# pylint: disable-msg=E1101,W0613,W0603 """ High level interface to PyTables for reading and writing pandas data structures to disk """ import copy from datetime import date, datetime from distutils.version import LooseVersion import itertools import os import re import time import warnings import numpy as np from pandas._libs import algos, lib, writers as libwriters from pandas._libs.tslibs import timezones from pandas.compat import PY3, filter, lrange, range, string_types from pandas.errors import PerformanceWarning from pandas.core.dtypes.common import ( ensure_int64, ensure_object, ensure_platform_int, is_categorical_dtype, is_datetime64_dtype, is_datetime64tz_dtype, is_list_like, is_timedelta64_dtype) from pandas.core.dtypes.missing import array_equivalent from pandas import ( DataFrame, DatetimeIndex, Index, Int64Index, MultiIndex, Panel, PeriodIndex, Series, SparseDataFrame, SparseSeries, TimedeltaIndex, compat, concat, isna, to_datetime) from pandas.core import config from pandas.core.algorithms import match, unique from pandas.core.arrays.categorical import ( Categorical, _factorize_from_iterables) from pandas.core.arrays.sparse import BlockIndex, IntIndex from pandas.core.base import StringMixin import pandas.core.common as com from pandas.core.computation.pytables import Expr, maybe_expression from pandas.core.config import get_option from pandas.core.index import ensure_index from pandas.core.internals import ( BlockManager, _block2d_to_blocknd, _block_shape, _factor_indexer, make_block) from pandas.io.common import _stringify_path from pandas.io.formats.printing import adjoin, pprint_thing # versioning attribute _version = '0.15.2' # encoding # PY3 encoding if we don't specify _default_encoding = 'UTF-8' def _ensure_decoded(s): """ if we have bytes, decode them to unicode """ if isinstance(s, np.bytes_): s = s.decode('UTF-8') return s def _ensure_encoding(encoding): # set the encoding if we need if encoding is None: if PY3: encoding = _default_encoding return encoding def _ensure_str(name): """Ensure that an index / column name is a str (python 3) or unicode (python 2); otherwise they may be np.string dtype. Non-string dtypes are passed through unchanged. https://github.com/pandas-dev/pandas/issues/13492 """ if isinstance(name, compat.string_types): name = compat.text_type(name) return name Term = Expr def _ensure_term(where, scope_level): """ ensure that the where is a Term or a list of Term this makes sure that we are capturing the scope of variables that are passed create the terms here with a frame_level=2 (we are 2 levels down) """ # only consider list/tuple here as an ndarray is automatically a coordinate # list level = scope_level + 1 if isinstance(where, (list, tuple)): wlist = [] for w in filter(lambda x: x is not None, where): if not maybe_expression(w): wlist.append(w) else: wlist.append(Term(w, scope_level=level)) where = wlist elif maybe_expression(where): where = Term(where, scope_level=level) return where class PossibleDataLossError(Exception): pass class ClosedFileError(Exception): pass class IncompatibilityWarning(Warning): pass incompatibility_doc = """ where criteria is being ignored as this version [%s] is too old (or not-defined), read the file in and write it out to a new file to upgrade (with the copy_to method) """ class AttributeConflictWarning(Warning): pass attribute_conflict_doc = """ the [%s] attribute of the existing index is [%s] which conflicts with the new [%s], resetting the attribute to None """ class DuplicateWarning(Warning): pass duplicate_doc = """ duplicate entries in table, taking most recently appended """ performance_doc = """ your performance may suffer as PyTables will pickle object types that it cannot map directly to c-types [inferred_type->%s,key->%s] [items->%s] """ # formats _FORMAT_MAP = { u'f': 'fixed', u'fixed': 'fixed', u't': 'table', u'table': 'table', } format_deprecate_doc = """ the table keyword has been deprecated use the format='fixed(f)|table(t)' keyword instead fixed(f) : specifies the Fixed format and is the default for put operations table(t) : specifies the Table format and is the default for append operations """ # map object types _TYPE_MAP = { Series: u'series', SparseSeries: u'sparse_series', DataFrame: u'frame', SparseDataFrame: u'sparse_frame', Panel: u'wide', } # storer class map _STORER_MAP = { u'Series': 'LegacySeriesFixed', u'DataFrame': 'LegacyFrameFixed', u'DataMatrix': 'LegacyFrameFixed', u'series': 'SeriesFixed', u'sparse_series': 'SparseSeriesFixed', u'frame': 'FrameFixed', u'sparse_frame': 'SparseFrameFixed', u'wide': 'PanelFixed', } # table class map _TABLE_MAP = { u'generic_table': 'GenericTable', u'appendable_series': 'AppendableSeriesTable', u'appendable_multiseries': 'AppendableMultiSeriesTable', u'appendable_frame': 'AppendableFrameTable', u'appendable_multiframe': 'AppendableMultiFrameTable', u'appendable_panel': 'AppendablePanelTable', u'worm': 'WORMTable', u'legacy_frame': 'LegacyFrameTable', u'legacy_panel': 'LegacyPanelTable', } # axes map _AXES_MAP = { DataFrame: [0], Panel: [1, 2] } # register our configuration options dropna_doc = """ : boolean drop ALL nan rows when appending to a table """ format_doc = """ : format default format writing format, if None, then put will default to 'fixed' and append will default to 'table' """ with config.config_prefix('io.hdf'): config.register_option('dropna_table', False, dropna_doc, validator=config.is_bool) config.register_option( 'default_format', None, format_doc, validator=config.is_one_of_factory(['fixed', 'table', None]) ) # oh the troubles to reduce import time _table_mod = None _table_file_open_policy_is_strict = False def _tables(): global _table_mod global _table_file_open_policy_is_strict if _table_mod is None: import tables _table_mod = tables # version requirements if LooseVersion(tables.__version__) < LooseVersion('3.0.0'): raise ImportError("PyTables version >= 3.0.0 is required") # set the file open policy # return the file open policy; this changes as of pytables 3.1 # depending on the HDF5 version try: _table_file_open_policy_is_strict = ( tables.file._FILE_OPEN_POLICY == 'strict') except AttributeError: pass return _table_mod # interface to/from ### def to_hdf(path_or_buf, key, value, mode=None, complevel=None, complib=None, append=None, **kwargs): """ store this object, close it if we opened it """ if append: f = lambda store: store.append(key, value, **kwargs) else: f = lambda store: store.put(key, value, **kwargs) path_or_buf = _stringify_path(path_or_buf) if isinstance(path_or_buf, string_types): with HDFStore(path_or_buf, mode=mode, complevel=complevel, complib=complib) as store: f(store) else: f(path_or_buf) def read_hdf(path_or_buf, key=None, mode='r', **kwargs): """ Read from the store, close it if we opened it. Retrieve pandas object stored in file, optionally based on where criteria Parameters ---------- path_or_buf : string, buffer or path object Path to the file to open, or an open :class:`pandas.HDFStore` object. Supports any object implementing the ``__fspath__`` protocol. This includes :class:`pathlib.Path` and py._path.local.LocalPath objects. .. versionadded:: 0.19.0 support for pathlib, py.path. .. versionadded:: 0.21.0 support for __fspath__ protocol. key : object, optional The group identifier in the store. Can be omitted if the HDF file contains a single pandas object. mode : {'r', 'r+', 'a'}, optional Mode to use when opening the file. Ignored if path_or_buf is a :class:`pandas.HDFStore`. Default is 'r'. where : list, optional A list of Term (or convertible) objects. start : int, optional Row number to start selection. stop : int, optional Row number to stop selection. columns : list, optional A list of columns names to return. iterator : bool, optional Return an iterator object. chunksize : int, optional Number of rows to include in an iteration when using an iterator. errors : str, default 'strict' Specifies how encoding and decoding errors are to be handled. See the errors argument for :func:`open` for a full list of options. **kwargs Additional keyword arguments passed to HDFStore. Returns ------- item : object The selected object. Return type depends on the object stored. See Also -------- pandas.DataFrame.to_hdf : Write a HDF file from a DataFrame. pandas.HDFStore : Low-level access to HDF files. Examples -------- >>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z']) >>> df.to_hdf('./store.h5', 'data') >>> reread = pd.read_hdf('./store.h5') """ if mode not in ['r', 'r+', 'a']: raise ValueError('mode {0} is not allowed while performing a read. ' 'Allowed modes are r, r+ and a.'.format(mode)) # grab the scope if 'where' in kwargs: kwargs['where'] = _ensure_term(kwargs['where'], scope_level=1) if isinstance(path_or_buf, HDFStore): if not path_or_buf.is_open: raise IOError('The HDFStore must be open for reading.') store = path_or_buf auto_close = False else: path_or_buf = _stringify_path(path_or_buf) if not isinstance(path_or_buf, string_types): raise NotImplementedError('Support for generic buffers has not ' 'been implemented.') try: exists = os.path.exists(path_or_buf) # if filepath is too long except (TypeError, ValueError): exists = False if not exists: raise compat.FileNotFoundError( 'File {path} does not exist'.format(path=path_or_buf)) store = HDFStore(path_or_buf, mode=mode, **kwargs) # can't auto open/close if we are using an iterator # so delegate to the iterator auto_close = True try: if key is None: groups = store.groups() if len(groups) == 0: raise ValueError('No dataset in HDF5 file.') candidate_only_group = groups[0] # For the HDF file to have only one dataset, all other groups # should then be metadata groups for that candidate group. (This # assumes that the groups() method enumerates parent groups # before their children.) for group_to_check in groups[1:]: if not _is_metadata_of(group_to_check, candidate_only_group): raise ValueError('key must be provided when HDF5 file ' 'contains multiple datasets.') key = candidate_only_group._v_pathname return store.select(key, auto_close=auto_close, **kwargs) except (ValueError, TypeError): # if there is an error, close the store try: store.close() except AttributeError: pass raise def _is_metadata_of(group, parent_group): """Check if a given group is a metadata group for a given parent_group.""" if group._v_depth <= parent_group._v_depth: return False current = group while current._v_depth > 1: parent = current._v_parent if parent == parent_group and current._v_name == 'meta': return True current = current._v_parent return False class HDFStore(StringMixin): """ Dict-like IO interface for storing pandas objects in PyTables either Fixed or Table format. Parameters ---------- path : string File path to HDF5 file mode : {'a', 'w', 'r', 'r+'}, default 'a' ``'r'`` Read-only; no data can be modified. ``'w'`` Write; a new file is created (an existing file with the same name would be deleted). ``'a'`` Append; an existing file is opened for reading and writing, and if the file does not exist it is created. ``'r+'`` It is similar to ``'a'``, but the file must already exist. complevel : int, 0-9, default None Specifies a compression level for data. A value of 0 disables compression. complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib' Specifies the compression library to be used. As of v0.20.2 these additional compressors for Blosc are supported (default if no compressor specified: 'blosc:blosclz'): {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy', 'blosc:zlib', 'blosc:zstd'}. Specifying a compression library which is not available issues a ValueError. fletcher32 : bool, default False If applying compression use the fletcher32 checksum Examples -------- >>> bar = pd.DataFrame(np.random.randn(10, 4)) >>> store = pd.HDFStore('test.h5') >>> store['foo'] = bar # write to HDF5 >>> bar = store['foo'] # retrieve >>> store.close() """ def __init__(self, path, mode=None, complevel=None, complib=None, fletcher32=False, **kwargs): if 'format' in kwargs: raise ValueError('format is not a defined argument for HDFStore') try: import tables # noqa except ImportError as ex: # pragma: no cover raise ImportError('HDFStore requires PyTables, "{ex!s}" problem ' 'importing'.format(ex=ex)) if complib is not None and complib not in tables.filters.all_complibs: raise ValueError( "complib only supports {libs} compression.".format( libs=tables.filters.all_complibs)) if complib is None and complevel is not None: complib = tables.filters.default_complib self._path = _stringify_path(path) if mode is None: mode = 'a' self._mode = mode self._handle = None self._complevel = complevel if complevel else 0 self._complib = complib self._fletcher32 = fletcher32 self._filters = None self.open(mode=mode, **kwargs) def __fspath__(self): return self._path @property def root(self): """ return the root node """ self._check_if_open() return self._handle.root @property def filename(self): return self._path def __getitem__(self, key): return self.get(key) def __setitem__(self, key, value): self.put(key, value) def __delitem__(self, key): return self.remove(key) def __getattr__(self, name): """ allow attribute access to get stores """ try: return self.get(name) except (KeyError, ClosedFileError): pass raise AttributeError( "'{object}' object has no attribute '{name}'".format( object=type(self).__name__, name=name)) def __contains__(self, key): """ check for existence of this key can match the exact pathname or the pathnm w/o the leading '/' """ node = self.get_node(key) if node is not None: name = node._v_pathname if name == key or name[1:] == key: return True return False def __len__(self): return len(self.groups()) def __unicode__(self): return '{type}\nFile path: {path}\n'.format( type=type(self), path=pprint_thing(self._path)) def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() def keys(self): """ Return a (potentially unordered) list of the keys corresponding to the objects stored in the HDFStore. These are ABSOLUTE path-names (e.g. have the leading '/' """ return [n._v_pathname for n in self.groups()] def __iter__(self): return iter(self.keys()) def items(self): """ iterate on key->group """ for g in self.groups(): yield g._v_pathname, g iteritems = items def open(self, mode='a', **kwargs): """ Open the file in the specified mode Parameters ---------- mode : {'a', 'w', 'r', 'r+'}, default 'a' See HDFStore docstring or tables.open_file for info about modes """ tables = _tables() if self._mode != mode: # if we are changing a write mode to read, ok if self._mode in ['a', 'w'] and mode in ['r', 'r+']: pass elif mode in ['w']: # this would truncate, raise here if self.is_open: raise PossibleDataLossError( "Re-opening the file [{0}] with mode [{1}] " "will delete the current file!" .format(self._path, self._mode) ) self._mode = mode # close and reopen the handle if self.is_open: self.close() if self._complevel and self._complevel > 0: self._filters = _tables().Filters(self._complevel, self._complib, fletcher32=self._fletcher32) try: self._handle = tables.open_file(self._path, self._mode, **kwargs) except (IOError) as e: # pragma: no cover if 'can not be written' in str(e): print( 'Opening {path} in read-only mode'.format(path=self._path)) self._handle = tables.open_file(self._path, 'r', **kwargs) else: raise except (ValueError) as e: # trap PyTables >= 3.1 FILE_OPEN_POLICY exception # to provide an updated message if 'FILE_OPEN_POLICY' in str(e): e = ValueError( "PyTables [{version}] no longer supports opening multiple " "files\n" "even in read-only mode on this HDF5 version " "[{hdf_version}]. You can accept this\n" "and not open the same file multiple times at once,\n" "upgrade the HDF5 version, or downgrade to PyTables 3.0.0 " "which allows\n" "files to be opened multiple times at once\n" .format(version=tables.