Method from_product

pandas/core/indexes/multi.py:594–650

Make a MultiIndex from the cartesian product of multiple iterables. Parameters ---------- iterables : list / sequence of iterables Each iterable has unique labels for each level of the index. sortorder : int or None Level of sortednes

(
        cls,
        iterables: Sequence[Iterable[Hashable]],
        sortorder: int | None = None,
        names: Sequence[Hashable] | Hashable | lib.NoDefault = lib.no_default,
    )

    @classmethod
    def from_product(
        cls,
        iterables: Sequence[Iterable[Hashable]],
        sortorder: int | None = None,
        names: Sequence[Hashable] | Hashable | lib.NoDefault = lib.no_default,
    ) -> MultiIndex:
        """
        Make a MultiIndex from the cartesian product of multiple iterables.

        Parameters
        ----------
        iterables : list / sequence of iterables
            Each iterable has unique labels for each level of the index.
        sortorder : int or None
            Level of sortedness (must be lexicographically sorted by that
            level).
        names : list / sequence of str, optional
            Names for the levels in the index.
            If not explicitly provided, names will be inferred from the
            elements of iterables if an element has a name attribute.

        Returns
        -------
        MultiIndex

        See Also
        --------
        MultiIndex.from_arrays : Convert list of arrays to MultiIndex.
        MultiIndex.from_tuples : Convert list of tuples to MultiIndex.
        MultiIndex.from_frame : Make a MultiIndex from a DataFrame.

        Examples
        --------
        >>> numbers = [0, 1, 2]
        >>> colors = ["green", "purple"]
        >>> pd.MultiIndex.from_product([numbers, colors], names=["number", "color"])
        MultiIndex([(0,  'green'),
                    (0, 'purple'),
                    (1,  'green'),
                    (1, 'purple'),
                    (2,  'green'),
                    (2, 'purple')],
                   names=['number', 'color'])
        """

        if not is_list_like(iterables):
            raise TypeError("Input must be a list / sequence of iterables.")
        if is_iterator(iterables):
            iterables = list(iterables)

        codes, levels = factorize_from_iterables(iterables)
        if names is lib.no_default:
            names = [getattr(it, "name", None) for it in iterables]

        # codes are all ndarrays, so cartesian_product is lossless
        codes = cartesian_product(codes)
        return cls(levels, codes, sortorder=sortorder, names=names)