text: """
  High level interface to PyTables for reading and writing pandas data structures
  to disk
  """
  from contextlib import suppress
  import copy
  from datetime import date, tzinfo
  import itertools
  import os
  import re
  from textwrap import dedent
  from typing import (
      TYPE_CHECKING,
      Any,
      Callable,
      Dict,
      List,
      Optional,
      Sequence,
      Tuple,
      Type,
      Union,
  )
  import warnings
  
  import numpy as np
  
  from pandas._config import config, get_option
  
  from pandas._libs import lib, writers as libwriters
  from pandas._libs.tslibs import timezones
  from pandas._typing import (
      ArrayLike,
      DtypeArg,
      FrameOrSeries,
      FrameOrSeriesUnion,
      Label,
      Shape,
  )
  from pandas.compat._optional import import_optional_dependency
  from pandas.compat.pickle_compat import patch_pickle
  from pandas.errors import PerformanceWarning
  from pandas.util._decorators import cache_readonly
  
  from pandas.core.dtypes.common import (
      ensure_object,
      is_categorical_dtype,
      is_complex_dtype,
      is_datetime64_dtype,
      is_datetime64tz_dtype,
      is_extension_array_dtype,
      is_list_like,
      is_string_dtype,
      is_timedelta64_dtype,
      needs_i8_conversion,
  )
  from pandas.core.dtypes.missing import array_equivalent
  
  from pandas import (
      DataFrame,
      DatetimeIndex,
      Index,
      Int64Index,
      MultiIndex,
      PeriodIndex,
      Series,
      TimedeltaIndex,
      concat,
      isna,
  )
  from pandas.core.arrays import Categorical, DatetimeArray, PeriodArray
  import pandas.core.common as com
  from pandas.core.computation.pytables import PyTablesExpr, maybe_expression
  from pandas.core.construction import extract_array
  from pandas.core.indexes.api import ensure_index
  
  from pandas.io.common import stringify_path
  from pandas.io.formats.printing import adjoin, pprint_thing
  
  if TYPE_CHECKING:
      from tables import Col, File, Node
  
      from pandas.core.internals import Block
  
  
  # versioning attribute
  _version = "0.15.2"
  
  # encoding
  _default_encoding = "UTF-8"
  
  
  def _ensure_decoded(s):
      """ if we have bytes, decode them to unicode """
      if isinstance(s, np.bytes_):
          s = s.decode("UTF-8")
      return s
  
  
  def _ensure_encoding(encoding):
      # set the encoding if we need
      if encoding is None:
          encoding = _default_encoding
  
      return encoding
  
  
  def _ensure_str(name):
      """
      Ensure that an index / column name is a str (python 3); otherwise they
      may be np.string dtype. Non-string dtypes are passed through unchanged.
  
      https://github.com/pandas-dev/pandas/issues/13492
      """
      if isinstance(name, str):
          name = str(name)
      return name
  
  
  Term = PyTablesExpr
  
  
  def _ensure_term(where, scope_level: int):
      """
      Ensure that the where is a Term or a list of Term.
  
      This makes sure that we are capturing the scope of variables that are
      passed create the terms here with a frame_level=2 (we are 2 levels down)
      """
      # only consider list/tuple here as an ndarray is automatically a coordinate
      # list
      level = scope_level + 1
      if isinstance(where, (list, tuple)):
          where = [
              Term(term, scope_level=level + 1) if maybe_expression(term) else term
              for term in where
              if term is not None
          ]
      elif maybe_expression(where):
          where = Term(where, scope_level=level)
      return where if where is None or len(where) else None
  
  
  class PossibleDataLossError(Exception):
      pass
  
  
  class ClosedFileError(Exception):
      pass
  
  
  class IncompatibilityWarning(Warning):
      pass
  
  
  incompatibility_doc = """
  where criteria is being ignored as this version [%s] is too old (or
  not-defined), read the file in and write it out to a new file to upgrade (with
  the copy_to method)
  """
  
  
  class AttributeConflictWarning(Warning):
      pass
  
  
  attribute_conflict_doc = """
  the [%s] attribute of the existing index is [%s] which conflicts with the new
  [%s], resetting the attribute to None
  """
  
  
  class DuplicateWarning(Warning):
      pass
  
  
  duplicate_doc = """
  duplicate entries in table, taking most recently appended
  """
  
  performance_doc = """
  your performance may suffer as PyTables will pickle object types that it cannot
  map directly to c-types [inferred_type->%s,key->%s] [items->%s]
  """
  
  # formats
  _FORMAT_MAP = {"f": "fixed", "fixed": "fixed", "t": "table", "table": "table"}
  
  # axes map
  _AXES_MAP = {DataFrame: [0]}
  
  # register our configuration options
  dropna_doc = """
  : boolean
      drop ALL nan rows when appending to a table
  """
  format_doc = """
  : format
      default format writing format, if None, then
      put will default to 'fixed' and append will default to 'table'
  """
  
  with config.config_prefix("io.hdf"):
      config.register_option("dropna_table", False, dropna_doc, validator=config.is_bool)
      config.register_option(
          "default_format",
          None,
          format_doc,
          validator=config.is_one_of_factory(["fixed", "table", None]),
      )
  
  # oh the troubles to reduce import time
  _table_mod = None
  _table_file_open_policy_is_strict = False
  
  
  def _tables():
      global _table_mod
      global _table_file_open_policy_is_strict
      if _table_mod is None:
          import tables
  
          _table_mod = tables
  
          # set the file open policy
          # return the file open policy; this changes as of pytables 3.1
          # depending on the HDF5 version
          with suppress(AttributeError):
              _table_file_open_policy_is_strict = (
                  tables.file._FILE_OPEN_POLICY == "strict"
              )
  
      return _table_mod
  
  
  # interface to/from ###
  
  
  def to_hdf(
      path_or_buf,
      key: str,
      value: FrameOrSeries,
      mode: str = "a",
      complevel: Optional[int] = None,
      complib: Optional[str] = None,
      append: bool = False,
      format: Optional[str] = None,
      index: bool = True,
      min_itemsize: Optional[Union[int, Dict[str, int]]] = None,
      nan_rep=None,
      dropna: Optional[bool] = None,
      data_columns: Optional[Union[bool, List[str]]] = None,
      errors: str = "strict",
      encoding: str = "UTF-8",
  ):
      """ store this object, close it if we opened it """
      if append:
          f = lambda store: store.append(
              key,
              value,
              format=format,
              index=index,
              min_itemsize=min_itemsize,
              nan_rep=nan_rep,
              dropna=dropna,
              data_columns=data_columns,
              errors=errors,
              encoding=encoding,
          )
      else:
          # NB: dropna is not passed to `put`
          f = lambda store: store.put(
              key,
              value,
              format=format,
              index=index,
              min_itemsize=min_itemsize,
              nan_rep=nan_rep,
              data_columns=data_columns,
              errors=errors,
              encoding=encoding,
              dropna=dropna,
          )
  
      path_or_buf = stringify_path(path_or_buf)
      if isinstance(path_or_buf, str):
          with HDFStore(
              path_or_buf, mode=mode, complevel=complevel, complib=complib
          ) as store:
              f(store)
      else:
          f(path_or_buf)
  
  
  def read_hdf(
      path_or_buf,
      key=None,
      mode: str = "r",
      errors: str = "strict",
      where=None,
      start: Optional[int] = None,
      stop: Optional[int] = None,
      columns=None,
      iterator=False,
      chunksize: Optional[int] = None,
      **kwargs,
  ):
      """
      Read from the store, close it if we opened it.
  
      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
      ----------
      path_or_buf : str, path object, pandas.HDFStore or file-like object
          Any valid string path is acceptable. The string could be a URL. Valid
          URL schemes include http, ftp, s3, and file. For file URLs, a host is
          expected. A local file could be: ``file://localhost/path/to/table.h5``.
  
          If you want to pass in a path object, pandas accepts any
          ``os.PathLike``.
  
          Alternatively, pandas accepts an open :class:`pandas.HDFStore` object.
  
          By file-like object, we refer to objects with a ``read()`` method,
          such as a file handle (e.g. via builtin ``open`` function)
          or ``StringIO``.
      key : object, optional
          The group identifier in the store. Can be omitted if the HDF file
          contains a single pandas object.
      mode : {'r', 'r+', 'a'}, default 'r'
          Mode to use when opening the file. Ignored if path_or_buf is a
          :class:`pandas.HDFStore`. Default is 'r'.
      errors : str, default 'strict'
          Specifies how encoding and decoding errors are to be handled.
          See the errors argument for :func:`open` for a full list
          of options.
      where : list, optional
          A list of Term (or convertible) objects.
      start : int, optional
          Row number to start selection.
      stop  : int, optional
          Row number to stop selection.
      columns : list, optional
          A list of columns names to return.
      iterator : bool, optional
          Return an iterator object.
      chunksize : int, optional
          Number of rows to include in an iteration when using an iterator.
      **kwargs
          Additional keyword arguments passed to HDFStore.
  
      Returns
      -------
      item : object
          The selected object. Return type depends on the object stored.
  
      See Also
      --------
      DataFrame.to_hdf : Write a HDF file from a DataFrame.
      HDFStore : Low-level access to HDF files.
  
      Examples
      --------
      >>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z'])
      >>> df.to_hdf('./store.h5', 'data')
      >>> reread = pd.read_hdf('./store.h5')
      """
      if mode not in ["r", "r+", "a"]:
          raise ValueError(
              f"mode {mode} is not allowed while performing a read. "
              f"Allowed modes are r, r+ and a."
          )
      # grab the scope
      if where is not None:
          where = _ensure_term(where, scope_level=1)
  
      if isinstance(path_or_buf, HDFStore):
          if not path_or_buf.is_open:
              raise OSError("The HDFStore must be open for reading.")
  
          store = path_or_buf
          auto_close = False
      else:
          path_or_buf = stringify_path(path_or_buf)
          if not isinstance(path_or_buf, str):
              raise NotImplementedError(
                  "Support for generic buffers has not been implemented."
              )
          try:
              exists = os.path.exists(path_or_buf)
  
          # if filepath is too long
          except (TypeError, ValueError):
              exists = False
  
          if not exists:
              raise FileNotFoundError(f"File {path_or_buf} does not exist")
  
          store = HDFStore(path_or_buf, mode=mode, errors=errors, **kwargs)
          # can't auto open/close if we are using an iterator
          # so delegate to the iterator
          auto_close = True
  
      try:
          if key is None:
              groups = store.groups()
              if len(groups) == 0:
                  raise ValueError(
                      "Dataset(s) incompatible with Pandas data types, "
                      "not table, or no datasets found in HDF5 file."
                  )
              candidate_only_group = groups[0]
  
              # For the HDF file to have only one dataset, all other groups
              # should then be metadata groups for that candidate group. (This
              # assumes that the groups() method enumerates parent groups
              # before their children.)
              for group_to_check in groups[1:]:
                  if not _is_metadata_of(group_to_check, candidate_only_group):
                      raise ValueError(
                          "key must be provided when HDF5 "
                          "file contains multiple datasets."
                      )
              key = candidate_only_group._v_pathname
          return store.select(
              key,
              where=where,
              start=start,
              stop=stop,
              columns=columns,
              iterator=iterator,
              chunksize=chunksize,
              auto_close=auto_close,
          )
      except (ValueError, TypeError, KeyError):
          if not isinstance(path_or_buf, HDFStore):
              # if there is an error, close the store if we opened it.
              with suppress(AttributeError):
                  store.close()
  
          raise
  
  
  def _is_metadata_of(group: "Node", parent_group: "Node") -> bool:
      """Check if a given group is a metadata group for a given parent_group."""
      if group._v_depth <= parent_group._v_depth:
          return False
  
      current = group
      while current._v_depth > 1:
          parent = current._v_parent
          if parent == parent_group and current._v_name == "meta":
              return True
          current = current._v_parent
      return False
  
  
  class HDFStore:
      """
      Dict-like IO interface for storing pandas objects in PyTables.
  
      Either Fixed or Table format.
  
      .. 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
      ----------
      path : str
          File path to HDF5 file.
      mode : {'a', 'w', 'r', 'r+'}, default 'a'
  
          ``'r'``
              Read-only; no data can be modified.
          ``'w'``
              Write; a new file is created (an existing file with the same
              name would be deleted).
          ``'a'``
              Append; an existing file is opened for reading and writing,
              and if the file does not exist it is created.
          ``'r+'``
              It is similar to ``'a'``, but the file must already exist.
      complevel : int, 0-9, default None
          Specifies a compression level for data.
          A value of 0 or None disables compression.
      complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
          Specifies the compression library to be used.
          As of v0.20.2 these additional compressors for Blosc are supported
          (default if no compressor specified: 'blosc:blosclz'):
          {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
           'blosc:zlib', 'blosc:zstd'}.
          Specifying a compression library which is not available issues
          a ValueError.
      fletcher32 : bool, default False
          If applying compression use the fletcher32 checksum.
      **kwargs
          These parameters will be passed to the PyTables open_file method.
  
      Examples
      --------
      >>> bar = pd.DataFrame(np.random.randn(10, 4))
      >>> store = pd.HDFStore('test.h5')
      >>> store['foo'] = bar   # write to HDF5
      >>> bar = store['foo']   # retrieve
      >>> store.close()
  
      **Create or load HDF5 file in-memory**
  
      When passing the `driver` option to the PyTables open_file method through
      **kwargs, the HDF5 file is loaded or created in-memory and will only be
      written when closed:
  
      >>> bar = pd.DataFrame(np.random.randn(10, 4))
      >>> store = pd.HDFStore('test.h5', driver='H5FD_CORE')
      >>> store['foo'] = bar
      >>> store.close()   # only now, data is written to disk
      """
  
      _handle: Optional["File"]
      _mode: str
      _complevel: int
      _fletcher32: bool
  
      def __init__(
          self,
          path,
          mode: str = "a",
          complevel: Optional[int] = None,
          complib=None,
          fletcher32: bool = False,
          **kwargs,
      ):
  
          if "format" in kwargs:
              raise ValueError("format is not a defined argument for HDFStore")
  
          tables = import_optional_dependency("tables")
  
          if complib is not None and complib not in tables.filters.all_complibs:
              raise ValueError(
                  f"complib only supports {tables.filters.all_complibs} compression."
              )
  
          if complib is None and complevel is not None:
              complib = tables.filters.default_complib
  
          self._path = stringify_path(path)
          if mode is None:
              mode = "a"
          self._mode = mode
          self._handle = None
          self._complevel = complevel if complevel else 0
          self._complib = complib
          self._fletcher32 = fletcher32
          self._filters = None
          self.open(mode=mode, **kwargs)
  
      def __fspath__(self):
          return self._path
  
      @property
      def root(self):
          """ return the root node """
          self._check_if_open()
          assert self._handle is not None  # for mypy
          return self._handle.root
  
      @property
      def filename(self):
          return self._path
  
      def __getitem__(self, key: str):
          return self.get(key)
  
      def __setitem__(self, key: str, value):
          self.put(key, value)
  
      def __delitem__(self, key: str):
          return self.remove(key)
  
      def __getattr__(self, name: str):
          """ allow attribute access to get stores """
          try:
              return self.get(name)
          except (KeyError, ClosedFileError):
              pass
          raise AttributeError(
              f"'{type(self).__name__}' object has no attribute '{name}'"
          )
  
      def __contains__(self, key: str) -> bool:
          """
          check for existence of this key
          can match the exact pathname or the pathnm w/o the leading '/'
          """
          node = self.get_node(key)
          if node is not None:
              name = node._v_pathname
              if name == key or name[1:] == key:
                  return True
          return False
  
      def __len__(self) -> int:
          return len(self.groups())
  
      def __repr__(self) -> str:
          pstr = pprint_thing(self._path)
          return f"{type(self)}\nFile path: {pstr}\n"
  
      def __enter__(self):
          return self
  
      def __exit__(self, exc_type, exc_value, traceback):
          self.close()
  
      def keys(self, include: str = "pandas") -> List[str]:
          """
          Return a list of keys corresponding to objects stored in HDFStore.
  
          Parameters
          ----------
  
          include : str, default 'pandas'
                  When kind equals 'pandas' return pandas objects.
                  When kind equals 'native' return native HDF5 Table objects.
  
                  .. versionadded:: 1.1.0
  
          Returns
          -------
          list
              List of ABSOLUTE path-names (e.g. have the leading '/').
  