__version__, hdf_version=tables.get_hdf5_version())) raise e except (Exception) as e: # trying to read from a non-existent file causes an error which # is not part of IOError, make it one if self._mode == 'r' and 'Unable to open/create file' in str(e): raise IOError(str(e)) raise def close(self): """ Close the PyTables file handle """ if self._handle is not None: self._handle.close() self._handle = None @property def is_open(self): """ return a boolean indicating whether the file is open """ if self._handle is None: return False return bool(self._handle.isopen) def flush(self, fsync=False): """ Force all buffered modifications to be written to disk. Parameters ---------- fsync : bool (default False) call ``os.fsync()`` on the file handle to force writing to disk. Notes ----- Without ``fsync=True``, flushing may not guarantee that the OS writes to disk. With fsync, the operation will block until the OS claims the file has been written; however, other caching layers may still interfere. """ if self._handle is not None: self._handle.flush() if fsync: try: os.fsync(self._handle.fileno()) except OSError: pass def get(self, key): """ Retrieve pandas object stored in file Parameters ---------- key : object Returns ------- obj : same type as object stored in file """ group = self.get_node(key) if group is None: raise KeyError('No object named {key} in the file'.format(key=key)) return self._read_group(group) def select(self, key, where=None, start=None, stop=None, columns=None, iterator=False, chunksize=None, auto_close=False, **kwargs): """ Retrieve pandas object stored in file, optionally based on where criteria Parameters ---------- key : object where : list of Term (or convertible) objects, optional start : integer (defaults to None), row number to start selection stop : integer (defaults to None), row number to stop selection columns : a list of columns that if not None, will limit the return columns iterator : boolean, return an iterator, default False chunksize : nrows to include in iteration, return an iterator auto_close : boolean, should automatically close the store when finished, default is False Returns ------- The selected object """ group = self.get_node(key) if group is None: raise KeyError('No object named {key} in the file'.format(key=key)) # create the storer and axes where = _ensure_term(where, scope_level=1) s = self._create_storer(group) s.infer_axes() # function to call on iteration def func(_start, _stop, _where): return s.read(start=_start, stop=_stop, where=_where, columns=columns) # create the iterator it = TableIterator(self, s, func, where=where, nrows=s.nrows, start=start, stop=stop, iterator=iterator, chunksize=chunksize, auto_close=auto_close) return it.get_result() def select_as_coordinates( self, key, where=None, start=None, stop=None, **kwargs): """ return the selection as an Index Parameters ---------- key : object where : list of Term (or convertible) objects, optional start : integer (defaults to None), row number to start selection stop : integer (defaults to None), row number to stop selection """ where = _ensure_term(where, scope_level=1) return self.get_storer(key).read_coordinates(where=where, start=start, stop=stop, **kwargs) def select_column(self, key, column, **kwargs): """ return a single column from the table. This is generally only useful to select an indexable Parameters ---------- key : object column: the column of interest Exceptions ---------- raises KeyError if the column is not found (or key is not a valid store) raises ValueError if the column can not be extracted individually (it is part of a data block) """ return self.get_storer(key).read_column(column=column, **kwargs) def select_as_multiple(self, keys, where=None, selector=None, columns=None, start=None, stop=None, iterator=False, chunksize=None, auto_close=False, **kwargs): """ Retrieve pandas objects from multiple tables Parameters ---------- keys : a list of the tables selector : the table to apply the where criteria (defaults to keys[0] if not supplied) columns : the columns I want back start : integer (defaults to None), row number to start selection stop : integer (defaults to None), row number to stop selection iterator : boolean, return an iterator, default False chunksize : nrows to include in iteration, return an iterator Exceptions ---------- raises KeyError if keys or selector is not found or keys is empty raises TypeError if keys is not a list or tuple raises ValueError if the tables are not ALL THE SAME DIMENSIONS """ # default to single select where = _ensure_term(where, scope_level=1) if isinstance(keys, (list, tuple)) and len(keys) == 1: keys = keys[0] if isinstance(keys, string_types): return self.select(key=keys, where=where, columns=columns, start=start, stop=stop, iterator=iterator, chunksize=chunksize, **kwargs) if not isinstance(keys, (list, tuple)): raise TypeError("keys must be a list/tuple") if not len(keys): raise ValueError("keys must have a non-zero length") if selector is None: selector = keys[0] # collect the tables tbls = [self.get_storer(k) for k in keys] s = self.get_storer(selector) # validate rows nrows = None for t, k in itertools.chain([(s, selector)], zip(tbls, keys)): if t is None: raise KeyError("Invalid table [{key}]".format(key=k)) if not t.is_table: raise TypeError( "object [{obj}] is not a table, and cannot be used in all " "select as multiple".format(obj=t.pathname) ) if nrows is None: nrows = t.nrows elif t.nrows != nrows: raise ValueError( "all tables must have exactly the same nrows!") # axis is the concentation axes axis = list({t.non_index_axes[0][0] for t in tbls})[0] def func(_start, _stop, _where): # retrieve the objs, _where is always passed as a set of # coordinates here objs = [t.read(where=_where, columns=columns, start=_start, stop=_stop, **kwargs) for t in tbls] # concat and return return concat(objs, axis=axis, verify_integrity=False)._consolidate() # create the iterator it = TableIterator(self, s, func, where=where, nrows=nrows, start=start, stop=stop, iterator=iterator, chunksize=chunksize, auto_close=auto_close) return it.get_result(coordinates=True) def put(self, key, value, format=None, append=False, **kwargs): """ Store object in HDFStore Parameters ---------- key : object value : {Series, DataFrame, Panel} format : 'fixed(f)|table(t)', default is 'fixed' fixed(f) : Fixed format Fast writing/reading. Not-appendable, nor searchable table(t) : Table format Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data append : boolean, default False This will force Table format, append the input data to the existing. data_columns : list of columns to create as data columns, or True to use all columns. See `here `__ # noqa encoding : default None, provide an encoding for strings dropna : boolean, default False, do not write an ALL nan row to the store settable by the option 'io.hdf.dropna_table' """ if format is None: format = get_option("io.hdf.default_format") or 'fixed' kwargs = self._validate_format(format, kwargs) self._write_to_group(key, value, append=append, **kwargs) def remove(self, key, where=None, start=None, stop=None): """ Remove pandas object partially by specifying the where condition Parameters ---------- key : string Node to remove or delete rows from where : list of Term (or convertible) objects, optional start : integer (defaults to None), row number to start selection stop : integer (defaults to None), row number to stop selection Returns ------- number of rows removed (or None if not a Table) Exceptions ---------- raises KeyError if key is not a valid store """ where = _ensure_term(where, scope_level=1) try: s = self.get_storer(key) except KeyError: # the key is not a valid store, re-raising KeyError raise except Exception: if where is not None: raise ValueError( "trying to remove a node with a non-None where clause!") # we are actually trying to remove a node (with children) s = self.get_node(key) if s is not None: s._f_remove(recursive=True) return None # remove the node if com._all_none(where, start, stop): s.group._f_remove(recursive=True) # delete from the table else: if not s.is_table: raise ValueError( 'can only remove with where on objects written as tables') return s.delete(where=where, start=start, stop=stop) def append(self, key, value, format=None, append=True, columns=None, dropna=None, **kwargs): """ Append to Table in file. Node must already exist and be Table format. Parameters ---------- key : object value : {Series, DataFrame, Panel} format : 'table' is the default table(t) : table format Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data append : boolean, default True, append the input data to the existing data_columns : list of columns, or True, default None List of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See `here `__. min_itemsize : dict of columns that specify minimum string sizes nan_rep : string to use as string nan represenation chunksize : size to chunk the writing expectedrows : expected TOTAL row size of this table encoding : default None, provide an encoding for strings dropna : boolean, default False, do not write an ALL nan row to the store settable by the option 'io.hdf.dropna_table' Notes ----- Does *not* check if data being appended overlaps with existing data in the table, so be careful """ if columns is not None: raise TypeError("columns is not a supported keyword in append, " "try data_columns") if dropna is None: dropna = get_option("io.hdf.dropna_table") if format is None: format = get_option("io.hdf.default_format") or 'table' kwargs = self._validate_format(format, kwargs) self._write_to_group(key, value, append=append, dropna=dropna, **kwargs) def append_to_multiple(self, d, value, selector, data_columns=None, axes=None, dropna=False, **kwargs): """ Append to multiple tables Parameters ---------- d : a dict of table_name to table_columns, None is acceptable as the values of one node (this will get all the remaining columns) value : a pandas object selector : a string that designates the indexable table; all of its columns will be designed as data_columns, unless data_columns is passed, in which case these are used data_columns : list of columns to create as data columns, or True to use all columns dropna : if evaluates to True, drop rows from all tables if any single row in each table has all NaN. Default False. Notes ----- axes parameter is currently not accepted """ if axes is not None: raise TypeError("axes is currently not accepted as a parameter to" " append_to_multiple; you can create the " "tables independently instead") if not isinstance(d, dict): raise ValueError( "append_to_multiple must have a dictionary specified as the " "way to split the value" ) if selector not in d: raise ValueError( "append_to_multiple requires a selector that is in passed dict" ) # figure out the splitting axis (the non_index_axis) axis = list(set(range(value.ndim)) - set(_AXES_MAP[type(value)]))[0] # figure out how to split the value remain_key = None remain_values = [] for k, v in d.items(): if v is None: if remain_key is not None: raise ValueError( "append_to_multiple can only have one value in d that " "is None" ) remain_key = k else: remain_values.extend(v) if remain_key is not None: ordered = value.axes[axis] ordd = ordered.difference(Index(remain_values)) ordd = sorted(ordered.get_indexer(ordd)) d[remain_key] = ordered.take(ordd) # data_columns if data_columns is None: data_columns = d[selector] # ensure rows are synchronized across the tables if dropna: idxs = (value[cols].dropna(how='all').index for cols in d.values()) valid_index = next(idxs) for index in idxs: valid_index = valid_index.intersection(index) value = value.loc[valid_index] # append for k, v in d.items(): dc = data_columns if k == selector else None # compute the val val = value.reindex(v, axis=axis) self.append(k, val, data_columns=dc, **kwargs) def create_table_index(self, key, **kwargs): """ Create a pytables index on the table Parameters ---------- key : object (the node to index) Exceptions ---------- raises if the node is not a table """ # version requirements _tables() s = self.get_storer(key) if s is None: return if not s.is_table: raise TypeError( "cannot create table index on a Fixed format store") s.create_index(**kwargs) def groups(self): """return a list of all the top-level nodes (that are not themselves a pandas storage object) """ _tables() self._check_if_open() return [ g for g in self._handle.walk_groups() if (not isinstance(g, _table_mod.link.Link) and (getattr(g._v_attrs, 'pandas_type', None) or getattr(g, 'table', None) or (isinstance(g, _table_mod.table.Table) and g._v_name != u'table'))) ] def walk(self, where="/"): """ Walk the pytables group hierarchy for pandas objects This generator will yield the group path, subgroups and pandas object names for each group. Any non-pandas PyTables objects that are not a group will be ignored. The `where` group itself is listed first (preorder), then each of its child groups (following an alphanumerical order) is also traversed, following the same procedure. .. versionadded:: 0.24.0 Parameters ---------- where : str, optional Group where to start walking. If not supplied, the root group is used. Yields ------ path : str Full path to a group (without trailing '/') groups : list of str names of the groups contained in `path` leaves : list of str names of the pandas objects contained in `path` """ _tables() self._check_if_open() for g in self._handle.walk_groups(where): if getattr(g._v_attrs, 'pandas_type', None) is not None: continue groups = [] leaves = [] for child in g._v_children.values(): pandas_type = getattr(child._v_attrs, 'pandas_type', None) if pandas_type is None: if isinstance(child, _table_mod.group.Group): groups.append(child._v_name) else: leaves.append(child._v_name) yield (g._v_pathname.rstrip('/'), groups, leaves) def get_node(self, key): """ return the node with the key or None if it does not exist """ self._check_if_open() try: if not key.startswith('/'): key = '/' + key return self._handle.get_node(self.root, key) except _table_mod.exceptions.NoSuchNodeError: return None def get_storer(self, key): """ return the storer object for a key, raise if not in the file """ group = self.get_node(key) if group is None: raise KeyError('No object named {key} in the file'.format(key=key)) s = self._create_storer(group) s.infer_axes() return s def copy(self, file, mode='w', propindexes=True, keys=None, complib=None, complevel=None, fletcher32=False, overwrite=True): """ copy the existing store to a new file, upgrading in place Parameters ---------- propindexes: restore indexes in copied file (defaults to True) keys : list of keys to include in the copy (defaults to all) overwrite : overwrite (remove and replace) existing nodes in the new store (default is True) mode, complib, complevel, fletcher32 same as in HDFStore.