""" 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, Final, Literal, cast, overload, ) import warnings import numpy as np from pandas._config import ( config, get_option, using_copy_on_write, using_pyarrow_string_dtype, ) from pandas._libs import ( lib, writers as libwriters, ) from pandas._libs.lib import is_string_array from pandas._libs.tslibs import timezones from pandas.compat._optional import import_optional_dependency from pandas.compat.pickle_compat import patch_pickle from pandas.errors import ( AttributeConflictWarning, ClosedFileError, IncompatibilityWarning, PerformanceWarning, PossibleDataLossError, ) from pandas.util._decorators import cache_readonly from pandas.util._exceptions import find_stack_level from pandas.core.dtypes.common import ( ensure_object, is_bool_dtype, is_complex_dtype, is_list_like, is_string_dtype, needs_i8_conversion, ) from pandas.core.dtypes.dtypes import ( CategoricalDtype, DatetimeTZDtype, ExtensionDtype, PeriodDtype, ) from pandas.core.dtypes.missing import array_equivalent from pandas import ( DataFrame, DatetimeIndex, Index, MultiIndex, PeriodIndex, RangeIndex, Series, TimedeltaIndex, concat, isna, ) from pandas.core.arrays import ( Categorical, DatetimeArray, PeriodArray, ) import pandas.core.common as com from pandas.core.computation.pytables import ( PyTablesExpr, maybe_expression, ) from pandas.core.construction import extract_array from pandas.core.indexes.api import ensure_index from pandas.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 collections.abc import ( Hashable, Iterator, Sequence, ) from types import TracebackType from tables import ( Col, File, Node, ) from pandas._typing import ( AnyArrayLike, ArrayLike, AxisInt, DtypeArg, FilePath, Self, Shape, npt, ) 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: str | None) -> str: # 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 incompatibility_doc: Final = """ 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) """ attribute_conflict_doc: Final = """ the [%s] attribute of the existing index is [%s] which conflicts with the new [%s], resetting the attribute to None """ performance_doc: Final = """ 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: Final = """ : boolean drop ALL nan rows when appending to a table """ format_doc: Final = """ : 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: FilePath | HDFStore, 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: Literal[True] | 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: FilePath | HDFStore, key=None, mode: str = "r", errors: str = "strict", where: str | list | None = None, start: int | None = None, stop: int | None = None, columns: list[str] | None = None, iterator: bool = 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 ------- 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']) # doctest: +SKIP >>> df.to_hdf('./store.h5', 'data') # doctest: +SKIP >>> reread = pd.read_hdf('./store.h5') # doctest: +SKIP """ 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, LookupError): 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. 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 def __init__( self, path, mode: str = "a", complevel: int | None = None, complib=None, fletcher32: bool = False, **kwargs, ) -> None: 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) -> str: 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) -> str: return self._path def __getitem__(self, key: str): return self.get(key) def __setitem__(self, key: str, value) -> None: self.put(key, value) def __delitem__(self, key: str) -> None: 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 key in (name, name[1:]): 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) -> Self: return self def __exit__( self, exc_type: type[BaseException] | None, exc_value: BaseException | None, traceback: TracebackType | None, ) -> None: 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. Returns ------- list List of ABSOLUTE path-names (e.g. have the leading '/'). Raises ------ raises ValueError if kind has an illegal value Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B']) >>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP >>> store.put('data', df) # doctest: +SKIP >>> store.get('data') # doctest: +SKIP >>> print(store.keys()) # doctest: +SKIP ['/data1', '/data2'] >>> store.close() # doctest: +SKIP """ 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) -> Iterator[str]: return iter(self.keys()) def items(self) -> Iterator[tuple[str, list]]: """ iterate on key->group """ for g in self.groups(): yield g._v_pathname, g def open(self, mode: str = "a", **kwargs) -> None: """ 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) -> None: """ 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) -> None: """ 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. Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B']) >>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP >>> store.put('data', df) # doctest: +SKIP >>> store.get('data') # doctest: +SKIP >>> store.close() # doctest: +SKIP """ 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: bool = False, chunksize: int | None = None, auto_close: bool = False, ): """ Retrieve pandas object stored in file, optionally based on where criteria. .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- key : str Object being retrieved from file. where : list or None List of Term (or convertible) objects, optional. start : int or None Row number to start selection. stop : int, default None Row number to stop selection. columns : list or None A list of columns that if not None, will limit the return columns. iterator : bool or False Returns an iterator. chunksize : int or None Number or rows to include in iteration, return an iterator. auto_close : bool or False Should automatically close the store when finished. Returns ------- object Retrieved object from file. Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B']) >>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP >>> store.put('data', df) # doctest: +SKIP >>> store.get('data') # doctest: +SKIP >>> print(store.keys()) # doctest: +SKIP ['/data1', '/data2'] >>> store.select('/data1') # doctest: +SKIP A B 0 1 2 1 3 4 >>> store.select('/data1', where='columns == A') # doctest: +SKIP A 0 1 1 3 >>> store.close() # doctest: +SKIP """ 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: bool = False, chunksize: int | None = 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 = {t.non_index_axes[0][0] for t in _tbls}.pop() 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: bool = True, append: bool = False, complib=None, complevel: int | None = None, min_itemsize: int | dict[str, int] | None = None, nan_rep=None, data_columns: Literal[True] | list[str] | None = None, encoding=None, errors: str = "strict", track_times: bool = True, dropna: bool = False, ) -> None: """ 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. index : bool, default True Write DataFrame index as a column. append : bool, default False This will force Table format, append the input data to the existing. data_columns : list of columns or True, default None List of columns to create as data columns, or True to use all columns. See `here `__. 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. dropna : bool, default False, optional Remove missing values. Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B']) >>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP >>> store.put('data', df) # doctest: +SKIP """ 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) -> 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: bool | list[str] = True, append: bool = True, complib=None, complevel: int | None = None, columns=None, min_itemsize: int | dict[str, int] | None = None, nan_rep=None, chunksize: int | None = None, expectedrows=None, dropna: bool | None = None, data_columns: Literal[True] | list[str] | None = None, encoding=None, errors: str = "strict", ) -> None: """ 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. index : bool, default True Write DataFrame index as a column. 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 `__. 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, optional 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 Examples -------- >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B']) >>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP >>> store.put('data', df1, format='table') # doctest: +SKIP >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B']) >>> store.append('data', df2) # doctest: +SKIP >>> store.close() # doctest: +SKIP A B 0 1 2 1 3 4 0 5 6 1 7 8 """ 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: bool = False, **kwargs, ) -> None: """ 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 = next(iter(set(range(value.ndim)) - set(_AXES_MAP[type(value)]))) # 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, ) -> 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) -> list: """ Return a list of all the top-level nodes. Each node returned is not a pandas storage object. Returns ------- list List of objects. Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B']) >>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP >>> store.put('data', df) # doctest: +SKIP >>> print(store.groups()) # doctest: +SKIP >>> store.close() # doctest: +SKIP [/data (Group) '' children := ['axis0' (Array), 'axis1' (Array), 'block0_values' (Array), 'block0_items' (Array)]] """ _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: str = "/") -> Iterator[tuple[str, list[str], list[str]]]: """ 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`. Examples -------- >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B']) >>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP >>> store.put('data', df1, format='table') # doctest: +SKIP >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B']) >>> store.append('data', df2) # doctest: +SKIP >>> store.close() # doctest: +SKIP >>> for group in store.walk(): # doctest: +SKIP ... print(group) # doctest: +SKIP >>> store.close() # doctest: +SKIP """ _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: str = "w", propindexes: bool = True, keys=None, complib=None, complevel: int | None = None, fletcher32: bool = False, overwrite: bool = True, ) -> HDFStore: """ 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 Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B']) >>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP >>> store.put('data', df) # doctest: +SKIP >>> print(store.info()) # doctest: +SKIP >>> store.close() # doctest: +SKIP File path: store.h5 /data frame (shape->[2,2]) """ 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) -> None: if not self.is_open: raise ClosedFileError(f"{self._path} file is not open!") def _validate_format(self, format: str) -> str: """validate / deprecate formats""" # validate try: format = _FORMAT_MAP[format.lower()] except KeyError as err: raise TypeError(f"invalid HDFStore format specified [{format}]") from err return format def _create_storer( self, group, format=None, value: DataFrame | Series | None = None, encoding: str = "UTF-8", errors: str = "strict", ) -> GenericFixed | Table: """return a suitable class to operate""" cls: type[GenericFixed | Table] if value is not None and not isinstance(value, (Series, DataFrame)): raise TypeError("value must be None, Series, or DataFrame") pt = _ensure_decoded(getattr(group._v_attrs, "pandas_type", None)) tt = _ensure_decoded(getattr(group._v_attrs, "table_type", None)) # infer the pt from the passed value if pt is None: if value is None: _tables() assert _table_mod is not None # for mypy if getattr(group, "table", None) or isinstance( group, _table_mod.table.Table ): pt = "frame_table" tt = "generic_table" else: raise TypeError( "cannot create a storer if the object is not existing " "nor a value are passed" ) else: if isinstance(value, Series): pt = "series" else: pt = "frame" # we are actually a table if format == "table": pt += "_table" # a storer node if "table" not in pt: _STORER_MAP = {"series": SeriesFixed, "frame": FrameFixed} try: cls = _STORER_MAP[pt] except KeyError as err: raise TypeError( f"cannot properly create the storer for: [_STORER_MAP] [group->" f"{group},value->{type(value)},format->{format}" ) from err return cls(self, group, encoding=encoding, errors=errors) # existing node (and must be a table) if tt is None: # if we are a writer, determine the tt if value is not None: if pt == "series_table": index = getattr(value, "index", None) if index is not None: if index.nlevels == 1: tt = "appendable_series" elif index.nlevels > 1: tt = "appendable_multiseries" elif pt == "frame_table": index = getattr(value, "index", None) if