""" High level interface to PyTables for reading and writing pandas data structures to disk """ import copy from datetime import date, tzinfo import itertools import os import re from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, Union import warnings import numpy as np from pandas._config import config, get_option from pandas._libs import lib, writers as libwriters from pandas._libs.tslibs import timezones from pandas._typing import ArrayLike, FrameOrSeries, Label from pandas.compat._optional import import_optional_dependency from pandas.compat.pickle_compat import patch_pickle from pandas.errors import PerformanceWarning from pandas.util._decorators import cache_readonly from pandas.core.dtypes.common import ( ensure_object, is_categorical_dtype, is_complex_dtype, is_datetime64_dtype, is_datetime64tz_dtype, is_extension_array_dtype, is_list_like, is_string_dtype, is_timedelta64_dtype, ) from pandas.core.dtypes.generic import ABCExtensionArray from pandas.core.dtypes.missing import array_equivalent from pandas import ( DataFrame, DatetimeIndex, Index, Int64Index, MultiIndex, PeriodIndex, Series, TimedeltaIndex, concat, isna, ) from pandas.core.arrays import Categorical, DatetimeArray, PeriodArray import pandas.core.common as com from pandas.core.computation.pytables import PyTablesExpr, maybe_expression from pandas.core.indexes.api import ensure_index from pandas.io.common import stringify_path from pandas.io.formats.printing import adjoin, pprint_thing if TYPE_CHECKING: from tables import Col, File, Node # noqa:F401 # versioning attribute _version = "0.15.2" # encoding _default_encoding = "UTF-8" def _ensure_decoded(s): """ if we have bytes, decode them to unicode """ if isinstance(s, np.bytes_): s = s.decode("UTF-8") return s def _ensure_encoding(encoding): # set the encoding if we need if encoding is None: encoding = _default_encoding return encoding def _ensure_str(name): """ Ensure that an index / column name is a str (python 3); otherwise they may be np.string dtype. Non-string dtypes are passed through unchanged. https://github.com/pandas-dev/pandas/issues/13492 """ if isinstance(name, str): name = str(name) return name Term = PyTablesExpr def _ensure_term(where, scope_level: int): """ ensure that the where is a Term or a list of Term this makes sure that we are capturing the scope of variables that are passed create the terms here with a frame_level=2 (we are 2 levels down) """ # only consider list/tuple here as an ndarray is automatically a coordinate # list level = scope_level + 1 if isinstance(where, (list, tuple)): wlist = [] for w in filter(lambda x: x is not None, where): if not maybe_expression(w): wlist.append(w) else: wlist.append(Term(w, scope_level=level)) where = wlist elif maybe_expression(where): where = Term(where, scope_level=level) return where if where is None or len(where) else None class PossibleDataLossError(Exception): pass class ClosedFileError(Exception): pass class IncompatibilityWarning(Warning): pass incompatibility_doc = """ where criteria is being ignored as this version [%s] is too old (or not-defined), read the file in and write it out to a new file to upgrade (with the copy_to method) """ class AttributeConflictWarning(Warning): pass attribute_conflict_doc = """ the [%s] attribute of the existing index is [%s] which conflicts with the new [%s], resetting the attribute to None """ class DuplicateWarning(Warning): pass duplicate_doc = """ duplicate entries in table, taking most recently appended """ performance_doc = """ your performance may suffer as PyTables will pickle object types that it cannot map directly to c-types [inferred_type->%s,key->%s] [items->%s] """ # formats _FORMAT_MAP = {"f": "fixed", "fixed": "fixed", "t": "table", "table": "table"} # axes map _AXES_MAP = {DataFrame: [0]} # register our configuration options dropna_doc = """ : boolean drop ALL nan rows when appending to a table """ format_doc = """ : format default format writing format, if None, then put will default to 'fixed' and append will default to 'table' """ with config.config_prefix("io.hdf"): config.register_option("dropna_table", False, dropna_doc, validator=config.is_bool) config.register_option( "default_format", None, format_doc, validator=config.is_one_of_factory(["fixed", "table", None]), ) # oh the troubles to reduce import time _table_mod = None _table_file_open_policy_is_strict = False def _tables(): global _table_mod global _table_file_open_policy_is_strict if _table_mod is None: import tables _table_mod = tables # set the file open policy # return the file open policy; this changes as of pytables 3.1 # depending on the HDF5 version try: _table_file_open_policy_is_strict = ( tables.file._FILE_OPEN_POLICY == "strict" ) except AttributeError: pass return _table_mod # interface to/from ### def to_hdf( path_or_buf, key: str, value: FrameOrSeries, mode: str = "a", complevel: Optional[int] = None, complib: Optional[str] = None, append: bool = False, format: Optional[str] = None, index: bool = True, min_itemsize: Optional[Union[int, Dict[str, int]]] = None, nan_rep=None, dropna: Optional[bool] = None, data_columns: Optional[Union[bool, List[str]]] = None, errors: str = "strict", encoding: str = "UTF-8", ): """ store this object, close it if we opened it """ if append: f = lambda store: store.append( key, value, format=format, index=index, min_itemsize=min_itemsize, nan_rep=nan_rep, dropna=dropna, data_columns=data_columns, errors=errors, encoding=encoding, ) else: # NB: dropna is not passed to `put` f = lambda store: store.put( key, value, format=format, index=index, min_itemsize=min_itemsize, nan_rep=nan_rep, data_columns=data_columns, errors=errors, encoding=encoding, ) path_or_buf = stringify_path(path_or_buf) if isinstance(path_or_buf, str): with HDFStore( path_or_buf, mode=mode, complevel=complevel, complib=complib ) as store: f(store) else: f(path_or_buf) def read_hdf( path_or_buf, key=None, mode: str = "r", errors: str = "strict", where=None, start: Optional[int] = None, stop: Optional[int] = None, columns=None, iterator=False, chunksize: Optional[int] = None, **kwargs, ): """ Read from the store, close it if we opened it. Retrieve pandas object stored in file, optionally based on where criteria. .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- path_or_buf : str, path object, pandas.HDFStore or file-like object Any valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. A local file could be: ``file://localhost/path/to/table.h5``. If you want to pass in a path object, pandas accepts any ``os.PathLike``. Alternatively, pandas accepts an open :class:`pandas.HDFStore` object. By file-like object, we refer to objects with a ``read()`` method, such as a file handler (e.g. via builtin ``open`` function) or ``StringIO``. key : object, optional The group identifier in the store. Can be omitted if the HDF file contains a single pandas object. mode : {'r', 'r+', 'a'}, default 'r' Mode to use when opening the file. Ignored if path_or_buf is a :class:`pandas.HDFStore`. Default is 'r'. errors : str, default 'strict' Specifies how encoding and decoding errors are to be handled. See the errors argument for :func:`open` for a full list of options. where : list, optional A list of Term (or convertible) objects. start : int, optional Row number to start selection. stop : int, optional Row number to stop selection. columns : list, optional A list of columns names to return. iterator : bool, optional Return an iterator object. chunksize : int, optional Number of rows to include in an iteration when using an iterator. **kwargs Additional keyword arguments passed to HDFStore. Returns ------- item : object The selected object. Return type depends on the object stored. See Also -------- DataFrame.to_hdf : Write a HDF file from a DataFrame. HDFStore : Low-level access to HDF files. Examples -------- >>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z']) >>> df.to_hdf('./store.h5', 'data') >>> reread = pd.read_hdf('./store.h5') """ if mode not in ["r", "r+", "a"]: raise ValueError( f"mode {mode} is not allowed while performing a read. " f"Allowed modes are r, r+ and a." ) # grab the scope if where is not None: where = _ensure_term(where, scope_level=1) if isinstance(path_or_buf, HDFStore): if not path_or_buf.is_open: raise IOError("The HDFStore must be open for reading.") store = path_or_buf auto_close = False else: path_or_buf = stringify_path(path_or_buf) if not isinstance(path_or_buf, str): raise NotImplementedError( "Support for generic buffers has not been implemented." ) try: exists = os.path.exists(path_or_buf) # if filepath is too long except (TypeError, ValueError): exists = False if not exists: raise FileNotFoundError(f"File {path_or_buf} does not exist") store = HDFStore(path_or_buf, mode=mode, errors=errors, **kwargs) # can't auto open/close if we are using an iterator # so delegate to the iterator auto_close = True try: if key is None: groups = store.groups() if len(groups) == 0: raise ValueError( "Dataset(s) incompatible with Pandas data types, " "not table, or no datasets found in HDF5 file." ) candidate_only_group = groups[0] # For the HDF file to have only one dataset, all other groups # should then be metadata groups for that candidate group. (This # assumes that the groups() method enumerates parent groups # before their children.) for group_to_check in groups[1:]: if not _is_metadata_of(group_to_check, candidate_only_group): raise ValueError( "key must be provided when HDF5 " "file contains multiple datasets." ) key = candidate_only_group._v_pathname return store.select( key, where=where, start=start, stop=stop, columns=columns, iterator=iterator, chunksize=chunksize, auto_close=auto_close, ) except (ValueError, TypeError, KeyError): if not isinstance(path_or_buf, HDFStore): # if there is an error, close the store if we opened it. try: store.close() except AttributeError: pass raise def _is_metadata_of(group: "Node", parent_group: "Node") -> bool: """Check if a given group is a metadata group for a given parent_group.""" if group._v_depth <= parent_group._v_depth: return False current = group while current._v_depth > 1: parent = current._v_parent if parent == parent_group and current._v_name == "meta": return True current = current._v_parent return False class HDFStore: """ Dict-like IO interface for storing pandas objects in PyTables. Either Fixed or Table format. .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- path : str File path to HDF5 file. mode : {'a', 'w', 'r', 'r+'}, default 'a' ``'r'`` Read-only; no data can be modified. ``'w'`` Write; a new file is created (an existing file with the same name would be deleted). ``'a'`` Append; an existing file is opened for reading and writing, and if the file does not exist it is created. ``'r+'`` It is similar to ``'a'``, but the file must already exist. complevel : int, 0-9, default None Specifies a compression level for data. A value of 0 or None disables compression. complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib' Specifies the compression library to be used. As of v0.20.2 these additional compressors for Blosc are supported (default if no compressor specified: 'blosc:blosclz'): {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy', 'blosc:zlib', 'blosc:zstd'}. Specifying a compression library which is not available issues a ValueError. fletcher32 : bool, default False If applying compression use the fletcher32 checksum. **kwargs These parameters will be passed to the PyTables open_file method. Examples -------- >>> bar = pd.DataFrame(np.random.randn(10, 4)) >>> store = pd.HDFStore('test.h5') >>> store['foo'] = bar # write to HDF5 >>> bar = store['foo'] # retrieve >>> store.close() **Create or load HDF5 file in-memory** When passing the `driver` option to the PyTables open_file method through **kwargs, the HDF5 file is loaded or created in-memory and will only be written when closed: >>> bar = pd.DataFrame(np.random.randn(10, 4)) >>> store = pd.HDFStore('test.h5', driver='H5FD_CORE') >>> store['foo'] = bar >>> store.close() # only now, data is written to disk """ _handle: Optional["File"] _mode: str _complevel: int _fletcher32: bool def __init__( self, path, mode: str = "a", complevel: Optional[int] = None, complib=None, fletcher32: bool = False, **kwargs, ): if "format" in kwargs: raise ValueError("format is not a defined argument for HDFStore") tables = import_optional_dependency("tables") if complib is not None and complib not in tables.filters.all_complibs: raise ValueError( f"complib only supports {tables.filters.all_complibs} compression." ) if complib is None and complevel is not None: complib = tables.filters.default_complib self._path = stringify_path(path) if mode is None: mode = "a" self._mode = mode self._handle = None self._complevel = complevel if complevel else 0 self._complib = complib self._fletcher32 = fletcher32 self._filters = None self.open(mode=mode, **kwargs) def __fspath__(self): return self._path @property def root(self): """ return the root node """ self._check_if_open() return self._handle.root @property def filename(self): return self._path def __getitem__(self, key: str): return self.get(key) def __setitem__(self, key: str, value): self.put(key, value) def __delitem__(self, key: str): return self.remove(key) def __getattr__(self, name: str): """ allow attribute access to get stores """ try: return self.get(name) except (KeyError, ClosedFileError): pass raise AttributeError( f"'{type(self).__name__}' object has no attribute '{name}'" ) def __contains__(self, key: str) -> bool: """ check for existence of this key can match the exact pathname or the pathnm w/o the leading '/' """ node = self.get_node(key) if node is not None: name = node._v_pathname if name == key or name[1:] == key: return True return False def __len__(self) -> int: return len(self.groups()) def __repr__(self) -> str: pstr = pprint_thing(self._path) return f"{type(self)}\nFile path: {pstr}\n" def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() def keys(self, include: str = "pandas") -> List[str]: """ Return a list of keys corresponding to objects stored in HDFStore. Parameters ---------- include : str, default 'pandas' When kind equals 'pandas' return pandas objects When kind equals 'native' return native HDF5 Table objects .. versionadded:: 1.1.0 Returns ------- list List of ABSOLUTE path-names (e.g. have the leading '/'). Raises ------ raises ValueError if kind has an illegal value """ if include == "pandas": return [n._v_pathname for n in self.groups()] elif include == "native": assert self._handle is not None # mypy return [ n._v_pathname for n in self._handle.walk_nodes("/", classname="Table") ] raise ValueError( f"`include` should be either 'pandas' or 'native' but is '{include}'" ) def __iter__(self): return iter(self.keys()) def items(self): """ iterate on key->group """ for g in self.groups(): yield g._v_pathname, g iteritems = items def open(self, mode: str = "a", **kwargs): """ Open the file in the specified mode Parameters ---------- mode : {'a', 'w', 'r', 'r+'}, default 'a' See HDFStore docstring or tables.open_file for info about modes **kwargs These parameters will be passed to the PyTables open_file method. """ tables = _tables() if self._mode != mode: # if we are changing a write mode to read, ok if self._mode in ["a", "w"] and mode in ["r", "r+"]: pass elif mode in ["w"]: # this would truncate, raise here if self.is_open: raise PossibleDataLossError( f"Re-opening the file [{self._path}] with mode [{self._mode}] " "will delete the current file!" ) self._mode = mode # close and reopen the handle if self.is_open: self.close() if self._complevel and self._complevel > 0: self._filters = _tables().Filters( self._complevel, self._complib, fletcher32=self._fletcher32 ) try: self._handle = tables.open_file(self._path, self._mode, **kwargs) except IOError as err: # pragma: no cover if "can not be written" in str(err): print(f"Opening {self._path} in read-only mode") self._handle = tables.open_file(self._path, "r", **kwargs) else: raise except ValueError as err: # trap PyTables >= 3.1 FILE_OPEN_POLICY exception # to provide an updated message if "FILE_OPEN_POLICY" in str(err): hdf_version = tables.get_hdf5_version() err = ValueError( f"PyTables [{tables.