__init__ Returns ------- open file handle of the new store """ new_store = HDFStore( file, mode=mode, complib=complib, complevel=complevel, fletcher32=fletcher32) if keys is None: keys = list(self.keys()) if not isinstance(keys, (tuple, list)): keys = [keys] for k in keys: s = self.get_storer(k) if s is not None: if k in new_store: if overwrite: new_store.remove(k) data = self.select(k) if s.is_table: index = False if propindexes: index = [a.name for a in s.axes if a.is_indexed] new_store.append( k, data, index=index, data_columns=getattr(s, 'data_columns', None), encoding=s.encoding ) else: new_store.put(k, data, encoding=s.encoding) return new_store def info(self): """ Print detailed information on the store. .. versionadded:: 0.21.0 """ output = '{type}\nFile path: {path}\n'.format( type=type(self), path=pprint_thing(self._path)) if self.is_open: lkeys = sorted(list(self.keys())) if len(lkeys): keys = [] values = [] for k in lkeys: try: s = self.get_storer(k) if s is not None: keys.append(pprint_thing(s.pathname or k)) values.append( pprint_thing(s or 'invalid_HDFStore node')) except Exception as detail: keys.append(k) values.append( "[invalid_HDFStore node: {detail}]".format( detail=pprint_thing(detail))) output += adjoin(12, keys, values) else: output += 'Empty' else: output += "File is CLOSED" return output # private methods ###### def _check_if_open(self): if not self.is_open: raise ClosedFileError("{0} file is not open!".format(self._path)) def _validate_format(self, format, kwargs): """ validate / deprecate formats; return the new kwargs """ kwargs = kwargs.copy() # validate try: kwargs['format'] = _FORMAT_MAP[format.lower()] except KeyError: raise TypeError("invalid HDFStore format specified [{0}]" .format(format)) return kwargs def _create_storer(self, group, format=None, value=None, append=False, **kwargs): """ return a suitable class to operate """ def error(t): raise TypeError( "cannot properly create the storer for: [{t}] [group->" "{group},value->{value},format->{format},append->{append}," "kwargs->{kwargs}]".format(t=t, group=group, value=type(value), format=format, append=append, kwargs=kwargs)) pt = _ensure_decoded(getattr(group._v_attrs, 'pandas_type', None)) tt = _ensure_decoded(getattr(group._v_attrs, 'table_type', None)) # infer the pt from the passed value if pt is None: if value is None: _tables() if (getattr(group, 'table', None) or isinstance(group, _table_mod.table.Table)): pt = u'frame_table' tt = u'generic_table' else: raise TypeError( "cannot create a storer if the object is not existing " "nor a value are passed") else: try: pt = _TYPE_MAP[type(value)] except KeyError: error('_TYPE_MAP') # we are actually a table if format == 'table': pt += u'_table' # a storer node if u'table' not in pt: try: return globals()[_STORER_MAP[pt]](self, group, **kwargs) except KeyError: error('_STORER_MAP') # existing node (and must be a table) if tt is None: # if we are a writer, determine the tt if value is not None: if pt == u'series_table': index = getattr(value, 'index', None) if index is not None: if index.nlevels == 1: tt = u'appendable_series' elif index.nlevels > 1: tt = u'appendable_multiseries' elif pt == u'frame_table': index = getattr(value, 'index', None) if index is not None: if index.nlevels == 1: tt = u'appendable_frame' elif index.nlevels > 1: tt = u'appendable_multiframe' elif pt == u'wide_table': tt = u'appendable_panel' elif pt == u'ndim_table': tt = u'appendable_ndim' else: # distiguish between a frame/table tt = u'legacy_panel' try: fields = group.table._v_attrs.fields if len(fields) == 1 and fields[0] == u'value': tt = u'legacy_frame' except IndexError: pass try: return globals()[_TABLE_MAP[tt]](self, group, **kwargs) except KeyError: error('_TABLE_MAP') def _write_to_group(self, key, value, format, index=True, append=False, complib=None, encoding=None, **kwargs): group = self.get_node(key) # remove the node if we are not appending if group is not None and not append: self._handle.remove_node(group, recursive=True) group = None # we don't want to store a table node at all if are object is 0-len # as there are not dtypes if getattr(value, 'empty', None) and (format == 'table' or append): return if group is None: paths = key.split('/') # recursively create the groups path = '/' for p in paths: if not len(p): continue new_path = path if not path.endswith('/'): new_path += '/' new_path += p group = self.get_node(new_path) if group is None: group = self._handle.create_group(path, p) path = new_path s = self._create_storer(group, format, value, append=append, encoding=encoding, **kwargs) if append: # raise if we are trying to append to a Fixed format, # or a table that exists (and we are putting) if (not s.is_table or (s.is_table and format == 'fixed' and s.is_exists)): raise ValueError('Can only append to Tables') if not s.is_exists: s.set_object_info() else: s.set_object_info() if not s.is_table and complib: raise ValueError( 'Compression not supported on Fixed format stores' ) # write the object s.write(obj=value, append=append, complib=complib, **kwargs) if s.is_table and index: s.create_index(columns=index) def _read_group(self, group, **kwargs): s = self._create_storer(group) s.infer_axes() return s.read(**kwargs) class TableIterator(object): """ define the iteration interface on a table Parameters ---------- store : the reference store s : the referred storer func : the function to execute the query where : the where of the query nrows : the rows to iterate on start : the passed start value (default is None) stop : the passed stop value (default is None) iterator : boolean, whether to use the default iterator chunksize : the passed chunking value (default is 50000) auto_close : boolean, automatically close the store at the end of iteration, default is False kwargs : the passed kwargs """ def __init__(self, store, s, func, where, nrows, start=None, stop=None, iterator=False, chunksize=None, auto_close=False): self.store = store self.s = s self.func = func self.where = where # set start/stop if they are not set if we are a table if self.s.is_table: if nrows is None: nrows = 0 if start is None: start = 0 if stop is None: stop = nrows stop = min(nrows, stop) self.nrows = nrows self.start = start self.stop = stop self.coordinates = None if iterator or chunksize is not None: if chunksize is None: chunksize = 100000 self.chunksize = int(chunksize) else: self.chunksize = None self.auto_close = auto_close def __iter__(self): # iterate current = self.start while current < self.stop: stop = min(current + self.chunksize, self.stop) value = self.func(None, None, self.coordinates[current:stop]) current = stop if value is None or not len(value): continue yield value self.close() def close(self): if self.auto_close: self.store.close() def get_result(self, coordinates=False): # return the actual iterator if self.chunksize is not None: if not self.s.is_table: raise TypeError( "can only use an iterator or chunksize on a table") self.coordinates = self.s.read_coordinates(where=self.where) return self # if specified read via coordinates (necessary for multiple selections if coordinates: where = self.s.read_coordinates(where=self.where, start=self.start, stop=self.stop) else: where = self.where # directly return the result results = self.func(self.start, self.stop, where) self.close() return results class IndexCol(StringMixin): """ an index column description class Parameters ---------- axis : axis which I reference values : the ndarray like converted values kind : a string description of this type typ : the pytables type pos : the position in the pytables """ is_an_indexable = True is_data_indexable = True _info_fields = ['freq', 'tz', 'index_name'] def __init__(self, values=None, kind=None, typ=None, cname=None, itemsize=None, name=None, axis=None, kind_attr=None, pos=None, freq=None, tz=None, index_name=None, **kwargs): self.values = values self.kind = kind self.typ = typ self.itemsize = itemsize self.name = name self.cname = cname self.kind_attr = kind_attr self.axis = axis self.pos = pos self.freq = freq self.tz = tz self.index_name = index_name self.table = None self.meta = None self.metadata = None if name is not None: self.set_name(name, kind_attr) if pos is not None: self.set_pos(pos) def set_name(self, name, kind_attr=None): """ set the name of this indexer """ self.name = name self.kind_attr = kind_attr or "{name}_kind".format(name=name) if self.cname is None: self.cname = name return self def set_axis(self, axis): """ set the axis over which I index """ self.axis = axis return self def set_pos(self, pos): """ set the position of this column in the Table """ self.pos = pos if pos is not None and self.typ is not None: self.typ._v_pos = pos return self def set_table(self, table): self.table = table return self def __unicode__(self): temp = tuple( map(pprint_thing, (self.name, self.cname, self.axis, self.pos, self.kind))) return ','.join(("{key}->{value}".format(key=key, value=value) for key, value in zip( ['name', 'cname', 'axis', 'pos', 'kind'], temp))) def __eq__(self, other): """ compare 2 col items """ return all(getattr(self, a, None) == getattr(other, a, None) for a in ['name', 'cname', 'axis', 'pos']) def __ne__(self, other): return not self.__eq__(other) @property def is_indexed(self): """ return whether I am an indexed column """ try: return getattr(self.table.cols, self.cname).is_indexed except AttributeError: False def copy(self): new_self = copy.copy(self) return new_self def infer(self, handler): """infer this column from the table: create and return a new object""" table = handler.table new_self = self.copy() new_self.set_table(table) new_self.get_attr() new_self.read_metadata(handler) return new_self def convert(self, values, nan_rep, encoding, errors): """ set the values from this selection: take = take ownership """ # values is a recarray if values.dtype.fields is not None: values = values[self.cname] values = _maybe_convert(values, self.kind, encoding, errors) kwargs = dict() if self.freq is not None: kwargs['freq'] = _ensure_decoded(self.freq) if self.index_name is not None: kwargs['name'] = _ensure_decoded(self.index_name) # making an Index instance could throw a number of different errors try: self.values = Index(values, **kwargs) except Exception: # noqa: E722 # if the output freq is different that what we recorded, # it should be None (see also 'doc example part 2') if 'freq' in kwargs: kwargs['freq'] = None self.values = Index(values, **kwargs) self.values = _set_tz(self.values, self.tz) return self def take_data(self): """ return the values & release the memory """ self.values, values = None, self.values return values @property def attrs(self): return self.table._v_attrs @property def description(self): return self.table.description @property def col(self): """ return my current col description """ return getattr(self.description, self.cname, None) @property def cvalues(self): """ return my cython values """ return self.values def __iter__(self): return iter(self.values) def maybe_set_size(self, min_itemsize=None): """ maybe set a string col itemsize: min_itemsize can be an integer or a dict with this columns name with an integer size """ if _ensure_decoded(self.kind) == u'string': if isinstance(min_itemsize, dict): min_itemsize = min_itemsize.get(self.name) if min_itemsize is not None and self.typ.itemsize < min_itemsize: self.typ = _tables( ).StringCol(itemsize=min_itemsize, pos=self.pos) def validate(self, handler, append): self.validate_names() def validate_names(self): pass def validate_and_set(self, handler, append): self.set_table(handler.table) self.validate_col() self.validate_attr(append) self.validate_metadata(handler) self.write_metadata(handler) self.set_attr() def validate_col(self, itemsize=None): """ validate this column: return the compared against itemsize """ # validate this column for string truncation (or reset to the max size) if _ensure_decoded(self.kind) == u'string': c = self.col if c is not None: if itemsize is None: itemsize = self.itemsize if c.itemsize < itemsize: raise ValueError( "Trying to store a string with len [{itemsize}] in " "[{cname}] column but\nthis column has a limit of " "[{c_itemsize}]!\nConsider using min_itemsize to " "preset the sizes on these columns".format( itemsize=itemsize, cname=self.cname, c_itemsize=c.itemsize)) return c.itemsize return None def validate_attr(self, append): # check for backwards incompatibility if append: existing_kind = getattr(self.attrs, self.kind_attr, None) if existing_kind is not None and existing_kind != self.kind: raise TypeError( "incompatible kind in col [{existing} - " "{self_kind}]".format( existing=existing_kind, self_kind=self.kind)) def update_info(self, info): """ set/update the info for this indexable with the key/value if there is a conflict raise/warn as needed """ for key in self._info_fields: value = getattr(self, key, None) idx = _get_info(info, self.name) existing_value = idx.get(key) if key in idx and value is not None and existing_value != value: # frequency/name just warn if key in ['freq', 'index_name']: ws = attribute_conflict_doc % (key, existing_value, value) warnings.warn(ws, AttributeConflictWarning, stacklevel=6) # reset idx[key] = None setattr(self, key, None) else: raise ValueError( "invalid info for [{name}] for [{key}], " "existing_value [{existing_value}] conflicts with " "new value [{value}]".format( name=self.name, key=key, existing_value=existing_value, value=value)) else: if value is not None or existing_value is not None: idx[key] = value return self def set_info(self, info): """ set my state from the passed info """ idx = info.get(self.name) if idx is not None: self.__dict__.update(idx) def get_attr(self): """ set the kind for this column """ self.kind = getattr(self.attrs, self.kind_attr, None) def set_attr(self): """ set the kind for this column """ setattr(self.attrs, self.kind_attr, self.kind) def read_metadata(self, handler): """ retrieve the metadata for this columns """ self.metadata = handler.read_metadata(self.cname) def validate_metadata(self, handler): """ validate that kind=category does not change the categories """ if self.meta == 'category': new_metadata = self.metadata cur_metadata = handler.read_metadata(self.cname) if (new_metadata is not None and cur_metadata is not None and not array_equivalent(new_metadata, cur_metadata)): raise ValueError("cannot append a categorical with " "different categories to the existing") def write_metadata(self, handler): """ set the meta data """ if self.metadata is not None: handler.write_metadata(self.cname, self.metadata) class GenericIndexCol(IndexCol): """ an index which is not represented in the data of the table """ @property def is_indexed(self): return False def convert(self, values, nan_rep, encoding, errors): """ set the values from this selection: take = take ownership """ self.values = Int64Index(np.arange(self.table.nrows)) return self def get_attr(self): pass def set_attr(self): pass class DataCol(IndexCol): """ a data holding column, by definition this is not indexable Parameters ---------- data : the actual data cname : the column name in the table to hold the data (typically values) meta : a string description of the metadata metadata : the actual metadata """ is_an_indexable = False is_data_indexable = False _info_fields = ['tz', 'ordered'] @classmethod def create_for_block( cls, i=None, name=None, cname=None, version=None, **kwargs): """ return a new datacol with the block i """ if cname is None: cname = name or 'values_block_{idx}'.format(idx=i) if name is None: name = cname # prior to 0.10.1, we named values blocks like: values_block_0 an the # name values_0 try: if version[0] == 0 and version[1] <= 10 and version[2] == 0: m = re.search(r"values_block_(\d+)", name) if m: name = "values_{group}".format(group=m.groups()[0]) except IndexError: pass return cls(name=name, cname=cname, **kwargs) def __init__(self, values=None, kind=None, typ=None, cname=None, data=None, meta=None, metadata=None, block=None, **kwargs): super(DataCol, self).