/pandas/io/pytables.py

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"""
High level interface to PyTables for reading and writing pandas data structures
to disk
"""
from __future__ import annotations

from contextlib import suppress
import copy
from datetime import (
    date,
    tzinfo,
)
import itertools
import os
import re
from textwrap import dedent
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Hashable,
    Sequence,
    cast,
)
import warnings

import numpy as np

from pandas._config import (
    config,
    get_option,
)

from pandas._libs import (
    lib,
    writers as libwriters,
)
from pandas._libs.tslibs import timezones
from pandas._typing import (
    ArrayLike,
    DtypeArg,
    Shape,
)
from pandas.compat._optional import import_optional_dependency
from pandas.compat.pickle_compat import patch_pickle
from pandas.errors import PerformanceWarning
from pandas.util._decorators import cache_readonly

from pandas.core.dtypes.common import (
    ensure_object,
    is_categorical_dtype,
    is_complex_dtype,
    is_datetime64_dtype,
    is_datetime64tz_dtype,
    is_extension_array_dtype,
    is_list_like,
    is_string_dtype,
    is_timedelta64_dtype,
    needs_i8_conversion,
)
from pandas.core.dtypes.missing import array_equivalent

from pandas import (
    DataFrame,
    DatetimeIndex,
    Index,
    MultiIndex,
    PeriodIndex,
    Series,
    TimedeltaIndex,
    concat,
    isna,
)
from pandas.core.api import Int64Index
from pandas.core.arrays import (
    Categorical,
    DatetimeArray,
    PeriodArray,
)
import pandas.core.common as com
from pandas.core.computation.pytables import (
    PyTablesExpr,
    maybe_expression,
)
from pandas.core.construction import extract_array
from pandas.core.indexes.api import ensure_index
from pandas.core.internals import (
    ArrayManager,
    BlockManager,
)

from pandas.io.common import stringify_path
from pandas.io.formats.printing import (
    adjoin,
    pprint_thing,
)

if TYPE_CHECKING:
    from tables import (
        Col,
        File,
        Node,
    )

    from pandas.core.internals import Block


# versioning attribute
_version = "0.15.2"

# encoding
_default_encoding = "UTF-8"


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


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

    return encoding


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

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


Term = PyTablesExpr


def _ensure_term(where, scope_level: int):
    """
    Ensure that the where is a Term or a list of Term.

    This makes sure that we are capturing the scope of variables that are
    passed create the terms here with a frame_level=2 (we are 2 levels down)
    """
    # only consider list/tuple here as an ndarray is automatically a coordinate
    # list
    level = scope_level + 1
    if isinstance(where, (list, tuple)):
        where = [
            Term(term, scope_level=level + 1) if maybe_expression(term) else term
            for term in where
            if term is not None
        ]
    elif maybe_expression(where):
        where = Term(where, scope_level=level)
    return where if where is None or len(where) else None


class PossibleDataLossError(Exception):
    pass


class ClosedFileError(Exception):
    pass


class IncompatibilityWarning(Warning):
    pass


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


class AttributeConflictWarning(Warning):
    pass


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


class DuplicateWarning(Warning):
    pass


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

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

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

# axes map
_AXES_MAP = {DataFrame: [0]}

# register our configuration options
dropna_doc = """
: boolean
    drop ALL nan rows when appending to a table
"""
format_doc = """
: format
    default format writing format, if None, then
    put will default to 'fixed' and append will default to 'table'
"""

with config.config_prefix("io.hdf"):
    config.register_option("dropna_table", False, dropna_doc, validator=config.is_bool)
    config.register_option(
        "default_format",
        None,
        format_doc,
        validator=config.is_one_of_factory(["fixed", "table", None]),
    )

# oh the troubles to reduce import time
_table_mod = None
_table_file_open_policy_is_strict = False


def _tables():
    global _table_mod
    global _table_file_open_policy_is_strict
    if _table_mod is None:
        import tables

