pytables.py

"""
High level interface to PyTables for reading and writing pandas data structures
to disk
"""

import copy
from datetime import date, datetime
import itertools
import os
import re
import time
from typing import List, Optional, Type, Union
import warnings

import numpy as np

from pandas._config import config, get_option

from pandas._libs import lib, writers as libwriters
from pandas._libs.tslibs import timezones
from pandas.compat._optional import import_optional_dependency
from pandas.errors import PerformanceWarning

from pandas.core.dtypes.common import (
    ensure_object,
    is_categorical_dtype,
    is_datetime64_dtype,
    is_datetime64tz_dtype,
    is_extension_type,
    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,
    concat,
    isna,
    to_datetime,
)
from pandas.core.arrays.categorical import Categorical
from pandas.core.arrays.sparse import BlockIndex, IntIndex
import pandas.core.common as com
from pandas.core.computation.pytables import Expr, maybe_expression
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
_default_encoding = "UTF-8"


def _ensure_decoded(s):
    """ if we have bytes, decode them to unicode """
    if isinstance(s, np.bytes_):
        s = s.decode("UTF-8")
    return s


def _ensure_encoding(encoding):
    # set the encoding if we need
    if encoding is None:
        encoding = _default_encoding

    return encoding


def _ensure_str(name):
    """
    Ensure that an index / column name is a str (python 3); otherwise they
    may be np.string dtype. Non-string dtypes are passed through unchanged.

    https://github.com/pandas-dev/pandas/issues/13492
    """
    if isinstance(name, str):
        name = str(name)
    return name


Term = 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 if where is None or len(where) else None


class PossibleDataLossError(Exception):
    pass


class ClosedFileError(Exception):
    pass


class IncompatibilityWarning(Warning):
    pass


incompatibility_doc = """
where criteria is being ignored as this version [%s] is too old (or
not-defined), read the file in and write it out to a new file to upgrade (with
the copy_to method)
"""


class AttributeConflictWarning(Warning):
    pass


attribute_conflict_doc = """
the [%s] attribute of the existing index is [%s] which conflicts with the new
[%s], resetting the attribute to None
"""


class DuplicateWarning(Warning):
    pass


duplicate_doc = """
duplicate entries in table, taking most recently appended
"""

performance_doc = """
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->%s,key->%s] [items->%s]
"""

# formats
_FORMAT_MAP = {"f": "fixed", "fixed": "fixed", "t": "table", "table": "table"}

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: "series",
    SparseSeries: "sparse_series",
    DataFrame: "frame",
    SparseDataFrame: "sparse_frame",
}

# storer class map
_STORER_MAP = {
    "Series": "LegacySeriesFixed",
    "DataFrame": "LegacyFrameFixed",
    "DataMatrix": "LegacyFrameFixed",
    "series": "SeriesFixed",
    "sparse_series": "SparseSeriesFixed",
    "frame": "FrameFixed",
    "sparse_frame": "SparseFrameFixed",
}

# table class map
_TABLE_MAP = {
    "generic_table": "GenericTable",
    "appendable_series": "AppendableSeriesTable",
    "appendable_multiseries": "AppendableMultiSeriesTable",
    "appendable_frame": "AppendableFrameTable",
    "appendable_multiframe": "AppendableMultiFrameTable",
    "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

        # 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, str):
        with HDFStore(
            path_or_buf, mode=mode, complevel=complevel, complib=complib
        ) as store:
            f(store)
    else:
        f(path_or_buf)


def read_hdf(path_or_buf, key=None, mode="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 : str, path object, pandas.HDFStore or file-like object
        Any valid string path is acceptable. The string could be a URL. Valid
        URL schemes include http, ftp, s3, and file. For file URLs, a host is
        expected. A local file could be: ``file://localhost/path/to/table.h5``.

        If you want to pass in a path object, pandas accepts any
        ``os.PathLike``.

        Alternatively, pandas accepts an open :class:`pandas.HDFStore` object.

        By file-like object, we refer to objects with a ``read()`` method,
        such as a file handler (e.g. via builtin ``open`` function)
        or ``StringIO``.