__init__(values=values, kind=kind, typ=typ, cname=cname, **kwargs) self.dtype = None self.dtype_attr = u'{name}_dtype'.format(name=self.name) self.meta = meta self.meta_attr = u'{name}_meta'.format(name=self.name) self.set_data(data) self.set_metadata(metadata) def __unicode__(self): temp = tuple( map(pprint_thing, (self.name, self.cname, self.dtype, self.kind, self.shape))) return ','.join(("{key}->{value}".format(key=key, value=value) for key, value in zip( ['name', 'cname', 'dtype', 'kind', 'shape'], temp))) def __eq__(self, other): """ compare 2 col items """ return all(getattr(self, a, None) == getattr(other, a, None) for a in ['name', 'cname', 'dtype', 'pos']) def set_data(self, data, dtype=None): self.data = data if data is not None: if dtype is not None: self.dtype = dtype self.set_kind() elif self.dtype is None: self.dtype = data.dtype.name self.set_kind() def take_data(self): """ return the data & release the memory """ self.data, data = None, self.data return data def set_metadata(self, metadata): """ record the metadata """ if metadata is not None: metadata = np.array(metadata, copy=False).ravel() self.metadata = metadata def set_kind(self): # set my kind if we can if self.dtype is not None: dtype = _ensure_decoded(self.dtype) if dtype.startswith(u'string') or dtype.startswith(u'bytes'): self.kind = 'string' elif dtype.startswith(u'float'): self.kind = 'float' elif dtype.startswith(u'complex'): self.kind = 'complex' elif dtype.startswith(u'int') or dtype.startswith(u'uint'): self.kind = 'integer' elif dtype.startswith(u'date'): self.kind = 'datetime' elif dtype.startswith(u'timedelta'): self.kind = 'timedelta' elif dtype.startswith(u'bool'): self.kind = 'bool' else: raise AssertionError( "cannot interpret dtype of [{dtype}] in [{obj}]".format( dtype=dtype, obj=self)) # set my typ if we need if self.typ is None: self.typ = getattr(self.description, self.cname, None) def set_atom(self, block, block_items, existing_col, min_itemsize, nan_rep, info, encoding=None, errors='strict'): """ create and setup my atom from the block b """ self.values = list(block_items) # short-cut certain block types if block.is_categorical: return self.set_atom_categorical(block, items=block_items, info=info) elif block.is_datetimetz: return self.set_atom_datetime64tz(block, info=info) elif block.is_datetime: return self.set_atom_datetime64(block) elif block.is_timedelta: return self.set_atom_timedelta64(block) elif block.is_complex: return self.set_atom_complex(block) dtype = block.dtype.name inferred_type = lib.infer_dtype(block.values, skipna=False) if inferred_type == 'date': raise TypeError( "[date] is not implemented as a table column") elif inferred_type == 'datetime': # after 8260 # this only would be hit for a mutli-timezone dtype # which is an error raise TypeError( "too many timezones in this block, create separate " "data columns" ) elif inferred_type == 'unicode': raise TypeError( "[unicode] is not implemented as a table column") # this is basically a catchall; if say a datetime64 has nans then will # end up here ### elif inferred_type == 'string' or dtype == 'object': self.set_atom_string( block, block_items, existing_col, min_itemsize, nan_rep, encoding, errors) # set as a data block else: self.set_atom_data(block) def get_atom_string(self, block, itemsize): return _tables().StringCol(itemsize=itemsize, shape=block.shape[0]) def set_atom_string(self, block, block_items, existing_col, min_itemsize, nan_rep, encoding, errors): # fill nan items with myself, don't disturb the blocks by # trying to downcast block = block.fillna(nan_rep, downcast=False) if isinstance(block, list): block = block[0] data = block.values # see if we have a valid string type inferred_type = lib.infer_dtype(data.ravel(), skipna=False) if inferred_type != 'string': # we cannot serialize this data, so report an exception on a column # by column basis for i, item in enumerate(block_items): col = block.iget(i) inferred_type = lib.infer_dtype(col.ravel(), skipna=False) if inferred_type != 'string': raise TypeError( "Cannot serialize the column [{item}] because\n" "its data contents are [{type}] object dtype".format( item=item, type=inferred_type) ) # itemsize is the maximum length of a string (along any dimension) data_converted = _convert_string_array(data, encoding, errors) itemsize = data_converted.itemsize # specified min_itemsize? if isinstance(min_itemsize, dict): min_itemsize = int(min_itemsize.get( self.name) or min_itemsize.get('values') or 0) itemsize = max(min_itemsize or 0, itemsize) # check for column in the values conflicts if existing_col is not None: eci = existing_col.validate_col(itemsize) if eci > itemsize: itemsize = eci self.itemsize = itemsize self.kind = 'string' self.typ = self.get_atom_string(block, itemsize) self.set_data(data_converted.astype( '|S{size}'.format(size=itemsize), copy=False)) def get_atom_coltype(self, kind=None): """ return the PyTables column class for this column """ if kind is None: kind = self.kind if self.kind.startswith('uint'): col_name = "UInt{name}Col".format(name=kind[4:]) else: col_name = "{name}Col".format(name=kind.capitalize()) return getattr(_tables(), col_name) def get_atom_data(self, block, kind=None): return self.get_atom_coltype(kind=kind)(shape=block.shape[0]) def set_atom_complex(self, block): self.kind = block.dtype.name itemsize = int(self.kind.split('complex')[-1]) // 8 self.typ = _tables().ComplexCol( itemsize=itemsize, shape=block.shape[0]) self.set_data(block.values.astype(self.typ.type, copy=False)) def set_atom_data(self, block): self.kind = block.dtype.name self.typ = self.get_atom_data(block) self.set_data(block.values.astype(self.typ.type, copy=False)) def set_atom_categorical(self, block, items, info=None, values=None): # currently only supports a 1-D categorical # in a 1-D block values = block.values codes = values.codes self.kind = 'integer' self.dtype = codes.dtype.name if values.ndim > 1: raise NotImplementedError("only support 1-d categoricals") if len(items) > 1: raise NotImplementedError("only support single block categoricals") # write the codes; must be in a block shape self.ordered = values.ordered self.typ = self.get_atom_data(block, kind=codes.dtype.name) self.set_data(_block_shape(codes)) # write the categories self.meta = 'category' self.set_metadata(block.values.categories) # update the info self.update_info(info) def get_atom_datetime64(self, block): return _tables().Int64Col(shape=block.shape[0]) def set_atom_datetime64(self, block, values=None): self.kind = 'datetime64' self.typ = self.get_atom_datetime64(block) if values is None: values = block.values.view('i8') self.set_data(values, 'datetime64') def set_atom_datetime64tz(self, block, info, values=None): if values is None: values = block.values # convert this column to i8 in UTC, and save the tz values = values.asi8.reshape(block.shape) # store a converted timezone self.tz = _get_tz(block.values.tz) self.update_info(info) self.kind = 'datetime64' self.typ = self.get_atom_datetime64(block) self.set_data(values, 'datetime64') def get_atom_timedelta64(self, block): return _tables().Int64Col(shape=block.shape[0]) def set_atom_timedelta64(self, block, values=None): self.kind = 'timedelta64' self.typ = self.get_atom_timedelta64(block) if values is None: values = block.values.view('i8') self.set_data(values, 'timedelta64') @property def shape(self): return getattr(self.data, 'shape', None) @property def cvalues(self): """ return my cython values """ return self.data def validate_attr(self, append): """validate that we have the same order as the existing & same dtype""" if append: existing_fields = getattr(self.attrs, self.kind_attr, None) if (existing_fields is not None and existing_fields != list(self.values)): raise ValueError("appended items do not match existing items" " in table!") existing_dtype = getattr(self.attrs, self.dtype_attr, None) if (existing_dtype is not None and existing_dtype != self.dtype): raise ValueError("appended items dtype do not match existing " "items dtype in table!") def convert(self, values, nan_rep, encoding, errors): """set the data from this selection (and convert to the correct dtype if we can) """ # values is a recarray if values.dtype.fields is not None: values = values[self.cname] self.set_data(values) # use the meta if needed meta = _ensure_decoded(self.meta) # convert to the correct dtype if self.dtype is not None: dtype = _ensure_decoded(self.dtype) # reverse converts if dtype == u'datetime64': # recreate with tz if indicated self.data = _set_tz(self.data, self.tz, coerce=True) elif dtype == u'timedelta64': self.data = np.asarray(self.data, dtype='m8[ns]') elif dtype == u'date': try: self.data = np.asarray( [date.fromordinal(v) for v in self.data], dtype=object) except ValueError: self.data = np.asarray( [date.fromtimestamp(v) for v in self.data], dtype=object) elif dtype == u'datetime': self.data = np.asarray( [datetime.fromtimestamp(v) for v in self.data], dtype=object) elif meta == u'category': # we have a categorical categories = self.metadata codes = self.data.ravel() # if we have stored a NaN in the categories # then strip it; in theory we could have BOTH # -1s in the codes and nulls :< if categories is None: # Handle case of NaN-only categorical columns in which case # the categories are an empty array; when this is stored, # pytables cannot write a zero-len array, so on readback # the categories would be None and `read_hdf()` would fail. categories = Index([], dtype=np.float64) else: mask = isna(categories) if mask.any(): categories = categories[~mask] codes[codes != -1] -= mask.astype(int).cumsum().values self.data = Categorical.from_codes(codes, categories=categories, ordered=self.ordered) else: try: self.data = self.data.astype(dtype, copy=False) except TypeError: self.data = self.data.astype('O', copy=False) # convert nans / decode if _ensure_decoded(self.kind) == u'string': self.data = _unconvert_string_array( self.data, nan_rep=nan_rep, encoding=encoding, errors=errors) return self def get_attr(self): """ get the data for this column """ self.values = getattr(self.attrs, self.kind_attr, None) self.dtype = getattr(self.attrs, self.dtype_attr, None) self.meta = getattr(self.attrs, self.meta_attr, None) self.set_kind() def set_attr(self): """ set the data for this column """ setattr(self.attrs, self.kind_attr, self.values) setattr(self.attrs, self.meta_attr, self.meta) if self.dtype is not None: setattr(self.attrs, self.dtype_attr, self.dtype) class DataIndexableCol(DataCol): """ represent a data column that can be indexed """ is_data_indexable = True def validate_names(self): if not Index(self.values).is_object(): raise ValueError("cannot have non-object label DataIndexableCol") def get_atom_string(self, block, itemsize): return _tables().StringCol(itemsize=itemsize) def get_atom_data(self, block, kind=None): return self.get_atom_coltype(kind=kind)() def get_atom_datetime64(self, block): return _tables().Int64Col() def get_atom_timedelta64(self, block): return _tables().Int64Col() class GenericDataIndexableCol(DataIndexableCol): """ represent a generic pytables data column """ def get_attr(self): pass class Fixed(StringMixin): """ represent an object in my store facilitate read/write of various types of objects this is an abstract base class Parameters ---------- parent : my parent HDFStore group : the group node where the table resides """ pandas_kind = None obj_type = None ndim = None is_table = False def __init__(self, parent, group, encoding=None, errors='strict', **kwargs): self.parent = parent self.group = group self.encoding = _ensure_encoding(encoding) self.errors = errors self.set_version() @property def is_old_version(self): return (self.version[0] <= 0 and self.version[1] <= 10 and self.version[2] < 1) def set_version(self): """ compute and set our version """ version = _ensure_decoded( getattr(self.group._v_attrs, 'pandas_version', None)) try: self.version = tuple(int(x) for x in version.split('.')) if len(self.version) == 2: self.version = self.version + (0,) except AttributeError: self.version = (0, 0, 0) @property def pandas_type(self): return _ensure_decoded(getattr(self.group._v_attrs, 'pandas_type', None)) @property def format_type(self): return 'fixed' def __unicode__(self): """ return a pretty representation of myself """ self.infer_axes() s = self.shape if s is not None: if isinstance(s, (list, tuple)): s = "[{shape}]".format( shape=','.join(pprint_thing(x) for x in s)) return "{type:12.12} (shape->{shape})".format( type=self.pandas_type, shape=s) return self.pandas_type def set_object_info(self): """ set my pandas type & version """ self.attrs.pandas_type = str(self.pandas_kind) self.attrs.pandas_version = str(_version) self.set_version() def copy(self): new_self = copy.copy(self) return new_self @property def storage_obj_type(self): return self.obj_type @property def shape(self): return self.nrows @property def pathname(self): return self.group._v_pathname @property def _handle(self): return self.parent._handle @property def _filters(self): return self.parent._filters @property def _complevel(self): return self.parent._complevel @property def _fletcher32(self): return self.parent._fletcher32 @property def _complib(self): return self.parent._complib @property def attrs(self): return self.group._v_attrs def set_attrs(self): """ set our object attributes """ pass def get_attrs(self): """ get our object attributes """ pass @property def storable(self): """ return my storable """ return self.group @property def is_exists(self): return False @property def nrows(self): return getattr(self.storable, 'nrows', None) def validate(self, other): """ validate against an existing storable """ if other is None: return return True def validate_version(self, where=None): """ are we trying to operate on an old version? """ return True def infer_axes(self): """ infer the axes of my storer return a boolean indicating if we have a valid storer or not """ s = self.storable if s is None: return False self.get_attrs() return True def read(self, **kwargs): raise NotImplementedError( "cannot read on an abstract storer: subclasses should implement") def write(self, **kwargs): raise NotImplementedError( "cannot write on an abstract storer: sublcasses should implement") def delete(self, where=None, start=None, stop=None, **kwargs): """ support fully deleting the node in its entirety (only) - where specification must be None """ if com._all_none(where, start, stop): self._handle.remove_node(self.group, recursive=True) return None raise TypeError("cannot delete on an abstract storer") class GenericFixed(Fixed): """ a generified fixed version """ _index_type_map = {DatetimeIndex: 'datetime', PeriodIndex: 'period'} _reverse_index_map = {v: k for k, v in compat.iteritems(_index_type_map)} attributes = [] # indexer helpders def _class_to_alias(self, cls): return self._index_type_map.get(cls, '') def _alias_to_class(self, alias): if isinstance(alias, type): # pragma: no cover # compat: for a short period of time master stored types return alias return self._reverse_index_map.get(alias, Index) def _get_index_factory(self, klass): if klass == DatetimeIndex: def f(values, freq=None, tz=None): # data are already in UTC, localize and convert if tz present result = DatetimeIndex._simple_new(values.values, name=None, freq=freq) if tz is not None: result = result.tz_localize('UTC').tz_convert(tz) return result return f elif klass == PeriodIndex: def