Method read

pandas/io/pytables.py:4789–4889

(
        self,
        where=None,
        columns=None,
        start: int | None = None,
        stop: int | None = None,
    )

        return obj

    def read(
        self,
        where=None,
        columns=None,
        start: int | None = None,
        stop: int | None = None,
    ):
        # validate the version
        self.validate_version(where)

        # infer the data kind
        if not self.infer_axes():
            return None

        result = self._read_axes(where=where, start=start, stop=stop)

        info = (
            self.info.get(self.non_index_axes[0][0], {})
            if len(self.non_index_axes)
            else {}
        )

        inds = [i for i, ax in enumerate(self.axes) if ax is self.index_axes[0]]
        assert len(inds) == 1
        ind = inds[0]

        index = result[ind][0]

        frames = []
        for i, a in enumerate(self.axes):
            if a not in self.values_axes:
                continue
            index_vals, cvalues = result[i]

            # we could have a multi-index constructor here
            # ensure_index doesn't recognized our list-of-tuples here
            if info.get("type") != "MultiIndex":
                cols = Index(index_vals)
            else:
                cols = MultiIndex.from_tuples(index_vals)

            names = info.get("names")
            if names is not None:
                cols.set_names(names, inplace=True)

            if self.is_transposed:
                values = cvalues
                index_ = cols
                cols_ = Index(index, name=getattr(index, "name", None))
            else:
                values = cvalues.T
                index_ = Index(index, name=getattr(index, "name", None))
                cols_ = cols

            # if we have a DataIndexableCol, its shape will only be 1 dim
            if values.ndim == 1 and isinstance(values, np.ndarray):
                values = values.reshape((1, values.shape[0]))