]j  %SrSSKJr SSKJr SSKrSSKJrJr SSK r SSK r SSK r SSK J r SSKJrJrJrJrJrJrJrJr SSKrSSKrSSKJrJrJr SS KJrJ r! 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Non-string dtypes are passed through unchanged. https://github.com/pandas-dev/pandas/issues/13492 ) isinstancestrnames r\ _ensure_strrds$4y Kr^r TermcUS-n[U[[45(a4UVs/sH&nUcM[U5(a [ X2S-S9OUPM( nnO[U5(a [ XS9nUb[ U5(aU$S$s snf)z Ensure that the where is a Term or a list of Term. This makes sure that we are capturing the scope of variables that are passed create the terms here with a frame_level=2 (we are 2 levels down) N scope_level)r`listtupler>relen)whererilevelterms r\ _ensure_termrps !OE%$''  R1A$1G1GD19 -T Q   % U.MSZZ59T9 s B"Bz where criteria is being ignored as this version [%s] is too old (or not-defined), read the file in and write it out to a new file to upgrade (with the copy_to method) r incompatibility_doczu the [%s] attribute of the existing index is [%s] which conflicts with the new [%s], resetting the attribute to None attribute_conflict_docz your performance may suffer as PyTables will pickle object types that it cannot map directly to c-types [inferred_type->%s,key->%s] [items->%s] performance_docfixedtable)frttruz; : boolean drop ALL nan rows when appending to a table dropna_docz~ : format default format writing format, if None, then put will default to 'fixed' and append will default to 'table' format_doczio.hdf dropna_tableF) validatordefault_format)rtruNzModuleType | None _table_modc[c=SSKnUq[[5 URR S:HqSSS5 [$[$!,(df  [$=f)Nrstrict)r}tablesrAttributeErrorfile_FILE_OPEN_POLICY!_table_file_open_policy_is_strict)rs r\_tablesrsV n % --9 .& : & % s A  Ac ^^^^^ ^ ^ ^ ^ ^U(aU U UU UUUU U U4 SjnOU U UU UUUU U U4 Sjn[U[5(a U"U5 g[U5n[XXES9nU"U5 SSS5 g!,(df  g=f)z+store this object, close it if we opened itc 4> URTT TTTT TTTTS9 $)N)formatindex min_itemsizenan_repdropna data_columnserrorsr[)append storerrr[rrrkeyrrvalues r\to_hdf..s1%,,  %%'  r^c 4> URTT TTTT TTTTS9 $)N)rrrrrrr[rputrs r\rr(s1%))  %%$  r^)mode complevelcomplibN)r`HDFStorerB) path_or_bufrrrrrrrrrrrrrr[rvrs `` ```````` r\to_hdfrsh$  +x(( +$[1  i  eH   s $ A66 Bpandasc US;a[SUS35eUb [USS9n[U[5(a!UR(d [ S5eUn Sn Oq[ U5n[U[5(d [S 5e[RRU5n U (d[S US 35e[U4X#S .U D6n S n UccU R5n[U5S:Xa [S5eUSnUSSHn[!UU5(aM[S5e UR"nU R%UUUUUUU U S9$![[4a Sn Nf=f![[[&4aQ [U[5(d:[)[*5 U R-5 SSS5 e!,(df  e=fef=f)aN Read from the store, close it if we opened it. Retrieve pandas object stored in file, optionally based on where criteria. .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- path_or_buf : str, path object, pandas.HDFStore Any valid string path is acceptable. Only supports the local file system, remote URLs and file-like objects are not supported. If you want to pass in a path object, pandas accepts any ``os.PathLike``. Alternatively, pandas accepts an open :class:`pandas.HDFStore` object. key : object, optional The group identifier in the store. Can be omitted if the HDF file contains a single pandas object. mode : {'r', 'r+', 'a'}, default 'r' Mode to use when opening the file. Ignored if path_or_buf is a :class:`pandas.HDFStore`. Default is 'r'. errors : str, default 'strict' Specifies how encoding and decoding errors are to be handled. See the errors argument for :func:`open` for a full list of options. where : list, optional A list of Term (or convertible) objects. start : int, optional Row number to start selection. stop : int, optional Row number to stop selection. columns : list, optional A list of columns names to return. iterator : bool, optional Return an iterator object. chunksize : int, optional Number of rows to include in an iteration when using an iterator. **kwargs Additional keyword arguments passed to HDFStore. Returns ------- object The selected object. Return type depends on the object stored. See Also -------- DataFrame.to_hdf : Write an HDF file from a DataFrame. HDFStore : Low-level access to HDF files. Notes ----- When ``errors="surrogatepass"``, ``pd.options.future.infer_string`` is true, and PyArrow is installed, if a UTF-16 surrogate is encountered when decoding to UTF-8, the resulting dtype will be ``pd.StringDtype(storage="python", na_value=np.nan)``. Examples -------- >>> df = pd.DataFrame([[1, 1.0, "a"]], columns=["x", "y", "z"]) # doctest: +SKIP >>> df.to_hdf("./store.h5", "data") # doctest: +SKIP >>> reread = pd.read_hdf("./store.h5") # doctest: +SKIP )rr+azmode zG is not allowed while performing a read. Allowed modes are r, r+ and a.Nrgrhz&The HDFStore must be open for reading.Fz5Support for generic buffers has not been implemented.zFile z does not exist)rrTrz]Dataset(s) incompatible with Pandas data types, not table, or no datasets found in HDF5 file.z?key must be provided when HDF5 file contains multiple datasets.)rmstartstopcolumnsiterator chunksize auto_close) ValueErrorrpr`ris_openOSErrorrBraNotImplementedErrorospathexists TypeErrorFileNotFoundErrorgroupsrl_is_metadata_of _v_pathnameselect LookupErrorrrclose)rrrrrmrrrrrkwargsrrrrcandidate_only_groupgroup_to_checks r\read_hdfr?sn ##D6- .   U2+x((""BC C $[1 +s++%G  WW^^K0F #eK=$HI II4I&I % ;\\^F6{a D$*!9 #)*&~7KLL$;#- '22C|| !  A:& F T  ; /+x00.) * *)  s=E A E.EEE9GF2) G2 G < GcURUR::agUnURS:a@URnX1:XaURS:XagURnURS:aM@g)zDCheck if a given group is a metadata group for a given parent_group.FrgmetaT)_v_depth _v_parent_v_name)group parent_groupcurrentparents r\rrsi ~~...G   Q ""  !goo&?##   Q  r^c\rSrSr%SrS\S'S\S'S4S5SjjrS6S jr\S 5r \S6S j5r S7S jr S8S jr S9Sjr S:SjrS;SjrSSjrS?S@SjjrSASjrSBSjrSCSDSjjrSESjr\SFSj5rSGSHSjjrS7SjrSISJSjjrSKSLSjjrSMSNSjjrSOSPS jjrSQSRS!jjr SKS9S"jjr!SSSTS#jjr"SUSVS$jjr#SKSWS%jjr$SXS&jr%SYSZS'jjr&S[S(jr'S\S)jr(S]S^S*jjr)S6S+jr*SES,jr+S_S-jr,S`SaS.jjr-SbScS/jjr.SdS0jr/SeS1jr0SfS2jr1S3r2g)gria| Dict-like IO interface for storing pandas objects in PyTables. Either Fixed or Table format. .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- path : str File path to HDF5 file. mode : {'a', 'w', 'r', 'r+'}, default 'a' ``'r'`` Read-only; no data can be modified. ``'w'`` Write; a new file is created (an existing file with the same name would be deleted). ``'a'`` Append; an existing file is opened for reading and writing, and if the file does not exist it is created. ``'r+'`` It is similar to ``'a'``, but the file must already exist. complevel : int, 0-9, default None Specifies a compression level for data. A value of 0 or None disables compression. complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib' Specifies the compression library to be used. These additional compressors for Blosc are supported (default if no compressor specified: 'blosc:blosclz'): {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy', 'blosc:zlib', 'blosc:zstd'}. Specifying a compression library which is not available issues a ValueError. fletcher32 : bool, default False If applying compression use the fletcher32 checksum. **kwargs These parameters will be passed to the PyTables open_file method. Examples -------- >>> bar = pd.DataFrame(np.random.randn(10, 4)) >>> store = pd.HDFStore("test.h5") >>> store["foo"] = bar # write to HDF5 >>> bar = store["foo"] # retrieve >>> store.close() **Create or load HDF5 file in-memory** When passing the `driver` option to the PyTables open_file method through **kwargs, the HDF5 file is loaded or created in-memory and will only be written when closed: >>> bar = pd.DataFrame(np.random.randn(10, 4)) >>> store = pd.HDFStore("test.h5", driver="H5FD_CORE") >>> store["foo"] = bar >>> store.close() # only now, data is written to disk z File | None_handlera_modeNc SU;a [S5e[S5nUb<XGRR;a#[SURRS35eUcUbURRn[ U5UlUcSnX lSUlU(aUOSUl X@l XPl SUl UR"S SU0UD6 g) Nrz-format is not a defined argument for HDFStorerzcomplib only supports z compression.rrr)rrfilters all_complibsdefault_complibrB_pathrr _complevel_complib _fletcher32_filtersopen)selfrrrr fletcher32rrs r\__init__HDFStore.__init__6s v LM M+H5  7..2M2M#M()D)D(E]S  ?y4nn44G#D) <D  '0)a %  &t&v&r^cUR$rXrrs r\ __fspath__HDFStore.__fspath__Ws zzr^clUR5 URceURR$)zreturn the root node)_check_if_openrrootrs r\r HDFStore.rootZs0 ||'''||   r^cUR$rXrrs r\filenameHDFStore.filenamea zzr^c$URU5$rX)getrrs r\ __getitem__HDFStore.__getitem__esxx}r^c&URX5 grXr)rrrs r\ __setitem__HDFStore.__setitem__hs r^c$URU5$rX)removers r\ __delitem__HDFStore.