        _table_mod = tables

        # set the file open policy
        # return the file open policy; this changes as of pytables 3.1
        # depending on the HDF5 version
        with suppress(AttributeError):
            _table_file_open_policy_is_strict = (
                tables.file._FILE_OPEN_POLICY == "strict"
            )

    return _table_mod


# interface to/from ###


def to_hdf(
    path_or_buf,
    key: str,
    value: DataFrame | Series,
    mode: str = "a",
    complevel: int | None = None,
    complib: str | None = None,
    append: bool = False,
    format: str | None = None,
    index: bool = True,
    min_itemsize: int | dict[str, int] | None = None,
    nan_rep=None,
    dropna: bool | None = None,
    data_columns: bool | list[str] | None = None,
    errors: str = "strict",
    encoding: str = "UTF-8",
) -> None:
    """store this object, close it if we opened it"""
    if append:
        f = lambda store: store.append(
            key,
            value,
            format=format,
            index=index,
            min_itemsize=min_itemsize,
            nan_rep=nan_rep,
            dropna=dropna,
            data_columns=data_columns,
            errors=errors,
            encoding=encoding,
        )
    else:
        # NB: dropna is not passed to `put`
        f = lambda store: store.put(
            key,
            value,
            format=format,
            index=index,
            min_itemsize=min_itemsize,
            nan_rep=nan_rep,
            data_columns=data_columns,
            errors=errors,
            encoding=encoding,
            dropna=dropna,
        )

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


def read_hdf(
    path_or_buf,
    key=None,
    mode: str = "r",
    errors: str = "strict",
    where=None,
    start: int | None = None,
    stop: int | None = None,
    columns=None,
    iterator=False,
    chunksize: int | None = None,
    **kwargs,
):
    """
    Read from the store, close it if we opened it.

    Retrieve pandas object stored in file, optionally based on where
    criteria.

    .. warning::

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

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

    Parameters
    ----------
    path_or_buf : str, path object, pandas.HDFStore
        Any valid string path is acceptable. Only supports the local file system,
        remote URLs and file-like objects are not supported.

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

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

    key : object, optional
        The group identifier in the store. Can be omitted if the HDF file
        contains a single pandas object.
    mode : {'r', 'r+', 'a'}, default 'r'
        Mode to use when opening the file. Ignored if path_or_buf is a
        :class:`pandas.HDFStore`. Default is 'r'.
    errors : str, default 'strict'
        Specifies how encoding and decoding errors are to be handled.
        See the errors argument for :func:`open` for a full list
        of options.
    where : list, optional
        A list of Term (or convertible) objects.
    start : int, optional
        Row number to start selection.
    stop  : int, optional
        Row number to stop selection.
    columns : list, optional
        A list of columns names to return.
    iterator : bool, optional
        Return an iterator object.
    chunksize : int, optional
        Number of rows to include in an iteration when using an iterator.
    **kwargs
        Additional keyword arguments passed to HDFStore.

    Returns
    -------
    item : object
        The selected object. Return type depends on the object stored.

    See Also
    --------
    DataFrame.to_hdf : Write a HDF file from a DataFrame.
    HDFStore : Low-level access to HDF files.

    Examples
    --------
    >>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z'])
    >>> df.to_hdf('./store.h5', 'data')
    >>> reread = pd.read_hdf('./store.h5')
    """
    if mode not in ["r", "r+", "a"]:
        raise ValueError(
            f"mode {mode} is not allowed while performing a read. "
            f"Allowed modes are r, r+ and a."
        )
    # grab the scope
    if where is not None:
        where = _ensure_term(where, scope_level=1)

    if isinstance(path_or_buf, HDFStore):
        if not path_or_buf.is_open:
            raise OSError("The HDFStore must be open for reading.")

        store = path_or_buf
        auto_close = False
    else:
        path_or_buf = stringify_path(path_or_buf)
        if not isinstance(path_or_buf, str):
            raise NotImplementedError(
                "Support for generic buffers has not been implemented."
            )
        try:
            exists = os.path.exists(path_or_buf)

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

        if not exists:
            raise FileNotFoundError(f"File {path_or_buf} does not exist")

        store = HDFStore(path_or_buf, mode=mode, errors=errors, **kwargs)
        # can't auto open/close if we are using an iterator
        # so delegate to the iterator
        auto_close = True

    try:
        if key is None:
            groups = store.groups()
            if len(groups) == 0:
                raise ValueError(
                    "Dataset(s) incompatible with Pandas data types, "
                    "not table, or no datasets found in HDF5 file."
                )
            candidate_only_group = groups[0]

            # For the HDF file to have only one dataset, all other groups
            # should then be metadata groups for that candidate group. (This
            # assumes that the groups() method enumerates parent groups
            # before their children.)
            for group_to_check in groups[1:]:
                if not _is_metadata_of(group_to_check, candidate_only_group):
                    raise ValueError(
                        "key must be provided when HDF5 "
                        "file contains multiple datasets."
                    )
            key = candidate_only_group._v_pathname
        return store.select(
            key,
            where=where,
            start=start,
            stop=stop,
            columns=columns,
            iterator=iterator,
            chunksize=chunksize,
            auto_close=auto_close,
        )
    except (ValueError, TypeError, KeyError):
        if not isinstance(path_or_buf, HDFStore):
            # if there is an error, close the store if we opened it.
            with suppress(AttributeError):
                store.close()

        raise


def _is_metadata_of(group: Node, parent_group: Node) -> bool:
    """Check if a given group is a metadata group for a given parent_group."""
    if group._v_depth <= parent_group._v_depth:
        return False

    current = group
    while current._v_depth > 1:
        parent = current._v_parent
        if parent == parent_group and current._v_name == "meta":
            return True
        current = current._v_parent
    return False


class HDFStore:
    """
    Dict-like IO interface for storing pandas objects in PyTables.

    Either Fixed or Table format.

    .. warning::

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

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

    Parameters
    ----------
    path : str
        File path to HDF5 file.
    mode : {'a', 'w', 'r', 'r+'}, default 'a'

        ``'r'``
            Read-only; no data can be modified.
        ``'w'``
            Write; a new file is created (an existing file with the same
            name would be deleted).
        ``'a'``
            Append; an existing file is opened for reading and writing,
            and if the file does not exist it is created.
        ``'r+'``
            It is similar to ``'a'``, but the file must already exist.
    complevel : int, 0-9, default None
        Specifies a compression level for data.
        A value of 0 or None disables compression.
    complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
        Specifies the compression library to be used.
        As of v0.20.2 these additional compressors for Blosc are supported
        (default if no compressor specified: 'blosc:blosclz'):
        {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
         'blosc:zlib', 'blosc:zstd'}.
        Specifying a compression library which is not available issues
        a ValueError.
    fletcher32 : bool, default False
        If applying compression use the fletcher32 checksum.
    **kwargs
        These parameters will be passed to the PyTables open_file method.

    Examples
    --------
    >>> bar = pd.DataFrame(np.random.randn(10, 4))
    >>> store = pd.HDFStore('test.h5')
    >>> store['foo'] = bar   # write to HDF5
    >>> bar = store['foo']   # retrieve
    >>> store.close()

    **Create or load HDF5 file in-memory**

    When passing the `driver` option to the PyTables open_file method through
    **kwargs, the HDF5 file is loaded or created in-memory and will only be
    written when closed:

    >>> bar = pd.DataFrame(np.random.randn(10, 4))
    >>> store = pd.HDFStore('test.h5', driver='H5FD_CORE')
    >>> store['foo'] = bar
    >>> store.close()   # only now, data is written to disk
    """

    _handle: File | None
    _mode: str
    _complevel: int
    _fletcher32: bool

    def __init__(
        self,
        path,
        mode: str = "a",
        complevel: int | None = None,
        complib=None,
        fletcher32: bool = False,
        **kwargs,
    ):

        if "format" in kwargs:
            raise ValueError("format is not a defined argument for HDFStore")

        tables = import_optional_dependency("tables")

        if complib is not None and complib not in tables.filters.all_complibs:
            raise ValueError(
                f"complib only supports {tables.filters.all_complibs} compression."
            )

        if complib is None and complevel is not None:
            complib = tables.filters.default_complib

        self._path = stringify_path(path)
        if mode is None:
            mode = "a"
        self._mode = mode
        self._handle = None
        self._complevel = complevel if complevel else 0
        self._complib = complib
        self._fletcher32 = fletcher32
        self._filters = None
        self.open(mode=mode, **kwargs)

    def __fspath__(self):
        return self._path

    @property
    def root(self):
        """return the root node"""
        self._check_if_open()
        assert self._handle is not None  # for mypy
        return self._handle.root

    @property
    def filename(self):
        return self._path

    def __getitem__(self, key: str):
        return self.get(key)

    def __setitem__(self, key: str, value):
        self.put(key, value)

    def __delitem__(self, key: str):
        return self.remove(key)

    def __getattr__(self, name: str):
        """allow attribute access to get stores"""
        try:
            return self.get(name)
        except (KeyError, ClosedFileError):
            pass
        raise AttributeError(
            f"'{type(self).__name__}' object has no attribute '{name}'"
        )

    def __contains__(self, key: str) -> bool:
        """
        check for existence of this key
        can match the exact pathname or the pathnm w/o the leading '/'
        """
        node = self.get_node(key)
        if node is not None:
            name = node._v_pathname
            if name == key or name[1:] == key:
                return True
        return False

    def __len__(self) -> int:
        return len(self.groups())

    def __repr__(self) -> str:
        pstr = pprint_thing(self._path)
        return f"{type(self)}\nFile path: {pstr}\n"

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        self.close()

    def keys(self, include: str = "pandas") -> list[str]:
        """
        Return a list of keys corresponding to objects stored in HDFStore.

