Method select

pandas/io/pytables.py:839–936

Retrieve pandas object stored in file, optionally based on where criteria. .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled d

(
        self,
        key: str,
        where=None,
        start=None,
        stop=None,
        columns=None,
        iterator: bool = False,
        chunksize: int | None = None,
        auto_close: bool = False,
    )

            return self._read_group(group)

    def select(
        self,
        key: str,
        where=None,
        start=None,
        stop=None,
        columns=None,
        iterator: bool = False,
        chunksize: int | None = None,
        auto_close: bool = False,
    ):
        """
        Retrieve pandas object stored in file, optionally based on where criteria.

        .. warning::

           Pandas uses PyTables for reading and writing HDF5 files, which allows
           serializing object-dtype data with pickle when using the "fixed" format.
           Loading pickled data received from untrusted sources can be unsafe.

           See: https://docs.python.org/3/library/pickle.html for more.

        Parameters
        ----------
        key : str
            Object being retrieved from file.
        where : list or None
            List of Term (or convertible) objects, optional.
        start : int or None
            Row number to start selection.
        stop : int, default None
            Row number to stop selection.
        columns : list or None
            A list of columns that if not None, will limit the return columns.
        iterator : bool or False
            Returns an iterator.
        chunksize : int or None
            Number or rows to include in iteration, return an iterator.
        auto_close : bool or False
            Should automatically close the store when finished.

        Returns
        -------
        object
            Retrieved object from file.

        See Also
        --------
        HDFStore.select_as_coordinates : Returns the selection as an index.
        HDFStore.select_column : Returns a single column from the table.
        HDFStore.select_as_multiple : Retrieves pandas objects from multiple tables.

        Examples
        --------
        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
        >>> store = pd.HDFStore("store.h5", "w")  # doctest: +SKIP
        >>> store.put("data", df)  # doctest: +SKIP
        >>> store.get("data")  # doctest: +SKIP