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d)Zd*ed+< d,Zd*ed-< d.Zd*ed/< d0d0d1d1d2ZeEdgiZd3Zd*ed4< d5Zd*ed6< ej d7« 5 ej d8d9eej ¬:« ej d;de ej" g d<¢« ¬:« ddd« dad9ad= Z d} d~d>Z d dd?Zdd@Z G dA dB« Z G dC dD« Z G dE dF« Z G dG dHe« Z G dI dJe« Z G dK dLe« Z G dM dNe« Z G dO dP« Z G dQ dRe« Z G dS dTe « Z¡ G dU dVe « Z¢ G dW dXe¢« Z£ G dY dZe« Z¤ G d[ d\e¤« Z¥ G d] d^e¤« Z¦ G d_ d`e¦« Z§ G da dbe§« Z¨ G dc dde¨« Z© G de dfe§« Zª G dg dhe§« Z« d ddiZ¬ddjZe d
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|
High level interface to PyTables for reading and writing pandas data structures
|
to disk
|
é )Ú annotations)ÚsuppressN)ÚdateÚtzinfo)Údedent)Ú TYPE_CHECKINGÚAnyÚCallableÚFinalÚLiteralÚcastÚoverload)ÚconfigÚ
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Ensure that an index / column name is a str (python 3); otherwise they
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Ensure that the where is a Term or a list of Term.
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|
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|
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|
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|
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|
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|
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|
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|
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|
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¤} d } |[| j « }t |« dk( r t d« |d }|dd D ] }t! ||« rt d« |j" }| j% ||||||| | ¬« S # t t f$ r d} Y ¬w xY w# t t t& f$ rG t | t « s5t) t* « 5 | j- « ddd« # 1 sw Y xY w w xY w)a>
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Read from the store, close it if we opened it.
|
|
Retrieve pandas object stored in file, optionally based on where
|
criteria.
|
|
.. warning::
|
|
Pandas uses PyTables for reading and writing HDF5 files, which allows
|
serializing object-dtype data with pickle when using the "fixed" format.
|
Loading pickled data received from untrusted sources can be unsafe.
|
|
See: https://docs.python.org/3/library/pickle.html for more.
|
|
Parameters
|
----------
|
path_or_buf : str, path object, pandas.HDFStore
|
Any valid string path is acceptable. Only supports the local file system,
|
remote URLs and file-like objects are not supported.
|
|
If you want to pass in a path object, pandas accepts any
|
``os.PathLike``.
|
|
Alternatively, pandas accepts an open :class:`pandas.HDFStore` object.
|
|
key : object, optional
|
The group identifier in the store. Can be omitted if the HDF file
|
contains a single pandas object.
|
mode : {'r', 'r+', 'a'}, default 'r'
|
Mode to use when opening the file. Ignored if path_or_buf is a
|
:class:`pandas.HDFStore`. Default is 'r'.
|
errors : str, default 'strict'
|
Specifies how encoding and decoding errors are to be handled.
|
See the errors argument for :func:`open` for a full list
|
of options.
|
where : list, optional
|
A list of Term (or convertible) objects.
|
start : int, optional
|
Row number to start selection.
|
stop : int, optional
|
Row number to stop selection.
|
columns : list, optional
|
A list of columns names to return.
|
iterator : bool, optional
|
Return an iterator object.
|
chunksize : int, optional
|
Number of rows to include in an iteration when using an iterator.
|
**kwargs
|
Additional keyword arguments passed to HDFStore.
|
|
Returns
|
-------
|
object
|
The selected object. Return type depends on the object stored.
|
|
See Also
|
--------
|
DataFrame.to_hdf : Write a HDF file from a DataFrame.
|
HDFStore : Low-level access to HDF files.
|
|
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
|
)Úrúr+Úazmode zG is not allowed while performing a read. Allowed modes are r, r+ and a.Nrk rl z&The HDFStore must be open for reading.Fz5Support for generic buffers has not been implemented.zFile z does not exist)r r Tr z]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.)rr ÚstartÚstopÚcolumnsÚiteratorÚ chunksizeÚ
|
auto_close)Ú
|
ValueErrorru rW r Úis_openÚOSErrorrB rf ÚNotImplementedErrorÚosÚpathÚexistsÚ TypeErrorÚFileNotFoundErrorÚgroupsrq Ú_is_metadata_ofÚ _v_pathnameÚselectÚ LookupErrorr r Úclose)r¡ r r r rr r§ r¨ r© rª r« Úkwargsr r¬ r³ r¶ Úcandidate_only_groupÚgroup_to_checks r\ Úread_hdfr¿ B sô ðl Ð#Ñ#ÜØD6ð -ð .ó
|
ð
|
ð
|
ÐÜ U°Ô2ä+xÔ(Ø×"Ò"ÜÐBÓCÐ CàØ
|
ä$ [Ó1 Ü+¤sÔ+Ü%ØGóð ð ÜWW^^ KÓ0Fñ Ü# e¨K¨=¸Ð$HÓIÐ IäÐI¨4¸ÑIÀ&ÑIð
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ð%Ø ;Ø\\^FÜ6{aÒ Ü ðDóð ð $*¨!¡9Ð ð #)¨¨ *ò Ü& ~Ð7KÕLÜ$ð;óð ð ð '×2Ñ2CØ||Ø ØØØØØ Ø Ø!ð ó
|
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|
øôA :Ð&ò ØFð ûôT ¤;Ð /ò Ü+¤xÔ0ä.Ó)ñ Ø ÷ ð ÷ ð úð ús= Á6 D. Â8AE Ã=0E Ä.EÅEÅ3F Å8FÆ F ÆF Æ F c óÔ | j |j k ry| }|j dkD r=|j }||k( r|j dk( ry|j }|j dkD r=y)zDCheck if a given group is a metadata group for a given parent_group.Frk ÚmetaT)Ú_v_depthÚ _v_parentÚ_v_name)ÚgroupÚ parent_groupÚcurrentÚparents r\ r· r· ä sk à ~~ ×.Ñ.Ò.ØàGØ
|
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|
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|
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|
Ø×"Ñ"Ø \Ò ! g§o¡o¸Ò&?ØØ×#Ñ#ð ×
|
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|
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|
r^ c ój e Zd ZU dZded< ded< d2 d3dZd4dZed « Zed4d
|
« Z d5d Z
|
d6d Z d6d Z d7dZ d8dZd9dZd4dZd:dZ d;dZd<d=dZd>dZd?dZd@dAdZdBdZedCd« ZdDdEdZd5dZ dF dGd Z dH dId Z dJ dKd Z dL dMd Z dN dOd Z dHd6d!Z dP dQd"Z! dR dSd#Z" dH dTd$Z#dUd%Z$dVdWd&Z%dXd'Z&dYd(Z' dZ d[d)Z(d4d*Z)dBd+Z*d\d,Z+ d] d^d-Z, d_ d`d.Z-dad/Z.dbd0Z/dcd1Z0y)dr aS
|
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 | NoneÚ_handlerf Ú_modeNc ó d|v r t d« t d« }|;||j j vr#t d|j j d« |||j j }t |« | _ |d}|| _ d | _ |r|nd| _ || _
|
|| _ d | _ | j d d|i|¤ y )
|
Nr z-format is not a defined argument for HDFStorer
zcomplib only supports z compression.r¦ r r © )r r ÚfiltersÚ all_complibsÚdefault_complibrB Ú_pathrË rÊ Ú
|
_complevelÚ_complibÚ _fletcher32Ú_filtersÚopen)Úselfr² r r r Ú
|
fletcher32r¼ r
s r\ Ú__init__zHDFStore.__init__7 sÓ ð vÑ Ü Ð LÓMÐ Mä+¨HÓ5à Ð 7°&·.±.×2MÑ2MÑ#MÜ Ø(¨¯ © ×)DÑ)DÐ(EÀ]ÐSóð ð ?yÐ 4Ø nn×4Ñ4Gä# DÓ)
|
Ø <ØDØ
|
Ø Ù'0)°aØ Ø%ÔØ Ø Ñ&tÐ&vÓ&r^ c ó | j S r` ©rÑ ©r× s r\ Ú
|
__fspath__zHDFStore.__fspath__X s ØzzÐr^ c ój | j « | j J | j j S )zreturn the root node)Ú_check_if_openrÊ ÚrootrÜ s r\ rà z HDFStore.root[ s0 ð ×ÑÔ Ø||Ð'Ð'Ð'Ø||× Ñ Ð r^ c ó | j S r` rÛ rÜ s r\ ÚfilenamezHDFStore.filenameb ó àzzÐr^ c ó$ | j |« S r` )Úget©r× r s r\ Ú __getitem__zHDFStore.__getitem__f s Øxx}Ð r^ c ó( | j ||« y r` r )r× r r s r\ Ú __setitem__zHDFStore.__setitem__i s Ø eÕ r^ c ó$ | j |« S r` )Úremoveræ s r\ Ú __delitem__zHDFStore.__delitem__l s Ø{{3Ó Ð r^ c ó | j |« S # t t f$ r Y nw xY wt dt | « j
|