f(values, freq=None, tz=None): return PeriodIndex._simple_new(values, name=None, freq=freq) return f return klass def validate_read(self, kwargs): """ remove table keywords from kwargs and return raise if any keywords are passed which are not-None """ kwargs = copy.copy(kwargs) columns = kwargs.pop('columns', None) if columns is not None: raise TypeError("cannot pass a column specification when reading " "a Fixed format store. this store must be " "selected in its entirety") where = kwargs.pop('where', None) if where is not None: raise TypeError("cannot pass a where specification when reading " "from a Fixed format store. this store must be " "selected in its entirety") return kwargs @property def is_exists(self): return True def set_attrs(self): """ set our object attributes """ self.attrs.encoding = self.encoding self.attrs.errors = self.errors def get_attrs(self): """ retrieve our attributes """ self.encoding = _ensure_encoding(getattr(self.attrs, 'encoding', None)) self.errors = _ensure_decoded(getattr(self.attrs, 'errors', 'strict')) for n in self.attributes: setattr(self, n, _ensure_decoded(getattr(self.attrs, n, None))) def write(self, obj, **kwargs): self.set_attrs() def read_array(self, key, start=None, stop=None): """ read an array for the specified node (off of group """ import tables node = getattr(self.group, key) attrs = node._v_attrs transposed = getattr(attrs, 'transposed', False) if isinstance(node, tables.VLArray): ret = node[0][start:stop] else: dtype = getattr(attrs, 'value_type', None) shape = getattr(attrs, 'shape', None) if shape is not None: # length 0 axis ret = np.empty(shape, dtype=dtype) else: ret = node[start:stop] if dtype == u'datetime64': # reconstruct a timezone if indicated ret = _set_tz(ret, getattr(attrs, 'tz', None), coerce=True) elif dtype == u'timedelta64': ret = np.asarray(ret, dtype='m8[ns]') if transposed: return ret.T else: return ret def read_index(self, key, **kwargs): variety = _ensure_decoded( getattr(self.attrs, '{key}_variety'.format(key=key))) if variety == u'multi': return self.read_multi_index(key, **kwargs) elif variety == u'block': return self.read_block_index(key, **kwargs) elif variety == u'sparseint': return self.read_sparse_intindex(key, **kwargs) elif variety == u'regular': _, index = self.read_index_node(getattr(self.group, key), **kwargs) return index else: # pragma: no cover raise TypeError( 'unrecognized index variety: {variety}'.format( variety=variety)) def write_index(self, key, index): if isinstance(index, MultiIndex): setattr(self.attrs, '{key}_variety'.format(key=key), 'multi') self.write_multi_index(key, index) elif isinstance(index, BlockIndex): setattr(self.attrs, '{key}_variety'.format(key=key), 'block') self.write_block_index(key, index) elif isinstance(index, IntIndex): setattr(self.attrs, '{key}_variety'.format(key=key), 'sparseint') self.write_sparse_intindex(key, index) else: setattr(self.attrs, '{key}_variety'.format(key=key), 'regular') converted = _convert_index(index, self.encoding, self.errors, self.format_type).set_name('index') self.write_array(key, converted.values) node = getattr(self.group, key) node._v_attrs.kind = converted.kind node._v_attrs.name = index.name if isinstance(index, (DatetimeIndex, PeriodIndex)): node._v_attrs.index_class = self._class_to_alias(type(index)) if hasattr(index, 'freq'): node._v_attrs.freq = index.freq if hasattr(index, 'tz') and index.tz is not None: node._v_attrs.tz = _get_tz(index.tz) def write_block_index(self, key, index): self.write_array('{key}_blocs'.format(key=key), index.blocs) self.write_array('{key}_blengths'.format(key=key), index.blengths) setattr(self.attrs, '{key}_length'.format(key=key), index.length) def read_block_index(self, key, **kwargs): length = getattr(self.attrs, '{key}_length'.format(key=key)) blocs = self.read_array('{key}_blocs'.format(key=key), **kwargs) blengths = self.read_array('{key}_blengths'.format(key=key), **kwargs) return BlockIndex(length, blocs, blengths) def write_sparse_intindex(self, key, index): self.write_array('{key}_indices'.format(key=key), index.indices) setattr(self.attrs, '{key}_length'.format(key=key), index.length) def read_sparse_intindex(self, key, **kwargs): length = getattr(self.attrs, '{key}_length'.format(key=key)) indices = self.read_array('{key}_indices'.format(key=key), **kwargs) return IntIndex(length, indices) def write_multi_index(self, key, index): setattr(self.attrs, '{key}_nlevels'.format(key=key), index.nlevels) for i, (lev, level_codes, name) in enumerate(zip(index.levels, index.codes, index.names)): # write the level level_key = '{key}_level{idx}'.format(key=key, idx=i) conv_level = _convert_index(lev, self.encoding, self.errors, self.format_type).set_name(level_key) self.write_array(level_key, conv_level.values) node = getattr(self.group, level_key) node._v_attrs.kind = conv_level.kind node._v_attrs.name = name # write the name setattr(node._v_attrs, '{key}_name{name}'.format( key=key, name=name), name) # write the labels label_key = '{key}_label{idx}'.format(key=key, idx=i) self.write_array(label_key, level_codes) def read_multi_index(self, key, **kwargs): nlevels = getattr(self.attrs, '{key}_nlevels'.format(key=key)) levels = [] codes = [] names = [] for i in range(nlevels): level_key = '{key}_level{idx}'.format(key=key, idx=i) name, lev = self.read_index_node(getattr(self.group, level_key), **kwargs) levels.append(lev) names.append(name) label_key = '{key}_label{idx}'.format(key=key, idx=i) level_codes = self.read_array(label_key, **kwargs) codes.append(level_codes) return MultiIndex(levels=levels, codes=codes, names=names, verify_integrity=True) def read_index_node(self, node, start=None, stop=None): data = node[start:stop] # If the index was an empty array write_array_empty() will # have written a sentinel. Here we relace it with the original. if ('shape' in node._v_attrs and self._is_empty_array(getattr(node._v_attrs, 'shape'))): data = np.empty(getattr(node._v_attrs, 'shape'), dtype=getattr(node._v_attrs, 'value_type')) kind = _ensure_decoded(node._v_attrs.kind) name = None if 'name' in node._v_attrs: name = _ensure_str(node._v_attrs.name) name = _ensure_decoded(name) index_class = self._alias_to_class(_ensure_decoded( getattr(node._v_attrs, 'index_class', ''))) factory = self._get_index_factory(index_class) kwargs = {} if u'freq' in node._v_attrs: kwargs['freq'] = node._v_attrs['freq'] if u'tz' in node._v_attrs: kwargs['tz'] = node._v_attrs['tz'] if kind in (u'date', u'datetime'): index = factory(_unconvert_index(data, kind, encoding=self.encoding, errors=self.errors), dtype=object, **kwargs) else: index = factory(_unconvert_index(data, kind, encoding=self.encoding, errors=self.errors), **kwargs) index.name = name return name, index def write_array_empty(self, key, value): """ write a 0-len array """ # ugly hack for length 0 axes arr = np.empty((1,) * value.ndim) self._handle.create_array(self.group, key, arr) getattr(self.group, key)._v_attrs.value_type = str(value.dtype) getattr(self.group, key)._v_attrs.shape = value.shape def _is_empty_array(self, shape): """Returns true if any axis is zero length.""" return any(x == 0 for x in shape) def write_array(self, key, value, items=None): if key in self.group: self._handle.remove_node(self.group, key) # Transform needed to interface with pytables row/col notation empty_array = self._is_empty_array(value.shape) transposed = False if is_categorical_dtype(value): raise NotImplementedError('Cannot store a category dtype in ' 'a HDF5 dataset that uses format=' '"fixed". Use format="table".') if not empty_array: if hasattr(value, 'T'): # ExtensionArrays (1d) may not have transpose. value = value.T transposed = True if self._filters is not None: atom = None try: # get the atom for this datatype atom = _tables().Atom.from_dtype(value.dtype) except ValueError: pass if atom is not None: # create an empty chunked array and fill it from value if not empty_array: ca = self._handle.create_carray(self.group, key, atom, value.shape, filters=self._filters) ca[:] = value getattr(self.group, key)._v_attrs.transposed = transposed else: self.write_array_empty(key, value) return if value.dtype.type == np.object_: # infer the type, warn if we have a non-string type here (for # performance) inferred_type = lib.infer_dtype(value.ravel(), skipna=False) if empty_array: pass elif inferred_type == 'string': pass else: try: items = list(items) except TypeError: pass ws = performance_doc % (inferred_type, key, items) warnings.warn(ws, PerformanceWarning, stacklevel=7) vlarr = self._handle.create_vlarray(self.group, key, _tables().ObjectAtom()) vlarr.append(value) else: if empty_array: self.write_array_empty(key, value) else: if is_datetime64_dtype(value.dtype): self._handle.create_array( self.group, key, value.view('i8')) getattr( self.group, key)._v_attrs.value_type = 'datetime64' elif is_datetime64tz_dtype(value.dtype): # store as UTC # with a zone self._handle.create_array(self.group, key, value.asi8) node = getattr(self.group, key) node._v_attrs.tz = _get_tz(value.tz) node._v_attrs.value_type = 'datetime64' elif is_timedelta64_dtype(value.dtype): self._handle.create_array( self.group, key, value.view('i8')) getattr( self.group, key)._v_attrs.value_type = 'timedelta64' else: self._handle.create_array(self.group, key, value) getattr(self.group, key)._v_attrs.transposed = transposed class LegacyFixed(GenericFixed): def read_index_legacy(self, key, start=None, stop=None): node = getattr(self.group, key) data = node[start:stop] kind = node._v_attrs.kind return _unconvert_index_legacy(data, kind, encoding=self.encoding, errors=self.errors) class LegacySeriesFixed(LegacyFixed): def read(self, **kwargs): kwargs = self.validate_read(kwargs) index = self.read_index_legacy('index') values = self.read_array('values') return Series(values, index=index) class LegacyFrameFixed(LegacyFixed): def read(self, **kwargs): kwargs = self.validate_read(kwargs) index = self.read_index_legacy('index') columns = self.read_index_legacy('columns') values = self.read_array('values') return DataFrame(values, index=index, columns=columns) class SeriesFixed(GenericFixed): pandas_kind = u'series' attributes = ['name'] @property def shape(self): try: return len(getattr(self.group, 'values')), except (TypeError, AttributeError): return None def read(self, **kwargs): kwargs = self.validate_read(kwargs) index = self.read_index('index', **kwargs) values = self.read_array('values', **kwargs) return Series(values, index=index, name=self.name) def write(self, obj, **kwargs): super(SeriesFixed, self).write(obj, **kwargs) self.write_index('index', obj.index) self.write_array('values', obj.values) self.attrs.name = obj.name class SparseFixed(GenericFixed): def validate_read(self, kwargs): """ we don't support start, stop kwds in Sparse """ kwargs = super(SparseFixed, self).validate_read(kwargs) if 'start' in kwargs or 'stop' in kwargs: raise NotImplementedError("start and/or stop are not supported " "in fixed Sparse reading") return kwargs class SparseSeriesFixed(SparseFixed): pandas_kind = u'sparse_series' attributes = ['name', 'fill_value', 'kind'] def read(self, **kwargs): kwargs = self.validate_read(kwargs) index = self.read_index('index') sp_values = self.read_array('sp_values') sp_index = self.read_index('sp_index') return SparseSeries(sp_values, index=index, sparse_index=sp_index, kind=self.kind or u'block', fill_value=self.fill_value, name=self.name) def write(self, obj, **kwargs): super(SparseSeriesFixed, self).write(obj, **kwargs) self.write_index('index', obj.index) self.write_index('sp_index', obj.sp_index) self.write_array('sp_values', obj.sp_values) self.attrs.name = obj.name self.attrs.fill_value = obj.fill_value self.attrs.kind = obj.kind class SparseFrameFixed(SparseFixed): pandas_kind = u'sparse_frame' attributes = ['default_kind', 'default_fill_value'] def read(self, **kwargs): kwargs = self.validate_read(kwargs) columns = self.read_index('columns') sdict = {} for c in columns: key = 'sparse_series_{columns}'.format(columns=c) s = SparseSeriesFixed(self.parent, getattr(self.group, key)) s.infer_axes() sdict[c] = s.read() return SparseDataFrame(sdict, columns=columns, default_kind=self.default_kind, default_fill_value=self.default_fill_value) def write(self, obj, **kwargs): """ write it as a collection of individual sparse series """ super(SparseFrameFixed, self).write(obj, **kwargs) for name, ss in compat.iteritems(obj): key = 'sparse_series_{name}'.format(name=name) if key not in self.group._v_children: node = self._handle.create_group(self.group, key) else: node = getattr(self.group, key) s = SparseSeriesFixed(self.parent, node) s.write(ss) self.attrs.default_fill_value = obj.default_fill_value self.attrs.default_kind = obj.default_kind self.write_index('columns', obj.columns) class BlockManagerFixed(GenericFixed): attributes = ['ndim', 'nblocks'] is_shape_reversed = False @property def shape(self): try: ndim = self.ndim # items items = 0 for i in range(self.nblocks): node = getattr(self.group, 'block{idx}_items'.format(idx=i)) shape = getattr(node, 'shape', None) if shape is not None: items += shape[0] # data shape node = getattr(self.group, 'block0_values') shape = getattr(node, 'shape', None) if shape is not None: shape = list(shape[0:(ndim - 1)]) else: shape = [] shape.append(items) # hacky - this works for frames, but is reversed for panels if self.is_shape_reversed: shape = shape[::-1] return shape except AttributeError: return None def read(self, start=None, stop=None, **kwargs): # start, stop applied to rows, so 0th axis only kwargs = self.validate_read(kwargs) select_axis = self.obj_type()._get_block_manager_axis(0) axes = [] for i in range(self.ndim): _start, _stop = (start, stop) if i == select_axis else (None, None) ax = self.read_index('axis{idx}'.format( idx=i), start=_start, stop=_stop) axes.append(ax) items = axes[0] blocks = [] for i in range(self.nblocks): blk_items = self.read_index('block{idx}_items'.format(idx=i)) values = self.read_array('block{idx}_values'.format(idx=i), start=_start, stop=_stop) blk = make_block(values, placement=items.get_indexer(blk_items)) blocks.append(blk) return self.obj_type(BlockManager(blocks, axes)) def write(self, obj, **kwargs): super(BlockManagerFixed, self).write(obj, **kwargs) data = obj._data if not data.is_consolidated(): data = data.consolidate() self.attrs.ndim = data.ndim for i, ax in enumerate(data.axes): if i == 0: if not ax.is_unique: raise ValueError( "Columns index has to be unique for fixed format") self.write_index('axis{idx}'.format(idx=i), ax) # Supporting mixed-type DataFrame objects...nontrivial self.attrs.nblocks = len(data.blocks) for i, blk in enumerate(data.blocks): # I have no idea why, but writing values before items fixed #2299 blk_items = data.items.take(blk.mgr_locs) self.write_array('block{idx}_values'.format(idx=i), blk.values, items=blk_items) self.write_index('block{idx}_items'.format(idx=i), blk_items) class FrameFixed(BlockManagerFixed): pandas_kind = u'frame' obj_type = DataFrame class PanelFixed(BlockManagerFixed): pandas_kind = u'wide' obj_type = Panel is_shape_reversed = True def write(self, obj, **kwargs): obj._consolidate_inplace() return super(PanelFixed, self).write(obj, **kwargs) class Table(Fixed): """ represent a table: facilitate read/write of various types of tables Attrs in Table Node ------------------- These are attributes that are store in the main table node, they are necessary to recreate these tables when read back in. index_axes : a list of tuples of the (original indexing axis and index column) non_index_axes: a list of tuples of the (original index axis and columns on a non-indexing axis) values_axes : a list of the columns which comprise the data of this table data_columns : a list of the columns that we are allowing indexing (these become single columns in values_axes), or True to force all columns nan_rep : the string to use for nan representations for string objects levels : the names of levels metadata : the names of the metadata columns """ pandas_kind = u'wide_table' table_type = None levels = 1 is_table = True is_shape_reversed = False def __init__(self, *args, **kwargs): super(Table, self).