Transpose index and columns.

        Reflect the DataFrame over its main diagonal by writing rows as columns
        and vice-versa. The property :attr:`.T` is an accessor to the method
        :meth:`transpose`.

        Parameters
        ----------
        copy : bool, default False
            If True, the underlying data is copied. Otherwise (default), no
            copy is made if possible.
        *args, **kwargs
            Additional keywords have no effect but might be accepted for
            compatibility with numpy.

        Returns
        -------
        DataFrame
            The transposed DataFrame.

        See Also
        --------
        numpy.transpose : Permute the dimensions of a given array.

        Notes
        -----
        Transposing a DataFrame with mixed dtypes will result in a homogeneous
        DataFrame with the `object` dtype. In such a case, a copy of the data
        is always made.

        Examples
        --------
        **Square DataFrame with homogeneous dtype**

        >>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
        >>> df1 = pd.DataFrame(data=d1)
        >>> df1
           col1  col2
        0     1     3
        1     2     4

        >>> df1_transposed = df1.T # or df1.transpose()
        >>> df1_transposed
              0  1
        col1  1  2
        col2  3  4

        When the dtype is homogeneous in the original DataFrame, we get a
        transposed DataFrame with the same dtype:

        >>> df1.dtypes
        col1    int64
        col2    int64
        dtype: object
        >>> df1_transposed.dtypes
        0    int64
        1    int64
        dtype: object

        **Non-square DataFrame with mixed dtypes**

        >>> d2 = {'name': ['Alice', 'Bob'],
        ...       'score': [9.5, 8],
        ...       'employed': [False, True],
        ...       'kids': [0, 0]}
        >>> df2 = pd.DataFrame(data=d2)
        >>> df2
            name  score  employed  kids
        0  Alice    9.5     False     0
        1    Bob    8.0      True     0

        >>> df2_transposed = df2.T # or df2.transpose()
        >>> df2_transposed
                      0     1
        name      Alice   Bob
        score       9.5     8
        employed  False  True
        kids          0     0

        When the DataFrame has mixed dtypes, we get a transposed DataFrame with
        the `object` dtype:

        >>> df2.dtypes
        name         object
        score       float64
        employed       bool
        kids          int64
        dtype: object
        >>> df2_transposed.dtypes
        0    object
        1    object
        dtype: object

def transpose(self, *args, **kwargs):
        """
        Transpose index and columns.

        Reflect the DataFrame over its main diagonal by writing rows as columns
        and vice-versa. The property :attr:`.T` is an accessor to the method
        :meth:`transpose`.

        Parameters
        ----------
        copy : bool, default False
            If True, the underlying data is copied. Otherwise (default), no
            copy is made if possible.
        *args, **kwargs
            Additional keywords have no effect but might be accepted for
            compatibility with numpy.

        Returns
        -------
        DataFrame
            The transposed DataFrame.

        See Also
        --------
        numpy.transpose : Permute the dimensions of a given array.

        Notes
        -----
        Transposing a DataFrame with mixed dtypes will result in a homogeneous
        DataFrame with the `object` dtype. In such a case, a copy of the data
        is always made.

        Examples
        --------
        **Square DataFrame with homogeneous dtype**

        >>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
        >>> df1 = pd.DataFrame(data=d1)
        >>> df1
           col1  col2
        0     1     3
        1     2     4

        >>> df1_transposed = df1.T # or df1.transpose()
        >>> df1_transposed
              0  1
        col1  1  2
        col2  3  4

        When the dtype is homogeneous in the original DataFrame, we get a
        transposed DataFrame with the same dtype:

        >>> df1.dtypes
        col1    int64
        col2    int64
        dtype: object
        >>> df1_transposed.dtypes
        0    int64
        1    int64
        dtype: object

        **Non-square DataFrame with mixed dtypes**

        >>> d2 = {'name': ['Alice', 'Bob'],
        ...       'score': [9.5, 8],
        ...       'employed': [False, True],
        ...       'kids': [0, 0]}
        >>> df2 = pd.DataFrame(data=d2)
        >>> df2
            name  score  employed  kids
        0  Alice    9.5     False     0
        1    Bob    8.0      True     0

        >>> df2_transposed = df2.T # or df2.transpose()
        >>> df2_transposed
                      0     1
        name      Alice   Bob
        score       9.5     8
        employed  False  True
        kids          0     0

        When the DataFrame has mixed dtypes, we get a transposed DataFrame with
        the `object` dtype:

        >>> df2.dtypes
        name         object
        score       float64
        employed       bool
        kids          int64
        dtype: object
        >>> df2_transposed.dtypes
        0    object
        1    object
        dtype: object
        """
        nv.validate_transpose(args, dict())
        return super().transpose(1, 0, **kwargs)