          Raises
          ------
          raises ValueError if kind has an illegal value
          """
          if include == "pandas":
              return [n._v_pathname for n in self.groups()]
  
          elif include == "native":
              assert self._handle is not None  # mypy
              return [
                  n._v_pathname for n in self._handle.walk_nodes("/", classname="Table")
              ]
          raise ValueError(
              f"`include` should be either 'pandas' or 'native' but is '{include}'"
          )
  
      def __iter__(self):
          return iter(self.keys())
  
      def items(self):
          """
          iterate on key->group
          """
          for g in self.groups():
              yield g._v_pathname, g
  
      iteritems = items
  
      def open(self, mode: str = "a", **kwargs):
          """
          Open the file in the specified mode
  
          Parameters
          ----------
          mode : {'a', 'w', 'r', 'r+'}, default 'a'
              See HDFStore docstring or tables.open_file for info about modes
          **kwargs
              These parameters will be passed to the PyTables open_file method.
          """
          tables = _tables()
  
          if self._mode != mode:
              # if we are changing a write mode to read, ok
              if self._mode in ["a", "w"] and mode in ["r", "r+"]:
                  pass
              elif mode in ["w"]:
                  # this would truncate, raise here
                  if self.is_open:
                      raise PossibleDataLossError(
                          f"Re-opening the file [{self._path}] with mode [{self._mode}] "
                          "will delete the current file!"
                      )
  
              self._mode = mode
  
          # close and reopen the handle
          if self.is_open:
              self.close()
  
          if self._complevel and self._complevel > 0:
              self._filters = _tables().Filters(
                  self._complevel, self._complib, fletcher32=self._fletcher32
              )
  
          if _table_file_open_policy_is_strict and self.is_open:
              msg = (
                  "Cannot open HDF5 file, which is already opened, "
                  "even in read-only mode."
              )
              raise ValueError(msg)
  
          self._handle = tables.open_file(self._path, self._mode, **kwargs)
  
      def close(self):
          """
          Close the PyTables file handle
          """
          if self._handle is not None:
              self._handle.close()
          self._handle = None
  
      @property
      def is_open(self) -> bool:
          """
          return a boolean indicating whether the file is open
          """
          if self._handle is None:
              return False
          return bool(self._handle.isopen)
  
      def flush(self, fsync: bool = False):
          """
          Force all buffered modifications to be written to disk.
  
          Parameters
          ----------
          fsync : bool (default False)
            call ``os.fsync()`` on the file handle to force writing to disk.
  
          Notes
          -----
          Without ``fsync=True``, flushing may not guarantee that the OS writes
          to disk. With fsync, the operation will block until the OS claims the
          file has been written; however, other caching layers may still
          interfere.
          """
          if self._handle is not None:
              self._handle.flush()
              if fsync:
                  with suppress(OSError):
                      os.fsync(self._handle.fileno())
  
      def get(self, key: str):
          """
          Retrieve pandas object stored in file.
  
          Parameters
          ----------
          key : str
  
          Returns
          -------
          object
              Same type as object stored in file.
          """
          with patch_pickle():
              # GH#31167 Without this patch, pickle doesn't know how to unpickle
              #  old DateOffset objects now that they are cdef classes.
              group = self.get_node(key)
              if group is None:
                  raise KeyError(f"No object named {key} in the file")
              return self._read_group(group)
  
      def select(
          self,
          key: str,
          where=None,
          start=None,
          stop=None,
          columns=None,
          iterator=False,
          chunksize=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.
          """
          group = self.get_node(key)
          if group is None:
              raise KeyError(f"No object named {key} in the file")
  
          # create the storer and axes
          where = _ensure_term(where, scope_level=1)
          s = self._create_storer(group)
          s.infer_axes()
  
          # function to call on iteration
          def func(_start, _stop, _where):
              return s.read(start=_start, stop=_stop, where=_where, columns=columns)
  
          # create the iterator
          it = TableIterator(
              self,
              s,
              func,
              where=where,
              nrows=s.nrows,
              start=start,
              stop=stop,
              iterator=iterator,
              chunksize=chunksize,
              auto_close=auto_close,
          )
  
          return it.get_result()
  
      def select_as_coordinates(
          self,
          key: str,
          where=None,
          start: Optional[int] = None,
          stop: Optional[int] = None,
      ):
          """
          return the selection as an Index
  
          .. 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
          where : list of Term (or convertible) objects, optional
          start : integer (defaults to None), row number to start selection
          stop  : integer (defaults to None), row number to stop selection
          """
          where = _ensure_term(where, scope_level=1)
          tbl = self.get_storer(key)
          if not isinstance(tbl, Table):
              raise TypeError("can only read_coordinates with a table")
          return tbl.read_coordinates(where=where, start=start, stop=stop)
  
      def select_column(
          self,
          key: str,
          column: str,
          start: Optional[int] = None,
          stop: Optional[int] = None,
      ):
          """
          return a single column from the table. This is generally only useful to
          select an indexable
  
          .. 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
          column : str
              The column of interest.
          start : int or None, default None
          stop : int or None, default None
  
          Raises
          ------
          raises KeyError if the column is not found (or key is not a valid
              store)
          raises ValueError if the column can not be extracted individually (it
              is part of a data block)
  
          """
          tbl = self.get_storer(key)
          if not isinstance(tbl, Table):
              raise TypeError("can only read_column with a table")
          return tbl.read_column(column=column, start=start, stop=stop)
  
      def select_as_multiple(
          self,
          keys,
          where=None,
          selector=None,
          columns=None,
          start=None,
          stop=None,
          iterator=False,
          chunksize=None,
          auto_close: bool = False,
      ):
          """
          Retrieve pandas objects from multiple tables.
  
          .. 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
          ----------
          keys : a list of the tables
          selector : the table to apply the where criteria (defaults to keys[0]
              if not supplied)
          columns : the columns I want back
          start : integer (defaults to None), row number to start selection
          stop  : integer (defaults to None), row number to stop selection
          iterator : boolean, return an iterator, default False
          chunksize : nrows to include in iteration, return an iterator
          auto_close : bool, default False
              Should automatically close the store when finished.
  
          Raises
          ------
          raises KeyError if keys or selector is not found or keys is empty
          raises TypeError if keys is not a list or tuple
          raises ValueError if the tables are not ALL THE SAME DIMENSIONS
          """
          # default to single select
          where = _ensure_term(where, scope_level=1)
          if isinstance(keys, (list, tuple)) and len(keys) == 1:
              keys = keys[0]
          if isinstance(keys, str):
              return self.select(
                  key=keys,
                  where=where,
                  columns=columns,
                  start=start,
                  stop=stop,
                  iterator=iterator,
                  chunksize=chunksize,
                  auto_close=auto_close,
              )
  
          if not isinstance(keys, (list, tuple)):
              raise TypeError("keys must be a list/tuple")
  
          if not len(keys):
              raise ValueError("keys must have a non-zero length")
  
          if selector is None:
              selector = keys[0]
  
          # collect the tables
          tbls = [self.get_storer(k) for k in keys]
          s = self.get_storer(selector)
  
          # validate rows
          nrows = None
          for t, k in itertools.chain([(s, selector)], zip(tbls, keys)):
              if t is None:
                  raise KeyError(f"Invalid table [{k}]")
              if not t.is_table:
                  raise TypeError(
                      f"object [{t.pathname}] is not a table, and cannot be used in all "
                      "select as multiple"
                  )
  
              if nrows is None:
                  nrows = t.nrows
              elif t.nrows != nrows:
                  raise ValueError("all tables must have exactly the same nrows!")
  
          # The isinstance checks here are redundant with the check above,
          #  but necessary for mypy; see GH#29757
          _tbls = [x for x in tbls if isinstance(x, Table)]
  
          # axis is the concentration axes
          axis = list({t.non_index_axes[0][0] for t in _tbls})[0]
  
          def func(_start, _stop, _where):
  
              # retrieve the objs, _where is always passed as a set of
              # coordinates here
              objs = [
                  t.read(where=_where, columns=columns, start=_start, stop=_stop)
                  for t in tbls
              ]
  
              # concat and return
              return concat(objs, axis=axis, verify_integrity=False)._consolidate()
  
          # create the iterator
          it = TableIterator(
              self,
              s,
              func,
              where=where,
              nrows=nrows,
              start=start,
              stop=stop,
              iterator=iterator,
              chunksize=chunksize,
              auto_close=auto_close,
          )
  
          return it.get_result(coordinates=True)
  
      def put(
          self,
          key: str,
          value: FrameOrSeries,
          format=None,
          index=True,
          append=False,
          complib=None,
          complevel: Optional[int] = None,
          min_itemsize: Optional[Union[int, Dict[str, int]]] = None,
          nan_rep=None,
          data_columns: Optional[List[str]] = None,
          encoding=None,
          errors: str = "strict",
          track_times: bool = True,
          dropna: bool = False,
      ):
          """
          Store object in HDFStore.
  
          Parameters
          ----------
          key : str
          value : {Series, DataFrame}
          format : 'fixed(f)|table(t)', default is 'fixed'
              Format to use when storing object in HDFStore. Value can be one of:
  
              ``'fixed'``
                  Fixed format.  Fast writing/reading. Not-appendable, nor searchable.
              ``'table'``
                  Table format.  Write as a PyTables Table structure which may perform
                  worse but allow more flexible operations like searching / selecting
                  subsets of the data.
          append : bool, default False
              This will force Table format, append the input data to the existing.
          data_columns : list, default None
              List of columns to create as data columns, or True to use all columns.
              See `here
              <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
          encoding : str, default None
              Provide an encoding for strings.
          track_times : bool, default True
              Parameter is propagated to 'create_table' method of 'PyTables'.
              If set to False it enables to have the same h5 files (same hashes)
              independent on creation time.
  
              .. versionadded:: 1.1.0
          """
          if format is None:
              format = get_option("io.hdf.default_format") or "fixed"
          format = self._validate_format(format)
          self._write_to_group(
              key,
              value,
              format=format,
              index=index,
              append=append,
              complib=complib,
              complevel=complevel,
              min_itemsize=min_itemsize,
              nan_rep=nan_rep,
              data_columns=data_columns,
              encoding=encoding,
              errors=errors,
              track_times=track_times,
              dropna=dropna,
          )
  
      def remove(self, key: str, where=None, start=None, stop=None):
          """
          Remove pandas object partially by specifying the where condition
  
          Parameters
          ----------
          key : string
              Node to remove or delete rows from
          where : list of Term (or convertible) objects, optional
          start : integer (defaults to None), row number to start selection
          stop  : integer (defaults to None), row number to stop selection
  
          Returns
          -------
          number of rows removed (or None if not a Table)
  
          Raises
          ------
          raises KeyError if key is not a valid store
  
          """
          where = _ensure_term(where, scope_level=1)
          try:
              s = self.get_storer(key)
          except KeyError:
              # the key is not a valid store, re-raising KeyError
              raise
          except AssertionError:
              # surface any assertion errors for e.g. debugging
              raise
          except Exception as err:
              # In tests we get here with ClosedFileError, TypeError, and
              #  _table_mod.NoSuchNodeError.  TODO: Catch only these?
  
              if where is not None:
                  raise ValueError(
                      "trying to remove a node with a non-None where clause!"
                  ) from err
  
              # we are actually trying to remove a node (with children)
              node = self.get_node(key)
              if node is not None:
                  node._f_remove(recursive=True)
                  return None
  
          # remove the node
          if com.all_none(where, start, stop):
              s.group._f_remove(recursive=True)
  
          # delete from the table
          else:
              if not s.is_table:
                  raise ValueError(
                      "can only remove with where on objects written as tables"
                  )
              return s.delete(where=where, start=start, stop=stop)
  
      def append(
          self,
          key: str,
          value: FrameOrSeries,
          format=None,
          axes=None,
          index=True,
          append=True,
          complib=None,
          complevel: Optional[int] = None,
          columns=None,
          min_itemsize: Optional[Union[int, Dict[str, int]]] = None,
          nan_rep=None,
          chunksize=None,
          expectedrows=None,
          dropna: Optional[bool] = None,
          data_columns: Optional[List[str]] = None,
          encoding=None,
          errors: str = "strict",
      ):
          """
          Append to Table in file. Node must already exist and be Table
          format.
  
          Parameters
          ----------
          key : str
          value : {Series, DataFrame}
          format : 'table' is the default
              Format to use when storing object in HDFStore.  Value can be one of:
  
              ``'table'``
                  Table format. Write as a PyTables Table structure which may perform
                  worse but allow more flexible operations like searching / selecting
                  subsets of the data.
          append       : bool, default True
              Append the input data to the existing.
          data_columns : list of columns, or True, default None
              List of columns to create as indexed data columns for on-disk
              queries, or True to use all columns. By default only the axes
              of the object are indexed. See `here
              <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
          min_itemsize : dict of columns that specify minimum str sizes
          nan_rep      : str to use as str nan representation
          chunksize    : size to chunk the writing
          expectedrows : expected TOTAL row size of this table
          encoding     : default None, provide an encoding for str
          dropna : bool, default False
              Do not write an ALL nan row to the store settable
              by the option 'io.hdf.dropna_table'.
  
          Notes
          -----
          Does *not* check if data being appended overlaps with existing
          data in the table, so be careful
          """
          if columns is not None:
              raise TypeError(
                  "columns is not a supported keyword in append, try data_columns"
              )
  
          if dropna is None:
              dropna = get_option("io.hdf.dropna_table")
          if format is None:
              format = get_option("io.hdf.default_format") or "table"
          format = self._validate_format(format)
          self._write_to_group(
              key,
              value,
              format=format,
              axes=axes,
              index=index,
              append=append,
              complib=complib,
              complevel=complevel,
              min_itemsize=min_itemsize,
              nan_rep=nan_rep,
              chunksize=chunksize,
              expectedrows=expectedrows,
              dropna=dropna,
              data_columns=data_columns,
              encoding=encoding,
              errors=errors,
          )
  
      def append_to_multiple(
          self,
          d: Dict,
          value,
          selector,
          data_columns=None,
          axes=None,
          dropna=False,
          **kwargs,
      ):
          """
          Append to multiple tables
  
          Parameters
          ----------
          d : a dict of table_name to table_columns, None is acceptable as the
              values of one node (this will get all the remaining columns)
          value : a pandas object
          selector : a string that designates the indexable table; all of its
              columns will be designed as data_columns, unless data_columns is
              passed, in which case these are used
          data_columns : list of columns to create as data columns, or True to
              use all columns
          dropna : if evaluates to True, drop rows from all tables if any single
                   row in each table has all NaN. Default False.
  
          Notes
          -----
          axes parameter is currently not accepted
  
          """
          if axes is not None:
              raise TypeError(
                  "axes is currently not accepted as a parameter to append_to_multiple; "
                  "you can create the tables independently instead"
              )
  
          if not isinstance(d, dict):
              raise ValueError(
                  "append_to_multiple must have a dictionary specified as the "
                  "way to split the value"
              )
  
          if selector not in d:
              raise ValueError(
                  "append_to_multiple requires a selector that is in passed dict"
              )
  
          # figure out the splitting axis (the non_index_axis)
          axis = list(set(range(value.ndim)) - set(_AXES_MAP[type(value)]))[0]
  
          # figure out how to split the value
          remain_key = None
          remain_values: List = []
          for k, v in d.items():
              if v is None:
                  if remain_key is not None:
                      raise ValueError(
                          "append_to_multiple can only have one value in d that is None"
                      )
                  remain_key = k
              else:
                  remain_values.extend(v)
          if remain_key is not None:
              ordered = value.axes[axis]
              ordd = ordered.difference(Index(remain_values))
              ordd = sorted(ordered.get_indexer(ordd))
              d[remain_key] = ordered.take(ordd)
  
          # data_columns
          if data_columns is None:
              data_columns = d[selector]
  
          # ensure rows are synchronized across the tables
          if dropna:
              idxs = (value[cols].dropna(how="all").index for cols in d.values())
              valid_index = next(idxs)
              for index in idxs:
                  valid_index = valid_index.intersection(index)
              value = value.loc[valid_index]
  
          min_itemsize = kwargs.pop("min_itemsize", None)
  
          # append
          for k, v in d.items():
              dc = data_columns if k == selector else None
  
              # compute the val
              val = value.reindex(v, axis=axis)
  
              filtered = (
                  {key: value for (key, value) in min_itemsize.items() if key in v}
                  if min_itemsize is not None
                  else None
              )
              self.append(k, val, data_columns=dc, min_itemsize=filtered, **kwargs)
  
      def create_table_index(
          self,
          key: str,
          columns=None,
          optlevel: Optional[int] = None,
          kind: Optional[str] = None,
      ):
          """
          Create a pytables index on the table.
  
          Parameters
          ----------
          key : str
          columns : None, bool, or listlike[str]
              Indicate which columns to create an index on.
  
              * False : Do not create any indexes.
              * True : Create indexes on all columns.
              * None : Create indexes on all columns.
              * listlike : Create indexes on the given columns.
  
          optlevel : int or None, default None
              Optimization level, if None, pytables defaults to 6.
          kind : str or None, default None
              Kind of index, if None, pytables defaults to "medium".
  
          Raises
          ------
          TypeError: raises if the node is not a table
          """
          # version requirements
          _tables()
          s = self.get_storer(key)
          if s is None:
              return
  
          if not isinstance(s, Table):
              raise TypeError("cannot create table index on a Fixed format store")
          s.create_index(columns=columns, optlevel=optlevel, kind=kind)
  
      def groups(self):
          """
          Return a list of all the top-level nodes.
  
          Each node returned is not a pandas storage object.
  
          Returns
          -------
          list
              List of objects.
          """
          _tables()
          self._check_if_open()
          assert self._handle is not None  # for mypy
          assert _table_mod is not None  # for mypy
          return [
              g
              for g in self._handle.walk_groups()
              if (
                  not isinstance(g, _table_mod.link.Link)
                  and (
                      getattr(g._v_attrs, "pandas_type", None)
                      or getattr(g, "table", None)
                      or (isinstance(g, _table_mod.table.Table) and g._v_name != "table")
                  )
              )
          ]
  
      def walk(self, where="/"):
          """
          Walk the pytables group hierarchy for pandas objects.
  
