Method transpose

pandas/core/frame.py:4075–4258

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 ---------- *args : tuple, optional

(
        self,
        *args,
        copy: bool | lib.NoDefault = lib.no_default,
    )

        return result

    def transpose(
        self,
        *args,
        copy: bool | lib.NoDefault = lib.no_default,
    ) -> DataFrame:
        """
        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
        ----------
        *args : tuple, optional
            Accepted for compatibility with NumPy.
        copy : bool, default False
            This keyword is now ignored; changing its value will have no
            impact on the method.

            Note that a copy is always required for mixed dtype DataFrames,
            or for DataFrames with any extension types.

            .. deprecated:: 3.0.0

                This keyword is ignored and will be removed in pandas 4.0. Since
                pandas 3.0, this method always returns a new object using a lazy
                copy mechanism that defers copies until necessary
                (Copy-on-Write). See the `user guide on Copy-on-Write
                <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__
                for more details.

        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