__version__}] no longer supports " "opening multiple files\n" "even in read-only mode on this HDF5 version " f"[{hdf_version}]. You can accept this\n" "and not open the same file multiple times at once,\n" "upgrade the HDF5 version, or downgrade to PyTables 3.0.0 " "which allows\n" "files to be opened multiple times at once\n" ) raise err except Exception as err: # trying to read from a non-existent file causes an error which # is not part of IOError, make it one if self._mode == "r" and "Unable to open/create file" in str(err): raise IOError(str(err)) from err raise def close(self): """ Close the PyTables file handle """ if self._handle is not None: self._handle.close() self._handle = None @property def is_open(self) -> bool: """ return a boolean indicating whether the file is open """ if self._handle is None: return False return bool(self._handle.isopen) def flush(self, fsync: bool = False): """ Force all buffered modifications to be written to disk. Parameters ---------- fsync : bool (default False) call ``os.fsync()`` on the file handle to force writing to disk. Notes ----- Without ``fsync=True``, flushing may not guarantee that the OS writes to disk. With fsync, the operation will block until the OS claims the file has been written; however, other caching layers may still interfere. """ if self._handle is not None: self._handle.flush() if fsync: try: os.fsync(self._handle.fileno()) except OSError: pass def get(self, key: str): """ Retrieve pandas object stored in file. Parameters ---------- key : str Returns ------- object Same type as object stored in file. """ with patch_pickle(): # GH#31167 Without this patch, pickle doesn't know how to unpickle # old DateOffset objects now that they are cdef classes. group = self.get_node(key) if group is None: raise KeyError(f"No object named {key} in the file") return self._read_group(group) def select( self, key: str, where=None, start=None, stop=None, columns=None, iterator=False, chunksize=None, auto_close: bool = False, ): """ Retrieve pandas object stored in file, optionally based on where criteria. .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- key : str Object being retrieved from file. where : list, default None List of Term (or convertible) objects, optional. start : int, default None Row number to start selection. stop : int, default None Row number to stop selection. columns : list, default None A list of columns that if not None, will limit the return columns. iterator : bool, default False Returns an iterator. chunksize : int, default None Number or rows to include in iteration, return an iterator. auto_close : bool, default False Should automatically close the store when finished. Returns ------- object Retrieved object from file. """ group = self.get_node(key) if group is None: raise KeyError(f"No object named {key} in the file") # create the storer and axes where = _ensure_term(where, scope_level=1) s = self._create_storer(group) s.infer_axes() # function to call on iteration def func(_start, _stop, _where): return s.read(start=_start, stop=_stop, where=_where, columns=columns) # create the iterator it = TableIterator( self, s, func, where=where, nrows=s.nrows, start=start, stop=stop, iterator=iterator, chunksize=chunksize, auto_close=auto_close, ) return it.get_result() def select_as_coordinates( self, key: str, where=None, start: Optional[int] = None, stop: Optional[int] = None, ): """ return the selection as an Index .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- key : str where : list of Term (or convertible) objects, optional start : integer (defaults to None), row number to start selection stop : integer (defaults to None), row number to stop selection """ where = _ensure_term(where, scope_level=1) tbl = self.get_storer(key) if not isinstance(tbl, Table): raise TypeError("can only read_coordinates with a table") return tbl.read_coordinates(where=where, start=start, stop=stop) def select_column( self, key: str, column: str, start: Optional[int] = None, stop: Optional[int] = None, ): """ return a single column from the table. This is generally only useful to select an indexable .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- key : str column : str The column of interest. start : int or None, default None stop : int or None, default None Raises ------ raises KeyError if the column is not found (or key is not a valid store) raises ValueError if the column can not be extracted individually (it is part of a data block) """ tbl = self.get_storer(key) if not isinstance(tbl, Table): raise TypeError("can only read_column with a table") return tbl.read_column(column=column, start=start, stop=stop) def select_as_multiple( self, keys, where=None, selector=None, columns=None, start=None, stop=None, iterator=False, chunksize=None, auto_close: bool = False, ): """ Retrieve pandas objects from multiple tables. .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- keys : a list of the tables selector : the table to apply the where criteria (defaults to keys[0] if not supplied) columns : the columns I want back start : integer (defaults to None), row number to start selection stop : integer (defaults to None), row number to stop selection iterator : boolean, return an iterator, default False chunksize : nrows to include in iteration, return an iterator auto_close : bool, default False Should automatically close the store when finished. Raises ------ raises KeyError if keys or selector is not found or keys is empty raises TypeError if keys is not a list or tuple raises ValueError if the tables are not ALL THE SAME DIMENSIONS """ # default to single select where = _ensure_term(where, scope_level=1) if isinstance(keys, (list, tuple)) and len(keys) == 1: keys = keys[0] if isinstance(keys, str): return self.select( key=keys, where=where, columns=columns, start=start, stop=stop, iterator=iterator, chunksize=chunksize, auto_close=auto_close, ) if not isinstance(keys, (list, tuple)): raise TypeError("keys must be a list/tuple") if not len(keys): raise ValueError("keys must have a non-zero length") if selector is None: selector = keys[0] # collect the tables tbls = [self.get_storer(k) for k in keys] s = self.get_storer(selector) # validate rows nrows = None for t, k in itertools.chain([(s, selector)], zip(tbls, keys)): if t is None: raise KeyError(f"Invalid table [{k}]") if not t.is_table: raise TypeError( f"object [{t.pathname}] is not a table, and cannot be used in all " "select as multiple" ) if nrows is None: nrows = t.nrows elif t.nrows != nrows: raise ValueError("all tables must have exactly the same nrows!") # The isinstance checks here are redundant with the check above, # but necessary for mypy; see GH#29757 _tbls = [x for x in tbls if isinstance(x, Table)] # axis is the concentration axes axis = list({t.non_index_axes[0][0] for t in _tbls})[0] def func(_start, _stop, _where): # retrieve the objs, _where is always passed as a set of # coordinates here objs = [ t.read(where=_where, columns=columns, start=_start, stop=_stop) for t in tbls ] # concat and return return concat(objs, axis=axis, verify_integrity=False)._consolidate() # create the iterator it = TableIterator( self, s, func, where=where, nrows=nrows, start=start, stop=stop, iterator=iterator, chunksize=chunksize, auto_close=auto_close, ) return it.get_result(coordinates=True) def put( self, key: str, value: FrameOrSeries, format=None, index=True, append=False, complib=None, complevel: Optional[int] = None, min_itemsize: Optional[Union[int, Dict[str, int]]] = None, nan_rep=None, data_columns: Optional[List[str]] = None, encoding=None, errors: str = "strict", track_times: bool = True, ): """ Store object in HDFStore. Parameters ---------- key : str value : {Series, DataFrame} format : 'fixed(f)|table(t)', default is 'fixed' Format to use when storing object in HDFStore. Value can be one of: ``'fixed'`` Fixed format. Fast writing/reading. Not-appendable, nor searchable. ``'table'`` Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data. append : bool, default False This will force Table format, append the input data to the existing. data_columns : list, default None List of columns to create as data columns, or True to use all columns. See `here `__. encoding : str, default None Provide an encoding for strings. dropna : bool, default False, do not write an ALL nan row to The store settable by the option 'io.hdf.dropna_table'. track_times : bool, default True Parameter is propagated to 'create_table' method of 'PyTables'. If set to False it enables to have the same h5 files (same hashes) independent on creation time. .. versionadded:: 1.1.0 """ if format is None: format = get_option("io.hdf.default_format") or "fixed" format = self._validate_format(format) self._write_to_group( key, value, format=format, index=index, append=append, complib=complib, complevel=complevel, min_itemsize=min_itemsize, nan_rep=nan_rep, data_columns=data_columns, encoding=encoding, errors=errors, track_times=track_times, ) def remove(self, key: str, where=None, start=None, stop=None): """ Remove pandas object partially by specifying the where condition Parameters ---------- key : string Node to remove or delete rows from where : list of Term (or convertible) objects, optional start : integer (defaults to None), row number to start selection stop : integer (defaults to None), row number to stop selection Returns ------- number of rows removed (or None if not a Table) Raises ------ raises KeyError if key is not a valid store """ where = _ensure_term(where, scope_level=1) try: s = self.get_storer(key) except KeyError: # the key is not a valid store, re-raising KeyError raise except AssertionError: # surface any assertion errors for e.g. debugging raise except Exception as err: # In tests we get here with ClosedFileError, TypeError, and # _table_mod.NoSuchNodeError. TODO: Catch only these? if where is not None: raise ValueError( "trying to remove a node with a non-None where clause!" ) from err # we are actually trying to remove a node (with children) node = self.get_node(key) if node is not None: node._f_remove(recursive=True) return None # remove the node if com.all_none(where, start, stop): s.group._f_remove(recursive=True) # delete from the table else: if not s.is_table: raise ValueError( "can only remove with where on objects written as tables" ) return s.delete(where=where, start=start, stop=stop) def append( self, key: str, value: FrameOrSeries, format=None, axes=None, index=True, append=True, complib=None, complevel: Optional[int] = None, columns=None, min_itemsize: Optional[Union[int, Dict[str, int]]] = None, nan_rep=None, chunksize=None, expectedrows=None, dropna: Optional[bool] = None, data_columns: Optional[List[str]] = None, encoding=None, errors: str = "strict", ): """ Append to Table in file. Node must already exist and be Table format. Parameters ---------- key : str value : {Series, DataFrame} format : 'table' is the default Format to use when storing object in HDFStore. Value can be one of: ``'table'`` Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data. append : bool, default True Append the input data to the existing. data_columns : list of columns, or True, default None List of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See `here `__. min_itemsize : dict of columns that specify minimum str sizes nan_rep : str to use as str nan representation chunksize : size to chunk the writing expectedrows : expected TOTAL row size of this table encoding : default None, provide an encoding for str dropna : bool, default False Do not write an ALL nan row to the store settable by the option 'io.hdf.dropna_table'. Notes ----- Does *not* check if data being appended overlaps with existing data in the table, so be careful """ if columns is not None: raise TypeError( "columns is not a supported keyword in append, try data_columns" ) if dropna is None: dropna = get_option("io.hdf.dropna_table") if format is None: format = get_option("io.hdf.default_format") or "table" format = self._validate_format(format) self._write_to_group( key, value, format=format, axes=axes, index=index, append=append, complib=complib, complevel=complevel, min_itemsize=min_itemsize, nan_rep=nan_rep, chunksize=chunksize, expectedrows=expectedrows, dropna=dropna, data_columns=data_columns, encoding=encoding, errors=errors, ) def append_to_multiple( self, d: Dict, value, selector, data_columns=None, axes=None, dropna=False, **kwargs, ): """ Append to multiple tables Parameters ---------- d : a dict of table_name to table_columns, None is acceptable as the values of one node (this will get all the remaining columns) value : a pandas object selector : a string that designates the indexable table; all of its columns will be designed as data_columns, unless data_columns is passed, in which case these are used data_columns : list of columns to create as data columns, or True to use all columns dropna : if evaluates to True, drop rows from all tables if any single row in each table has all NaN. Default False. Notes ----- axes parameter is currently not accepted """ if axes is not None: raise TypeError( "axes is currently not accepted as a parameter to append_to_multiple; " "you can create the tables independently instead" ) if not isinstance(d, dict): raise ValueError( "append_to_multiple must have a dictionary specified as the " "way to split the value" ) if selector not in d: raise ValueError( "append_to_multiple requires a selector that is in passed dict" ) # figure out the splitting axis (the non_index_axis) axis = list(set(range(value.ndim)) - set(_AXES_MAP[type(value)]))[0] # figure out how to split the value remain_key = None remain_values: List = [] for k, v in d.items(): if v is None: if remain_key is not None: raise ValueError( "append_to_multiple can only have one value in d that is None" ) remain_key = k