        Parameters
        ----------

        include : str, default 'pandas'
                When kind equals 'pandas' return pandas objects.
                When kind equals 'native' return native HDF5 Table objects.

                .. versionadded:: 1.1.0

        Returns
        -------
        list
            List of ABSOLUTE path-names (e.g. have the leading '/').

        Raises
        ------
        raises ValueError if kind has an illegal value
        """
        if include == "pandas":
            return [n._v_pathname for n in self.groups()]

        elif include == "native":
            assert self._handle is not None  # mypy
            return [
                n._v_pathname for n in self._handle.walk_nodes("/", classname="Table")
            ]
        raise ValueError(
            f"`include` should be either 'pandas' or 'native' but is '{include}'"
        )

    def __iter__(self):
        return iter(self.keys())

    def items(self):
        """
        iterate on key->group
        """
        for g in self.groups():
            yield g._v_pathname, g

    iteritems = items

    def open(self, mode: str = "a", **kwargs):
        """
        Open the file in the specified mode

        Parameters
        ----------
        mode : {'a', 'w', 'r', 'r+'}, default 'a'
            See HDFStore docstring or tables.open_file for info about modes
        **kwargs
            These parameters will be passed to the PyTables open_file method.
        """
        tables = _tables()

        if self._mode != mode:
            # if we are changing a write mode to read, ok
            if self._mode in ["a", "w"] and mode in ["r", "r+"]:
                pass
            elif mode in ["w"]:
                # this would truncate, raise here
                if self.is_open:
                    raise PossibleDataLossError(
                        f"Re-opening the file [{self._path}] with mode [{self._mode}] "
                        "will delete the current file!"
                    )

            self._mode = mode

        # close and reopen the handle
        if self.is_open:
            self.close()

        if self._complevel and self._complevel > 0:
            self._filters = _tables().Filters(
                self._complevel, self._complib, fletcher32=self._fletcher32
            )

        if _table_file_open_policy_is_strict and self.is_open:
            msg = (
                "Cannot open HDF5 file, which is already opened, "
                "even in read-only mode."
            )
            raise ValueError(msg)

        self._handle = tables.open_file(self._path, self._mode, **kwargs)

    def close(self):
        """
        Close the PyTables file handle
        """
        if self._handle is not None:
            self._handle.close()
        self._handle = None

    @property
    def is_open(self) -> bool:
        """
        return a boolean indicating whether the file is open
        """
        if self._handle is None:
            return False
        return bool(self._handle.isopen)

    def flush(self, fsync: bool = False):
        """
        Force all buffered modifications to be written to disk.

        Parameters
        ----------
        fsync : bool (default False)
          call ``os.fsync()`` on the file handle to force writing to disk.

        Notes
        -----
        Without ``fsync=True``, flushing may not guarantee that the OS writes
        to disk. With fsync, the operation will block until the OS claims the
        file has been written; however, other caching layers may still
        interfere.
        """
        if self._handle is not None:
            self._handle.flush()
            if fsync:
                with suppress(OSError):
                    os.fsync(self._handle.fileno())

    def get(self, key: str):
        """
        Retrieve pandas object stored in file.

        Parameters
        ----------
        key : str

        Returns
        -------
        object
            Same type as object stored in file.
        """
        with patch_pickle():
            # GH#31167 Without this patch, pickle doesn't know how to unpickle
            #  old DateOffset objects now that they are cdef classes.
            group = self.get_node(key)
            if group is None:
                raise KeyError(f"No object named {key} in the file")
            return self._read_group(group)

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

        .. warning::

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

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

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

        Returns
        -------
        object
            Retrieved object from file.
        """
        group = self.get_node(key)
        if group is None:
            raise KeyError(f"No object named {key} in the file")

        # create the storer and axes
        where = _ensure_term(where, scope_level=1)
        s = self._create_storer(group)
        s.infer_axes()

        # function to call on iteration
        def func(_start, _stop, _where):
            return s.read(start=_start, stop=_stop, where=_where, columns=columns)

        # create the iterator
        it = TableIterator(
            self,
            s,
            func,
            where=where,
            nrows=s.nrows,
            start=start,
            stop=stop,
            iterator=iterator,
            chunksize=chunksize,
            auto_close=auto_close,
        )

        return it.get_result()

    def select_as_coordinates(
        self,
        key: str,
        where=None,
        start: int | None = None,
        stop: int | None = None,
    ):
        """
        return the selection as an Index

        .. warning::

