Method mode

pandas/core/series.py:2093–2161

Return the mode(s) of the Series. The mode is the value that appears most often. There can be multiple modes. Always returns Series even if only one value is returned. Parameters ---------- dropna : bool, default True Don't consider cou

(self, dropna: bool = True)

        return maybe_unbox_numpy_scalar(notna(self._values).sum().astype("int64"))

    def mode(self, dropna: bool = True) -> Series:
        """
        Return the mode(s) of the Series.

        The mode is the value that appears most often. There can be multiple modes.

        Always returns Series even if only one value is returned.

        Parameters
        ----------
        dropna : bool, default True
            Don't consider counts of NaN/NaT.

        Returns
        -------
        Series
            Modes of the Series in sorted order.

        See Also
        --------
        numpy.mode : Equivalent numpy function for computing median.
        Series.sum : Sum of the values.
        Series.median : Median of the values.
        Series.std : Standard deviation of the values.
        Series.var : Variance of the values.
        Series.min : Minimum value.
        Series.max : Maximum value.

        Examples
        --------
        >>> s = pd.Series([2, 4, 2, 2, 4, None])
        >>> s.mode()
        0    2.0
        dtype: float64

        More than one mode:

        >>> s = pd.Series([2, 4, 8, 2, 4, None])
        >>> s.mode()
        0    2.0
        1    4.0
        dtype: float64

        With and without considering null value:

        >>> s = pd.Series([2, 4, None, None, 4, None])
        >>> s.mode(dropna=False)
        0   NaN
        dtype: float64
        >>> s = pd.Series([2, 4, None, None, 4, None])
        >>> s.mode()
        0    4.0
        dtype: float64
        """
        # TODO: Add option for bins like value_counts()
        values = self._values
        if isinstance(values, np.ndarray):
            res_values, _ = algorithms.mode(values, dropna=dropna)