Function _convert_index

pandas/io/pytables.py:5125–5186

(name: str, index: Index, encoding: str, errors: str)

def _convert_index(name: str, index: Index, encoding: str, errors: str) -> IndexCol:
    assert isinstance(name, str)

    index_name = index.name
    # error: Argument 1 to "_get_data_and_dtype_name" has incompatible type "Index";
    # expected "Union[ExtensionArray, ndarray]"
    converted, dtype_name = _get_data_and_dtype_name(index)  # type: ignore[arg-type]
    kind = _dtype_to_kind(dtype_name)
    atom = DataIndexableCol._get_atom(converted)

    if (
        lib.is_np_dtype(index.dtype, "iu")
        or needs_i8_conversion(index.dtype)
        or is_bool_dtype(index.dtype)
    ):
        # Includes Index, RangeIndex, DatetimeIndex, TimedeltaIndex, PeriodIndex,
        #  in which case "kind" is "integer", "integer", "datetime64",
        #  "timedelta64", and "integer", respectively.
        return IndexCol(
            name,
            values=converted,
            kind=kind,
            typ=atom,
            freq=getattr(index, "freq", None),
            tz=getattr(index, "tz", None),
            index_name=index_name,
        )

    if isinstance(index, MultiIndex):
        raise TypeError("MultiIndex not supported here!")

    inferred_type = lib.infer_dtype(index, skipna=False)
    # we won't get inferred_type of "datetime64" or "timedelta64" as these
    #  would go through the DatetimeIndex/TimedeltaIndex paths above

    values = np.asarray(index)

    if inferred_type == "date":
        converted = np.asarray([v.toordinal() for v in values], dtype=np.int32)
        return IndexCol(
            name, converted, "date", _tables().Time32Col(), index_name=index_name
        )
    elif inferred_type == "string":
        converted = _convert_string_array(values, encoding, errors)
        itemsize = converted.dtype.itemsize
        return IndexCol(
            name,
            converted,
            "string",
            _tables().StringCol(itemsize),
            index_name=index_name,
        )

    elif inferred_type in ["integer", "floating"]:
        return IndexCol(
            name, values=converted, kind=kind, typ=atom, index_name=index_name
        )
    else: