text: # pylint: disable-msg=E1101,W0613,W0603
  """
  High level interface to PyTables for reading and writing pandas data structures
  to disk
  """
  
  import copy
  from datetime import date, datetime
  from distutils.version import LooseVersion
  import itertools
  import os
  import re
  import time
  import warnings
  
  import numpy as np
  
  from pandas._libs import lib, writers as libwriters
  from pandas._libs.tslibs import timezones
  from pandas.compat import PY3, filter, lrange, range, string_types
  from pandas.errors import PerformanceWarning
  
  from pandas.core.dtypes.common import (
      ensure_object, is_categorical_dtype, is_datetime64_dtype,
      is_datetime64tz_dtype, is_list_like, is_timedelta64_dtype)
  from pandas.core.dtypes.missing import array_equivalent
  
  from pandas import (
      DataFrame, DatetimeIndex, Index, Int64Index, MultiIndex, PeriodIndex,
      Series, SparseDataFrame, SparseSeries, TimedeltaIndex, compat, concat,
      isna, to_datetime)
  from pandas.core import config
  from pandas.core.arrays.categorical import Categorical
  from pandas.core.arrays.sparse import BlockIndex, IntIndex
  from pandas.core.base import StringMixin
  import pandas.core.common as com
  from pandas.core.computation.pytables import Expr, maybe_expression
  from pandas.core.config import get_option
  from pandas.core.index import ensure_index
  from pandas.core.internals import BlockManager, _block_shape, make_block
  
  from pandas.io.common import _stringify_path
  from pandas.io.formats.printing import adjoin, pprint_thing
  
  # versioning attribute
  _version = '0.15.2'
  
  # encoding
  # PY3 encoding if we don't specify
  _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:
          if PY3:
              encoding = _default_encoding
      return encoding
  
  
  def _ensure_str(name):
      """Ensure that an index / column name is a str (python 3) or
      unicode (python 2); 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, compat.string_types):
          name = compat.text_type(name)
      return name
  
  
  Term = Expr
  
  
  def _ensure_term(where, scope_level):
      """
      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)):
          wlist = []
          for w in filter(lambda x: x is not None, where):
              if not maybe_expression(w):
                  wlist.append(w)
              else:
                  wlist.append(Term(w, scope_level=level))
          where = wlist
      elif maybe_expression(where):
          where = Term(where, scope_level=level)
      return where
  
  
  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 = {
      u'f': 'fixed',
      u'fixed': 'fixed',
      u't': 'table',
      u'table': 'table',
  }
  
  format_deprecate_doc = """
  the table keyword has been deprecated
  use the format='fixed(f)|table(t)' keyword instead
    fixed(f) : specifies the Fixed format
               and is the default for put operations
    table(t) : specifies the Table format
               and is the default for append operations
  """
  
  # map object types
  _TYPE_MAP = {
  
      Series: u'series',
      SparseSeries: u'sparse_series',
      DataFrame: u'frame',
      SparseDataFrame: u'sparse_frame',
  }
  
  # storer class map
  _STORER_MAP = {
      u'Series': 'LegacySeriesFixed',
      u'DataFrame': 'LegacyFrameFixed',
      u'DataMatrix': 'LegacyFrameFixed',
      u'series': 'SeriesFixed',
      u'sparse_series': 'SparseSeriesFixed',
      u'frame': 'FrameFixed',
      u'sparse_frame': 'SparseFrameFixed',
  }
  
  # table class map
  _TABLE_MAP = {
      u'generic_table': 'GenericTable',
      u'appendable_series': 'AppendableSeriesTable',
      u'appendable_multiseries': 'AppendableMultiSeriesTable',
      u'appendable_frame': 'AppendableFrameTable',
      u'appendable_multiframe': 'AppendableMultiFrameTable',
      u'worm': 'WORMTable',
  }
  
  # 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
  
          # version requirements
          if LooseVersion(tables.__version__) < LooseVersion('3.0.0'):
              raise ImportError("PyTables version >= 3.0.0 is required")
  
          # set the file open policy
          # return the file open policy; this changes as of pytables 3.1
          # depending on the HDF5 version
          try:
              _table_file_open_policy_is_strict = (
                  tables.file._FILE_OPEN_POLICY == 'strict')
          except AttributeError:
              pass
  
      return _table_mod
  
  # interface to/from ###
  
  
  def to_hdf(path_or_buf, key, value, mode=None, complevel=None, complib=None,
             append=None, **kwargs):
      """ store this object, close it if we opened it """
  
      if append:
          f = lambda store: store.append(key, value, **kwargs)
      else:
          f = lambda store: store.put(key, value, **kwargs)
  
      path_or_buf = _stringify_path(path_or_buf)
      if isinstance(path_or_buf, string_types):
          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='r', **kwargs):
      """
      Read from the store, close it if we opened it.
  
      Retrieve pandas object stored in file, optionally based on where
      criteria
  
      Parameters
      ----------
      path_or_buf : string, buffer or path object
          Path to the file to open, or an open :class:`pandas.HDFStore` object.
          Supports any object implementing the ``__fspath__`` protocol.
          This includes :class:`pathlib.Path` and py._path.local.LocalPath
          objects.
  
          .. versionadded:: 0.19.0 support for pathlib, py.path.
          .. versionadded:: 0.21.0 support for __fspath__ protocol.
  
      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'}, optional
          Mode to use when opening the file. Ignored if path_or_buf is a
          :class:`pandas.HDFStore`. Default is 'r'.
      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.
      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.
      **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('mode {0} is not allowed while performing a read. '
                           'Allowed modes are r, r+ and a.'.format(mode))
      # grab the scope
      if 'where' in kwargs:
          kwargs['where'] = _ensure_term(kwargs['where'], scope_level=1)
  
      if isinstance(path_or_buf, HDFStore):
          if not path_or_buf.is_open:
              raise IOError('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, string_types):
              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 compat.FileNotFoundError(
                  'File {path} does not exist'.format(path=path_or_buf))
  
          store = HDFStore(path_or_buf, mode=mode, **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('No dataset 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, auto_close=auto_close, **kwargs)
      except (ValueError, TypeError):
          # if there is an error, close the store
          try:
              store.close()
          except AttributeError:
              pass
  
          raise
  
  
  def _is_metadata_of(group, parent_group):
      """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(StringMixin):
  
      """
      Dict-like IO interface for storing pandas objects in PyTables
      either Fixed or Table format.
  
      Parameters
      ----------
      path : string
          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 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
  
      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()
      """
  
      def __init__(self, path, mode=None, complevel=None, complib=None,
                   fletcher32=False, **kwargs):
  
          if 'format' in kwargs:
              raise ValueError('format is not a defined argument for HDFStore')
  
          try:
              import tables  # noqa
          except ImportError as ex:  # pragma: no cover
              raise ImportError('HDFStore requires PyTables, "{ex!s}" problem '
                                'importing'.format(ex=ex))
  
          if complib is not None and complib not in tables.filters.all_complibs:
              raise ValueError(
                  "complib only supports {libs} compression.".format(
                      libs=tables.filters.all_complibs))
  
          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()
          return self._handle.root
  
      @property
      def filename(self):
          return self._path
  
      def __getitem__(self, key):
          return self.get(key)
  
      def __setitem__(self, key, value):
          self.put(key, value)
  
      def __delitem__(self, key):
          return self.remove(key)
  
      def __getattr__(self, name):
          """ allow attribute access to get stores """
          try:
              return self.get(name)
          except (KeyError, ClosedFileError):
              pass
          raise AttributeError(
              "'{object}' object has no attribute '{name}'".format(
                  object=type(self).__name__, name=name))
  
      def __contains__(self, key):
          """ 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):
          return len(self.groups())
  
      def __unicode__(self):
          return '{type}\nFile path: {path}\n'.format(
              type=type(self), path=pprint_thing(self._path))
  
      def __enter__(self):
          return self
  
      def __exit__(self, exc_type, exc_value, traceback):
          self.close()
  
      def keys(self):
          """
          Return a (potentially unordered) list of the keys corresponding to the
          objects stored in the HDFStore. These are ABSOLUTE path-names (e.g.
          have the leading '/'
          """
          return [n._v_pathname for n in self.groups()]
  
      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='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
          """
          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(
                          "Re-opening the file [{0}] with mode [{1}] "
                          "will delete the current file!"
                          .format(self._path, self._mode)
                      )
  
              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)
  
          try:
              self._handle = tables.open_file(self._path, self._mode, **kwargs)
          except (IOError) as e:  # pragma: no cover
              if 'can not be written' in str(e):
                  print(
                      'Opening {path} in read-only mode'.format(path=self._path))
                  self._handle = tables.open_file(self._path, 'r', **kwargs)
              else:
                  raise
  
          except (ValueError) as e:
  
              # trap PyTables >= 3.1 FILE_OPEN_POLICY exception
              # to provide an updated message
              if 'FILE_OPEN_POLICY' in str(e):
                  e = ValueError(
                      "PyTables [{version}] no longer supports opening multiple "
                      "files\n"
                      "even in read-only mode on this HDF5 version "
                      "[{hdf_version}]. You can accept this\n"
                      "and not open the same file multiple times at once,\n"
                      "upgrade the HDF5 version, or downgrade to PyTables 3.0.0 "
                      "which allows\n"
                      "files to be opened multiple times at once\n"
                      .format(version=tables.__version__,
                              hdf_version=tables.get_hdf5_version()))
  
              raise e
  
          except (Exception) as e:
  
              # trying to read from a non-existent file causes an error which
              # is not part of IOError, make it one
              if self._mode == 'r' and 'Unable to open/create file' in str(e):
                  raise IOError(str(e))
              raise
  
      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):
          """
          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=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:
                  try:
                      os.fsync(self._handle.fileno())
                  except OSError:
                      pass
  
      def get(self, key):
          """
          Retrieve pandas object stored in file
  
          Parameters
          ----------
          key : object
  
          Returns
          -------
          obj : same type as object stored in file
          """
          group = self.get_node(key)
          if group is None:
              raise KeyError('No object named {key} in the file'.format(key=key))
          return self._read_group(group)
  
      def select(self, key, where=None, start=None, stop=None, columns=None,
                 iterator=False, chunksize=None, auto_close=False, **kwargs):
          """
          Retrieve pandas object stored in file, optionally based on where
          criteria
  
          Parameters
          ----------
          key : object
          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
          columns : a list of columns that if not None, will limit the return
              columns
          iterator : boolean, return an iterator, default False
          chunksize : nrows to include in iteration, return an iterator
          auto_close : boolean, should automatically close the store when
              finished, default is False
  