        .. 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, str):
            raise NotImplementedError(
                "Support for generic buffers has not been implemented."
            )
        try:
            exists = os.path.exists(path_or_buf)

        # if filepath is too long
        except (TypeError, ValueError):
            exists = False

        if not exists:
            raise FileNotFoundError(
                "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, KeyError):
        # 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:

    """
    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 or None disables compression.
    complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
            Specifies the compression library to be used.
            As of v0.20.2 these additional compressors for Blosc are supported
            (default if no compressor specified: 'blosc:blosclz'):
            {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
             'blosc:zlib', 'blosc:zstd'}.
            Specifying a compression library which is not available issues
            a ValueError.
    fletcher32 : bool, default False
            If applying compression use the fletcher32 checksum

    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")

        tables = import_optional_dependency("tables")

        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 __repr__(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 '/'

        Returns
        -------
        list
        """
        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

        Raises
        ------
        raises KeyError if the column is not found (or key is not a valid
            store)
        raises ValueError if the column can not be extracted individually (it
            is part of a data block)

        """
        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

        Raises
        ------
        raises KeyError if keys or selector is not found or keys is empty
        raises TypeError if keys is not a list or tuple
        raises ValueError if the tables are not ALL THE SAME DIMENSIONS
        """

        # default to single select
        where = _ensure_term(where, scope_level=1)
        if isinstance(keys, (list, tuple)) and len(keys) == 1:
            keys = keys[0]
        if isinstance(keys, str):
            return self.select(
                key=keys,
                where=where,
                columns=columns,
                start=start,
                stop=stop,
                iterator=iterator,
                chunksize=chunksize,
                **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 concentration axes
        axis = list({t.non_index_axes[0][0] for t in tbls})[0]

        def func(_start, _stop, _where):

            # retrieve the objs, _where is always passed as a set of
            # coordinates here
            objs = [
                t.read(
                    where=_where, columns=columns, start=_start, stop=_stop, **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/user_guide/io.html#query-via-data-columns>`__.
        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)

        Raises
        ------
        raises KeyError if key is not a valid store

        """
        where = _ensure_term(where, scope_level=1)
        try:
            s = self.get_storer(key)
        except KeyError:
            # the key is not a valid store, re-raising KeyError
            raise
        except 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/user_guide/io.html#query-via-data-columns>`__.
        min_itemsize : dict of columns that specify minimum string sizes
        nan_rep      : string to use as string nan representation
        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)

        Raises
        ------
        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)

        Returns
        -------
        list
        """
        _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 != "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

        Returns
        -------
        str
        """
        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 = "frame_table"
                    tt = "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 += "_table"

        # a storer node
        if "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 == "series_table":
                    index = getattr(value, "index", None)
                    if index is not None:
                        if index.nlevels == 1:
                            tt = "appendable_series"
                        elif index.nlevels > 1:
                            tt = "appendable_multiseries"
                elif pt == "frame_table":
                    index = getattr(value, "index", None)
                    if index is not None:
                        if index.nlevels == 1:
                            tt = "appendable_frame"
                        elif index.nlevels > 1:
                            tt = "appendable_multiframe"
                elif pt == "wide_table":
                    tt = "appendable_panel"
                elif pt == "ndim_table":
                    tt = "appendable_ndim"

            else:

                # distinguish between a frame/table
                tt = "legacy_panel"
                try:
                    fields = group.table._v_attrs.fields
                    if len(fields) == 1 and fields[0] == "value":
                        tt = "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:

    """ 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:

    """ 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 __repr__(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, start=None, stop=None):
        """ 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) == "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) == "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, start=None, stop=None):
        """ set the values from this selection: take = take ownership

        Parameters
        ----------

        values : np.ndarray
        nan_rep : str
        encoding : str
        errors : str
        start : int, optional
            Table row number: the start of the sub-selection.
        stop : int, optional
            Table row number: the end of the sub-selection. Values larger than
            the underlying table's row count are normalized to that.
        """

        start = start if start is not None else 0
        stop = min(stop, self.table.nrows) if stop is not None else self.table.nrows
        self.values = Int64Index(np.arange(stop - start))

        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().__init__(values=values, kind=kind, typ=typ, cname=cname, **kwargs)
        self.dtype = None
        self.dtype_attr = "{name}_dtype".format(name=self.name)
        self.meta = meta
        self.meta_attr = "{name}_meta".format(name=self.name)
        self.set_data(data)
        self.set_metadata(metadata)

    def __repr__(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("string") or dtype.startswith("bytes"):
                self.kind = "string"
            elif dtype.startswith("float"):
                self.kind = "float"
            elif dtype.startswith("complex"):
                self.kind = "complex"
            elif dtype.startswith("int") or dtype.startswith("uint"):
                self.kind = "integer"
            elif dtype.startswith("date"):
                self.kind = "datetime"
            elif dtype.startswith("timedelta"):
                self.kind = "timedelta"
            elif dtype.startswith("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, start=None, stop=None):
        """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 == "datetime64":