index is not None: if index.nlevels == 1: tt = "appendable_frame" elif index.nlevels > 1: tt = "appendable_multiframe" _TABLE_MAP = { "generic_table": GenericTable, "appendable_series": AppendableSeriesTable, "appendable_multiseries": AppendableMultiSeriesTable, "appendable_frame": AppendableFrameTable, "appendable_multiframe": AppendableMultiFrameTable, "worm": WORMTable, } try: cls = _TABLE_MAP[tt] except KeyError as err: raise TypeError( f"cannot properly create the storer for: [_TABLE_MAP] [group->" f"{group},value->{type(value)},format->{format}" ) from err return cls(self, group, encoding=encoding, errors=errors) def _write_to_group( self, key: str, value: DataFrame | Series, format, axes=None, index: bool | list[str] = True, append: bool = False, complib=None, complevel: int | None = None, fletcher32=None, min_itemsize: int | dict[str, int] | None = None, chunksize: int | None = None, expectedrows=None, dropna: bool = False, nan_rep=None, data_columns=None, encoding=None, errors: str = "strict", track_times: bool = True, ) -> None: # we don't want to store a table node at all if our object is 0-len # as there are not dtypes if getattr(value, "empty", None) and (format == "table" or append): return group = self._identify_group(key, append) s = self._create_storer(group, format, value, encoding=encoding, errors=errors) if append: # raise if we are trying to append to a Fixed format, # or a table that exists (and we are putting) if not s.is_table or (s.is_table and format == "fixed" and s.is_exists): raise ValueError("Can only append to Tables") if not s.is_exists: s.set_object_info() else: s.set_object_info() if not s.is_table and complib: raise ValueError("Compression not supported on Fixed format stores") # write the object s.write( obj=value, axes=axes, append=append, complib=complib, complevel=complevel, fletcher32=fletcher32, min_itemsize=min_itemsize, chunksize=chunksize, expectedrows=expectedrows, dropna=dropna, nan_rep=nan_rep, data_columns=data_columns, track_times=track_times, ) if isinstance(s, Table) and index: s.create_index(columns=index) def _read_group(self, group: Node): s = self._create_storer(group) s.infer_axes() return s.read() def _identify_group(self, key: str, append: bool) -> Node: """Identify HDF5 group based on key, delete/create group if needed.""" group = self.get_node(key) # we make this assertion for mypy; the get_node call will already # have raised if this is incorrect assert self._handle is not None # remove the node if we are not appending if group is not None and not append: self._handle.remove_node(group, recursive=True) group = None if group is None: group = self._create_nodes_and_group(key) return group def _create_nodes_and_group(self, key: str) -> Node: """Create nodes from key and return group name.""" # assertion for mypy assert self._handle is not None paths = key.split("/") # recursively create the groups path = "/" for p in paths: if not len(p): continue new_path = path if not path.endswith("/"): new_path += "/" new_path += p group = self.get_node(new_path) if group is None: group = self._handle.create_group(path, p) path = new_path return group class TableIterator: """ Define the iteration interface on a table Parameters ---------- store : HDFStore s : the referred storer func : the function to execute the query where : the where of the query nrows : the rows to iterate on start : the passed start value (default is None) stop : the passed stop value (default is None) iterator : bool, default False Whether to use the default iterator. chunksize : the passed chunking value (default is 100000) auto_close : bool, default False Whether to automatically close the store at the end of iteration. """ chunksize: int | None store: HDFStore s: GenericFixed | Table def __init__( self, store: HDFStore, s: GenericFixed | Table, func, where, nrows, start=None, stop=None, iterator: bool = False, chunksize: int | None = None, auto_close: bool = False, ) -> None: 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) -> Iterator: # iterate current = self.start if self.coordinates is None: raise ValueError("Cannot iterate until get_result is called.") while current < self.stop: stop = min(current + self.chunksize, self.stop) value = self.func(None, None, self.coordinates[current:stop]) current = stop if value is None or not len(value): continue yield value self.close() def close(self) -> None: if self.auto_close: self.store.close() def get_result(self, coordinates: bool = False): # return the actual iterator if self.chunksize is not None: if not isinstance(self.s, Table): raise TypeError("can only use an iterator or chunksize on a table") self.coordinates = self.s.read_coordinates(where=self.where) return self # if specified read via coordinates (necessary for multiple selections if coordinates: if not isinstance(self.s, Table): raise TypeError("can only read_coordinates on a table") where = self.s.read_coordinates( where=self.where, start=self.start, stop=self.stop ) else: where = self.where # directly return the result results = self.func(self.start, self.stop, where) self.close() return results class IndexCol: """ an index column description class Parameters ---------- axis : axis which I reference values : the ndarray like converted values kind : a string description of this type typ : the pytables type pos : the position in the pytables """ is_an_indexable: bool = True is_data_indexable: bool = True _info_fields = ["freq", "tz", "index_name"] def __init__( self, name: str, values=None, kind=None, typ=None, cname: str | None = None, axis=None, pos=None, freq=None, tz=None, index_name=None, ordered=None, table=None, meta=None, metadata=None, ) -> None: if not isinstance(name, str): raise ValueError("`name` must be a str.") self.values = values self.kind = kind self.typ = typ self.name = name self.cname = cname or name self.axis = axis self.pos = pos self.freq = freq self.tz = tz self.index_name = index_name self.ordered = ordered self.table = table self.meta = meta self.metadata = metadata if pos is not None: self.set_pos(pos) # These are ensured as long as the passed arguments match the # constructor annotations. assert isinstance(self.name, str) assert isinstance(self.cname, str) @property def itemsize(self) -> int: # Assumes self.typ has already been initialized return self.typ.itemsize @property def kind_attr(self) -> str: return f"{self.name}_kind" def set_pos(self, pos: int) -> None: """set the position of this column in the Table""" self.pos = pos if pos is not None and self.typ is not None: self.typ._v_pos = pos def __repr__(self) -> str: temp = tuple( map(pprint_thing, (self.name, self.cname, self.axis, self.pos, self.kind)) ) return ",".join( [ f"{key}->{value}" for key, value in zip(["name", "cname", "axis", "pos", "kind"], temp) ] ) def __eq__(self, other: object) -> bool: """compare 2 col items""" return all( getattr(self, a, None) == getattr(other, a, None) for a in ["name", "cname", "axis", "pos"] ) def __ne__(self, other) -> bool: return not self.__eq__(other) @property def is_indexed(self) -> bool: """return whether I am an indexed column""" if not hasattr(self.table, "cols"): # e.g. if infer hasn't been called yet, self.table will be None. return False return getattr(self.table.cols, self.cname).is_indexed def convert( self, values: np.ndarray, nan_rep, encoding: str, errors: str ) -> tuple[np.ndarray, np.ndarray] | tuple[Index, Index]: """ Convert the data from this selection to the appropriate pandas type. """ assert isinstance(values, np.ndarray), type(values) # values is a recarray if values.dtype.fields is not None: # Copy, otherwise values will be a view # preventing the original recarry from being free'ed values = values[self.cname].copy() val_kind = _ensure_decoded(self.kind) values = _maybe_convert(values, val_kind, encoding, errors) kwargs = {} kwargs["name"] = _ensure_decoded(self.index_name) if self.freq is not None: kwargs["freq"] = _ensure_decoded(self.freq) factory: type[Index | DatetimeIndex] = Index if lib.is_np_dtype(values.dtype, "M") or isinstance( values.dtype, DatetimeTZDtype ): factory = DatetimeIndex elif values.dtype == "i8" and "freq" in kwargs: # PeriodIndex data is stored as i8 # error: Incompatible types in assignment (expression has type # "Callable[[Any, KwArg(Any)], PeriodIndex]", variable has type # "Union[Type[Index], Type[DatetimeIndex]]") factory = lambda x, **kwds: PeriodIndex.from_ordinals( # type: ignore[assignment] x, freq=kwds.get("freq", None) )._rename( kwds["name"] ) # making an Index instance could throw a number of different errors try: new_pd_index = factory(values, **kwargs) except ValueError: # if the output freq is different that what we recorded, # it should be None (see also 'doc example part 2') if "freq" in kwargs: kwargs["freq"] = None new_pd_index = factory(values, **kwargs) final_pd_index = _set_tz(new_pd_index, self.tz) return final_pd_index, final_pd_index def take_data(self): """return the values""" return self.values @property def attrs(self): return self.table._v_attrs @property def description(self): return self.table.description @property def col(self): """return my current col description""" return getattr(self.description, self.cname, None) @property def cvalues(self): """return my cython values""" return self.values def __iter__(self) -> Iterator: return iter(self.values) def maybe_set_size(self, min_itemsize=None) -> None: """ maybe set a string col itemsize: min_itemsize can be an integer or a dict with this columns name with an integer size """ if _ensure_decoded(self.kind) == "string": if isinstance(min_itemsize, dict): min_itemsize = min_itemsize.get(self.name) if min_itemsize is not None and self.typ.itemsize < min_itemsize: self.typ = _tables().StringCol(itemsize=min_itemsize, pos=self.pos) def validate_names(self) -> None: pass def validate_and_set(self, handler: AppendableTable, append: bool) -> None: self.table = handler.table self.validate_col() self.validate_attr(append) self.validate_metadata(handler) self.write_metadata(handler) self.set_attr() def validate_col(self, itemsize=None): """validate this column: return the compared against itemsize""" # validate this column for string truncation (or reset to the max size) if _ensure_decoded(self.kind) == "string": c = self.col if c is not None: if itemsize is None: itemsize = self.itemsize if c.itemsize < itemsize: raise ValueError( f"Trying to store a string with len [{itemsize}] in " f"[{self.cname}] column but\nthis column has a limit of " f"[{c.itemsize}]!\nConsider using min_itemsize to " "preset the sizes on these columns" ) return c.itemsize return None def validate_attr(self, append: bool) -> None: # check for backwards incompatibility if append: existing_kind = getattr(self.attrs, self.kind_attr, None) if existing_kind is not None and existing_kind != self.kind: raise TypeError( f"incompatible kind in col [{existing_kind} - {self.kind}]" ) def update_info(self, info) -> None: """ set/update the info for this indexable with the key/value if there is a conflict raise/warn as needed """ for key in self._info_fields: value = getattr(self, key, None) idx = info.setdefault(self.name, {}) existing_value = idx.get(key) if key in idx and value is not None and existing_value != value: # frequency/name just warn if key in ["freq", "index_name"]: ws = attribute_conflict_doc % (key, existing_value, value) warnings.warn( ws, AttributeConflictWarning, stacklevel=find_stack_level() ) # reset idx[key] = None setattr(self, key, None) else: raise ValueError( f"invalid info for [{self.name}] for [{key}], " f"existing_value [{existing_value}] conflicts with " f"new value [{value}]" ) elif value is not None or existing_value is not None: idx[key] = value def set_info(self, info) -> None: """set my state from the passed info""" idx = info.get(self.name) if idx is not None: self.__dict__.update(idx) def set_attr(self) -> None: """set the kind for this column""" setattr(self.attrs, self.kind_attr, self.kind) def validate_metadata(self, handler: AppendableTable) -> None: """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, strict_nan=True, dtype_equal=True ) ): raise ValueError( "cannot append a categorical with " "different categories to the existing" ) def write_metadata(self, handler: AppendableTable) -> None: """set the meta data""" if self.metadata is not None: handler.write_metadata(self.cname, self.metadata) class GenericIndexCol(IndexCol): """an index which is not represented in the data of the table""" @property def is_indexed(self) -> bool: return False def convert( self, values: np.ndarray, nan_rep, encoding: str, errors: str ) -> tuple[Index, Index]: """ Convert the data from this selection to the appropriate pandas type. Parameters ---------- values : np.ndarray nan_rep : str encoding : str errors : str """ assert isinstance(values, np.ndarray), type(values) index = RangeIndex(len(values)) return index, index def set_attr(self) -> None: pass class DataCol(IndexCol): """ a data holding column, by definition this is not indexable Parameters ---------- data : the actual data cname : the column name in the table to hold the data (typically values) meta : a string description of the metadata metadata : the actual metadata """ is_an_indexable = False is_data_indexable = False _info_fields = ["tz", "ordered"] def __init__( self, name: str, values=None, kind=None, typ=None, cname: str | None = None, pos=None, tz=None, ordered=None, table=None, meta=None, metadata=None, dtype: DtypeArg | None = None, data=None, ) -> None: super().