__delitem__ks{{3r^cURU5$![[4a Of=f[S[ U5R SUS35e)z$allow attribute access to get stores'z' object has no attribute ')rKeyErrorrrtype__name__)rrcs r\ __getattr__HDFStore.__getattr__nsW 88D> !/*   T ##$$?vQ G  s &&cZURU5nUbURnXUSS4;agg)z` check for existence of this key can match the exact pathname or the pathnm w/o the leading '/' NrgTF)get_noder)rrnodercs r\ __contains__HDFStore.__contains__xs: }}S!  ##DT!"X&&r^c4[UR55$rX)rlrrs r\__len__HDFStore.__len__s4;;=!!r^cN[UR5n[U5SUS3$)N File path:  )rDrr)rpstrs r\__repr__HDFStore.__repr__s'DJJ't*]4&33r^cU$rXrrs r\ __enter__HDFStore.__enter__s r^c$UR5 grX)r)rexc_type exc_value tracebacks r\__exit__HDFStore.__exit__s r^c&US:Xa*UR5Vs/sHo"RPM sn$US:XaCURceURRSSS9Vs/sHo"RPM sn$[ SUS35es snfs snf)a Return a list of keys corresponding to objects stored in HDFStore. Parameters ---------- include : str, default 'pandas' When kind equals 'pandas' return pandas objects. When kind equals 'native' return native HDF5 Table objects. Returns ------- list List of ABSOLUTE path-names (e.g. have the leading '/'). Raises ------ raises ValueError if kind has an illegal value See Also -------- HDFStore.info : Prints detailed information on the store. HDFStore.get_node : Returns the node with the key. HDFStore.get_storer : Returns the storer object for a key. Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data", df) # doctest: +SKIP >>> store.get("data") # doctest: +SKIP >>> print(store.keys()) # doctest: +SKIP ['/data1', '/data2'] >>> store.close() # doctest: +SKIP rnative/Table) classnamez8`include` should be either 'pandas' or 'native' but is 'r)rrr walk_nodesr)rincludens r\keys HDFStore.keyssH h +/;;=9=aMM=9 9  <<+ ++'+||'>'>sg'>'V'V! 'V Fwiq Q  :s B "Bc4[UR55$rX)iterrrs r\__iter__HDFStore.__iter__sDIIK  r^c#Z# UR5HnURU4v M g7f)z iterate on key->group N)rrrgs r\itemsHDFStore.itemss&A--" "s)+c [5nURU:waZURS;aUS;aO=US;a7UR(a&[SURSURS35eXlUR(aUR 5 UR (aLUR S:a<[5RUR URURS9Ul [(aUR(a S n[U5eUR"URUR40UD6Ulg ) z Open the file in the specified mode Parameters ---------- mode : {'a', 'w', 'r', 'r+'}, default 'a' See HDFStore docstring or tables.open_file for info about modes **kwargs These parameters will be passed to the PyTables open_file method. )rw)rr)r#zRe-opening the file [z ] with mode [z] will delete the current file!r)rzGCannot open HDF5 file, which is already opened, even in read-only mode.N)rrrrrrrFiltersrrrrr open_filer)rrrrmsgs r\r HDFStore.opens :: zzZ'DK,?<<// |= U88 J << JJL ??t2#I--4;K;K.DM - ,* S/ !'' DJJI&I r^c`URbURR5 SUlg)z Close the PyTables file handle N)rrrs r\rHDFStore.closes% << # LL    r^c\URcg[URR5$)z6 return a boolean indicating whether the file is open F)rboolisopenrs r\rHDFStore.is_opens& << DLL''((r^cURbiURR5 U(aG[[5 [R "URR 55 SSS5 ggg!,(df  g=f)a 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. N)rflushrrrfsyncfileno)rr0s r\r/HDFStore.flushs] << # LL   g&HHT\\0023'& $'&s /A88 Bc[5 URU5nUc[SUS35eURU5sSSS5 $!,(df  g=f)aL Retrieve pandas object stored in file. Parameters ---------- key : str Object to retrieve from file. Raises KeyError if not found. Returns ------- object Same type as object stored in file. See Also -------- HDFStore.get_node : Returns the node with the key. HDFStore.get_storer : Returns the storer object for a key. Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data", df) # doctest: +SKIP >>> store.get("data") # doctest: +SKIP >>> store.close() # doctest: +SKIP NNo object named in the file)rrr _read_grouprrrs r\r HDFStore.get$sK6^MM#&E}!1#lCDD##E* ^^s 4A  Ac ^^ URU5n U c[SUS35e[USS9nURU 5m T R 5 UU 4Sjn [ UT U UT R UUUUUS9 n U R5$)a Retrieve pandas object stored in file, optionally based on where criteria. .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- key : str Object being retrieved from file. where : list or None List of Term (or convertible) objects, optional. start : int or None Row number to start selection. stop : int, default None Row number to stop selection. columns : list or None A list of columns that if not None, will limit the return columns. iterator : bool or False Returns an iterator. chunksize : int or None Number or rows to include in iteration, return an iterator. auto_close : bool or False Should automatically close the store when finished. Returns ------- object Retrieved object from file. See Also -------- HDFStore.select_as_coordinates : Returns the selection as an index. HDFStore.select_column : Returns a single column from the table. HDFStore.select_as_multiple : Retrieves pandas objects from multiple tables. Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data", df) # doctest: +SKIP >>> store.get("data") # doctest: +SKIP >>> print(store.keys()) # doctest: +SKIP ['/data1', '/data2'] >>> store.select("/data1") # doctest: +SKIP A B 0 1 2 1 3 4 >>> store.select("/data1", where="columns == A") # doctest: +SKIP A 0 1 1 3 >>> store.close() # doctest: +SKIP r4r5rgrhc&>TRXUTS9$)N)rrrmr)read)_start_stop_whererss r\funcHDFStore.select..funcs66&'6R Rr^rmnrowsrrrrr)rrrp_create_storer infer_axes TableIteratorrC get_result) rrrmrrrrrrrr@itr?s ` @r\rHDFStore.selectGsL c" =-cU,?@ @U2    &  S  ''!  }}r^c[USS9nURU5n[U[5(d [ S5eUR X#US9$)a+ return the selection as an Index .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- key : str where : list of Term (or convertible) objects, optional start : integer (defaults to None), row number to start selection stop : integer (defaults to None), row number to stop selection rgrhz&can only read_coordinates with a tablermrr)rp get_storerr`rrread_coordinates)rrrmrrtbls r\select_as_coordinatesHDFStore.select_as_coordinatessM4U2ooc"#u%%DE E##%4#HHr^cURU5n[U[5(d [S5eUR X#US9$)a return a single column from the table. This is generally only useful to select an indexable .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- key : str column : str The column of interest. start : int or None, default None stop : int or None, default None Raises ------ raises KeyError if the column is not found (or key is not a valid store) raises ValueError if the column can not be extracted individually (it is part of a data block) z!can only read_column with a table)columnrr)rLr`rr read_column)rrrRrrrNs r\ select_columnHDFStore.select_columns?Fooc"#u%%?@ @fEEr^c ^^^[USS9n[U[[45(a[ U5S:XaUSn[U[ 5(aUR UUTUUUUU S9$[U[[45(d [S5e[ U5(d [S5eUcUSnUV s/sHoRU 5PM sn mURU5n Sn [R"X4/[TUSS 95HmupU c[S U S 35eU R(d[S U RS 35eU cU R n MRU R U :wdMd[S5e TVs/sHn[U["5(dMUPM nnUV s1sHoR$SSiM sn R'5mUUU4Sjn[)UU UUU UUUUU S9 nUR+SS9$s sn fs snfs sn f)a Retrieve pandas objects from multiple tables. .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- keys : a list of the tables selector : the table to apply the where criteria (defaults to keys[0] if not supplied) columns : the columns I want back start : integer (defaults to None), row number to start selection stop : integer (defaults to None), row number to stop selection iterator : bool, return an iterator, default False chunksize : nrows to include in iteration, return an iterator auto_close : bool, default False Should automatically close the store when finished. Raises ------ raises KeyError if keys or selector is not found or keys is empty raises TypeError if keys is not a list or tuple raises ValueError if the tables are not ALL THE SAME DIMENSIONS rgrhr)rrmrrrrrrzkeys must be a list/tuplez keys must have a non-zero lengthNTrzInvalid table []zobject [z>] is not a table, and cannot be used in all select as multiplez,all tables must have exactly the same nrows!c >TVs/sHnURUTXS9PM nn[UTSS9R5$s snf)NrmrrrF)axisverify_integrity)r;r6 _consolidate)r<r=r>rwobjsr[rtblss r\r@)HDFStore.select_as_multiple..funcPsS AVWFO  $TEBOOQ Q s<rB) coordinates)rpr`rjrkrlrarrrrL itertoolschainzipris_tablepathnamerCrnon_index_axespoprFrG)rrrmselectorrrrrrrkr?rCrwx_tblsr@rHr[r_s ` @@r\select_as_multipleHDFStore.select_as_multiplesVU2 dT5M * *s4yA~7D dC ;;!#%  $u ..78 84yy?@ @  AwH-11Dq"D1 OOH %OOa]OStD5QRDAy1566::qzzl+)) }E! !OPPS !9DqJq%$8D91661  #A&6::< R  !  }}}..g2*:7s5G:<G?G?