d| d« )z$allow attribute access to get storesú'z' object has no attribute ')rå ÚKeyErrorr r ÚtypeÚ__name__)r× rh s r\ Ú __getattr__zHDFStore.__getattr__o sW ð Ø88D>Ð !øÜ/Ð*ò Ù ð úä ØT
|
×#Ñ#Ð$Ð$?À¸vÀQÐ Gó
|
ð
|
s %¤%c óV | j |« }||j }|||dd fv ryy)zx
|
check for existence of this key
|
can match the exact pathname or the pathnm w/o the leading '/'
|
Nrk TF)Úget_noder¸ )r× r Únoderh s r\ Ú __contains__zHDFStore.__contains__y s< ð
|
}}SÓ!Ø Ð Ø×#Ñ#DØtT ! "XÐ&Ñ&ØØr^ c ó4 t | j « « S r` )rq r¶ rÜ s r\ Ú__len__zHDFStore.__len__
s Ü4;;=Ó!Ð!r^ c óN t | j « }t | « d| dS )Nú
|
File path: ú
|
)rD rÑ rð )r× Úpstrs r\ Ú__repr__zHDFStore.__repr__ s' ÜDJJÓ'Üt* ]¨4¨&°Ð3Ð3r^ c ó | S r` rÍ rÜ s r\ Ú __enter__zHDFStore.__enter__ s Ø r^ c ó$ | j « y r` )r» )r× Úexc_typeÚ exc_valueÚ tracebacks r\ Ú__exit__zHDFStore.__exit__ s ð
|
|
r^ c ó |dk( r(| j « D cg c] }|j c}S |dk( rC| j J | j j dd¬« D cg c] }|j c}S t d| d« c c}w c c}w )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
|
|
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
|
ÚpandasÚnativeú/ÚTable)Ú classnamez8`include` should be either 'pandas' or 'native' but is 'rî )r¶ r¸ rÊ Ú
|
walk_nodesr )r× ÚincludeÚns r\ Úkeysz HDFStore.keys s ð< hÒ Ø+/¯;©;«=Ö9 aAMMÒ9Ð 9à Ò Ø<<Ð+Ð +Ð+à'+§|¡|×'>Ñ'>¸sÈgÐ'>Ó'VöØ"# òð ô ØFÀwÀiÈqÐ Qó
|
ð
|
ùò :ùòs BÁ B
|
c ó4 t | j « « S r` )Úiterr rÜ s r\ Ú__iter__zHDFStore.__iter__Á s ÜDIIKÓ Ð r^ c # óV K | j « D ] }|j |f yw)z'
|
iterate on key->group
|
N)r¶ r¸ ©r× Úgs r\ ÚitemszHDFStore.itemsÄ s, è ø ð ò #AØ-- Ð"Ó "ñ #ùs ')c óZ t « }| j |k7 rP| j dv r|dv rn6|dv r2| j r&t d| j d| j d« || _ | j r| j « | j rN| j dkD r?t « j | j | j | j ¬« | _
|
t r| j r d }t |« |j | j | j fi |¤| _ y
|
) a9
|
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.
|
)r¦ Úw)r¤ r¥ )r zRe-opening the file [z ] with mode [z ] will delete the current file!r )rØ zGCannot open HDF5 file, which is already opened, even in read-only mode.N)r rË r® r rÑ r» rÒ ÚFiltersrÓ rÔ rÕ r r Ú open_filerÊ )r× r r¼ r
Úmsgs r\ rÖ z HDFStore.openË s ô à ::Ò àzzZÑ'¨D°KÑ,?ØØ à<<Ü/Ø/°·
|
±
|
¨|¸=ÈÏÉÈ ð U8ð 8óð ð
|
DJð <<Ø JJLà ??t°Ò 2Ü #I× -Ñ -Ø § ¡ ¸4×;KÑ;Kð .ó DMõ -°· ² ð*ð ô S/Ð !à'v×'Ñ'¨¯
|
©
|
°D·J±JÑIÀ&ÑI r^ c ó^ | j | j j « d| _ y)z0
|
Close the PyTables file handle
|
N)rÊ r» rÜ s r\ r» zHDFStore.closeø s% ð <<Ð #Ø LL× Ñ Ô Ø r^ c óZ | j yt | j j « S )zF
|
return a boolean indicating whether the file is open
|
F)rÊ ÚboolÚisopenrÜ s r\ r® zHDFStore.is_open s& ð
|
<<Ð ØÜDLL×'Ñ'Ó(Ð(r^ c óú | j c| j j « |rFt t « 5 t j
|
| j j « « ddd« yyy# 1 sw Y yxY w)aó
|
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)rÊ Úflushr r¯ r± ÚfsyncÚfileno)r× r! s r\ r zHDFStore.flush sf ð <<Ð #Ø LL× Ñ Ô ÙÜ gÓ&ñ 4ÜHHT\\× 0Ñ 0Ó 2Ô3÷4ð 4ð ð $÷4ð 4ús ¸.A1Á1A:c ó¨ t « 5 | j |« }|t d| d« | j |« cddd« S # 1 sw Y yxY w)a
|
Retrieve pandas object stored in file.