else: remain_values.extend(v) if remain_key is not None: ordered = value.axes[axis] ordd = ordered.difference(Index(remain_values)) ordd = sorted(ordered.get_indexer(ordd)) d[remain_key] = ordered.take(ordd) # data_columns if data_columns is None: data_columns = d[selector] # ensure rows are synchronized across the tables if dropna: idxs = (value[cols].dropna(how="all").index for cols in d.values()) valid_index = next(idxs) for index in idxs: valid_index = valid_index.intersection(index) value = value.loc[valid_index] min_itemsize = kwargs.pop("min_itemsize", None) # append for k, v in d.items(): dc = data_columns if k == selector else None # compute the val val = value.reindex(v, axis=axis) filtered = ( {key: value for (key, value) in min_itemsize.items() if key in v} if min_itemsize is not None else None ) self.append(k, val, data_columns=dc, min_itemsize=filtered, **kwargs) def create_table_index( self, key: str, columns=None, optlevel: Optional[int] = None, kind: Optional[str] = None, ): """ Create a pytables index on the table. Parameters ---------- key : str columns : None, bool, or listlike[str] Indicate which columns to create an index on. * False : Do not create any indexes. * True : Create indexes on all columns. * None : Create indexes on all columns. * listlike : Create indexes on the given columns. optlevel : int or None, default None Optimization level, if None, pytables defaults to 6. kind : str or None, default None Kind of index, if None, pytables defaults to "medium". Raises ------ TypeError: raises if the node is not a table """ # version requirements _tables() s = self.get_storer(key) if s is None: return if not isinstance(s, Table): raise TypeError("cannot create table index on a Fixed format store") s.create_index(columns=columns, optlevel=optlevel, kind=kind) def groups(self): """ Return a list of all the top-level nodes. Each node returned is not a pandas storage object. Returns ------- list List of objects. """ _tables() self._check_if_open() return [ g for g in self._handle.walk_groups() if ( not isinstance(g, _table_mod.link.Link) and ( getattr(g._v_attrs, "pandas_type", None) or getattr(g, "table", None) or (isinstance(g, _table_mod.table.Table) and g._v_name != "table") ) ) ] def walk(self, where="/"): """ Walk the pytables group hierarchy for pandas objects. This generator will yield the group path, subgroups and pandas object names for each group. Any non-pandas PyTables objects that are not a group will be ignored. The `where` group itself is listed first (preorder), then each of its child groups (following an alphanumerical order) is also traversed, following the same procedure. .. versionadded:: 0.24.0 Parameters ---------- where : str, default "/" Group where to start walking. Yields ------ path : str Full path to a group (without trailing '/'). groups : list Names (strings) of the groups contained in `path`. leaves : list Names (strings) of the pandas objects contained in `path`. """ _tables() self._check_if_open() for g in self._handle.walk_groups(where): if getattr(g._v_attrs, "pandas_type", None) is not None: continue groups = [] leaves = [] for child in g._v_children.values(): pandas_type = getattr(child._v_attrs, "pandas_type", None) if pandas_type is None: if isinstance(child, _table_mod.group.Group): groups.append(child._v_name) else: leaves.append(child._v_name) yield (g._v_pathname.rstrip("/"), groups, leaves) def get_node(self, key: str) -> Optional["Node"]: """ return the node with the key or None if it does not exist """ self._check_if_open() if not key.startswith("/"): key = "/" + key assert self._handle is not None assert _table_mod is not None # for mypy try: node = self._handle.get_node(self.root, key) except _table_mod.exceptions.NoSuchNodeError: return None assert isinstance(node, _table_mod.Node), type(node) return node def get_storer(self, key: str) -> Union["GenericFixed", "Table"]: """ return the storer object for a key, raise if not in the file """ group = self.get_node(key) if group is None: raise KeyError(f"No object named {key} in the file") s = self._create_storer(group) s.infer_axes() return s def copy( self, file, mode="w", propindexes: bool = True, keys=None, complib=None, complevel: Optional[int] = None, fletcher32: bool = False, overwrite=True, ): """ Copy the existing store to a new file, updating in place. Parameters ---------- propindexes: bool, default True Restore indexes in copied file. keys : list of keys to include in the copy (defaults to all) overwrite : overwrite (remove and replace) existing nodes in the new store (default is True) mode, complib, complevel, fletcher32 same as in HDFStore.__init__ Returns ------- open file handle of the new store """ new_store = HDFStore( file, mode=mode, complib=complib, complevel=complevel, fletcher32=fletcher32 ) if keys is None: keys = list(self.keys()) if not isinstance(keys, (tuple, list)): keys = [keys] for k in keys: s = self.get_storer(k) if s is not None: if k in new_store: if overwrite: new_store.remove(k) data = self.select(k) if isinstance(s, Table): index: Union[bool, List[str]] = False if propindexes: index = [a.name for a in s.axes if a.is_indexed] new_store.append( k, data, index=index, data_columns=getattr(s, "data_columns", None), encoding=s.encoding, ) else: new_store.put(k, data, encoding=s.encoding) return new_store def info(self) -> str: """ Print detailed information on the store. Returns ------- str """ path = pprint_thing(self._path) output = f"{type(self)}\nFile path: {path}\n" if self.is_open: lkeys = sorted(self.keys()) if len(lkeys): keys = [] values = [] for k in lkeys: try: s = self.get_storer(k) if s is not None: keys.append(pprint_thing(s.pathname or k)) values.append(pprint_thing(s or "invalid_HDFStore node")) except AssertionError: # surface any assertion errors for e.g. debugging raise except Exception as detail: keys.append(k) dstr = pprint_thing(detail) values.append(f"[invalid_HDFStore node: {dstr}]") output += adjoin(12, keys, values) else: output += "Empty" else: output += "File is CLOSED" return output # ------------------------------------------------------------------------ # private methods def _check_if_open(self): if not self.is_open: raise ClosedFileError(f"{self._path} file is not open!") def _validate_format(self, format: str) -> str: """ validate / deprecate formats """ # validate try: format = _FORMAT_MAP[format.lower()] except KeyError as err: raise TypeError(f"invalid HDFStore format specified [{format}]") from err return format def _create_storer( self, group, format=None, value: Optional[FrameOrSeries] = None, encoding: str = "UTF-8", errors: str = "strict", ) -> Union["GenericFixed", "Table"]: """ return a suitable class to operate """ cls: Union[Type["GenericFixed"], Type["Table"]] if value is not None and not isinstance(value, (Series, DataFrame)): raise TypeError("value must be None, Series, or DataFrame") def error(t): # return instead of raising so mypy can tell where we are raising return TypeError( f"cannot properly create the storer for: [{t}] [group->" f"{group},value->{type(value)},format->{format}" ) pt = _ensure_decoded(getattr(group._v_attrs, "pandas_type", None)) tt = _ensure_decoded(getattr(group._v_attrs, "table_type", None)) # infer the pt from the passed value if pt is None: if value is None: _tables() assert _table_mod is not None # for mypy if getattr(group, "table", None) or isinstance( group, _table_mod.table.Table ): pt = "frame_table" tt = "generic_table" else: raise TypeError( "cannot create a storer if the object is not existing " "nor a value are passed" ) else: _TYPE_MAP = {Series: "series", DataFrame: "frame"} pt = _TYPE_MAP[type(value)] # we are actually a table if format == "table": pt += "_table" # a storer node if "table" not in pt: _STORER_MAP = {"series": SeriesFixed, "frame": FrameFixed} try: cls = _STORER_MAP[pt] except KeyError as err: raise error("_STORER_MAP") from err return cls(self, group, encoding=encoding, errors=errors) # existing node (and must be a table) if tt is None: # if we are a writer, determine the tt if value is not None: if pt == "series_table": index = getattr(value, "index", None) if index is not None: if index.nlevels == 1: tt = "appendable_series" elif index.nlevels > 1: tt = "appendable_multiseries" elif pt == "frame_table": index = getattr(value, "index", None) if index is not None: if index.nlevels == 1: tt = "appendable_frame" elif index.nlevels > 1: tt = "appendable_multiframe" _TABLE_MAP = { "generic_table": GenericTable, "appendable_series": AppendableSeriesTable, "appendable_multiseries": AppendableMultiSeriesTable, "appendable_frame": AppendableFrameTable, "appendable_multiframe": AppendableMultiFrameTable, "worm": WORMTable, } try: cls = _TABLE_MAP[tt] except KeyError as err: raise error("_TABLE_MAP") from err return cls(self, group, encoding=encoding, errors=errors) def _write_to_group( self, key: str, value: FrameOrSeries, format, axes=None, index=True, append=False, complib=None, complevel: Optional[int] = None, fletcher32=None, min_itemsize: Optional[Union[int, Dict[str, int]]] = None, chunksize=None, expectedrows=None, dropna=False, nan_rep=None, data_columns=None, encoding=None, errors: str = "strict", track_times: bool = True, ): 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 # 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 if group is None: paths = key.split("/") # recursively create the groups path = "/" for p in paths: if not len(p): continue new_path = path if not path.endswith("/"): new_path += "/" new_path += p group = self.get_node(new_path) if group is None: group = self._handle.create_group(path, p) path = new_path s = self._create_storer(group, format, value, 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() class TableIterator: """ Define the iteration interface on a table Parameters ---------- store : HDFStore s : the referred storer func : the function to execute the query where : the where of the query nrows : the rows to iterate on start : the passed start value (default is None) stop : the passed stop value (default is None) iterator : bool, default False Whether to use the default iterator. chunksize : the passed chunking value (default is 100000) auto_close : bool, default False Whether to automatically close the store at the end of iteration. """ chunksize: Optional[int] store: HDFStore s: Union["GenericFixed", "Table"] def __init__( self, store: HDFStore, s: Union["GenericFixed", "Table"], func, where, nrows, start=None, stop=None, iterator: bool = False, chunksize: Optional[int] = None, auto_close: bool = False, ): self.store = store self.s = s self.func = func self.where = where # set start/stop if they are not set if we are a table if self.s.is_table: if nrows is None: nrows = 0 if start is None: start = 0 if stop is None: stop = nrows stop = min(nrows, stop) self.nrows = nrows self.start = start self.stop = stop self.coordinates = None if iterator or chunksize is not None: if chunksize is None: chunksize = 100000 self.chunksize = int(chunksize) else: self.chunksize = None self.auto_close = auto_close def __iter__(self): # iterate current = self.start while current < self.stop: stop = min(current + self.chunksize, self.stop) value = self.func(None, None, self.coordinates[current:stop]) current = stop if value is None or not len(value): continue yield value self.close() def close(self): if self.auto_close: self.store.close() def get_result(self, coordinates: bool = False): # return the actual iterator if self.chunksize is not None: if not isinstance(self.s, Table): raise TypeError("can only use an iterator or chunksize on a table") self.coordinates = self.s.read_coordinates(where=self.where) return self # if specified read via coordinates (necessary for multiple selections if coordinates: if not isinstance(self.s, Table): raise TypeError("can only read_coordinates on a table") where = self.s.read_coordinates( where=self.where, start=self.start, stop=self.stop ) else: where = self.where # directly return the result results = self.func(self.start, self.stop, where) self.close() return results class IndexCol: """ an index column description class Parameters ---------- axis : axis which I reference values : the ndarray like converted values kind : a string description of this type typ : the pytables type pos : the position in the pytables """ is_an_indexable = True is_data_indexable = True _info_fields = ["freq", "tz", "index_name"] name: str cname: str def __init__( self, name: str, values=None, kind=None, typ=None, cname: Optional[str] = None, axis=None, pos=None, freq=None, tz=None, index_name=None, ordered=None, table=None, meta=None, metadata=None, ): if not isinstance(name, str): raise ValueError("`name` must be a str.") self.values = values self.kind = kind self.typ = typ self.name = name self.cname = cname or name self.axis = axis self.pos = pos self.freq = freq self.tz = tz self.index_name = index_name self.ordered = ordered self.table = table self.meta = meta self.metadata = metadata if pos is not None: self.set_pos(pos) # These are ensured as long as the passed arguments match the # constructor annotations. assert isinstance(self.name, str) assert isinstance(self.cname, str) @property def itemsize(self) -> int: # Assumes self.typ has already been initialized return self.typ.itemsize @property def kind_attr(self) -> str: return f"{self.name}_kind" def set_pos(self, pos: int): """ set the position of this column in the Table """ self.pos = pos if pos is not None and self.typ is not None: self.typ._v_pos = pos def __repr__(self) -> str: temp = tuple( map(pprint_thing, (self.name, self.cname, self.axis, self.pos, self.kind)) ) return ",".join( ( f"{key}->{value}" for key, value in zip(["name", "cname", "axis", "pos", "kind"], temp) ) ) def __eq__(self, other: Any) -> bool: """ compare 2 col items """ return all( getattr(self, a, None) == getattr(other, a, None) for a in ["name", "cname", "axis", "pos"] ) def __ne__(self, other) -> bool: return not self.