%HcUc[S5=(d SnURU5nURUUUUUUUUU U U U U US9 g)ad Store object in HDFStore. This method writes a pandas DataFrame or Series into an HDF5 file using either the fixed or table format. The `table` format allows additional operations like incremental appends and queries but may have performance trade-offs. The `fixed` format provides faster read/write operations but does not support appends or queries. Parameters ---------- key : str Key of object to store in file. value : {Series, DataFrame} Value of object to store in file. format : 'fixed(f)|table(t)', default is 'fixed' Format to use when storing object in HDFStore. Value can be one of: ``'fixed'`` Fixed format. Fast writing/reading. Not-appendable, nor searchable. ``'table'`` Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data. index : bool, default True Write DataFrame index as a column. append : bool, default False This will force Table format, append the input data to the existing. complib : default None This parameter is currently not accepted. complevel : int, 0-9, default None Specifies a compression level for data. A value of 0 or None disables compression. min_itemsize : int, dict, or None Dict of columns that specify minimum str sizes. nan_rep : str Str to use as str nan representation. data_columns : list of columns or True, default None List of columns to create as data columns, or True to use all columns. See `here `__. encoding : str, default None Provide an encoding for strings. errors : str, default 'strict' The error handling scheme to use for encoding errors. The default is 'strict' meaning that encoding errors raise a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and 'xmlcharrefreplace' as well as any other name registered with codecs.register_error that can handle UnicodeEncodeErrors. track_times : bool, default True Parameter is propagated to 'create_table' method of 'PyTables'. If set to False it enables to have the same h5 files (same hashes) independent on creation time. dropna : bool, default False, optional Remove missing values. See Also -------- HDFStore.info : Prints detailed information on the store. HDFStore.get_storer : Returns the storer object for a key. Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data", df) # doctest: +SKIP Nio.hdf.default_formatrt) rrrrrrrrr[r track_timesr)r_validate_format_write_to_group)rrrrrrrrrrrr[rrqrs r\r HDFStore.putksgh > 78CGF&&v.   %%#  r^c[USS9nURU5n[R"X#U5(aWRRSS9 gWR(d [ S5eURX#US9$![a e[a e[aAnUb [ S5UeUR U5nUbURSS9 SnAgSnANSnAff=f) a Remove pandas object partially by specifying the where condition Parameters ---------- key : str Node to remove or delete rows from where : list of Term (or convertible) objects, optional start : integer (defaults to None), row number to start selection stop : integer (defaults to None), row number to stop selection Returns ------- number of rows removed (or None if not a Table) Raises ------ raises KeyError if key is not a valid store rgrhNz5trying to remove a node with a non-None where clause!T recursivez7can only remove with where on objects written as tablesrK) rprLrAssertionError Exceptionrr _f_removecomall_nonerredelete)rrrmrrr?errrs r\rHDFStore.removes*U2 $A. <<d + + GG    -zzVW Wxxetx<<;       K ==%D.  sA??C2CCcU b [S5eUc [S5nUc[S5=(d SnURU5nURUUUUUUUUU U U U UUUUS9 g)a{ Append to Table in file. Node must already exist and be Table format. Parameters ---------- key : str Key of object to append. value : {Series, DataFrame} Value of object to append. format : 'table' is the default Format to use when storing object in HDFStore. Value can be one of: ``'table'`` Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data. axes : default None This parameter is currently not accepted. index : bool, default True Write DataFrame index as a column. append : bool, default True Append the input data to the existing. complib : default None This parameter is currently not accepted. complevel : int, 0-9, default None Specifies a compression level for data. A value of 0 or None disables compression. columns : default None This parameter is currently not accepted, try data_columns. min_itemsize : int, dict, or None Dict of columns that specify minimum str sizes. nan_rep : str Str to use as str nan representation. chunksize : int or None Size to chunk the writing. expectedrows : int Expected TOTAL row size of this table. dropna : bool, default False, optional Do not write an ALL nan row to the store settable by the option 'io.hdf.dropna_table'. 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 `__. encoding : default None Provide an encoding for str. errors : str, default 'strict' The error handling scheme to use for encoding errors. The default is 'strict' meaning that encoding errors raise a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and 'xmlcharrefreplace' as well as any other name registered with codecs.register_error that can handle UnicodeEncodeErrors. See Also -------- HDFStore.append_to_multiple : Append to multiple tables. Notes ----- Does *not* check if data being appended overlaps with existing data in the table, so be careful Examples -------- >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data", df1, format="table") # doctest: +SKIP >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=["A", "B"]) >>> store.append("data", df2) # doctest: +SKIP >>> store.close() # doctest: +SKIP A B 0 1 2 1 3 4 0 5 6 1 7 8 Nz>columns is not a supported keyword in append, try data_columnszio.hdf.dropna_tablerpru)raxesrrrrrrr expectedrowsrrr[r)rrrrrs)rrrrrrrrrrrrrrrrr[rs r\rHDFStore.append sF  P  > 56F > 78CGF&&v.   %%%!  r^c 2^Ub [S5e[U[5(d [S5eX1;a [S5e[ [ [ [TR55[ [[T55- 55nSn /n UR5H+upU cU b [S5eU n MU RU 5 M- U bVTRUn U R[U 55n[!U R#U55nU R%U5X'UcXnU(aNU4SjUR'55n[ U5nUHnUR)U5nM TR*UmUR-SS5nUR5HhupX:XaUOSnTR/XS9nUb/UR5VVs0sHunnUU ;dMUU_M snnOSnUR0"U U4UUS .UD6 Mj gs snnf) a 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 Nztaxes is currently not accepted as a parameter to append_to_multiple; you can create the tables independently insteadzQappend_to_multiple must have a dictionary specified as the way to split the valuez=append_to_multiple requires a selector that is in passed dictz# UH!nTURSS9Rv M# g7f)all)howN)rr).0colsrs r\ .HDFStore.append_to_multiple..s)OJDE$K&&5&177Js),rr[)rr)rr`dictrnextrsetrangendim _AXES_MAPrr extendr differencer/sorted get_indexertakevalues intersectionlocrhreindexr)rdrrirrrrr[ remain_key remain_valuesrjvorderedorddidxs valid_indexrrdcvalrfiltereds ` r\append_to_multipleHDFStore.append_to_multiples>  B  !T"")   O  DU5::./#iU 6L2MMNO  GGIDAy)$V $$Q'  !jj&G%%eM&:;D'--d34D#LL.AM  ;L OAHHJODt*K)66u= IIk*Ezz.$7 GGIDA!"DB----C +1=0B0B0DQ0D eqe0DQ  KK3 QRh Q& QRs  H*Hc[5 URU5nUcg[U[5(d [ S5eUR X#US9 g)aP Create a pytables index on the table. Parameters ---------- key : str columns : None, bool, or listlike[str] Indicate which columns to create an index on. * False : Do not create any indexes. * True : Create indexes on all columns. * None : Create indexes on all columns. * listlike : Create indexes on the given columns. optlevel : int or None, default None Optimization level, if None, pytables defaults to 6. kind : str or None, default None Kind of index, if None, pytables defaults to "medium". Raises ------ TypeError: raises if the node is not a table Nz1cannot create table index on a Fixed format store)roptlevelkind)rrLr`rr create_index)rrrrrr?s r\create_table_indexHDFStore.create_table_indexsH>  OOC  9 !U##OP P wEr^c[5 UR5 URce[ceURR 5Vs/sHn[ U[R R5(aM.[URSS5(dO[USS5(d=[ U[RR5(dMURS:wdMUPM sn$s snf)aM Return a list of all the top-level nodes. Each node returned is not a pandas storage object. Returns ------- list List of objects. See Also -------- HDFStore.get_node : Returns the node with the key. Examples -------- >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data", df) # doctest: +SKIP >>> print(store.groups()) # doctest: +SKIP >>> store.close() # doctest: +SKIP [/data (Group) '' children := ['axis0' (Array), 'axis1' (Array), 'block0_values' (Array), 'block0_items' (Array)]] N pandas_typeru) rrrr} walk_groupsr`linkLinkgetattr_v_attrsrurrrs r\rHDFStore.groupss4   ||'''%%%\\--/  /q*//"6"67  AJJ t<<q'400"1j&6&6&<&<= CD))wBV /   s-C4AC4C4+C4c#j# [5 UR5 URce[ceURR U5Hn[ UR SS5bM/n/nURR5Hn[ UR SS5nUcH[U[RR5(aURUR5 McMeURUR5 M URRS5X44v M g7f)a Walk the pytables group hierarchy for pandas objects. This generator will yield the group path, subgroups and pandas object names for each group. Any non-pandas PyTables objects that are not a group will be ignored. The `where` group itself is listed first (preorder), then each of its child groups (following an alphanumerical order) is also traversed, following the same procedure. Parameters ---------- where : str, default "/" Group where to start walking. Yields ------ path : str Full path to a group (without trailing '/'). groups : list Names (strings) of the groups contained in `path`. leaves : list Names (strings) of the pandas objects contained in `path`. See Also -------- HDFStore.info : Prints detailed information on the store. Examples -------- >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data", df1, format="table") # doctest: +SKIP >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=["A", "B"]) >>> store.append("data", df2) # doctest: +SKIP >>> store.close() # doctest: +SKIP >>> for group in store.walk(): # doctest: +SKIP ... print(group) # doctest: +SKIP >>> store.close() # doctest: +SKIP Nrr)rrrr}rrr _v_childrenrr`rGrouprrrrstrip)rrmrrleaveschildrs r\walk HDFStore.walk>sV   ||'''%%%))%0Aqzz=$7CFF--/%ennmTJ &!