__init__( name=name, values=values, kind=kind, typ=typ, pos=pos, cname=cname, tz=tz, ordered=ordered, table=table, meta=meta, metadata=metadata, ) self.dtype = dtype self.data = data @property def dtype_attr(self) -> str: return f"{self.name}_dtype" @property def meta_attr(self) -> str: return f"{self.name}_meta" def __repr__(self) -> str: temp = tuple( map( pprint_thing, (self.name, self.cname, self.dtype, self.kind, self.shape) ) ) return ",".join( [ f"{key}->{value}" for key, value in zip(["name", "cname", "dtype", "kind", "shape"], temp) ] ) def __eq__(self, other: object) -> bool: """compare 2 col items""" return all( getattr(self, a, None) == getattr(other, a, None) for a in ["name", "cname", "dtype", "pos"] ) def set_data(self, data: ArrayLike) -> None: assert data is not None assert self.dtype is None data, dtype_name = _get_data_and_dtype_name(data) self.data = data self.dtype = dtype_name self.kind = _dtype_to_kind(dtype_name) def take_data(self): """return the data""" return self.data @classmethod def _get_atom(cls, values: ArrayLike) -> Col: """ Get an appropriately typed and shaped pytables.Col object for values. """ dtype = values.dtype # error: Item "ExtensionDtype" of "Union[ExtensionDtype, dtype[Any]]" has no # attribute "itemsize" itemsize = dtype.itemsize # type: ignore[union-attr] shape = values.shape if values.ndim == 1: # EA, use block shape pretending it is 2D # TODO(EA2D): not necessary with 2D EAs shape = (1, values.size) if isinstance(values, Categorical): codes = values.codes atom = cls.get_atom_data(shape, kind=codes.dtype.name) elif lib.is_np_dtype(dtype, "M") or isinstance(dtype, DatetimeTZDtype): atom = cls.get_atom_datetime64(shape) elif lib.is_np_dtype(dtype, "m"): atom = cls.get_atom_timedelta64(shape) elif is_complex_dtype(dtype): atom = _tables().ComplexCol(itemsize=itemsize, shape=shape[0]) elif is_string_dtype(dtype): atom = cls.get_atom_string(shape, itemsize) else: atom = cls.get_atom_data(shape, kind=dtype.name) return atom @classmethod def get_atom_string(cls, shape, itemsize): return _tables().StringCol(itemsize=itemsize, shape=shape[0]) @classmethod def get_atom_coltype(cls, kind: str) -> type[Col]: """return the PyTables column class for this column""" if kind.startswith("uint"): k4 = kind[4:] col_name = f"UInt{k4}Col" elif kind.startswith("period"): # we store as integer col_name = "Int64Col" else: kcap = kind.capitalize() col_name = f"{kcap}Col" return getattr(_tables(), col_name) @classmethod def get_atom_data(cls, shape, kind: str) -> Col: return cls.get_atom_coltype(kind=kind)(shape=shape[0]) @classmethod def get_atom_datetime64(cls, shape): return _tables().Int64Col(shape=shape[0]) @classmethod def get_atom_timedelta64(cls, shape): return _tables().Int64Col(shape=shape[0]) @property def shape(self): return getattr(self.data, "shape", None) @property def cvalues(self): """return my cython values""" return self.data def validate_attr(self, append) -> None: """validate that we have the same order as the existing & same dtype""" if append: existing_fields = getattr(self.attrs, self.kind_attr, None) if existing_fields is not None and existing_fields != list(self.values): raise ValueError("appended items do not match existing items in table!") existing_dtype = getattr(self.attrs, self.dtype_attr, None) if existing_dtype is not None and existing_dtype != self.dtype: raise ValueError( "appended items dtype do not match existing items dtype in table!" ) def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str): """ Convert the data from this selection to the appropriate pandas type. Parameters ---------- values : np.ndarray nan_rep : encoding : str errors : str Returns ------- index : listlike to become an Index data : ndarraylike to become a column """ assert isinstance(values, np.ndarray), type(values) # values is a recarray if values.dtype.fields is not None: values = values[self.cname] assert self.typ is not None if self.dtype is None: # Note: in tests we never have timedelta64 or datetime64, # so the _get_data_and_dtype_name may be unnecessary converted, dtype_name = _get_data_and_dtype_name(values) kind = _dtype_to_kind(dtype_name) else: converted = values dtype_name = self.dtype kind = self.kind assert isinstance(converted, np.ndarray) # for mypy # use the meta if needed meta = _ensure_decoded(self.meta) metadata = self.metadata ordered = self.ordered tz = self.tz assert dtype_name is not None # convert to the correct dtype dtype = _ensure_decoded(dtype_name) # reverse converts if dtype.startswith("datetime64"): # recreate with tz if indicated converted = _set_tz(converted, tz, coerce=True) elif dtype == "timedelta64": converted = np.asarray(converted, dtype="m8[ns]") elif dtype == "date": try: converted = np.asarray( [date.fromordinal(v) for v in converted], dtype=object ) except ValueError: converted = np.asarray( [date.fromtimestamp(v) for v in converted], dtype=object ) elif meta == "category": # we have a categorical categories = metadata codes = converted.ravel() # if we have stored a NaN in the categories # then strip it; in theory we could have BOTH # -1s in the codes and nulls :< if categories is None: # Handle case of NaN-only categorical columns in which case # the categories are an empty array; when this is stored, # pytables cannot write a zero-len array, so on readback # the categories would be None and `read_hdf()` would fail. categories = Index([], dtype=np.float64) else: mask = isna(categories) if mask.any(): categories = categories[~mask] codes[codes != -1] -= mask.astype(int).cumsum()._values converted = Categorical.from_codes( codes, categories=categories, ordered=ordered, validate=False ) else: try: converted = converted.astype(dtype, copy=False) except TypeError: converted = converted.astype("O", copy=False) # convert nans / decode if _ensure_decoded(kind) == "string": converted = _unconvert_string_array( converted, nan_rep=nan_rep, encoding=encoding, errors=errors ) return self.values, converted def set_attr(self) -> None: """set the data for this column""" setattr(self.attrs, self.kind_attr, self.values) setattr(self.attrs, self.meta_attr, self.meta) assert self.dtype is not None setattr(self.attrs, self.dtype_attr, self.dtype) class DataIndexableCol(DataCol): """represent a data column that can be indexed""" is_data_indexable = True def validate_names(self) -> None: if not is_string_dtype(Index(self.values).dtype): # TODO: should the message here be more specifically non-str? raise ValueError("cannot have non-object label DataIndexableCol") @classmethod def get_atom_string(cls, shape, itemsize): return _tables().StringCol(itemsize=itemsize) @classmethod def get_atom_data(cls, shape, kind: str) -> Col: return cls.get_atom_coltype(kind=kind)() @classmethod def get_atom_datetime64(cls, shape): return _tables().Int64Col() @classmethod def get_atom_timedelta64(cls, shape): return _tables().Int64Col() class GenericDataIndexableCol(DataIndexableCol): """represent a generic pytables data column""" class Fixed: """ represent an object in my store facilitate read/write of various types of objects this is an abstract base class Parameters ---------- parent : HDFStore group : Node The group node where the table resides. """ pandas_kind: str format_type: str = "fixed" # GH#30962 needed by dask obj_type: type[DataFrame | Series] ndim: int parent: HDFStore is_table: bool = False def __init__( self, parent: HDFStore, group: Node, encoding: str | None = "UTF-8", errors: str = "strict", ) -> None: assert isinstance(parent, HDFStore), type(parent) assert _table_mod is not None # needed for mypy assert isinstance(group, _table_mod.Node), type(group) self.parent = parent self.group = group self.encoding = _ensure_encoding(encoding) self.errors = errors @property def is_old_version(self) -> bool: return self.version[0] <= 0 and self.version[1] <= 10 and self.version[2] < 1 @property def version(self) -> tuple[int, int, int]: """compute and set our version""" version = _ensure_decoded(getattr(self.group._v_attrs, "pandas_version", None)) try: version = tuple(int(x) for x in version.split(".")) if len(version) == 2: version = version + (0,) except AttributeError: version = (0, 0, 0) return version @property def pandas_type(self): return _ensure_decoded(getattr(self.group._v_attrs, "pandas_type", None)) def __repr__(self) -> str: """return a pretty representation of myself""" self.infer_axes() s = self.shape if s is not None: if isinstance(s, (list, tuple)): jshape = ",".join([pprint_thing(x) for x in s]) s = f"[{jshape}]" return f"{self.pandas_type:12.12} (shape->{s})" return self.pandas_type def set_object_info(self) -> None: """set my pandas type & version""" self.attrs.pandas_type = str(self.pandas_kind) self.attrs.pandas_version = str(_version) def copy(self) -> Fixed: new_self = copy.copy(self) return new_self @property def shape(self): return self.nrows @property def pathname(self): return self.group._v_pathname @property def _handle(self): return self.parent._handle @property def _filters(self): return self.parent._filters @property def _complevel(self) -> int: return self.parent._complevel @property def _fletcher32(self) -> bool: return self.parent._fletcher32 @property def attrs(self): return self.group._v_attrs def set_attrs(self) -> None: """set our object attributes""" def get_attrs(self) -> None: """get our object attributes""" @property def storable(self): """return my storable""" return self.group @property def is_exists(self) -> bool: return False @property def nrows(self): return getattr(self.storable, "nrows", None) def validate(self, other) -> Literal[True] | None: """validate against an existing storable""" if other is None: return None return True def validate_version(self, where=None) -> None: """are we trying to operate on an old version?""" def infer_axes(self) -> bool: """ infer the axes of my storer return a boolean indicating if we have a valid storer or not """ s = self.storable if s is None: return False self.get_attrs() return True def read( self, where=None, columns=None, start: int | None = None, stop: int | None = None, ): raise NotImplementedError( "cannot read on an abstract storer: subclasses should implement" ) def write(self, obj, **kwargs) -> None: raise NotImplementedError( "cannot write on an abstract storer: subclasses should implement" ) def delete( self, where=None, start: int | None = None, stop: int | None = None ) -> None: """ support fully deleting the node in its entirety (only) - where specification must be None """ if com.all_none(where, start, stop): self._handle.remove_node(self.group, recursive=True) return None raise TypeError("cannot delete on an abstract storer") class GenericFixed(Fixed): """a generified fixed version""" _index_type_map = {DatetimeIndex: "datetime", PeriodIndex: "period"} _reverse_index_map = {v: k for k, v in _index_type_map.items()} attributes: list[str] = [] # indexer helpers def _class_to_alias(self, cls) -> str: return self._index_type_map.get(cls, "") def _alias_to_class(self, alias): if isinstance(alias, type): # pragma: no cover # compat: for a short period of time master stored types return alias return self._reverse_index_map.get(alias, Index) def _get_index_factory(self, attrs): index_class = self._alias_to_class( _ensure_decoded(getattr(attrs, "index_class", "")) ) factory: Callable if index_class == DatetimeIndex: def f(values, freq=None, tz=None): # data are already in UTC, localize and convert if tz present dta = DatetimeArray._simple_new( values.values, dtype=values.dtype, freq=freq ) result = DatetimeIndex._simple_new(dta, name=None) if tz is not None: result = result.tz_localize("UTC").tz_convert(tz) return result factory = f elif index_class == PeriodIndex: def f(values, freq=None, tz=None): dtype = PeriodDtype(freq) parr = PeriodArray._simple_new(values, dtype=dtype) return PeriodIndex._simple_new(parr, name=None) factory = f else: factory = index_class