                # recreate with tz if indicated
                self.data = _set_tz(self.data, self.tz, coerce=True)

            elif dtype == "timedelta64":
                self.data = np.asarray(self.data, dtype="m8[ns]")
            elif dtype == "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 == "datetime":
                self.data = np.asarray(
                    [datetime.fromtimestamp(v) for v in self.data], dtype=object
                )

            elif meta == "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) == "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:

    """ 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  # type: str
    obj_type = None  # type: Type[Union[DataFrame, Series]]
    ndim = None  # type: int
    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 __repr__(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 _index_type_map.items()}
    attributes = []  # type: List[str]

    # 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 == "datetime64":

                # reconstruct a timezone if indicated
                ret = _set_tz(ret, getattr(attrs, "tz", None), coerce=True)

            elif dtype == "timedelta64":
                ret = np.asarray(ret, dtype="m8[ns]")

        if transposed:
            return ret.T
        else:
            return ret

    def read_index(self, key, **kwargs):
        variety = _ensure_decoded(getattr(self.attrs, "{key}_variety".format(key=key)))

        if variety == "multi":
            return self.read_multi_index(key, **kwargs)
        elif variety == "block":
            return self.read_block_index(key, **kwargs)
        elif variety == "sparseint":
            return self.read_sparse_intindex(key, **kwargs)
        elif variety == "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
            if is_extension_type(lev):
                raise NotImplementedError(
                    "Saving a MultiIndex with an extension dtype is not supported."
                )
            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 "freq" in node._v_attrs:
            kwargs["freq"] = node._v_attrs["freq"]

        if "tz" in node._v_attrs:
            kwargs["tz"] = node._v_attrs["tz"]

        if kind in ("date", "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 = "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().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().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 = "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 "block",
            fill_value=self.fill_value,
            name=self.name,
        )

    def write(self, obj, **kwargs):
        super().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 = "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().write(obj, **kwargs)
        for name, ss in obj.items():
            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), ndim=len(axes)
            )
            blocks.append(blk)

        return self.obj_type(BlockManager(blocks, axes))

    def write(self, obj, **kwargs):
        super().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 = "frame"
    obj_type = DataFrame


class Table(Fixed):

    """ represent a table:
          facilitate read/write of various types of tables

        Attrs in Table Node
        -------------------
        These are attributes that are store in the main table node, they are
        necessary to recreate these tables when read back in.

        index_axes    : a list of tuples of the (original indexing axis and
            index column)
        non_index_axes: a list of tuples of the (original index axis and
            columns on a non-indexing axis)
        values_axes   : a list of the columns which comprise the data of this
            table
        data_columns  : a list of the columns that we are allowing indexing
            (these become single columns in values_axes), or True to force all
            columns
        nan_rep       : the string to use for nan representations for string
            objects
        levels        : the names of levels
        metadata      : the names of the metadata columns

        """

    pandas_kind = "wide_table"
    table_type = None  # type: str
    levels = 1
    is_table = True
    is_shape_reversed = False

    def __init__(self, *args, **kwargs):
        super().__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 __repr__(self):
        """ return a pretty representation 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 "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_itemsize doesn't contain items that are not in the
        axes this needs data_columns to be defined
        """
        if min_itemsize is None:
            return
        if not isinstance(min_itemsize, dict):
            return

        q = self.queryables()
        for k, v in min_itemsize.items():

            # ok, apply generally
            if k == "values":
                continue
            if k not in q:
                raise ValueError(
                    "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')

        Raises
        ------
        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)
            # `kwargs` may contain `start` and `stop` arguments if passed to
            # `store.select()`. If set they determine the index size.
            a.convert(
                values,
                nan_rep=self.nan_rep,
                encoding=self.encoding,
                errors=self.errors,
                start=kwargs.get("start"),
                stop=kwargs.get("stop"),
            )

        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
        legacy 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)
                        assert axis_number is not None

                        # see if the field is the name of an axis
                        if field == axis_name:

                            # if we have a multi-index, then need to include
                            # the levels
                            if self.is_multi_index:
                                filt = filt.union(Index(self.levels))