%)9)9)?)?@@ emm4AMM%--0 0=='',f= =1sD1D3cUR5 URS5(dSU-nURce[ceURR UR U5n[U[R5(d[U55eU$![R Ra gf=f)z9return the node with the key or None if it does not existrN) r startswithrr}rr exceptionsNoSuchNodeErrorr`rMr)rrrs r\rHDFStore.get_node~s ~~c"")C||'''%%% <<((C8D$ 00<$t*<0 $$44  s&BB=<B=cURU5nUc[SUS35eURU5nUR5 U$)z !((+{{1~a''.3E"12 HA<< H$$#%,Q%E!" %MM!AJJM?),!Is ;D/D/c~[UR5n[U5SUS3nUR(a[ UR 55nU(a/n/nUHjnUR U5nUbRUR[UR=(d U55 UR[U=(d S55 MjMl U[SXE5- nU$US- nU$US - nU$![a e[a<nURU5 [U5n URSU S35 SnAMSnAff=f) a/ Print detailed information on the store. Returns ------- str A String containing the python pandas class name, filepath to the HDF5 file and all the object keys along with their respective dataframe shapes. See Also -------- HDFStore.get_storer : Returns the storer object for a key. Examples -------- >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) >>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=["C", "D"]) >>> store = pd.HDFStore("store.h5", "w") # doctest: +SKIP >>> store.put("data1", df1) # doctest: +SKIP >>> store.put("data2", df2) # doctest: +SKIP >>> print(store.info()) # doctest: +SKIP >>> store.close() # doctest: +SKIP File path: store.h5 /data1 frame (shape->[2,2]) /data2 frame (shape->[2,2]) rrNzinvalid_HDFStore nodez[invalid_HDFStore node: rX EmptyzFile is CLOSED) rDrrrrrrLrrfrxryrC) rroutputlkeysrrrjr?detaildstrs r\info HDFStore.infos08DJJ'J<}TF"5 <<499;'EA J OOA.= KK QZZ_1(EF"MM,q7S,value->z ,format->r[r series_tablerrgappendable_seriesappendable_multiseriesappendable_frameappendable_multiframe)rrrrrwormz)r`r3r-rrrrr}rur SeriesFixed FrameFixedrrnlevels GenericTableAppendableSeriesTableAppendableMultiSeriesTableAppendableFrameTableAppendableMultiFrameTable WORMTable) rrrrr[rpttt _STORER_MAPclsr~r _TABLE_MAPs r\rDHDFStore._create_storersR  Z 7J%K%KFG G U^^]D 9 U^^\4 8 :} !---5'400J:++1155'B(B#1 eV,,!B BW$(NB " %0:FK !o tXE E : '#E7D9E( ==A-!4B"]]Q.!9B=(#E7D9E( ==A-!3B"]]Q.!8B*!6&@ 4%>   .C4AAU SgXd5k])F8E H N'$u+ixA  s05F*G* G4GG H$HHcZ[USS5(aUS:XdU(agURX5nURUX2UUS9nU(afUR(a(UR(a"US:XaUR(a [ S5eUR(dUR 5 OUR 5 UR(dU(a [ S5eURUUUUUU U U U U UUUS9 [U[5(aU(aURUS9 ggg) NemptyrurrtzCan only append to Tablesz0Compression not supported on Fixed format stores) objrrrrrrrrrrrrqr) r_identify_grouprDre is_existsrset_object_infowriter`rr)rrrrrrrrrrrrrrrrr[rrqrr?s r\rsHDFStore._write_to_groupzs . 5'4 ( (f.?6 $$S1   vxPV  W ::!**71Bq{{ !<==;;!!#   zzgOP P !%%%#  a  E NN5N )%* r^cdURU5nUR5 UR5$rX)rDrEr;)rrr?s r\r6HDFStore._read_groups&    & vvxr^cURU5nURceUb#U(dURRUSS9 SnUcURU5nU$)z@Identify HDF5 group based on key, delete/create group if needed.NTrv)rr remove_node_create_nodes_and_group)rrrrs r\rHDFStore._identify_groupsb c"||'''  V LL $ $Ud $ ;E =005E r^c$URceURS5nSnUHgn[U5(dMUnURS5(dUS- nXT- nUR U5nUcURR X45nUnMi W$)z,Create nodes from key and return group name.r)rsplitrlendswithr create_group)rrpathsrpnew_pathrs r\r HDFStore._create_nodes_and_groups||''' #Aq66H==%%C MHMM(+E} 11$:D r^)rrrrrrr)rNNF)rrar int | Nonerr+returnNonerrarra)rrarr)rrarr)rcra)rrarr+rint)rr )r ztype[BaseException] | Noner zBaseException | Noner zTracebackType | Nonerr)r)rrar list[str])rz Iterator[str])rzIterator[tuple[str, list]])r)rrarrrrrr+F)r0r+rr)NNNNFNF)rrarr+rrrr+NNNrrarrrrNN)rrarRrarrrr)NNNNNFNF)rr+rrrr+) NTFNNNNNNrTF)rrarDataFrame | Seriesrr+rr+rrrint | dict[str, int] | Noner Literal[True] | list[str] | Nonerrarqr+rr+rr)NNTTNNNNNNNNNNr)rrarr rbool | list[str]rr+rrrr!rrr bool | Nonerr"rrarr)NNF)rrrr+rr)rrarrr str | Nonerrrrj)r)rmrarz*Iterator[tuple[str, list[str], list[str]]])rrarz Node | None)rrarGenericFixed | Table)r#TNNNFT) rrarr+rrrr+rr+rr)rrarra)NNrVr)rzDataFrame | Series | Noner[rarrarr')NTFNNNNNNFNNNrT)rrarr rr#rr+rrrr!rrrr+rrarqr+rr)rrM)rrarr+rrM)rrarrM)3r __module__ __qualname____firstlineno____doc____annotations__rrpropertyrrrrrrrrrrr rrr rrrr/rrrOrTrmrrrrrrrrrLrrrrrrDrsr6rr__static_attributes__rr^r\rrs>?B J  $ '' '  ' 'B!!    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Define the iteration interface on a table Parameters ---------- store : HDFStore s : the referred storer func : the function to execute the query where : the where of the query nrows : the rows to iterate on start : the passed start value (default is None) stop : the passed stop value (default is None) iterator : bool, default False Whether to use the default iterator. chunksize : the passed chunking value (default is 100000) auto_close : bool, default False Whether to automatically close the store at the end of iteration. rrrrr'r?Nc *XlX lX0lX@lURR(aUcSnUcSnUcUn[ XW5nXPlX`lXplSUl U(dU bU cSn [U 5Ul OSUl Xl g)Nr順) rr?r@rmreminrCrrrarrr) rrr?r@rmrCrrrrrs r\rTableIterator.__init__s    66??}}|u#D    y, "  ^DN!DN$r^c#l# URnURc [S5eXR:am[ XR -UR5nUR SSURX5nUnUb[U5(dMgUv XR:aMmUR5 g7f)Nz*Cannot iterate until get_result is called.) rrarrr2rr@rlr)rrrrs r\rTableIterator.__iter__'s**    #IJ J !w/;DIIdD$*:*:7*HIEG}CJJK ! s BB4#B4c\UR(aURR5 ggrX)rrrrs r\rTableIterator.close7s ?? JJ    r^c$URbT[UR[5(d [ S5eURR UR S9UlU$U(ad[UR[5(d [ S5eURR UR URURS9nO UR nURURURU5nUR5 U$)Nz0can only use an iterator or chunksize on a table)rmz$can only read_coordinates on a tablerK) rr`r?rrrMrmrarrr@r)rrarmresultss r\rGTableIterator.get_result;s >> %dffe,, RSS#vv66TZZ6HD K dffe,, FGGFF++jj ,EJJE))DJJ 59 r^) rrrar@rCr?rrrrm)NNFNF) rrr?r'rr+rrrr+rrrrGrr)rar+) rr(r)r*r+r,rrrrGr.rr^r\rFrFs& O  $ (%(% (%(%(%(% (%T r^rFc\rSrSr%SrSrS\S'SrS\S'/SQrS#S$S jjr \ S%S j5r \ S&S j5r S'S jr S&S jrS(SjrS)Sjr\ S)Sj5rS*SjrSr\ S5r\ S5r\ S5r\ S5rS+SjrS,S-SjjrS-SjrS.SjrS,SjrS/SjrS-SjrS-SjrS-Sjr S0S jr!S0S!jr"S"r#g)1IndexColiUz 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 Tr+is_an_indexableis_data_indexable)freqtz index_nameNc[U[5(d [S5eX lX0lX@lXlU=(d UUlX`lXpl Xl Xl Xl Xl XlXlXlUbUR#U5 [UR [5(de[UR[5(deg)Nz`name` must be a str.)r`rarrrtyprccnamer[posr@rArBrrurmetadataset_pos)rrcrrrDrEr[rFr@rArBrrurrGs r\rIndexCol.__init__gs"$$$45 5   ]d   $    ? 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((.S-M' !r^cgrXrrs r\validate_namesIndexCol.validate_names)  r^cURUlUR5 URU5 URU5 UR U5 UR 5 grX)ru validate_col validate_attrvalidate_metadatawrite_metadataset_attr)rhandlerrs r\validate_and_setIndexCol.validate_and_set, sL]]   6" w' G$ r^c URS:XacURnUbTUc URnURU:a)[SUSURSURS35eUR$g)z:validate this column: return the compared against itemsizerNz#Trying to store a string with len [z] in [z)] column but this column has a limit of [zC]! 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MM  % r^cZ[URURUR5 g)zset the kind for this columnN)rrrOrrs r\rIndexCol.set_attru s DNNDII6r^cURS:XaKURnURUR5nUb Ub[ X#SSS9(d [ S5egggg)z:validate that kind=category does not change the categoriescategoryNT strict_nan dtype_equalzEcannot append a categorical with different categories to the existing)rrG read_metadatarEr,r)rr new_metadata cur_metadatas r\rIndexCol.validate_metadatay sn 99 "==L"00r,r?rrr-rKrOrHrrhrkrrrrrrrrrrrrrrrrrrr.rr^r\r=r=Usv !OT "t"/L        )+)+  )+ !)+V!!##"   &??A. A.58A.BEA. <A.F##&&;;! T &!>& 7">r^r=cP\rSrSrSr\SSj5rS SjrS SjrSr g) GenericIndexColi z:an index which is not represented in the data of the tablecgNFrrs r\rGenericIndexCol.is_indexed r^c[U[R5(d[U55e[ [ U55nXU4$)z Convert the data from this selection to the appropriate pandas type. Parameters ---------- values : np.ndarray nan_rep : str encoding : str errors : str )r`rrrr2rl)rrrr[rrs r\rGenericIndexCol.convert s:&"**--;tF|;-3v;'|r^cgrXrrs r\rGenericIndexCol.set_attr rr^rNr)rrr[rarrarztuple[Index, Index]r) rr(r)r*r+r-rrrr.rr^r\rr sAD  58BE $ r^rc`^\rSrSrSrSrSrSS/rSSU4Sjjjr\ SSj5r \ SSj5r SS jr SS jr SS jrS r\SS j5r\S5r\SSj5r\S Sj5r\S5r\S5r\ S5r\ S5rS!SjrS"SjrS!SjrSrU=r$)#DataColi a a data holding column, by definition this is not indexable Parameters ---------- data : the actual data cname : the column name in the table to hold the data (typically values) meta : a string description of the metadata metadata : the actual metadata FrArcL>[TU]UUUUUUUUU U U S9 XlXlg)N) rcrrrDrFrErArrurrG)superrrzr)rrcrrrDrErFrArrurrGrzr __class__s r\rDataCol.