__init__(*args, **kwargs) self.index_axes = [] self.non_index_axes = [] self.values_axes = [] self.data_columns = [] self.metadata = [] self.info = dict() self.nan_rep = None self.selection = None @property def table_type_short(self): return self.table_type.split('_')[0] @property def format_type(self): return 'table' def __unicode__(self): """ return a pretty representatgion of myself """ self.infer_axes() dc = ",dc->[{columns}]".format(columns=(','.join( self.data_columns) if len(self.data_columns) else '')) ver = '' if self.is_old_version: ver = "[{version}]".format( version='.'.join(str(x) for x in self.version)) return ( "{pandas_type:12.12}{ver} (typ->{table_type},nrows->{nrows}," "ncols->{ncols},indexers->[{index_axes}]{dc})".format( pandas_type=self.pandas_type, ver=ver, table_type=self.table_type_short, nrows=self.nrows, ncols=self.ncols, index_axes=(','.join(a.name for a in self.index_axes)), dc=dc )) def __getitem__(self, c): """ return the axis for c """ for a in self.axes: if c == a.name: return a return None def validate(self, other): """ validate against an existing table """ if other is None: return if other.table_type != self.table_type: raise TypeError( "incompatible table_type with existing " "[{other} - {self}]".format( other=other.table_type, self=self.table_type)) for c in ['index_axes', 'non_index_axes', 'values_axes']: sv = getattr(self, c, None) ov = getattr(other, c, None) if sv != ov: # show the error for the specific axes for i, sax in enumerate(sv): oax = ov[i] if sax != oax: raise ValueError( "invalid combinate of [{c}] on appending data " "[{sax}] vs current table [{oax}]".format( c=c, sax=sax, oax=oax)) # should never get here raise Exception( "invalid combinate of [{c}] on appending data [{sv}] vs " "current table [{ov}]".format(c=c, sv=sv, ov=ov)) @property def is_multi_index(self): """the levels attribute is 1 or a list in the case of a multi-index""" return isinstance(self.levels, list) def validate_metadata(self, existing): """ create / validate metadata """ self.metadata = [ c.name for c in self.values_axes if c.metadata is not None] def validate_multiindex(self, obj): """validate that we can store the multi-index; reset and return the new object """ levels = [l if l is not None else "level_{0}".format(i) for i, l in enumerate(obj.index.names)] try: return obj.reset_index(), levels except ValueError: raise ValueError("duplicate names/columns in the multi-index when " "storing as a table") @property def nrows_expected(self): """ based on our axes, compute the expected nrows """ return np.prod([i.cvalues.shape[0] for i in self.index_axes]) @property def is_exists(self): """ has this table been created """ return u'table' in self.group @property def storable(self): return getattr(self.group, 'table', None) @property def table(self): """ return the table group (this is my storable) """ return self.storable @property def dtype(self): return self.table.dtype @property def description(self): return self.table.description @property def axes(self): return itertools.chain(self.index_axes, self.values_axes) @property def ncols(self): """ the number of total columns in the values axes """ return sum(len(a.values) for a in self.values_axes) @property def is_transposed(self): return False @property def data_orientation(self): """return a tuple of my permutated axes, non_indexable at the front""" return tuple(itertools.chain([int(a[0]) for a in self.non_index_axes], [int(a.axis) for a in self.index_axes])) def queryables(self): """ return a dict of the kinds allowable columns for this object """ # compute the values_axes queryables return dict( [(a.cname, a) for a in self.index_axes] + [(self.storage_obj_type._AXIS_NAMES[axis], None) for axis, values in self.non_index_axes] + [(v.cname, v) for v in self.values_axes if v.name in set(self.data_columns)] ) def index_cols(self): """ return a list of my index cols """ return [(i.axis, i.cname) for i in self.index_axes] def values_cols(self): """ return a list of my values cols """ return [i.cname for i in self.values_axes] def _get_metadata_path(self, key): """ return the metadata pathname for this key """ return "{group}/meta/{key}/meta".format(group=self.group._v_pathname, key=key) def write_metadata(self, key, values): """ write out a meta data array to the key as a fixed-format Series Parameters ---------- key : string values : ndarray """ values = Series(values) self.parent.put(self._get_metadata_path(key), values, format='table', encoding=self.encoding, errors=self.errors, nan_rep=self.nan_rep) def read_metadata(self, key): """ return the meta data array for this key """ if getattr(getattr(self.group, 'meta', None), key, None) is not None: return self.parent.select(self._get_metadata_path(key)) return None def set_info(self): """ update our table index info """ self.attrs.info = self.info def set_attrs(self): """ set our table type & indexables """ self.attrs.table_type = str(self.table_type) self.attrs.index_cols = self.index_cols() self.attrs.values_cols = self.values_cols() self.attrs.non_index_axes = self.non_index_axes self.attrs.data_columns = self.data_columns self.attrs.nan_rep = self.nan_rep self.attrs.encoding = self.encoding self.attrs.errors = self.errors self.attrs.levels = self.levels self.attrs.metadata = self.metadata self.set_info() def get_attrs(self): """ retrieve our attributes """ self.non_index_axes = getattr( self.attrs, 'non_index_axes', None) or [] self.data_columns = getattr( self.attrs, 'data_columns', None) or [] self.info = getattr( self.attrs, 'info', None) or dict() self.nan_rep = getattr(self.attrs, 'nan_rep', None) self.encoding = _ensure_encoding( getattr(self.attrs, 'encoding', None)) self.errors = _ensure_decoded(getattr(self.attrs, 'errors', 'strict')) self.levels = getattr( self.attrs, 'levels', None) or [] self.index_axes = [ a.infer(self) for a in self.indexables if a.is_an_indexable ] self.values_axes = [ a.infer(self) for a in self.indexables if not a.is_an_indexable ] self.metadata = getattr( self.attrs, 'metadata', None) or [] def validate_version(self, where=None): """ are we trying to operate on an old version? """ if where is not None: if (self.version[0] <= 0 and self.version[1] <= 10 and self.version[2] < 1): ws = incompatibility_doc % '.'.join( [str(x) for x in self.version]) warnings.warn(ws, IncompatibilityWarning) def validate_min_itemsize(self, min_itemsize): """validate the min_itemisze doesn't contain items that are not in the axes this needs data_columns to be defined """ if min_itemsize is None: return if not isinstance(min_itemsize, dict): return q = self.queryables() for k, v in min_itemsize.items(): # ok, apply generally if k == 'values': continue if k not in q: raise ValueError( "min_itemsize has the key [{key}] which is not an axis or " "data_column".format(key=k)) @property def indexables(self): """ create/cache the indexables if they don't exist """ if self._indexables is None: self._indexables = [] # index columns self._indexables.extend([ IndexCol(name=name, axis=axis, pos=i) for i, (axis, name) in enumerate(self.attrs.index_cols) ]) # values columns dc = set(self.data_columns) base_pos = len(self._indexables) def f(i, c): klass = DataCol if c in dc: klass = DataIndexableCol return klass.create_for_block(i=i, name=c, pos=base_pos + i, version=self.version) self._indexables.extend( [f(i, c) for i, c in enumerate(self.attrs.values_cols)]) return self._indexables def create_index(self, columns=None, optlevel=None, kind=None): """ Create a pytables index on the specified columns note: cannot index Time64Col() or ComplexCol currently; PyTables must be >= 3.0 Parameters ---------- columns : False (don't create an index), True (create all columns index), None or list_like (the indexers to index) optlevel: optimization level (defaults to 6) kind : kind of index (defaults to 'medium') Exceptions ---------- raises if the node is not a table """ if not self.infer_axes(): return if columns is False: return # index all indexables and data_columns if columns is None or columns is True: columns = [a.cname for a in self.axes if a.is_data_indexable] if not isinstance(columns, (tuple, list)): columns = [columns] kw = dict() if optlevel is not None: kw['optlevel'] = optlevel if kind is not None: kw['kind'] = kind table = self.table for c in columns: v = getattr(table.cols, c, None) if v is not None: # remove the index if the kind/optlevel have changed if v.is_indexed: index = v.index cur_optlevel = index.optlevel cur_kind = index.kind if kind is not None and cur_kind != kind: v.remove_index() else: kw['kind'] = cur_kind if optlevel is not None and cur_optlevel != optlevel: v.remove_index() else: kw['optlevel'] = cur_optlevel # create the index if not v.is_indexed: if v.type.startswith('complex'): raise TypeError( 'Columns containing complex values can be stored ' 'but cannot' ' be indexed when using table format. Either use ' 'fixed format, set index=False, or do not include ' 'the columns containing complex values to ' 'data_columns when initializing the table.') v.create_index(**kw) def read_axes(self, where, **kwargs): """create and return the axes sniffed from the table: return boolean for success """ # validate the version self.validate_version(where) # infer the data kind if not self.infer_axes(): return False # create the selection self.selection = Selection(self, where=where, **kwargs) values = self.selection.select() # convert the data for a in self.axes: a.set_info(self.info) a.convert(values, nan_rep=self.nan_rep, encoding=self.encoding, errors=self.errors) return True def get_object(self, obj): """ return the data for this obj """ return obj def validate_data_columns(self, data_columns, min_itemsize): """take the input data_columns and min_itemize and create a data columns spec """ if not len(self.non_index_axes): return [] axis, axis_labels = self.non_index_axes[0] info = self.info.get(axis, dict()) if info.get('type') == 'MultiIndex' and data_columns: raise ValueError("cannot use a multi-index on axis [{0}] with " "data_columns {1}".format(axis, data_columns)) # evaluate the passed data_columns, True == use all columns # take only valide axis labels if data_columns is True: data_columns = list(axis_labels) elif data_columns is None: data_columns = [] # if min_itemsize is a dict, add the keys (exclude 'values') if isinstance(min_itemsize, dict): existing_data_columns = set(data_columns) data_columns.extend([ k for k in min_itemsize.keys() if k != 'values' and k not in existing_data_columns ]) # return valid columns in the order of our axis return [c for c in data_columns if c in axis_labels] def create_axes(self, axes, obj, validate=True, nan_rep=None, data_columns=None, min_itemsize=None, **kwargs): """ create and return the axes leagcy tables create an indexable column, indexable index, non-indexable fields Parameters: ----------- axes: a list of the axes in order to create (names or numbers of the axes) obj : the object to create axes on validate: validate the obj against an existing object already written min_itemsize: a dict of the min size for a column in bytes nan_rep : a values to use for string column nan_rep encoding : the encoding for string values data_columns : a list of columns that we want to create separate to allow indexing (or True will force all columns) """ # set the default axes if needed if axes is None: try: axes = _AXES_MAP[type(obj)] except KeyError: raise TypeError( "cannot properly create the storer for: [group->{group}," "value->{value}]".format( group=self.group._v_name, value=type(obj))) # map axes to numbers axes = [obj._get_axis_number(a) for a in axes] # do we have an existing table (if so, use its axes & data_columns) if self.infer_axes(): existing_table = self.copy() existing_table.infer_axes() axes = [a.axis for a in existing_table.index_axes] data_columns = existing_table.data_columns nan_rep = existing_table.nan_rep self.encoding = existing_table.encoding self.errors = existing_table.errors self.info = copy.copy(existing_table.info) else: existing_table = None # currently support on ndim-1 axes if len(axes) != self.ndim - 1: raise ValueError( "currently only support ndim-1 indexers in an AppendableTable") # create according to the new data self.non_index_axes = [] self.data_columns = [] # nan_representation if nan_rep is None: nan_rep = 'nan' self.nan_rep = nan_rep # create axes to index and non_index index_axes_map = dict() for i, a in enumerate(obj.axes): if i in axes: name = obj._AXIS_NAMES[i] index_axes_map[i] = _convert_index( a, self.encoding, self.errors, self.format_type ).set_name(name).set_axis(i) else: # we might be able to change the axes on the appending data if # necessary append_axis = list(a) if existing_table is not None: indexer = len(self.non_index_axes) exist_axis = existing_table.non_index_axes[indexer][1] if not array_equivalent(np.array(append_axis), np.array(exist_axis)): # ahah! -> reindex if array_equivalent(np.array(sorted(append_axis)), np.array(sorted(exist_axis))): append_axis = exist_axis # the non_index_axes info info = _get_info(self.info, i) info['names'] = list(a.names) info['type'] = a.__class__.