          This generator will yield the group path, subgroups and pandas object
          names for each group.
  
          Any non-pandas PyTables objects that are not a group will be ignored.
  
          The `where` group itself is listed first (preorder), then each of its
          child groups (following an alphanumerical order) is also traversed,
          following the same procedure.
  
          .. versionadded:: 0.24.0
  
          Parameters
          ----------
          where : str, default "/"
              Group where to start walking.
  
          Yields
          ------
          path : str
              Full path to a group (without trailing '/').
          groups : list
              Names (strings) of the groups contained in `path`.
          leaves : list
              Names (strings) of the pandas objects contained in `path`.
          """
          _tables()
          self._check_if_open()
          assert self._handle is not None  # for mypy
          assert _table_mod is not None  # for mypy
  
          for g in self._handle.walk_groups(where):
              if getattr(g._v_attrs, "pandas_type", None) is not None:
                  continue
  
              groups = []
              leaves = []
              for child in g._v_children.values():
                  pandas_type = getattr(child._v_attrs, "pandas_type", None)
                  if pandas_type is None:
                      if isinstance(child, _table_mod.group.Group):
                          groups.append(child._v_name)
                  else:
                      leaves.append(child._v_name)
  
              yield (g._v_pathname.rstrip("/"), groups, leaves)
  
      def get_node(self, key: str) -> Optional["Node"]:
          """ return the node with the key or None if it does not exist """
          self._check_if_open()
          if not key.startswith("/"):
              key = "/" + key
  
          assert self._handle is not None
          assert _table_mod is not None  # for mypy
          try:
              node = self._handle.get_node(self.root, key)
          except _table_mod.exceptions.NoSuchNodeError:
              return None
  
          assert isinstance(node, _table_mod.Node), type(node)
          return node
  
      def get_storer(self, key: str) -> Union["GenericFixed", "Table"]:
          """ return the storer object for a key, raise if not in the file """
          group = self.get_node(key)
          if group is None:
              raise KeyError(f"No object named {key} in the file")
  
          s = self._create_storer(group)
          s.infer_axes()
          return s
  
      def copy(
          self,
          file,
          mode="w",
          propindexes: bool = True,
          keys=None,
          complib=None,
          complevel: Optional[int] = None,
          fletcher32: bool = False,
          overwrite=True,
      ):
          """
          Copy the existing store to a new file, updating in place.
  
          Parameters
          ----------
          propindexes : bool, default True
              Restore indexes in copied file.
          keys : list, optional
              List of keys to include in the copy (defaults to all).
          overwrite : bool, default True
              Whether to overwrite (remove and replace) existing nodes in the new store.
          mode, complib, complevel, fletcher32 same as in HDFStore.__init__
  
          Returns
          -------
          open file handle of the new store
          """
          new_store = HDFStore(
              file, mode=mode, complib=complib, complevel=complevel, fletcher32=fletcher32
          )
          if keys is None:
              keys = list(self.keys())
          if not isinstance(keys, (tuple, list)):
              keys = [keys]
          for k in keys:
              s = self.get_storer(k)
              if s is not None:
  
                  if k in new_store:
                      if overwrite:
                          new_store.remove(k)
  
                  data = self.select(k)
                  if isinstance(s, Table):
  
                      index: Union[bool, List[str]] = False
                      if propindexes:
                          index = [a.name for a in s.axes if a.is_indexed]
                      new_store.append(
                          k,
                          data,
                          index=index,
                          data_columns=getattr(s, "data_columns", None),
                          encoding=s.encoding,
                      )
                  else:
                      new_store.put(k, data, encoding=s.encoding)
  
          return new_store
  
      def info(self) -> str:
          """
          Print detailed information on the store.
  
          Returns
          -------
          str
          """
          path = pprint_thing(self._path)
          output = f"{type(self)}\nFile path: {path}\n"
  
          if self.is_open:
              lkeys = sorted(self.keys())
              if len(lkeys):
                  keys = []
                  values = []
  
                  for k in lkeys:
                      try:
                          s = self.get_storer(k)
                          if s is not None:
                              keys.append(pprint_thing(s.pathname or k))
                              values.append(pprint_thing(s or "invalid_HDFStore node"))
                      except AssertionError:
                          # surface any assertion errors for e.g. debugging
                          raise
                      except Exception as detail:
                          keys.append(k)
                          dstr = pprint_thing(detail)
                          values.append(f"[invalid_HDFStore node: {dstr}]")
  
                  output += adjoin(12, keys, values)
              else:
                  output += "Empty"
          else:
              output += "File is CLOSED"
  
          return output
  
      # ------------------------------------------------------------------------
      # private methods
  
      def _check_if_open(self):
          if not self.is_open:
              raise ClosedFileError(f"{self._path} file is not open!")
  
      def _validate_format(self, format: str) -> str:
          """ validate / deprecate formats """
          # validate
          try:
              format = _FORMAT_MAP[format.lower()]
          except KeyError as err:
              raise TypeError(f"invalid HDFStore format specified [{format}]") from err
  
          return format
  
      def _create_storer(
          self,
          group,
          format=None,
          value: Optional[FrameOrSeries] = None,
          encoding: str = "UTF-8",
          errors: str = "strict",
      ) -> Union["GenericFixed", "Table"]:
          """ return a suitable class to operate """
          cls: Union[Type["GenericFixed"], Type["Table"]]
  
          if value is not None and not isinstance(value, (Series, DataFrame)):
              raise TypeError("value must be None, Series, or DataFrame")
  
          def error(t):
              # return instead of raising so mypy can tell where we are raising
              return TypeError(
                  f"cannot properly create the storer for: [{t}] [group->"
                  f"{group},value->{type(value)},format->{format}"
              )
  
          pt = _ensure_decoded(getattr(group._v_attrs, "pandas_type", None))
          tt = _ensure_decoded(getattr(group._v_attrs, "table_type", None))
  
          # infer the pt from the passed value
          if pt is None:
              if value is None:
                  _tables()
                  assert _table_mod is not None  # for mypy
                  if getattr(group, "table", None) or isinstance(
                      group, _table_mod.table.Table
                  ):
                      pt = "frame_table"
                      tt = "generic_table"
                  else:
                      raise TypeError(
                          "cannot create a storer if the object is not existing "
                          "nor a value are passed"
                      )
              else:
                  if isinstance(value, Series):
                      pt = "series"
                  else:
                      pt = "frame"
  
                  # we are actually a table
                  if format == "table":
                      pt += "_table"
  
          # a storer node
          if "table" not in pt:
              _STORER_MAP = {"series": SeriesFixed, "frame": FrameFixed}
              try:
                  cls = _STORER_MAP[pt]
              except KeyError as err:
                  raise error("_STORER_MAP") from err
              return cls(self, group, encoding=encoding, errors=errors)
  
          # existing node (and must be a table)
          if tt is None:
              # if we are a writer, determine the tt
              if value is not None:
                  if pt == "series_table":
                      index = getattr(value, "index", None)
                      if index is not None:
                          if index.nlevels == 1:
                              tt = "appendable_series"
                          elif index.nlevels > 1:
                              tt = "appendable_multiseries"
                  elif pt == "frame_table":
                      index = getattr(value, "index", None)
                      if index is not None:
                          if index.nlevels == 1:
                              tt = "appendable_frame"
                          elif index.nlevels > 1:
                              tt = "appendable_multiframe"
  
          _TABLE_MAP = {
              "generic_table": GenericTable,
              "appendable_series": AppendableSeriesTable,
              "appendable_multiseries": AppendableMultiSeriesTable,
              "appendable_frame": AppendableFrameTable,
              "appendable_multiframe": AppendableMultiFrameTable,
              "worm": WORMTable,
          }
          try:
              cls = _TABLE_MAP[tt]
          except KeyError as err:
              raise error("_TABLE_MAP") from err
  
          return cls(self, group, encoding=encoding, errors=errors)
  
      def _write_to_group(
          self,
          key: str,
          value: FrameOrSeries,
          format,
          axes=None,
          index=True,
          append=False,
          complib=None,
          complevel: Optional[int] = None,
          fletcher32=None,
          min_itemsize: Optional[Union[int, Dict[str, int]]] = None,
          chunksize=None,
          expectedrows=None,
          dropna=False,
          nan_rep=None,
          data_columns=None,
          encoding=None,
          errors: str = "strict",
          track_times: bool = True,
      ):
          # we don't want to store a table node at all if our object is 0-len
          # as there are not dtypes
          if getattr(value, "empty", None) and (format == "table" or append):
              return
  
          group = self._identify_group(key, append)
  
          s = self._create_storer(group, format, value, encoding=encoding, errors=errors)
          if append:
              # raise if we are trying to append to a Fixed format,
              #       or a table that exists (and we are putting)
              if not s.is_table or (s.is_table and format == "fixed" and s.is_exists):
                  raise ValueError("Can only append to Tables")
              if not s.is_exists:
                  s.set_object_info()
          else:
              s.set_object_info()
  
          if not s.is_table and complib:
              raise ValueError("Compression not supported on Fixed format stores")
  
          # write the object
          s.write(
              obj=value,
              axes=axes,
              append=append,
              complib=complib,
              complevel=complevel,
              fletcher32=fletcher32,
              min_itemsize=min_itemsize,
              chunksize=chunksize,
              expectedrows=expectedrows,
              dropna=dropna,
              nan_rep=nan_rep,
              data_columns=data_columns,
              track_times=track_times,
          )
  
          if isinstance(s, Table) and index:
              s.create_index(columns=index)
  
      def _read_group(self, group: "Node"):
          s = self._create_storer(group)
          s.infer_axes()
          return s.read()
  
      def _identify_group(self, key: str, append: bool) -> "Node":
          """Identify HDF5 group based on key, delete/create group if needed."""
          group = self.get_node(key)
  
          # we make this assertion for mypy; the get_node call will already
          # have raised if this is incorrect
          assert self._handle is not None
  
          # remove the node if we are not appending
          if group is not None and not append:
              self._handle.remove_node(group, recursive=True)
              group = None
  
          if group is None:
              group = self._create_nodes_and_group(key)
  
          return group
  
      def _create_nodes_and_group(self, key: str) -> "Node":
          """Create nodes from key and return group name."""
          # assertion for mypy
          assert self._handle is not None
  
          paths = key.split("/")
          # recursively create the groups
          path = "/"
          for p in paths:
              if not len(p):
                  continue
              new_path = path
              if not path.endswith("/"):
                  new_path += "/"
              new_path += p
              group = self.get_node(new_path)
              if group is None:
                  group = self._handle.create_group(path, p)
              path = new_path
          return group
  
  
  class TableIterator:
      """
      Define the iteration interface on a table
  
      Parameters
      ----------
      store : HDFStore
      s     : the referred storer
      func  : the function to execute the query
      where : the where of the query
      nrows : the rows to iterate on
      start : the passed start value (default is None)
      stop  : the passed stop value (default is None)
      iterator : bool, default False
          Whether to use the default iterator.
      chunksize : the passed chunking value (default is 100000)
      auto_close : bool, default False
          Whether to automatically close the store at the end of iteration.
      """
  
      chunksize: Optional[int]
      store: HDFStore
      s: Union["GenericFixed", "Table"]
  
      def __init__(
          self,
          store: HDFStore,
          s: Union["GenericFixed", "Table"],
          func,
          where,
          nrows,
          start=None,
          stop=None,
          iterator: bool = False,
          chunksize: Optional[int] = None,
          auto_close: bool = False,
      ):
          self.store = store
          self.s = s
          self.func = func
          self.where = where
  
          # set start/stop if they are not set if we are a table
          if self.s.is_table:
              if nrows is None:
                  nrows = 0
              if start is None:
                  start = 0
              if stop is None:
                  stop = nrows
              stop = min(nrows, stop)
  
          self.nrows = nrows
          self.start = start
          self.stop = stop
  
          self.coordinates = None
          if iterator or chunksize is not None:
              if chunksize is None:
                  chunksize = 100000
              self.chunksize = int(chunksize)
          else:
              self.chunksize = None
  
          self.auto_close = auto_close
  
      def __iter__(self):
          # iterate
          current = self.start
          if self.coordinates is None:
              raise ValueError("Cannot iterate until get_result is called.")
          while current < self.stop:
              stop = min(current + self.chunksize, self.stop)
              value = self.func(None, None, self.coordinates[current:stop])
              current = stop
              if value is None or not len(value):
                  continue
  
              yield value
  
          self.close()
  
      def close(self):
          if self.auto_close:
              self.store.close()
  
      def get_result(self, coordinates: bool = False):
          #  return the actual iterator
          if self.chunksize is not None:
              if not isinstance(self.s, Table):
                  raise TypeError("can only use an iterator or chunksize on a table")
  
              self.coordinates = self.s.read_coordinates(where=self.where)
  
              return self
  
          # if specified read via coordinates (necessary for multiple selections
          if coordinates:
              if not isinstance(self.s, Table):
                  raise TypeError("can only read_coordinates on a table")
              where = self.s.read_coordinates(
                  where=self.where, start=self.start, stop=self.stop
              )
          else:
              where = self.where
  
          # directly return the result
          results = self.func(self.start, self.stop, where)
          self.close()
          return results
  
  
  class IndexCol:
      """
      an index column description class
  
      Parameters
      ----------
      axis   : axis which I reference
      values : the ndarray like converted values
      kind   : a string description of this type
      typ    : the pytables type
      pos    : the position in the pytables
  
      """
  
      is_an_indexable = True
      is_data_indexable = True
      _info_fields = ["freq", "tz", "index_name"]
  
      name: str
      cname: str
  
      def __init__(
          self,
          name: str,
          values=None,
          kind=None,
          typ=None,
          cname: Optional[str] = None,
          axis=None,
          pos=None,
          freq=None,
          tz=None,
          index_name=None,
          ordered=None,
          table=None,
          meta=None,
          metadata=None,
      ):
  
          if not isinstance(name, str):
              raise ValueError("`name` must be a str.")
  
          self.values = values
          self.kind = kind
          self.typ = typ
          self.name = name
          self.cname = cname or name
          self.axis = axis
          self.pos = pos
          self.freq = freq
          self.tz = tz
          self.index_name = index_name
          self.ordered = ordered
          self.table = table
          self.meta = meta
          self.metadata = metadata
  
          if pos is not None:
              self.set_pos(pos)
  
          # These are ensured as long as the passed arguments match the
          #  constructor annotations.
          assert isinstance(self.name, str)
          assert isinstance(self.cname, str)
  
      @property
      def itemsize(self) -> int:
          # Assumes self.typ has already been initialized
          return self.typ.itemsize
  
      @property
      def kind_attr(self) -> str:
          return f"{self.name}_kind"
  
      def set_pos(self, pos: int):
          """ set the position of this column in the Table """
          self.pos = pos
          if pos is not None and self.typ is not None:
              self.typ._v_pos = pos
  
      def __repr__(self) -> str:
          temp = tuple(
              map(pprint_thing, (self.name, self.cname, self.axis, self.pos, self.kind))
          )
          return ",".join(
              (
                  f"{key}->{value}"
                  for key, value in zip(["name", "cname", "axis", "pos", "kind"], temp)
              )
          )
  
      def __eq__(self, other: Any) -> bool:
          """ compare 2 col items """
          return all(
              getattr(self, a, None) == getattr(other, a, None)
              for a in ["name", "cname", "axis", "pos"]
          )
  
      def __ne__(self, other) -> bool:
          return not self.__eq__(other)
  
      @property
      def is_indexed(self) -> bool:
          """ return whether I am an indexed column """
          if not hasattr(self.table, "cols"):
              # e.g. if infer hasn't been called yet, self.table will be None.
              return False
          return getattr(self.table.cols, self.cname).is_indexed
  
      def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str):
          """
          Convert the data from this selection to the appropriate pandas type.
          """
          assert isinstance(values, np.ndarray), type(values)
  
          # values is a recarray
          if values.dtype.fields is not None:
              values = values[self.cname]
  
          val_kind = _ensure_decoded(self.kind)
          values = _maybe_convert(values, val_kind, encoding, errors)
  
          kwargs = {}
          kwargs["name"] = _ensure_decoded(self.index_name)
  
          if self.freq is not None:
              kwargs["freq"] = _ensure_decoded(self.freq)
  
          factory: Union[Type[Index], Type[DatetimeIndex]] = Index
          if is_datetime64_dtype(values.dtype) or is_datetime64tz_dtype(values.dtype):
              factory = DatetimeIndex
  
          # making an Index instance could throw a number of different errors
          try:
              new_pd_index = factory(values, **kwargs)
          except ValueError:
              # if the output freq is different that what we recorded,
              # it should be None (see also 'doc example part 2')
              if "freq" in kwargs:
                  kwargs["freq"] = None
              new_pd_index = factory(values, **kwargs)
  
          new_pd_index = _set_tz(new_pd_index, self.tz)
          return new_pd_index, new_pd_index
  
      def take_data(self):
          """ return the values"""
          return self.values
  