__init__ sD     r^c URS3$)N_dtyperbrs r\ dtype_attrDataCol.dtype_attr s))F##r^c URS3$)N_metarbrs r\ meta_attrDataCol.meta_attr rQr^c [[[URURUR UR UR455nSR[/SQUSS9VVs/sH up#USU3PM snn5$s snnf)NrV)rcrErzrshapeTrWrW) rkrXrDrcrErzrrrYrdrZs r\rDataCol.__repr__ s tyy$**djj$))TZZX   xx#&?d# #JC%r%!#    r]c2^^[UU4SjS55$)r_c3Z># UH n[TUS5[TUS5:Hv M" g7frXrarbs r\r!DataCol.__eq__.. s/ 6 D!T "geQ&= =6re)rcrErzrFrfrgs``r\rhDataCol.__eq__ s 6   r^c~UceURbe[U5upXlX l[U5UlgrX)rz_get_data_and_dtype_namer_dtype_to_kindr)rr dtype_names r\set_dataDataCol.set_data sAzz!!!3D9  ":. r^cUR$)zreturn the datarrs r\rDataCol.take_data s yyr^cURnURnURnURS:XaSUR4n[ U[ 5(a1URnURXERRS9nU$[R"US5(d[ U[5(aURU5nU$[R"US5(aURU5nU$[U5(a[!5R#X4SS9nU$[%U5(aUR'XC5nU$URXBRS9nU$)zG Get an appropriately typed and shaped pytables.Col object for values. rgrrqmrrKr)rzrKrrsizer`r8codes get_atom_datarcrrr)get_atom_datetime64get_atom_timedelta64r$r ComplexColr&get_atom_string)rrrzrKrratoms r\ _get_atomDataCol._get_atom sF  >>  ;;!  $E fk * *LLE$$U1A1A$BD __UC ( (Juo,N,N**51D __UC ( (++E2D e $ $9''q'JD  U # #&&u7D $$U$>r^c6[5RUSS9$Nrr(rr rrs r\rDataCol.get_atom_datetime64A y!!a!11r^c6[5RUSS9$r-r.r/s r\rDataCol.get_atom_timedelta64E r1r^c0[URSS5$)Nr)rrrs r\r DataCol.shapeI styy'400r^cUR$rrrs r\rDataCol.cvaluesM syyr^c"U(a[URURS5nUb$U[UR5:wa [ S5e[URUR S5nUbX0R:wa [ S5eggg)zAvalidate that we have the same order as the existing & same dtypeNz4appended items do not match existing items in table!z@appended items dtype do not match existing items dtype in table!)rrrOrjrrrrz)rrexisting_fieldsexisting_dtypes r\rDataCol.validate_attrR s %djj$..$GO*$t{{BS/S !WXX$TZZ$GN)n .J V/K) r^c [U[R5(d[U55eURR bXR nURceURc[U5upV[U5nOUnURnURn[U[R5(deURnURn URn URn UceUn U RS5(aU S:XaSn [!X[U 5nGOKU RS5(a3U S:Xa[R""USS9nGO[R""X\S9nGOU S:Xa@[R""UV s/sHn [$R&"U 5PM sn [(S9nOUS:XaU nUR/5nUc[1/[R2S9nO`[5U5nUR75(a@UU)nXS:g==UR9[:5R=5R>-ss'[@RB"XU S S 9nOUR9U S S 9nUS :Xa [GXRX4S9nURHU4$s sn f![*aG [R""UV s/sHn [$R,"U 5PM Os sn fsn [(S9nNsf=f![Da UR9S S S 9nNf=f)z Convert the data from this selection to the appropriate pandas type. Parameters ---------- values : np.ndarray nan_rep : encoding : str errors : str Returns ------- index : listlike to become an Index data : ndarraylike to become a column datetime64datetime64[ns] timedelta64m8[ns]rzrrF) categoriesrvalidaterOrrr[r)%r`rrrrzrrErDrrrrrGrrAr_set_tzasarrayr fromordinalrr fromtimestampravelr/float64r7anyastypercumsum_valuesr8 from_codesr_unconvert_string_arrayr)rrrr[r convertedrrrrGrrArzrrCrmasks r\rDataCol.convert_ s &"**--;tF|;- <<   *JJ'Fxx### :: %=V$D !I!*-DIJ99D)RZZ0000yy==,, WW%%%   L ) ) $( u5I   m , , %JJyA JJy> f_ JJ2;<)QT%%a()%,,U,? 8 /XI{{I%%W= JJ4=>IqT''*I>f  @ >%,,Su,=  >sB8J J, J#K*JK'4 K K'&K'*LLc$[URURUR5 [URURUR 5 UR ce[URURUR 5 g)zset the data for this columnN)rrrOrrrrzrrs r\rDataCol.set_attr sZ DNNDKK8 DNNDII6zz%%% DOOTZZ8r^)rrzr) NNNNNNNNNNNN)rcrarEr%rzzDtypeArg | Nonerrrr)rrOrr)rrOrrK)rrarz type[Col]rrarrKr)rrr[rarra)rr(r)r*r+r>r?rrr-rrrrhrr classmethodrrr%rrrrrrrrr. __classcell__rs@r\rr sj O)$L      !%    @$$##   />FF , ,??222211 h&T99r^rcj\rSrSrSrSrS Sjr\S5r\S Sj5r \S5r \S5r S r g ) DataIndexableColi z+represent a data column that can be indexedTct[[UR5R5(d [ S5eg)N-cannot have non-object label DataIndexableCol)r&r/rrzrrs r\rDataIndexableCol.validate_names s-uT[[17788LM M9r^c0[5RUS9$)N)rKrrs r\r DataIndexableCol.get_atom_string sy""H"55r^c*URUS9"5$)Nr r)r*s r\rDataIndexableCol.get_atom_data s###.00r^c2[5R5$rXr.r/s r\r$DataIndexableCol.get_atom_datetime64 y!!##r^c2[5R5$rXr.r/s r\r%DataIndexableCol.get_atom_timedelta64 rhr^rNrrY) rr(r)r*r+r?rrZrrrrr.rr^r\r^r^ sa5N 6611$$$$r^r^c\rSrSrSrSrg)GenericDataIndexableColi z(represent a generic pytables data columnrN)rr(r)r*r+r.rr^r\rlrl s2r^rlc\rSrSr%SrS\S'SrS\S'S\S'S \S 'S \S 'S rS\S'S+S,Sjjr\ S-Sj5r \ S.Sj5r \ S5r S/Sjr S0SjrS1Sjr\ S5r\ S5r\ S5r\ S5r\ S2Sj5r\ S-Sj5r\ S5rS0SjrS0Sjr\ S 5r\ S-S!j5r\ S"5rS3S#jrS4S0S%jjrS-S&jrS5S6S'jjrS0S(jr S7S8S)jjr!S*r"g$)9Fixedi z represent an object in my store facilitate read/write of various types of objects this is an abstract base class Parameters ---------- parent : HDFStore group : Node The group node where the table resides. ra pandas_kindrt format_typetype[DataFrame | Series]obj_typerrrrFr+rec[U[5(d[U55e[ce[U[R5(d[U55eXlX l[U5UlX@l grX) r`rrr}rMrrr]r[r)rrrr[rs r\rFixed.__init__ sg&(++9T&\9+%%%%11>4;>1  (2  r^cURSS:*=(a+ URSS:*=(a URSS:$)Nrrg )versionrs r\is_old_versionFixed.is_old_version s:||A!#U Q2(=U$,,q/TUBUUr^c[URRSS5n[U[5(aJ[ SUR S555n[U5S:Xa/UQSP7n[U5S:XdeU$g) zcompute and set our versionpandas_versionNc38# UHn[U5v M g7frX)r)rrks r\r Fixed.version.. sC0B1A0Bs.rwr)rrr)rrrr`rarkr rl)rrx version_tups r\rx Fixed.version s{$**--/?F gs # #C c0BCCK;1$/ /Q/ {#q( (( r^cD[URRSS5$)Nr)rrrrs r\rFixed.pandas_type$ stzz**M4@@r^c UR5 URnUb_[U[[45(a1SR UVs/sHn[ U5PM sn5nSUS3nURSSUS3$UR$s snf)(return a pretty representation of myselfrV[rX12.12z (shape->))rErr`rjrkrYrDr)rr?rkjshapes r\rFixed.__repr__( s  JJ =!dE]++A">Aq<?A">?xqM&&u-Yqc; ;#?s B c[UR5URl[[5URlg)zset my pandas type & versionN)rarorr_versionr|rs r\rFixed.set_object_info3 s)!$T%5%5!6 $'M !r^c2[R"U5nU$rXrE)rnew_selfs r\r Fixed.copy8 s99T?r^cUR$rX)rCrs r\r Fixed.shape< rr^c.URR$rXrrrs r\rfFixed.pathname@ rr^c.URR$rX)rrrs r\r Fixed._handleD s{{"""r^c.URR$rX)rrrs r\rFixed._filtersH s{{###r^c.URR$rX)rrrs r\rFixed._complevelL s{{%%%r^c.URR$rX)rrrs r\rFixed._fletcher32P s{{&&&r^c.URR$rX)rrrs r\r Fixed.attrsT rr^cgzset our object attributesNrrs r\ set_attrsFixed.set_attrsX r^cg)zget our object attributesNrrs r\ get_attrsFixed.get_attrs[ rr^cUR$)zreturn my storablerrs r\storableFixed.storable^ szzr^cgrrrs r\rFixed.is_existsc rr^c0[URSS5$)NrC)rrrs r\rC Fixed.nrowsg st}}gt44r^c Ucgg)z%validate against an existing storableNTrrgs r\rDFixed.validatek s =r^Ncg)+are we trying to operate on an old version?Nrrrms r\validate_versionFixed.validate_versionq rr^cDURnUcgUR5 g)zZ infer the axes of my storer return a boolean indicating if we have a valid storer or not FT)rr)rr?s r\rEFixed.infer_axest s" MM 9 r^c[S5e)Nz>cannot read on an abstract storer: subclasses should implementrrrmrrrs r\r; Fixed.read s" L  r^c [S5e)Nz?cannot write on an abstract storer: subclasses should implementrrrrs r\r Fixed.write s! M  r^c[R"XU5(a%URRURSS9 g[ S5e)z[ support fully deleting the node in its entirety (only) - where specification must be None TrvNz#cannot delete on an abstract storer)r{r|rrrr)rrmrrs r\r} Fixed.delete s? <<d + + LL $ $TZZ4 $ @=>>r^)r[rrr)rVr) rrrrMr[r%rrarrr)rztuple[int, int, int]rr)rrnr)rzLiteral[True] | NonerXNNNN)rrrrrzSeries | DataFramerrrrrrr)#rr(r)r*r+r,rprerr-ryrxrrrrrrfrrrrrrrrrrCrDrrEr;rr}r.rr^r\rnrn s K&& I Hd '        VV  AA  2 &&##$$&&''##((55 :           HL ?!+ ?:D ?  ? ?r^rnc\rSrSr%Sr\S\S0r\R5VVVs0sHupX!