          Returns
          -------
          The selected object
          """
          group = self.get_node(key)
          if group is None:
              raise KeyError('No object named {key} in the file'.format(key=key))
  
          # 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, where=None, start=None, stop=None, **kwargs):
          """
          return the selection as an Index
  
          Parameters
          ----------
          key : object
          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)
          return self.get_storer(key).read_coordinates(where=where, start=start,
                                                       stop=stop, **kwargs)
  
      def select_column(self, key, column, **kwargs):
          """
          return a single column from the table. This is generally only useful to
          select an indexable
  
          Parameters
          ----------
          key : object
          column: the column of interest
  
          Exceptions
          ----------
          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)
  
          """
          return self.get_storer(key).read_column(column=column, **kwargs)
  
      def select_as_multiple(self, keys, where=None, selector=None, columns=None,
                             start=None, stop=None, iterator=False,
                             chunksize=None, auto_close=False, **kwargs):
          """ Retrieve pandas objects from multiple tables
  
          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
  
          Exceptions
          ----------
          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, string_types):
              return self.select(key=keys, where=where, columns=columns,
                                 start=start, stop=stop, iterator=iterator,
                                 chunksize=chunksize, **kwargs)
  
          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("Invalid table [{key}]".format(key=k))
              if not t.is_table:
                  raise TypeError(
                      "object [{obj}] is not a table, and cannot be used in all "
                      "select as multiple".format(obj=t.pathname)
                  )
  
              if nrows is None:
                  nrows = t.nrows
              elif t.nrows != nrows:
                  raise ValueError(
                      "all tables must have exactly the same nrows!")
  
          # axis is the concentation 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, **kwargs) 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, value, format=None, append=False, **kwargs):
          """
          Store object in HDFStore
  
          Parameters
          ----------
          key      : object
          value    : {Series, DataFrame}
          format   : 'fixed(f)|table(t)', default is 'fixed'
              fixed(f) : Fixed format
                         Fast writing/reading. Not-appendable, nor searchable
              table(t) : 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   : boolean, default False
              This will force Table format, append the input data to the
              existing.
          data_columns : list of columns to create as data columns, or True to
              use all columns. See
              `here <http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns>`__ # noqa
          encoding : default None, provide an encoding for strings
          dropna   : boolean, default False, do not write an ALL nan row to
              the store settable by the option 'io.hdf.dropna_table'
          """
          if format is None:
              format = get_option("io.hdf.default_format") or 'fixed'
          kwargs = self._validate_format(format, kwargs)
          self._write_to_group(key, value, append=append, **kwargs)
  
      def remove(self, key, 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)
  
          Exceptions
          ----------
          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 Exception:
  
              if where is not None:
                  raise ValueError(
                      "trying to remove a node with a non-None where clause!")
  
              # we are actually trying to remove a node (with children)
              s = self.get_node(key)
              if s is not None:
                  s._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, value, format=None, append=True, columns=None,
                 dropna=None, **kwargs):
          """
          Append to Table in file. Node must already exist and be Table
          format.
  
          Parameters
          ----------
          key : object
          value : {Series, DataFrame}
          format : 'table' is the default
              table(t) : 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       : boolean, 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
              <http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns>`__.
          min_itemsize : dict of columns that specify minimum string sizes
          nan_rep      : string to use as string nan represenation
          chunksize    : size to chunk the writing
          expectedrows : expected TOTAL row size of this table
          encoding     : default None, provide an encoding for strings
          dropna       : boolean, 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'
          kwargs = self._validate_format(format, kwargs)
          self._write_to_group(key, value, append=append, dropna=dropna,
                               **kwargs)
  
      def append_to_multiple(self, d, 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 = []
          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]
  
          # append
          for k, v in d.items():
              dc = data_columns if k == selector else None
  
              # compute the val
              val = value.reindex(v, axis=axis)
  
              self.append(k, val, data_columns=dc, **kwargs)
  
      def create_table_index(self, key, **kwargs):
          """ Create a pytables index on the table
          Parameters
          ----------
          key : object (the node to index)
  
          Exceptions
          ----------
          raises if the node is not a table
  
          """
  
          # version requirements
          _tables()
          s = self.get_storer(key)
          if s is None:
              return
  
          if not s.is_table:
              raise TypeError(
                  "cannot create table index on a Fixed format store")
          s.create_index(**kwargs)
  
      def groups(self):
          """return a list of all the top-level nodes (that are not themselves a
          pandas storage object)
          """
          _tables()
          self._check_if_open()
          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 != u'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, optional
              Group where to start walking.
              If not supplied, the root group is used.
  
          Yields
          ------
          path : str
              Full path to a group (without trailing '/')
          groups : list of str
              names of the groups contained in `path`
          leaves : list of str
              names of the pandas objects contained in `path`
          """
          _tables()
          self._check_if_open()
          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):
          """ return the node with the key or None if it does not exist """
          self._check_if_open()
          try:
              if not key.startswith('/'):
                  key = '/' + key
              return self._handle.get_node(self.root, key)
          except _table_mod.exceptions.NoSuchNodeError:
              return None
  
      def get_storer(self, key):
          """ return the storer object for a key, raise if not in the file """
          group = self.get_node(key)
          if group is None:
              raise KeyError('No object named {key} in the file'.format(key=key))
  
          s = self._create_storer(group)
          s.infer_axes()
          return s
  
      def copy(self, file, mode='w', propindexes=True, keys=None, complib=None,
               complevel=None, fletcher32=False, overwrite=True):
          """ copy the existing store to a new file, upgrading in place
  
              Parameters
              ----------
              propindexes: restore indexes in copied file (defaults to True)
              keys       : list of keys to include in the copy (defaults to all)
              overwrite  : overwrite (remove and replace) existing nodes in the
                  new store (default is True)
              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 s.is_table:
  
                      index = 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):
          """
          Print detailed information on the store.
  
          .. versionadded:: 0.21.0
          """
          output = '{type}\nFile path: {path}\n'.format(
              type=type(self), path=pprint_thing(self._path))
          if self.is_open:
              lkeys = sorted(list(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 Exception as detail:
                          keys.append(k)
                          values.append(
                              "[invalid_HDFStore node: {detail}]".format(
                                  detail=pprint_thing(detail)))
  
                  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("{0} file is not open!".format(self._path))
  
      def _validate_format(self, format, kwargs):
          """ validate / deprecate formats; return the new kwargs """
          kwargs = kwargs.copy()
  
          # validate
          try:
              kwargs['format'] = _FORMAT_MAP[format.lower()]
          except KeyError:
              raise TypeError("invalid HDFStore format specified [{0}]"
                              .format(format))
  
          return kwargs
  
      def _create_storer(self, group, format=None, value=None, append=False,
                         **kwargs):
          """ return a suitable class to operate """
  
          def error(t):
              raise TypeError(
                  "cannot properly create the storer for: [{t}] [group->"
                  "{group},value->{value},format->{format},append->{append},"
                  "kwargs->{kwargs}]".format(t=t, group=group,
                                             value=type(value), format=format,
                                             append=append, kwargs=kwargs))
  
          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()
                  if (getattr(group, 'table', None) or
                          isinstance(group, _table_mod.table.Table)):
                      pt = u'frame_table'
                      tt = u'generic_table'
                  else:
                      raise TypeError(
                          "cannot create a storer if the object is not existing "
                          "nor a value are passed")
              else:
  
                  try:
                      pt = _TYPE_MAP[type(value)]
                  except KeyError:
                      error('_TYPE_MAP')
  
                  # we are actually a table
                  if format == 'table':
                      pt += u'_table'
  
          # a storer node
          if u'table' not in pt:
              try:
                  return globals()[_STORER_MAP[pt]](self, group, **kwargs)
              except KeyError:
                  error('_STORER_MAP')
  
          # 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 == u'series_table':
                      index = getattr(value, 'index', None)
                      if index is not None:
                          if index.nlevels == 1:
                              tt = u'appendable_series'
                          elif index.nlevels > 1:
                              tt = u'appendable_multiseries'
                  elif pt == u'frame_table':
                      index = getattr(value, 'index', None)
                      if index is not None:
                          if index.nlevels == 1:
                              tt = u'appendable_frame'
                          elif index.nlevels > 1:
                              tt = u'appendable_multiframe'
                  elif pt == u'wide_table':
                      tt = u'appendable_panel'
                  elif pt == u'ndim_table':
                      tt = u'appendable_ndim'
  
              else:
  
                  # distiguish between a frame/table
                  tt = u'legacy_panel'
                  try:
                      fields = group.table._v_attrs.fields
                      if len(fields) == 1 and fields[0] == u'value':
                          tt = u'legacy_frame'
                  except IndexError:
                      pass
  
          try:
              return globals()[_TABLE_MAP[tt]](self, group, **kwargs)
          except KeyError:
              error('_TABLE_MAP')
  
      def _write_to_group(self, key, value, format, index=True, append=False,
                          complib=None, encoding=None, **kwargs):
          group = self.get_node(key)
  
          # 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
  
          # we don't want to store a table node at all if are object is 0-len
          # as there are not dtypes
          if getattr(value, 'empty', None) and (format == 'table' or append):
              return
  
          if group is 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
  
          s = self._create_storer(group, format, value, append=append,
                                  encoding=encoding, **kwargs)
          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, append=append, complib=complib, **kwargs)
  
          if s.is_table and index:
              s.create_index(columns=index)
  
      def _read_group(self, group, **kwargs):
          s = self._create_storer(group)
          s.infer_axes()
          return s.read(**kwargs)
  
  
  class TableIterator(object):
  
      """ define the iteration interface on a table
  
          Parameters
          ----------
  
          store : the reference store
          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 : boolean, whether to use the default iterator
          chunksize : the passed chunking value (default is 50000)
          auto_close : boolean, automatically close the store at the end of
              iteration, default is False
          kwargs : the passed kwargs
          """
  
      def __init__(self, store, s, func, where, nrows, start=None, stop=None,
                   iterator=False, chunksize=None, auto_close=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
          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=False):
  
          #  return the actual iterator
          if self.chunksize is not None:
              if not self.s.is_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:
              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(StringMixin):
  