kwargs = {} if "freq" in attrs: kwargs["freq"] = attrs["freq"] if index_class is Index: # DTI/PI would be gotten by _alias_to_class factory = TimedeltaIndex if "tz" in attrs: if isinstance(attrs["tz"], bytes): # created by python2 kwargs["tz"] = attrs["tz"].decode("utf-8") else: # created by python3 kwargs["tz"] = attrs["tz"] assert index_class is DatetimeIndex # just checking return factory, kwargs def validate_read(self, columns, where) -> None: """ raise if any keywords are passed which are not-None """ if columns is not None: raise TypeError( "cannot pass a column specification when reading " "a Fixed format store. this store must be selected in its entirety" ) if where is not None: raise TypeError( "cannot pass a where specification when reading " "from a Fixed format store. this store must be selected in its entirety" ) @property def is_exists(self) -> bool: return True def set_attrs(self) -> None: """set our object attributes""" self.attrs.encoding = self.encoding self.attrs.errors = self.errors def get_attrs(self) -> None: """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) -> None: self.set_attrs() def read_array(self, key: str, start: int | None = None, stop: int | None = None): """read an array for the specified node (off of group""" import tables node = getattr(self.group, key) attrs = node._v_attrs transposed = getattr(attrs, "transposed", False) if isinstance(node, tables.VLArray): ret = node[0][start:stop] else: dtype = _ensure_decoded(getattr(attrs, "value_type", None)) shape = getattr(attrs, "shape", None) if shape is not None: # length 0 axis ret = np.empty(shape, dtype=dtype) else: ret = node[start:stop] if dtype and dtype.startswith("datetime64"): # reconstruct a timezone if indicated tz = getattr(attrs, "tz", None) ret = _set_tz(ret, tz, coerce=True) elif dtype == "timedelta64": ret = np.asarray(ret, dtype="m8[ns]") if transposed: return ret.T else: return ret def read_index( self, key: str, start: int | None = None, stop: int | None = None ) -> Index: variety = _ensure_decoded(getattr(self.attrs, f"{key}_variety")) if variety == "multi": return self.read_multi_index(key, start=start, stop=stop) elif variety == "regular": node = getattr(self.group, key) index = self.read_index_node(node, start=start, stop=stop) return index else: # pragma: no cover raise TypeError(f"unrecognized index variety: {variety}") def write_index(self, key: str, index: Index) -> None: if isinstance(index, MultiIndex): setattr(self.attrs, f"{key}_variety", "multi") self.write_multi_index(key, index) else: setattr(self.attrs, f"{key}_variety", "regular") converted = _convert_index("index", index, self.encoding, self.errors) self.write_array(key, converted.values) node = getattr(self.group, key) node._v_attrs.kind = converted.kind node._v_attrs.name = index.name if isinstance(index, (DatetimeIndex, PeriodIndex)): node._v_attrs.index_class = self._class_to_alias(type(index)) if isinstance(index, (DatetimeIndex, PeriodIndex, TimedeltaIndex)): node._v_attrs.freq = index.freq if isinstance(index, DatetimeIndex) and index.tz is not None: node._v_attrs.tz = _get_tz(index.tz) def write_multi_index(self, key: str, index: MultiIndex) -> None: setattr(self.attrs, f"{key}_nlevels", index.nlevels) for i, (lev, level_codes, name) in enumerate( zip(index.levels, index.codes, index.names) ): # write the level if isinstance(lev.dtype, ExtensionDtype): raise NotImplementedError( "Saving a MultiIndex with an extension dtype is not supported." ) level_key = f"{key}_level{i}" conv_level = _convert_index(level_key, lev, self.encoding, self.errors) self.write_array(level_key, conv_level.values) node = getattr(self.group, level_key) node._v_attrs.kind = conv_level.kind node._v_attrs.name = name # write the name setattr(node._v_attrs, f"{key}_name{name}", name) # write the labels label_key = f"{key}_label{i}" self.write_array(label_key, level_codes) def read_multi_index( self, key: str, start: int | None = None, stop: int | None = None ) -> MultiIndex: nlevels = getattr(self.attrs, f"{key}_nlevels") levels = [] codes = [] names: list[Hashable] = [] for i in range(nlevels): level_key = f"{key}_level{i}" node = getattr(self.group, level_key) lev = self.read_index_node(node, start=start, stop=stop) levels.append(lev) names.append(lev.name) label_key = f"{key}_label{i}" level_codes = self.read_array(label_key, start=start, stop=stop) codes.append(level_codes) return MultiIndex( levels=levels, codes=codes, names=names, verify_integrity=True ) def read_index_node( self, node: Node, start: int | None = None, stop: int | None = None ) -> Index: data = node[start:stop] # If the index was an empty array write_array_empty() will # have written a sentinel. Here we replace it with the original. if "shape" in node._v_attrs and np.prod(node._v_attrs.shape) == 0: data = np.empty(node._v_attrs.shape, dtype=node._v_attrs.value_type) kind = _ensure_decoded(node._v_attrs.kind) name = None if "name" in node._v_attrs: name = _ensure_str(node._v_attrs.name) name = _ensure_decoded(name) attrs = node._v_attrs factory, kwargs = self._get_index_factory(attrs) if kind in ("date", "object"): index = factory( _unconvert_index( data, kind, encoding=self.encoding, errors=self.errors ), dtype=object, **kwargs, ) else: index = factory( _unconvert_index( data, kind, encoding=self.encoding, errors=self.errors ), **kwargs, ) index.name = name return index def write_array_empty(self, key: str, value: ArrayLike) -> None: """write a 0-len array""" # ugly hack for length 0 axes arr = np.empty((1,) * value.ndim) self._handle.create_array(self.group, key, arr) node = getattr(self.group, key) node._v_attrs.value_type = str(value.dtype) node._v_attrs.shape = value.shape def write_array( self, key: str, obj: AnyArrayLike, items: Index | None = None ) -> None: # TODO: we only have a few tests that get here, the only EA # that gets passed is DatetimeArray, and we never have # both self._filters and EA value = extract_array(obj, extract_numpy=True) if key in self.group: self._handle.remove_node(self.group, key) # Transform needed to interface with pytables row/col notation empty_array = value.size == 0 transposed = False if isinstance(value.dtype, CategoricalDtype): raise NotImplementedError( "Cannot store a category dtype in a HDF5 dataset that uses format=" '"fixed". Use format="table".' ) if not empty_array: if hasattr(value, "T"): # ExtensionArrays (1d) may not have transpose. value = value.T transposed = True atom = None if self._filters is not None: with suppress(ValueError): # get the atom for this datatype atom = _tables().Atom.from_dtype(value.dtype) if atom is not None: # We only get here if self._filters is non-None and # the Atom.from_dtype call succeeded # create an empty chunked array and fill it from value if not empty_array: ca = self._handle.create_carray( self.group, key, atom, value.shape, filters=self._filters ) ca[:] = value else: self.write_array_empty(key, value) elif value.dtype.type == np.object_: # infer the type, warn if we have a non-string type here (for # performance) inferred_type = lib.infer_dtype(value, skipna=False) if empty_array: pass elif inferred_type == "string": pass else: ws = performance_doc % (inferred_type, key, items) warnings.warn(ws, PerformanceWarning, stacklevel=find_stack_level()) vlarr = self._handle.create_vlarray(self.group, key, _tables().ObjectAtom()) vlarr.append(value) elif lib.is_np_dtype(value.dtype, "M"): self._handle.create_array(self.group, key, value.view("i8")) getattr(self.group, key)._v_attrs.value_type = str(value.dtype) elif isinstance(value.dtype, DatetimeTZDtype): # store as UTC # with a zone # error: Item "ExtensionArray" of "Union[Any, ExtensionArray]" has no # attribute "asi8" self._handle.create_array( self.group, key, value.asi8 # type: ignore[union-attr] ) node = getattr(self.group, key) # error: Item "ExtensionArray" of "Union[Any, ExtensionArray]" has no # attribute "tz" node._v_attrs.tz = _get_tz(value.tz) # type: ignore[union-attr] node._v_attrs.value_type = f"datetime64[{value.dtype.unit}]" elif lib.is_np_dtype(value.dtype, "m"): self._handle.create_array(self.group, key, value.view("i8")) getattr(self.group, key)._v_attrs.value_type = "timedelta64" elif empty_array: self.write_array_empty(key, value) else: self._handle.create_array(self.group, key, value) getattr(self.group, key)._v_attrs.transposed = transposed class SeriesFixed(GenericFixed): pandas_kind = "series" attributes = ["name"] name: Hashable @property def shape(self): try: return (len(self.group.values),) except (TypeError, AttributeError): return None def read( self, where=None, columns=None, start: int | None = None, stop: int | None = None, ) -> Series: self.validate_read(columns, where) index = self.read_index("index", start=start, stop=stop) values = self.read_array("values", start=start, stop=stop) result = Series(values, index=index, name=self.name, copy=False) if using_pyarrow_string_dtype() and is_string_array(values, skipna=True): result = result.astype("string[pyarrow_numpy]") return result def write(self, obj, **kwargs) -> None: super().write(obj, **kwargs) self.write_index("index", obj.index) self.write_array("values", obj) self.attrs.name = obj.name class BlockManagerFixed(GenericFixed): attributes = ["ndim", "nblocks"] nblocks: int @property def shape(self) -> Shape | None: try: ndim = self.ndim # items items = 0 for i in range(self.nblocks): node = getattr(self.group, f"block{i}_items") shape = getattr(node, "shape", None) if shape is not None: items += shape[0] # data shape node = self.group.block0_values shape = getattr(node, "shape", None) if shape is not None: shape = list(shape[0 : (ndim - 1)]) else: shape = [] shape.append(items) return shape except AttributeError: return None def read( self, where=None, columns=None, start: int | None = None, stop: int | None = None, ) -> DataFrame: # start, stop applied to rows, so 0th axis only self.validate_read(columns, where) select_axis = self.obj_type()._get_block_manager_axis(0) axes = [] for i in range(self.ndim): _start, _stop = (start, stop) if i == select_axis else (None, None) ax = self.read_index(f"axis{i}", start=_start, stop=_stop) axes.append(ax) items = axes[0] dfs = [] for i in range(self.nblocks): blk_items = self.read_index(f"block{i}_items") values = self.read_array(f"block{i}_values", start=_start, stop=_stop) columns = items[items.get_indexer(blk_items)] df = DataFrame(values.T, columns=columns, index=axes[1], copy=False) if using_pyarrow_string_dtype() and is_string_array(values, skipna=True): df = df.astype("string[pyarrow_numpy]") dfs.append(df) if len(dfs) > 0: out = concat(dfs, axis=1, copy=True) if using_copy_on_write(): # with CoW, concat ignores the copy keyword. Here, we still want # to copy to enforce optimized column-major layout out = out.copy() out = out.reindex(columns=items, copy=False) return out return DataFrame(columns=axes[0], index=axes[1]) def write(self, obj, **kwargs) -> None: super().write(obj, **kwargs) # TODO(ArrayManager) HDFStore relies on accessing the blocks if isinstance(obj._mgr, ArrayManager): obj = obj._as_manager("block") data = obj._mgr if not data.is_consolidated(): data = data.consolidate() self.attrs.ndim = data.ndim for i, ax in enumerate(data.axes): if i == 0 and (not ax.is_unique): raise ValueError("Columns index has to be unique for fixed format") self.write_index(f"axis{i}", ax) # Supporting mixed-type DataFrame objects...nontrivial self.attrs.nblocks = len(data.blocks) for i, blk in enumerate(data.blocks): # I have no idea why, but writing values before items fixed #2299 blk_items = data.items.take(blk.mgr_locs) self.write_array(f"block{i}_values", blk.values, items=blk_items) self.write_index(f"block{i}_items", blk_items) class FrameFixed(BlockManagerFixed): pandas_kind = "frame" obj_type = DataFrame class Table(Fixed): """ represent a table: facilitate read/write of various types of tables Attrs in Table Node ------------------- These are attributes that are store in the main table node, they are necessary to recreate these tables when read back in. index_axes : a list of tuples of the (original indexing axis and index column) non_index_axes: a list of tuples of the (original index axis and columns on a non-indexing axis) values_axes : a list of the columns which comprise the data of this table data_columns : a list of the columns that we are allowing indexing (these become single columns in values_axes) nan_rep : the string to use for nan representations for string objects levels : the names of levels metadata : the names of the metadata columns """ pandas_kind = "wide_table" format_type: str = "table" # GH#30962 needed by dask table_type: str levels: int | list[Hashable] = 1 is_table = True metadata: list def __init__( self, parent: HDFStore, group: Node, encoding: str | None = None, errors: str = "strict", index_axes: list[IndexCol] | None = None, non_index_axes: list[tuple[AxisInt, Any]] | None = None, values_axes: list[DataCol] | None = None, data_columns: list | None = None, info: dict | None = None, nan_rep=None, ) -> None: super().