                            takers = op(axis_values, filt)
                            return obj.loc(axis=axis_number)[takers]

                        # this might be the name of a file IN an axis
                        elif field in axis_values:

                            # we need to filter on this dimension
                            values = ensure_index(getattr(obj, field).values)
                            filt = ensure_index(filt)

                            # hack until we support reversed dim flags
                            if isinstance(obj, DataFrame):
                                axis_number = 1 - axis_number
                            takers = op(values, filt)
                            return obj.loc(axis=axis_number)[takers]

                    raise ValueError(
                        "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 = "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),
    ]  # type: Optional[List[IndexCol]]
    table_type = "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


class AppendableTable(LegacyTable):
    """ support the new appendable table formats """

    _indexables = None
    table_type = "appendable"

    def write(
        self,
        obj,
        axes=None,
        append=False,
        complib=None,
        complevel=None,
        fletcher32=None,
        min_itemsize=None,
        chunksize=None,
        expectedrows=None,
        dropna=False,
        **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(range(g, pg))
                table.remove_rows(
                    start=rows[rows.index[0]], stop=rows[rows.index[-1]] + 1
                )
                pg = g

            self.table.flush()

        # return the number of rows removed
        return ln


class AppendableFrameTable(AppendableTable):
    """ support the new appendable table formats """

    pandas_kind = "frame_table"
    table_type = "appendable_frame"
    ndim = 2
    obj_type = DataFrame  # type: Type[Union[DataFrame, Series]]

    @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_)), ndim=2)
            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 = "series_table"
    table_type = "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().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().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 = "series_table"
    table_type = "appendable_multiseries"

    def write(self, obj, **kwargs):
        """ we are going to write this as a frame table """
        name = obj.name or "values"
        obj, self.levels = self.validate_multiindex(obj)
        cols = list(self.levels)
        cols.append(name)
        obj.columns = cols
        return super().write(obj=obj, **kwargs)


class GenericTable(AppendableFrameTable):
    """ a table that read/writes the generic pytables table format """

    pandas_kind = "frame_table"
    table_type = "generic_table"
    ndim = 2
    obj_type = DataFrame

    @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 = "appendable_multiframe"
    obj_type = DataFrame
    ndim = 2
    _re_levels = re.compile(r"^level_\d+$")

    @property
    def table_type_short(self):
        return "appendable_multi"

    def write(self, obj, data_columns=None, **kwargs):
        if data_columns is None:
            data_columns = []
        elif data_columns is True:
            data_columns = obj.columns.tolist()
        obj, self.levels = self.validate_multiindex(obj)
        for n in self.levels:
            if n not in data_columns:
                data_columns.insert(0, n)
        return super().write(obj=obj, data_columns=data_columns, **kwargs)

    def read(self, **kwargs):

        df = super().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 == "datetime64":
        index = DatetimeIndex(data)
    elif kind == "timedelta64":
        index = TimedeltaIndex(data)
    elif kind == "datetime":
        index = np.asarray([datetime.fromtimestamp(v) for v in data], dtype=object)
    elif kind == "date":
        try:
            index = np.asarray([date.fromordinal(v) for v in data], dtype=object)
        except (ValueError):
            index = np.asarray([date.fromtimestamp(v) for v in data], dtype=object)
    elif kind in ("integer", "float"):
        index = np.asarray(data)
    elif kind in ("string"):
        index = _unconvert_string_array(
            data, nan_rep=None, encoding=encoding, errors=errors
        )
    elif kind == "object":
        index = np.asarray(data[0])
    else:  # pragma: no cover
        raise ValueError("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 == "datetime":
        index = to_datetime(data)
    elif kind in ("integer"):
        index = np.asarray(data, dtype=object)
    elif kind in ("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 (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))
        dtype = "U{0}".format(itemsize)

        if isinstance(data[0], bytes):
            data = Series(data).str.decode(encoding, errors=errors).values
        else:
            data = data.astype(dtype, copy=False).astype(object, copy=False)

    if nan_rep is None:
        nan_rep = "nan"

    data = libwriters.string_array_replace_from_nan_rep(data, nan_rep)
    return data.reshape(shape)


def _maybe_convert(values, 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 ("datetime", "datetime64", "string"):
        return True
    return False


class Selection:

    """
    Carries out a selection operation on a tables.Table object.

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

    """

    def __init__(self, table, 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)