass = self._constructor_sliced
        for k, v in zip(self.index, self.values):
            s = klass(v, index=columns, name=k)
            yield k, s