      @property
      def attrs(self):
          return self.table._v_attrs
  
      @property
      def description(self):
          return self.table.description
  
      @property
      def col(self):
          """ return my current col description """
          return getattr(self.description, self.cname, None)
  
      @property
      def cvalues(self):
          """ return my cython values """
          return self.values
  
      def __iter__(self):
          return iter(self.values)
  
      def maybe_set_size(self, min_itemsize=None):
          """
          maybe set a string col itemsize:
              min_itemsize can be an integer or a dict with this columns name
              with an integer size
          """
          if _ensure_decoded(self.kind) == "string":
              if isinstance(min_itemsize, dict):
                  min_itemsize = min_itemsize.get(self.name)
  
              if min_itemsize is not None and self.typ.itemsize < min_itemsize:
                  self.typ = _tables().StringCol(itemsize=min_itemsize, pos=self.pos)
  
      def validate_names(self):
          pass
  
      def validate_and_set(self, handler: "AppendableTable", append: bool):
          self.table = handler.table
          self.validate_col()
          self.validate_attr(append)
          self.validate_metadata(handler)
          self.write_metadata(handler)
          self.set_attr()
  
      def validate_col(self, itemsize=None):
          """ validate this column: return the compared against itemsize """
          # validate this column for string truncation (or reset to the max size)
          if _ensure_decoded(self.kind) == "string":
              c = self.col
              if c is not None:
                  if itemsize is None:
                      itemsize = self.itemsize
                  if c.itemsize < itemsize:
                      raise ValueError(
                          f"Trying to store a string with len [{itemsize}] in "
                          f"[{self.cname}] column but\nthis column has a limit of "
                          f"[{c.itemsize}]!\nConsider using min_itemsize to "
                          "preset the sizes on these columns"
                      )
                  return c.itemsize
  
          return None
  
      def validate_attr(self, append: bool):
          # check for backwards incompatibility
          if append:
              existing_kind = getattr(self.attrs, self.kind_attr, None)
              if existing_kind is not None and existing_kind != self.kind:
                  raise TypeError(
                      f"incompatible kind in col [{existing_kind} - {self.kind}]"
                  )
  
      def update_info(self, info):
          """
          set/update the info for this indexable with the key/value
          if there is a conflict raise/warn as needed
          """
          for key in self._info_fields:
  
              value = getattr(self, key, None)
              idx = info.setdefault(self.name, {})
  
              existing_value = idx.get(key)
              if key in idx and value is not None and existing_value != value:
                  # frequency/name just warn
                  if key in ["freq", "index_name"]:
                      ws = attribute_conflict_doc % (key, existing_value, value)
                      warnings.warn(ws, AttributeConflictWarning, stacklevel=6)
  
                      # reset
                      idx[key] = None
                      setattr(self, key, None)
  
                  else:
                      raise ValueError(
                          f"invalid info for [{self.name}] for [{key}], "
                          f"existing_value [{existing_value}] conflicts with "
                          f"new value [{value}]"
                      )
              else:
                  if value is not None or existing_value is not None:
                      idx[key] = value
  
      def set_info(self, info):
          """ set my state from the passed info """
          idx = info.get(self.name)
          if idx is not None:
              self.__dict__.update(idx)
  
      def set_attr(self):
          """ set the kind for this column """
          setattr(self.attrs, self.kind_attr, self.kind)
  
      def validate_metadata(self, handler: "AppendableTable"):
          """ validate that kind=category does not change the categories """
          if self.meta == "category":
              new_metadata = self.metadata
              cur_metadata = handler.read_metadata(self.cname)
              if (
                  new_metadata is not None
                  and cur_metadata is not None
                  and not array_equivalent(new_metadata, cur_metadata)
              ):
                  raise ValueError(
                      "cannot append a categorical with "
                      "different categories to the existing"
                  )
  
      def write_metadata(self, handler: "AppendableTable"):
          """ set the meta data """
          if self.metadata is not None:
              handler.write_metadata(self.cname, self.metadata)
  
  
  class GenericIndexCol(IndexCol):
      """ an index which is not represented in the data of the table """
  
      @property
      def is_indexed(self) -> bool:
          return False
  
      def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str):
          """
          Convert the data from this selection to the appropriate pandas type.
  
          Parameters
          ----------
          values : np.ndarray
          nan_rep : str
          encoding : str
          errors : str
          """
          assert isinstance(values, np.ndarray), type(values)
  
          values = Int64Index(np.arange(len(values)))
          return values, values
  
      def set_attr(self):
          pass
  
  
  class DataCol(IndexCol):
      """
      a data holding column, by definition this is not indexable
  
      Parameters
      ----------
      data   : the actual data
      cname  : the column name in the table to hold the data (typically
                  values)
      meta   : a string description of the metadata
      metadata : the actual metadata
      """
  
      is_an_indexable = False
      is_data_indexable = False
      _info_fields = ["tz", "ordered"]
  
      def __init__(
          self,
          name: str,
          values=None,
          kind=None,
          typ=None,
          cname=None,
          pos=None,
          tz=None,
          ordered=None,
          table=None,
          meta=None,
          metadata=None,
          dtype: Optional[DtypeArg] = None,
          data=None,
      ):
          super().__init__(
              name=name,
              values=values,
              kind=kind,
              typ=typ,
              pos=pos,
              cname=cname,
              tz=tz,
              ordered=ordered,
              table=table,
              meta=meta,
              metadata=metadata,
          )
          self.dtype = dtype
          self.data = data
  
      @property
      def dtype_attr(self) -> str:
          return f"{self.name}_dtype"
  
      @property
      def meta_attr(self) -> str:
          return f"{self.name}_meta"
  
      def __repr__(self) -> str:
          temp = tuple(
              map(
                  pprint_thing, (self.name, self.cname, self.dtype, self.kind, self.shape)
              )
          )
          return ",".join(
              (
                  f"{key}->{value}"
                  for key, value in zip(["name", "cname", "dtype", "kind", "shape"], temp)
              )
          )
  
      def __eq__(self, other: Any) -> bool:
          """ compare 2 col items """
          return all(
              getattr(self, a, None) == getattr(other, a, None)
              for a in ["name", "cname", "dtype", "pos"]
          )
  
      def set_data(self, data: ArrayLike):
          assert data is not None
          assert self.dtype is None
  
          data, dtype_name = _get_data_and_dtype_name(data)
  
          self.data = data
          self.dtype = dtype_name
          self.kind = _dtype_to_kind(dtype_name)
  
      def take_data(self):
          """ return the data """
          return self.data
  
      @classmethod
      def _get_atom(cls, values: ArrayLike) -> "Col":
          """
          Get an appropriately typed and shaped pytables.Col object for values.
          """
          dtype = values.dtype
          # error: "ExtensionDtype" has no attribute "itemsize"
          itemsize = dtype.itemsize  # type: ignore[attr-defined]
  
          shape = values.shape
          if values.ndim == 1:
              # EA, use block shape pretending it is 2D
              # TODO(EA2D): not necessary with 2D EAs
              shape = (1, values.size)
  
          if isinstance(values, Categorical):
              codes = values.codes
              atom = cls.get_atom_data(shape, kind=codes.dtype.name)
          elif is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):
              atom = cls.get_atom_datetime64(shape)
          elif is_timedelta64_dtype(dtype):
              atom = cls.get_atom_timedelta64(shape)
          elif is_complex_dtype(dtype):
              atom = _tables().ComplexCol(itemsize=itemsize, shape=shape[0])
          elif is_string_dtype(dtype):
              atom = cls.get_atom_string(shape, itemsize)
          else:
              atom = cls.get_atom_data(shape, kind=dtype.name)
  
          return atom
  
      @classmethod
      def get_atom_string(cls, shape, itemsize):
          return _tables().StringCol(itemsize=itemsize, shape=shape[0])
  
      @classmethod
      def get_atom_coltype(cls, kind: str) -> Type["Col"]:
          """ return the PyTables column class for this column """
          if kind.startswith("uint"):
              k4 = kind[4:]
              col_name = f"UInt{k4}Col"
          elif kind.startswith("period"):
              # we store as integer
              col_name = "Int64Col"
          else:
              kcap = kind.capitalize()
              col_name = f"{kcap}Col"
  
          return getattr(_tables(), col_name)
  
      @classmethod
      def get_atom_data(cls, shape, kind: str) -> "Col":
          return cls.get_atom_coltype(kind=kind)(shape=shape[0])
  
      @classmethod
      def get_atom_datetime64(cls, shape):
          return _tables().Int64Col(shape=shape[0])
  
      @classmethod
      def get_atom_timedelta64(cls, shape):
          return _tables().Int64Col(shape=shape[0])
  
      @property
      def shape(self):
          return getattr(self.data, "shape", None)
  
      @property
      def cvalues(self):
          """ return my cython values """
          return self.data
  
      def validate_attr(self, append):
          """validate that we have the same order as the existing & same dtype"""
          if append:
              existing_fields = getattr(self.attrs, self.kind_attr, None)
              if existing_fields is not None and existing_fields != list(self.values):
                  raise ValueError("appended items do not match existing items in table!")
  
              existing_dtype = getattr(self.attrs, self.dtype_attr, None)
              if existing_dtype is not None and existing_dtype != self.dtype:
                  raise ValueError(
                      "appended items dtype do not match existing items dtype in table!"
                  )
  
      def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str):
          """
          Convert the data from this selection to the appropriate pandas type.
  
          Parameters
          ----------
          values : np.ndarray
          nan_rep :
          encoding : str
          errors : str
  
          Returns
          -------
          index : listlike to become an Index
          data : ndarraylike to become a column
          """
          assert isinstance(values, np.ndarray), type(values)
  
          # values is a recarray
          if values.dtype.fields is not None:
              values = values[self.cname]
  
          assert self.typ is not None
          if self.dtype is None:
              # Note: in tests we never have timedelta64 or datetime64,
              #  so the _get_data_and_dtype_name may be unnecessary
              converted, dtype_name = _get_data_and_dtype_name(values)
              kind = _dtype_to_kind(dtype_name)
          else:
              converted = values
              dtype_name = self.dtype
              kind = self.kind
  
          assert isinstance(converted, np.ndarray)  # for mypy
  
          # use the meta if needed
          meta = _ensure_decoded(self.meta)
          metadata = self.metadata
          ordered = self.ordered
          tz = self.tz
  
          assert dtype_name is not None
          # convert to the correct dtype
          dtype = _ensure_decoded(dtype_name)
  
          # reverse converts
          if dtype == "datetime64":
              # recreate with tz if indicated
              converted = _set_tz(converted, tz, coerce=True)
  
          elif dtype == "timedelta64":
              converted = np.asarray(converted, dtype="m8[ns]")
          elif dtype == "date":
              try:
                  converted = np.asarray(
                      [date.fromordinal(v) for v in converted], dtype=object
                  )
              except ValueError:
                  converted = np.asarray(
                      [date.fromtimestamp(v) for v in converted], dtype=object
                  )
  
          elif meta == "category":
              # we have a categorical
              categories = metadata
              codes = converted.ravel()
  
              # if we have stored a NaN in the categories
              # then strip it; in theory we could have BOTH
              # -1s in the codes and nulls :<
              if categories is None:
                  # Handle case of NaN-only categorical columns in which case
                  # the categories are an empty array; when this is stored,
                  # pytables cannot write a zero-len array, so on readback
                  # the categories would be None and `read_hdf()` would fail.
                  categories = Index([], dtype=np.float64)
              else:
                  mask = isna(categories)
                  if mask.any():
                      categories = categories[~mask]
                      codes[codes != -1] -= mask.astype(int).cumsum()._values
  
              converted = Categorical.from_codes(
                  codes, categories=categories, ordered=ordered
              )
  
          else:
  
              try:
                  converted = converted.astype(dtype, copy=False)
              except TypeError:
                  converted = converted.astype("O", copy=False)
  
          # convert nans / decode
          if _ensure_decoded(kind) == "string":
              converted = _unconvert_string_array(
                  converted, nan_rep=nan_rep, encoding=encoding, errors=errors
              )
  
          return self.values, converted
  
      def set_attr(self):
          """ set the data for this column """
          setattr(self.attrs, self.kind_attr, self.values)
          setattr(self.attrs, self.meta_attr, self.meta)
          assert self.dtype is not None
          setattr(self.attrs, self.dtype_attr, self.dtype)
  
  
  class DataIndexableCol(DataCol):
      """ represent a data column that can be indexed """
  
      is_data_indexable = True
  
      def validate_names(self):
          if not Index(self.values).is_object():
              # TODO: should the message here be more specifically non-str?
              raise ValueError("cannot have non-object label DataIndexableCol")
  
      @classmethod
      def get_atom_string(cls, shape, itemsize):
          return _tables().StringCol(itemsize=itemsize)
  
      @classmethod
      def get_atom_data(cls, shape, kind: str) -> "Col":
          return cls.get_atom_coltype(kind=kind)()
  
      @classmethod
      def get_atom_datetime64(cls, shape):
          return _tables().Int64Col()
  
      @classmethod
      def get_atom_timedelta64(cls, shape):
          return _tables().Int64Col()
  
  
  class GenericDataIndexableCol(DataIndexableCol):
      """ represent a generic pytables data column """
  
      pass
  
  
  class Fixed:
      """
      represent an object in my store
      facilitate read/write of various types of objects
      this is an abstract base class
  
      Parameters
      ----------
      parent : HDFStore
      group : Node
          The group node where the table resides.
      """
  
      pandas_kind: str
      format_type: str = "fixed"  # GH#30962 needed by dask
      obj_type: Type[FrameOrSeriesUnion]
      ndim: int
      encoding: str
      parent: HDFStore
      group: "Node"
      errors: str
      is_table = False
  
      def __init__(
          self,
          parent: HDFStore,
          group: "Node",
          encoding: str = "UTF-8",
          errors: str = "strict",
      ):
          assert isinstance(parent, HDFStore), type(parent)
          assert _table_mod is not None  # needed for mypy
          assert isinstance(group, _table_mod.Node), type(group)
          self.parent = parent
          self.group = group
          self.encoding = _ensure_encoding(encoding)
          self.errors = errors
  
      @property
      def is_old_version(self) -> bool:
          return self.version[0] <= 0 and self.version[1] <= 10 and self.version[2] < 1
  
      @property
      def version(self) -> Tuple[int, int, int]:
          """ compute and set our version """
          version = _ensure_decoded(getattr(self.group._v_attrs, "pandas_version", None))
          try:
              version = tuple(int(x) for x in version.split("."))
              if len(version) == 2:
                  version = version + (0,)
          except AttributeError:
              version = (0, 0, 0)
          return version
  
      @property
      def pandas_type(self):
          return _ensure_decoded(getattr(self.group._v_attrs, "pandas_type", None))
  
      def __repr__(self) -> str:
          """ return a pretty representation of myself """
          self.infer_axes()
          s = self.shape
          if s is not None:
              if isinstance(s, (list, tuple)):
                  jshape = ",".join(pprint_thing(x) for x in s)
                  s = f"[{jshape}]"
              return f"{self.pandas_type:12.12} (shape->{s})"
          return self.pandas_type
  
      def set_object_info(self):
          """ set my pandas type & version """
          self.attrs.pandas_type = str(self.pandas_kind)
          self.attrs.pandas_version = str(_version)
  
      def copy(self):
          new_self = copy.copy(self)
          return new_self
  
      @property
      def shape(self):
          return self.nrows
  
      @property
      def pathname(self):
          return self.group._v_pathname
  
      @property
      def _handle(self):
          return self.parent._handle
  
      @property
      def _filters(self):
          return self.parent._filters
  
      @property
      def _complevel(self) -> int:
          return self.parent._complevel
  
      @property
      def _fletcher32(self) -> bool:
          return self.parent._fletcher32
  
      @property
      def attrs(self):
          return self.group._v_attrs
  
      def set_attrs(self):
          """ set our object attributes """
          pass
  
      def get_attrs(self):
          """ get our object attributes """
          pass
  
      @property
      def storable(self):
          """ return my storable """
          return self.group
  
      @property
      def is_exists(self) -> bool:
          return False
  
      @property
      def nrows(self):
          return getattr(self.storable, "nrows", None)
  
      def validate(self, other):
          """ validate against an existing storable """
          if other is None:
              return
          return True
  
      def validate_version(self, where=None):
          """ are we trying to operate on an old version? """
          return True
  
      def infer_axes(self):
          """
          infer the axes of my storer
          return a boolean indicating if we have a valid storer or not
          """
          s = self.storable
          if s is None:
              return False
          self.get_attrs()
          return True
  
      def read(
          self,
          where=None,
          columns=None,
          start: Optional[int] = None,
          stop: Optional[int] = None,
      ):
          raise NotImplementedError(
              "cannot read on an abstract storer: subclasses should implement"
          )
  
      def write(self, **kwargs):
          raise NotImplementedError(
              "cannot write on an abstract storer: subclasses should implement"
          )
  
      def delete(
          self, where=None, start: Optional[int] = None, stop: Optional[int] = None
      ):
          """
          support fully deleting the node in its entirety (only) - where
          specification must be None
          """
          if com.all_none(where, start, stop):
              self._handle.remove_node(self.group, recursive=True)
              return None
  
          raise TypeError("cannot delete on an abstract storer")
  
  
  class GenericFixed(Fixed):
      """ a generified fixed version """
  