      """ 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']
  
      def __init__(self, values=None, kind=None, typ=None, cname=None,
                   itemsize=None, name=None, axis=None, kind_attr=None,
                   pos=None, freq=None, tz=None, index_name=None, **kwargs):
          self.values = values
          self.kind = kind
          self.typ = typ
          self.itemsize = itemsize
          self.name = name
          self.cname = cname
          self.kind_attr = kind_attr
          self.axis = axis
          self.pos = pos
          self.freq = freq
          self.tz = tz
          self.index_name = index_name
          self.table = None
          self.meta = None
          self.metadata = None
  
          if name is not None:
              self.set_name(name, kind_attr)
          if pos is not None:
              self.set_pos(pos)
  
      def set_name(self, name, kind_attr=None):
          """ set the name of this indexer """
          self.name = name
          self.kind_attr = kind_attr or "{name}_kind".format(name=name)
          if self.cname is None:
              self.cname = name
  
          return self
  
      def set_axis(self, axis):
          """ set the axis over which I index """
          self.axis = axis
  
          return self
  
      def set_pos(self, pos):
          """ 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
          return self
  
      def set_table(self, table):
          self.table = table
          return self
  
      def __unicode__(self):
          temp = tuple(
              map(pprint_thing,
                      (self.name,
                       self.cname,
                       self.axis,
                       self.pos,
                       self.kind)))
          return ','.join(("{key}->{value}".format(key=key, value=value)
                           for key, value in zip(
              ['name', 'cname', 'axis', 'pos', 'kind'], temp)))
  
      def __eq__(self, other):
          """ 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):
          return not self.__eq__(other)
  
      @property
      def is_indexed(self):
          """ return whether I am an indexed column """
          try:
              return getattr(self.table.cols, self.cname).is_indexed
          except AttributeError:
              False
  
      def copy(self):
          new_self = copy.copy(self)
          return new_self
  
      def infer(self, handler):
          """infer this column from the table: create and return a new object"""
          table = handler.table
          new_self = self.copy()
          new_self.set_table(table)
          new_self.get_attr()
          new_self.read_metadata(handler)
          return new_self
  
      def convert(self, values, nan_rep, encoding, errors):
          """ set the values from this selection: take = take ownership """
  
          # values is a recarray
          if values.dtype.fields is not None:
              values = values[self.cname]
  
          values = _maybe_convert(values, self.kind, encoding, errors)
  
          kwargs = dict()
          if self.freq is not None:
              kwargs['freq'] = _ensure_decoded(self.freq)
          if self.index_name is not None:
              kwargs['name'] = _ensure_decoded(self.index_name)
          # making an Index instance could throw a number of different errors
          try:
              self.values = Index(values, **kwargs)
          except Exception:  # noqa: E722
  
              # 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
              self.values = Index(values, **kwargs)
  
          self.values = _set_tz(self.values, self.tz)
  
          return self
  
      def take_data(self):
          """ return the values & release the memory """
          self.values, values = None, self.values
          return 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) == u'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(self, handler, append):
          self.validate_names()
  
      def validate_names(self):
          pass
  
      def validate_and_set(self, handler, append):
          self.set_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) == u'string':
              c = self.col
              if c is not None:
                  if itemsize is None:
                      itemsize = self.itemsize
                  if c.itemsize < itemsize:
                      raise ValueError(
                          "Trying to store a string with len [{itemsize}] in "
                          "[{cname}] column but\nthis column has a limit of "
                          "[{c_itemsize}]!\nConsider using min_itemsize to "
                          "preset the sizes on these columns".format(
                              itemsize=itemsize, cname=self.cname,
                              c_itemsize=c.itemsize))
                  return c.itemsize
  
          return None
  
      def validate_attr(self, append):
          # 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(
                      "incompatible kind in col [{existing} - "
                      "{self_kind}]".format(
                          existing=existing_kind, self_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 = _get_info(info, 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(
                          "invalid info for [{name}] for [{key}], "
                          "existing_value [{existing_value}] conflicts with "
                          "new value [{value}]".format(
                              name=self.name, key=key,
                              existing_value=existing_value, value=value))
              else:
                  if value is not None or existing_value is not None:
                      idx[key] = value
  
          return self
  
      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 get_attr(self):
          """ set the kind for this column """
          self.kind = getattr(self.attrs, self.kind_attr, None)
  
      def set_attr(self):
          """ set the kind for this column """
          setattr(self.attrs, self.kind_attr, self.kind)
  
      def read_metadata(self, handler):
          """ retrieve the metadata for this columns """
          self.metadata = handler.read_metadata(self.cname)
  
      def validate_metadata(self, handler):
          """ 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):
          """ 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):
          return False
  
      def convert(self, values, nan_rep, encoding, errors):
          """ set the values from this selection: take = take ownership """
  
          self.values = Int64Index(np.arange(self.table.nrows))
          return self
  
      def get_attr(self):
          pass
  
      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']
  
      @classmethod
      def create_for_block(
              cls, i=None, name=None, cname=None, version=None, **kwargs):
          """ return a new datacol with the block i """
  
          if cname is None:
              cname = name or 'values_block_{idx}'.format(idx=i)
          if name is None:
              name = cname
  
          # prior to 0.10.1, we named values blocks like: values_block_0 an the
          # name values_0
          try:
              if version[0] == 0 and version[1] <= 10 and version[2] == 0:
                  m = re.search(r"values_block_(\d+)", name)
                  if m:
                      name = "values_{group}".format(group=m.groups()[0])
          except IndexError:
              pass
  
          return cls(name=name, cname=cname, **kwargs)
  
      def __init__(self, values=None, kind=None, typ=None,
                   cname=None, data=None, meta=None, metadata=None,
                   block=None, **kwargs):
          super(DataCol, self).__init__(values=values, kind=kind, typ=typ,
                                        cname=cname, **kwargs)
          self.dtype = None
          self.dtype_attr = u'{name}_dtype'.format(name=self.name)
          self.meta = meta
          self.meta_attr = u'{name}_meta'.format(name=self.name)
          self.set_data(data)
          self.set_metadata(metadata)
  
      def __unicode__(self):
          temp = tuple(
              map(pprint_thing,
                      (self.name,
                       self.cname,
                       self.dtype,
                       self.kind,
                       self.shape)))
          return ','.join(("{key}->{value}".format(key=key, value=value)
                           for key, value in zip(
              ['name', 'cname', 'dtype', 'kind', 'shape'], temp)))
  
      def __eq__(self, other):
          """ 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, dtype=None):
          self.data = data
          if data is not None:
              if dtype is not None:
                  self.dtype = dtype
                  self.set_kind()
              elif self.dtype is None:
                  self.dtype = data.dtype.name
                  self.set_kind()
  
      def take_data(self):
          """ return the data & release the memory """
          self.data, data = None, self.data
          return data
  
      def set_metadata(self, metadata):
          """ record the metadata """
          if metadata is not None:
              metadata = np.array(metadata, copy=False).ravel()
          self.metadata = metadata
  
      def set_kind(self):
          # set my kind if we can
  
          if self.dtype is not None:
              dtype = _ensure_decoded(self.dtype)
  
              if dtype.startswith(u'string') or dtype.startswith(u'bytes'):
                  self.kind = 'string'
              elif dtype.startswith(u'float'):
                  self.kind = 'float'
              elif dtype.startswith(u'complex'):
                  self.kind = 'complex'
              elif dtype.startswith(u'int') or dtype.startswith(u'uint'):
                  self.kind = 'integer'
              elif dtype.startswith(u'date'):
                  self.kind = 'datetime'
              elif dtype.startswith(u'timedelta'):
                  self.kind = 'timedelta'
              elif dtype.startswith(u'bool'):
                  self.kind = 'bool'
              else:
                  raise AssertionError(
                      "cannot interpret dtype of [{dtype}] in [{obj}]".format(
                          dtype=dtype, obj=self))
  
              # set my typ if we need
              if self.typ is None:
                  self.typ = getattr(self.description, self.cname, None)
  
      def set_atom(self, block, block_items, existing_col, min_itemsize,
                   nan_rep, info, encoding=None, errors='strict'):
          """ create and setup my atom from the block b """
  
          self.values = list(block_items)
  
          # short-cut certain block types
          if block.is_categorical:
              return self.set_atom_categorical(block, items=block_items,
                                               info=info)
          elif block.is_datetimetz:
              return self.set_atom_datetime64tz(block, info=info)
          elif block.is_datetime:
              return self.set_atom_datetime64(block)
          elif block.is_timedelta:
              return self.set_atom_timedelta64(block)
          elif block.is_complex:
              return self.set_atom_complex(block)
  
          dtype = 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 8260
              # this only would be hit for a mutli-timezone dtype
              # which is an error
  
              raise TypeError(
                  "too many timezones in this block, create separate "
                  "data columns"
              )
          elif inferred_type == 'unicode':
              raise TypeError(
                  "[unicode] is not implemented as a table column")
  
          # this is basically a catchall; if say a datetime64 has nans then will
          # end up here ###
          elif inferred_type == 'string' or dtype == 'object':
              self.set_atom_string(
                  block, block_items,
                  existing_col,
                  min_itemsize,
                  nan_rep,
                  encoding,
                  errors)
  
          # set as a data block
          else:
              self.set_atom_data(block)
  
      def get_atom_string(self, block, itemsize):
          return _tables().StringCol(itemsize=itemsize, shape=block.shape[0])
  
      def set_atom_string(self, block, block_items, existing_col, min_itemsize,
                          nan_rep, encoding, errors):
          # fill nan items with myself, don't disturb the blocks by
          # trying to downcast
          block = block.fillna(nan_rep, downcast=False)
          if isinstance(block, list):
              block = block[0]
          data = block.values
  
          # see if we have a valid string type
          inferred_type = lib.infer_dtype(data.ravel(), skipna=False)
          if inferred_type != 'string':
  
              # we cannot serialize this data, so report an exception on a column
              # by column basis
              for i, item in enumerate(block_items):
  
                  col = block.iget(i)
                  inferred_type = lib.infer_dtype(col.ravel(), skipna=False)
                  if inferred_type != 'string':
                      raise TypeError(
                          "Cannot serialize the column [{item}] because\n"
                          "its data contents are [{type}] object dtype".format(
                              item=item, type=inferred_type)
                      )
  
          # itemsize is the maximum length of a string (along any dimension)
          data_converted = _convert_string_array(data, encoding, errors)
          itemsize = data_converted.itemsize
  
