Method memory_usage

pandas/core/series.py:5946–6000

Return the memory usage of the Series. The memory usage can optionally include the contribution of the index and of elements of `object` dtype. Parameters ---------- index : bool, default True Specifies whether to include the memory usag

(self, index: bool = True, deep: bool = False)

        )

    def memory_usage(self, index: bool = True, deep: bool = False) -> int:
        """
        Return the memory usage of the Series.

        The memory usage can optionally include the contribution of
        the index and of elements of `object` dtype.

        Parameters
        ----------
        index : bool, default True
            Specifies whether to include the memory usage of the Series index.
        deep : bool, default False
            If True, introspect the data deeply by interrogating
            `object` dtypes for system-level memory consumption, and include
            it in the returned value.

        Returns
        -------
        int
            Bytes of memory consumed.

        See Also
        --------
        numpy.ndarray.nbytes : Total bytes consumed by the elements of the
            array.
        DataFrame.memory_usage : Bytes consumed by a DataFrame.

        Examples
        --------
        >>> s = pd.Series(range(3))
        >>> s.memory_usage()
        156

        Not including the index gives the size of the rest of the data, which
        is necessarily smaller:

        >>> s.memory_usage(index=False)
        24

        The memory footprint of `object` values is ignored by default:

        >>> s = pd.Series(["a", "b"])
        >>> s.values
        <ArrowStringArray>
        ['a', 'b']
        Length: 2, dtype: str
        >>> s.memory_usage()
        150
        >>> s.memory_usage(deep=True)
        150
        """
        v = self._memory_usage(deep=deep)
        if index:
            v += self.index.memory_usage(deep=deep)
        return v

    def isin(self, values) -> Series:
        """