Method from_codes

pandas/core/arrays/categorical.py:716–787

Make a Categorical type from codes and categories or dtype. This constructor is useful if you already have codes and categories/dtype and so do not need the (computation intensive) factorization step, which is usually done on the constructor. If your data d

(
        cls,
        codes,
        categories=None,
        ordered=None,
        dtype: Dtype | None = None,
        validate: bool = True,
    )

    @classmethod
    def from_codes(
        cls,
        codes,
        categories=None,
        ordered=None,
        dtype: Dtype | None = None,
        validate: bool = True,
    ) -> Self:
        """
        Make a Categorical type from codes and categories or dtype.

        This constructor is useful if you already have codes and
        categories/dtype and so do not need the (computation intensive)
        factorization step, which is usually done on the constructor.

        If your data does not follow this convention, please use the normal
        constructor.

        Parameters
        ----------
        codes : array-like of int
            An integer array, where each integer points to a category in
            categories or dtype.categories, or else is -1 for NaN.
        categories : index-like, optional
            The categories for the categorical. Items need to be unique.
            If the categories are not given here, then they must be provided
            in `dtype`.
        ordered : bool, optional
            Whether or not this categorical is treated as an ordered
            categorical. If not given here or in `dtype`, the resulting
            categorical will be unordered.
        dtype : CategoricalDtype or "category", optional
            If :class:`CategoricalDtype`, cannot be used together with
            `categories` or `ordered`.
        validate : bool, default True
            If True, validate that the codes are valid for the dtype.
            If False, don't validate that the codes are valid. Be careful about skipping
            validation, as invalid codes can lead to severe problems, such as segfaults.

            .. versionadded:: 2.1.0

        Returns
        -------
        Categorical

        See Also
        --------
        codes : The category codes of the categorical.
        CategoricalIndex : An Index with an underlying ``Categorical``.

        Examples
        --------
        >>> dtype = pd.CategoricalDtype(["a", "b"], ordered=True)
        >>> pd.Categorical.from_codes(codes=[0, 1, 0, 1], dtype=dtype)
        ['a', 'b', 'a', 'b']
        Categories (2, str): ['a' < 'b']
        """
        dtype = CategoricalDtype._from_values_or_dtype(