          # specified min_itemsize?
          if isinstance(min_itemsize, dict):
              min_itemsize = int(min_itemsize.get(
                  self.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
  
          self.itemsize = itemsize
          self.kind = 'string'
          self.typ = self.get_atom_string(block, itemsize)
          self.set_data(data_converted.astype(
              '|S{size}'.format(size=itemsize), copy=False))
  
      def get_atom_coltype(self, kind=None):
          """ return the PyTables column class for this column """
          if kind is None:
              kind = self.kind
          if self.kind.startswith('uint'):
              col_name = "UInt{name}Col".format(name=kind[4:])
          else:
              col_name = "{name}Col".format(name=kind.capitalize())
  
          return getattr(_tables(), col_name)
  
      def get_atom_data(self, block, kind=None):
          return self.get_atom_coltype(kind=kind)(shape=block.shape[0])
  
      def set_atom_complex(self, block):
          self.kind = block.dtype.name
          itemsize = int(self.kind.split('complex')[-1]) // 8
          self.typ = _tables().ComplexCol(
              itemsize=itemsize, shape=block.shape[0])
          self.set_data(block.values.astype(self.typ.type, copy=False))
  
      def set_atom_data(self, block):
          self.kind = block.dtype.name
          self.typ = self.get_atom_data(block)
          self.set_data(block.values.astype(self.typ.type, copy=False))
  
      def set_atom_categorical(self, block, items, info=None, values=None):
          # currently only supports a 1-D categorical
          # in a 1-D block
  
          values = block.values
          codes = values.codes
          self.kind = 'integer'
          self.dtype = codes.dtype.name
          if values.ndim > 1:
              raise NotImplementedError("only support 1-d categoricals")
          if len(items) > 1:
              raise NotImplementedError("only support single block categoricals")
  
          # write the codes; must be in a block shape
          self.ordered = values.ordered
          self.typ = self.get_atom_data(block, kind=codes.dtype.name)
          self.set_data(_block_shape(codes))
  
          # write the categories
          self.meta = 'category'
          self.set_metadata(block.values.categories)
  
          # update the info
          self.update_info(info)
  
      def get_atom_datetime64(self, block):
          return _tables().Int64Col(shape=block.shape[0])
  
      def set_atom_datetime64(self, block, values=None):
          self.kind = 'datetime64'
          self.typ = self.get_atom_datetime64(block)
          if values is None:
              values = block.values.view('i8')
          self.set_data(values, 'datetime64')
  
      def set_atom_datetime64tz(self, block, info, values=None):
  
          if values is None:
              values = block.values
  
          # convert this column to i8 in UTC, and save the tz
          values = values.asi8.reshape(block.shape)
  
          # store a converted timezone
          self.tz = _get_tz(block.values.tz)
          self.update_info(info)
  
          self.kind = 'datetime64'
          self.typ = self.get_atom_datetime64(block)
          self.set_data(values, 'datetime64')
  
      def get_atom_timedelta64(self, block):
          return _tables().Int64Col(shape=block.shape[0])
  
      def set_atom_timedelta64(self, block, values=None):
          self.kind = 'timedelta64'
          self.typ = self.get_atom_timedelta64(block)
          if values is None:
              values = block.values.view('i8')
          self.set_data(values, 'timedelta64')
  
      @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, nan_rep, encoding, errors):
          """set the data from this selection (and convert to the correct dtype
          if we can)
          """
  
          # values is a recarray
          if values.dtype.fields is not None:
              values = values[self.cname]
  
          self.set_data(values)
  
          # use the meta if needed
          meta = _ensure_decoded(self.meta)
  
          # convert to the correct dtype
          if self.dtype is not None:
              dtype = _ensure_decoded(self.dtype)
  
              # reverse converts
              if dtype == u'datetime64':
  
                  # recreate with tz if indicated
                  self.data = _set_tz(self.data, self.tz, coerce=True)
  
              elif dtype == u'timedelta64':
                  self.data = np.asarray(self.data, dtype='m8[ns]')
              elif dtype == u'date':
                  try:
                      self.data = np.asarray(
                          [date.fromordinal(v) for v in self.data], dtype=object)
                  except ValueError:
                      self.data = np.asarray(
                          [date.fromtimestamp(v) for v in self.data],
                          dtype=object)
              elif dtype == u'datetime':
                  self.data = np.asarray(
                      [datetime.fromtimestamp(v) for v in self.data],
                      dtype=object)
  
              elif meta == u'category':
  
                  # we have a categorical
                  categories = self.metadata
                  codes = self.data.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
  
                  self.data = Categorical.from_codes(codes,
                                                     categories=categories,
                                                     ordered=self.ordered)
  
              else:
  
                  try:
                      self.data = self.data.astype(dtype, copy=False)
                  except TypeError:
                      self.data = self.data.astype('O', copy=False)
  
          # convert nans / decode
          if _ensure_decoded(self.kind) == u'string':
              self.data = _unconvert_string_array(
                  self.data, nan_rep=nan_rep, encoding=encoding, errors=errors)
  
          return self
  
      def get_attr(self):
          """ get the data for this column """
          self.values = getattr(self.attrs, self.kind_attr, None)
          self.dtype = getattr(self.attrs, self.dtype_attr, None)
          self.meta = getattr(self.attrs, self.meta_attr, None)
          self.set_kind()
  
      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)
          if 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():
              raise ValueError("cannot have non-object label DataIndexableCol")
  
      def get_atom_string(self, block, itemsize):
          return _tables().StringCol(itemsize=itemsize)
  
      def get_atom_data(self, block, kind=None):
          return self.get_atom_coltype(kind=kind)()
  
      def get_atom_datetime64(self, block):
          return _tables().Int64Col()
  
      def get_atom_timedelta64(self, block):
          return _tables().Int64Col()
  
  
  class GenericDataIndexableCol(DataIndexableCol):
  
      """ represent a generic pytables data column """
  
      def get_attr(self):
          pass
  
  
  class Fixed(StringMixin):
  
      """ represent an object in my store
          facilitate read/write of various types of objects
          this is an abstract base class
  
          Parameters
          ----------
  
          parent : my parent HDFStore
          group  : the group node where the table resides
          """
      pandas_kind = None
      obj_type = None
      ndim = None
      is_table = False
  
      def __init__(self, parent, group, encoding=None, errors='strict',
                   **kwargs):
          self.parent = parent
          self.group = group
          self.encoding = _ensure_encoding(encoding)
          self.errors = errors
          self.set_version()
  
      @property
      def is_old_version(self):
          return (self.version[0] <= 0 and self.version[1] <= 10 and
                  self.version[2] < 1)
  
      def set_version(self):
          """ compute and set our version """
          version = _ensure_decoded(
              getattr(self.group._v_attrs, 'pandas_version', None))
          try:
              self.version = tuple(int(x) for x in version.split('.'))
              if len(self.version) == 2:
                  self.version = self.version + (0,)
          except AttributeError:
              self.version = (0, 0, 0)
  
      @property
      def pandas_type(self):
          return _ensure_decoded(getattr(self.group._v_attrs,
                                         'pandas_type', None))
  
      @property
      def format_type(self):
          return 'fixed'
  
      def __unicode__(self):
          """ return a pretty representation of myself """
          self.infer_axes()
          s = self.shape
          if s is not None:
              if isinstance(s, (list, tuple)):
                  s = "[{shape}]".format(
                      shape=','.join(pprint_thing(x) for x in s))
              return "{type:12.12} (shape->{shape})".format(
                  type=self.pandas_type, 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)
          self.set_version()
  
      def copy(self):
          new_self = copy.copy(self)
          return new_self
  
      @property
      def storage_obj_type(self):
          return self.obj_type
  
      @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):
          return self.parent._complevel
  
      @property
      def _fletcher32(self):
          return self.parent._fletcher32
  
      @property
      def _complib(self):
          return self.parent._complib
  
      @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):
          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, **kwargs):
          raise NotImplementedError(
              "cannot read on an abstract storer: subclasses should implement")
  
      def write(self, **kwargs):
          raise NotImplementedError(
              "cannot write on an abstract storer: sublcasses should implement")
  
      def delete(self, where=None, start=None, stop=None, **kwargs):
          """
          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 compat.iteritems(_index_type_map)}
      attributes = []
  
      # indexer helpders
      def _class_to_alias(self, cls):
          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, klass):
          if klass == DatetimeIndex:
              def f(values, freq=None, tz=None):
                  # data are already in UTC, localize and convert if tz present
                  result = DatetimeIndex._simple_new(values.values, name=None,
                                                     freq=freq)
                  if tz is not None:
                      result = result.tz_localize('UTC').tz_convert(tz)
                  return result
              return f
          elif klass == PeriodIndex:
              def f(values, freq=None, tz=None):
                  return PeriodIndex._simple_new(values, name=None, freq=freq)
              return f
  
          return klass
  
      def validate_read(self, kwargs):
          """
          remove table keywords from kwargs and return
          raise if any keywords are passed which are not-None
          """
          kwargs = copy.copy(kwargs)
  
          columns = kwargs.pop('columns', 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")
          where = kwargs.pop('where', None)
          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")
          return kwargs
  
      @property
      def is_exists(self):
          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, start=None, stop=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 = 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 == u'datetime64':
  
                  # reconstruct a timezone if indicated
                  ret = _set_tz(ret, getattr(attrs, 'tz', None), coerce=True)
  
              elif dtype == u'timedelta64':
                  ret = np.asarray(ret, dtype='m8[ns]')
  
          if transposed:
              return ret.T
          else:
              return ret
  
      def read_index(self, key, **kwargs):
          variety = _ensure_decoded(
              getattr(self.attrs, '{key}_variety'.format(key=key)))
  
          if variety == u'multi':
              return self.read_multi_index(key, **kwargs)
          elif variety == u'block':
              return self.read_block_index(key, **kwargs)
          elif variety == u'sparseint':
              return self.read_sparse_intindex(key, **kwargs)
          elif variety == u'regular':
              _, index = self.read_index_node(getattr(self.group, key), **kwargs)
              return index
          else:  # pragma: no cover
              raise TypeError(
                  'unrecognized index variety: {variety}'.format(
                      variety=variety))
  
      def write_index(self, key, index):
          if isinstance(index, MultiIndex):
              setattr(self.attrs, '{key}_variety'.format(key=key), 'multi')
              self.write_multi_index(key, index)
          elif isinstance(index, BlockIndex):
              setattr(self.attrs, '{key}_variety'.format(key=key), 'block')
              self.write_block_index(key, index)
          elif isinstance(index, IntIndex):
              setattr(self.attrs, '{key}_variety'.format(key=key), 'sparseint')
              self.write_sparse_intindex(key, index)
          else:
              setattr(self.attrs, '{key}_variety'.format(key=key), 'regular')
              converted = _convert_index(index, self.encoding, self.errors,
                                         self.format_type).set_name('index')
  