      _index_type_map = {DatetimeIndex: "datetime", PeriodIndex: "period"}
      _reverse_index_map = {v: k for k, v in _index_type_map.items()}
      attributes: List[str] = []
  
      # indexer helpers
      def _class_to_alias(self, cls) -> str:
          return self._index_type_map.get(cls, "")
  
      def _alias_to_class(self, alias):
          if isinstance(alias, type):  # pragma: no cover
              # compat: for a short period of time master stored types
              return alias
          return self._reverse_index_map.get(alias, Index)
  
      def _get_index_factory(self, attrs):
          index_class = self._alias_to_class(
              _ensure_decoded(getattr(attrs, "index_class", ""))
          )
  
          factory: Callable
  
          if index_class == DatetimeIndex:
  
              def f(values, freq=None, tz=None):
                  # data are already in UTC, localize and convert if tz present
                  dta = DatetimeArray._simple_new(values.values, freq=freq)
                  result = DatetimeIndex._simple_new(dta, name=None)
                  if tz is not None:
                      result = result.tz_localize("UTC").tz_convert(tz)
                  return result
  
              factory = f
          elif index_class == PeriodIndex:
  
              def f(values, freq=None, tz=None):
                  parr = PeriodArray._simple_new(values, freq=freq)
                  return PeriodIndex._simple_new(parr, name=None)
  
              factory = f
          else:
              factory = index_class
  
          kwargs = {}
          if "freq" in attrs:
              kwargs["freq"] = attrs["freq"]
              if index_class is Index:
                  # DTI/PI would be gotten by _alias_to_class
                  factory = TimedeltaIndex
  
          if "tz" in attrs:
              if isinstance(attrs["tz"], bytes):
                  # created by python2
                  kwargs["tz"] = attrs["tz"].decode("utf-8")
              else:
                  # created by python3
                  kwargs["tz"] = attrs["tz"]
              assert index_class is DatetimeIndex  # just checking
  
          return factory, kwargs
  
      def validate_read(self, columns, where):
          """
          raise if any keywords are passed which are not-None
          """
          if columns is not None:
              raise TypeError(
                  "cannot pass a column specification when reading "
                  "a Fixed format store. this store must be selected in its entirety"
              )
          if where is not None:
              raise TypeError(
                  "cannot pass a where specification when reading "
                  "from a Fixed format store. this store must be selected in its entirety"
              )
  
      @property
      def is_exists(self) -> bool:
          return True
  
      def set_attrs(self):
          """ set our object attributes """
          self.attrs.encoding = self.encoding
          self.attrs.errors = self.errors
  
      def get_attrs(self):
          """ retrieve our attributes """
          self.encoding = _ensure_encoding(getattr(self.attrs, "encoding", None))
          self.errors = _ensure_decoded(getattr(self.attrs, "errors", "strict"))
          for n in self.attributes:
              setattr(self, n, _ensure_decoded(getattr(self.attrs, n, None)))
  
      def write(self, obj, **kwargs):
          self.set_attrs()
  
      def read_array(
          self, key: str, start: Optional[int] = None, stop: Optional[int] = None
      ):
          """ read an array for the specified node (off of group """
          import tables
  
          node = getattr(self.group, key)
          attrs = node._v_attrs
  
          transposed = getattr(attrs, "transposed", False)
  
          if isinstance(node, tables.VLArray):
              ret = node[0][start:stop]
          else:
              dtype = _ensure_decoded(getattr(attrs, "value_type", None))
              shape = getattr(attrs, "shape", None)
  
              if shape is not None:
                  # length 0 axis
                  ret = np.empty(shape, dtype=dtype)
              else:
                  ret = node[start:stop]
  
              if dtype == "datetime64":
                  # reconstruct a timezone if indicated
                  tz = getattr(attrs, "tz", None)
                  ret = _set_tz(ret, tz, coerce=True)
  
              elif dtype == "timedelta64":
                  ret = np.asarray(ret, dtype="m8[ns]")
  
          if transposed:
              return ret.T
          else:
              return ret
  
      def read_index(
          self, key: str, start: Optional[int] = None, stop: Optional[int] = None
      ) -> Index:
          variety = _ensure_decoded(getattr(self.attrs, f"{key}_variety"))
  
          if variety == "multi":
              return self.read_multi_index(key, start=start, stop=stop)
          elif variety == "regular":
              node = getattr(self.group, key)
              index = self.read_index_node(node, start=start, stop=stop)
              return index
          else:  # pragma: no cover
              raise TypeError(f"unrecognized index variety: {variety}")
  
      def write_index(self, key: str, index: Index):
          if isinstance(index, MultiIndex):
              setattr(self.attrs, f"{key}_variety", "multi")
              self.write_multi_index(key, index)
          else:
              setattr(self.attrs, f"{key}_variety", "regular")
              converted = _convert_index("index", index, self.encoding, self.errors)
  
              self.write_array(key, converted.values)
  
              node = getattr(self.group, key)
              node._v_attrs.kind = converted.kind
              node._v_attrs.name = index.name
  
              if isinstance(index, (DatetimeIndex, PeriodIndex)):
                  node._v_attrs.index_class = self._class_to_alias(type(index))
  
              if isinstance(index, (DatetimeIndex, PeriodIndex, TimedeltaIndex)):
                  node._v_attrs.freq = index.freq
  
              if isinstance(index, DatetimeIndex) and index.tz is not None:
                  node._v_attrs.tz = _get_tz(index.tz)
  
      def write_multi_index(self, key: str, index: MultiIndex):
          setattr(self.attrs, f"{key}_nlevels", index.nlevels)
  
          for i, (lev, level_codes, name) in enumerate(
              zip(index.levels, index.codes, index.names)
          ):
              # write the level
              if is_extension_array_dtype(lev):
                  raise NotImplementedError(
                      "Saving a MultiIndex with an extension dtype is not supported."
                  )
              level_key = f"{key}_level{i}"
              conv_level = _convert_index(level_key, lev, self.encoding, self.errors)
              self.write_array(level_key, conv_level.values)
              node = getattr(self.group, level_key)
              node._v_attrs.kind = conv_level.kind
              node._v_attrs.name = name
  
              # write the name
              setattr(node._v_attrs, f"{key}_name{name}", name)
  
              # write the labels
              label_key = f"{key}_label{i}"
              self.write_array(label_key, level_codes)
  
      def read_multi_index(
          self, key: str, start: Optional[int] = None, stop: Optional[int] = None
      ) -> MultiIndex:
          nlevels = getattr(self.attrs, f"{key}_nlevels")
  
          levels = []
          codes = []
          names: List[Label] = []
          for i in range(nlevels):
              level_key = f"{key}_level{i}"
              node = getattr(self.group, level_key)
              lev = self.read_index_node(node, start=start, stop=stop)
              levels.append(lev)
              names.append(lev.name)
  
              label_key = f"{key}_label{i}"
              level_codes = self.read_array(label_key, start=start, stop=stop)
              codes.append(level_codes)
  
          return MultiIndex(
              levels=levels, codes=codes, names=names, verify_integrity=True
          )
  
      def read_index_node(
          self, node: "Node", start: Optional[int] = None, stop: Optional[int] = None
      ) -> Index:
          data = node[start:stop]
          # If the index was an empty array write_array_empty() will
          # have written a sentinel. Here we replace it with the original.
          if "shape" in node._v_attrs and np.prod(node._v_attrs.shape) == 0:
              data = np.empty(node._v_attrs.shape, dtype=node._v_attrs.value_type)
          kind = _ensure_decoded(node._v_attrs.kind)
          name = None
  
          if "name" in node._v_attrs:
              name = _ensure_str(node._v_attrs.name)
              name = _ensure_decoded(name)
  
          attrs = node._v_attrs
          factory, kwargs = self._get_index_factory(attrs)
  
          if kind == "date":
              index = factory(
                  _unconvert_index(
                      data, kind, encoding=self.encoding, errors=self.errors
                  ),
                  dtype=object,
                  **kwargs,
              )
          else:
              index = factory(
                  _unconvert_index(
                      data, kind, encoding=self.encoding, errors=self.errors
                  ),
                  **kwargs,
              )
  
          index.name = name
  
          return index
  
      def write_array_empty(self, key: str, value: ArrayLike):
          """ write a 0-len array """
          # ugly hack for length 0 axes
          arr = np.empty((1,) * value.ndim)
          self._handle.create_array(self.group, key, arr)
          node = getattr(self.group, key)
          node._v_attrs.value_type = str(value.dtype)
          node._v_attrs.shape = value.shape
  
      def write_array(self, key: str, obj: FrameOrSeries, items: Optional[Index] = None):
          # TODO: we only have a few tests that get here, the only EA
          #  that gets passed is DatetimeArray, and we never have
          #  both self._filters and EA
  
          value = extract_array(obj, extract_numpy=True)
  
          if key in self.group:
              self._handle.remove_node(self.group, key)
  
          # Transform needed to interface with pytables row/col notation
          empty_array = value.size == 0
          transposed = False
  
          if is_categorical_dtype(value.dtype):
              raise NotImplementedError(
                  "Cannot store a category dtype in a HDF5 dataset that uses format="
                  '"fixed". Use format="table".'
              )
          if not empty_array:
              if hasattr(value, "T"):
                  # ExtensionArrays (1d) may not have transpose.
                  value = value.T
                  transposed = True
  
          atom = None
          if self._filters is not None:
              with suppress(ValueError):
                  # get the atom for this datatype
                  atom = _tables().Atom.from_dtype(value.dtype)
  
          if atom is not None:
              # We only get here if self._filters is non-None and
              #  the Atom.from_dtype call succeeded
  
              # create an empty chunked array and fill it from value
              if not empty_array:
                  ca = self._handle.create_carray(
                      self.group, key, atom, value.shape, filters=self._filters
                  )
                  ca[:] = value
  
              else:
                  self.write_array_empty(key, value)
  
          elif value.dtype.type == np.object_:
              # infer the type, warn if we have a non-string type here (for
              # performance)
              inferred_type = lib.infer_dtype(value, skipna=False)
              if empty_array:
                  pass
              elif inferred_type == "string":
                  pass
              else:
                  ws = performance_doc % (inferred_type, key, items)
                  warnings.warn(ws, PerformanceWarning, stacklevel=7)
  
              vlarr = self._handle.create_vlarray(self.group, key, _tables().ObjectAtom())
              vlarr.append(value)
  
          elif is_datetime64_dtype(value.dtype):
              self._handle.create_array(self.group, key, value.view("i8"))
              getattr(self.group, key)._v_attrs.value_type = "datetime64"
          elif is_datetime64tz_dtype(value.dtype):
              # store as UTC
              # with a zone
              self._handle.create_array(self.group, key, value.asi8)
  
              node = getattr(self.group, key)
              node._v_attrs.tz = _get_tz(value.tz)
              node._v_attrs.value_type = "datetime64"
          elif is_timedelta64_dtype(value.dtype):
              self._handle.create_array(self.group, key, value.view("i8"))
              getattr(self.group, key)._v_attrs.value_type = "timedelta64"
          elif empty_array:
              self.write_array_empty(key, value)
          else:
              self._handle.create_array(self.group, key, value)
  
          getattr(self.group, key)._v_attrs.transposed = transposed
  
  
  class SeriesFixed(GenericFixed):
      pandas_kind = "series"
      attributes = ["name"]
  
      name: Label
  
      @property
      def shape(self):
          try:
              return (len(self.group.values),)
          except (TypeError, AttributeError):
              return None
  
      def read(
          self,
          where=None,
          columns=None,
          start: Optional[int] = None,
          stop: Optional[int] = None,
      ):
          self.validate_read(columns, where)
          index = self.read_index("index", start=start, stop=stop)
          values = self.read_array("values", start=start, stop=stop)
          return Series(values, index=index, name=self.name)
  
      def write(self, obj, **kwargs):
          super().write(obj, **kwargs)
          self.write_index("index", obj.index)
          self.write_array("values", obj)
          self.attrs.name = obj.name
  
  
  class BlockManagerFixed(GenericFixed):
      attributes = ["ndim", "nblocks"]
  
      nblocks: int
  
      @property
      def shape(self) -> Optional[Shape]:
          try:
              ndim = self.ndim
  
              # items
              items = 0
              for i in range(self.nblocks):
                  node = getattr(self.group, f"block{i}_items")
                  shape = getattr(node, "shape", None)
                  if shape is not None:
                      items += shape[0]
  
              # data shape
              node = self.group.block0_values
              shape = getattr(node, "shape", None)
              if shape is not None:
                  shape = list(shape[0 : (ndim - 1)])
              else:
                  shape = []
  
              shape.append(items)
  
              return shape
          except AttributeError:
              return None
  
      def read(
          self,
          where=None,
          columns=None,
          start: Optional[int] = None,
          stop: Optional[int] = None,
      ):
          # start, stop applied to rows, so 0th axis only
          self.validate_read(columns, where)
          select_axis = self.obj_type()._get_block_manager_axis(0)
  
          axes = []
          for i in range(self.ndim):
  
              _start, _stop = (start, stop) if i == select_axis else (None, None)
              ax = self.read_index(f"axis{i}", start=_start, stop=_stop)
              axes.append(ax)
  
          items = axes[0]
          dfs = []
  
          for i in range(self.nblocks):
  
              blk_items = self.read_index(f"block{i}_items")
              values = self.read_array(f"block{i}_values", start=_start, stop=_stop)
  
              columns = items[items.get_indexer(blk_items)]
              df = DataFrame(values.T, columns=columns, index=axes[1])
              dfs.append(df)
  
          if len(dfs) > 0:
              out = concat(dfs, axis=1)
              out = out.reindex(columns=items, copy=False)
              return out
  
          return DataFrame(columns=axes[0], index=axes[1])
  
      def write(self, obj, **kwargs):
          super().write(obj, **kwargs)
          data = obj._mgr
          if not data.is_consolidated():
              data = data.consolidate()
  
          self.attrs.ndim = data.ndim
          for i, ax in enumerate(data.axes):
              if i == 0 and (not ax.is_unique):
                  raise ValueError("Columns index has to be unique for fixed format")
              self.write_index(f"axis{i}", ax)
  
          # Supporting mixed-type DataFrame objects...nontrivial
          self.attrs.nblocks = len(data.blocks)
          for i, blk in enumerate(data.blocks):
              # I have no idea why, but writing values before items fixed #2299
              blk_items = data.items.take(blk.mgr_locs)
              self.write_array(f"block{i}_values", blk.values, items=blk_items)
              self.write_index(f"block{i}_items", blk_items)
  
  
  class FrameFixed(BlockManagerFixed):
      pandas_kind = "frame"
      obj_type = DataFrame
  
  
  class Table(Fixed):
      """
      represent a table:
          facilitate read/write of various types of tables
  
      Attrs in Table Node
      -------------------
      These are attributes that are store in the main table node, they are
      necessary to recreate these tables when read back in.
  
      index_axes    : a list of tuples of the (original indexing axis and
          index column)
      non_index_axes: a list of tuples of the (original index axis and
          columns on a non-indexing axis)
      values_axes   : a list of the columns which comprise the data of this
          table
      data_columns  : a list of the columns that we are allowing indexing
          (these become single columns in values_axes), or True to force all
          columns
      nan_rep       : the string to use for nan representations for string
          objects
      levels        : the names of levels
      metadata      : the names of the metadata columns
      """
  
      pandas_kind = "wide_table"
      format_type: str = "table"  # GH#30962 needed by dask
      table_type: str
      levels: Union[int, List[Label]] = 1
      is_table = True
  
      index_axes: List[IndexCol]
      non_index_axes: List[Tuple[int, Any]]
      values_axes: List[DataCol]
      data_columns: List
      metadata: List
      info: Dict
  
      def __init__(
          self,
          parent: HDFStore,
          group: "Node",
          encoding=None,
          errors: str = "strict",
          index_axes=None,
          non_index_axes=None,
          values_axes=None,
          data_columns=None,
          info=None,
          nan_rep=None,
      ):
          super().__init__(parent, group, encoding=encoding, errors=errors)
          self.index_axes = index_axes or []
          self.non_index_axes = non_index_axes or []
          self.values_axes = values_axes or []
          self.data_columns = data_columns or []
          self.info = info or {}
          self.nan_rep = nan_rep
  
      @property
      def table_type_short(self) -> str:
          return self.table_type.split("_")[0]
  
      def __repr__(self) -> str:
          """ return a pretty representation of myself """
          self.infer_axes()
          jdc = ",".join(self.data_columns) if len(self.data_columns) else ""
          dc = f",dc->[{jdc}]"
  
          ver = ""
          if self.is_old_version:
              jver = ".".join(str(x) for x in self.version)
              ver = f"[{jver}]"
  
          jindex_axes = ",".join(a.name for a in self.index_axes)
          return (
              f"{self.pandas_type:12.12}{ver} "
              f"(typ->{self.table_type_short},nrows->{self.nrows},"
              f"ncols->{self.ncols},indexers->[{jindex_axes}]{dc})"
          )
  
      def __getitem__(self, c: str):
          """ return the axis for c """
          for a in self.axes:
              if c == a.name:
                  return a
          return None
  
      def validate(self, other):
          """ validate against an existing table """
          if other is None:
              return
  
          if other.table_type != self.table_type:
              raise TypeError(
                  "incompatible table_type with existing "
                  f"[{other.table_type} - {self.table_type}]"
              )
  
          for c in ["index_axes", "non_index_axes", "values_axes"]:
              sv = getattr(self, c, None)
              ov = getattr(other, c, None)
              if sv != ov:
  