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

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


        Parameters
        ----------
        key : str
        where : list of Term (or convertible) objects, optional
        start : integer (defaults to None), row number to start selection
        stop  : integer (defaults to None), row number to stop selection
        """
        where = _ensure_term(where, scope_level=1)
        tbl = self.get_storer(key)
        if not isinstance(tbl, Table):
            raise TypeError("can only read_coordinates with a table")
        return tbl.read_coordinates(where=where, start=start, stop=stop)

    def select_column(
        self,
        key: str,
        column: str,
        start: int | None = None,
        stop: int | None = None,
    ):
        """
        return a single column from the table. This is generally only useful to
        select an indexable

        .. warning::

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

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

        Parameters
        ----------
        key : str
        column : str
            The column of interest.
        start : int or None, default None
        stop : int or None, default None

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

        """
        tbl = self.get_storer(key)
        if not isinstance(tbl, Table):
            raise TypeError("can only read_column with a table")
        return tbl.read_column(column=column, start=start, stop=stop)

    def select_as_multiple(
        self,
        keys,
        where=None,
        selector=None,
        columns=None,
        start=None,
        stop=None,
        iterator=False,
        chunksize=None,
        auto_close: bool = False,
    ):
        """
        Retrieve pandas objects from multiple tables.

        .. warning::

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

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

        Parameters
        ----------
        keys : a list of the tables
        selector : the table to apply the where criteria (defaults to keys[0]
            if not supplied)
        columns : the columns I want back
        start : integer (defaults to None), row number to start selection
        stop  : integer (defaults to None), row number to stop selection
        iterator : bool, return an iterator, default False
        chunksize : nrows to include in iteration, return an iterator
        auto_close : bool, default False
            Should automatically close the store when finished.

        Raises
        ------
        raises KeyError if keys or selector is not found or keys is empty
        raises TypeError if keys is not a list or tuple
        raises ValueError if the tables are not ALL THE SAME DIMENSIONS
        """
        # default to single select
        where = _ensure_term(where, scope_level=1)
        if isinstance(keys, (list, tuple)) and len(keys) == 1:
            keys = keys[0]
        if isinstance(keys, str):
            return self.select(
                key=keys,
                where=where,
                columns=columns,
                start=start,
                stop=stop,
                iterator=iterator,
                chunksize=chunksize,
                auto_close=auto_close,
            )

        if not isinstance(keys, (list, tuple)):
            raise TypeError("keys must be a list/tuple")

        if not len(keys):
            raise ValueError("keys must have a non-zero length")

        if selector is None:
            selector = keys[0]

        # collect the tables
        tbls = [self.get_storer(k) for k in keys]
        s = self.get_storer(selector)

        # validate rows
        nrows = None
        for t, k in itertools.chain([(s, selector)], zip(tbls, keys)):
            if t is None:
                raise KeyError(f"Invalid table [{k}]")
            if not t.is_table:
                raise TypeError(
                    f"object [{t.pathname}] is not a table, and cannot be used in all "
                    "select as multiple"
                )

            if nrows is None:
                nrows = t.nrows
            elif t.nrows != nrows:
                raise ValueError("all tables must have exactly the same nrows!")

        # The isinstance checks here are redundant with the check above,
        #  but necessary for mypy; see GH#29757
        _tbls = [x for x in tbls if isinstance(x, Table)]

        # axis is the concentration axes
        axis = list({t.non_index_axes[0][0] for t in _tbls})[0]

        def func(_start, _stop, _where):

            # retrieve the objs, _where is always passed as a set of
            # coordinates here
            objs = [
                t.read(where=_where, columns=columns, start=_start, stop=_stop)
                for t in tbls
            ]

            # concat and return
            return concat(objs, axis=axis, verify_integrity=False)._consolidate()

        # create the iterator
        it = TableIterator(
            self,
            s,
            func,
            where=where,
            nrows=nrows,
            start=start,
            stop=stop,
            iterator=iterator,
            chunksize=chunksize,
            auto_close=auto_close,
        )

        return it.get_result(coordinates=True)

    def put(
        self,
        key: str,
        value: DataFrame | Series,
        format=None,
        index=True,
        append=False,
        complib=None,
        complevel: int | None = None,
        min_itemsize: int | dict[str, int] | None = None,
        nan_rep=None,
        data_columns: list[str] | None = None,
        encoding=None,
        errors: str = "strict",
        track_times: bool = True,
        dropna: bool = False,
    ):
        """
        Store object in HDFStore.

        Parameters
        ----------
        key : str
        value : {Series, DataFrame}
        format : 'fixed(f)|table(t)', default is 'fixed'
            Format to use when storing object in HDFStore. Value can be one of:

            ``'fixed'``
                Fixed format.  Fast writing/reading. Not-appendable, nor searchable.
            ``'table'``
                Table format.  Write as a PyTables Table structure which may perform
                worse but allow more flexible operations like searching / selecting
                subsets of the data.
        append : bool, default False
            This will force Table format, append the input data to the existing.
        data_columns : list, default None
            List of columns to create as data columns, or True to use all columns.
            See `here
            <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
        encoding : str, default None
            Provide an encoding for strings.
        track_times : bool, default True
            Parameter is propagated to 'create_table' method of 'PyTables'.
            If set to False it enables to have the same h5 files (same hashes)
            independent on creation time.

            .. versionadded:: 1.1.0
        """
        if format is None:
            format = get_option("io.hdf.default_format") or "fixed"
        format = self._validate_format(format)
        self._write_to_group(
            key,
            value,
            format=format,
            index=index,
            append=append,
            complib=complib,
            complevel=complevel,
            min_itemsize=min_itemsize,
            nan_rep=nan_rep,
            data_columns=data_columns,
            encoding=encoding,
            errors=errors,
            track_times=track_times,
            dropna=dropna,
        )

    def remove(self, key: str, where=None, start=None, stop=None):
        """
        Remove pandas object partially by specifying the where condition

        Parameters
        ----------
        key : str
            Node to remove or delete rows from
        where : list of Term (or convertible) objects, optional
        start : integer (defaults to None), row number to start selection
        stop  : integer (defaults to None), row number to stop selection

        Returns
        -------
        number of rows removed (or None if not a Table)

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

        """
        where = _ensure_term(where, scope_level=1)
        try:
            s = self.get_storer(key)
        except KeyError:
            # the key is not a valid store, re-raising KeyError
            raise
        except AssertionError:
            # surface any assertion errors for e.g. debugging
            raise
        except Exception as err:
            # In tests we get here with ClosedFileError, TypeError, and
            #  _table_mod.NoSuchNodeError.  TODO: Catch only these?

            if where is not None:
                raise ValueError(
                    "trying to remove a node with a non-None where clause!"
                ) from err

            # we are actually trying to remove a node (with children)
            node = self.get_node(key)
            if node is not None:
                node._f_remove(recursive=True)
                return None

        # remove the node
        if com.all_none(where, start, stop):
            s.group._f_remove(recursive=True)

        # delete from the table
        else:
            if not s.is_table:
                raise ValueError(
                    "can only remove with where on objects written as tables"
                )
            return s.delete(where=where, start=start, stop=stop)

    def append(
        self,
        key: str,
        value: DataFrame | Series,
        format=None,
        axes=None,
        index=True,
        append=True,
        complib=None,
        complevel: int | None = None,
        columns=None,
        min_itemsize: int | dict[str, int] | None = None,
        nan_rep=None,
        chunksize=None,
        expectedrows=None,
        dropna: bool | None = None,
        data_columns: list[str] | None = None,
        encoding=None,
        errors: str = "strict",
    ):
        """
        Append to Table in file. Node must already exist and be Table
        format.

        Parameters
        ----------
        key : str
        value : {Series, DataFrame}
        format : 'table' is the default
            Format to use when storing object in HDFStore.  Value can be one of:

            ``'table'``
                Table format. Write as a PyTables Table structure which may perform
                worse but allow more flexible operations like searching / selecting
                subsets of the data.
        append       : bool, default True
            Append the input data to the existing.
        data_columns : list of columns, or True, default None
            List of columns to create as indexed data columns for on-disk
            queries, or True to use all columns. By default only the axes
            of the object are indexed. See `here
            <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
        min_itemsize : dict of columns that specify minimum str sizes
        nan_rep      : str to use as str nan representation
        chunksize    : size to chunk the writing
        expectedrows : expected TOTAL row size of this table
        encoding     : default None, provide an encoding for str
        dropna : bool, default False
            Do not write an ALL nan row to the store settable
            by the option 'io.hdf.dropna_table'.

        Notes
        -----
        Does *not* check if data being appended overlaps with existing
        data in the table, so be careful
        """
        if columns is not None:
            raise TypeError(
                "columns is not a supported keyword in append, try data_columns"
            )

        if dropna is None:
            dropna = get_option("io.hdf.dropna_table")
        if format is None:
            format = get_option("io.hdf.default_format") or "table"
        format = self._validate_format(format)
        self._write_to_group(
            key,
            value,
            format=format,
            axes=axes,
            index=index,
            append=append,
            complib=complib,
            complevel=complevel,
            min_itemsize=min_itemsize,
            nan_rep=nan_rep,
            chunksize=chunksize,
            expectedrows=expectedrows,
            dropna=dropna,
            data_columns=data_columns,
            encoding=encoding,
            errors=errors,
        )

    def append_to_multiple(
        self,
        d: dict,
        value,
        selector,
        data_columns=None,
        axes=None,
        dropna=False,
        **kwargs,
    ):
        """
        Append to multiple tables

        Parameters
        ----------
        d : a dict of table_name to table_columns, None is acceptable as the
            values of one node (this will get all the remaining columns)
        value : a pandas object
        selector : a string that designates the indexable table; all of its
            columns will be designed as data_columns, unless data_columns is
            passed, in which case these are used
        data_columns : list of columns to create as data columns, or True to
            use all columns
        dropna : if evaluates to True, drop rows from all tables if any single
                 row in each table has all NaN. Default False.

        Notes
        -----
        axes parameter is currently not accepted

        """
        if axes is not None:
            raise TypeError(
                "axes is currently not accepted as a parameter to append_to_multiple; "
                "you can create the tables independently instead"
            )

        if not isinstance(d, dict):
            raise ValueError(
                "append_to_multiple must have a dictionary specified as the "
                "way to split the value"
            )

        if selector not in d:
            raise ValueError(
                "append_to_multiple requires a selector that is in passed dict"
            )

        # figure out the splitting axis (the non_index_axis)
        axis = list(set(range(value.ndim)) - set(_AXES_MAP[type(value)]))[0]

        # figure out how to split the value
        remain_key = None
        remain_values: list = []
        for k, v in d.items():
            if v is None:
                if remain_key is not None:
                    raise ValueError(
                        "append_to_multiple can only have one value in d that is None"
                    )
                remain_key = k
            else:
                remain_values.extend(v)
        if remain_key is not None:
            ordered = value.axes[axis]
            ordd = ordered.difference(Index(remain_values))
            ordd = sorted(ordered.get_indexer(ordd))
            d[remain_key] = ordered.take(ordd)

        # data_columns
        if data_columns is None:
            data_columns = d[selector]

        # ensure rows are synchronized across the tables
        if dropna:
            idxs = (value[cols].dropna(how="all").index for cols in d.values())
            valid_index = next(idxs)
            for index in idxs:
                valid_index = valid_index.intersection(index)
            value = value.loc[valid_index]

        min_itemsize = kwargs.pop("min_itemsize", None)

        # append
        for k, v in d.items():
            dc = data_columns if k == selector else None

            # compute the val
            val = value.reindex(v, axis=axis)

            filtered = (
                {key: value for (key, value) in min_itemsize.items() if key in v}
                if min_itemsize is not None
                else None
            )
            self.append(k, val, data_columns=dc, min_itemsize=filtered, **kwargs)

    def create_table_index(
        self,
        key: str,
        columns=None,
        optlevel: int | None = None,
        kind: str | None = None,
    ):
        """
        Create a pytables index on the table.

        Parameters
        ----------
        key : str
        columns : None, bool, or listlike[str]
            Indicate which columns to create an index on.

            * False : Do not create any indexes.
            * True : Create indexes on all columns.
            * None : Create indexes on all columns.
            * listlike : Create indexes on the given columns.

        optlevel : int or None, default None
            Optimization level, if None, pytables defaults to 6.
        kind : str or None, default None
            Kind of index, if None, pytables defaults to "medium".

        Raises
        ------
        TypeError: raises if the node is not a table
        """
        # version requirements
        _tables()
        s = self.get_storer(key)
        if s is None:
            return

        if not isinstance(s, Table):
            raise TypeError("cannot create table index on a Fixed format store")
        s.create_index(columns=columns, optlevel=optlevel, kind=kind)

    def groups(self):
        """
        Return a list of all the top-level nodes.

        Each node returned is not a pandas storage object.

        Returns
        -------
        list
            List of objects.
        """
        _tables()
        self._check_if_open()
        assert self._handle is not None  # for mypy
        assert _table_mod is not None  # for mypy
        return [
            g
            for g in self._handle.walk_groups()
            if (
                not isinstance(g, _table_mod.link.Link)
                and (
                    getattr(g._v_attrs, "pandas_type", None)
                    or getattr(g, "table", None)
                    or (isinstance(g, _table_mod.table.Table) and g._v_name != "table")
                )
            )
        ]

    def walk(self, where="/"):
        """
        Walk the pytables group hierarchy for pandas objects.

        This generator will yield the group path, subgroups and pandas object
        names for each group.

        Any non-pandas PyTables objects that are not a group will be ignored.

        The `where` group itself is listed first (preorder), then each of its
        child groups (following an alphanumerical order) is also traversed,
        following the same procedure.

        Parameters
        ----------
        where : str, default "/"
            Group where to start walking.

        Yields
        ------
        path : str
            Full path to a group (without trailing '/').
        groups : list
            Names (strings) of the groups contained in `path`.
        leaves : list
            Names (strings) of the pandas objects contained in `path`.
        """
        _tables()
        self._check_if_open()
        assert self._handle is not None  # for mypy
        assert _table_mod is not None  # for mypy

        for g in self._handle.walk_groups(where):
            if getattr(g._v_attrs, "pandas_type", None) is not None:
                continue

            groups = []
            leaves = []
            for child in g._v_children.values():
                pandas_type = getattr(child._v_attrs, "pandas_type", None)
                if pandas_type is None:
                    if isinstance(child, _table_mod.group.Group):
                        groups.append(child._v_name)
                else:
                    leaves.append(child._v_name)

            yield (g._v_pathname.rstrip("/"), groups, leaves)

    def get_node(self, key: str) -> Node | None:
        """return the node with the key or None if it does not exist"""
        self._check_if_open()
        if not key.startswith("/"):
            key = "/" + key

        assert self._handle is not None
        assert _table_mod is not None  # for mypy
        try:
            node = self._handle.get_node(self.root, key)
        except _table_mod.exceptions.NoSuchNodeError:
            return None

        assert isinstance(node, _table_mod.Node), type(node)
        return node

    def get_storer(self, key: str) -> GenericFixed | Table:
        """return the storer object for a key, raise if not in the file"""
        group = self.get_node(key)
        if group is None:
            raise KeyError(f"No object named {key} in the file")

        s = self._create_storer(group)
        s.infer_axes()
        return s

    def copy(
        self,
        file,
        mode="w",
        propindexes: bool = True,
        keys=None,
        complib=None,
        complevel: int | None = None,
        fletcher32: bool = False,
        overwrite=True,
    ):
        """
        Copy the existing store to a new file, updating in place.

        Parameters
        ----------
        propindexes : bool, default True
            Restore indexes in copied file.
        keys : list, optional
            List of keys to include in the copy (defaults to all).
        overwrite : bool, default True
            Whether to overwrite (remove and replace) existing nodes in the new store.
        mode, complib, complevel, fletcher32 same as in HDFStore.__init__

        Returns
        -------
        open file handle of the new store
        """
        new_store = HDFStore(
            file, mode=mode, complib=complib, complevel=complevel, fletcher32=fletcher32
        )
        if keys is None:
            keys = list(self.keys())
        if not isinstance(keys, (tuple, list)):
            keys = [keys]
        for k in keys:
            s = self.get_storer(k)
            if s is not None:

                if k in new_store:
                    if overwrite:
                        new_store.remove(k)

                data = self.select(k)
                if isinstance(s, Table):

                    index: bool | list[str] = False
                    if propindexes:
                        index = [a.name for a in s.axes if a.is_indexed]
                    new_store.append(
                        k,
                        data,
                        index=index,
                        data_columns=getattr(s, "data_columns", None),
                        encoding=s.encoding,
                    )
                else:
                    new_store.put(k, data, encoding=s.encoding)

        return new_store

    def info(self) -> str:
        """
        Print detailed information on the store.

        Returns
        -------
        str
        """
        path = pprint_thing(self._path)
        output = f"{type(self)}\nFile path: {path}\n"

        if self.is_open:
            lkeys = sorted(self.keys())
            if len(lkeys):
                keys = []
                values = []

                for k in lkeys:
                    try:
                        s = self.get_storer(k)
                        if s is not None:
                            keys.append(pprint_thing(s.pathname or k))
                            values.append(pprint_thing(s or "invalid_HDFStore node"))
                    except AssertionError:
                        # surface any assertion errors for e.g. debugging
                        raise
                    except Exception as detail:
                        keys.append(k)
                        dstr = pprint_thing(detail)
                        values.append(f"[invalid_HDFStore node: {dstr}]")

                output += adjoin(12, keys, values)
            else:
                output += "Empty"
        else:
            output += "File is CLOSED"

        return output

    # ------------------------------------------------------------------------
    # private methods

    def _check_if_open(self):
        if not self.is_open:
            raise ClosedFileError(f"{self._path} file is not open!")

    def _validate_format(self, format: str) -> str:
        """validate / deprecate formats"""
        # validate
        try:
            format = _FORMAT_MAP[format.lower()]
        except KeyError as err:
            raise TypeError(f"invalid HDFStore format specified [{format}]") from err

        return for…