              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 hasattr(index, 'freq'):
                  node._v_attrs.freq = index.freq
  
              if hasattr(index, 'tz') and index.tz is not None:
                  node._v_attrs.tz = _get_tz(index.tz)
  
      def write_block_index(self, key, index):
          self.write_array('{key}_blocs'.format(key=key), index.blocs)
          self.write_array('{key}_blengths'.format(key=key), index.blengths)
          setattr(self.attrs, '{key}_length'.format(key=key), index.length)
  
      def read_block_index(self, key, **kwargs):
          length = getattr(self.attrs, '{key}_length'.format(key=key))
          blocs = self.read_array('{key}_blocs'.format(key=key), **kwargs)
          blengths = self.read_array('{key}_blengths'.format(key=key), **kwargs)
          return BlockIndex(length, blocs, blengths)
  
      def write_sparse_intindex(self, key, index):
          self.write_array('{key}_indices'.format(key=key), index.indices)
          setattr(self.attrs, '{key}_length'.format(key=key), index.length)
  
      def read_sparse_intindex(self, key, **kwargs):
          length = getattr(self.attrs, '{key}_length'.format(key=key))
          indices = self.read_array('{key}_indices'.format(key=key), **kwargs)
          return IntIndex(length, indices)
  
      def write_multi_index(self, key, index):
          setattr(self.attrs, '{key}_nlevels'.format(key=key), index.nlevels)
  
          for i, (lev, level_codes, name) in enumerate(zip(index.levels,
                                                           index.codes,
                                                           index.names)):
              # write the level
              level_key = '{key}_level{idx}'.format(key=key, idx=i)
              conv_level = _convert_index(lev, self.encoding, self.errors,
                                          self.format_type).set_name(level_key)
              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, '{key}_name{name}'.format(
                  key=key, name=name), name)
  
              # write the labels
              label_key = '{key}_label{idx}'.format(key=key, idx=i)
              self.write_array(label_key, level_codes)
  
      def read_multi_index(self, key, **kwargs):
          nlevels = getattr(self.attrs, '{key}_nlevels'.format(key=key))
  
          levels = []
          codes = []
          names = []
          for i in range(nlevels):
              level_key = '{key}_level{idx}'.format(key=key, idx=i)
              name, lev = self.read_index_node(getattr(self.group, level_key),
                                               **kwargs)
              levels.append(lev)
              names.append(name)
  
              label_key = '{key}_label{idx}'.format(key=key, idx=i)
              level_codes = self.read_array(label_key, **kwargs)
              codes.append(level_codes)
  
          return MultiIndex(levels=levels, codes=codes, names=names,
                            verify_integrity=True)
  
      def read_index_node(self, node, start=None, stop=None):
          data = node[start:stop]
          # If the index was an empty array write_array_empty() will
          # have written a sentinel. Here we relace it with the original.
          if ('shape' in node._v_attrs and
                  self._is_empty_array(getattr(node._v_attrs, 'shape'))):
              data = np.empty(getattr(node._v_attrs, 'shape'),
                              dtype=getattr(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)
  
          index_class = self._alias_to_class(_ensure_decoded(
              getattr(node._v_attrs, 'index_class', '')))
          factory = self._get_index_factory(index_class)
  
          kwargs = {}
          if u'freq' in node._v_attrs:
              kwargs['freq'] = node._v_attrs['freq']
  
          if u'tz' in node._v_attrs:
              kwargs['tz'] = node._v_attrs['tz']
  
          if kind in (u'date', u'datetime'):
              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 name, index
  
      def write_array_empty(self, key, value):
          """ 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)
          getattr(self.group, key)._v_attrs.value_type = str(value.dtype)
          getattr(self.group, key)._v_attrs.shape = value.shape
  
      def _is_empty_array(self, shape):
          """Returns true if any axis is zero length."""
          return any(x == 0 for x in shape)
  
      def write_array(self, key, value, items=None):
          if key in self.group:
              self._handle.remove_node(self.group, key)
  
          # Transform needed to interface with pytables row/col notation
          empty_array = self._is_empty_array(value.shape)
          transposed = False
  
          if is_categorical_dtype(value):
              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
  
          if self._filters is not None:
              atom = None
              try:
                  # get the atom for this datatype
                  atom = _tables().Atom.from_dtype(value.dtype)
              except ValueError:
                  pass
  
              if atom is not None:
                  # 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
                      getattr(self.group, key)._v_attrs.transposed = transposed
  
                  else:
                      self.write_array_empty(key, value)
  
                  return
  
          if 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.ravel(), skipna=False)
              if empty_array:
                  pass
              elif inferred_type == 'string':
                  pass
              else:
                  try:
                      items = list(items)
                  except TypeError:
                      pass
                  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)
          else:
              if empty_array:
                  self.write_array_empty(key, value)
              else:
                  if 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'
                  else:
                      self._handle.create_array(self.group, key, value)
  
          getattr(self.group, key)._v_attrs.transposed = transposed
  
  
  class LegacyFixed(GenericFixed):
  
      def read_index_legacy(self, key, start=None, stop=None):
          node = getattr(self.group, key)
          data = node[start:stop]
          kind = node._v_attrs.kind
          return _unconvert_index_legacy(data, kind, encoding=self.encoding,
                                         errors=self.errors)
  
  
  class LegacySeriesFixed(LegacyFixed):
  
      def read(self, **kwargs):
          kwargs = self.validate_read(kwargs)
          index = self.read_index_legacy('index')
          values = self.read_array('values')
          return Series(values, index=index)
  
  
  class LegacyFrameFixed(LegacyFixed):
  
      def read(self, **kwargs):
          kwargs = self.validate_read(kwargs)
          index = self.read_index_legacy('index')
          columns = self.read_index_legacy('columns')
          values = self.read_array('values')
          return DataFrame(values, index=index, columns=columns)
  
  
  class SeriesFixed(GenericFixed):
      pandas_kind = u'series'
      attributes = ['name']
  
      @property
      def shape(self):
          try:
              return len(getattr(self.group, 'values')),
          except (TypeError, AttributeError):
              return None
  
      def read(self, **kwargs):
          kwargs = self.validate_read(kwargs)
          index = self.read_index('index', **kwargs)
          values = self.read_array('values', **kwargs)
          return Series(values, index=index, name=self.name)
  
      def write(self, obj, **kwargs):
          super(SeriesFixed, self).write(obj, **kwargs)
          self.write_index('index', obj.index)
          self.write_array('values', obj.values)
          self.attrs.name = obj.name
  
  
  class SparseFixed(GenericFixed):
  
      def validate_read(self, kwargs):
          """
          we don't support start, stop kwds in Sparse
          """
          kwargs = super(SparseFixed, self).validate_read(kwargs)
          if 'start' in kwargs or 'stop' in kwargs:
              raise NotImplementedError("start and/or stop are not supported "
                                        "in fixed Sparse reading")
          return kwargs
  
  
  class SparseSeriesFixed(SparseFixed):
      pandas_kind = u'sparse_series'
      attributes = ['name', 'fill_value', 'kind']
  
      def read(self, **kwargs):
          kwargs = self.validate_read(kwargs)
          index = self.read_index('index')
          sp_values = self.read_array('sp_values')
          sp_index = self.read_index('sp_index')
          return SparseSeries(sp_values, index=index, sparse_index=sp_index,
                              kind=self.kind or u'block',
                              fill_value=self.fill_value,
                              name=self.name)
  
      def write(self, obj, **kwargs):
          super(SparseSeriesFixed, self).write(obj, **kwargs)
          self.write_index('index', obj.index)
          self.write_index('sp_index', obj.sp_index)
          self.write_array('sp_values', obj.sp_values)
          self.attrs.name = obj.name
          self.attrs.fill_value = obj.fill_value
          self.attrs.kind = obj.kind
  
  
  class SparseFrameFixed(SparseFixed):
      pandas_kind = u'sparse_frame'
      attributes = ['default_kind', 'default_fill_value']
  
      def read(self, **kwargs):
          kwargs = self.validate_read(kwargs)
          columns = self.read_index('columns')
          sdict = {}
          for c in columns:
              key = 'sparse_series_{columns}'.format(columns=c)
              s = SparseSeriesFixed(self.parent, getattr(self.group, key))
              s.infer_axes()
              sdict[c] = s.read()
          return SparseDataFrame(sdict, columns=columns,
                                 default_kind=self.default_kind,
                                 default_fill_value=self.default_fill_value)
  
      def write(self, obj, **kwargs):
          """ write it as a collection of individual sparse series """
          super(SparseFrameFixed, self).write(obj, **kwargs)
          for name, ss in compat.iteritems(obj):
              key = 'sparse_series_{name}'.format(name=name)
              if key not in self.group._v_children:
                  node = self._handle.create_group(self.group, key)
              else:
                  node = getattr(self.group, key)
              s = SparseSeriesFixed(self.parent, node)
              s.write(ss)
          self.attrs.default_fill_value = obj.default_fill_value
          self.attrs.default_kind = obj.default_kind
          self.write_index('columns', obj.columns)
  
  
  class BlockManagerFixed(GenericFixed):
      attributes = ['ndim', 'nblocks']
      is_shape_reversed = False
  
      @property
      def shape(self):
          try:
              ndim = self.ndim
  
              # items
              items = 0
              for i in range(self.nblocks):
                  node = getattr(self.group, 'block{idx}_items'.format(idx=i))
                  shape = getattr(node, 'shape', None)
                  if shape is not None:
                      items += shape[0]
  
              # data shape
              node = getattr(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)
  
              # hacky - this works for frames, but is reversed for panels
              if self.is_shape_reversed:
                  shape = shape[::-1]
  
              return shape
          except AttributeError:
              return None
  
      def read(self, start=None, stop=None, **kwargs):
          # start, stop applied to rows, so 0th axis only
  
          kwargs = self.validate_read(kwargs)
          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('axis{idx}'.format(
                  idx=i), start=_start, stop=_stop)
              axes.append(ax)
  
          items = axes[0]
          blocks = []
          for i in range(self.nblocks):
  
              blk_items = self.read_index('block{idx}_items'.format(idx=i))
              values = self.read_array('block{idx}_values'.format(idx=i),
                                       start=_start, stop=_stop)
              blk = make_block(values,
                               placement=items.get_indexer(blk_items))
              blocks.append(blk)
  
          return self.obj_type(BlockManager(blocks, axes))
  
      def write(self, obj, **kwargs):
          super(BlockManagerFixed, self).write(obj, **kwargs)
          data = obj._data
          if not data.is_consolidated():
              data = data.consolidate()
  
          self.attrs.ndim = data.ndim
          for i, ax in enumerate(data.axes):
              if i == 0:
                  if not ax.is_unique:
                      raise ValueError(
                          "Columns index has to be unique for fixed format")
              self.write_index('axis{idx}'.format(idx=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('block{idx}_values'.format(idx=i),
                               blk.values, items=blk_items)
              self.write_index('block{idx}_items'.format(idx=i), blk_items)
  
  
  class FrameFixed(BlockManagerFixed):
      pandas_kind = u'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 = u'wide_table'
      table_type = None
      levels = 1
      is_table = True
      is_shape_reversed = False
  
      def __init__(self, *args, **kwargs):
          super(Table, self).__init__(*args, **kwargs)
          self.index_axes = []
          self.non_index_axes = []
          self.values_axes = []
          self.data_columns = []
          self.metadata = []
          self.info = dict()
          self.nan_rep = None
          self.selection = None
  