                  # show the error for the specific axes
                  for i, sax in enumerate(sv):
                      oax = ov[i]
                      if sax != oax:
                          raise ValueError(
                              f"invalid combination of [{c}] on appending data "
                              f"[{sax}] vs current table [{oax}]"
                          )
  
                  # should never get here
                  raise Exception(
                      f"invalid combination of [{c}] on appending data [{sv}] vs "
                      f"current table [{ov}]"
                  )
  
      @property
      def is_multi_index(self) -> bool:
          """the levels attribute is 1 or a list in the case of a multi-index"""
          return isinstance(self.levels, list)
  
      def validate_multiindex(
          self, obj: FrameOrSeriesUnion
      ) -> Tuple[DataFrame, List[Label]]:
          """
          validate that we can store the multi-index; reset and return the
          new object
          """
          levels = [
              l if l is not None else f"level_{i}" for i, l in enumerate(obj.index.names)
          ]
          try:
              reset_obj = obj.reset_index()
          except ValueError as err:
              raise ValueError(
                  "duplicate names/columns in the multi-index when storing as a table"
              ) from err
          assert isinstance(reset_obj, DataFrame)  # for mypy
          return reset_obj, levels
  
      @property
      def nrows_expected(self) -> int:
          """ based on our axes, compute the expected nrows """
          return np.prod([i.cvalues.shape[0] for i in self.index_axes])
  
      @property
      def is_exists(self) -> bool:
          """ has this table been created """
          return "table" in self.group
  
      @property
      def storable(self):
          return getattr(self.group, "table", None)
  
      @property
      def table(self):
          """ return the table group (this is my storable) """
          return self.storable
  
      @property
      def dtype(self):
          return self.table.dtype
  
      @property
      def description(self):
          return self.table.description
  
      @property
      def axes(self):
          return itertools.chain(self.index_axes, self.values_axes)
  
      @property
      def ncols(self) -> int:
          """ the number of total columns in the values axes """
          return sum(len(a.values) for a in self.values_axes)
  
      @property
      def is_transposed(self) -> bool:
          return False
  
      @property
      def data_orientation(self):
          """return a tuple of my permutated axes, non_indexable at the front"""
          return tuple(
              itertools.chain(
                  [int(a[0]) for a in self.non_index_axes],
                  [int(a.axis) for a in self.index_axes],
              )
          )
  
      def queryables(self) -> Dict[str, Any]:
          """ return a dict of the kinds allowable columns for this object """
          # mypy doesn't recognize DataFrame._AXIS_NAMES, so we re-write it here
          axis_names = {0: "index", 1: "columns"}
  
          # compute the values_axes queryables
          d1 = [(a.cname, a) for a in self.index_axes]
          d2 = [(axis_names[axis], None) for axis, values in self.non_index_axes]
          d3 = [
              (v.cname, v) for v in self.values_axes if v.name in set(self.data_columns)
          ]
  
          # error: Unsupported operand types for + ("List[Tuple[str, IndexCol]]"
          # and "List[Tuple[str, None]]")
          return dict(d1 + d2 + d3)  # type: ignore[operator]
  
      def index_cols(self):
          """ return a list of my index cols """
          # Note: each `i.cname` below is assured to be a str.
          return [(i.axis, i.cname) for i in self.index_axes]
  
      def values_cols(self) -> List[str]:
          """ return a list of my values cols """
          return [i.cname for i in self.values_axes]
  
      def _get_metadata_path(self, key: str) -> str:
          """ return the metadata pathname for this key """
          group = self.group._v_pathname
          return f"{group}/meta/{key}/meta"
  
      def write_metadata(self, key: str, values: np.ndarray):
          """
          Write out a metadata array to the key as a fixed-format Series.
  
          Parameters
          ----------
          key : str
          values : ndarray
          """
          values = Series(values)
          self.parent.put(
              self._get_metadata_path(key),
              values,
              format="table",
              encoding=self.encoding,
              errors=self.errors,
              nan_rep=self.nan_rep,
          )
  
      def read_metadata(self, key: str):
          """ return the meta data array for this key """
          if getattr(getattr(self.group, "meta", None), key, None) is not None:
              return self.parent.select(self._get_metadata_path(key))
          return None
  
      def set_attrs(self):
          """ set our table type & indexables """
          self.attrs.table_type = str(self.table_type)
          self.attrs.index_cols = self.index_cols()
          self.attrs.values_cols = self.values_cols()
          self.attrs.non_index_axes = self.non_index_axes
          self.attrs.data_columns = self.data_columns
          self.attrs.nan_rep = self.nan_rep
          self.attrs.encoding = self.encoding
          self.attrs.errors = self.errors
          self.attrs.levels = self.levels
          self.attrs.info = self.info
  
      def get_attrs(self):
          """ retrieve our attributes """
          self.non_index_axes = getattr(self.attrs, "non_index_axes", None) or []
          self.data_columns = getattr(self.attrs, "data_columns", None) or []
          self.info = getattr(self.attrs, "info", None) or {}
          self.nan_rep = getattr(self.attrs, "nan_rep", None)
          self.encoding = _ensure_encoding(getattr(self.attrs, "encoding", None))
          self.errors = _ensure_decoded(getattr(self.attrs, "errors", "strict"))
          self.levels: List[Label] = getattr(self.attrs, "levels", None) or []
          self.index_axes = [a for a in self.indexables if a.is_an_indexable]
          self.values_axes = [a for a in self.indexables if not a.is_an_indexable]
  
      def validate_version(self, where=None):
          """ are we trying to operate on an old version? """
          if where is not None:
              if self.version[0] <= 0 and self.version[1] <= 10 and self.version[2] < 1:
                  ws = incompatibility_doc % ".".join([str(x) for x in self.version])
                  warnings.warn(ws, IncompatibilityWarning)
  
      def validate_min_itemsize(self, min_itemsize):
          """
          validate the min_itemsize doesn't contain items that are not in the
          axes this needs data_columns to be defined
          """
          if min_itemsize is None:
              return
          if not isinstance(min_itemsize, dict):
              return
  
          q = self.queryables()
          for k, v in min_itemsize.items():
  
              # ok, apply generally
              if k == "values":
                  continue
              if k not in q:
                  raise ValueError(
                      f"min_itemsize has the key [{k}] which is not an axis or "
                      "data_column"
                  )
  
      @cache_readonly
      def indexables(self):
          """ create/cache the indexables if they don't exist """
          _indexables = []
  
          desc = self.description
          table_attrs = self.table.attrs
  
          # Note: each of the `name` kwargs below are str, ensured
          #  by the definition in index_cols.
          # index columns
          for i, (axis, name) in enumerate(self.attrs.index_cols):
              atom = getattr(desc, name)
              md = self.read_metadata(name)
              meta = "category" if md is not None else None
  
              kind_attr = f"{name}_kind"
              kind = getattr(table_attrs, kind_attr, None)
  
              index_col = IndexCol(
                  name=name,
                  axis=axis,
                  pos=i,
                  kind=kind,
                  typ=atom,
                  table=self.table,
                  meta=meta,
                  metadata=md,
              )
              _indexables.append(index_col)
  
          # values columns
          dc = set(self.data_columns)
          base_pos = len(_indexables)
  
          def f(i, c):
              assert isinstance(c, str)
              klass = DataCol
              if c in dc:
                  klass = DataIndexableCol
  
              atom = getattr(desc, c)
              adj_name = _maybe_adjust_name(c, self.version)
  
              # TODO: why kind_attr here?
              values = getattr(table_attrs, f"{adj_name}_kind", None)
              dtype = getattr(table_attrs, f"{adj_name}_dtype", None)
              kind = _dtype_to_kind(dtype)
  
              md = self.read_metadata(c)
              # TODO: figure out why these two versions of `meta` dont always match.
              #  meta = "category" if md is not None else None
              meta = getattr(table_attrs, f"{adj_name}_meta", None)
  
              obj = klass(
                  name=adj_name,
                  cname=c,
                  values=values,
                  kind=kind,
                  pos=base_pos + i,
                  typ=atom,
                  table=self.table,
                  meta=meta,
                  metadata=md,
                  dtype=dtype,
              )
              return obj
  
          # Note: the definition of `values_cols` ensures that each
          #  `c` below is a str.
          _indexables.extend([f(i, c) for i, c in enumerate(self.attrs.values_cols)])
  
          return _indexables
  
      def create_index(self, columns=None, optlevel=None, kind: Optional[str] = None):
          """
          Create a pytables index on the specified columns.
  
          Parameters
          ----------
          columns : None, bool, or listlike[str]
              Indicate which columns to create an index on.
  
              * False : Do not create any indexes.
              * True : Create indexes on all columns.
              * None : Create indexes on all columns.
              * listlike : Create indexes on the given columns.
  
          optlevel : int or None, default None
              Optimization level, if None, pytables defaults to 6.
          kind : str or None, default None
              Kind of index, if None, pytables defaults to "medium".
  
          Raises
          ------
          TypeError if trying to create an index on a complex-type column.
  
          Notes
          -----
          Cannot index Time64Col or ComplexCol.
          Pytables must be >= 3.0.
          """
          if not self.infer_axes():
              return
          if columns is False:
              return
  
          # index all indexables and data_columns
          if columns is None or columns is True:
              columns = [a.cname for a in self.axes if a.is_data_indexable]
          if not isinstance(columns, (tuple, list)):
              columns = [columns]
  
          kw = {}
          if optlevel is not None:
              kw["optlevel"] = optlevel
          if kind is not None:
              kw["kind"] = kind
  
          table = self.table
          for c in columns:
              v = getattr(table.cols, c, None)
              if v is not None:
                  # remove the index if the kind/optlevel have changed
                  if v.is_indexed:
                      index = v.index
                      cur_optlevel = index.optlevel
                      cur_kind = index.kind
  
                      if kind is not None and cur_kind != kind:
                          v.remove_index()
                      else:
                          kw["kind"] = cur_kind
  
                      if optlevel is not None and cur_optlevel != optlevel:
                          v.remove_index()
                      else:
                          kw["optlevel"] = cur_optlevel
  
                  # create the index
                  if not v.is_indexed:
                      if v.type.startswith("complex"):
                          raise TypeError(
                              "Columns containing complex values can be stored but "
                              "cannot be indexed when using table format. Either use "
                              "fixed format, set index=False, or do not include "
                              "the columns containing complex values to "
                              "data_columns when initializing the table."
                          )
                      v.create_index(**kw)
              elif c in self.non_index_axes[0][1]:
                  # GH 28156
                  raise AttributeError(
                      f"column {c} is not a data_column.\n"
                      f"In order to read column {c} you must reload the dataframe \n"
                      f"into HDFStore and include {c} with the data_columns argument."
                  )
  
      def _read_axes(
          self, where, start: Optional[int] = None, stop: Optional[int] = None
      ) -> List[Tuple[ArrayLike, ArrayLike]]:
          """
          Create the axes sniffed from the table.
  
          Parameters
          ----------
          where : ???
          start : int or None, default None
          stop : int or None, default None
  
          Returns
          -------
          List[Tuple[index_values, column_values]]
          """
          # create the selection
          selection = Selection(self, where=where, start=start, stop=stop)
          values = selection.select()
  
          results = []
          # convert the data
          for a in self.axes:
              a.set_info(self.info)
              res = a.convert(
                  values,
                  nan_rep=self.nan_rep,
                  encoding=self.encoding,
                  errors=self.errors,
              )
              results.append(res)
  
          return results
  
      @classmethod
      def get_object(cls, obj, transposed: bool):
          """ return the data for this obj """
          return obj
  
      def validate_data_columns(self, data_columns, min_itemsize, non_index_axes):
          """
          take the input data_columns and min_itemize and create a data
          columns spec
          """
          if not len(non_index_axes):
              return []
  
          axis, axis_labels = non_index_axes[0]
          info = self.info.get(axis, {})
          if info.get("type") == "MultiIndex" and data_columns:
              raise ValueError(
                  f"cannot use a multi-index on axis [{axis}] with "
                  f"data_columns {data_columns}"
              )
  
          # evaluate the passed data_columns, True == use all columns
          # take only valid axis labels
          if data_columns is True:
              data_columns = list(axis_labels)
          elif data_columns is None:
              data_columns = []
  
          # if min_itemsize is a dict, add the keys (exclude 'values')
          if isinstance(min_itemsize, dict):
              existing_data_columns = set(data_columns)
              data_columns = list(data_columns)  # ensure we do not modify
              data_columns.extend(
                  [
                      k
                      for k in min_itemsize.keys()
                      if k != "values" and k not in existing_data_columns
                  ]
              )
  
          # return valid columns in the order of our axis
          return [c for c in data_columns if c in axis_labels]
  
      def _create_axes(
          self,
          axes,
          obj: DataFrame,
          validate: bool = True,
          nan_rep=None,
          data_columns=None,
          min_itemsize=None,
      ):
          """
          Create and return the axes.
  
          Parameters
          ----------
          axes: list or None
              The names or numbers of the axes to create.
          obj : DataFrame
              The object to create axes on.
          validate: bool, default True
              Whether to validate the obj against an existing object already written.
          nan_rep :
              A value to use for string column nan_rep.
          data_columns : List[str], True, or None, default None
              Specify the columns that we want to create to allow indexing on.
  
              * True : Use all available columns.
              * None : Use no columns.
              * List[str] : Use the specified columns.
  
          min_itemsize: Dict[str, int] or None, default None
              The min itemsize for a column in bytes.
          """
          if not isinstance(obj, DataFrame):
              group = self.group._v_name
              raise TypeError(
                  f"cannot properly create the storer for: [group->{group},"
                  f"value->{type(obj)}]"
              )
  
          # set the default axes if needed
          if axes is None:
              axes = [0]
  
          # map axes to numbers
          axes = [obj._get_axis_number(a) for a in axes]
  
          # do we have an existing table (if so, use its axes & data_columns)
          if self.infer_axes():
              table_exists = True
              axes = [a.axis for a in self.index_axes]
              data_columns = list(self.data_columns)
              nan_rep = self.nan_rep
              # TODO: do we always have validate=True here?
          else:
              table_exists = False
  
          new_info = self.info
  
          assert self.ndim == 2  # with next check, we must have len(axes) == 1
          # currently support on ndim-1 axes
          if len(axes) != self.ndim - 1:
              raise ValueError(
                  "currently only support ndim-1 indexers in an AppendableTable"
              )
  
          # create according to the new data
          new_non_index_axes: List = []
  
          # nan_representation
          if nan_rep is None:
              nan_rep = "nan"
  
          # We construct the non-index-axis first, since that alters new_info
          idx = [x for x in [0, 1] if x not in axes][0]
  
          a = obj.axes[idx]
          # we might be able to change the axes on the appending data if necessary
          append_axis = list(a)
          if table_exists:
              indexer = len(new_non_index_axes)  # i.e. 0
              exist_axis = self.non_index_axes[indexer][1]
              if not array_equivalent(np.array(append_axis), np.array(exist_axis)):
  
                  # ahah! -> reindex
                  if array_equivalent(
                      np.array(sorted(append_axis)), np.array(sorted(exist_axis))
                  ):
                      append_axis = exist_axis
  
          # the non_index_axes info
          info = new_info.setdefault(idx, {})
          info["names"] = list(a.names)
          info["type"] = type(a).__name__
  
          new_non_index_axes.append((idx, append_axis))
  
          # Now we can construct our new index axis
          idx = axes[0]
          a = obj.axes[idx]
          axis_name = obj._get_axis_name(idx)
          new_index = _convert_index(axis_name, a, self.encoding, self.errors)
          new_index.axis = idx
  
          # Because we are always 2D, there is only one new_index, so
          #  we know it will have pos=0
          new_index.set_pos(0)
          new_index.update_info(new_info)
          new_index.maybe_set_size(min_itemsize)  # check for column conflicts
  
          new_index_axes = [new_index]
          j = len(new_index_axes)  # i.e. 1
          assert j == 1
  
          # reindex by our non_index_axes & compute data_columns
          assert len(new_non_index_axes) == 1
          for a in new_non_index_axes:
              obj = _reindex_axis(obj, a[0], a[1])
  
          transposed = new_index.axis == 1
  
          # figure out data_columns and get out blocks
          data_columns = self.validate_data_columns(
              data_columns, min_itemsize, new_non_index_axes
          )
  
          frame = self.get_object(obj, transposed)._consolidate()
  
          blocks, blk_items = self._get_blocks_and_items(
              frame, table_exists, new_non_index_axes, self.values_axes, data_columns
          )
  
