Class Selection

pandas/io/pytables.py:5483–5603

Carries out a selection operation on a tables.Table object. Parameters ---------- table : a Table object where : list of Terms (or convertible to) start, stop: indices to start and/or stop selection

class Selection:
    """
    Carries out a selection operation on a tables.Table object.

    Parameters
    ----------
    table : a Table object
    where : list of Terms (or convertible to)
    start, stop: indices to start and/or stop selection

    """

    def __init__(
        self,
        table: Table,
        where=None,
        start: int | None = None,
        stop: int | None = None,
    ) -> None:
        self.table = table
        self.where = where
        self.start = start
        self.stop = stop
        self.condition = None
        self.filter = None
        self.terms = None
        self.coordinates = None

        if is_list_like(where):
            # see if we have a passed coordinate like
            with suppress(ValueError):
                inferred = lib.infer_dtype(where, skipna=False)
                if inferred in ("integer", "boolean"):
                    where = np.asarray(where)
                    if where.dtype == np.bool_:
                        start, stop = self.start, self.stop
                        if start is None:
                            start = 0
                        if stop is None:
                            stop = self.table.nrows
                        self.coordinates = np.arange(start, stop)[where]
                    elif issubclass(where.dtype.type, np.integer):
                        if (self.start is not None and (where < self.start).any()) or (
                            self.stop is not None and (where >= self.stop).any()
                        ):
                            raise ValueError(
                                "where must have index locations >= start and < stop"
                            )
                        self.coordinates = where

        if self.coordinates is None:
            self.terms = self.generate(where)

            # create the numexpr & the filter
            if self.terms is not None:
                self.condition, self.filter = self.terms.evaluate()

    @overload