__init__(parent, group, encoding=encoding, errors=errors) self.index_axes = index_axes or [] self.non_index_axes = non_index_axes or [] self.values_axes = values_axes or [] self.data_columns = data_columns or [] self.info = info or {} self.nan_rep = nan_rep @property def table_type_short(self) -> str: return self.table_type.split("_")[0] def __repr__(self) -> str: """return a pretty representation of myself""" self.infer_axes() jdc = ",".join(self.data_columns) if len(self.data_columns) else "" dc = f",dc->[{jdc}]" ver = "" if self.is_old_version: jver = ".".join([str(x) for x in self.version]) ver = f"[{jver}]" jindex_axes = ",".join([a.name for a in self.index_axes]) return ( f"{self.pandas_type:12.12}{ver} " f"(typ->{self.table_type_short},nrows->{self.nrows}," f"ncols->{self.ncols},indexers->[{jindex_axes}]{dc})" ) def __getitem__(self, c: str): """return the axis for c""" for a in self.axes: if c == a.name: return a return None def validate(self, other) -> None: """validate against an existing table""" if other is None: return if other.table_type != self.table_type: raise TypeError( "incompatible table_type with existing " f"[{other.table_type} - {self.table_type}]" ) for c in ["index_axes", "non_index_axes", "values_axes"]: sv = getattr(self, c, None) ov = getattr(other, c, None) if sv != ov: # show the error for the specific axes # Argument 1 to "enumerate" has incompatible type # "Optional[Any]"; expected "Iterable[Any]" [arg-type] for i, sax in enumerate(sv): # type: ignore[arg-type] # Value of type "Optional[Any]" is not indexable [index] oax = ov[i] # type: ignore[index] if sax != oax: raise ValueError( f"invalid combination of [{c}] on appending data " f"[{sax}] vs current table [{oax}]" ) # should never get here raise Exception( f"invalid combination of [{c}] on appending data [{sv}] vs " f"current table [{ov}]" ) @property def is_multi_index(self) -> bool: """the levels attribute is 1 or a list in the case of a multi-index""" return isinstance(self.levels, list) def validate_multiindex( self, obj: DataFrame | Series ) -> tuple[DataFrame, list[Hashable]]: """ validate that we can store the multi-index; reset and return the new object """ levels = com.fill_missing_names(obj.index.names) try: reset_obj = obj.reset_index() except ValueError as err: raise ValueError( "duplicate names/columns in the multi-index when storing as a table" ) from err assert isinstance(reset_obj, DataFrame) # for mypy return reset_obj, levels @property def nrows_expected(self) -> int: """based on our axes, compute the expected nrows""" return np.prod([i.cvalues.shape[0] for i in self.index_axes]) @property def is_exists(self) -> bool: """has this table been created""" return "table" in self.group @property def storable(self): return getattr(self.group, "table", None) @property def table(self): """return the table group (this is my storable)""" return self.storable @property def dtype(self): return self.table.dtype @property def description(self): return self.table.description @property def axes(self) -> itertools.chain[IndexCol]: return itertools.chain(self.index_axes, self.values_axes) @property def ncols(self) -> int: """the number of total columns in the values axes""" return sum(len(a.values) for a in self.values_axes) @property def is_transposed(self) -> bool: return False @property def data_orientation(self) -> tuple[int, ...]: """return a tuple of my permutated axes, non_indexable at the front""" return tuple( itertools.chain( [int(a[0]) for a in self.non_index_axes], [int(a.axis) for a in self.index_axes], ) ) def queryables(self) -> dict[str, Any]: """return a dict of the kinds allowable columns for this object""" # mypy doesn't recognize DataFrame._AXIS_NAMES, so we re-write it here axis_names = {0: "index", 1: "columns"} # compute the values_axes queryables d1 = [(a.cname, a) for a in self.index_axes] d2 = [(axis_names[axis], None) for axis, values in self.non_index_axes] d3 = [ (v.cname, v) for v in self.values_axes if v.name in set(self.data_columns) ] return dict(d1 + d2 + d3) def index_cols(self): """return a list of my index cols""" # Note: each `i.cname` below is assured to be a str. return [(i.axis, i.cname) for i in self.index_axes] def values_cols(self) -> list[str]: """return a list of my values cols""" return [i.cname for i in self.values_axes] def _get_metadata_path(self, key: str) -> str: """return the metadata pathname for this key""" group = self.group._v_pathname return f"{group}/meta/{key}/meta" def write_metadata(self, key: str, values: np.ndarray) -> None: """ Write out a metadata array to the key as a fixed-format Series. Parameters ---------- key : str values : ndarray """ self.parent.put( self._get_metadata_path(key), Series(values, copy=False), format="table", encoding=self.encoding, errors=self.errors, nan_rep=self.nan_rep, ) def read_metadata(self, key: str): """return the meta data array for this key""" if getattr(getattr(self.group, "meta", None), key, None) is not None: return self.parent.select(self._get_metadata_path(key)) return None def set_attrs(self) -> None: """set our table type & indexables""" self.attrs.table_type = str(self.table_type) self.attrs.index_cols = self.index_cols() self.attrs.values_cols = self.values_cols() self.attrs.non_index_axes = self.non_index_axes self.attrs.data_columns = self.data_columns self.attrs.nan_rep = self.nan_rep self.attrs.encoding = self.encoding self.attrs.errors = self.errors self.attrs.levels = self.levels self.attrs.info = self.info def get_attrs(self) -> None: """retrieve our attributes""" self.non_index_axes = getattr(self.attrs, "non_index_axes", None) or [] self.data_columns = getattr(self.attrs, "data_columns", None) or [] self.info = getattr(self.attrs, "info", None) or {} self.nan_rep = getattr(self.attrs, "nan_rep", None) self.encoding = _ensure_encoding(getattr(self.attrs, "encoding", None)) self.errors = _ensure_decoded(getattr(self.attrs, "errors", "strict")) self.levels: list[Hashable] = getattr(self.attrs, "levels", None) or [] self.index_axes = [a for a in self.indexables if a.is_an_indexable] self.values_axes = [a for a in self.indexables if not a.is_an_indexable] def validate_version(self, where=None) -> None: """are we trying to operate on an old version?""" if where is not None: if self.is_old_version: ws = incompatibility_doc % ".".join([str(x) for x in self.version]) warnings.warn( ws, IncompatibilityWarning, stacklevel=find_stack_level(), ) def validate_min_itemsize(self, min_itemsize) -> None: """ 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 in min_itemsize: # ok, apply generally if k == "values": continue if k not in q: raise ValueError( f"min_itemsize has the key [{k}] which is not an axis or " "data_column" ) @cache_readonly def indexables(self): """create/cache the indexables if they don't exist""" _indexables = [] desc = self.description table_attrs = self.table.attrs # Note: each of the `name` kwargs below are str, ensured # by the definition in index_cols. # index columns for i, (axis, name) in enumerate(self.attrs.index_cols): atom = getattr(desc, name) md = self.read_metadata(name) meta = "category" if md is not None else None kind_attr = f"{name}_kind" kind = getattr(table_attrs, kind_attr, None) index_col = IndexCol( name=name, axis=axis, pos=i, kind=kind, typ=atom, table=self.table, meta=meta, metadata=md, ) _indexables.append(index_col) # values columns dc = set(self.data_columns) base_pos = len(_indexables) def f(i, c): assert isinstance(c, str) klass = DataCol if c in dc: klass = DataIndexableCol atom = getattr(desc, c) adj_name = _maybe_adjust_name(c, self.version) # TODO: why kind_attr here? values = getattr(table_attrs, f"{adj_name}_kind", None) dtype = getattr(table_attrs, f"{adj_name}_dtype", None) # Argument 1 to "_dtype_to_kind" has incompatible type # "Optional[Any]"; expected "str" [arg-type] kind = _dtype_to_kind(dtype) # type: ignore[arg-type] md = self.read_metadata(c) # TODO: figure out why these two versions of `meta` dont always match. # meta = "category" if md is not None else None meta = getattr(table_attrs, f"{adj_name}_meta", None) obj = klass( name=adj_name, cname=c, values=values, kind=kind, pos=base_pos + i, typ=atom, table=self.table, meta=meta, metadata=md, dtype=dtype, ) return obj # Note: the definition of `values_cols` ensures that each # `c` below is a str. _indexables.extend([f(i, c) for i, c in enumerate(self.attrs.values_cols)]) return _indexables def create_index( self, columns=None, optlevel=None, kind: str | None = None ) -> None: """ Create a pytables index on the specified columns. Parameters ---------- columns : None, bool, or listlike[str] Indicate which columns to create an index on. * False : Do not create any indexes. * True : Create indexes on all columns. * None : Create indexes on all columns. * listlike : Create indexes on the given columns. optlevel : int or None, default None Optimization level, if None, pytables defaults to 6. kind : str or None, default None Kind of index, if None, pytables defaults to "medium". Raises ------ TypeError if trying to create an index on a complex-type column. Notes ----- Cannot index Time64Col or ComplexCol. Pytables must be >= 3.0. """ if not self.infer_axes(): return if columns is False: return # index all indexables and data_columns if columns is None or columns is True: columns = [a.cname for a in self.axes if a.is_data_indexable] if not isinstance(columns, (tuple, list)): columns = [columns] kw = {} if optlevel is not None: kw["optlevel"] = optlevel if kind is not None: kw["kind"] = kind table = self.table for c in columns: v = getattr(table.cols, c, None) if v is not None: # remove the index if the kind/optlevel have changed if v.is_indexed: index = v.index cur_optlevel = index.optlevel cur_kind = index.kind if kind is not None and cur_kind != kind: v.remove_index() else: kw["kind"] = cur_kind if optlevel is not None and cur_optlevel != optlevel: v.remove_index() else: kw["optlevel"] = cur_optlevel # create the index if not v.is_indexed: if v.type.startswith("complex"): raise TypeError( "Columns containing complex values can be stored but " "cannot be indexed when using table format. Either use " "fixed format, set index=False, or do not include " "the columns containing complex values to " "data_columns when initializing the table." ) v.create_index(**kw) elif c in self.non_index_axes[0][1]: # GH 28156 raise AttributeError( f"column {c} is not a data_column.\n" f"In order to read column {c} you must reload the dataframe \n" f"into HDFStore and include {c} with the data_columns argument." ) def _read_axes( self, where, start: int | None = None, stop: int | None = None ) -> list[tuple[np.ndarray, np.ndarray] | tuple[Index, Index]]: """ Create the axes sniffed from the table. Parameters ---------- where : ??? start : int or None, default None stop : int or None, default None Returns ------- List[Tuple[index_values, column_values]] """ # create the selection selection = Selection(self, where=where, start=start, stop=stop) values = selection.select() results = [] # convert the data for a in self.axes: a.set_info(self.info) res = a.convert( values, nan_rep=self.nan_rep, encoding=self.encoding, errors=self.errors, ) results.append(res) return results @classmethod def get_object(cls, obj, transposed: bool): """return the data for this obj""" return obj def validate_data_columns(self, data_columns, min_itemsize, non_index_axes): """ take the input data_columns and min_itemize and create a data columns spec """ if not len(non_index_axes): return [] axis, axis_labels = non_index_axes[0] info = self.info.get(axis, {}) if info.get("type") == "MultiIndex" and data_columns: raise ValueError( f"cannot use a multi-index on axis [{axis}] with " f"data_columns {data_columns}" ) # evaluate the passed data_columns, True == use all columns # take only valid axis labels if data_columns is True: data_columns = list(axis_labels) elif data_columns is None: data_columns = [] # if min_itemsize is a dict, add the keys (exclude 'values') if isinstance(min_itemsize, dict): existing_data_columns = set(data_columns) data_columns = list(data_columns) # ensure we do not modify data_columns.extend( [ k for k in min_itemsize.keys() if k != "values" and k not in existing_data_columns ] ) # return valid columns in the order of our axis return [c for c in data_columns if c in axis_labels] def _create_axes( self, axes, obj: DataFrame, validate: bool = True, nan_rep=None, data_columns=None, min_itemsize=None, ): """ Create and return the axes. Parameters ---------- axes: list or None The names or numbers of the axes to create. obj : DataFrame The object to create axes on. validate: bool, default True Whether to validate the obj against an existing object already written. nan_rep : A value to use for string column nan_rep. data_columns : List[str], True, or None, default None Specify the columns that we want to create to allow indexing on. * True : Use all available columns. * None : Use no columns. * List[str] : Use the specified columns. min_itemsize: Dict[str, int] or None, default None The min itemsize for a column in bytes. """ if not isinstance(obj, DataFrame): group = self.group._v_name raise TypeError( f"cannot properly create the storer for: [group->{group}," f"value->{type(obj)}]" ) # set the default axes if needed if axes is None: axes = [0] # map axes to numbers axes = [obj._get_axis_number(a) for a in axes] # do we have an existing table (if so, use its axes & data_columns) if self.infer_axes(): table_exists = True axes = [a.axis for a in self.index_axes] data_columns = list(self.data_columns) nan_rep = self.nan_rep # TODO: do we always have validate=True here? else: table_exists = False new_info = self.info assert self.ndim == 2 # with next check, we must have len(axes) == 1 # currently support on ndim-1 axes if len(axes) != self.ndim - 1: raise ValueError( "currently only support ndim-1 indexers in an AppendableTable" ) # create according to the new data new_non_index_axes: list = [] # nan_representation if nan_rep is None: nan_rep = "nan" # We construct the non-index-axis first, since that alters new_info idx = next(x for x in [0, 1] if x not in axes) a = obj.axes[idx] # we might be able to change the axes on the appending data if necessary append_axis = list(a) if table_exists: indexer = len(new_non_index_axes) # i.e. 0 exist_axis = self.non_index_axes[indexer][1] if not array_equivalent( np.array(append_axis), np.array(exist_axis), strict_nan=True, dtype_equal=True, ): # ahah! -> reindex if array_equivalent( np.array(sorted(append_axis)), np.array(sorted(exist_axis)), strict_nan=True, dtype_equal=True, ): append_axis = exist_axis # the non_index_axes info info = new_info.setdefault(idx, {}) info["names"] = list(a.names) info["type"] = type(a).__name__ new_non_index_axes.append((idx, append_axis)) # Now we can construct our new index axis idx = axes[0] a = obj.axes[idx] axis_name = obj._get_axis_name(idx) new_index = _convert_index(axis_name, a, self.encoding, self.errors) new_index.axis = idx # Because we are always 2D, there is only one new_index, so # we know it will have pos=0 new_index.set_pos(0) new_index.update_info(new_info) new_index.maybe_set_size(min_itemsize) # check for column conflicts new_index_axes = [new_index] j = len(new_index_axes) # i.e. 1 assert j == 1 # reindex by our non_index_axes & compute data_columns assert len(new_non_index_axes) == 1 for a in new_non_index_axes: obj = _reindex_axis(obj, a[0], a[1]) transposed = new_index.axis == 1 # figure out data_columns and get out blocks data_columns = self.validate_data_columns( data_columns, min_itemsize, new_non_index_axes ) frame = self.get_object(obj, transposed)._consolidate() blocks, blk_items = self._get_blocks_and_items( frame, table_exists, new_non_index_axes, self.values_axes, data_columns ) # add my values vaxes = [] for i, (blk, b_items) in enumerate(zip(blocks, blk_items)): # shape of the data column are the indexable axes klass = DataCol name = None # we have a data_column if data_columns and len(b_items) == 1 and b_items[0] in data_columns: klass = DataIndexableCol name = b_items[0] if not (name is None or isinstance(name, str)): # TODO: should the message here be more specifically non-str? raise ValueError("cannot have non-object label DataIndexableCol") # make sure that we match up the existing columns # if we have an existing table existing_col: DataCol | None if table_exists and validate: try: existing_col = self.values_axes[i] except (IndexError, KeyError) as err: raise ValueError( f"Incompatible appended table [{blocks}]" f"with existing table [{self.values_axes}]" ) from err else: existing_col = None new_name = name or f"values_block_{i}" data_converted = _maybe_convert_for_string_atom( new_name, blk.values, existing_col=existing_col, min_itemsize=min_itemsize, nan_rep=nan_rep, encoding=self.encoding, errors=self.errors, columns=b_items, ) adj_name = _maybe_adjust_name(new_name, self.version) typ = klass._get_atom(data_converted) kind = _dtype_to_kind(data_converted.dtype.name) tz = None if getattr(data_converted, "tz", None) is not None: tz = _get_tz(data_converted.tz) meta = metadata = ordered = None if isinstance(data_converted.dtype, CategoricalDtype): ordered = data_converted.ordered meta = "category" metadata = np.asarray(data_converted.categories).ravel() data, dtype_name = _get_data_and_dtype_name(data_converted) col = klass( name=adj_name, cname=new_name, values=list(b_items), typ=typ, pos=j, kind=kind, tz=tz, ordered=ordered, meta=meta, metadata=metadata, dtype=dtype_name, data=data, ) col.update_info(new_info) vaxes.append(col) j += 1 dcs = [col.name for col in vaxes if col.is_data_indexable] new_table = type(self)( parent=self.parent, group=self.group, encoding=self.encoding, errors=self.errors, index_axes=new_index_axes, non_index_axes=new_non_index_axes, values_axes=vaxes, data_columns=dcs, info=new_info, nan_rep=nan_rep, ) if hasattr(self, "levels"): # TODO: get this into constructor, only for appropriate subclass new_table.levels = self.levels new_table.validate_min_itemsize(min_itemsize) if validate and table_exists: new_table.validate(self) return new_table @staticmethod def _get_blocks_and_items( frame: DataFrame, table_exists: bool, new_non_index_axes, values_axes, data_columns, ): # Helper to clarify non-state-altering parts of _create_axes # TODO(ArrayManager) HDFStore relies on accessing the blocks if isinstance(frame._mgr, ArrayManager): frame = frame._as_manager("block") def get_blk_items(mgr): return [mgr.items.take(blk.mgr_locs) for blk in mgr.blocks] mgr = frame._mgr mgr = cast(BlockManager, mgr) blocks: list[Block] = list(mgr.blocks) blk_items: list[Index] = get_blk_items(mgr) if len(data_columns): # TODO: prove that we only get here with axis == 1? # It is the case in all extant tests, but NOT the case # outside this `if len(data_columns)` check. axis, axis_labels = new_non_index_axes[0] new_labels = Index(axis_labels).difference(Index(data_columns)) mgr = frame.reindex(new_labels, axis=axis)._mgr mgr = cast(BlockManager, mgr) blocks = list(mgr.blocks) blk_items = get_blk_items(mgr) for c in data_columns: # This reindex would raise ValueError if we had a duplicate # index, so we can infer that (as long as axis==1) we # get a single column back, so a single block. mgr = frame.reindex([c], axis=axis)._mgr mgr = cast(BlockManager, mgr) blocks.extend(mgr.blocks) blk_items.extend(get_blk_items(mgr)) # reorder the blocks in the same order as the existing table if we can if table_exists: by_items = { tuple(b_items.tolist()): (b, b_items) for b, b_items in zip(blocks, blk_items) } new_blocks: list[Block] = [] new_blk_items = [] for ea in values_axes: items = tuple(ea.values) try: b, b_items = by_items.pop(items) new_blocks.append(b) new_blk_items.append(b_items) except (IndexError, KeyError) as err: jitems = ",".join([pprint_thing(item) for item in items]) raise ValueError( f"cannot match existing table structure for [{jitems}] " "on appending data" ) from err blocks = new_blocks blk_items = new_blk_items return blocks, blk_items def process_axes(self, obj, selection: Selection, columns=None) -> DataFrame: """process axes filters""" # make a copy to avoid side effects if columns is not None: columns = list(columns) # make sure to include levels if we have them if columns is not None and self.is_multi_index: assert isinstance(self.levels, list) # assured by is_multi_index for n in self.levels: if n not in columns: columns.insert(0, n) # reorder by any non_index_axes & limit to the select columns for axis, labels in self.non_index_axes: obj = _reindex_axis(obj, axis, labels, columns) def process_filter(field, filt, op): for axis_name in obj._AXIS_ORDERS: axis_number = obj._get_axis_number(axis_name) axis_values = obj._get_axis(axis_name) assert axis_number is not None # see if the field is the name of an axis if field == axis_name: # if we have a multi-index, then need to include # the levels if self.is_multi_index: filt = filt.union(Index(self.levels)) takers = op(axis_values, filt) return obj.loc(axis=axis_number)[takers] # this might be the name of a file IN an axis elif field in axis_values: # we need to filter on this dimension values = ensure_index(getattr(obj, field).values) filt = ensure_index(filt) # hack until we support reversed dim flags if isinstance(obj, DataFrame): axis_number = 1 - axis_number takers = op(values, filt) return obj.loc(axis=axis_number)[takers] raise ValueError(f"cannot find the field [{field}] for filtering!") # apply the selection filters (but keep in the same order) if selection.filter is not None: for field, op, filt in selection.filter.format(): obj = process_filter(field, filt, op) return obj def create_description( self, complib, complevel: int | None, fletcher32: bool, expectedrows: int | None, ) -> dict[str, Any]: """create the description of the table from the axes & values""" # provided expected rows if its passed if expectedrows is None: expectedrows = max(self.nrows_expected, 10000) d = {"name": "table", "expectedrows": expectedrows} # description from the axes & values d["description"] = {a.cname: a.typ for a in self.axes} if complib: if complevel is None: complevel = self._complevel or 9 filters = _tables().Filters( complevel=complevel, complib=complib, fletcher32=fletcher32 or self._fletcher32, ) d["filters"] = filters elif self._filters is not None: d["filters"] = self._filters return d def read_coordinates( self, where=None, start: int | None = None, stop: int | None = None ): """ select coordinates (row numbers) from a table; return the coordinates object """ # validate the version self.validate_version(where) # infer the data kind if not self.infer_axes(): return False # create the selection selection = Selection(self, where=where, start=start, stop=stop) coords = selection.select_coords() if selection.filter is not None: for field, op, filt in selection.filter.format(): data = self.read_column( field, start=coords.min(), stop=coords.max() + 1 ) coords = coords[op(data.iloc[coords - coords.min()], filt).values] return Index(coords) def read_column( self, column: str, where=None, start: int | None = None, stop: int | None = None, ): """ return a single column from the table, generally only indexables are interesting """ # validate the version self.validate_version() # infer the data kind if not self.infer_axes(): return False if where is not None: raise TypeError("read_column does not currently accept a where clause") # find the axes for a in self.axes: if column == a.name: if not a.is_data_indexable: raise ValueError( f"column [{column}] can not be extracted individually; " "it is not data indexable" ) # column must be an indexable or a data column c = getattr(self.table.cols, column) a.set_info(self.info) col_values = a.convert( c[start:stop], nan_rep=self.nan_rep, encoding=self.encoding, errors=self.errors, ) return Series(_set_tz(col_values[1], a.tz), name=column, copy=False) raise KeyError(f"column [{column}] not found in the table") class WORMTable(Table): """ a write-once read-many table: this format DOES NOT ALLOW appending to a table. writing is a one-time operation the data are stored in a format that allows for searching the data on disk """ table_type = "worm" def read( self, where=None, columns=None, start: int | None = None, stop: int | None = None, ): """ read the indices and the indexing array, calculate offset rows and return """ raise NotImplementedError("WORMTable needs to implement read") def write(self, obj, **kwargs) -> None: """ write in a format that we can search later on (but cannot append to): write out the indices and the values using _write_array (e.g. a CArray) create an indexing table so that we can search """ raise NotImplementedError("WORMTable needs to implement write") class AppendableTable(Table): """support the new appendable table formats""" table_type = "appendable" # error: Signature of "write" incompatible with supertype "Fixed" def write( # type: ignore[override] self, obj, axes=None, append: bool = False, complib=None, complevel=None, fletcher32=None, min_itemsize=None, chunksize: int | None = None, expectedrows=None, dropna: bool = False, nan_rep=None, data_columns=None, track_times: bool = True, ) -> None: if not append and self.is_exists: self._handle.remove_node(self.group, "table") # create the axes table = self._create_axes( axes=axes, obj=obj, validate=append, min_itemsize=min_itemsize, nan_rep=nan_rep, data_columns=data_columns, ) for a in table.axes: a.validate_names() if not table.is_exists: # create the table options = table.create_description( complib=complib, complevel=complevel, fletcher32=fletcher32, expectedrows=expectedrows, ) # set the table attributes table.set_attrs() options["track_times"] = track_times # create the table table._handle.create_table(table.group, **options) # update my info table.attrs.info = table.info # validate the axes and set the kinds for a in table.axes: a.validate_and_set(table, append) # add the rows table.write_data(chunksize, dropna=dropna) def write_data(self, chunksize: int | None, dropna: bool = False) -> None: """ we form the data into a 2-d including indexes,values,mask write chunk-by-chunk """ names = self.dtype.names nrows = self.nrows_expected # if dropna==True, then drop ALL nan rows masks = [] if dropna: for a in self.values_axes: # figure the mask: only do if we can successfully process this # column, otherwise ignore the mask mask = isna(a.data).all(axis=0) if isinstance(mask, np.ndarray): masks.append(mask.astype("u1", copy=False)) # consolidate masks if len(masks): mask = masks[0] for m in masks[1:]: mask = mask & m mask = mask.ravel() else: mask = None # broadcast the indexes if needed indexes = [a.cvalues for a in self.index_axes] nindexes = len(indexes) assert nindexes == 1, nindexes # ensures we dont need to broadcast # transpose the values so first dimension is last # reshape the values if needed values = [a.take_data() for a in self.values_axes] values = [v.transpose(np.roll(np.arange(v.ndim), v.ndim - 1)) for v in values] bvalues = [] for i, v in enumerate(values): new_shape = (nrows,) + self.dtype[names[nindexes + i]].shape bvalues.append(v.reshape(new_shape)) # write the chunks if chunksize is None: chunksize = 100000 rows = np.empty(min(chunksize, nrows), dtype=self.dtype) chunks = nrows // chunksize + 1 for i in range(chunks): start_i = i * chunksize end_i = min((i + 1) * chunksize, nrows) if start_i >= end_i: break self.write_data_chunk( rows, indexes=[a[start_i:end_i] for a in indexes], mask=mask[start_i:end_i] if mask is not None else None, values=[v[start_i:end_i] for v in bvalues], ) def write_data_chunk( self, rows: np.ndarray, indexes: list[np.ndarray], mask: npt.NDArray[np.bool_] | None, values: list[np.ndarray], ) -> None: """ Parameters ---------- rows : an empty memory space where we are putting the chunk indexes : an array of the indexes mask : an array of the masks values : an array of the values """ # 0 len for v in values: if not np.prod(v.shape): return nrows = indexes[0].shape[0] if nrows != len(rows): rows = np.empty(nrows, dtype=self.dtype) names = self.dtype.names nindexes = len(indexes) # indexes for i, idx in enumerate(indexes): rows[names[i]] = idx # values for i, v in enumerate(values): rows[names[i + nindexes]] = v # mask if mask is not None: m = ~mask.ravel().astype(bool, copy=False) if not m.all(): rows = rows[m] if len(rows): self.table.append(rows) self.table.flush() def delete(self, where=None, start: int | None = None, stop: int | None = None): # delete all rows (and return the nrows) if where is None or not len(where): if start is None and stop is None: nrows = self.nrows self._handle.remove_node(self.group, recursive=True) else: # pytables<3.0 would remove a single row with stop=None if stop is None: stop = self.nrows nrows = self.table.remove_rows(start=start, stop=stop) self.table.flush() return nrows # infer the data kind if not self.infer_axes(): return None # create the selection table = self.table selection = Selection(self, where, start=start, stop=stop) values = selection.select_coords() # delete the rows in reverse order sorted_series = Series(values, copy=False).sort_values() ln = len(sorted_series) if ln: # construct groups of consecutive rows diff = sorted_series.diff() groups = list(diff[diff > 1].index) # 1 group if not len(groups): groups = [0] # final element if groups[-1] != ln: groups.append(ln) # initial element if groups[0] != 0: groups.insert(0, 0) # we must remove in reverse order! pg = groups.pop() for g in reversed(groups): rows = sorted_series.take(range(g, pg)) table.remove_rows( start=rows[rows.index[0]], stop=rows[rows.index[-1]] + 1 ) pg = g self.table.flush() # return the number of rows removed return ln class AppendableFrameTable(AppendableTable): """support the new appendable table formats""" pandas_kind = "frame_table" table_type = "appendable_frame" ndim = 2 obj_type: type[DataFrame | Series] = DataFrame @property def is_transposed(self) -> bool: return self.index_axes[0].axis == 1 @classmethod def get_object(cls, obj, transposed: bool): """these are written transposed""" if transposed: obj = obj.T return obj def read( self, where=None, columns=None, start: int | None = None, stop: int | None = None, ): # validate the version self.validate_version(where) # infer the data kind if not self.infer_axes(): return None result = self._read_axes(where=where, start=start, stop=stop) info = ( self.info.get(self.non_index_axes[0][0], {}) if len(self.non_index_axes) else {} ) inds = [i for i, ax in enumerate(self.axes) if ax is self.index_axes[0]] assert len(inds) == 1 ind = inds[0] index = result[ind][0] frames = [] for i, a in enumerate(self.axes): if a not in self.values_axes: continue index_vals, cvalues = result[i] # we could have a multi-index constructor here # ensure_index doesn't recognized our list-of-tuples here if info.get("type") != "MultiIndex": cols = Index(index_vals) else: cols = MultiIndex.from_tuples(index_vals) names = info.get("names") if names is not None: cols.set_names(names, inplace=True) if self.is_transposed: values = cvalues index_ = cols cols_ = Index(index, name=getattr(index, "name", None)) else: values = cvalues.T index_ = Index(index, name=getattr(index, "name", None)) cols_ = cols # if we have a DataIndexableCol, its shape will only be 1 dim if values.ndim == 1 and isinstance(values, np.ndarray): values = values.reshape((1, values.shape[0])) if isinstance(values, np.ndarray): df = DataFrame(values.T, columns=cols_, index=index_, copy=False) elif isinstance(values, Index): df = DataFrame(values, columns=cols_, index=index_) else: # Categorical df = DataFrame._from_arrays([values], columns=cols_, index=index_) if not (using_pyarrow_string_dtype() and values.dtype.kind == "O"): assert (df.dtypes == values.dtype).all(), (df.dtypes, values.dtype) if using_pyarrow_string_dtype() and is_string_array( values, # type: ignore[arg-type] skipna=True, ): df = df.astype("string[pyarrow_numpy]") frames.append(df) if len(frames) == 1: df = frames[0] else: df = concat(frames, axis=1) selection = Selection(self, where=where, start=start, stop=stop) # apply the selection filters & axis orderings df = self.process_axes(df, selection=selection, columns=columns) return df class AppendableSeriesTable(AppendableFrameTable): """support the new appendable table formats""" pandas_kind = "series_table" table_type = "appendable_series" ndim = 2 obj_type = Series @property def is_transposed(self) -> bool: return False @classmethod def get_object(cls, obj, transposed: bool): return obj # error: Signature of "write" incompatible with supertype "Fixed" def write(self, obj, data_columns=None, **kwargs) -> None: # type: ignore[override] """we are going to write this as a frame table""" if not isinstance(obj, DataFrame): name = obj.name or "values" obj = obj.to_frame(name) super().write(obj=obj, data_columns=obj.columns.tolist(), **kwargs) def read( self, where=None, columns=None, start: int | None = None, stop: int | None = None, ) -> Series: is_multi_index = self.is_multi_index if columns is not None and is_multi_index: assert isinstance(self.levels, list) # needed for mypy for n in self.levels: if n not in columns: columns.insert(0, n) s = super().read(where=where, columns=columns, start=start, stop=stop) if is_multi_index: s.set_index(self.levels, inplace=True) s = s.iloc[:, 0] # remove the default name if s.name == "values": s.name = None return s class AppendableMultiSeriesTable(AppendableSeriesTable): """support the new appendable table formats""" pandas_kind = "series_table" table_type = "appendable_multiseries" # error: Signature of "write" incompatible with supertype "Fixed" def write(self, obj, **kwargs) -> None: # type: ignore[override] """we are going to write this as a frame table""" name = obj.name or "values" newobj, self.levels = self.validate_multiindex(obj) assert isinstance(self.levels, list) # for mypy cols = list(self.levels) cols.append(name) newobj.columns = Index(cols) super().write(obj=newobj, **kwargs) class GenericTable(AppendableFrameTable): """a table that read/writes the generic pytables table format""" pandas_kind = "frame_table" table_type = "generic_table" ndim = 2 obj_type = DataFrame levels: list[Hashable] @property def pandas_type(self) -> str: return self.pandas_kind @property def storable(self): return getattr(self.group, "table", None) or self.group def get_attrs(self) -> None: """retrieve our attributes""" self.non_index_axes = [] self.nan_rep = None self.levels = [] self.index_axes = [a for a in self.indexables if a.is_an_indexable] self.values_axes = [a for a in self.indexables if not a.is_an_indexable] self.data_columns = [a.name for a in self.values_axes] @cache_readonly def indexables(self): """create the indexables from the table description""" d = self.description # TODO: can we get a typ for this? AFAICT it is the only place # where we aren't passing one # the index columns is just a simple index md = self.read_metadata("index") meta = "category" if md is not None else None index_col = GenericIndexCol( name="index", axis=0, table=self.table, meta=meta, metadata=md ) _indexables: list[GenericIndexCol | GenericDataIndexableCol] = [index_col] for i, n in enumerate(d._v_names): assert isinstance(n, str) atom = getattr(d, n) md = self.read_metadata(n) meta = "category" if md is not None else None dc = GenericDataIndexableCol( name=n, pos=i, values=[n], typ=atom, table=self.table, meta=meta, metadata=md, ) _indexables.append(dc) return _indexables # error: Signature of "write" incompatible with supertype "AppendableTable" def write(self, **kwargs) -> None: # type: ignore[override] raise NotImplementedError("cannot write on an generic table") class AppendableMultiFrameTable(AppendableFrameTable): """a frame with a multi-index""" table_type = "appendable_multiframe" obj_type = DataFrame ndim = 2 _re_levels = re.compile(r"^level_\d+$") @property def table_type_short(self) -> str: return "appendable_multi" # error: Signature of "write" incompatible with supertype "Fixed" def write(self, obj, data_columns=None, **kwargs) -> None: # type: ignore[override] if data_columns is None: data_columns = [] elif data_columns is True: data_columns = obj.columns.tolist() obj, self.levels = self.validate_multiindex(obj) assert isinstance(self.levels, list) # for mypy for n in self.levels: if n not in data_columns: data_columns.insert(0, n) super().write(obj=obj, data_columns=data_columns, **kwargs) def read( self, where=None, columns=None, start: int | None = None, stop: int | None = None, ): df = super().read(where=where, columns=columns, start=start, stop=stop) df = df.set_index(self.levels) # remove names for 'level_%d' df.index = df.index.set_names( [None if self._re_levels.search(name) else name for name in df.index.names] ) return df def _reindex_axis( obj: DataFrame, axis: AxisInt, labels: Index, other=None ) -> DataFrame: ax = obj._get_axis(axis) labels = ensure_index(labels) # try not to reindex even if other is provided # if it equals our current index if other is not None: other = ensure_index(other) if (other is None or labels.equals(other)) and labels.equals(ax): return obj labels = ensure_index(labels.unique()) if other is not None: labels = ensure_index(other.unique()).intersection(labels, sort=False) if not labels.equals(ax): slicer: list[slice | Index] = [slice(None, None)] * obj.ndim slicer[axis] = labels obj = obj.loc[tuple(slicer)] return obj # tz to/from coercion def _get_tz(tz: tzinfo) -> str | tzinfo: """for a tz-aware type, return an encoded zone""" zone = timezones.get_timezone(tz) return zone @overload def _set_tz( values: np.ndarray | Index, tz: str | tzinfo, coerce: bool = False ) -> DatetimeIndex: ... @overload def _set_tz(values: np.ndarray | Index, tz: None, coerce: bool = False) -> np.ndarray: ... def _set_tz( values: np.ndarray | Index, tz: str | tzinfo | None, coerce: bool = False ) -> np.ndarray | DatetimeIndex: """ coerce the values to a DatetimeIndex if tz is set preserve the input shape if possible Parameters ---------- values : ndarray or Index tz : str or tzinfo coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray """ if isinstance(values, DatetimeIndex): # If values is tzaware, the tz gets dropped in the values.ravel() # call below (which returns an ndarray). So we are only non-lossy # if `tz` matches `values.tz`. assert values.tz is None or values.tz == tz if values.tz is not None: return values if tz is not None: if isinstance(values, DatetimeIndex): name = values.name else: name = None values = values.ravel() tz = _ensure_decoded(tz) values = DatetimeIndex(values, name=name) values = values.tz_localize("UTC").tz_convert(tz) elif coerce: values = np.asarray(values, dtype="M8[ns]") # error: Incompatible return value type (got "Union[ndarray, Index]", # expected "Union[ndarray, DatetimeIndex]") return values # type: ignore[return-value] def _convert_index(name: str, index: Index, encoding: str, errors: str) -> IndexCol: assert isinstance(name, str) index_name = index.name # error: Argument 1 to "_get_data_and_dtype_name" has incompatible type "Index"; # expected "Union[ExtensionArray, ndarray]" converted, dtype_name = _get_data_and_dtype_name(index) # type: ignore[arg-type] kind = _dtype_to_kind(dtype_name) atom = DataIndexableCol._get_atom(converted) if ( lib.is_np_dtype(index.dtype, "iu") or needs_i8_conversion(index.dtype) or is_bool_dtype(index.dtype) ): # Includes Index, RangeIndex, DatetimeIndex, TimedeltaIndex, PeriodIndex, # in which case "kind" is "integer", "integer", "datetime64", # "timedelta64", and "integer", respectively. return IndexCol( name, values=converted, kind=kind, typ=atom, freq=getattr(index, "freq", None), tz=getattr(index, "tz", None), index_name=index_name, ) if isinstance(index, MultiIndex): raise TypeError("MultiIndex not supported here!") inferred_type = lib.infer_dtype(index, skipna=False) # we won't get inferred_type of "datetime64" or "timedelta64" as these # would go through the DatetimeIndex/TimedeltaIndex paths above values = np.asarray(index) if inferred_type == "date": converted = np.asarray([v.toordinal() for v in values], dtype=np.int32) return IndexCol( name, converted, "date", _tables().Time32Col(), index_name=index_name ) elif inferred_type == "string": converted = _convert_string_array(values, encoding, errors) itemsize = converted.dtype.itemsize return IndexCol( name, converted, "string", _tables().StringCol(itemsize), index_name=index_name, ) elif inferred_type in ["integer", "floating"]: return IndexCol( name, values=converted, kind=kind, typ=atom, index_name=index_name ) else: assert isinstance(converted, np.ndarray) and converted.dtype == object assert kind == "object", kind atom = _tables().ObjectAtom() return IndexCol(name, converted, kind, atom, index_name=index_name) def _unconvert_index(data, kind: str, encoding: str, errors: str) -> np.ndarray | Index: index: Index | np.ndarray if kind.startswith("datetime64"): if kind == "datetime64": # created before we stored resolution information index = DatetimeIndex(data) else: index = DatetimeIndex(data.view(kind)) elif kind == "timedelta64": index = TimedeltaIndex(data) elif kind == "date": try: index = np.asarray([date.fromordinal(v) for v in data], dtype=object) except ValueError: index = np.asarray([date.fromtimestamp(v) for v in data], dtype=object) elif kind in ("integer", "float", "bool"): index = np.asarray(data) elif kind in ("string"): index = _unconvert_string_array( data, nan_rep=None, encoding=encoding, errors=errors ) elif kind == "object": index = np.asarray(data[0]) else: # pragma: no cover raise ValueError(f"unrecognized index type {kind}") return index def _maybe_convert_for_string_atom( name: str, bvalues: ArrayLike, existing_col, min_itemsize, nan_rep, encoding, errors, columns: list[str], ): if bvalues.dtype != object: return bvalues bvalues = cast(np.ndarray, bvalues) dtype_name = bvalues.dtype.name inferred_type = lib.infer_dtype(bvalues, skipna=False) if inferred_type == "date": raise TypeError("[date] is not implemented as a table column") if inferred_type == "datetime": # after GH#8260 # this only would be hit for a multi-timezone dtype which is an error raise TypeError( "too many timezones in this block, create separate data columns" ) if not (inferred_type == "string" or dtype_name == "object"): return bvalues mask = isna(bvalues) data = bvalues.copy() data[mask] = nan_rep # see if we have a valid string type inferred_type = lib.infer_dtype(data, skipna=False) if inferred_type != "string": # we cannot serialize this data, so report an exception on a column # by column basis # expected behaviour: # search block for a non-string object column by column for i in range(data.shape[0]): col = data[i] inferred_type = lib.infer_dtype(col, skipna=False) if inferred_type != "string": error_column_label = columns[i] if len(columns) > i else f"No.{i}" raise TypeError( f"Cannot serialize the column [{error_column_label}]\n" f"because its data contents are not [string] but " f"[{inferred_type}] object dtype" ) # itemsize is the maximum length of a string (along any dimension) data_converted = _convert_string_array(data, encoding, errors).reshape(data.shape) itemsize = data_converted.itemsize # specified min_itemsize? if isinstance(min_itemsize, dict): min_itemsize = int(min_itemsize.get(name) or min_itemsize.get("values") or 0) itemsize = max(min_itemsize or 0, itemsize) # check for column in the values conflicts if existing_col is not None: eci = existing_col.validate_col(itemsize) if eci is not None and eci > itemsize: itemsize = eci data_converted = data_converted.astype(f"|S{itemsize}", copy=False) return data_converted def _convert_string_array(data: np.ndarray, encoding: str, errors: str) -> np.ndarray: """ Take a string-like that is object dtype and coerce to a fixed size string type. Parameters ---------- data : np.ndarray[object] encoding : str errors : str Handler for encoding errors. Returns ------- np.ndarray[fixed-length-string] """ # encode if needed if len(data): data = ( Series(data.ravel(), copy=False) .str.encode(encoding, errors) ._values.reshape(data.shape) ) # create the sized dtype ensured = ensure_object(data.ravel()) itemsize = max(1, libwriters.max_len_string_array(ensured)) data = np.asarray(data, dtype=f"S{itemsize}") return data def _unconvert_string_array( data: np.ndarray, nan_rep, encoding: str, errors: str ) -> np.ndarray: """ Inverse of _convert_string_array. Parameters ---------- data : np.ndarray[fixed-length-string] nan_rep : the storage repr of NaN encoding : str errors : str Handler for encoding errors. Returns ------- np.ndarray[object] Decoded data. """ shape = data.shape data = np.asarray(data.ravel(), dtype=object) if len(data): itemsize = libwriters.max_len_string_array(ensure_object(data)) dtype = f"U{itemsize}" if isinstance(data[0], bytes): data = Series(data, copy=False).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" libwriters.string_array_replace_from_nan_rep(data, nan_rep) return data.reshape(shape) def _maybe_convert(values: np.ndarray, val_kind: str, encoding: str, errors: str): assert isinstance(val_kind, str), type(val_kind) if _need_convert(val_kind): conv = _get_converter(val_kind, encoding, errors) values = conv(values) return values def _get_converter(kind: str, encoding: str, errors: str): if kind == "datetime64": return lambda x: np.asarray(x, dtype="M8[ns]") elif "datetime64" in kind: return lambda x: np.asarray(x, dtype=kind) elif kind == "string": return lambda x: _unconvert_string_array( x, nan_rep=None, encoding=encoding, errors=errors ) else: # pragma: no cover raise ValueError(f"invalid kind {kind}") def _need_convert(kind: str) -> bool: if kind in ("datetime64", "string") or "datetime64" in kind: return True return False def _maybe_adjust_name(name: str, version: Sequence[int]) -> str: """ Prior to 0.10.1, we named values blocks like: values_block_0 an the name values_0, adjust the given name if necessary. Parameters ---------- name : str version : Tuple[int, int, int] Returns ------- str """ if isinstance(version, str) or len(version) < 3: raise ValueError("Version is incorrect, expected sequence of 3 integers.") if version[0] == 0 and version[1] <= 10 and version[2] == 0: m = re.search(r"values_block_(\d+)", name) if m: grp = m.groups()[0] name = f"values_{grp}" return name def _dtype_to_kind(dtype_str: str) -> str: """ Find the "kind" string describing the given dtype name. """ dtype_str = _ensure_decoded(dtype_str) if dtype_str.startswith(("string", "bytes")): kind = "string" elif dtype_str.startswith("float"): kind = "float" elif dtype_str.startswith("complex"): kind = "complex" elif dtype_str.startswith(("int", "uint")): kind = "integer" elif dtype_str.startswith("datetime64"): kind = dtype_str elif dtype_str.startswith("timedelta"): kind = "timedelta64" elif dtype_str.startswith("bool"): kind = "bool" elif dtype_str.startswith("category"): kind = "category" elif dtype_str.startswith("period"): # We store the `freq` attr so we can restore from integers kind = "integer" elif dtype_str == "object": kind = "object" else: raise ValueError(f"cannot interpret dtype of [{dtype_str}]") return kind def _get_data_and_dtype_name(data: ArrayLike): """ Convert the passed data into a storable form and a dtype string. """ if isinstance(data, Categorical): data = data.codes if isinstance(data.dtype, DatetimeTZDtype): # For datetime64tz we need to drop the TZ in tests TODO: why? dtype_name = f"datetime64[{data.dtype.unit}]" else: dtype_name = data.dtype.name if data.dtype.kind in "mM": data = np.asarray(data.view("i8")) # TODO: we used to reshape for the dt64tz case, but no longer # doing that doesn't seem to break anything. why? elif isinstance(data, PeriodIndex): data = data.asi8 data = np.asarray(data) return data, dtype_name class Selection: """ Carries out a selection operation on a tables.Table object. Parameters ---------- table : a Table object where : list of Terms (or convertible to) start, stop: indices to start and/or stop selection """ def __init__( self, table: Table, where=None, start: int | None = None, stop: int | None = None, ) -> None: self.table = table self.where = where self.start = start self.stop = stop self.condition = None self.filter = None self.terms = None self.coordinates = None if is_list_like(where): # see if we have a passed coordinate like with suppress(ValueError): inferred = lib.infer_dtype(where, skipna=False) if inferred in ("integer", "boolean"): where = np.asarray(where) if where.dtype == np.bool_: start, stop = self.start, self.stop if start is None: start = 0 if stop is None: stop = self.table.nrows self.coordinates = np.arange(start, stop)[where] elif issubclass(where.dtype.type, np.integer): if (self.start is not None and (where < self.start).any()) or ( self.stop is not None and (where >= self.stop).any() ): raise ValueError( "where must have index locations >= start and < stop" ) self.coordinates = where if self.coordinates is None: self.terms = self.generate(where) # create the numexpr & the filter if self.terms is not None: self.condition, self.filter = self.terms.evaluate() def generate(self, where): """where can be a : dict,list,tuple,string""" if where is None: return None q = self.table.queryables() try: return PyTablesExpr(where, queryables=q, encoding=self.table.encoding) except NameError as err: # raise a nice message, suggesting that the user should use # data_columns qkeys = ",".join(q.keys()) msg = dedent( f"""\ The passed where expression: {where} contains an invalid variable reference all of the variable references must be a reference to an axis (e.g. 'index' or 'columns'), or a data_column The currently defined references are: {qkeys} """ ) raise ValueError(msg) from err def select(self): """ generate the selection """ if self.condition is not None: return self.table.table.read_where( self.condition.format(), start=self.start, stop=self.stop ) elif self.coordinates is not None: return self.table.table.read_coordinates(self.coordinates) return self.table.table.read(start=self.start, stop=self.stop) def select_coords(self): """ generate the selection """ start, stop = self.start, self.stop nrows = self.table.nrows if start is None: start = 0 elif start < 0: start += nrows if stop is None: stop = nrows elif stop < 0: stop += nrows if self.condition is not None: return self.table.table.get_where_list( self.condition.format(), start=start, stop=stop, sort=True ) elif self.coordinates is not None: return self.coordinates return np.arange(start, stop)