__eq__(other) @property def is_indexed(self) -> bool: """ return whether I am an indexed column """ if not hasattr(self.table, "cols"): # e.g. if infer hasn't been called yet, self.table will be None. return False return getattr(self.table.cols, self.cname).is_indexed def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str): """ Convert the data from this selection to the appropriate pandas type. """ assert isinstance(values, np.ndarray), type(values) # values is a recarray if values.dtype.fields is not None: values = values[self.cname] val_kind = _ensure_decoded(self.kind) values = _maybe_convert(values, val_kind, encoding, errors) kwargs = dict() kwargs["name"] = _ensure_decoded(self.index_name) if self.freq is not None: kwargs["freq"] = _ensure_decoded(self.freq) # making an Index instance could throw a number of different errors try: new_pd_index = Index(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 = Index(values, **kwargs) new_pd_index = _set_tz(new_pd_index, self.tz) return new_pd_index, new_pd_index def take_data(self): """ return the values""" return self.values @property def attrs(self): return self.table._v_attrs @property def description(self): return self.table.description @property def col(self): """ return my current col description """ return getattr(self.description, self.cname, None) @property def cvalues(self): """ return my cython values """ return self.values def __iter__(self): return iter(self.values) def maybe_set_size(self, min_itemsize=None): """ maybe set a string col itemsize: min_itemsize can be an integer or a dict with this columns name with an integer size """ if _ensure_decoded(self.kind) == "string": if isinstance(min_itemsize, dict): min_itemsize = min_itemsize.get(self.name) if min_itemsize is not None and self.typ.itemsize < min_itemsize: self.typ = _tables().StringCol(itemsize=min_itemsize, pos=self.pos) def validate_names(self): pass def validate_and_set(self, handler: "AppendableTable", append: bool): self.table = handler.table self.validate_col() self.validate_attr(append) self.validate_metadata(handler) self.write_metadata(handler) self.set_attr() def validate_col(self, itemsize=None): """ validate this column: return the compared against itemsize """ # validate this column for string truncation (or reset to the max size) if _ensure_decoded(self.kind) == "string": c = self.col if c is not None: if itemsize is None: itemsize = self.itemsize if c.itemsize < itemsize: raise ValueError( f"Trying to store a string with len [{itemsize}] in " f"[{self.cname}] column but\nthis column has a limit of " f"[{c.itemsize}]!\nConsider using min_itemsize to " "preset the sizes on these columns" ) return c.itemsize return None def validate_attr(self, append: bool): # check for backwards incompatibility if append: existing_kind = getattr(self.attrs, self.kind_attr, None) if existing_kind is not None and existing_kind != self.kind: raise TypeError( f"incompatible kind in col [{existing_kind} - {self.kind}]" ) def update_info(self, info): """ set/update the info for this indexable with the key/value if there is a conflict raise/warn as needed """ for key in self._info_fields: value = getattr(self, key, None) idx = info.setdefault(self.name, {}) existing_value = idx.get(key) if key in idx and value is not None and existing_value != value: # frequency/name just warn if key in ["freq", "index_name"]: ws = attribute_conflict_doc % (key, existing_value, value) warnings.warn(ws, AttributeConflictWarning, stacklevel=6) # reset idx[key] = None setattr(self, key, None) else: raise ValueError( f"invalid info for [{self.name}] for [{key}], " f"existing_value [{existing_value}] conflicts with " f"new value [{value}]" ) else: if value is not None or existing_value is not None: idx[key] = value def set_info(self, info): """ set my state from the passed info """ idx = info.get(self.name) if idx is not None: self.__dict__.update(idx) def set_attr(self): """ set the kind for this column """ setattr(self.attrs, self.kind_attr, self.kind) def validate_metadata(self, handler: "AppendableTable"): """ validate that kind=category does not change the categories """ if self.meta == "category": new_metadata = self.metadata cur_metadata = handler.read_metadata(self.cname) if ( new_metadata is not None and cur_metadata is not None and not array_equivalent(new_metadata, cur_metadata) ): raise ValueError( "cannot append a categorical with " "different categories to the existing" ) def write_metadata(self, handler: "AppendableTable"): """ set the meta data """ if self.metadata is not None: handler.write_metadata(self.cname, self.metadata) class GenericIndexCol(IndexCol): """ an index which is not represented in the data of the table """ @property def is_indexed(self) -> bool: return False def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str): """ Convert the data from this selection to the appropriate pandas type. Parameters ---------- values : np.ndarray nan_rep : str encoding : str errors : str """ assert isinstance(values, np.ndarray), type(values) values = Int64Index(np.arange(len(values))) return values, values def set_attr(self): pass class DataCol(IndexCol): """ a data holding column, by definition this is not indexable Parameters ---------- data : the actual data cname : the column name in the table to hold the data (typically values) meta : a string description of the metadata metadata : the actual metadata """ is_an_indexable = False is_data_indexable = False _info_fields = ["tz", "ordered"] def __init__( self, name: str, values=None, kind=None, typ=None, cname=None, pos=None, tz=None, ordered=None, table=None, meta=None, metadata=None, dtype=None, data=None, ): super().__init__( name=name, values=values, kind=kind, typ=typ, pos=pos, cname=cname, tz=tz, ordered=ordered, table=table, meta=meta, metadata=metadata, ) self.dtype = dtype self.data = data @property def dtype_attr(self) -> str: return f"{self.name}_dtype" @property def meta_attr(self) -> str: return f"{self.name}_meta" def __repr__(self) -> str: temp = tuple( map( pprint_thing, (self.name, self.cname, self.dtype, self.kind, self.shape) ) ) return ",".join( ( f"{key}->{value}" for key, value in zip(["name", "cname", "dtype", "kind", "shape"], temp) ) ) def __eq__(self, other: Any) -> bool: """ compare 2 col items """ return all( getattr(self, a, None) == getattr(other, a, None) for a in ["name", "cname", "dtype", "pos"] ) def set_data(self, data: ArrayLike): assert data is not None assert self.dtype is None data, dtype_name = _get_data_and_dtype_name(data) self.data = data self.dtype = dtype_name self.kind = _dtype_to_kind(dtype_name) def take_data(self): """ return the data """ return self.data @classmethod def _get_atom(cls, values: ArrayLike) -> "Col": """ Get an appropriately typed and shaped pytables.Col object for values. """ dtype = values.dtype itemsize = dtype.itemsize # type: ignore shape = values.shape if values.ndim == 1: # EA, use block shape pretending it is 2D # TODO(EA2D): not necessary with 2D EAs shape = (1, values.size) if isinstance(values, Categorical): codes = values.codes atom = cls.get_atom_data(shape, kind=codes.dtype.name) elif is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype): atom = cls.get_atom_datetime64(shape) elif is_timedelta64_dtype(dtype): atom = cls.get_atom_timedelta64(shape) elif is_complex_dtype(dtype): atom = _tables().ComplexCol(itemsize=itemsize, shape=shape[0]) elif is_string_dtype(dtype): atom = cls.get_atom_string(shape, itemsize) else: atom = cls.get_atom_data(shape, kind=dtype.name) return atom @classmethod def get_atom_string(cls, shape, itemsize): return _tables().StringCol(itemsize=itemsize, shape=shape[0]) @classmethod def get_atom_coltype(cls, kind: str) -> Type["Col"]: """ return the PyTables column class for this column """ if kind.startswith("uint"): k4 = kind[4:] col_name = f"UInt{k4}Col" elif kind.startswith("period"): # we store as integer col_name = "Int64Col" else: kcap = kind.capitalize() col_name = f"{kcap}Col" return getattr(_tables(), col_name) @classmethod def get_atom_data(cls, shape, kind: str) -> "Col": return cls.get_atom_coltype(kind=kind)(shape=shape[0]) @classmethod def get_atom_datetime64(cls, shape): return _tables().Int64Col(shape=shape[0]) @classmethod def get_atom_timedelta64(cls, shape): return _tables().Int64Col(shape=shape[0]) @property def shape(self): return getattr(self.data, "shape", None) @property def cvalues(self): """ return my cython values """ return self.data def validate_attr(self, append): """validate that we have the same order as the existing & same dtype""" if append: existing_fields = getattr(self.attrs, self.kind_attr, None) if existing_fields is not None and existing_fields != list(self.values): raise ValueError("appended items do not match existing items in table!") existing_dtype = getattr(self.attrs, self.dtype_attr, None) if existing_dtype is not None and existing_dtype != self.dtype: raise ValueError( "appended items dtype do not match existing items dtype in table!" ) def convert(self, values: np.ndarray, nan_rep, encoding: str, errors: str): """ Convert the data from this selection to the appropriate pandas type. Parameters ---------- values : np.ndarray nan_rep : encoding : str errors : str Returns ------- index : listlike to become an Index data : ndarraylike to become a column """ assert isinstance(values, np.ndarray), type(values) # values is a recarray if values.dtype.fields is not None: values = values[self.cname] assert self.typ is not None if self.dtype is None: # Note: in tests we never have timedelta64 or datetime64, # so the _get_data_and_dtype_name may be unnecessary converted, dtype_name = _get_data_and_dtype_name(values) kind = _dtype_to_kind(dtype_name) else: converted = values dtype_name = self.dtype kind = self.kind assert isinstance(converted, np.ndarray) # for mypy # use the meta if needed meta = _ensure_decoded(self.meta) metadata = self.metadata ordered = self.ordered tz = self.tz assert dtype_name is not None # convert to the correct dtype dtype = _ensure_decoded(dtype_name) # reverse converts if dtype == "datetime64": # recreate with tz if indicated converted = _set_tz(converted, tz, coerce=True) elif dtype == "timedelta64": converted = np.asarray(converted, dtype="m8[ns]") elif dtype == "date": try: converted = np.asarray( [date.fromordinal(v) for v in converted], dtype=object ) except ValueError: converted = np.asarray( [date.fromtimestamp(v) for v in converted], dtype=object ) elif meta == "category": # we have a categorical categories = metadata codes = converted.ravel() # if we have stored a NaN in the categories # then strip it; in theory we could have BOTH # -1s in the codes and nulls :< if categories is None: # Handle case of NaN-only categorical columns in which case # the categories are an empty array; when this is stored, # pytables cannot write a zero-len array, so on readback # the categories would be None and `read_hdf()` would fail. categories = Index([], dtype=np.float64) else: mask = isna(categories) if mask.any(): categories = categories[~mask] codes[codes != -1] -= mask.astype(int).cumsum()._values converted = Categorical.from_codes( codes, categories=categories, ordered=ordered ) else: try: converted = converted.astype(dtype, copy=False) except TypeError: converted = converted.astype("O", copy=False) # convert nans / decode if _ensure_decoded(kind) == "string": converted = _unconvert_string_array( converted, nan_rep=nan_rep, encoding=encoding, errors=errors ) return self.values, converted def set_attr(self): """ set the data for this column """ setattr(self.attrs, self.kind_attr, self.values) setattr(self.attrs, self.meta_attr, self.meta) assert self.dtype is not None setattr(self.attrs, self.dtype_attr, self.dtype) class DataIndexableCol(DataCol): """ represent a data column that can be indexed """ is_data_indexable = True def validate_names(self): if not Index(self.values).is_object(): # TODO: should the message here be more specifically non-str? raise ValueError("cannot have non-object label DataIndexableCol") @classmethod def get_atom_string(cls, shape, itemsize): return _tables().StringCol(itemsize=itemsize) @classmethod def get_atom_data(cls, shape, kind: str) -> "Col": return cls.get_atom_coltype(kind=kind)() @classmethod def get_atom_datetime64(cls, shape): return _tables().Int64Col() @classmethod def get_atom_timedelta64(cls, shape): return _tables().Int64Col() class GenericDataIndexableCol(DataIndexableCol): """ represent a generic pytables data column """ pass class Fixed: """ represent an object in my store facilitate read/write of various types of objects this is an abstract base class Parameters ---------- parent : HDFStore group : Node The group node where the table resides. """ pandas_kind: str format_type: str = "fixed" # GH#30962 needed by dask obj_type: Type[Union[DataFrame, Series]] ndim: int encoding: str parent: HDFStore group: "Node" errors: str is_table = False def __init__( self, parent: HDFStore, group: "Node", encoding: str = "UTF-8", errors: str = "strict", ): assert isinstance(parent, HDFStore), type(parent) assert _table_mod is not None # needed for mypy assert isinstance(group, _table_mod.Node), type(group) self.parent = parent self.group = group self.encoding = _ensure_encoding(encoding) self.errors = errors @property def is_old_version(self) -> bool: return self.version[0] <= 0 and self.version[1] <= 10 and self.version[2] < 1 @property def version(self) -> Tuple[int, int, int]: """ compute and set our version """ version = _ensure_decoded(getattr(self.group._v_attrs, "pandas_version", None)) try: version = tuple(int(x) for x in version.split(".")) if len(version) == 2: version = version + (0,) except AttributeError: version = (0, 0, 0) return version @property def pandas_type(self): return _ensure_decoded(getattr(self.group._v_attrs, "pandas_type", None)) def __repr__(self) -> str: """ return a pretty representation of myself """ self.infer_axes() s = self.shape if s is not None: if isinstance(s, (list, tuple)): jshape = ",".join(pprint_thing(x) for x in s) s = f"[{jshape}]" return f"{self.pandas_type:12.12} (shape->{s})" return self.pandas_type def set_object_info(self): """ set my pandas type & version """ self.attrs.pandas_type = str(self.pandas_kind) self.attrs.pandas_version = str(_version) def copy(self): new_self = copy.copy(self) return new_self @property def shape(self): return self.nrows @property def pathname(self): return self.group._v_pathname @property def _handle(self): return self.parent._handle @property def _filters(self): return self.parent._filters @property def _complevel(self) -> int: return self.parent._complevel @property def _fletcher32(self) -> bool: return self.parent._fletcher32 @property def attrs(self): return self.group._v_attrs def set_attrs(self): """ set our object attributes """ pass def get_attrs(self): """ get our object attributes """ pass @property def storable(self): """ return my storable """ return self.group @property def is_exists(self) -> bool: return False @property def nrows(self): return getattr(self.storable, "nrows", None) def validate(self, other): """ validate against an existing storable """ if other is None: return return True def validate_version(self, where=None): """ are we trying to operate on an old version? """ return True def infer_axes(self): """ infer the axes of my storer return a boolean indicating if we have a valid storer or not """ s = self.storable if s is None: return False self.get_attrs() return True def read( self, where=None, columns=None, start: Optional[int] = None, stop: Optional[int] = None, ): raise NotImplementedError( "cannot read on an abstract storer: subclasses should implement" ) def write(self, **kwargs): raise NotImplementedError( "cannot write on an abstract storer: subclasses should implement" ) def delete( self, where=None, start: Optional[int] = None, stop: Optional[int] = None ): """ support fully deleting the node in its entirety (only) - where specification must be None """ if com.all_none(where, start, stop): self._handle.remove_node(self.group, recursive=True) return None raise