      @property
      def table_type_short(self):
          return self.table_type.split('_')[0]
  
      @property
      def format_type(self):
          return 'table'
  
      def __unicode__(self):
          """ return a pretty representatgion of myself """
          self.infer_axes()
          dc = ",dc->[{columns}]".format(columns=(','.join(
              self.data_columns) if len(self.data_columns) else ''))
  
          ver = ''
          if self.is_old_version:
              ver = "[{version}]".format(
                  version='.'.join(str(x) for x in self.version))
  
          return (
              "{pandas_type:12.12}{ver} (typ->{table_type},nrows->{nrows},"
              "ncols->{ncols},indexers->[{index_axes}]{dc})".format(
                  pandas_type=self.pandas_type, ver=ver,
                  table_type=self.table_type_short, nrows=self.nrows,
                  ncols=self.ncols,
                  index_axes=(','.join(a.name for a in self.index_axes)), dc=dc
              ))
  
      def __getitem__(self, c):
          """ 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 "
                  "[{other} - {self}]".format(
                      other=other.table_type, self=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(
                              "invalid combinate of [{c}] on appending data "
                              "[{sax}] vs current table [{oax}]".format(
                                  c=c, sax=sax, oax=oax))
  
                  # should never get here
                  raise Exception(
                      "invalid combinate of [{c}] on appending data [{sv}] vs "
                      "current table [{ov}]".format(c=c, sv=sv, ov=ov))
  
      @property
      def is_multi_index(self):
          """the levels attribute is 1 or a list in the case of a multi-index"""
          return isinstance(self.levels, list)
  
      def validate_metadata(self, existing):
          """ create / validate metadata """
          self.metadata = [
              c.name for c in self.values_axes if c.metadata is not None]
  
      def validate_multiindex(self, obj):
          """validate that we can store the multi-index; reset and return the
          new object
          """
          levels = [l if l is not None else "level_{0}".format(i)
                    for i, l in enumerate(obj.index.names)]
          try:
              return obj.reset_index(), levels
          except ValueError:
              raise ValueError("duplicate names/columns in the multi-index when "
                               "storing as a table")
  
      @property
      def nrows_expected(self):
          """ 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):
          """ has this table been created """
          return u'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):
          """ 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):
          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):
          """ return a dict of the kinds allowable columns for this object """
  
          # compute the values_axes queryables
          return dict(
              [(a.cname, a) for a in self.index_axes] +
              [(self.storage_obj_type._AXIS_NAMES[axis], None)
               for axis, values in self.non_index_axes] +
              [(v.cname, v) for v in self.values_axes
               if v.name in set(self.data_columns)]
          )
  
      def index_cols(self):
          """ return a list of my index cols """
          return [(i.axis, i.cname) for i in self.index_axes]
  
      def values_cols(self):
          """ return a list of my values cols """
          return [i.cname for i in self.values_axes]
  
      def _get_metadata_path(self, key):
          """ return the metadata pathname for this key """
          return "{group}/meta/{key}/meta".format(group=self.group._v_pathname,
                                                  key=key)
  
      def write_metadata(self, key, values):
          """
          write out a meta data array to the key as a fixed-format Series
  
          Parameters
          ----------
          key : string
          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):
          """ 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_info(self):
          """ update our table index info """
          self.attrs.info = self.info
  
      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.metadata = self.metadata
          self.set_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 dict()
          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 = getattr(
              self.attrs, 'levels', None) or []
          self.index_axes = [
              a.infer(self) for a in self.indexables if a.is_an_indexable
          ]
          self.values_axes = [
              a.infer(self) for a in self.indexables if not a.is_an_indexable
          ]
          self.metadata = getattr(
              self.attrs, 'metadata', None) or []
  
      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_itemisze 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(
                      "min_itemsize has the key [{key}] which is not an axis or "
                      "data_column".format(key=k))
  
      @property
      def indexables(self):
          """ create/cache the indexables if they don't exist """
          if self._indexables is None:
  
              self._indexables = []
  
              # index columns
              self._indexables.extend([
                  IndexCol(name=name, axis=axis, pos=i)
                  for i, (axis, name) in enumerate(self.attrs.index_cols)
              ])
  
              # values columns
              dc = set(self.data_columns)
              base_pos = len(self._indexables)
  
              def f(i, c):
                  klass = DataCol
                  if c in dc:
                      klass = DataIndexableCol
                  return klass.create_for_block(i=i, name=c, pos=base_pos + i,
                                                version=self.version)
  
              self._indexables.extend(
                  [f(i, c) for i, c in enumerate(self.attrs.values_cols)])
  
          return self._indexables
  
      def create_index(self, columns=None, optlevel=None, kind=None):
          """
          Create a pytables index on the specified columns
            note: cannot index Time64Col() or ComplexCol currently;
            PyTables must be >= 3.0
  
          Parameters
          ----------
          columns : False (don't create an index), True (create all columns
              index), None or list_like (the indexers to index)
          optlevel: optimization level (defaults to 6)
          kind    : kind of index (defaults to 'medium')
  
          Exceptions
          ----------
          raises if the node is not a table
  
          """
  
          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 = dict()
          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)
  
      def read_axes(self, where, **kwargs):
          """create and return the axes sniffed from the table: return boolean
          for success
          """
  
          # validate the version
          self.validate_version(where)
  
          # infer the data kind
          if not self.infer_axes():
              return False
  
          # create the selection
          self.selection = Selection(self, where=where, **kwargs)
          values = self.selection.select()
  
          # convert the data
          for a in self.axes:
              a.set_info(self.info)
              a.convert(values, nan_rep=self.nan_rep, encoding=self.encoding,
                        errors=self.errors)
  
          return True
  
      def get_object(self, obj):
          """ return the data for this obj """
          return obj
  
      def validate_data_columns(self, data_columns, min_itemsize):
          """take the input data_columns and min_itemize and create a data
          columns spec
          """
  
          if not len(self.non_index_axes):
              return []
  
          axis, axis_labels = self.non_index_axes[0]
          info = self.info.get(axis, dict())
          if info.get('type') == 'MultiIndex' and data_columns:
              raise ValueError("cannot use a multi-index on axis [{0}] with "
                               "data_columns {1}".format(axis, data_columns))
  
          # evaluate the passed data_columns, True == use all columns
          # take only valide 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.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, validate=True, nan_rep=None,
                      data_columns=None, min_itemsize=None, **kwargs):
          """ create and return the axes
          leagcy tables create an indexable column, indexable index,
          non-indexable fields
  
              Parameters:
              -----------
              axes: a list of the axes in order to create (names or numbers of
                  the axes)
              obj : the object to create axes on
              validate: validate the obj against an existing object already
                  written
              min_itemsize: a dict of the min size for a column in bytes
              nan_rep : a values to use for string column nan_rep
              encoding : the encoding for string values
              data_columns : a list of columns that we want to create separate to
                  allow indexing (or True will force all columns)
  
          """
  
          # set the default axes if needed
          if axes is None:
              try:
                  axes = _AXES_MAP[type(obj)]
              except KeyError:
                  raise TypeError(
                      "cannot properly create the storer for: [group->{group},"
                      "value->{value}]".format(
                          group=self.group._v_name, value=type(obj)))
  
          # 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():
              existing_table = self.copy()
              existing_table.infer_axes()
              axes = [a.axis for a in existing_table.index_axes]
              data_columns = existing_table.data_columns
              nan_rep = existing_table.nan_rep
              self.encoding = existing_table.encoding
              self.errors = existing_table.errors
              self.info = copy.copy(existing_table.info)
          else:
              existing_table = None
  
          # 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
          self.non_index_axes = []
          self.data_columns = []
  
          # nan_representation
          if nan_rep is None:
              nan_rep = 'nan'
  
          self.nan_rep = nan_rep
  
          # create axes to index and non_index
          index_axes_map = dict()
          for i, a in enumerate(obj.axes):
  
              if i in axes:
                  name = obj._AXIS_NAMES[i]
                  index_axes_map[i] = _convert_index(
                      a, self.encoding, self.errors, self.format_type
                  ).set_name(name).set_axis(i)
              else:
  
                  # we might be able to change the axes on the appending data if
                  # necessary
                  append_axis = list(a)
                  if existing_table is not None:
                      indexer = len(self.non_index_axes)
                      exist_axis = existing_table.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 = _get_info(self.info, i)
                  info['names'] = list(a.names)
                  info['type'] = a.__class__.__name__
  
                  self.non_index_axes.append((i, append_axis))
  
          # set axis positions (based on the axes)
          self.index_axes = [
              index_axes_map[a].set_pos(j).update_info(self.info)
              for j, a in enumerate(axes)
          ]
          j = len(self.index_axes)
  
          # check for column conflicts
          for a in self.axes:
              a.maybe_set_size(min_itemsize=min_itemsize)
  
          # reindex by our non_index_axes & compute data_columns
          for a in self.non_index_axes:
              obj = _reindex_axis(obj, a[0], a[1])
  
          def get_blk_items(mgr, blocks):
              return [mgr.items.take(blk.mgr_locs) for blk in blocks]
  
          # figure out data_columns and get out blocks
          block_obj = self.get_object(obj)._consolidate()
          blocks = block_obj._data.blocks
          blk_items = get_blk_items(block_obj._data, blocks)
          if len(self.non_index_axes):
              axis, axis_labels = self.non_index_axes[0]
              data_columns = self.validate_data_columns(
                  data_columns, min_itemsize)
              if len(data_columns):
                  mgr = block_obj.reindex(
                      Index(axis_labels).difference(Index(data_columns)),
                      axis=axis
                  )._data
  
                  blocks = list(mgr.blocks)
                  blk_items = get_blk_items(mgr, blocks)
                  for c in data_columns:
                      mgr = block_obj.reindex([c], axis=axis)._data
                      blocks.extend(mgr.blocks)
                      blk_items.extend(get_blk_items(mgr, mgr.blocks))
  
