def mode(self, axis=0, numeric_only=False, dropna=True):
        """
        Get the mode(s) of each element along the selected axis.

        The mode of a set of values is the value that appears most often.
        It can be multiple values.

        Parameters
        ----------
        axis : {0 or 'index', 1 or 'columns'}, default 0
            The axis to iterate over while searching for the mode:

            * 0 or 'index' : get mode of each column
            * 1 or 'columns' : get mode of each row
        numeric_only : bool, default False
            If True, only apply to numeric columns.
        dropna : bool, default True
            Don't consider counts of NaN/NaT.

            .. versionadded:: 0.24.0

        Returns
        -------
        DataFrame
            The modes of each column or row.

        See Also
        --------
        Series.mode : Return the highest frequency value in a Series.
        Series.value_counts : Return the counts of values in a Series.

        Examples
        --------
        >>> df = pd.DataFrame([('bird', 2, 2),
        ...                    ('mammal', 4, np.nan),
        ...                    ('arthropod', 8, 0),
        ...                    ('bird', 2, np.nan)],
        ...                   index=('falcon', 'horse', 'spider', 'ostrich'),
        ...                   columns=('species', 'legs', 'wings'))
        >>> df
                   species  legs  wings
        falcon        bird     2    2.0
        horse       mammal     4    NaN
        spider   arthropod     8    0.0
        ostrich       bird     2    NaN

        By default, missing values are not considered, and the mode of wings
        are both 0 and 2. The second row of species and legs contains ``NaN``,
        because they have only one mode, but the DataFrame has two rows.

        >>> df.mode()
          species  legs  wings
        0    bird   2.0    0.0
        1     NaN   NaN    2.0

        Setting ``dropna=False`` ``NaN`` values are considered and they can be
        the mode (like for wings).

        >>> df.mode(dropna=False)
          species  legs  wings
        0    bird     2    NaN

        Setting ``numeric_only=True``, only the mode of numeric columns is
        computed, and columns of other types are ignored.

        >>> df.mode(numeric_only=True)
           legs  wings
        0   2.0    0.0
        1   NaN    2.0

        To compute the mode over columns and not rows, use the axis parameter:

        >>> df.mode(axis='columns', numeric_only=True)
                   0    1
        falcon   2.0  NaN
        horse    4.0  NaN
        spider   0.0  8.0
        ostrich  2.0  NaN
        """
        data = self if not numeric_only else self._get_numeric_data()

        def f(s):
            return s.mode(dropna=dropna)

        return data.apply(f, axis=axis)
Get the mode(s) of each element along the selected axis.

        The mode of a set of values is the value that appears most often.
        It can be multiple values.

        Parameters
        ----------
        axis : {0 or 'index', 1 or 'columns'}, default 0
            The axis to iterate over while searching for the mode:

            * 0 or 'index' : get mode of each column
            * 1 or 'columns' : get mode of each row
        numeric_only : bool, default False
            If True, only apply to numeric columns.
        dropna : bool, default True
            Don't consider counts of NaN/NaT.

            .. versionadded:: 0.24.0

        Returns
        -------
        DataFrame
            The modes of each column or row.

        See Also
        --------
        Series.mode : Return the highest frequency value in a Series.
        Series.value_counts : Return the counts of values in a Series.

        Examples
        --------
        >>> df = pd.DataFrame([('bird', 2, 2),
        ...                    ('mammal', 4, np.nan),
        ...                    ('arthropod', 8, 0),
        ...                    ('bird', 2, np.nan)],
        ...                   index=('falcon', 'horse', 'spider', 'ostrich'),
        ...                   columns=('species', 'legs', 'wings'))
        >>> df
                   species  legs  wings
        falcon        bird     2    2.0
        horse       mammal     4    NaN
        spider   arthropod     8    0.0
        ostrich       bird     2    NaN

        By default, missing values are not considered, and the mode of wings
        are both 0 and 2. The second row of species and legs contains ``NaN``,
        because they have only one mode, but the DataFrame has two rows.

        >>> df.mode()
          species  legs  wings
        0    bird   2.0    0.0
        1     NaN   NaN    2.0

        Setting ``dropna=False`` ``NaN`` values are considered and they can be
        the mode (like for wings).

        >>> df.mode(dropna=False)
          species  legs  wings
        0    bird     2    NaN

        Setting ``numeric_only=True``, only the mode of numeric columns is
        computed, and columns of other types are ignored.

        >>> df.mode(numeric_only=True)
           legs  wings
        0   2.0    0.0
        1   NaN    2.0

        To compute the mode over columns and not rows, use the axis parameter:

        >>> df.mode(axis='columns', numeric_only=True)
                   0    1
        falcon   2.0  NaN
        horse    4.0  NaN
        spider   0.0  8.0
        ostrich  2.0  NaN