TypeError("cannot delete on an abstract storer") class GenericFixed(Fixed): """ a generified fixed version """ _index_type_map = {DatetimeIndex: "datetime", PeriodIndex: "period"} _reverse_index_map = {v: k for k, v in _index_type_map.items()} attributes: List[str] = [] # indexer helpers def _class_to_alias(self, cls) -> str: return self._index_type_map.get(cls, "") def _alias_to_class(self, alias): if isinstance(alias, type): # pragma: no cover # compat: for a short period of time master stored types return alias return self._reverse_index_map.get(alias, Index) def _get_index_factory(self, klass): if klass == DatetimeIndex: def f(values, freq=None, tz=None): # data are already in UTC, localize and convert if tz present dta = DatetimeArray._simple_new(values.values, freq=freq) result = DatetimeIndex._simple_new(dta, name=None) if tz is not None: result = result.tz_localize("UTC").tz_convert(tz) return result return f elif klass == PeriodIndex: def f(values, freq=None, tz=None): parr = PeriodArray._simple_new(values, freq=freq) return PeriodIndex._simple_new(parr, name=None) return f return klass def validate_read(self, columns, where): """ raise if any keywords are passed which are not-None """ if columns is not None: raise TypeError( "cannot pass a column specification when reading " "a Fixed format store. this store must be selected in its entirety" ) if where is not None: raise TypeError( "cannot pass a where specification when reading " "from a Fixed format store. this store must be selected in its entirety" ) @property def is_exists(self) -> bool: return True def set_attrs(self): """ set our object attributes """ self.attrs.encoding = self.encoding self.attrs.errors = self.errors def get_attrs(self): """ retrieve our attributes """ self.encoding = _ensure_encoding(getattr(self.attrs, "encoding", None)) self.errors = _ensure_decoded(getattr(self.attrs, "errors", "strict")) for n in self.attributes: setattr(self, n, _ensure_decoded(getattr(self.attrs, n, None))) def write(self, obj, **kwargs): self.set_attrs() def read_array( self, key: str, start: Optional[int] = None, stop: Optional[int] = None ): """ read an array for the specified node (off of group """ import tables node = getattr(self.group, key) attrs = node._v_attrs transposed = getattr(attrs, "transposed", False) if isinstance(node, tables.VLArray): ret = node[0][start:stop] else: dtype = _ensure_decoded(getattr(attrs, "value_type", None)) shape = getattr(attrs, "shape", None) if shape is not None: # length 0 axis ret = np.empty(shape, dtype=dtype) else: ret = node[start:stop] if dtype == "datetime64": # reconstruct a timezone if indicated tz = getattr(attrs, "tz", None) ret = _set_tz(ret, tz, coerce=True) elif dtype == "timedelta64": ret = np.asarray(ret, dtype="m8[ns]") if transposed: return ret.T else: return ret def read_index( self, key: str, start: Optional[int] = None, stop: Optional[int] = None ) -> Index: variety = _ensure_decoded(getattr(self.attrs, f"{key}_variety")) if variety == "multi": return self.read_multi_index(key, start=start, stop=stop) elif variety == "regular": node = getattr(self.group, key) index = self.read_index_node(node, start=start, stop=stop) return index else: # pragma: no cover raise TypeError(f"unrecognized index variety: {variety}") def write_index(self, key: str, index: Index): if isinstance(index, MultiIndex): setattr(self.attrs, f"{key}_variety", "multi") self.write_multi_index(key, index) else: setattr(self.attrs, f"{key}_variety", "regular") converted = _convert_index("index", index, self.encoding, self.errors) self.write_array(key, converted.values) node = getattr(self.group, key) node._v_attrs.kind = converted.kind node._v_attrs.name = index.name if isinstance(index, (DatetimeIndex, PeriodIndex)): node._v_attrs.index_class = self._class_to_alias(type(index)) if isinstance(index, (DatetimeIndex, PeriodIndex, TimedeltaIndex)): node._v_attrs.freq = index.freq if isinstance(index, DatetimeIndex) and index.tz is not None: node._v_attrs.tz = _get_tz(index.tz) def write_multi_index(self, key: str, index: MultiIndex): setattr(self.attrs, f"{key}_nlevels", index.nlevels) for i, (lev, level_codes, name) in enumerate( zip(index.levels, index.codes, index.names) ): # write the level if is_extension_array_dtype(lev): raise NotImplementedError( "Saving a MultiIndex with an extension dtype is not supported." ) level_key = f"{key}_level{i}" conv_level = _convert_index(level_key, lev, self.encoding, self.errors) self.write_array(level_key, conv_level.values) node = getattr(self.group, level_key) node._v_attrs.kind = conv_level.kind node._v_attrs.name = name # write the name setattr(node._v_attrs, f"{key}_name{name}", name) # write the labels label_key = f"{key}_label{i}" self.write_array(label_key, level_codes) def read_multi_index( self, key: str, start: Optional[int] = None, stop: Optional[int] = None ) -> MultiIndex: nlevels = getattr(self.attrs, f"{key}_nlevels") levels = [] codes = [] names: List[Label] = [] for i in range(nlevels): level_key = f"{key}_level{i}" node = getattr(self.group, level_key) lev = self.read_index_node(node, start=start, stop=stop) levels.append(lev) names.append(lev.name) label_key = f"{key}_label{i}" level_codes = self.read_array(label_key, start=start, stop=stop) codes.append(level_codes) return MultiIndex( levels=levels, codes=codes, names=names, verify_integrity=True ) def read_index_node( self, node: "Node", start: Optional[int] = None, stop: Optional[int] = None ) -> Index: data = node[start:stop] # If the index was an empty array write_array_empty() will # have written a sentinel. Here we replace it with the original. if "shape" in node._v_attrs and np.prod(node._v_attrs.shape) == 0: data = np.empty(node._v_attrs.shape, dtype=node._v_attrs.value_type,) kind = _ensure_decoded(node._v_attrs.kind) name = None if "name" in node._v_attrs: name = _ensure_str(node._v_attrs.name) name = _ensure_decoded(name) index_class = self._alias_to_class( _ensure_decoded(getattr(node._v_attrs, "index_class", "")) ) factory = self._get_index_factory(index_class) kwargs = {} if "freq" in node._v_attrs: kwargs["freq"] = node._v_attrs["freq"] if "tz" in node._v_attrs: if isinstance(node._v_attrs["tz"], bytes): # created by python2 kwargs["tz"] = node._v_attrs["tz"].decode("utf-8") else: # created by python3 kwargs["tz"] = node._v_attrs["tz"] if kind == "date": index = factory( _unconvert_index( data, kind, encoding=self.encoding, errors=self.errors ), dtype=object, **kwargs, ) else: index = factory( _unconvert_index( data, kind, encoding=self.encoding, errors=self.errors ), **kwargs, ) index.name = name return index def write_array_empty(self, key: str, value: ArrayLike): """ write a 0-len array """ # ugly hack for length 0 axes arr = np.empty((1,) * value.ndim) self._handle.create_array(self.group, key, arr) node = getattr(self.group, key) node._v_attrs.value_type = str(value.dtype) node._v_attrs.shape = value.shape def write_array(self, key: str, value: ArrayLike, items: Optional[Index] = None): # TODO: we only have one test that gets here, the only EA # that gets passed is DatetimeArray, and we never have # both self._filters and EA assert isinstance(value, (np.ndarray, ABCExtensionArray)), type(value) if key in self.group: self._handle.remove_node(self.group, key) # Transform needed to interface with pytables row/col notation empty_array = value.size == 0 transposed = False if is_categorical_dtype(value.dtype): raise NotImplementedError( "Cannot store a category dtype in a HDF5 dataset that uses format=" '"fixed". Use format="table".' ) if not empty_array: if hasattr(value, "T"): # ExtensionArrays (1d) may not have transpose. value = value.T transposed = True atom = None if self._filters is not None: try: # get the atom for this datatype atom = _tables().Atom.from_dtype(value.dtype) except ValueError: pass if atom is not None: # We only get here if self._filters is non-None and # the Atom.from_dtype call succeeded # create an empty chunked array and fill it from value if not empty_array: ca = self._handle.create_carray( self.group, key, atom, value.shape, filters=self._filters ) ca[:] = value else: self.write_array_empty(key, value) elif value.dtype.type == np.object_: # infer the type, warn if we have a non-string type here (for # performance) inferred_type = lib.infer_dtype(value, skipna=False) if empty_array: pass elif inferred_type == "string": pass else: ws = performance_doc % (inferred_type, key, items) warnings.warn(ws, PerformanceWarning, stacklevel=7) vlarr = self._handle.create_vlarray(self.group, key, _tables().ObjectAtom()) vlarr.append(value) elif empty_array: self.write_array_empty(key, value) elif is_datetime64_dtype(value.dtype): self._handle.create_array(self.group, key, value.view("i8")) getattr(self.group, key)._v_attrs.value_type = "datetime64" elif is_datetime64tz_dtype(value.dtype): # store as UTC # with a zone self._handle.create_array(self.group, key, value.asi8) node = getattr(self.group, key) node._v_attrs.tz = _get_tz(value.tz) node._v_attrs.value_type = "datetime64" elif is_timedelta64_dtype(value.dtype): self._handle.create_array(self.group, key, value.view("i8")) getattr(self.group, key)._v_attrs.value_type = "timedelta64" else: self._handle.create_array(self.group, key, value) getattr(self.group, key)._v_attrs.transposed = transposed class SeriesFixed(GenericFixed): pandas_kind = "series" attributes = ["name"] name: Label @property def shape(self): try: return (len(self.group.values),) except (TypeError, AttributeError): return None def read( self, where=None, columns=None, start: Optional[int] = None, stop: Optional[int] = None, ): self.validate_read(columns, where) index = self.read_index("index", start=start, stop=stop) values = self.read_array("values", start=start, stop=stop) return Series(values, index=index, name=self.name) def write(self, obj, **kwargs): super().write(obj, **kwargs) self.write_index("index", obj.index) self.write_array("values", obj.values) self.attrs.name = obj.name class BlockManagerFixed(GenericFixed): attributes = ["ndim", "nblocks"] nblocks: int @property def shape(self): try: ndim = self.ndim # items items = 0 for i in range(self.nblocks): node = getattr(self.group, f"block{i}_items") shape = getattr(node, "shape", None) if shape is not None: items += shape[0] # data shape node = self.group.block0_values shape = getattr(node, "shape", None) if shape is not None: shape = list(shape[0 : (ndim - 1)]) else: shape = [] shape.append(items) return shape except AttributeError: return None def read( self, where=None, columns=None, start: Optional[int] = None, stop: Optional[int] = None, ): # start, stop applied to rows, so 0th axis only self.validate_read(columns, where) select_axis = self.obj_type()._get_block_manager_axis(0) axes = [] for i in range(self.ndim): _start, _stop = (start, stop) if i == select_axis else (None, None) ax = self.read_index(f"axis{i}", start=_start, stop=_stop) axes.append(ax) items = axes[0] dfs = [] for i in range(self.nblocks): blk_items = self.read_index(f"block{i}_items") values = self.read_array(f"block{i}_values", start=_start, stop=_stop) columns = items[items.get_indexer(blk_items)] df = DataFrame(values.T, columns=columns, index=axes[1]) dfs.append(df) if len(dfs) > 0: out = concat(dfs, axis=1) out = out.reindex(columns=items, copy=False) return out return DataFrame(columns=axes[0], index=axes[1]) def write(self, obj, **kwargs): super().write(obj, **kwargs) data = obj._mgr if not data.is_consolidated(): data = data.consolidate() self.attrs.ndim = data.ndim for i, ax in enumerate(data.axes): if i == 0 and (not ax.is_unique): raise ValueError("Columns index has to be unique for fixed format") self.write_index(f"axis{i}", ax) # Supporting mixed-type DataFrame objects...nontrivial self.attrs.nblocks = len(data.blocks) for i, blk in enumerate(data.blocks): # I have no idea why, but writing values before items fixed #2299 blk_items = data.items.take(blk.mgr_locs) self.write_array(f"block{i}_values", blk.values, items=blk_items) self.write_index(f"block{i}_items", blk_items) class FrameFixed(BlockManagerFixed): pandas_kind = "frame" obj_type = DataFrame class Table(Fixed): """ represent a table: facilitate read/write of various types of tables Attrs in Table Node ------------------- These are attributes that are store in the main table node, they are necessary to recreate these tables when read back in. index_axes : a list of tuples of the (original indexing axis and index column) non_index_axes: a list of tuples of the (original index axis and columns on a non-indexing axis) values_axes : a list of the columns which comprise the data of this table data_columns : a list of the columns that we are allowing indexing (these become single columns in values_axes), or True to force all columns nan_rep : the string to use for nan representations for string objects levels : the names of levels metadata : the names of the metadata columns """ pandas_kind = "wide_table" format_type: str = "table" # GH#30962 needed by dask table_type: str levels = 1 is_table = True index_axes: List[IndexCol] non_index_axes: List[Tuple[int, Any]] values_axes: List[DataCol] data_columns: List metadata: List info: Dict def __init__( self, parent: HDFStore, group: "Node", encoding=None, errors: str = "strict", index_axes=None, non_index_axes=None, values_axes=None, data_columns=None, info=None, nan_rep=None, ): super().__init__(parent, group, encoding=encoding, errors=errors) self.index_axes = index_axes or [] self.non_index_axes = non_index_axes or [] self.values_axes = values_axes or [] self.data_columns = data_columns or [] self.info = info or dict() self.nan_rep = nan_rep @property def table_type_short(self) -> str: return self.table_type.split("_")[0] def __repr__(self) -> str: """ return a pretty representation of myself """ self.infer_axes() jdc = ",".join(self.data_columns) if len(self.data_columns) else "" dc = f",dc->[{jdc}]" ver = "" if self.is_old_version: jver = ".".join(str(x) for x in self.version) ver = f"[{jver}]" jindex_axes = ",".join(a.name for a in self.index_axes) return ( f"{self.pandas_type:12.12}{ver} " f"(typ->{self.table_type_short},nrows->{self.nrows}," f"ncols->{self.ncols},indexers->[{jindex_axes}]{dc})" ) def __getitem__(self, c: str): """ return the axis for c """ for a in self.axes: if c == a.name: return a return None def validate(self, other): """ validate against an existing table """ if other is None: return if other.table_type != self.table_type: raise TypeError( "incompatible table_type with existing " f"[{other.table_type} - {self.table_type}]" ) for c in ["index_axes", "non_index_axes", "values_axes"]: sv = getattr(self, c, None) ov = getattr(other, c, None) if sv != ov: # show the error for the specific axes for i, sax in enumerate(sv): oax = ov[i] if sax != oax: raise ValueError( f"invalid combination of [{c}] on appending data " f"[{sax}] vs current table [{oax}]" ) # should never get here raise Exception( f"invalid combination of [{c}] on appending data [{sv}] vs " f"current table [{ov}]" ) @property def is_multi_index(self) -> bool: """the levels attribute is 1 or a list in the case of a multi-index""" return isinstance(self.levels, list) def validate_multiindex(self, obj): """ validate that we can store the multi-index; reset and return the new object """ levels = [ l if l is not None else f"level_{i}" for i, l in enumerate(obj.index.names) ] try: return obj.reset_index(), levels except ValueError as err: raise ValueError( "duplicate names/columns in the multi-index when storing as a table" ) from err @property def nrows_expected(self) -> int: """ based on our axes, compute the expected nrows """ return np.prod([i.cvalues.shape[0] for i in self.index_axes]) @property def is_exists(self) -> bool: """ has this table been created """ return "table" in self.group @property def storable(self): return getattr(self.group, "table", None) @property def table(self): """ return the table group (this is my storable) """ return self.storable @property def dtype(self): return self.table.dtype @property def description(self): return self.table.description @property def axes(self): return itertools.chain(self.index_axes, self.values_axes) @property def ncols(self) -> int: """ the number of total columns in the values axes """ return sum(len(a.values) for a in self.values_axes) @property def is_transposed(self) -> bool: return False @property def data_orientation(self): """return a tuple of my permutated axes, non_indexable at the front""" return tuple( itertools.chain( [int(a[0]) for a in self.non_index_axes], [int(a.axis) for a in self.index_axes], ) ) def queryables(self) -> Dict[str, Any]: """ return a dict of the kinds allowable columns for this object """ # mypy doesn't recognize DataFrame._AXIS_NAMES, so we re-write it here axis_names = {0: "index", 1: "columns"} # compute the values_axes queryables d1 = [(a.cname, a) for a in self.index_axes] d2 = [(axis_names[axis], None) for axis, values in self.non_index_axes] d3 = [ (v.cname, v) for v in self.values_axes if v.name in set(self.data_columns) ] return dict(d1 + d2 + d3) # type: ignore # error: List comprehension has incompatible type # List[Tuple[Any, None]]; expected List[Tuple[str, IndexCol]] def index_cols(self): """ return a list of my index cols """ # Note: each `i.cname` below is assured to be a str. return [(i.axis, i.cname) for i in self.index_axes] def values_cols(self) -> List[str]: """ return a list of my values cols """ return [i.cname for i in self.values_axes] def _get_metadata_path(self, key: str) -> str: """ return the metadata pathname for this key """ group = self.group._v_pathname return f"{group}/meta/{key}/meta" def write_metadata(self, key: str, values: np.ndarray): """ Write out a metadata array to the key as a fixed-format Series. Parameters ---------- key : str values : ndarray """ values = Series(values) self.parent.put( self._get_metadata_path(key), values, format="table", encoding=self.encoding, errors=self.errors, nan_rep=self.nan_rep, ) def read_metadata(self, key: str): """ return the meta data array for this key """ if getattr(getattr(self.group, "meta", None), key, None) is not None: return self.parent.select(self._get_metadata_path(key)) return None def set_attrs(self): """ set our table type & indexables """ self.attrs.table_type = str(self.table_type) self.attrs.index_cols = self.index_cols() self.attrs.values_cols = self.values_cols() self.attrs.non_index_axes = self.non_index_axes self.attrs.data_columns = self.data_columns self.attrs.nan_rep = self.nan_rep self.attrs.encoding = self.encoding self.attrs.errors = self.errors self.attrs.levels = self.levels self.attrs.info = self.info def get_attrs(self): """ retrieve our attributes """ self.non_index_axes = getattr(self.attrs, "non_index_axes", None) or [] self.data_columns = getattr(self.attrs, "data_columns", None) or [] self.info = getattr(self.attrs, "info", None) or dict() self.nan_rep = getattr(self.attrs, "nan_rep", None) self.encoding = _ensure_encoding(getattr(self.attrs, "encoding", None)) self.errors = _ensure_decoded(getattr(self.attrs, "errors", "strict")) self.levels = getattr(self.attrs, "levels", None) or [] self.index_axes = [a for a in self.indexables if a.is_an_indexable] self.values_axes = [a for a in self.indexables if not a.is_an_indexable] def validate_version(self, where=None): """ are we trying to operate on an old version? """ if where is not None: if self.version[0] <= 0 and self.version[1] <= 10 and self.version[2] < 1: ws = incompatibility_doc % ".".join([str(x) for x in self.version]) warnings.warn(ws, IncompatibilityWarning) def validate_min_itemsize(self, min_itemsize): """ validate the min_itemsize doesn't contain items that are not in the axes this needs data_columns to be defined """ if min_itemsize is None: return if not isinstance(min_itemsize, dict): return q = self.queryables() for k, v in min_itemsize.items(): # ok, apply generally if k == "values": continue if k not in q: raise ValueError( f"min_itemsize has the key [{k}] which is not an axis or " "data_column" ) @cache_readonly def indexables(self): """ create/cache the indexables if they don't exist """ _indexables = [] desc = self.description table_attrs = self.table.attrs # Note: each of the `name` kwargs below are str, ensured # by the definition in index_cols. # index columns for i, (axis, name) in enumerate(self.attrs.index_cols): atom = getattr(desc, name) md = self.read_metadata(name) meta = "category" if md is not None else None kind_attr = f"{name}_kind" kind = getattr(table_attrs, kind_attr, None) index_col = IndexCol( name=name, axis=axis, pos=i, kind=kind, typ=atom, table=self.table, meta=meta, metadata=md, ) _indexables.append(index_col) # values columns dc = set(self.data_columns) base_pos = len(_indexables) def f(i, c): assert isinstance(c, str) klass = DataCol if c in dc: klass = DataIndexableCol atom = getattr(desc, c) adj_name = _maybe_adjust_name(c, self.version) # TODO: why kind_attr here? values = getattr(table_attrs, f"{adj_name}_kind", None) dtype = getattr(table_attrs, f"{adj_name}_dtype", None) kind = _dtype_to_kind(dtype) md = self.read_metadata(c) # TODO: figure out why these two versions of `meta` dont always match. # meta = "category" if md is not None else None meta = getattr(table_attrs, f"{adj_name}_meta", None) obj = klass( name=adj_name, cname=c, values=values, kind=kind, pos=base_pos + i, typ=atom, table=self.table, meta=meta, metadata=md, dtype=dtype, ) return obj # Note: the definition of `values_cols` ensures that each # `c` below is a str. _indexables.extend([f(i, c) for i, c in enumerate(self.attrs.values_cols)]) return _indexables def create_index(self, columns=None, optlevel=None, kind: Optional[str] = None): """ Create a pytables index on the specified columns. Parameters ---------- columns : None, bool, or listlike[str] Indicate which columns to create an index on. * False : Do not create any indexes. * True : Create indexes on all columns. * None : Create indexes on all columns. * listlike : Create indexes on the given columns. optlevel : int or None, default None Optimization level, if None, pytables defaults to 6. kind : str or None, default None Kind of index, if None, pytables defaults to "medium". Raises ------ TypeError if trying to create an index on a complex-type column. Notes ----- Cannot index Time64Col or ComplexCol. Pytables must be >= 3.0. """ if not self.infer_axes(): return if columns is False: return # index all indexables and data_columns if columns is None or columns is True: columns = [a.cname for a in self.axes if a.is_data_indexable] if not isinstance(columns, (tuple, list)): columns = [columns] kw = dict() if optlevel is not None: kw["optlevel"] = optlevel if kind is not None: kw["kind"] = kind table = self.table for c in columns: v = getattr(table.cols, c, None) if v is not None: # remove the index if the kind/optlevel have changed if v.is_indexed: index = v.index cur_optlevel = index.optlevel cur_kind = index.kind if kind is not None and cur_kind != kind: v.remove_index() else: kw["kind"] = cur_kind if optlevel is not None and cur_optlevel != optlevel: v.remove_index() else: kw["optlevel"] = cur_optlevel # create the index if not v.is_indexed: if v.type.startswith("complex"): raise TypeError( "Columns containing complex values can be stored but " "cannot be indexed when using table format. Either use " "fixed format, set index=False, or do not include " "the columns containing complex values to " "data_columns when initializing the table." ) v.create_index(**kw) elif c in self.non_index_axes[0][1]: # GH 28156 raise AttributeError( f"column {c} is not a data_column.\n" f"In order to read column {c} you must reload the dataframe \n" f"into HDFStore and include {c} with the data_columns argument." ) def _read_axes( self, where, start: Optional[int] = None, stop: Optional[int] = None ) -> List[Tuple[ArrayLike, ArrayLike]]: """ Create the axes sniffed from the table. Parameters ---------- where : ??? start : int or None, default None stop : int or None, default None Returns ------- List[Tuple[index_values, column_values]] """ # create the selection selection = Selection(self, where=where, start=start, stop=stop) values = selection.select() results = [] # convert the data for a in self.axes: a.set_info(self.info) res = a.convert( values, nan_rep=self.nan_rep, encoding=self.encoding, errors=self.errors, ) results.append(res) return results @classmethod def get_object(cls, obj, transposed: bool): """ return the data for this obj """ return obj def validate_data_columns(self, data_columns, min_itemsize, non_index_axes): """ take the input data_columns and min_itemize and create a data columns spec """ if not len(non_index_axes): return [] axis, axis_labels = non_index_axes[0] info = self.info.get(axis, dict()) if info.get("type") == "MultiIndex" and data_columns: raise ValueError( f"cannot use a multi-index on axis [{axis}] with " f"data_columns {data_columns}" ) # evaluate the passed data_columns, True == use all columns # take only valid axis labels if data_columns is True: data_columns = list(axis_labels) elif data_columns is None: data_columns = [] # if min_itemsize is a dict, add the keys (exclude 'values') if isinstance(min_itemsize, dict): existing_data_columns = set(data_columns) data_columns = list(data_columns) # ensure we do not modify data_columns.extend( [ k for k in min_itemsize.keys() if k != "values" and k not in existing_data_columns ] ) # return valid columns in the order of our axis return [c for c in data_columns if c in axis_labels] def _create_axes( self, axes, obj: DataFrame, validate: bool = True, nan_rep=None, data_columns=None, min_itemsize=None, ): """ Create and return the axes. Parameters ---------- axes: list or None The names or numbers of the axes to create. obj : DataFrame The object to create axes on. validate: bool, default True Whether to validate the obj against an existing object already written. nan_rep : A value to use for string column nan_rep. data_columns : List[str], True, or None, default None Specify the columns that we want to create to allow indexing on. * True : Use all available columns. * None : Use no columns. * List[str] : Use the specified columns. min_itemsize: Dict[str, int] or None, default None The min itemsize for a column in bytes. """ if not isinstance(obj, DataFrame): group = self.group._v_name raise TypeError( f"cannot properly create the storer for: [group->{group}," f"value->{type(obj)}]" ) # set the default axes if needed if axes is None: axes = [0] # map axes to numbers axes = [obj._get_axis_number(a) for a in axes] # do we have an existing table (if so, use its axes & data_columns) if self.infer_axes(): table_exists = True axes = [a.axis for a in self.index_axes] data_columns = list(self.data_columns) nan_rep = self.nan_rep # TODO: do we always have validate=True here? else: table_exists = False new_info = self.info assert self.ndim == 2 # with next check, we must have len(axes) == 1 # currently support on ndim-1 axes if len(axes) != self.ndim - 1: raise ValueError( "currently only support ndim-1 indexers in an AppendableTable" ) # create according to the new data new_non_index_axes: List = [] # nan_representation if nan_rep is None: nan_rep = "nan" # We construct the non-index-axis first, since that alters new_info idx = [x for x in [0, 1] if x not in axes][0] a = obj.axes[idx] # we might be able to change the axes on the appending data if necessary append_axis = list(a) if table_exists: indexer = len(new_non_index_axes) # i.e. 0 exist_axis = self.non_index_axes[indexer][1] if not array_equivalent(np.array(append_axis), np.array(exist_axis)): # ahah! -> reindex if array_equivalent( np.array(sorted(append_axis)), np.array(sorted(exist_axis)) ): append_axis = exist_axis # the non_index_axes info info = new_info.setdefault(idx, {}) info["names"] = list(a.names) info["type"] = type(a).