Count distinct observations over requested axis.

        Return Series with number of distinct observations. Can ignore NaN
        values.

        .. versionadded:: 0.20.0

        Parameters
        ----------
        axis : {0 or 'index', 1 or 'columns'}, default 0
            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for
            column-wise.
        dropna : bool, default True
            Don't include NaN in the counts.

        Returns
        -------
        Series

        See Also
        --------
        Series.nunique: Method nunique for Series.
        DataFrame.count: Count non-NA cells for each column or row.

        Examples
        --------
        >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [1, 1, 1]})
        >>> df.nunique()
        A    3
        B    1
        dtype: int64

        >>> df.nunique(axis=1)
        0    1
        1    2
        2    2
        dtype: int64

def nunique(self, axis=0, dropna=True):
        """
        Count distinct observations over requested axis.

        Return Series with number of distinct observations. Can ignore NaN
        values.

        .. versionadded:: 0.20.0

        Parameters
        ----------
        axis : {0 or 'index', 1 or 'columns'}, default 0
            The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for
            column-wise.
        dropna : bool, default True
            Don't include NaN in the counts.

        Returns
        -------
        Series

        See Also
        --------
        Series.nunique: Method nunique for Series.
        DataFrame.count: Count non-NA cells for each column or row.

        Examples
        --------
        >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [1, 1, 1]})
        >>> df.nunique()
        A    3
        B    1
        dtype: int64

        >>> df.nunique(axis=1)
        0    1
        1    2
        2    2
        dtype: int64
        """
        return self.apply(Series.nunique, axis=axis, dropna=dropna)