          # add my values
          vaxes = []
          for i, (b, b_items) in enumerate(zip(blocks, blk_items)):
  
              # shape of the data column are the indexable axes
              klass = DataCol
              name = None
  
              # we have a data_column
              if data_columns and len(b_items) == 1 and b_items[0] in data_columns:
                  klass = DataIndexableCol
                  name = b_items[0]
                  if not (name is None or isinstance(name, str)):
                      # TODO: should the message here be more specifically non-str?
                      raise ValueError("cannot have non-object label DataIndexableCol")
  
              # make sure that we match up the existing columns
              # if we have an existing table
              existing_col: Optional[DataCol]
  
              if table_exists and validate:
                  try:
                      existing_col = self.values_axes[i]
                  except (IndexError, KeyError) as err:
                      raise ValueError(
                          f"Incompatible appended table [{blocks}]"
                          f"with existing table [{self.values_axes}]"
                      ) from err
              else:
                  existing_col = None
  
              new_name = name or f"values_block_{i}"
              data_converted = _maybe_convert_for_string_atom(
                  new_name,
                  b,
                  existing_col=existing_col,
                  min_itemsize=min_itemsize,
                  nan_rep=nan_rep,
                  encoding=self.encoding,
                  errors=self.errors,
                  block_columns=b_items,
              )
              adj_name = _maybe_adjust_name(new_name, self.version)
  
              typ = klass._get_atom(data_converted)
              kind = _dtype_to_kind(data_converted.dtype.name)
              tz = _get_tz(data_converted.tz) if hasattr(data_converted, "tz") else None
  
              meta = metadata = ordered = None
              if is_categorical_dtype(data_converted.dtype):
                  ordered = data_converted.ordered
                  meta = "category"
                  metadata = np.array(data_converted.categories, copy=False).ravel()
  
              data, dtype_name = _get_data_and_dtype_name(data_converted)
  
              col = klass(
                  name=adj_name,
                  cname=new_name,
                  values=list(b_items),
                  typ=typ,
                  pos=j,
                  kind=kind,
                  tz=tz,
                  ordered=ordered,
                  meta=meta,
                  metadata=metadata,
                  dtype=dtype_name,
                  data=data,
              )
              col.update_info(new_info)
  
              vaxes.append(col)
  
              j += 1
  
          dcs = [col.name for col in vaxes if col.is_data_indexable]
  
          new_table = type(self)(
              parent=self.parent,
              group=self.group,
              encoding=self.encoding,
              errors=self.errors,
              index_axes=new_index_axes,
              non_index_axes=new_non_index_axes,
              values_axes=vaxes,
              data_columns=dcs,
              info=new_info,
              nan_rep=nan_rep,
          )
          if hasattr(self, "levels"):
              # TODO: get this into constructor, only for appropriate subclass
              new_table.levels = self.levels
  
          new_table.validate_min_itemsize(min_itemsize)
  
          if validate and table_exists:
              new_table.validate(self)
  
          return new_table
  
      @staticmethod
      def _get_blocks_and_items(
          frame: DataFrame,
          table_exists: bool,
          new_non_index_axes,
          values_axes,
          data_columns,
      ):
          # Helper to clarify non-state-altering parts of _create_axes
  
          def get_blk_items(mgr):
              return [mgr.items.take(blk.mgr_locs) for blk in mgr.blocks]
  
          blocks: List["Block"] = list(frame._mgr.blocks)
          blk_items: List[Index] = get_blk_items(frame._mgr)
  
          if len(data_columns):
              axis, axis_labels = new_non_index_axes[0]
              new_labels = Index(axis_labels).difference(Index(data_columns))
              mgr = frame.reindex(new_labels, axis=axis)._mgr
  
              blocks = list(mgr.blocks)
              blk_items = get_blk_items(mgr)
              for c in data_columns:
                  mgr = frame.reindex([c], axis=axis)._mgr
                  blocks.extend(mgr.blocks)
                  blk_items.extend(get_blk_items(mgr))
  
          # reorder the blocks in the same order as the existing table if we can
          if table_exists:
              by_items = {
                  tuple(b_items.tolist()): (b, b_items)
                  for b, b_items in zip(blocks, blk_items)
              }
              new_blocks: List["Block"] = []
              new_blk_items = []
              for ea in values_axes:
                  items = tuple(ea.values)
                  try:
                      b, b_items = by_items.pop(items)
                      new_blocks.append(b)
                      new_blk_items.append(b_items)
                  except (IndexError, KeyError) as err:
                      jitems = ",".join(pprint_thing(item) for item in items)
                      raise ValueError(
                          f"cannot match existing table structure for [{jitems}] "
                          "on appending data"
                      ) from err
              blocks = new_blocks
              blk_items = new_blk_items
  
          return blocks, blk_items
  
      def process_axes(self, obj, selection: "Selection", columns=None):
          """ process axes filters """
          # make a copy to avoid side effects
          if columns is not None:
              columns = list(columns)
  
          # make sure to include levels if we have them
          if columns is not None and self.is_multi_index:
              assert isinstance(self.levels, list)  # assured by is_multi_index
              for n in self.levels:
                  if n not in columns:
                      columns.insert(0, n)
  
          # reorder by any non_index_axes & limit to the select columns
          for axis, labels in self.non_index_axes:
              obj = _reindex_axis(obj, axis, labels, columns)
  
          # apply the selection filters (but keep in the same order)
          if selection.filter is not None:
              for field, op, filt in selection.filter.format():
  
                  def process_filter(field, filt):
  
                      for axis_name in obj._AXIS_ORDERS:
                          axis_number = obj._get_axis_number(axis_name)
                          axis_values = obj._get_axis(axis_name)
                          assert axis_number is not None
  
                          # see if the field is the name of an axis
                          if field == axis_name:
  
                              # if we have a multi-index, then need to include
                              # the levels
                              if self.is_multi_index:
                                  filt = filt.union(Index(self.levels))
  
                              takers = op(axis_values, filt)
                              return obj.loc(axis=axis_number)[takers]
  
                          # this might be the name of a file IN an axis
                          elif field in axis_values:
  
                              # we need to filter on this dimension
                              values = ensure_index(getattr(obj, field).values)
                              filt = ensure_index(filt)
  
                              # hack until we support reversed dim flags
                              if isinstance(obj, DataFrame):
                                  axis_number = 1 - axis_number
                              takers = op(values, filt)
                              return obj.loc(axis=axis_number)[takers]
  
                      raise ValueError(f"cannot find the field [{field}] for filtering!")
  
                  obj = process_filter(field, filt)
  
          return obj
  
      def create_description(
          self,
          complib,
          complevel: Optional[int],
          fletcher32: bool,
          expectedrows: Optional[int],
      ) -> Dict[str, Any]:
          """ create the description of the table from the axes & values """
          # provided expected rows if its passed
          if expectedrows is None:
              expectedrows = max(self.nrows_expected, 10000)
  
          d = {"name": "table", "expectedrows": expectedrows}
  
          # description from the axes & values
          d["description"] = {a.cname: a.typ for a in self.axes}
  
          if complib:
              if complevel is None:
                  complevel = self._complevel or 9
              filters = _tables().Filters(
                  complevel=complevel,
                  complib=complib,
                  fletcher32=fletcher32 or self._fletcher32,
              )
              d["filters"] = filters
          elif self._filters is not None:
              d["filters"] = self._filters
  
          return d
  
      def read_coordinates(
          self, where=None, start: Optional[int] = None, stop: Optional[int] = None
      ):
          """
          select coordinates (row numbers) from a table; return the
          coordinates object
          """
          # validate the version
          self.validate_version(where)
  
          # infer the data kind
          if not self.infer_axes():
              return False
  
          # create the selection
          selection = Selection(self, where=where, start=start, stop=stop)
          coords = selection.select_coords()
          if selection.filter is not None:
              for field, op, filt in selection.filter.format():
                  data = self.read_column(
                      field, start=coords.min(), stop=coords.max() + 1
                  )
                  coords = coords[op(data.iloc[coords - coords.min()], filt).values]
  
          return Index(coords)
  
      def read_column(
          self,
          column: str,
          where=None,
          start: Optional[int] = None,
          stop: Optional[int] = None,
      ):
          """
          return a single column from the table, generally only indexables
          are interesting
          """
          # validate the version
          self.validate_version()
  
          # infer the data kind
          if not self.infer_axes():
              return False
  
          if where is not None:
              raise TypeError("read_column does not currently accept a where clause")
  
          # find the axes
          for a in self.axes:
              if column == a.name:
                  if not a.is_data_indexable:
                      raise ValueError(
                          f"column [{column}] can not be extracted individually; "
                          "it is not data indexable"
                      )
  
                  # column must be an indexable or a data column
                  c = getattr(self.table.cols, column)
                  a.set_info(self.info)
                  col_values = a.convert(
                      c[start:stop],
                      nan_rep=self.nan_rep,
                      encoding=self.encoding,
                      errors=self.errors,
                  )
                  return Series(_set_tz(col_values[1], a.tz), name=column)
  
          raise KeyError(f"column [{column}] not found in the table")
  
  
  class WORMTable(Table):
      """
      a write-once read-many table: this format DOES NOT ALLOW appending to a
      table. writing is a one-time operation the data are stored in a format
      that allows for searching the data on disk
      """
  
      table_type = "worm"
  
      def read(
          self,
          where=None,
          columns=None,
          start: Optional[int] = None,
          stop: Optional[int] = None,
      ):
          """
          read the indices and the indexing array, calculate offset rows and return
          """
          raise NotImplementedError("WORMTable needs to implement read")
  
      def write(self, **kwargs):
          """
          write in a format that we can search later on (but cannot append
          to): write out the indices and the values using _write_array
          (e.g. a CArray) create an indexing table so that we can search
          """
          raise NotImplementedError("WORMTable needs to implement write")
  
  
  class AppendableTable(Table):
      """ support the new appendable table formats """
  
      table_type = "appendable"
  
      def write(
          self,
          obj,
          axes=None,
          append=False,
          complib=None,
          complevel=None,
          fletcher32=None,
          min_itemsize=None,
          chunksize=None,
          expectedrows=None,
          dropna=False,
          nan_rep=None,
          data_columns=None,
          track_times=True,
      ):
          if not append and self.is_exists:
              self._handle.remove_node(self.group, "table")
  
          # create the axes
          table = self._create_axes(
              axes=axes,
              obj=obj,
              validate=append,
              min_itemsize=min_itemsize,
              nan_rep=nan_rep,
              data_columns=data_columns,
          )
  
          for a in table.axes:
              a.validate_names()
  
          if not table.is_exists:
  
              # create the table
              options = table.create_description(
                  complib=complib,
                  complevel=complevel,
                  fletcher32=fletcher32,
                  expectedrows=expectedrows,
              )
  
              # set the table attributes
              table.set_attrs()
  
              options["track_times"] = track_times
  
              # create the table
              table._handle.create_table(table.group, **options)
  
          # update my info
          table.attrs.info = table.info
  
          # validate the axes and set the kinds
          for a in table.axes:
              a.validate_and_set(table, append)
  
          # add the rows
          table.write_data(chunksize, dropna=dropna)
  
      def write_data(self, chunksize: Optional[int], dropna: bool = False):
          """
          we form the data into a 2-d including indexes,values,mask write chunk-by-chunk
          """
          names = self.dtype.names
          nrows = self.nrows_expected
  
          # if dropna==True, then drop ALL nan rows
          masks = []
          if dropna:
              for a in self.values_axes:
                  # figure the mask: only do if we can successfully process this
                  # column, otherwise ignore the mask
                  mask = isna(a.data).all(axis=0)
                  if isinstance(mask, np.ndarray):
                      masks.append(mask.astype("u1", copy=False))
  
          # consolidate masks
          if len(masks):
              mask = masks[0]
              for m in masks[1:]:
                  mask = mask & m
              mask = mask.ravel()
          else:
              mask = None
  
          # broadcast the indexes if needed
          indexes = [a.cvalues for a in self.index_axes]
          nindexes = len(indexes)
          assert nindexes == 1, nindexes  # ensures we dont need to broadcast
  
          # transpose the values so first dimension is last
          # reshape the values if needed
          values = [a.take_data() for a in self.values_axes]
          values = [v.transpose(np.roll(np.arange(v.ndim), v.ndim - 1)) for v in values]
          bvalues = []
          for i, v in enumerate(values):
              new_shape = (nrows,) + self.dtype[names[nindexes + i]].shape
              bvalues.append(values[i].reshape(new_shape))
  
          # write the chunks
          if chunksize is None:
              chunksize = 100000
  
          rows = np.empty(min(chunksize, nrows), dtype=self.dtype)
          chunks = nrows // chunksize + 1
          for i in range(chunks):
              start_i = i * chunksize
              end_i = min((i + 1) * chunksize, nrows)
              if start_i >= end_i:
                  break
  
              self.write_data_chunk(
                  rows,
                  indexes=[a[start_i:end_i] for a in indexes],
                  mask=mask[start_i:end_i] if mask is not None else None,
                  values=[v[start_i:end_i] for v in bvalues],
              )
  
      def write_data_chunk(
          self,
          rows: np.ndarray,
          indexes: List[np.ndarray],
          mask: Optional[np.ndarray],
          values: List[np.ndarray],
      ):
          """
          Parameters
          ----------
          rows : an empty memory space where we are putting the chunk
          indexes : an array of the indexes
          mask : an array of the masks
          values : an array of the values
          """
          # 0 len
          for v in values:
              if not np.prod(v.shape):
                  return
  
          nrows = indexes[0].shape[0]
          if nrows != len(rows):
              rows = np.empty(nrows, dtype=self.dtype)
          names = self.dtype.names
          nindexes = len(indexes)
  
          # indexes
          for i, idx in enumerate(indexes):
              rows[names[i]] = idx
  
          # values
          for i, v in enumerate(values):
              rows[names[i + nindexes]] = v
  
          # mask
          if mask is not None:
              m = ~mask.ravel().astype(bool, copy=False)
              if not m.all():
                  rows = rows[m]
  
          if len(rows):
              self.table.append(rows)
              self.table.flush()
  
      def delete(
          self, where=None, start: Optional[int] = None, stop: Optional[int] = None
      ):
  
          # delete all rows (and return the nrows)
          if where is None or not len(where):
              if start is None and stop is None:
                  nrows = self.nrows
                  self._handle.remove_node(self.group, recursive=True)
              else:
                  # pytables<3.0 would remove a single row with stop=None
                  if stop is None:
                      stop = self.nrows
                  nrows = self.table.remove_rows(start=start, stop=stop)
                  self.table.flush()
              return nrows
  
          # infer the data kind
          if not self.infer_axes():
              return None
  
          # create the selection
          table = self.table
          selection = Selection(self, where, start=start, stop=stop)
          values = selection.select_coords()
  
          # delete the rows in reverse order
          sorted_series = Series(values).sort_values()
          ln = len(sorted_series)
  
          if ln:
  
              # construct groups of consecutive rows
              diff = sorted_series.diff()
              groups = list(diff[diff > 1].index)
  
              # 1 group
              if not len(groups):
                  groups = [0]
  
              # final element
              if groups[-1] != ln:
                  groups.append(ln)
  
              # initial element
              if groups[0] != 0:
                  groups.insert(0, 0)
  
              # we must remove in reverse order!
              pg = groups.pop()
              for g in reversed(groups):
                  rows = sorted_series.take(range(g, pg))
                  table.remove_rows(
                      start=rows[rows.index[0]], stop=rows[rows.index[-1]] + 1
                  )
                  pg = g
  
              self.table.flush()
  
          # return the number of rows removed
          return ln
  
  
  class AppendableFrameTable(AppendableTable):
      """ support the new appendable table formats """
  
      pandas_kind = "frame_table"
      table_type = "appendable_frame"
      ndim = 2
      obj_type: Type[FrameOrSeriesUnion] = DataFrame
  
      @property
      def is_transposed(self) -> bool:
          return self.index_axes[0].axis == 1
  
      @classmethod
      def get_object(cls, obj, transposed: bool):
          """ these are written transposed """
          if transposed:
              obj = obj.T
          return obj
  
      def read(
          self,
          where=None,
          columns=None,
          start: Optional[int] = None,
          stop: Optional[int] = 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 = MultiIndex.from_tuples(index_vals)
              else:
                  cols = Index(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]))
  
              if isinstance(values, np.ndarray):
                  df = DataFrame(values.T, columns=cols_, index=index_)
              elif isinstance(values, Index):
                  df = DataFrame(values, columns=cols_, index=index_)
              else:
                  # Categorical
                  df = DataFrame([values], columns=cols_, index=index_)
              assert (df.dtypes == values.dtype).all(), (df.dtypes, values.dtype)
              frames.append(df)
  
          if len(frames) == 1:
              df = frames[0]
          else:
              df = concat(frames, axis=1)
  
          selection = Selection(self, where=where, start=start, stop=stop)
          # apply the selection filters & axis orderings
          df = self.process_axes(df, selection=selection, columns=columns)
  
          return df
  
  
  class AppendableSeriesTable(AppendableFrameTable):
      """ support the new appendable table formats """
  
      pandas_kind = "series_table"
      table_type = "appendable_series"
      ndim = 2
      obj_type = Series
  