          # reorder the blocks in the same order as the existing_table if we can
          if existing_table is not None:
              by_items = {tuple(b_items.tolist()): (b, b_items)
                          for b, b_items in zip(blocks, blk_items)}
              new_blocks = []
              new_blk_items = []
              for ea in existing_table.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):
                      raise ValueError(
                          "cannot match existing table structure for [{items}] "
                          "on appending data".format(
                              items=(','.join(pprint_thing(item) for
                                              item in items))))
              blocks = new_blocks
              blk_items = new_blk_items
  
          # add my values
          self.values_axes = []
          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]
                  self.data_columns.append(name)
  
              # make sure that we match up the existing columns
              # if we have an existing table
              if existing_table is not None and validate:
                  try:
                      existing_col = existing_table.values_axes[i]
                  except (IndexError, KeyError):
                      raise ValueError(
                          "Incompatible appended table [{blocks}]"
                          "with existing table [{table}]".format(
                              blocks=blocks,
                              table=existing_table.values_axes))
              else:
                  existing_col = None
  
              try:
                  col = klass.create_for_block(
                      i=i, name=name, version=self.version)
                  col.set_atom(block=b, block_items=b_items,
                               existing_col=existing_col,
                               min_itemsize=min_itemsize,
                               nan_rep=nan_rep,
                               encoding=self.encoding,
                               errors=self.errors,
                               info=self.info)
                  col.set_pos(j)
  
                  self.values_axes.append(col)
              except (NotImplementedError, ValueError, TypeError) as e:
                  raise e
              except Exception as detail:
                  raise Exception(
                      "cannot find the correct atom type -> "
                      "[dtype->{name},items->{items}] {detail!s}".format(
                          name=b.dtype.name, items=b_items, detail=detail))
              j += 1
  
          # validate our min_itemsize
          self.validate_min_itemsize(min_itemsize)
  
          # validate our metadata
          self.validate_metadata(existing_table)
  
          # validate the axes if we have an existing table
          if validate:
              self.validate(existing_table)
  
      def process_axes(self, obj, 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:
              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 self.selection.filter is not None:
              for field, op, filt in self.selection.filter.format():
  
                  def process_filter(field, filt):
  
                      for axis_name in obj._AXIS_NAMES.values():
                          axis_number = obj._get_axis_number(axis_name)
                          axis_values = obj._get_axis(axis_name)
  
                          # 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._getitem_axis(takers,
                                                           axis=axis_number)
  
                          # 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._getitem_axis(takers,
                                                           axis=axis_number)
  
                      raise ValueError("cannot find the field [{field}] for "
                                       "filtering!".format(field=field))
  
                  obj = process_filter(field, filt)
  
          return obj
  
      def create_description(self, complib=None, complevel=None,
                             fletcher32=False, expectedrows=None):
          """ 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 = dict(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=None, stop=None, **kwargs):
          """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
          self.selection = Selection(
              self, where=where, start=start, stop=stop, **kwargs)
          coords = self.selection.select_coords()
          if self.selection.filter is not None:
              for field, op, filt in self.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, where=None, start=None, stop=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(
                          "column [{column}] can not be extracted individually; "
                          "it is not data indexable".format(column=column))
  
                  # column must be an indexable or a data column
                  c = getattr(self.table.cols, column)
                  a.set_info(self.info)
                  return Series(_set_tz(a.convert(c[start:stop],
                                                  nan_rep=self.nan_rep,
                                                  encoding=self.encoding,
                                                  errors=self.errors
                                                  ).take_data(),
                                        a.tz, True), name=column)
  
          raise KeyError(
              "column [{column}] not found in the table".format(column=column))
  
  
  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 = u'worm'
  
      def read(self, **kwargs):
          """ 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("WORKTable needs to implement write")
  
  
  class LegacyTable(Table):
  
      """ an appendable table: allow append/query/delete operations to a
            (possibly) already existing appendable table this table ALLOWS
            append (but doesn't require them), and stores the data in a format
            that can be easily searched
  
      """
      _indexables = [
          IndexCol(name='index', axis=1, pos=0),
          IndexCol(name='column', axis=2, pos=1, index_kind='columns_kind'),
          DataCol(name='fields', cname='values', kind_attr='fields', pos=2)
      ]
      table_type = u'legacy'
      ndim = 3
  
      def write(self, **kwargs):
          raise TypeError("write operations are not allowed on legacy tables!")
  
      def read(self, where=None, columns=None, **kwargs):
          """we have n indexable columns, with an arbitrary number of data
          axes
          """
  
          if not self.read_axes(where=where, **kwargs):
              return None
  
          raise NotImplementedError("Panel is removed in pandas 0.25.0")
  
  
  class AppendableTable(LegacyTable):
      """ support the new appendable table formats """
      _indexables = None
      table_type = u'appendable'
  
      def write(self, obj, axes=None, append=False, complib=None,
                complevel=None, fletcher32=None, min_itemsize=None,
                chunksize=None, expectedrows=None, dropna=False, **kwargs):
  
          if not append and self.is_exists:
              self._handle.remove_node(self.group, 'table')
  
          # create the axes
          self.create_axes(axes=axes, obj=obj, validate=append,
                           min_itemsize=min_itemsize,
                           **kwargs)
  
          for a in self.axes:
              a.validate(self, append)
  
          if not self.is_exists:
  
              # create the table
              options = self.create_description(complib=complib,
                                                complevel=complevel,
                                                fletcher32=fletcher32,
                                                expectedrows=expectedrows)
  
              # set the table attributes
              self.set_attrs()
  
              # create the table
              self._handle.create_table(self.group, **options)
          else:
              pass
              # table = self.table
  
          # update my info
          self.set_info()
  
          # validate the axes and set the kinds
          for a in self.axes:
              a.validate_and_set(self, append)
  
          # add the rows
          self.write_data(chunksize, dropna=dropna)
  
      def write_data(self, chunksize, dropna=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)
          bindexes = []
          for i, idx in enumerate(indexes):
  
              # broadcast to all other indexes except myself
              if i > 0 and i < nindexes:
                  repeater = np.prod(
                      [indexes[bi].shape[0] for bi in range(0, i)])
                  idx = np.tile(idx, repeater)
  
              if i < nindexes - 1:
                  repeater = np.prod([indexes[bi].shape[0]
                                      for bi in range(i + 1, nindexes)])
                  idx = np.repeat(idx, repeater)
  
              bindexes.append(idx)
  
          # 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 = int(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 bindexes],
                  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, indexes, mask, values):
          """
          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
  
          try:
              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]
  
          except Exception as detail:
              raise Exception(
                  "cannot create row-data -> {detail}".format(detail=detail))
  
          try:
              if len(rows):
                  self.table.append(rows)
                  self.table.flush()
          except Exception as detail:
              raise TypeError(
                  "tables cannot write this data -> {detail}".format(
                      detail=detail))
  
      def delete(self, where=None, start=None, stop=None, **kwargs):
  
          # 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
          self.selection = Selection(
              self, where, start=start, stop=stop, **kwargs)
          values = self.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(lrange(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 = u'frame_table'
      table_type = u'appendable_frame'
      ndim = 2
      obj_type = DataFrame
  
      @property
      def is_transposed(self):
          return self.index_axes[0].axis == 1
  
      def get_object(self, obj):
          """ these are written transposed """
          if self.is_transposed:
              obj = obj.T
          return obj
  
      def read(self, where=None, columns=None, **kwargs):
  
          if not self.read_axes(where=where, **kwargs):
              return None
  
          info = (self.info.get(self.non_index_axes[0][0], dict())
                  if len(self.non_index_axes) else dict())
          index = self.index_axes[0].values
          frames = []
          for a in self.values_axes:
  
              # 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(a.values)
              else:
                  cols = Index(a.values)
              names = info.get('names')
              if names is not None:
                  cols.set_names(names, inplace=True)
  
              if self.is_transposed:
                  values = a.cvalues
                  index_ = cols
                  cols_ = Index(index, name=getattr(index, 'name', None))
              else:
                  values = a.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]))
  
              block = make_block(values, placement=np.arange(len(cols_)))
              mgr = BlockManager([block], [cols_, index_])
              frames.append(DataFrame(mgr))
  
          if len(frames) == 1:
              df = frames[0]
          else:
              df = concat(frames, axis=1)
  
          # apply the selection filters & axis orderings
          df = self.process_axes(df, columns=columns)
  
          return df
  
  
  class AppendableSeriesTable(AppendableFrameTable):
      """ support the new appendable table formats """
      pandas_kind = u'series_table'
      table_type = u'appendable_series'
      ndim = 2
      obj_type = Series
      storage_obj_type = DataFrame
  
      @property
      def is_transposed(self):
          return False
  
      def get_object(self, obj):
          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 = DataFrame({name: obj}, index=obj.index)
              obj.columns = [name]
          return super(AppendableSeriesTable, self).write(
              obj=obj, data_columns=obj.columns.tolist(), **kwargs)
  
      def read(self, columns=None, **kwargs):
  
          is_multi_index = self.is_multi_index
          if columns is not None and is_multi_index:
              for n in self.levels:
                  if n not in columns:
                      columns.insert(0, n)
          s = super(AppendableSeriesTable, self).read(columns=columns, **kwargs)
          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 = u'series_table'
      table_type = u'appendable_multiseries'
  
      def write(self, obj, **kwargs):
          """ we are going to write this as a frame table """
          name = obj.name or 'values'
          obj, self.levels = self.validate_multiindex(obj)
          cols = list(self.levels)
          cols.append(name)
          obj.columns = cols
          return super(AppendableMultiSeriesTable, self).write(obj=obj, **kwargs)
  
  
  class GenericTable(AppendableFrameTable):
      """ a table that read/writes the generic pytables table format """
      pandas_kind = u'frame_table'
      table_type = u'generic_table'
      ndim = 2
      obj_type = DataFrame
  
      @property
      def pandas_type(self):
          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.infer(self)
                             for a in self.indexables if a.is_an_indexable]
          self.values_axes = [a.infer(self)
                              for a in self.indexables if not a.is_an_indexable]
          self.data_columns = [a.name for a in self.values_axes]
  
      @property
      def indexables(self):
          """ create the indexables from the table description """
          if self._indexables is None:
  
              d = self.description
  
              # the index columns is just a simple index
              self._indexables = [GenericIndexCol(name='index', axis=0)]
  
              for i, n in enumerate(d._v_names):
  
                  dc = GenericDataIndexableCol(
                      name=n, pos=i, values=[n], version=self.version)
                  self._indexables.append(dc)
  
          return self._indexables
  
      def write(self, **kwargs):
          raise NotImplementedError("cannot write on an generic table")
  
  
  class AppendableMultiFrameTable(AppendableFrameTable):
  