_M snnnr /r S\ S'SSjr Sr S rSS jr\SS j5rSS jrSS jrSSjrSSSjjrSSSjjrSSjrS SjrSS!SjjrSS"SjjrS#SjrS$S%SjjrSrgs snnnf)& GenericFixedi za generified fixed versiondatetimerr attributesc:URRUS5$)N)_index_type_mapr)rrs r\_class_to_aliasGenericFixed._class_to_alias s##''R00r^cp[U[5(aU$URRU[5$rX)r`r_reverse_index_maprr/)raliass r\_alias_to_classGenericFixed._alias_to_class s- eT " "L&&**5%88r^cUR[USS55n0nU[:XaS SjnUnOU[:XaS SjnUnOUnSUS'SU;aUSUS'U[La[ nSU;aUSUS'U[LdeXS4$) N index_classrc[R"URURUS9n[R"USS9nUb UR S5R U5nU$)N)rzr@rbr)r9 _simple_newrrzr.rr)rr@rAdtaresults r\rv*GenericFixed._get_index_factory..f sZ#//MMD'223TB>#//6AA"EF r^cj[U5n[R"XS9n[R"USS9$)NrArb)r+r:rr1)rr@rArzparrs r\rvr s/#D)"..vC"..t$??r^Frr@rAr)rrr.r1r/r5)rrrrrvrs r\_get_index_factoryGenericFixed._get_index_factory s**75-+LM  - ' G K ' @ G!G"F6N U?"6]F6Ne#( 5= ;F4L-/ //r^c<Ub [S5eUb [S5eg)z5 raise if any keywords are passed which are not-None Nzqcannot pass a column specification when reading a Fixed format store. this store must be selected in its entiretyzucannot pass a where specification when reading from a Fixed format store. this store must be selected in its entirety)r)rrrms r\ validate_readGenericFixed.validate_read s=  T   Y  r^cg)NTrrs r\rGenericFixed.is_exists sr^cpURURlURURlgr)r[rrrs r\rGenericFixed.set_attrs s""mm  KK r^c [[URSS55Ul[URSS5UlUR H$n[ X[URUS55 M& g)retrieve our attributesr[Nrr)r]rrr[rrr)rrs r\rGenericFixed.get_attrs sT(Z)NO djj(H= A DWTZZD9 :!r^c $UR5 grX)rrs r\rGenericFixed.write s  r^NcSSKn[URU5nURn[USS5n[ XTR 5(a!USX#n[USS5n U b [ XS9nO[USS5n [USS5n U b[R"XS9nOXRUnU (a8U RS5(a"U S:XaS n [US S5n [XU 5nOMU (aFU RS 5(a0U S :Xa[R"US S9nO[R"XS9nU(a UR$U$) z2read an array for the specified node (off of grouprN transposedF value_typerArr=r>rAr?r@) rrrrr`VLArraypd_arrayrrrrHrIT) rrrrrrrrretrzrrAs r\ read_arrayGenericFixed.read_array s(tzz3' UL%8 dNN + +q'%%CE<6E s0E<6EE7D1E hhu2&)),77L(,EUD$/cu-5++M::M)**S9C**S6C 55LJr^c[URUS35nUS:XaURXUS9$US:Xa([URU5nUR XRUS9nU$[ SU35e)N_varietymultirrregularzunrecognized index variety: )rrread_multi_indexrread_index_noder)rrrrvarietyrrs r\ read_indexGenericFixed.read_index' s~$**X&67 g (((E E  !4::s+D(((FEL:7)DE Er^cX[U[5(a,[URUS3S5 UR X5 g[URUS3S5 [ SX R UR5nURXR5 [URU5nURURl URURl[U[[ 45(a)UR#[%U55URl[U[[ [(45(aUR*URl[U[5(a3UR,b%[/UR,5URlggg)Nrrrr)r`r0rrwrite_multi_index_convert_indexr[r write_arrayrrrrrrcr.r1rrrr5r@rA_get_tz)rrrrTrs r\ write_indexGenericFixed.write_index5 s* eZ ( ( DJJ3%x 0' :  " "3 . DJJ3%x 0) <&w}}dkkRI   S"2"2 34::s+D!*DMM !&DMM %-!=>>,0,@,@e,M )%-n!MNN%*ZZ "%//EHH4H#*588#4  5I/r^c [URUS3UR5 [[ UR UR URSS95GH unupEn[UR[5(a*[UR[5(d [S5eUSU3n[XtURUR5nUR!XxR"5 [%UR&U5n UR(U R*lXiR*l[U R*USU3U5 USU3n UR!X5 GM g)N_nlevelsTrWz=Saving a MultiIndex with an extension dtype is not supported._level_name_label)rrr enumeraterdlevelsrnamesr`rzr*r4rrr[rrrrrrrrc) rrrilev level_codesrc level_key conv_levelr label_keys r\rGenericFixed.write_multi_indexL s- se8,emm<+4  ekk5;;t D, 'A'$#))^44Z ;>>*S%vaS)I'  t{{SJ   Y(9(9 :4::y1D!+DMM !%MM  DMMcU%v#6 =%vaS)I   Y 4-, r^c|[URUS35n/n/n/n[U5HnUSU3n [URU 5n UR XUS9n UR U 5 UR U R 5 USU3n URXUS9n UR U 5 M [XVUSS9$)NrrrrT)r rr r\) rrrrrrrcrr0)rrrrrr rr r rrr rrs r\rGenericFixed.read_multi_indexg s$**X&67 "wA%vaS)I4::y1D&&tt&DC MM#  LL "%vaS)I//)t/LK LL % ed  r^c XUnSUR;ak[R"URR5S:Xa=[R"URRURR S9nURR nSnSUR;a[URR5nURnURU5upUS;a-U"[XEURURS94S[0U D6n O'U"[XEURURS940U D6n XjlU $![an URS:Xa|[5(am[!U 5R#S 5(aN[$(aCU"[XEURURS94S['S [R(S 90U D6n Sn A NeSn A ff=f) NrrrArc)rrrrzrxryr{r|)rrprodrrrrrdrcr_unconvert_indexr[rrrrrar rr4r) rrrrrrrcrrrrr~s r\rGenericFixed.read_index_node~ s$ dmm # 0C0C(D(I88DMM//t}}7O7OPD}}!! T]] "t}}112D 11%8 % % t{{   E $T]]4;; .  '& KK?2*,,C))*BCC# #( t{{*(RVVL  ! E s&E G$BGGG$c@[R"SUR-5nURR UR X5 [ UR U5n[UR5URl URURl g)zwrite a 0-len array)rgN) rrrr create_arrayrrrarzrrr)rrrarrrs r\write_array_emptyGenericFixed.write_array_empty shhhtejj() !!$**c7tzz3'#&u{{#3  #kk r^cj [USS9nXR;a&URRURU5 URS:HnSn[ UR [5(a [S5eU(d[US5(aURnSn[ U[5(aURRURU[5R5URS9nUR!UR#55 [%URU5n['UR 5UR(lGOSn URbE[-[.5 [5R0R3UR 5n SSS5 U bZU(d@URR5URXUR6URS9n XJSS&GOUR9X5 GOUR R:[<R>:Xa[@RB"USS9n U(aODU S :XaO=[ES 5(a-[FXU4-n [HRJ"U [L[O5S 9 URRURU[5R55nUR!U5 GO[@RP"UR S 5(aoURRSURXRUS 55 ['UR 5[%URU5R(lGO[ UR [V5(aURRSURUURX5 [%URU5n[[UR\5UR(l.SUR R^S3UR(lO[@RP"UR S5(anURRSURXRUS 55 ['UR 5[%URU5R(lO?U(aUR9X5 O&URRSURX5 U[%URU5R(l0g!,(df  GN=f)NT) extract_numpyrFz^Cannot store a category dtype in an HDF5 dataset that uses format="fixed". Use format="table".r)rskipnarperformance_warningsrrqrr datetime64[rXr )1r@rrrr r`rzr(rrnrr<create_vlarrayr ObjectAtomrrto_numpyrrarrrrAtom from_dtype create_carrayrrrrobject_r infer_dtyperrsrrrr!rrviewr)asi8rrAunitr) rrrr r empty_arrayrvlarrrrca inferred_typers r\rGenericFixed.write_array sc6 **  LL $ $TZZ 5jjAo  ekk#3 4 4%/ uc""! e_ - -LL// C!5!5!70E LL) *4::s+D'*5;;'7DMM $D}}(j)"9>>44U[[AD* #33 Cu{{DMM4B"qE**36!!RZZ/!$e D "h. 677(M+FFBMM"&8EUEWX 33JJWY%9%9%; U#c22 ))$**c::d;KL?B5;;?O C(11<EKK99  ))JJJJ tzz3/#*588#4  -89I9I8J!+L (c22 ))$**c::d;KL?B5;;?O C(11<&&s2 ))$**cA7A C ))4{*)s 5.T## T2rrrrrr)rrarrrrrr/)rrarr/rr)rrarr0rr)rrarrrrrr0)rrMrrrrrr/)rrarrOrrrX)rrarrNr z Index | Nonerr)rr(r)r*r+r.r1rr rrr,rrrrr-rrrrrrrrrrrrr.)rrjrs000r\rr s\$$j+xHO+:+@+@+BC+B41!$+BCJ 19 )V ( ;*ZFJ F F) F8B F  F5.58FJ  ) 8B  0HL44!+4:D4 4l*BFaBaB)aB2>aB aBaByDsB<rcv^\rSrSr%SrS/rS\S'\S Sj5rS S Sjjr S U4Sjjr Sr U=r $) ri! rrcrFcp[URR54$![[4a gf=frX)rlrrrrrs r\rSeriesFixed.shape' s6  ))*, ,>*  s "55c URX!5 URSX4S9nURSX4S9n[XeURSS9nU$![ anUR S:Xal[5(a][U5RS5(a>[(a3[UUURS[S[RS 9S 9nSnAU$eSnAff=f) NrrrF)rrcrrxryr{r|)rrcrrz)rrrr3rcrrrrar rr4rr) rrmrrrrrrr~s r\r;SeriesFixed.read. s 7*u@B FdiieLF" !"  .&((H%%&>??K%hH   sA CA6CCCc >[TU]"U40UD6 URSUR5 UR SU5 UR UR lg)Nrr)rrrrrrcr)rrrrs r\rSeriesFixed.writeL sG  c$V$ #)), 3'(( r^r)rztuple[int] | Nonerrrrrrr3r) rr(r)r*rorr,r-rr;rr.r[r\s@r\rr! saKJ N        <##r^rct^\rSrSr%SS/rS\S'\S Sj5rS S SjjrS U4Sjjr Sr U=r $) BlockManagerFixediS rnblocksrcxURnSn[UR5H6n[URSUS35n[USS5nUcM/X%S- nM8 URR n[USS5nUb[ USUS- 5nO/nURU5 U$![a gf=f)Nrblock_itemsrrg) rrr>rr block0_valuesrjrr)rrr r rrs r\rBlockManagerFixed.shapeX s 99DE4<<(tzzU1#V+<=gt4$1X%E )::++DD'40E U1q23 LL L  sAB,AB,, B98B9cbURX!5 UR5RS5n/n[UR5H3nXu:XaX44OSupUR SU3XS9n UR U 5 M5 USn /n [UR5HnUR SUS35n URSUS3WW S9nXRU 5n[URX&SS S 9n[5(aT[U[R5(a5[!US S 9(a&UR#[%[R&S 95nU R U5 M [)U 5S:a'[+U SS9R-5nUR/U S9$[USUSS9$)Nrrr[rr@rArQrgFrrrTr )r~rrrr)rrr_get_block_manager_axisrrrrr>rrr-rrr`rrrrOr4rrlr6rr)rrmrrr select_axisrr r<r=axr dfs blk_itemsrdfouts r\r;BlockManagerFixed.reads s 7*mmo==a@ tyy!A-.-=UM[TU]"U40UD6 URnUR5(dUR 5nUR UR l[UR5H<upEUS:XaUR(d [S5eURSU3U5 M> [UR5UR l[UR5H_upFURR!UR"5nUR%SUS3UR&US9 URSUS3U5 Ma g)Nrz/Columns index has to be unique for fixed formatr[r@rQ)r rA)rr_mgris_consolidated consolidaterrr r is_uniquerrrlblocksr>r rmgr_locsrr) rrrrr rIblkrKrs r\rBlockManagerFixed.write s   c$V$xx##%%##%D)) tyy)EAAvr|| !RSS   tA3Z ,* !-  ,FA  5I   uQCw/9  M   uQCv. : -r^r)rzlist[int] | Nonerrrrrrr-r) rr(r)r*rr,r-rr;rr.r[r\s@r\r=r=S s`)$J L 8  &9 &9  &9  &9P;;r^r=c\rSrSrSr\rSrg)ri rrN)rr(r)r*ror-rrr.rr^r\rr s KHr^rc^\rSrSr%SrSrSrS\S'S\S'SrS \S 'S r S \S 'S5S6U4Sjjjr \ S7Sj5r S7Sjr S8SjrS9Sjr\ S:Sj5rS;Sjr\ SSj5rS?SjrS@S jrSAS!jrSBS"jrSCS#jr SDS$jr!S9S%jr"S9S&jr#SES9S'jjr$S9S(jr%\&S)5r'SFSGS*jjr(SHSIS+jjr)\*SJS,j5r+SKS-jr,SLSMS.jjr-\.SNS/j5r/SESOS0jjr0SPS1jr1SFSQS2jjr2SFSRS3jjr3S4r4U=r5$)Sri a represent a table: facilitate read/write of various types of tables Attrs in Table Node ------------------- These are attributes that are store in the main table node, they are necessary to recreate these tables when read back in. index_axes : a list of tuples of the (original indexing axis and index column) non_index_axes: a list of tuples of the (original index axis and columns on a non-indexing axis) values_axes : a list of the columns which comprise the data of this table data_columns : a list of the columns that we are allowing indexing (these become single columns in values_axes) nan_rep : the string to use for nan representations for string objects levels : the names of levels metadata : the names of the metadata columns wide_tablerurarprrgzint | list[Hashable]r TrjrGc >[T U]XX4S9 U=(d /UlU=(d /UlU=(d /UlU=(d /UlU =(d 0UlXlg)Nr)rr index_axesrg values_axesrrr) rrrr[rr]rgr^rrrrs r\rTable.__init__ sZ I$*,2&,"(.BJB  r^c>URRS5S$)N_r)rr rs r\table_type_shortTable.table_type_short s$$S)!,,r^c 4UR5 [UR5(aSRUR5OSnSUS3nSnUR(a;SRUR Vs/sHn[ U5PM sn5nSUS3nSRURVs/sHofRPM sn5nURSUSURS URS URS USUS 3 $s snfs snf) rrVrz,dc->[rXrrrz (typ->z,nrows->z,ncols->z ,indexers->[r) rErlrrYryrxrar]rcrrbrCncols)rjdcrverrkjverr jindex_axess r\rTable.__repr__ s -01B1B-C-Cchht(()cU!_   88T\\:\SV\:;DdV1+Chh@1@A &se,**+8DJJ<@jj\k]!B4q B ; As :D5DcPURHnXR:XdMUs $ g)zreturn the axis for cN)rrc)rrrs r\rTable.__getitem__s#AFF{r^c UcgURUR:wa&[SURSURS35eSHn[XS5n[XS5nX4:wdM"[U5HyupVXEnXg:wdMUS:XaPURUR:wa6[ SUR SS URS URS 35e[ S US USUS35e [S US USUS35e g)z"validate against an existing tableNz'incompatible table_type with existing [rrX)r]rgr^r^Cannot serialize the column [rz%] because its data contents are not [z] but [] object dtypezinvalid combination of [z] on appending data [z] vs current table [)rrrr rrrry)rrcrsvovr saxoaxs r\rDTable.validatesM =    t .$$%S(9<  AA$'B4(Bx(mFA%Cz -#((chh2F","? 1 OFFIhhZP((+z!A# )6qc: #u$8Q@,  .qc1FrdK&&(T,/Ar^c6[UR[5$)z@the levels attribute is 1 or a list in the case of a multi-index)r`r rjrs r\is_multi_indexTable.is_multi_index/s$++t,,r^c[R"URR5nUR 5n[ U[5(deX24$![ an[ S5UeSnAff=f)zM validate that we can store the multi-index; reset and return the new object zBduplicate names/columns in the multi-index when storing as a tableN)r{fill_missing_namesrr  reset_indexrr`r-)rrr  reset_objr~s r\validate_multiindexTable.validate_multiindex4sp'' 8 )I )Y////   T  sA A1 A,,A1c[R"URVs/sHoRRSPM sn5$s snf)z-based on our axes, compute the expected nrowsr)rrr]rrrr s r\nrows_expectedTable.nrows_expectedEs4wwDOODOq *ODEEDs"Ac SUR;$)zhas this table been createdrurrs r\rTable.is_existsJs$**$$r^c0[URSS5$Nrurrrs r\rTable.storableOstzz7D11r^cUR$)z,return the table group (this is my storable))rrs r\ru Table.tableSs}}r^c.URR$rX)rurzrs r\rz Table.dtypeXszzr^c.URR$rXrrs r\rTable.description\rr^cX[R"URUR5$rX)rbrcr]r^rs r\r Table.axes`st0@0@AAr^c:[SUR55$)z.the number of total columns in the values axesc3L# UHn[UR5v M g7frX)rlr)rrs r\rTable.ncols..gs;*:Q3qxx==*:s"$)sumr^rs r\re Table.ncolsds;$*:*:;;;r^cgrrrs r\ is_transposedTable.is_transposedirr^c [[R"URVs/sHn[ US5PM snUR Vs/sHn[ UR 5PM sn55$s snfs snf)z@return a tuple of my permutated axes, non_indexable at the frontr)rkrbrcrgrr]r[rrs r\data_orientationTable.data_orientationmsc OO$($7$78$7qQqT$78&*oo6oQVVo6   86s A6 A;czSSS.nURVs/sHo"RU4PM nnURVVs/sH upEXS4PM nnnURVs/sH5owR[ UR 5;dM'URU4PM7 nn[X6-U-5$s snfs snnfs snf)zURRnUSUS3$)z)return the metadata pathname for this keyz/meta/z/metarr7s r\_get_metadata_pathTable._get_metadata_paths$ &&se5))r^c URRURU5[USS9SURUR UR S9 g)zs Write out a metadata array to the key as a fixed-format Series. Parameters ---------- key : str values : ndarray FrEru)rr[rrN)rrrr3r[rr)rrrs r\rTable.write_metadatasI   # #C ( 6 &]];;LL  r^c[[URSS5US5b*URRUR U55$g)z'return the meta data array for this keyrN)rrrrrrs r\rTable.read_metadatasA 74::vt4c4 @ L;;%%d&=&=c&BC Cr^cB[UR5URlUR5URlUR 5URlUR URlUR URlURURlURURlURURl URURl URURl g)zset our table type & indexablesN) rarrrrrgrrr[rr rrs r\rTable.set_attrss #DOO 4  $ 1 !%!1!1!3 $($7$7 !"&"3"3 !\\ "mm  KK  KK )) r^c[URSS5=(d /Ul[URSS5=(d /Ul[URSS5=(d 0Ul[URSS5Ul[ [URSS55Ul[URSS5Ul[URS S5=(d /Ul URVs/sHoR(dMUPM snUl URVs/sHoR(aMUPM snUl gs snfs snf) rrgNrrrr[rrr )rrrgrrrr]r[rr  indexablesr>r]r^rs r\rTable.get_attrss%djj2BDIOR#DJJEKDJJ5; tzz9d; (Z)NO djj(H= &-djj(D&I&OR &*ooKo9J9J1oK'+P!>O>OAPLPsEE4E  E c UbqUR(a_[SRURVs/sHn[ U5PM sn5-n[ R "U[[5S9 gggs snf)rNrr) ryrqrYrxrarrrr!)rrmrkrs r\rTable.validate_versions`  ""(388T\\4R\SV\4R+SS */1# 4RsA6 cUcg[U[5(dgUR5nUHnUS:XaM X2;dM[SUS35e g)zp validate the min_itemsize doesn't contain items that are not in the axes this needs data_columns to be defined Nrzmin_itemsize has the key [z%] which is not an axis or data_column)r`rrr)rrqrjs r\validate_min_itemsizeTable.validate_min_itemsizes`   ,--  OO AH}z 04"" r^c f^^ ^^^/nTRmTRRm[TRR5Hiunup4[ TU5nTR U5nUbSOSnUS3n[ TUS5n [UUUU UTRUUS9n URU 5 Mk [TR5m[U5m SU UUUU4Sjjn UR[TRR5VV s/sH up,U "X,5PM sn n5 U$s sn nf)z/create/cache the indexables if they don't existNrrN)rcr[rFrrDrurrGc n>[U[5(de[nUT ;a[n[ T U5n[ UTR 5n[ TUS3S5n[ TUS3S5n[U5nTRU5n[ TUS3S5n U"UUUUT U-UTRU UUS9 n U $)NrNrr) rcrErrrFrDrurrGrz) r`rarr^r_maybe_adjust_namerxrrru)r rklassradj_namerrzrmdrrbase_posrdescr table_attrss r\rvTable.indexables..fsa%% %%EBw(4#D)!T\\:H[XJe*)>?OA|4&D##D)B!#:TD&I; 48D jj I   y )% @*"" #{#! ! J  $**:P:P0QR0QAaG0QRSSsD- c XUR5(dgUSLagUbUSLa8URVs/sH!oDR(dMURPM# nn[ U[ [ 45(dU/n0nUbX%S'UbX5S'URnUGHn[URUS5nUbUR(a^URn U Rn U Rn UbX:waUR5 OXS'UbX:waUR5 OXS'UR(d?URR!S5(a [#S5eUR$"S0UD6 MMXpR&SS ;dM[)S US US US 35e gs snf)a Create a pytables index on the specified columns. Parameters ---------- columns : None, bool, or listlike[str] Indicate which columns to create an index on. * False : Do not create any indexes. * True : Create indexes on all columns. * None : Create indexes on all columns. * listlike : Create indexes on the given columns. optlevel : int or None, default None Optimization level, if None, pytables defaults to 6. kind : str or None, default None Kind of index, if None, pytables defaults to "medium". Raises ------ TypeError if trying to create an index on a complex-type column. Notes ----- Cannot index Time64Col or ComplexCol. Pytables must be >= 3.0. NFTrrcomplexzColumns 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.rrgzcolumn z/ is not a data_column. In order to read column z: you must reload the dataframe into HDFStore and include z with the data_columns argument.r)rErr?rEr`rkrjrurrrrrr remove_indexrrrrrgr) rrrrrkwrurrr cur_optlevelcur_kinds r\rTable.create_index0s<   e   ?go(, I 15H5Hwqww GI'E4=11iG   %zN  vJ A At,A}<<GGE#(>>L$zzH'H,<(%-6 + 0H()5:||vv((33'HNN(R($))!,Q//$aS!//0c21123SUAJs F' F'c[XX#S9nUR5n/nURH_nURUR5 UR UUR URURS9nURU5 Ma U$)z Create the axes sniffed from the table. Parameters ---------- where : ??? start : int or None, default None stop : int or None, default None Returns ------- List[Tuple[index_values, column_values]] rKrG) Selectionrrrrrrr[rr) rrmrr selectionrr9rress r\ _read_axesTable._read_axess"duH !!#A JJtyy !)) {{ C NN3 r^cU$)zreturn the data for this objrrrrs r\ get_objectTable.get_objects  r^c[U5(d/$USupEURRU05nURS5S:XaU(a[SUSU35eUSLa [ U5nOUc/n[ U[ 5(aU[U5n[ U5nURUR5Vs/sHnUS:wdM X;dMUPM sn5 UV s/sH oU;dM U PM sn $s snfs sn f)zL take the input data_columns and min_itemize and create a data columns spec rrr0z"cannot use a multi-index on axis [z] with data_columns Tr) rlrrrrjr`rrrr) rrrrgr[ axis_labelsrexisting_data_columnsrjrs r\validate_data_columnsTable.validate_data_columnss >""I*1-yy}}T2& 88F | + 4TF; ,~/  4  ,L  !L lD ) )$' $5 ! -L   *..00H})*)G0 (<5nU Ul URAS5 URCU 5 UREU5 U/n[U5nUS :Xde[U 5S :XdeU Hn[GX(SUS 5nM URS :HnURIXVU 5nURKUU5RM5nUROUXURPU5unn/n[S[UUUSS95GH ununn[VnSnU(aF[U5S :Xa7USU;a.[XnUSnUb [U[Z5(d [!S5eU (aU(aURPUnOSnU=(d SU3n![aU!URbUUUUR<UR>US9n"[eU!URf5n#URiU"5n$[kU"RlRn5n%Sn&[qU"SS5b[sU"Rt5n&S=n'=n(n)[U"Rl[v5(a=U"Rxn)Sn'[*Rz"U"R|5R5n(O4[URl[5(a[[URl5n'[U"5un*n+U"U#U![U5U$UU%U&U)U'U(U+U*S9 n,U,RCU 5 UR7U,5 US - nGM UV,s/sH"n,U,R(dMU,RnPM$ n-n,[ U5"URURUR<UR>UU UU-U US9 n.[US5(aURU.lEU.RU5 U(aU (aU.RU5 U.$s snfs snf![\[^4a"n [!SUSURPS35U eSn A ff=fs sn,f)a Create and return the axes. Parameters ---------- axes: list or None The names or numbers of the axes to create. obj : DataFrame The object to create axes on. validate: bool, default True Whether to validate the obj against an existing object already written. nan_rep : A value to use for string column nan_rep. data_columns : List[str], True, or None, default None Specify the columns that we want to create to allow indexing on. * True : Use all available columns. * None : Use no columns. * List[str] : Use the specified columns. min_itemsize: Dict[str, int] or None, default None The min itemsize for a column in bytes. z/cannot properly create the storer for: [group->rrXNrTFrwrgz# UHoT;dM Uv M g7frXr)rrkrs r\r%Table._create_axes..s6f 11fs  rrr rrWr`zIncompatible appended table [z]with existing table [ values_block_) existing_colrrr[rrrAr) rcrErrDrFrrArrrGrzr) rrr[rr]rgr^rrrr )Hr`r-rrrr_get_axis_numberrEr]r[rjrrrrrlrrrrgr,rr?rrr rr_get_axis_namerr[rrHrr _reindex_axisrrr]_get_blocks_and_itemsr^r rdrr^ra IndexErrorr_maybe_convert_for_string_atomrrrxrrrzrcrrrAr(rrIrCrLr4rr?rrnr rrD)/rrrrDrrrrr table_existsnew_infonew_non_index_axesr append_axisindexer exist_axisr axis_name new_indexnew_index_axesjrrrTrKvaxesr rVb_itemsrrcrr~new_namedata_convertedrrDrrArrGrrrrdcs new_tables/ ` r\ _create_axesTable._create_axess:@#y))JJ&&EA%Is)A'  <3D266A$$Q'6 ??  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BQ'BduH   rY  H ]QN* 6.00H--.FGG'K&"HH$)"(!&"-h"P s+N 3N +N PA6PPPrrr,rr-)rr(r)r*r+rorrr-rrr,r-rrZrr;r.rr^r\rrsu2K#J D)2H&2 ,,  d d  ddr^rc^\rSrSrSrSrSrSr\r \ S Sj5r \ S Sj5r S SU4SjjjrSSU4S jjjrS rU=r$)rir5rrrwcgrrrs r\r#AppendableSeriesTable.is_transposed$rr^cU$rXrrs r\r AppendableSeriesTable.get_object(s r^c >[U[5(d&UR=(d SnURU5n[TU]"SXR R5S.UD6 g)+we are going to write this as a frame tablerrrNr)r`r-rcto_framerrrr)rrrrrcrs r\rAppendableSeriesTable.write-sM#y))88'xD,,t$C  K#KK,>,>,@KFKr^c>URnUbTU(aM[UR[5(deURHnXb;dM UR SU5 M [ TU]XX4S9nU(aURURSS9 URSS2S4nURS:XaSUl U$)NrrZTr_r) rvr`r rjrrr; set_indexr$rc) rrmrrrrvrr?rs r\r;AppendableSeriesTable.read4s,,  >dkk400 00[[#NN1a(! GLuUL N  KK TK 2 FF1a4L 66X AFr^rrr,rXrrr;)rr(r)r*r+rorrr3rrr-rrZrrr;r.r[r\s@r\rrs2 K$J DH LL       r^rc8^\rSrSrSrSrSrSU4SjjrSrU=r $)riMr5rrc 2>UR=(d SnURU5uo@l[UR[5(de[ UR5nUR U5 [ U5Ul[TU]$"SSU0UD6 g)rrrrNr) rcr|r r`rjrr/rrr)rrrrcnewobjrrs r\r AppendableMultiSeriesTable.writeTsvxx#8"66s; $++t,,,,DKK  Dt  +&+F+r^r r) rr(r)r*r+rorrr.r[r\s@r\rrMs2 K)J,,r^rc|\rSrSr%SrSrSrSr\r S\ S'\ SSj5r \ S 5r SS jr\S 5rSS jrS rg)ri_z:a table that read/writes the generic pytables table formatrrrwzlist[Hashable]r cUR$rX)rors r\rGenericTable.pandas_typehsr^cV[URSS5=(d UR$rrrs r\rGenericTable.storablelstzz7D1?TZZ?r^cr/UlSUl/UlURVs/sHoR(dMUPM snUlURVs/sHoR(aMUPM snUlUR Vs/sHoRPM snUlgs snfs snfs snf)rN) rgrr rr>r]r^rcrrs r\rGenericTable.get_attrsps   &*ooKo9J9J1oK'+P!