      @property
      def is_transposed(self) -> bool:
          return False
  
      @classmethod
      def get_object(cls, obj, transposed: bool):
          return obj
  
      def write(self, obj, data_columns=None, **kwargs):
          """ we are going to write this as a frame table """
          if not isinstance(obj, DataFrame):
              name = obj.name or "values"
              obj = obj.to_frame(name)
          return super().write(obj=obj, data_columns=obj.columns.tolist(), **kwargs)
  
      def read(
          self,
          where=None,
          columns=None,
          start: Optional[int] = None,
          stop: Optional[int] = None,
      ) -> Series:
  
          is_multi_index = self.is_multi_index
          if columns is not None and is_multi_index:
              assert isinstance(self.levels, list)  # needed for mypy
              for n in self.levels:
                  if n not in columns:
                      columns.insert(0, n)
          s = super().read(where=where, columns=columns, start=start, stop=stop)
          if is_multi_index:
              s.set_index(self.levels, inplace=True)
  
          s = s.iloc[:, 0]
  
          # remove the default name
          if s.name == "values":
              s.name = None
          return s
  
  
  class AppendableMultiSeriesTable(AppendableSeriesTable):
      """ support the new appendable table formats """
  
      pandas_kind = "series_table"
      table_type = "appendable_multiseries"
  
      def write(self, obj, **kwargs):
          """ we are going to write this as a frame table """
          name = obj.name or "values"
          newobj, self.levels = self.validate_multiindex(obj)
          assert isinstance(self.levels, list)  # for mypy
          cols = list(self.levels)
          cols.append(name)
          newobj.columns = Index(cols)
          return super().write(obj=newobj, **kwargs)
  
  
  class GenericTable(AppendableFrameTable):
      """ a table that read/writes the generic pytables table format """
  
      pandas_kind = "frame_table"
      table_type = "generic_table"
      ndim = 2
      obj_type = DataFrame
      levels: List[Label]
  
      @property
      def pandas_type(self) -> str:
          return self.pandas_kind
  
      @property
      def storable(self):
          return getattr(self.group, "table", None) or self.group
  
      def get_attrs(self):
          """ retrieve our attributes """
          self.non_index_axes = []
          self.nan_rep = None
          self.levels = []
  
          self.index_axes = [a for a in self.indexables if a.is_an_indexable]
          self.values_axes = [a for a in self.indexables if not a.is_an_indexable]
          self.data_columns = [a.name for a in self.values_axes]
  
      @cache_readonly
      def indexables(self):
          """ create the indexables from the table description """
          d = self.description
  
          # TODO: can we get a typ for this?  AFAICT it is the only place
          #  where we aren't passing one
          # the index columns is just a simple index
          md = self.read_metadata("index")
          meta = "category" if md is not None else None
          index_col = GenericIndexCol(
              name="index", axis=0, table=self.table, meta=meta, metadata=md
          )
  
          _indexables: List[Union[GenericIndexCol, GenericDataIndexableCol]] = [index_col]
  
          for i, n in enumerate(d._v_names):
              assert isinstance(n, str)
  
              atom = getattr(d, n)
              md = self.read_metadata(n)
              meta = "category" if md is not None else None
              dc = GenericDataIndexableCol(
                  name=n,
                  pos=i,
                  values=[n],
                  typ=atom,
                  table=self.table,
                  meta=meta,
                  metadata=md,
              )
              _indexables.append(dc)
  
          return _indexables
  
      def write(self, **kwargs):
          raise NotImplementedError("cannot write on an generic table")
  
  
  class AppendableMultiFrameTable(AppendableFrameTable):
      """ a frame with a multi-index """
  
      table_type = "appendable_multiframe"
      obj_type = DataFrame
      ndim = 2
      _re_levels = re.compile(r"^level_\d+$")
  
      @property
      def table_type_short(self) -> str:
          return "appendable_multi"
  
      def write(self, obj, data_columns=None, **kwargs):
          if data_columns is None:
              data_columns = []
          elif data_columns is True:
              data_columns = obj.columns.tolist()
          obj, self.levels = self.validate_multiindex(obj)
          assert isinstance(self.levels, list)  # for mypy
          for n in self.levels:
              if n not in data_columns:
                  data_columns.insert(0, n)
          return super().write(obj=obj, data_columns=data_columns, **kwargs)
  
      def read(
          self,
          where=None,
          columns=None,
          start: Optional[int] = None,
          stop: Optional[int] = None,
      ):
  
          df = super().read(where=where, columns=columns, start=start, stop=stop)
          df = df.set_index(self.levels)
  
          # remove names for 'level_%d'
          df.index = df.index.set_names(
              [None if self._re_levels.search(name) else name for name in df.index.names]
          )
  
          return df
  
  
  def _reindex_axis(obj: DataFrame, axis: int, labels: Index, other=None) -> DataFrame:
      ax = obj._get_axis(axis)
      labels = ensure_index(labels)
  
      # try not to reindex even if other is provided
      # if it equals our current index
      if other is not None:
          other = ensure_index(other)
      if (other is None or labels.equals(other)) and labels.equals(ax):
          return obj
  
      labels = ensure_index(labels.unique())
      if other is not None:
          labels = ensure_index(other.unique()).intersection(labels, sort=False)
      if not labels.equals(ax):
          slicer: List[Union[slice, Index]] = [slice(None, None)] * obj.ndim
          slicer[axis] = labels
          obj = obj.loc[tuple(slicer)]
      return obj
  
  
  # tz to/from coercion
  
  
  def _get_tz(tz: tzinfo) -> Union[str, tzinfo]:
      """ for a tz-aware type, return an encoded zone """
      zone = timezones.get_timezone(tz)
      return zone
  
  
  def _set_tz(
      values: Union[np.ndarray, Index],
      tz: Optional[Union[str, tzinfo]],
      coerce: bool = False,
  ) -> Union[np.ndarray, DatetimeIndex]:
      """
      coerce the values to a DatetimeIndex if tz is set
      preserve the input shape if possible
  
      Parameters
      ----------
      values : ndarray or Index
      tz : str or tzinfo
      coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray
      """
      if isinstance(values, DatetimeIndex):
          # If values is tzaware, the tz gets dropped in the values.ravel()
          #  call below (which returns an ndarray).  So we are only non-lossy
          #  if `tz` matches `values.tz`.
          assert values.tz is None or values.tz == tz
  
      if tz is not None:
          if isinstance(values, DatetimeIndex):
              name = values.name
              values = values.asi8
          else:
              name = None
              values = values.ravel()
  
          tz = _ensure_decoded(tz)
          values = DatetimeIndex(values, name=name)
          values = values.tz_localize("UTC").tz_convert(tz)
      elif coerce:
          values = np.asarray(values, dtype="M8[ns]")
  
      return values
  
  
  def _convert_index(name: str, index: Index, encoding: str, errors: str) -> IndexCol:
      assert isinstance(name, str)
  
      index_name = index.name
      converted, dtype_name = _get_data_and_dtype_name(index)
      kind = _dtype_to_kind(dtype_name)
      atom = DataIndexableCol._get_atom(converted)
  
      if isinstance(index, Int64Index) or needs_i8_conversion(index.dtype):
          # Includes Int64Index, 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:
          assert isinstance(converted, np.ndarray) and converted.dtype == object
          assert kind == "object", kind
          atom = _tables().ObjectAtom()
          return IndexCol(name, converted, kind, atom, index_name=index_name)
  
  
  def _unconvert_index(
      data, kind: str, encoding: str, errors: str
  ) -> Union[np.ndarray, Index]:
      index: Union[Index, np.ndarray]
  
      if kind == "datetime64":
          index = DatetimeIndex(data)
      elif kind == "timedelta64":
          index = TimedeltaIndex(data)
      elif kind == "date":
          try:
              index = np.asarray([date.fromordinal(v) for v in data], dtype=object)
          except (ValueError):
              index = np.asarray([date.fromtimestamp(v) for v in data], dtype=object)
      elif kind in ("integer", "float"):
          index = np.asarray(data)
      elif kind in ("string"):
          index = _unconvert_string_array(
              data, nan_rep=None, encoding=encoding, errors=errors
          )
      elif kind == "object":
          index = np.asarray(data[0])
      else:  # pragma: no cover
          raise ValueError(f"unrecognized index type {kind}")
      return index
  
  
  def _maybe_convert_for_string_atom(
      name: str,
      block: "Block",
      existing_col,
      min_itemsize,
      nan_rep,
      encoding,
      errors,
      block_columns: List[str],
  ):
      if not block.is_object:
          return block.values
  
      dtype_name = block.dtype.name
      inferred_type = lib.infer_dtype(block.values, skipna=False)
  
      if inferred_type == "date":
          raise TypeError("[date] is not implemented as a table column")
      elif inferred_type == "datetime":
          # after GH#8260
          # this only would be hit for a multi-timezone dtype which is an error
          raise TypeError(
              "too many timezones in this block, create separate data columns"
          )
  
      elif not (inferred_type == "string" or dtype_name == "object"):
          return block.values
  
      blocks: List["Block"] = block.fillna(nan_rep, downcast=False)
      # Note: because block is always object dtype, fillna goes
      #  through a path such that the result is always a 1-element list
      assert len(blocks) == 1
      block = blocks[0]
  
      data = block.values
  
      # see if we have a valid string type
      inferred_type = lib.infer_dtype(data, skipna=False)
      if inferred_type != "string":
  
          # we cannot serialize this data, so report an exception on a column
          # by column basis
  
          # expected behaviour:
          # search block for a non-string object column by column
          for i in range(block.shape[0]):
              col = block.iget(i)
              inferred_type = lib.infer_dtype(col, skipna=False)
              if inferred_type != "string":
                  error_column_label = (
                      block_columns[i] if len(block_columns) > i else f"No.{i}"
                  )
                  raise TypeError(
                      f"Cannot serialize the column [{error_column_label}]\n"
                      f"because its data contents are not [string] but "
                      f"[{inferred_type}] object dtype"
                  )
  
      # itemsize is the maximum length of a string (along any dimension)
      data_converted = _convert_string_array(data, encoding, errors).reshape(data.shape)
      assert data_converted.shape == block.shape, (data_converted.shape, block.shape)
      itemsize = data_converted.itemsize
  
      # specified min_itemsize?
      if isinstance(min_itemsize, dict):
          min_itemsize = int(min_itemsize.get(name) or min_itemsize.get("values") or 0)
      itemsize = max(min_itemsize or 0, itemsize)
  
      # check for column in the values conflicts
      if existing_col is not None:
          eci = existing_col.validate_col(itemsize)
          if eci > itemsize:
              itemsize = eci
  
      data_converted = data_converted.astype(f"|S{itemsize}", copy=False)
      return data_converted
  
  
  def _convert_string_array(data: np.ndarray, encoding: str, errors: str) -> np.ndarray:
      """
      Take a string-like that is object dtype and coerce to a fixed size string type.
  
      Parameters
      ----------
      data : np.ndarray[object]
      encoding : str
      errors : str
          Handler for encoding errors.
  
      Returns
      -------
      np.ndarray[fixed-length-string]
      """
      # encode if needed
      if len(data):
          data = (
              Series(data.ravel())
              .str.encode(encoding, errors)
              ._values.reshape(data.shape)
          )
  
      # create the sized dtype
      ensured = ensure_object(data.ravel())
      itemsize = max(1, libwriters.max_len_string_array(ensured))
  
      data = np.asarray(data, dtype=f"S{itemsize}")
      return data
  
  
  def _unconvert_string_array(
      data: np.ndarray, nan_rep, encoding: str, errors: str
  ) -> np.ndarray:
      """
      Inverse of _convert_string_array.
  
      Parameters
      ----------
      data : np.ndarray[fixed-length-string]
      nan_rep : the storage repr of NaN
      encoding : str
      errors : str
          Handler for encoding errors.
  
      Returns
      -------
      np.ndarray[object]
          Decoded data.
      """
      shape = data.shape
      data = np.asarray(data.ravel(), dtype=object)
  
      if len(data):
  
          itemsize = libwriters.max_len_string_array(ensure_object(data))
          dtype = f"U{itemsize}"
  
          if isinstance(data[0], bytes):
              data = Series(data).str.decode(encoding, errors=errors)._values
          else:
              data = data.astype(dtype, copy=False).astype(object, copy=False)
  
      if nan_rep is None:
          nan_rep = "nan"
  
      data = libwriters.string_array_replace_from_nan_rep(data, nan_rep)
      return data.reshape(shape)
  
  
  def _maybe_convert(values: np.ndarray, val_kind: str, encoding: str, errors: str):
      assert isinstance(val_kind, str), type(val_kind)
      if _need_convert(val_kind):
          conv = _get_converter(val_kind, encoding, errors)
          values = conv(values)
      return values
  
  
  def _get_converter(kind: str, encoding: str, errors: str):
      if kind == "datetime64":
          return lambda x: np.asarray(x, dtype="M8[ns]")
      elif kind == "string":
          return lambda x: _unconvert_string_array(
              x, nan_rep=None, encoding=encoding, errors=errors
          )
      else:  # pragma: no cover
          raise ValueError(f"invalid kind {kind}")
  
  
  def _need_convert(kind: str) -> bool:
      if kind in ("datetime64", "string"):
          return True
      return False
  
  
  def _maybe_adjust_name(name: str, version: Sequence[int]) -> str:
      """
      Prior to 0.10.1, we named values blocks like: values_block_0 an the
      name values_0, adjust the given name if necessary.
  
      Parameters
      ----------
      name : str
      version : Tuple[int, int, int]
  
      Returns
      -------
      str
      """
      if isinstance(version, str) or len(version) < 3:
          raise ValueError("Version is incorrect, expected sequence of 3 integers.")
  
      if version[0] == 0 and version[1] <= 10 and version[2] == 0:
          m = re.search(r"values_block_(\d+)", name)
          if m:
              grp = m.groups()[0]
              name = f"values_{grp}"
      return name
  
  
  def _dtype_to_kind(dtype_str: str) -> str:
      """
      Find the "kind" string describing the given dtype name.
      """
      dtype_str = _ensure_decoded(dtype_str)
  
      if dtype_str.startswith("string") or dtype_str.startswith("bytes"):
          kind = "string"
      elif dtype_str.startswith("float"):
          kind = "float"
      elif dtype_str.startswith("complex"):
          kind = "complex"
      elif dtype_str.startswith("int") or dtype_str.startswith("uint"):
          kind = "integer"
      elif dtype_str.startswith("datetime64"):
          kind = "datetime64"
      elif dtype_str.startswith("timedelta"):
          kind = "timedelta64"
      elif dtype_str.startswith("bool"):
          kind = "bool"
      elif dtype_str.startswith("category"):
          kind = "category"
      elif dtype_str.startswith("period"):
          # We store the `freq` attr so we can restore from integers
          kind = "integer"
      elif dtype_str == "object":
          kind = "object"
      else:
          raise ValueError(f"cannot interpret dtype of [{dtype_str}]")
  
      return kind
  
  
  def _get_data_and_dtype_name(data: ArrayLike):
      """
      Convert the passed data into a storable form and a dtype string.
      """
      if isinstance(data, Categorical):
          data = data.codes
  
      # For datetime64tz we need to drop the TZ in tests TODO: why?
      dtype_name = data.dtype.name.split("[")[0]
  
      if data.dtype.kind in ["m", "M"]:
          data = np.asarray(data.view("i8"))
          # TODO: we used to reshape for the dt64tz case, but no longer
          #  doing that doesn't seem to break anything.  why?
  
      elif isinstance(data, PeriodIndex):
          data = data.asi8
  
      data = np.asarray(data)
      return data, dtype_name
  
  
  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: Optional[int] = None,
          stop: Optional[int] = 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 == "integer" or inferred == "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()
  
      def generate(self, where):
          """ where can be a : dict,list,tuple,string """
          if where is None:
              return None
  
          q = self.table.queryables()
          try:
              return PyTablesExpr(where, queryables=q, encoding=self.table.encoding)
          except NameError as err:
              # raise a nice message, suggesting that the user should use
              # data_columns
              qkeys = ",".join(q.keys())
              msg = dedent(
                  f"""\
                  The passed where expression: {where}
                              contains an invalid variable reference
                              all of the variable references must be a reference to
                              an axis (e.g. 'index' or 'columns'), or a data_column
                              The currently defined references are: {qkeys}
                  """
              )
              raise ValueError(msg) from err
  
      def select(self):
          """
          generate the selection
          """
          if self.condition is not None:
              return self.table.table.read_where(
                  self.condition.format(), start=self.start, stop=self.stop
              )
          elif self.coordinates is not None:
              return self.table.table.read_coordinates(self.coordinates)
          return self.table.table.read(start=self.start, stop=self.stop)
  
      def select_coords(self):
          """
          generate the selection
          """
          start, stop = self.start, self.stop
          nrows = self.table.nrows
          if start is None:
              start = 0
          elif start < 0:
              start += nrows
          if stop is None:
              stop = nrows
          elif stop < 0:
              stop += nrows
  
          if self.condition is not None:
              return self.table.table.get_where_list(
                  self.condition.format(), start=start, stop=stop, sort=True
              )
          elif self.coordinates is not None:
              return self.coordinates
  
          return np.arange(start, stop)
  