__name__ self.non_index_axes.append((i, append_axis)) # set axis positions (based on the axes) self.index_axes = [ index_axes_map[a].set_pos(j).update_info(self.info) for j, a in enumerate(axes) ] j = len(self.index_axes) # check for column conflicts for a in self.axes: a.maybe_set_size(min_itemsize=min_itemsize) # reindex by our non_index_axes & compute data_columns for a in self.non_index_axes: obj = _reindex_axis(obj, a[0], a[1]) def get_blk_items(mgr, blocks): return [mgr.items.take(blk.mgr_locs) for blk in blocks] # figure out data_columns and get out blocks block_obj = self.get_object(obj)._consolidate() blocks = block_obj._data.blocks blk_items = get_blk_items(block_obj._data, blocks) if len(self.non_index_axes): axis, axis_labels = self.non_index_axes[0] data_columns = self.validate_data_columns( data_columns, min_itemsize) if len(data_columns): mgr = block_obj.reindex( Index(axis_labels).difference(Index(data_columns)), axis=axis )._data blocks = list(mgr.blocks) blk_items = get_blk_items(mgr, blocks) for c in data_columns: mgr = block_obj.reindex([c], axis=axis)._data blocks.extend(mgr.blocks) blk_items.extend(get_blk_items(mgr, mgr.blocks)) # reorder the blocks in the same order as the existing_table if we can if existing_table is not None: by_items = {tuple(b_items.tolist()): (b, b_items) for b, b_items in zip(blocks, blk_items)} new_blocks = [] new_blk_items = [] for ea in existing_table.values_axes: items = tuple(ea.values) try: b, b_items = by_items.pop(items) new_blocks.append(b) new_blk_items.append(b_items) except (IndexError, KeyError): raise ValueError( "cannot match existing table structure for [{items}] " "on appending data".format( items=(','.join(pprint_thing(item) for item in items)))) blocks = new_blocks blk_items = new_blk_items # add my values self.values_axes = [] for i, (b, b_items) in enumerate(zip(blocks, blk_items)): # shape of the data column are the indexable axes klass = DataCol name = None # we have a data_column if (data_columns and len(b_items) == 1 and b_items[0] in data_columns): klass = DataIndexableCol name = b_items[0] self.data_columns.append(name) # make sure that we match up the existing columns # if we have an existing table if existing_table is not None and validate: try: existing_col = existing_table.values_axes[i] except (IndexError, KeyError): raise ValueError( "Incompatible appended table [{blocks}]" "with existing table [{table}]".format( blocks=blocks, table=existing_table.values_axes)) else: existing_col = None try: col = klass.create_for_block( i=i, name=name, version=self.version) col.set_atom(block=b, block_items=b_items, existing_col=existing_col, min_itemsize=min_itemsize, nan_rep=nan_rep, encoding=self.encoding, errors=self.errors, info=self.info) col.set_pos(j) self.values_axes.append(col) except (NotImplementedError, ValueError, TypeError) as e: raise e except Exception as detail: raise Exception( "cannot find the correct atom type -> " "[dtype->{name},items->{items}] {detail!s}".format( name=b.dtype.name, items=b_items, detail=detail)) j += 1 # validate our min_itemsize self.validate_min_itemsize(min_itemsize) # validate our metadata self.validate_metadata(existing_table) # validate the axes if we have an existing table if validate: self.validate(existing_table) def process_axes(self, obj, columns=None): """ process axes filters """ # make a copy to avoid side effects if columns is not None: columns = list(columns) # make sure to include levels if we have them if columns is not None and self.is_multi_index: for n in self.levels: if n not in columns: columns.insert(0, n) # reorder by any non_index_axes & limit to the select columns for axis, labels in self.non_index_axes: obj = _reindex_axis(obj, axis, labels, columns) # apply the selection filters (but keep in the same order) if self.selection.filter is not None: for field, op, filt in self.selection.filter.format(): def process_filter(field, filt): for axis_name in obj._AXIS_NAMES.values(): axis_number = obj._get_axis_number(axis_name) axis_values = obj._get_axis(axis_name) # see if the field is the name of an axis if field == axis_name: # if we have a multi-index, then need to include # the levels if self.is_multi_index: filt = filt.union(Index(self.levels)) takers = op(axis_values, filt) return obj.loc._getitem_axis(takers, axis=axis_number) # this might be the name of a file IN an axis elif field in axis_values: # we need to filter on this dimension values = ensure_index(getattr(obj, field).values) filt = ensure_index(filt) # hack until we support reversed dim flags if isinstance(obj, DataFrame): axis_number = 1 - axis_number takers = op(values, filt) return obj.loc._getitem_axis(takers, axis=axis_number) raise ValueError("cannot find the field [{field}] for " "filtering!".format(field=field)) obj = process_filter(field, filt) return obj def create_description(self, complib=None, complevel=None, fletcher32=False, expectedrows=None): """ create the description of the table from the axes & values """ # provided expected rows if its passed if expectedrows is None: expectedrows = max(self.nrows_expected, 10000) d = dict(name='table', expectedrows=expectedrows) # description from the axes & values d['description'] = {a.cname: a.typ for a in self.axes} if complib: if complevel is None: complevel = self._complevel or 9 filters = _tables().Filters( complevel=complevel, complib=complib, fletcher32=fletcher32 or self._fletcher32) d['filters'] = filters elif self._filters is not None: d['filters'] = self._filters return d def read_coordinates(self, where=None, start=None, stop=None, **kwargs): """select coordinates (row numbers) from a table; return the coordinates object """ # validate the version self.validate_version(where) # infer the data kind if not self.infer_axes(): return False # create the selection self.selection = Selection( self, where=where, start=start, stop=stop, **kwargs) coords = self.selection.select_coords() if self.selection.filter is not None: for field, op, filt in self.selection.filter.format(): data = self.read_column( field, start=coords.min(), stop=coords.max() + 1) coords = coords[ op(data.iloc[coords - coords.min()], filt).values] return Index(coords) def read_column(self, column, where=None, start=None, stop=None): """return a single column from the table, generally only indexables are interesting """ # validate the version self.validate_version() # infer the data kind if not self.infer_axes(): return False if where is not None: raise TypeError("read_column does not currently accept a where " "clause") # find the axes for a in self.axes: if column == a.name: if not a.is_data_indexable: raise ValueError( "column [{column}] can not be extracted individually; " "it is not data indexable".format(column=column)) # column must be an indexable or a data column c = getattr(self.table.cols, column) a.set_info(self.info) return Series(_set_tz(a.convert(c[start:stop], nan_rep=self.nan_rep, encoding=self.encoding, errors=self.errors ).take_data(), a.tz, True), name=column) raise KeyError( "column [{column}] not found in the table".format(column=column)) class WORMTable(Table): """ a write-once read-many table: this format DOES NOT ALLOW appending to a table. writing is a one-time operation the data are stored in a format that allows for searching the data on disk """ table_type = u'worm' def read(self, **kwargs): """ read the indices and the indexing array, calculate offset rows and return """ raise NotImplementedError("WORMTable needs to implement read") def write(self, **kwargs): """ write in a format that we can search later on (but cannot append to): write out the indices and the values using _write_array (e.g. a CArray) create an indexing table so that we can search """ raise NotImplementedError("WORKTable needs to implement write") class LegacyTable(Table): """ an appendable table: allow append/query/delete operations to a (possibly) already existing appendable table this table ALLOWS append (but doesn't require them), and stores the data in a format that can be easily searched """ _indexables = [ IndexCol(name='index', axis=1, pos=0), IndexCol(name='column', axis=2, pos=1, index_kind='columns_kind'), DataCol(name='fields', cname='values', kind_attr='fields', pos=2) ] table_type = u'legacy' ndim = 3 def write(self, **kwargs): raise TypeError("write operations are not allowed on legacy tables!") def read(self, where=None, columns=None, **kwargs): """we have n indexable columns, with an arbitrary number of data axes """ if not self.read_axes(where=where, **kwargs): return None lst_vals = [a.values for a in self.index_axes] labels, levels = _factorize_from_iterables(lst_vals) # labels and levels are tuples but lists are expected labels = list(labels) levels = list(levels) N = [len(lvl) for lvl in levels] # compute the key key = _factor_indexer(N[1:], labels) objs = [] if len(unique(key)) == len(key): sorter, _ = algos.groupsort_indexer( ensure_int64(key), np.prod(N)) sorter = ensure_platform_int(sorter) # create the objs for c in self.values_axes: # the data need to be sorted sorted_values = c.take_data().take(sorter, axis=0) if sorted_values.ndim == 1: sorted_values = sorted_values.reshape( (sorted_values.shape[0], 1)) take_labels = [l.take(sorter) for l in labels] items = Index(c.values) block = _block2d_to_blocknd( values=sorted_values, placement=np.arange(len(items)), shape=tuple(N), labels=take_labels, ref_items=items) # create the object mgr = BlockManager([block], [items] + levels) obj = self.obj_type(mgr) # permute if needed if self.is_transposed: obj = obj.transpose( *tuple(Series(self.data_orientation).argsort())) objs.append(obj) else: warnings.warn(duplicate_doc, DuplicateWarning, stacklevel=5) # reconstruct long_index = MultiIndex.from_arrays( [i.values for i in self.index_axes]) for c in self.values_axes: lp = DataFrame(c.data, index=long_index, columns=c.values) # need a better algorithm tuple_index = long_index.values unique_tuples = unique(tuple_index) unique_tuples = com.asarray_tuplesafe(unique_tuples) indexer = match(unique_tuples, tuple_index) indexer = ensure_platform_int(indexer) new_index = long_index.take(indexer) new_values = lp.values.take(indexer, axis=0) lp = DataFrame(new_values, index=new_index, columns=lp.columns) objs.append(lp.to_panel()) # create the composite object if len(objs) == 1: wp = objs[0] else: wp = concat(objs, axis=0, verify_integrity=False)._consolidate() # apply the selection filters & axis orderings wp = self.process_axes(wp, columns=columns) return wp class LegacyFrameTable(LegacyTable): """ support the legacy frame table """ pandas_kind = u'frame_table' table_type = u'legacy_frame' obj_type = Panel def read(self, *args, **kwargs): return super(LegacyFrameTable, self).read(*args, **kwargs)['value'] class LegacyPanelTable(LegacyTable): """ support the legacy panel table """ table_type = u'legacy_panel' obj_type = Panel class AppendableTable(LegacyTable): """ suppor the new appendable table formats """ _indexables = None table_type = u'appendable' def write(self, obj, axes=None, append=False, complib=None, complevel=None, fletcher32=None, min_itemsize=None, chunksize=None, expectedrows=None, dropna=False, **kwargs): if not append and self.is_exists: self._handle.remove_node(self.group, 'table') # create the axes self.create_axes(axes=axes, obj=obj, validate=append, min_itemsize=min_itemsize, **kwargs) for a in self.axes: a.validate(self, append) if not self.is_exists: # create the table options = self.create_description(complib=complib, complevel=complevel, fletcher32=fletcher32, expectedrows=expectedrows) # set the table attributes self.set_attrs() # create the table self._handle.create_table(self.group, **options) else: pass # table = self.table # update my info self.set_info() # validate the axes and set the kinds for a in self.axes: a.validate_and_set(self, append) # add the rows self.write_data(chunksize, dropna=dropna) def write_data(self, chunksize, dropna=False): """ we form the data into a 2-d including indexes,values,mask write chunk-by-chunk """ names = self.dtype.names nrows = self.nrows_expected # if dropna==True, then drop ALL nan rows masks = [] if dropna: for a in self.values_axes: # figure the mask: only do if we can successfully process this # column, otherwise ignore the mask mask = isna(a.data).all(axis=0) if isinstance(mask, np.ndarray): masks.append(mask.astype('u1', copy=False)) # consolidate masks if len(masks): mask = masks[0] for m in masks[1:]: mask = mask & m mask = mask.ravel() else: mask = None # broadcast the indexes if needed indexes = [a.cvalues for a in self.index_axes] nindexes = len(indexes) bindexes = [] for i, idx in enumerate(indexes): # broadcast to all other indexes except myself if i > 0 and i < nindexes: repeater = np.prod( [indexes[bi].shape[0] for bi in range(0, i)]) idx = np.tile(idx, repeater) if i < nindexes - 1: repeater = np.prod([indexes[bi].shape[0] for bi in range(i + 1, nindexes)]) idx = np.repeat(idx, repeater) bindexes.append(idx) # transpose the values so first dimension is last # reshape the values if needed values = [a.take_data() for a in self.values_axes] values = [v.transpose(np.roll(np.arange(v.ndim), v.ndim - 1)) for v in values] bvalues = [] for i, v in enumerate(values): new_shape = (nrows,) + self.dtype[names[nindexes + i]].shape bvalues.append(values[i].reshape(new_shape)) # write the chunks if chunksize is None: chunksize = 100000 rows = np.empty(min(chunksize, nrows), dtype=self.dtype) chunks = int(nrows / chunksize) + 1 for i in range(chunks): start_i = i * chunksize end_i = min((i + 1) * chunksize, nrows) if start_i >= end_i: break self.write_data_chunk( rows, indexes=[a[start_i:end_i] for a in bindexes], mask=mask[start_i:end_i] if mask is not None else None, values=[v[start_i:end_i] for v in bvalues]) def write_data_chunk(self, rows, indexes, mask, values): """ Parameters ---------- rows : an empty memory space where we are putting the chunk indexes : an array of the indexes mask : an array of the masks values : an array of the values """ # 0 len for v in values: if not np.prod(v.shape): return try: nrows = indexes[0].shape[0] if nrows != len(rows): rows = np.empty(nrows, dtype=self.dtype) names = self.dtype.names nindexes = len(indexes) # indexes for i, idx in enumerate(indexes): rows[names[i]] = idx # values for i, v in enumerate(values): rows[names[i + nindexes]] = v # mask if mask is not None: m = ~mask.ravel().astype(bool, copy=False) if not m.all(): rows = rows[m] except Exception as detail: raise Exception( "cannot create row-data -> {detail}".format(detail=detail)) try: if len(rows): self.table.append(rows) self.table.flush() except Exception as detail: raise TypeError( "tables cannot write this data -> {detail}".format( detail=detail)) def delete(self, where=None, start=None, stop=None, **kwargs): # delete all rows (and return the nrows) if where is None or not len(where): if start is None and stop is None: nrows = self.nrows self._handle.remove_node(self.group, recursive=True) else: # pytables<3.0 would remove a single row with stop=None if stop is None: stop = self.nrows nrows = self.table.remove_rows(start=start, stop=stop) self.table.flush() return nrows # infer the data kind if not self.infer_axes(): return None # create the selection table = self.table self.selection = Selection( self, where, start=start, stop=stop, **kwargs) values = self.selection.select_coords() # delete the rows in reverse order sorted_series = Series(values).sort_values() ln = len(sorted_series) if ln: # construct groups of consecutive rows diff = sorted_series.diff() groups = list(diff[diff > 1].index) # 1 group if not len(groups): groups = [0] # final element if groups[-1] != ln: groups.append(ln) # initial element if groups[0] != 0: groups.insert(0, 0) # we must remove in reverse order! pg = groups.pop() for g in reversed(groups): rows = sorted_series.take(lrange(g, pg)) table.remove_rows(start=rows[rows.index[0] ], stop=rows[rows.index[-1]] + 1) pg = g self.table.flush() # return the number of rows removed return ln class AppendableFrameTable(AppendableTable): """ suppor the new appendable table formats """ pandas_kind = u'frame_table' table_type = u'appendable_frame' ndim = 2 obj_type = DataFrame @property def is_transposed(self): return self.index_axes[0].axis == 1 def get_object(self, obj): """ these are written transposed """ if self.is_transposed: obj = obj.T return obj def read(self, where=None, columns=None, **kwargs): if not self.read_axes(where=where, **kwargs): return None info = (self.info.get(self.non_index_axes[0][0], dict()) if len(self.non_index_axes) else dict()) index = self.index_axes[0].values frames = [] for a in self.values_axes: # we could have a multi-index constructor here # ensure_index doesn't recognized our list-of-tuples here if info.get('type') == 'MultiIndex': cols = MultiIndex.from_tuples(a.values) else: cols = Index(a.values) names = info.get('names') if names is not None: cols.set_names(names, inplace=True) if self.is_transposed: values = a.cvalues index_ = cols cols_ = Index(index, name=getattr(index, 'name', None)) else: values = a.cvalues.T index_ = Index(index, name=getattr(index, 'name', None)) cols_ = cols # if we have a DataIndexableCol, its shape will only be 1 dim if values.ndim == 1 and isinstance(values, np.ndarray): values = values.reshape((1, values.shape[0])) block = make_block(values, placement=np.arange(len(cols_))) mgr = BlockManager([block], [cols_, index_]) frames.append(DataFrame(mgr)) if len(frames) == 1: df = frames[0] else: df = concat(frames, axis=1) # apply the selection filters & axis orderings df = self.process_axes(df, columns=columns) return df class AppendableSeriesTable(AppendableFrameTable): """ support the new appendable table formats """ pandas_kind = u'series_table' table_type = u'appendable_series' ndim = 2 obj_type = Series storage_obj_type = DataFrame @property def is_transposed(self): return False def get_object(self, obj): return obj def write(self, obj, data_columns=None, **kwargs): """ we are going to write this as a frame table """ if not isinstance(obj, DataFrame): name = obj.name or 'values' obj = DataFrame({name: obj}, index=obj.index) obj.columns = [name] return super(AppendableSeriesTable, self).write( obj=obj, data_columns=obj.columns.tolist(), **kwargs) def read(self, columns=None, **kwargs): is_multi_index = self.is_multi_index if columns is not None and is_multi_index: for n in self.levels: if n not in columns: columns.insert(0, n) s = super(AppendableSeriesTable, self).read(columns=columns, **kwargs) if is_multi_index: s.set_index(self.levels, inplace=True) s = s.iloc[:, 0] # remove the default name if s.name == 'values': s.name = None return s class AppendableMultiSeriesTable(AppendableSeriesTable): """ support the new appendable table formats """ pandas_kind = u'series_table' table_type = u'appendable_multiseries' def write(self, obj, **kwargs): """ we are going to write this as a frame table """ name = obj.name or 'values' obj, self.levels = self.validate_multiindex(obj) cols = list(self.levels) cols.append(name) obj.columns = cols return super(AppendableMultiSeriesTable, self).write(obj=obj, **kwargs) class GenericTable(AppendableFrameTable): """ a table that read/writes the generic pytables table format """ pandas_kind = u'frame_table' table_type = u'generic_table' ndim = 2 obj_type = DataFrame @property def pandas_type(self): return self.pandas_kind @property def storable(self): return getattr(self.group, 'table', None) or self.group def get_attrs(self): """ retrieve our attributes """ self.non_index_axes = [] self.nan_rep = None self.levels = [] self.index_axes = [a.infer(self) for a in self.indexables if a.is_an_indexable] self.values_axes = [a.infer(self) for a in self.indexables if not a.is_an_indexable] self.data_columns = [a.name for a in self.values_axes] @property def indexables(self): """ create the indexables from the table description """ if self._indexables is None: d = self.description # the index columns is just a simple index self._indexables = [GenericIndexCol(name='index', axis=0)] for i, n in enumerate(d._v_names): dc = GenericDataIndexableCol( name=n, pos=i, values=[n], version=self.version) self._indexables.append(dc) return self._indexables def write(self, **kwargs): raise NotImplementedError("cannot write on an generic table") class AppendableMultiFrameTable(AppendableFrameTable): """ a frame with a multi-index """ table_type = u'appendable_multiframe' obj_type = DataFrame ndim = 2 _re_levels = re.compile(r"^level_\d+$") @property def table_type_short(self): return u'appendable_multi' def write(self, obj, data_columns=None, **kwargs): if data_columns is None: data_columns = [] elif data_columns is True: data_columns = obj.columns.tolist() obj, self.levels = self.validate_multiindex(obj) for n in self.levels: if n not in data_columns: data_columns.insert(0, n) return super(AppendableMultiFrameTable, self).write( obj=obj, data_columns=data_columns, **kwargs) def read(self, **kwargs): df = super(AppendableMultiFrameTable, self).read(**kwargs) df = df.set_index(self.levels) # remove names for 'level_%d' df.index = df.index.set_names([ None if self._re_levels.search(l) else l for l in df.index.names ]) return df class AppendablePanelTable(AppendableTable): """ suppor the new appendable table formats """ table_type = u'appendable_panel' ndim = 3 obj_type = Panel def get_object(self, obj): """ these are written transposed """ if self.is_transposed: obj = obj.transpose(*self.data_orientation) return obj @property def is_transposed(self): return self.data_orientation != tuple(range(self.ndim)) def _reindex_axis(obj, axis, labels, other=None): ax = obj._get_axis(axis) labels = ensure_index(labels) # try not to reindex even if other is provided # if it equals our current index if other is not None: other = ensure_index(other) if (other is None or labels.equals(other)) and labels.equals(ax): return obj labels = ensure_index(labels.unique()) if other is not None: labels = ensure_index(other.unique()).intersection(labels, sort=False) if not labels.equals(ax): slicer = [slice(None, None)] * obj.ndim slicer[axis] = labels obj = obj.loc[tuple(slicer)] return obj def _get_info(info, name): """ get/create the info for this name """ try: idx = info[name] except KeyError: idx = info[name] = dict() return idx # tz to/from coercion def _get_tz(tz): """ for a tz-aware type, return an encoded zone """ zone = timezones.get_timezone(tz) if zone is None: zone = tz.utcoffset().total_seconds() return zone def _set_tz(values, tz, preserve_UTC=False, coerce=False): """ coerce the values to a DatetimeIndex if tz is set preserve the input shape if possible Parameters ---------- values : ndarray tz : string/pickled tz object preserve_UTC : boolean, preserve the UTC of the result coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray """ if tz is not None: name = getattr(values, 'name', None) values = values.ravel() tz = timezones.get_timezone(_ensure_decoded(tz)) values = DatetimeIndex(values, name=name) if values.tz is None: values = values.tz_localize('UTC').tz_convert(tz) if preserve_UTC: if tz == 'UTC': values = list(values) elif coerce: values = np.asarray(values, dtype='M8[ns]') return values def _convert_index(index, encoding=None, errors='strict', format_type=None): index_name = getattr(index, 'name', None) if isinstance(index, DatetimeIndex): converted = index.asi8 return IndexCol(converted, 'datetime64', _tables().Int64Col(), freq=getattr(index, 'freq', None), tz=getattr(index, 'tz', None), index_name=index_name) elif isinstance(index, TimedeltaIndex): converted = index.asi8 return IndexCol(converted, 'timedelta64', _tables().Int64Col(), freq=getattr(index, 'freq', None), index_name=index_name) elif isinstance(index, (Int64Index, PeriodIndex)): atom = _tables().Int64Col() # avoid to store ndarray of Period objects return IndexCol(index._ndarray_values, 'integer', atom, freq=getattr(index, 'freq', None), index_name=index_name) if isinstance(index, MultiIndex): raise TypeError('MultiIndex not supported here!') inferred_type = lib.infer_dtype(index, skipna=False) values = np.asarray(index) if inferred_type == 'datetime64': converted = values.view('i8') return IndexCol(converted, 'datetime64', _tables().Int64Col(), freq=getattr(index, 'freq', None), tz=getattr(index, 'tz', None), index_name=index_name) elif inferred_type == 'timedelta64': converted = values.view('i8') return IndexCol(converted, 'timedelta64', _tables().Int64Col(), freq=getattr(index, 'freq', None), index_name=index_name) elif inferred_type == 'datetime': converted = np.asarray([(time.mktime(v.timetuple()) + v.microsecond / 1E6) for v in values], dtype=np.float64) return IndexCol(converted, 'datetime', _tables().Time64Col(), index_name=index_name) elif inferred_type == 'date': converted = np.asarray([v.toordinal() for v in values], dtype=np.int32) return IndexCol(converted, 'date', _tables().Time32Col(), index_name=index_name) elif inferred_type == 'string': # atom = _tables().ObjectAtom() # return np.asarray(values, dtype='O'), 'object', atom converted = _convert_string_array(values, encoding, errors) itemsize = converted.dtype.itemsize return IndexCol( converted, 'string', _tables().StringCol(itemsize), itemsize=itemsize, index_name=index_name ) elif inferred_type == 'unicode': if format_type == 'fixed': atom = _tables().ObjectAtom() return IndexCol(np.asarray(values, dtype='O'), 'object', atom, index_name=index_name) raise TypeError( "[unicode] is not supported as a in index type for [{0}] formats" .format(format_type) ) elif inferred_type == 'integer': # take a guess for now, hope the values fit atom = _tables().Int64Col() return IndexCol(np.asarray(values, dtype=np.int64), 'integer', atom, index_name=index_name) elif inferred_type == 'floating': atom = _tables().Float64Col() return IndexCol(np.asarray(values, dtype=np.float64), 'float', atom, index_name=index_name) else: # pragma: no cover atom = _tables().ObjectAtom() return IndexCol(np.asarray(values, dtype='O'), 'object', atom, index_name=index_name) def _unconvert_index(data, kind, encoding=None, errors='strict'): kind = _ensure_decoded(kind) if kind == u'datetime64': index = DatetimeIndex(data) elif kind == u'timedelta64': index = TimedeltaIndex(data) elif kind == u'datetime': index = np.asarray([datetime.fromtimestamp(v) for v in data], dtype=object) elif kind == u'date': try: index = np.asarray( [date.fromordinal(v) for v in data], dtype=object) except (ValueError): index = np.asarray( [date.fromtimestamp(v) for v in data], dtype=object) elif kind in (u'integer', u'float'): index = np.asarray(data) elif kind in (u'string'): index = _unconvert_string_array(data, nan_rep=None, encoding=encoding, errors=errors) elif kind == u'object': index = np.asarray(data[0]) else: # pragma: no cover raise ValueError('unrecognized index type {kind}'.format(kind=kind)) return index def _unconvert_index_legacy(data, kind, legacy=False, encoding=None, errors='strict'): kind = _ensure_decoded(kind) if kind == u'datetime': index = to_datetime(data) elif kind in (u'integer'): index = np.asarray(data, dtype=object) elif kind in (u'string'): index = _unconvert_string_array(data, nan_rep=None, encoding=encoding, errors=errors) else: # pragma: no cover raise ValueError('unrecognized index type {kind}'.format(kind=kind)) return index def _convert_string_array(data, encoding, errors, itemsize=None): """ we take a string-like that is object dtype and coerce to a fixed size string type Parameters ---------- data : a numpy array of object dtype encoding : None or string-encoding errors : handler for encoding errors itemsize : integer, optional, defaults to the max length of the strings Returns ------- data in a fixed-length string dtype, encoded to bytes if needed """ # encode if needed if encoding is not None and len(data): data = Series(data.ravel()).str.encode( encoding, errors).values.reshape(data.shape) # create the sized dtype if itemsize is None: ensured = ensure_object(data.ravel()) itemsize = max(1, libwriters.max_len_string_array(ensured)) data = np.asarray(data, dtype="S{size}".format(size=itemsize)) return data def _unconvert_string_array(data, nan_rep=None, encoding=None, errors='strict'): """ inverse of _convert_string_array Parameters ---------- data : fixed length string dtyped array nan_rep : the storage repr of NaN, optional encoding : the encoding of the data, optional errors : handler for encoding errors, default 'strict' Returns ------- an object array of the decoded data """ shape = data.shape data = np.asarray(data.ravel(), dtype=object) # guard against a None encoding in PY3 (because of a legacy # where the passed encoding is actually None) encoding = _ensure_encoding(encoding) if encoding is not None and len(data): itemsize = libwriters.max_len_string_array(ensure_object(data)) if compat.PY3: dtype = "U{0}".format(itemsize) else: dtype = "S{0}".format(itemsize) if isinstance(data[0], compat.binary_type): data = Series(data).str.decode(encoding, errors=errors).values else: data = data.astype(dtype, copy=False).astype(object, copy=False) if nan_rep is None: nan_rep = 'nan' data = libwriters.string_array_replace_from_nan_rep(data, nan_rep) return data.reshape(shape) def _maybe_convert(values, val_kind, encoding, errors): if _need_convert(val_kind): conv = _get_converter(val_kind, encoding, errors) # conv = np.frompyfunc(conv, 1, 1) values = conv(values) return values def _get_converter(kind, encoding, errors): kind = _ensure_decoded(kind) if kind == 'datetime64': return lambda x: np.asarray(x, dtype='M8[ns]') elif kind == 'datetime': return lambda x: to_datetime(x, cache=True).to_pydatetime() elif kind == 'string': return lambda x: _unconvert_string_array(x, encoding=encoding, errors=errors) else: # pragma: no cover raise ValueError('invalid kind {kind}'.format(kind=kind)) def _need_convert(kind): kind = _ensure_decoded(kind) if kind in (u'datetime', u'datetime64', u'string'): return True return False class Selection(object): """ Carries out a selection operation on a tables.Table object. Parameters ---------- table : a Table object where : list of Terms (or convertible to) start, stop: indices to start and/or stop selection """ def __init__(self, table, where=None, start=None, stop=None): self.table = table self.where = where self.start = start self.stop = stop self.condition = None self.filter = None self.terms = None self.coordinates = None if is_list_like(where): # see if we have a passed coordinate like try: inferred = lib.infer_dtype(where, skipna=False) if inferred == 'integer' or inferred == 'boolean': where = np.asarray(where) if where.dtype == np.bool_: start, stop = self.start, self.stop if start is None: start = 0 if stop is None: stop = self.table.nrows self.coordinates = np.arange(start, stop)[where] elif issubclass(where.dtype.type, np.integer): if ((self.start is not None and (where < self.start).any()) or (self.stop is not None and (where >= self.stop).any())): raise ValueError( "where must have index locations >= start and " "< stop" ) self.coordinates = where except ValueError: pass if self.coordinates is None: self.terms = self.generate(where) # create the numexpr & the filter if self.terms is not None: self.condition, self.filter = self.terms.evaluate() def generate(self, where): """ where can be a : dict,list,tuple,string """ if where is None: return None q = self.table.queryables() try: return Expr(where, queryables=q, encoding=self.table.encoding) except NameError: # raise a nice message, suggesting that the user should use # data_columns raise ValueError( "The passed where expression: {0}\n" " contains an invalid variable reference\n" " all of the variable references must be a " "reference to\n" " an axis (e.g. 'index' or 'columns'), or a " "data_column\n" " The currently defined references are: {1}\n" .format(where, ','.join(q.keys())) ) def select(self): """ generate the selection """ if self.condition is not None: return self.table.table.read_where(self.condition.format(), start=self.start, stop=self.stop) elif self.coordinates is not None: return self.table.table.read_coordinates(self.coordinates) return self.table.table.read(start=self.start, stop=self.stop) def select_coords(self): """ generate the selection """ start, stop = self.start, self.stop nrows = self.table.nrows if start is None: start = 0 elif start < 0: start += nrows if self.stop is None: stop = nrows elif stop < 0: stop += nrows if self.condition is not None: return self.table.table.get_where_list(self.condition.format(), start=start, stop=stop, sort=True) elif self.coordinates is not None: return self.coordinates return np.arange(start, stop) # utilities ### def timeit(key, df, fn=None, remove=True, **kwargs): if fn is None: fn = 'timeit.h5' store = HDFStore(fn, mode='w') store.append(key, df, **kwargs) store.close() if remove: os.remove(fn)