>O>OAP-1-=-=>-=VV-=>LP>sB*B*B/0B/ B4c URnURS5nUbSOSn[SSURX2S9nU/n[ UR 5Hjupg[ U[5(de[X5nURU5nUbSOSn[UUU/UURUUS9n URU 5 Ml U$)z0create the indexables from the table descriptionrNrr)rcr[rurrG)rcrFrrDrurrG) rrrrur _v_namesr`rarrlr) rrrrrrr rrrs r\rGenericTable.indexableszs       (^z#q  IR{ ajj)DAa%% %%1=D##A&B!#:TD(sjjB   r "*"r^c [S5e)Nzcannot write on a generic tabler)rrs r\rGenericTable.writes!"CDDr^)rr]r rrgr^Nrr)rr(r)r*r+rorrr-rrr,r-rrrrrrr.rr^r\rr_sgDK J DH    @@?  FEr^rc^\rSrSrSrSr\rSr\ R"S5r \ S Sj5r S S U4SjjjrS SU4SjjjrS rU=r$)riza frame with a multi-indexrrwz ^level_\d+$cg)Nappendable_multirrs r\rb*AppendableMultiFrameTable.table_type_shorts!r^c >>Uc/nOUSLaURR5nURU5uol[ UR[ 5(deURHnXB;dM UR SU5 M [TU] "SXS.UD6 g)NTrrsr) rrr|r r`rjrrr)rrrrrrs r\rAppendableMultiFrameTable.writes  L T !;;--/L33C8[$++t,,,,A$##Aq)  C#CFCr^c0>[TU]XX4S9nURUR5nURR URR Vs/sH&o`RRU5(aSOUPM( sn5UlU$s snf)NrZ) rr;rwr rrbr  _re_levelssearch)rrmrrrrLrcrs r\r;AppendableMultiFrameTable.readsW\e\ O \\$++ &88%%HJ W__++D11Tt ; W   Xs-Br}rrXrrrX)rr(r)r*r+rr-rrrrecompilerr-rbrr;r.r[r\s@r\rrsw$(JH DN+J "" D D       r^rcURU5n[U5nUb [U5nUbURU5(aURU5(aU$[UR55nUb'[UR55R USS9nURU5(d6[ SS5/UR -nX%U'UR[U5nU$)NF)sort) rrAequalsuniquerslicerrrk)rr[rrcrIslicers r\rrs t B & !F U# u--6==3D3D &--/ *F elln-::6:N ==  ',T4'8&9CHH&Dt ggeFm$ Jr^c2[R"U5nU$)z+for a tz-aware type, return an encoded zone)r get_timezone)rAzones r\rrs  ! !" %D Kr^cURS:XdUR5e[R"U5up4[SU5n[ XS9n[ R "XS9nU$)z Coerce the values to a DatetimeArray with appropriate tz. Parameters ---------- values : ndarray[int64] tz : str, tzinfo, or None datetime64_dtype : str, e.g. "datetime64[ns]", "datetime64[25s]" rrrS)rAr.rA)rzr datetime_datarr;r9_from_sequence)rrAdatetime64_dtyper.rarzrs r\rHrHsa <<4 -- /0GD  D !D 2 )E  & &v ;C Jr^c [U[5(deURn[U5upV[ U5n[ R U5n[R"URS5(d4[UR5(d[UR5(a%[UUUU[USS5[USS5US9$[U[5(a [S5e[R "USS9n ["R$"U5n U S:Xac["R$"U V s/sHoR'5PM sn ["R(S 9n[XS[+5R-5US 9$U S :XaF[/XU5nURR0n [UUS [+5R3U 5US 9$U S ;a [XXxUS 9$[U["R45(aUR[6:XdeUS:XdU5e[+5R95n[XXxUS 9$s sn f)Niur@rA)rrrDr@rArBzMultiIndex not supported here!Fr rrA)rBr)integerfloating)rrrDrBr)r`rarcrrr^rrrrzr'r#r=rr0rr+rrI toordinalint32r Time32Col_convert_string_arrayrKrrrr%) rcrr[rrBrTrrrr2rrrKs r\rrs dC  J5U;I * %D  % %i 0D  T** u{{ + +  % %  -udD)!  %$$899OOE%8MZZ FJJv>v! v>bhhO  VWY%8%8%:z   ( ")&FC ??++    I   )!    1 1 J  )RZZ00Y__5NNNx%%y##% KK/ ?s5Ic URS5(a.US:Xa [USS9nU$[URU5SS9nU$URS5(a.US:Xa [USS9nU$[URU5SS9nU$US:XaA[R "UVs/sHn[ R"U5PM sn[S9nU$US;a[R "U5nU$US;a [USX#S 9nU$US :Xa[R "US 5nU$[S U35es snf![aH [R "UVs/sHn[ R"U5PM Os snfsn[S9nU$f=f) Nr=FrEr?rrA)rfloatr+rrGrrzunrecognized index type ) rr.r,r5rrIrrJrrrKrS)rrr[rrrs r\rrEs |$$ < !$U3E0 L-"$))D/>E, L+  ' ' = "4e4E$ L!#499T??E L  TJJTBT 0 0 3TB&QE L - - 4  L ( ' $  L   47# L3D6:;; C TJJtDt! 2 21 5tDFSE L Ts0D5$ D0 D50D55F E4 3FFc[UR[5(aUR5nUR[:waU$[ [ RU5nURRn[R"USS9n U S:Xa [S5eU S:Xa [S5eU S:XdUS:XdU$[U5n UR5n XKU 'U(a9U R5(a$[U5UR :a [#S 5e[R"U SS9n U S:wah[%U R&S 5HLn Xn [R"U SS9n U S:wdM$[U5U :aX|OS U 3n[S US U S35e [)XU5R+U R&5nUR n[U[,5(a;[/UR1U5=(d UR1S5=(d S 5n[3U=(d S U5nUbUR5U5nUbUU:aUnUR7SU3SS9nU$)NFr rz+[date] is not implemented as a table columnrz>too many timezones in this block, create separate data columnsrrz8NaN representation is too large for existing column sizerzNo.rnz2] because its data contents are not [string] but [rorz|SrE)r`rzr4r&rrrrrcrr+rr7rrNrlrKrrrrrBrrrrrrO)rcrFrrrr[rrrr2rUrr rerror_column_labelrrKecis r\rrfs='----""$}}2::w'G##JOOGE:MEFF " L   X %x)? =D <<>DJ s7|l6K6K'KSTTOOD7M tzz!}%A'COOC>M(36w?H ::dAhZ. 1D Kr^c$URn[R"UR5[S9n[ U5(a[ R"[U55nSU3n[US[5(aD[USS9RRX#SS9nUR5nSURlO"UR#USS9R#[SS9nUcS n[ R$"X5 UR'U5$) z Inverse of _convert_string_array. Parameters ---------- data : np.ndarray[fixed-length-string] nan_rep : the storage repr of NaN encoding : str errors : str Handler for encoding errors. Returns ------- np.ndarray[object] Decoded data. rAUrFrEr)rrzTr)rrrIrLrrlrrr"r`bytesr3radecoder&flags writeablerO!string_array_replace_from_nan_reprB)rrr[rrrKrzsers r\rSrSs& JJE ::djjl& 1D 4yy22=3FGH: d1gu % %E*..55x6C<<>D#'DJJ ;;u5;188e8LD00? << r^c[U[5(d[U55e[U5(a[ XU5nU"U5nU$rX)r`rar _need_convert_get_converter)rrr[rconvs r\rrsC h $ $4d8n4 $Xh&9f Mr^ch^^^TS:XaS$ST;aU4Sj$TS:XaUU4Sj$[ST35e)Nr=c,[R"USS9$)NzM8[ns]rArrI)rks r\r _get_converter..sAX6r^c.>[R"UTS9$)NrAr)rkrs r\rr sAT2r^rc>[USTTS9$)NrG)rS)rkr[rs r\rr s0 thv r^z invalid kind )r)rr[rs```r\rrsE |66  22    =/00r^cUS;dSU;agg)N)r=rr=TFrr s r\rrs ''<4+? r^c[U[5(d[U5S:a [S5eUSS:XaHUSS::a?USS:Xa6[R "SU5nU(aUR 5SnSU3nU$) z Prior to 0.10.1, we named values blocks like: values_block_0 and the name values_0, adjust the given name if necessary. Parameters ---------- name : str version : Tuple[int, int, int] Returns ------- str rz6Version is incorrect, expected sequence of 3 integers.rrgrvrwzvalues_block_(\d+)values_)r`rarlrrrr)rcrxr grps r\rrs{'33w= start and < stop)rurmrr conditionrtermsrar%rrrr+rrIrzbool_rCrA issubclassrrrNgenerateevaluate)rrurmrrinferreds r\rSelection.__init__ws         *%??5?55JJu-E{{bhh.&*jj$))t =$%E<#'::#3#3D+-99U+A%+H(#EKK$4$4bjjAA JJ2 8J7O7O7Q7Q II1u 7I6N6N6P6P", U#,1(%&(    #u-DJzz%.2jj.A.A.C+ & $)&%s EG<< H cgrXrrs r\rSelection.generatesJMr^cgrXrrs r\rrs-0r^cUcgURR5n[XURRS9$![aBnSR UR 55n[SUSUS35n[U5UeSnAff=f)z'where can be a : dict,list,tuple,stringN)rr[rVz- The passed where expression: a* contains an invalid variable reference all of the variable references must be a reference to an axis (e.g. 'index' or 'columns'), or a data_column The currently defined references are: z ) rurr=r[ NameErrorrYrrr)rrmrr~qkeysr&s r\rrs = JJ ! ! # +djj>Q>QR R +HHQVVX&E..3W5CDI'J CS/s * +s> B =BB cURbQURRRURR5URUR S9$UR b/URRRUR 5$URRRURUR S9$) generate the selection r) rru read_whererrrrarMr;rs r\rSelection.selects >> %::##..%%'tzz /    )::##44T5E5EF Fzz$$4::DII$FFr^cURURp!URRnUcSnO US:aX- nUcUnO US:aX#- nURb=URRR URR 5XSS9$URb UR$[R"X5$)rrT)rrr) rrrurCrget_where_listrrarrA)rrrrCs r\r#Selection.select_coordssjj$))t    =E QY NE <D AX MD >> %::##22%%'ud3    )## #yy%%r^)rrarrrrurrmr)rurrrrrrr)rmzdict | list | tuple | strrr=)rmrrr)rmz dict | list | tuple | str | NonerzPyTablesExpr | None) rr(r)r*r+rrrrr#r.rr^r\rrksq   +D+D +D  +D  +DZMM 00+. G&r^r)r[r%rra)rir) rNNFNTNNNNrrV)rFilePath | HDFStorerrarr rrarrrr%rr+rr%rr+rr!rr$rr"rrar[rarr) NrrNNNNFN)rrrrarrarmzstr | list | Nonerrrrrzlist[str] | Nonerr+rr)rrMrrMrr+rX)rr-r[rPrr/rr-)rArrz str | tzinfo)rznpt.NDArray[np.int64]rAzstr | tzinfo | Nonerrarr9) rcrarr/r[rarrarr=)rrar[rarrarznp.ndarray | Index)rcrarFrOrr)rrr[rarrarr)rrrrar[rarra)rrar[rarra)rrarr+)rcrarxz Sequence[int]rra)rrarra)rrO)r+ __future__r contextlibrrrrrrbrrtextwraprtypingrr r r r r rrrnumpyrpandas._configrrr pandas._libsrrrpandas._libs.librpandas._libs.tslibsr pandas.compatrpandas.compat._optionalrpandas.compat.pickle_compatr pandas.errorsrrrrrpandas.util._decoratorsrr pandas.util._exceptionsr!pandas.core.dtypes.commonr"r#r$r%r&r'pandas.core.dtypes.dtypesr(r)r*r+pandas.core.dtypes.missingr,rr-r.r/r0r1r2r3r4r5r6r7pandas.core.arraysr8r9r:pandas.core.arrays.datetimesr;pandas.core.arrays.string_r<pandas.core.commoncorecommonr{ pandas.core.computation.pytablesr=r>pandas.core.constructionr?rr@pandas.core.indexes.apirApandas.io.commonrBpandas.io.formats.printingrCrDcollections.abcrErFrGrHtypesrIrJrrKrLrMpandas._typingrNrOrPrQrRrSrTpandas.core.internalsrUrrYr]rdrer,rprqrrrsrrrxry config_prefixregister_optionis_boolis_one_of_factoryr}rrrrrrrFr=rrr^rlrnrrr=rrrrrrrrrrrrHrrrrrSrrrrrrrrr^r\rsn #      -)%>45 8     56 1+    ,  i:*U !  gGgN    E E  (# >5*W  **+CD $!% $$)!4 04 595$5 5 5  5  5  5 5 5 5.5 535 55  !5p H # $ _$_ _  _  _  _ _____D  Hqqqh/mm`x>x>v  h :a9ha9H $w$63.3j?j?ZAB5ABH /#,/#d[; [;|" fDEfDRHH' '),'69''T 1 2B2x&x&Qd$#s ,AN<< O