      """ a frame with a multi-index """
      table_type = u'appendable_multiframe'
      obj_type = DataFrame
      ndim = 2
      _re_levels = re.compile(r"^level_\d+$")
  
      @property
      def table_type_short(self):
          return u'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)
          for n in self.levels:
              if n not in data_columns:
                  data_columns.insert(0, n)
          return super(AppendableMultiFrameTable, self).write(
              obj=obj, data_columns=data_columns, **kwargs)
  
      def read(self, **kwargs):
  
          df = super(AppendableMultiFrameTable, self).read(**kwargs)
          df = df.set_index(self.levels)
  
          # remove names for 'level_%d'
          df.index = df.index.set_names([
              None if self._re_levels.search(l) else l for l in df.index.names
          ])
  
          return df
  
  
  def _reindex_axis(obj, axis, labels, other=None):
      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 = [slice(None, None)] * obj.ndim
          slicer[axis] = labels
          obj = obj.loc[tuple(slicer)]
      return obj
  
  
  def _get_info(info, name):
      """ get/create the info for this name """
      try:
          idx = info[name]
      except KeyError:
          idx = info[name] = dict()
      return idx
  
  # tz to/from coercion
  
  
  def _get_tz(tz):
      """ for a tz-aware type, return an encoded zone """
      zone = timezones.get_timezone(tz)
      if zone is None:
          zone = tz.utcoffset().total_seconds()
      return zone
  
  
  def _set_tz(values, tz, preserve_UTC=False, coerce=False):
      """
      coerce the values to a DatetimeIndex if tz is set
      preserve the input shape if possible
  
      Parameters
      ----------
      values : ndarray
      tz : string/pickled tz object
      preserve_UTC : boolean,
          preserve the UTC of the result
      coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray
      """
      if tz is not None:
          name = getattr(values, 'name', None)
          values = values.ravel()
          tz = timezones.get_timezone(_ensure_decoded(tz))
          values = DatetimeIndex(values, name=name)
          if values.tz is None:
              values = values.tz_localize('UTC').tz_convert(tz)
          if preserve_UTC:
              if tz == 'UTC':
                  values = list(values)
      elif coerce:
          values = np.asarray(values, dtype='M8[ns]')
  
      return values
  
  
  def _convert_index(index, encoding=None, errors='strict', format_type=None):
      index_name = getattr(index, 'name', None)
  
      if isinstance(index, DatetimeIndex):
          converted = index.asi8
          return IndexCol(converted, 'datetime64', _tables().Int64Col(),
                          freq=getattr(index, 'freq', None),
                          tz=getattr(index, 'tz', None),
                          index_name=index_name)
      elif isinstance(index, TimedeltaIndex):
          converted = index.asi8
          return IndexCol(converted, 'timedelta64', _tables().Int64Col(),
                          freq=getattr(index, 'freq', None),
                          index_name=index_name)
      elif isinstance(index, (Int64Index, PeriodIndex)):
          atom = _tables().Int64Col()
          # avoid to store ndarray of Period objects
          return IndexCol(index._ndarray_values, 'integer', atom,
                          freq=getattr(index, 'freq', None),
                          index_name=index_name)
  
      if isinstance(index, MultiIndex):
          raise TypeError('MultiIndex not supported here!')
  
      inferred_type = lib.infer_dtype(index, skipna=False)
  
      values = np.asarray(index)
  
      if inferred_type == 'datetime64':
          converted = values.view('i8')
          return IndexCol(converted, 'datetime64', _tables().Int64Col(),
                          freq=getattr(index, 'freq', None),
                          tz=getattr(index, 'tz', None),
                          index_name=index_name)
      elif inferred_type == 'timedelta64':
          converted = values.view('i8')
          return IndexCol(converted, 'timedelta64', _tables().Int64Col(),
                          freq=getattr(index, 'freq', None),
                          index_name=index_name)
      elif inferred_type == 'datetime':
          converted = np.asarray([(time.mktime(v.timetuple()) +
                                   v.microsecond / 1E6) for v in values],
                                 dtype=np.float64)
          return IndexCol(converted, 'datetime', _tables().Time64Col(),
                          index_name=index_name)
      elif inferred_type == 'date':
          converted = np.asarray([v.toordinal() for v in values],
                                 dtype=np.int32)
          return IndexCol(converted, 'date', _tables().Time32Col(),
                          index_name=index_name)
      elif inferred_type == 'string':
          # atom = _tables().ObjectAtom()
          # return np.asarray(values, dtype='O'), 'object', atom
  
          converted = _convert_string_array(values, encoding, errors)
          itemsize = converted.dtype.itemsize
          return IndexCol(
              converted, 'string', _tables().StringCol(itemsize),
              itemsize=itemsize, index_name=index_name
          )
      elif inferred_type == 'unicode':
          if format_type == 'fixed':
              atom = _tables().ObjectAtom()
              return IndexCol(np.asarray(values, dtype='O'), 'object', atom,
                              index_name=index_name)
          raise TypeError(
              "[unicode] is not supported as a in index type for [{0}] formats"
              .format(format_type)
          )
  
      elif inferred_type == 'integer':
          # take a guess for now, hope the values fit
          atom = _tables().Int64Col()
          return IndexCol(np.asarray(values, dtype=np.int64), 'integer', atom,
                          index_name=index_name)
      elif inferred_type == 'floating':
          atom = _tables().Float64Col()
          return IndexCol(np.asarray(values, dtype=np.float64), 'float', atom,
                          index_name=index_name)
      else:  # pragma: no cover
          atom = _tables().ObjectAtom()
          return IndexCol(np.asarray(values, dtype='O'), 'object', atom,
                          index_name=index_name)
  
  
  def _unconvert_index(data, kind, encoding=None, errors='strict'):
      kind = _ensure_decoded(kind)
      if kind == u'datetime64':
          index = DatetimeIndex(data)
      elif kind == u'timedelta64':
          index = TimedeltaIndex(data)
      elif kind == u'datetime':
          index = np.asarray([datetime.fromtimestamp(v) for v in data],
                             dtype=object)
      elif kind == u'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 (u'integer', u'float'):
          index = np.asarray(data)
      elif kind in (u'string'):
          index = _unconvert_string_array(data, nan_rep=None, encoding=encoding,
                                          errors=errors)
      elif kind == u'object':
          index = np.asarray(data[0])
      else:  # pragma: no cover
          raise ValueError('unrecognized index type {kind}'.format(kind=kind))
      return index
  
  
  def _unconvert_index_legacy(data, kind, legacy=False, encoding=None,
                              errors='strict'):
      kind = _ensure_decoded(kind)
      if kind == u'datetime':
          index = to_datetime(data)
      elif kind in (u'integer'):
          index = np.asarray(data, dtype=object)
      elif kind in (u'string'):
          index = _unconvert_string_array(data, nan_rep=None, encoding=encoding,
                                          errors=errors)
      else:  # pragma: no cover
          raise ValueError('unrecognized index type {kind}'.format(kind=kind))
      return index
  
  
  def _convert_string_array(data, encoding, errors, itemsize=None):
      """
      we take a string-like that is object dtype and coerce to a fixed size
      string type
  
      Parameters
      ----------
      data : a numpy array of object dtype
      encoding : None or string-encoding
      errors : handler for encoding errors
      itemsize : integer, optional, defaults to the max length of the strings
  
      Returns
      -------
      data in a fixed-length string dtype, encoded to bytes if needed
      """
  
      # encode if needed
      if encoding is not None and len(data):
          data = Series(data.ravel()).str.encode(
              encoding, errors).values.reshape(data.shape)
  
      # create the sized dtype
      if itemsize is None:
          ensured = ensure_object(data.ravel())
          itemsize = max(1, libwriters.max_len_string_array(ensured))
  
      data = np.asarray(data, dtype="S{size}".format(size=itemsize))
      return data
  
  
  def _unconvert_string_array(data, nan_rep=None, encoding=None,
                              errors='strict'):
      """
      inverse of _convert_string_array
  
      Parameters
      ----------
      data : fixed length string dtyped array
      nan_rep : the storage repr of NaN, optional
      encoding : the encoding of the data, optional
      errors : handler for encoding errors, default 'strict'
  
      Returns
      -------
      an object array of the decoded data
  
      """
      shape = data.shape
      data = np.asarray(data.ravel(), dtype=object)
  
      # guard against a None encoding in PY3 (because of a legacy
      # where the passed encoding is actually None)
      encoding = _ensure_encoding(encoding)
      if encoding is not None and len(data):
  
          itemsize = libwriters.max_len_string_array(ensure_object(data))
          if compat.PY3:
              dtype = "U{0}".format(itemsize)
          else:
              dtype = "S{0}".format(itemsize)
  
          if isinstance(data[0], compat.binary_type):
              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, val_kind, encoding, errors):
      if _need_convert(val_kind):
          conv = _get_converter(val_kind, encoding, errors)
          # conv = np.frompyfunc(conv, 1, 1)
          values = conv(values)
      return values
  
  
  def _get_converter(kind, encoding, errors):
      kind = _ensure_decoded(kind)
      if kind == 'datetime64':
          return lambda x: np.asarray(x, dtype='M8[ns]')
      elif kind == 'datetime':
          return lambda x: to_datetime(x, cache=True).to_pydatetime()
      elif kind == 'string':
          return lambda x: _unconvert_string_array(x, encoding=encoding,
                                                   errors=errors)
      else:  # pragma: no cover
          raise ValueError('invalid kind {kind}'.format(kind=kind))
  
  
  def _need_convert(kind):
      kind = _ensure_decoded(kind)
      if kind in (u'datetime', u'datetime64', u'string'):
          return True
      return False
  
  
  class Selection(object):
  
      """
      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, where=None, start=None, stop=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
              try:
                  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
  
              except ValueError:
                  pass
  
          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 Expr(where, queryables=q, encoding=self.table.encoding)
          except NameError:
              # raise a nice message, suggesting that the user should use
              # data_columns
              raise ValueError(
                  "The passed where expression: {0}\n"
                  "            contains an invalid variable reference\n"
                  "            all of the variable references must be a "
                  "reference to\n"
                  "            an axis (e.g. 'index' or 'columns'), or a "
                  "data_column\n"
                  "            The currently defined references are: {1}\n"
                  .format(where, ','.join(q.keys()))
              )
  
      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 self.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)
  