__name__ new_non_index_axes.append((idx, append_axis)) # Now we can construct our new index axis idx = axes[0] a = obj.axes[idx] axis_name = obj._get_axis_name(idx) new_index = _convert_index(axis_name, a, self.encoding, self.errors) new_index.axis = idx # Because we are always 2D, there is only one new_index, so # we know it will have pos=0 new_index.set_pos(0) new_index.update_info(new_info) new_index.maybe_set_size(min_itemsize) # check for column conflicts new_index_axes = [new_index] j = len(new_index_axes) # i.e. 1 assert j == 1 # reindex by our non_index_axes & compute data_columns assert len(new_non_index_axes) == 1 for a in new_non_index_axes: obj = _reindex_axis(obj, a[0], a[1]) def get_blk_items(mgr, blocks): return [mgr.items.take(blk.mgr_locs) for blk in blocks] 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 ) block_obj = self.get_object(obj, transposed)._consolidate() blocks, blk_items = self._get_blocks_and_items( block_obj, table_exists, new_non_index_axes, self.values_axes, data_columns ) # add my values vaxes = [] for i, (b, b_items) in enumerate(zip(blocks, blk_items)): # shape of the data column are the indexable axes klass = DataCol name = None # we have a data_column if data_columns and len(b_items) == 1 and b_items[0] in data_columns: klass = DataIndexableCol name = b_items[0] if not (name is None or isinstance(name, str)): # TODO: should the message here be more specifically non-str? raise ValueError("cannot have non-object label DataIndexableCol") # make sure that we match up the existing columns # if we have an existing table existing_col: Optional[DataCol] if table_exists and validate: try: existing_col = self.values_axes[i] except (IndexError, KeyError) as err: raise ValueError( f"Incompatible appended table [{blocks}]" f"with existing table [{self.values_axes}]" ) from err else: existing_col = None new_name = name or f"values_block_{i}" data_converted = _maybe_convert_for_string_atom( new_name, b, existing_col=existing_col, min_itemsize=min_itemsize, nan_rep=nan_rep, encoding=self.encoding, errors=self.errors, ) adj_name = _maybe_adjust_name(new_name, self.version) typ = klass._get_atom(data_converted) kind = _dtype_to_kind(data_converted.dtype.name) tz = _get_tz(data_converted.tz) if hasattr(data_converted, "tz") else None meta = metadata = ordered = None if is_categorical_dtype(data_converted.dtype): ordered = data_converted.ordered meta = "category" metadata = np.array(data_converted.categories, copy=False).ravel() data, dtype_name = _get_data_and_dtype_name(data_converted) col = klass( name=adj_name, cname=new_name, values=list(b_items), typ=typ, pos=j, kind=kind, tz=tz, ordered=ordered, meta=meta, metadata=metadata, dtype=dtype_name, data=data, ) col.update_info(new_info) vaxes.append(col) j += 1 dcs = [col.name for col in vaxes if col.is_data_indexable] new_table = type(self)( parent=self.parent, group=self.group, encoding=self.encoding, errors=self.errors, index_axes=new_index_axes, non_index_axes=new_non_index_axes, values_axes=vaxes, data_columns=dcs, info=new_info, nan_rep=nan_rep, ) if hasattr(self, "levels"): # TODO: get this into constructor, only for appropriate subclass new_table.levels = self.levels new_table.validate_min_itemsize(min_itemsize) if validate and table_exists: new_table.validate(self) return new_table @staticmethod def _get_blocks_and_items( block_obj, table_exists, new_non_index_axes, values_axes, data_columns ): # Helper to clarify non-state-altering parts of _create_axes def get_blk_items(mgr, blocks): return [mgr.items.take(blk.mgr_locs) for blk in blocks] blocks = block_obj._mgr.blocks blk_items = get_blk_items(block_obj._mgr, blocks) if len(data_columns): axis, axis_labels = new_non_index_axes[0] new_labels = Index(axis_labels).difference(Index(data_columns)) mgr = block_obj.reindex(new_labels, axis=axis)._mgr blocks = list(mgr.blocks) blk_items = get_blk_items(mgr, blocks) for c in data_columns: mgr = block_obj.reindex([c], axis=axis)._mgr blocks.extend(mgr.blocks) blk_items.extend(get_blk_items(mgr, mgr.blocks)) # reorder the blocks in the same order as the existing table if we can if table_exists: by_items = { tuple(b_items.tolist()): (b, b_items) for b, b_items in zip(blocks, blk_items) } new_blocks = [] new_blk_items = [] for ea in values_axes: items = tuple(ea.values) try: b, b_items = by_items.pop(items) new_blocks.append(b) new_blk_items.append(b_items) except (IndexError, KeyError) as err: jitems = ",".join(pprint_thing(item) for item in items) raise ValueError( f"cannot match existing table structure for [{jitems}] " "on appending data" ) from err blocks = new_blocks blk_items = new_blk_items return blocks, blk_items def process_axes(self, obj, selection: "Selection", columns=None): """ process axes filters """ # make a copy to avoid side effects if columns is not None: columns = list(columns) # make sure to include levels if we have them if columns is not None and self.is_multi_index: assert isinstance(self.levels, list) # assured by is_multi_index for n in self.levels: if n not in columns: columns.insert(0, n) # reorder by any non_index_axes & limit to the select columns for axis, labels in self.non_index_axes: obj = _reindex_axis(obj, axis, labels, columns) # apply the selection filters (but keep in the same order) if selection.filter is not None: for field, op, filt in selection.filter.format(): def process_filter(field, filt): for axis_name in obj._AXIS_ORDERS: axis_number = obj._get_axis_number(axis_name) axis_values = obj._get_axis(axis_name) assert axis_number is not None # see if the field is the name of an axis if field == axis_name: # if we have a multi-index, then need to include # the levels if self.is_multi_index: filt = filt.union(Index(self.levels)) takers = op(axis_values, filt) return obj.loc(axis=axis_number)[takers] # this might be the name of a file IN an axis elif field in axis_values: # we need to filter on this dimension values = ensure_index(getattr(obj, field).values) filt = ensure_index(filt) # hack until we support reversed dim flags if isinstance(obj, DataFrame): axis_number = 1 - axis_number takers = op(values, filt) return obj.loc(axis=axis_number)[takers] raise ValueError(f"cannot find the field [{field}] for filtering!") obj = process_filter(field, filt) return obj def create_description( self, complib, complevel: Optional[int], fletcher32: bool, expectedrows: Optional[int], ) -> Dict[str, Any]: """ create the description of the table from the axes & values """ # provided expected rows if its passed if expectedrows is None: expectedrows = max(self.nrows_expected, 10000) d = dict(name="table", expectedrows=expectedrows) # description from the axes & values d["description"] = {a.cname: a.typ for a in self.axes} if complib: if complevel is None: complevel = self._complevel or 9 filters = _tables().Filters( complevel=complevel, complib=complib, fletcher32=fletcher32 or self._fletcher32, ) d["filters"] = filters elif self._filters is not None: d["filters"] = self._filters return d def read_coordinates( self, where=None, start: Optional[int] = None, stop: Optional[int] = None, ): """ select coordinates (row numbers) from a table; return the coordinates object """ # validate the version self.validate_version(where) # infer the data kind if not self.infer_axes(): return False # create the selection selection = Selection(self, where=where, start=start, stop=stop) coords = selection.select_coords() if selection.filter is not None: for field, op, filt in selection.filter.format(): data = self.read_column( field, start=coords.min(), stop=coords.max() + 1 ) coords = coords[op(data.iloc[coords - coords.min()], filt).values] return Index(coords) def read_column( self, column: str, where=None, start: Optional[int] = None, stop: Optional[int] = None, ): """ return a single column from the table, generally only indexables are interesting """ # validate the version self.validate_version() # infer the data kind if not self.infer_axes(): return False if where is not None: raise TypeError("read_column does not currently accept a where clause") # find the axes for a in self.axes: if column == a.name: if not a.is_data_indexable: raise ValueError( f"column [{column}] can not be extracted individually; " "it is not data indexable" ) # column must be an indexable or a data column c = getattr(self.table.cols, column) a.set_info(self.info) col_values = a.convert( c[start:stop], nan_rep=self.nan_rep, encoding=self.encoding, errors=self.errors, ) return Series(_set_tz(col_values[1], a.tz), name=column) raise KeyError(f"column [{column}] not found in the table") class WORMTable(Table): """ a write-once read-many table: this format DOES NOT ALLOW appending to a table. writing is a one-time operation the data are stored in a format that allows for searching the data on disk """ table_type = "worm" def read( self, where=None, columns=None, start: Optional[int] = None, stop: Optional[int] = None, ): """ read the indices and the indexing array, calculate offset rows and return """ raise NotImplementedError("WORMTable needs to implement read") def write(self, **kwargs): """ write in a format that we can search later on (but cannot append to): write out the indices and the values using _write_array (e.g. a CArray) create an indexing table so that we can search """ raise NotImplementedError("WORMTable needs to implement write") class AppendableTable(Table): """ support the new appendable table formats """ table_type = "appendable" def write( self, obj, axes=None, append=False, complib=None, complevel=None, fletcher32=None, min_itemsize=None, chunksize=None, expectedrows=None, dropna=False, nan_rep=None, data_columns=None, track_times=True, ): if not append and self.is_exists: self._handle.remove_node(self.group, "table") # create the axes table = self._create_axes( axes=axes, obj=obj, validate=append, min_itemsize=min_itemsize, nan_rep=nan_rep, data_columns=data_columns, ) for a in table.axes: a.validate_names() if not table.is_exists: # create the table options = table.create_description( complib=complib, complevel=complevel, fletcher32=fletcher32, expectedrows=expectedrows, ) # set the table attributes table.set_attrs() options["track_times"] = track_times # create the table table._handle.create_table(table.group, **options) # update my info table.attrs.info = table.info # validate the axes and set the kinds for a in table.axes: a.validate_and_set(table, append) # add the rows table.write_data(chunksize, dropna=dropna) def write_data(self, chunksize: Optional[int], dropna: bool = False): """ we form the data into a 2-d including indexes,values,mask write chunk-by-chunk """ names = self.dtype.names nrows = self.nrows_expected # if dropna==True, then drop ALL nan rows masks = [] if dropna: for a in self.values_axes: # figure the mask: only do if we can successfully process this # column, otherwise ignore the mask mask = isna(a.data).all(axis=0) if isinstance(mask, np.ndarray): masks.append(mask.astype("u1", copy=False)) # consolidate masks if len(masks): mask = masks[0] for m in masks[1:]: mask = mask & m mask = mask.ravel() else: mask = None # broadcast the indexes if needed indexes = [a.cvalues for a in self.index_axes] nindexes = len(indexes) assert nindexes == 1, nindexes # ensures we dont need to broadcast # transpose the values so first dimension is last # reshape the values if needed values = [a.take_data() for a in self.values_axes] values = [v.transpose(np.roll(np.arange(v.ndim), v.ndim - 1)) for v in values] bvalues = [] for i, v in enumerate(values): new_shape = (nrows,) + self.dtype[names[nindexes + i]].shape bvalues.append(values[i].reshape(new_shape)) # write the chunks if chunksize is None: chunksize = 100000 rows = np.empty(min(chunksize, nrows), dtype=self.dtype) chunks = nrows // chunksize + 1 for i in range(chunks): start_i = i * chunksize end_i = min((i + 1) * chunksize, nrows) if start_i >= end_i: break self.write_data_chunk( rows, indexes=[a[start_i:end_i] for a in indexes], mask=mask[start_i:end_i] if mask is not None else None, values=[v[start_i:end_i] for v in bvalues], ) def write_data_chunk( self, rows: np.ndarray, indexes: List[np.ndarray], mask: Optional[np.ndarray], values: List[np.ndarray], ): """ Parameters ---------- rows : an empty memory space where we are putting the chunk indexes : an array of the indexes mask : an array of the masks values : an array of the values """ # 0 len for v in values: if not np.prod(v.shape): return nrows = indexes[0].shape[0] if nrows != len(rows): rows = np.empty(nrows, dtype=self.dtype) names = self.dtype.names nindexes = len(indexes) # indexes for i, idx in enumerate(indexes): rows[names[i]] = idx # values for i, v in enumerate(values): rows[names[i + nindexes]] = v # mask if mask is not None: m = ~mask.ravel().astype(bool, copy=False) if not m.all(): rows = rows[m] if len(rows): self.table.append(rows) self.table.flush() def delete( self, where=None, start: Optional[int] = None, stop: Optional[int] = None, ): # delete all rows (and return the nrows) if where is None or not len(where): if start is None and stop is None: nrows = self.nrows self._handle.remove_node(self.group, recursive=True) else: # pytables<3.0 would remove a single row with stop=None if stop is None: stop = self.nrows nrows = self.table.remove_rows(start=start, stop=stop) self.table.flush() return nrows # infer the data kind if not self.infer_axes(): return None # create the selection table = self.table selection = Selection(self, where, start=start, stop=stop) values = selection.select_coords() # delete the rows in reverse order sorted_series = Series(values).sort_values() ln = len(sorted_series) if ln: # construct groups of consecutive rows diff = sorted_series.diff() groups = list(diff[diff > 1].index) # 1 group if not len(groups): groups = [0] # final element if groups[-1] != ln: groups.append(ln) # initial element if groups[0] != 0: groups.insert(0, 0) # we must remove in reverse order! pg = groups.pop() for g in reversed(groups): rows = sorted_series.take(range(g, pg)) table.remove_rows( start=rows[rows.index[0]], stop=rows[rows.index[-1]] + 1 ) pg = g self.table.flush() # return the number of rows removed return ln class AppendableFrameTable(AppendableTable): """ support the new appendable table formats """ pandas_kind = "frame_table" table_type = "appendable_frame" ndim = 2 obj_type: Type[Union[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: Optional[int] = None, stop: Optional[int] = None, ): # validate the version self.validate_version(where) # infer the data kind if not self.infer_axes(): return None result = self._read_axes(where=where, start=start, stop=stop) info = ( self.info.get(self.non_index_axes[0][0], dict()) if len(self.non_index_axes) else dict() ) inds = [i for i, ax in enumerate(self.axes) if ax is self.index_axes[0]] assert len(inds) == 1 ind = inds[0] index = result[ind][0] frames = [] for i, a in enumerate(self.axes): if a not in self.values_axes: continue index_vals, cvalues = result[i] # we could have a multi-index constructor here # ensure_index doesn't recognized our list-of-tuples here if info.get("type") == "MultiIndex": cols = MultiIndex.from_tuples(index_vals) else: cols = Index(index_vals) names = info.get("names") if names is not None: cols.set_names(names, inplace=True) if self.is_transposed: values = cvalues index_ = cols cols_ = Index(index, name=getattr(index, "name", None)) else: values = cvalues.T index_ = Index(index, name=getattr(index, "name", None)) cols_ = cols # if we have a DataIndexableCol, its shape will only be 1 dim if values.ndim == 1 and isinstance(values, np.ndarray): values = values.reshape((1, values.shape[0])) if isinstance(values, np.ndarray): df = DataFrame(values.T, columns=cols_, index=index_) elif isinstance(values, Index): df = DataFrame(values, columns=cols_, index=index_) else: # Categorical df = DataFrame([values], columns=cols_, index=index_) assert (df.dtypes == values.dtype).all(), (df.dtypes, values.dtype) frames.append(df) if len(frames) == 1: df = frames[0] else: df = concat(frames, axis=1) selection = Selection(self, where=where, start=start, stop=stop) # apply the selection filters & axis orderings df = self.process_axes(df, selection=selection, columns=columns) return df class AppendableSeriesTable(AppendableFrameTable): """ support the new appendable table formats """ pandas_kind = "series_table" table_type = "appendable_series" ndim = 2 obj_type = Series @property def is_transposed(self) -> bool: return False @classmethod def get_object(cls, obj, transposed: bool): return obj def write(self, obj, data_columns=None, **kwargs): """ we are going to write this as a frame table """ if not isinstance(obj, DataFrame): name = obj.name or "values" obj = obj.to_frame(name) return super().write(obj=obj, data_columns=obj.columns.tolist(), **kwargs) def read( self, where=None, columns=None, start: Optional[int] = None, stop: Optional[int] = None, ) -> Series: is_multi_index = self.is_multi_index if columns is not None and is_multi_index: assert isinstance(self.levels, list) # needed for mypy for n in self.levels: if n not in columns: columns.insert(0, n) s = super().read(where=where, columns=columns, start=start, stop=stop) if is_multi_index: s.set_index(self.levels, inplace=True) s = s.iloc[:, 0] # remove the default name if s.name == "values": s.name = None return s class AppendableMultiSeriesTable(AppendableSeriesTable): """ support the new appendable table formats """ pandas_kind = "series_table" table_type = "appendable_multiseries" def write(self, obj, **kwargs): """ we are going to write this as a frame table """ name = obj.name or "values" obj, self.levels = self.validate_multiindex(obj) cols = list(self.levels) cols.append(name) obj.columns = cols return super().write(obj=obj, **kwargs) class GenericTable(AppendableFrameTable): """ a table that read/writes the generic pytables table format """ pandas_kind = "frame_table" table_type = "generic_table" ndim = 2 obj_type = DataFrame @property def pandas_type(self) -> str: return self.pandas_kind @property def storable(self): return getattr(self.group, "table", None) or self.group def get_attrs(self): """ retrieve our attributes """ self.non_index_axes = [] self.nan_rep = None self.levels = [] self.index_axes = [a for a in self.indexables if a.is_an_indexable] self.values_axes = [a for a in self.indexables if not a.is_an_indexable] self.data_columns = [a.name for a in self.values_axes] @cache_readonly def indexables(self): """ create the indexables from the table description """ d = self.description # TODO: can we get a typ for this? AFAICT it is the only place # where we aren't passing one # the index columns is just a simple index md = self.read_metadata("index") meta = "category" if md is not None else None index_col = GenericIndexCol( name="index", axis=0, table=self.table, meta=meta, metadata=md ) _indexables = [index_col] for i, n in enumerate(d._v_names): assert isinstance(n, str) atom = getattr(d, n) md = self.read_metadata(n) meta = "category" if md is not None else None dc = GenericDataIndexableCol( name=n, pos=i, values=[n], typ=atom, table=self.table, meta=meta, metadata=md, ) _indexables.append(dc) return _indexables def write(self, **kwargs): raise NotImplementedError("cannot write on an generic table") class AppendableMultiFrameTable(AppendableFrameTable): """ a frame with a multi-index """ table_type = "appendable_multiframe" obj_type = DataFrame ndim = 2 _re_levels = re.compile(r"^level_\d+$") @property def table_type_short(self) -> str: return "appendable_multi" def write(self, obj, data_columns=None, **kwargs): if data_columns is None: data_columns = [] elif data_columns is True: data_columns = obj.columns.tolist() obj, self.levels = self.validate_multiindex(obj) for n in self.levels: if n not in data_columns: data_columns.insert(0, n) return super().write(obj=obj, data_columns=data_columns, **kwargs) def read( self, where=None, columns=None, start: Optional[int] = None, stop: Optional[int] = None, ): df = super().read(where=where, columns=columns, start=start, stop=stop) df = df.set_index(self.levels) # remove names for 'level_%d' df.index = df.index.set_names( [None if self._re_levels.search(l) else l for l in df.index.names] ) return df def _reindex_axis(obj: DataFrame, axis: int, labels: Index, other=None) -> DataFrame: ax = obj._get_axis(axis) labels = ensure_index(labels) # try not to reindex even if other is provided # if it equals our current index if other is not None: other = ensure_index(other) if (other is None or labels.equals(other)) and labels.equals(ax): return obj labels = ensure_index(labels.unique()) if other is not None: labels = ensure_index(other.unique()).intersection(labels, sort=False) if not labels.equals(ax): slicer: List[Union[slice, Index]] = [slice(None, None)] * obj.ndim slicer[axis] = labels obj = obj.loc[tuple(slicer)] return obj # tz to/from coercion def _get_tz(tz: tzinfo) -> Union[str, tzinfo]: """ for a tz-aware type, return an encoded zone """ zone = timezones.get_timezone(tz) return zone def _set_tz( values: Union[np.ndarray, Index], tz: Optional[Union[str, tzinfo]], coerce: bool = False, ) -> Union[np.ndarray, DatetimeIndex]: """ coerce the values to a DatetimeIndex if tz is set preserve the input shape if possible Parameters ---------- values : ndarray or Index tz : str or tzinfo coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray """ if isinstance(values, DatetimeIndex): # If values is tzaware, the tz gets dropped in the values.ravel() # call below (which returns an ndarray). So we are only non-lossy # if `tz` matches `values.tz`. assert values.tz is None or values.tz == tz if tz is not None: name = getattr(values, "name", None) values = values.ravel() tz = _ensure_decoded(tz) values = DatetimeIndex(values, name=name) values = values.tz_localize("UTC").tz_convert(tz) elif coerce: values = np.asarray(values, dtype="M8[ns]") return values def _convert_index(name: str, index: Index, encoding: str, errors: str) -> IndexCol: assert isinstance(name, str) index_name = index.name converted, dtype_name = _get_data_and_dtype_name(index) kind = _dtype_to_kind(dtype_name) atom = DataIndexableCol._get_atom(converted) if isinstance(index, Int64Index): # Includes Int64Index, RangeIndex, DatetimeIndex, TimedeltaIndex, PeriodIndex, # in which case "kind" is "integer", "integer", "datetime64", # "timedelta64", and "integer", respectively. return IndexCol( name, values=converted, kind=kind, typ=atom, freq=getattr(index, "freq", None), tz=getattr(index, "tz", None), index_name=index_name, ) if isinstance(index, MultiIndex): raise TypeError("MultiIndex not supported here!") inferred_type = lib.infer_dtype(index, skipna=False) # we won't get inferred_type of "datetime64" or "timedelta64" as these # would go through the DatetimeIndex/TimedeltaIndex paths above values = np.asarray(index) if inferred_type == "date": converted = np.asarray([v.toordinal() for v in values], dtype=np.int32) return IndexCol( name, converted, "date", _tables().Time32Col(), index_name=index_name, ) elif inferred_type == "string": converted = _convert_string_array(values, encoding, errors) itemsize = converted.dtype.itemsize return IndexCol( name, converted, "string", _tables().StringCol(itemsize), index_name=index_name, ) elif inferred_type in ["integer", "floating"]: return IndexCol( name, values=converted, kind=kind, typ=atom, index_name=index_name, ) else: assert isinstance(converted, np.ndarray) and converted.dtype == object assert kind == "object", kind atom = _tables().ObjectAtom() return IndexCol(name, converted, kind, atom, index_name=index_name,) def _unconvert_index( data, kind: str, encoding: str, errors: str ) -> Union[np.ndarray, Index]: index: Union[Index, np.ndarray] if kind == "datetime64": index = DatetimeIndex(data) elif kind == "timedelta64": index = TimedeltaIndex(data) elif kind == "date": try: index = np.asarray([date.fromordinal(v) for v in data], dtype=object) except (ValueError): index = np.asarray([date.fromtimestamp(v) for v in data], dtype=object) elif kind in ("integer", "float"): index = np.asarray(data) elif kind in ("string"): index = _unconvert_string_array( data, nan_rep=None, encoding=encoding, errors=errors ) elif kind == "object": index = np.asarray(data[0]) else: # pragma: no cover raise ValueError(f"unrecognized index type {kind}") return index def _maybe_convert_for_string_atom( name: str, block, existing_col, min_itemsize, nan_rep, encoding, errors ): if not block.is_object: return block.values dtype_name = block.dtype.name inferred_type = lib.infer_dtype(block.values, skipna=False) if inferred_type == "date": raise TypeError("[date] is not implemented as a table column") elif inferred_type == "datetime": # after GH#8260 # this only would be hit for a multi-timezone dtype which is an error raise TypeError( "too many timezones in this block, create separate data columns" ) elif not (inferred_type == "string" or dtype_name == "object"): return block.values block = block.fillna(nan_rep, downcast=False) if isinstance(block, list): # Note: because block is always object dtype, fillna goes # through a path such that the result is always a 1-element list block = block[0] data = block.values # see if we have a valid string type inferred_type = lib.infer_dtype(data, skipna=False) if inferred_type != "string": # we cannot serialize this data, so report an exception on a column # by column basis for i in range(len(block.shape[0])): col = block.iget(i) inferred_type = lib.infer_dtype(col, skipna=False) if inferred_type != "string": iloc = block.mgr_locs.indexer[i] raise TypeError( f"Cannot serialize the column [{iloc}] because\n" f"its data contents are [{inferred_type}] object dtype" ) # itemsize is the maximum length of a string (along any dimension) data_converted = _convert_string_array(data, encoding, errors).reshape(data.shape) assert data_converted.shape == block.shape, (data_converted.shape, block.shape) itemsize = data_converted.itemsize # specified min_itemsize? if isinstance(min_itemsize, dict): min_itemsize = int(min_itemsize.get(name) or min_itemsize.get("values") or 0) itemsize = max(min_itemsize or 0, itemsize) # check for column in the values conflicts if existing_col is not None: eci = existing_col.validate_col(itemsize) if eci > itemsize: itemsize = eci data_converted = data_converted.astype(f"|S{itemsize}", copy=False) return data_converted def _convert_string_array(data: np.ndarray, encoding: str, errors: str) -> np.ndarray: """ Take a string-like that is object dtype and coerce to a fixed size string type. Parameters ---------- data : np.ndarray[object] encoding : str errors : str Handler for encoding errors. Returns ------- np.ndarray[fixed-length-string] """ # encode if needed if len(data): data = ( Series(data.ravel()) .str.encode(encoding, errors) ._values.reshape(data.shape) ) # create the sized dtype ensured = ensure_object(data.ravel()) itemsize = max(1, libwriters.max_len_string_array(ensured)) data = np.asarray(data, dtype=f"S{itemsize}") return data def _unconvert_string_array( data: np.ndarray, nan_rep, encoding: str, errors: str ) -> np.ndarray: """ Inverse of _convert_string_array. Parameters ---------- data : np.ndarray[fixed-length-string] nan_rep : the storage repr of NaN encoding : str errors : str Handler for encoding errors. Returns ------- np.ndarray[object] Decoded data. """ shape = data.shape data = np.asarray(data.ravel(), dtype=object) if len(data): itemsize = libwriters.max_len_string_array(ensure_object(data)) dtype = f"U{itemsize}" if isinstance(data[0], bytes): data = Series(data).str.decode(encoding, errors=errors)._values else: data = data.astype(dtype, copy=False).astype(object, copy=False) if nan_rep is None: nan_rep = "nan" data = libwriters.string_array_replace_from_nan_rep(data, nan_rep) return data.reshape(shape) def _maybe_convert(values: np.ndarray, val_kind: str, encoding: str, errors: str): assert isinstance(val_kind, str), type(val_kind) if _need_convert(val_kind): conv = _get_converter(val_kind, encoding, errors) values = conv(values) return values def _get_converter(kind: str, encoding: str, errors: str): if kind == "datetime64": return lambda x: np.asarray(x, dtype="M8[ns]") elif kind == "string": return lambda x: _unconvert_string_array( x, nan_rep=None, encoding=encoding, errors=errors ) else: # pragma: no cover raise ValueError(f"invalid kind {kind}") def _need_convert(kind: str) -> bool: if kind in ("datetime64", "string"): return True return False def _maybe_adjust_name(name: str, version) -> 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 """ try: 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}" except IndexError: pass return name def _dtype_to_kind(dtype_str: str) -> str: """ Find the "kind" string describing the given dtype name. """ dtype_str = _ensure_decoded(dtype_str) if dtype_str.startswith("string") or dtype_str.startswith("bytes"): kind = "string" elif dtype_str.startswith("float"): kind = "float" elif dtype_str.startswith("complex"): kind = "complex" elif dtype_str.startswith("int") or dtype_str.startswith("uint"): kind = "integer" elif dtype_str.startswith("datetime64"): kind = "datetime64" elif dtype_str.startswith("timedelta"): kind = "timedelta64" elif dtype_str.startswith("bool"): kind = "bool" elif dtype_str.startswith("category"): kind = "category" elif dtype_str.startswith("period"): # We store the `freq` attr so we can restore from integers kind = "integer" elif dtype_str == "object": kind = "object" else: raise ValueError(f"cannot interpret dtype of [{dtype_str}]") return kind def _get_data_and_dtype_name(data: ArrayLike): """ Convert the passed data into a storable form and a dtype string. """ if isinstance(data, Categorical): data = data.codes # For datetime64tz we need to drop the TZ in tests TODO: why? dtype_name = data.dtype.name.split("[")[0] if data.dtype.kind in ["m", "M"]: data = np.asarray(data.view("i8")) # TODO: we used to reshape for the dt64tz case, but no longer # doing that doesn't seem to break anything. why? elif isinstance(data, PeriodIndex): data = data.asi8 data = np.asarray(data) return data, dtype_name class Selection: """ Carries out a selection operation on a tables.Table object. Parameters ---------- table : a Table object where : list of Terms (or convertible to) start, stop: indices to start and/or stop selection """ def __init__( self, table: Table, where=None, start: Optional[int] = None, stop: Optional[int] = None, ): self.table = table self.where = where self.start = start self.stop = stop self.condition = None self.filter = None self.terms = None self.coordinates = None if is_list_like(where): # see if we have a passed coordinate like try: inferred = lib.infer_dtype(where, skipna=False) if inferred == "integer" or inferred == "boolean": where = np.asarray(where) if where.dtype == np.bool_: start, stop = self.start, self.stop if start is None: start = 0 if stop is None: stop = self.table.nrows self.coordinates = np.arange(start, stop)[where] elif issubclass(where.dtype.type, np.integer): if (self.start is not None and (where < self.start).any()) or ( self.stop is not None and (where >= self.stop).any() ): raise ValueError( "where must have index locations >= start and < stop" ) self.coordinates = where except ValueError: pass if self.coordinates is None: self.terms = self.generate(where) # create the numexpr & the filter if self.terms is not None: self.condition, self.filter = self.terms.evaluate() def generate(self, where): """ where can be a : dict,list,tuple,string """ if where is None: return None q = self.table.queryables() try: return 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()) raise ValueError( f"The passed where expression: {where}\n" " contains an invalid variable reference\n" " all of the variable references must be a " "reference to\n" " an axis (e.g. 'index' or 'columns'), or a " "data_column\n" f" The currently defined references are: {qkeys}\n" ) 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 self.stop is None: stop = nrows elif stop < 0: stop += nrows if self.condition is not None: return self.table.table.get_where_list( self.condition.format(), start=start, stop=stop, sort=True ) elif self.coordinates is not None: return self.coordinates return np.arange(start, stop)