|
|
Parameters
|
----------
|
key : str
|
|
Returns
|
-------
|
object
|
Same type as object stored in file.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
|
>>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP
|
>>> store.put('data', df) # doctest: +SKIP
|
>>> store.get('data') # doctest: +SKIP
|
>>> store.close() # doctest: +SKIP
|
NúNo object named ú in the file)r rô rï Ú _read_group©r× r rÅ s r\ rå z HDFStore.get sV ô* ^ñ +ð MM #Ó&EØ}Ü Ð!1°#°°lÐ CÓDÐDØ×#Ñ# EÓ*÷ +÷ +ò +ús 3AÁAc ó | j |« } | t d| d« t |d¬« }| j | « j « fd}
|
t | |
|
| j |||||¬«
|
} | j « S )a6
|
Retrieve pandas object stored in file, optionally based on where criteria.
|
|
.. warning::
|
|
Pandas uses PyTables for reading and writing HDF5 files, which allows
|
serializing object-dtype data with pickle when using the "fixed" format.
|
Loading pickled data received from untrusted sources can be unsafe.
|
|
See: https://docs.python.org/3/library/pickle.html for more.
|
|
Parameters
|
----------
|
key : str
|
Object being retrieved from file.
|
where : list or None
|
List of Term (or convertible) objects, optional.
|
start : int or None
|
Row number to start selection.
|
stop : int, default None
|
Row number to stop selection.
|
columns : list or None
|
A list of columns that if not None, will limit the return columns.
|
iterator : bool or False
|
Returns an iterator.
|
chunksize : int or None
|
Number or rows to include in iteration, return an iterator.
|
auto_close : bool or False
|
Should automatically close the store when finished.
|
|
Returns
|
-------
|
object
|
Retrieved object from file.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
|
>>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP
|
>>> store.put('data', df) # doctest: +SKIP
|
>>> store.get('data') # doctest: +SKIP
|
>>> print(store.keys()) # doctest: +SKIP
|
['/data1', '/data2']
|
>>> store.select('/data1') # doctest: +SKIP
|
A B
|
0 1 2
|
1 3 4
|
>>> store.select('/data1', where='columns == A') # doctest: +SKIP
|
A
|
0 1
|
1 3
|
>>> store.close() # doctest: +SKIP
|
r$ r% rk rl c ó. j | ||¬« S )N)r§ r¨ rr r© )Úread)Ú_startÚ_stopÚ_wherer© r[ s r\ Úfuncz HDFStore.select.<locals>.func s ø Ø66 ¨U¸&È'6ÓRÐ Rr^ ©rr Únrowsr§ r¨ rª r« r¬ )rô rï ru Ú_create_storerÚ
|
infer_axesÚ TableIteratorr0 Ú
|
get_result) r× r rr r§ r¨ r© rª r« r¬ rÅ r. Úitr[ s ` @r\ r¹ zHDFStore.select< s ù ð@ cÓ"Ø =ÜÐ -¨c¨U°,Ð?Ó@Ð @ô U°Ô2Ø × Ñ Ó &Ø õ Sô Ø Ø Ø ØØ''ØØØ Ø Ø!ô
|
ð }}Ð r^ c ó t |d¬« }| j |« }t |t « s t d« |j |||¬« S )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
|
rk rl z&can only read_coordinates with a table©rr r§ r¨ )ru Ú
|
get_storerrW r r´ Úread_coordinates)r× r rr r§ r¨ Útbls r\ Úselect_as_coordinatesz HDFStore.select_as_coordinates sL ô4 U°Ô2ØoocÓ"Ü#uÔ%ÜÐ DÓEÐ EØ×#Ñ#¨%°uÀ4Ð#ÓHÐHr^ c ó | j |« }t |t « s t d« |j |||¬« S )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)Úcolumnr§ r¨ )r8 rW r r´ Ú read_column)r× r r= r§ r¨ r: s r\ Ú select_columnzHDFStore.select_column¹ s> ðF oocÓ"Ü#uÔ%ÜÐ ?Ó@Ð @Ø f°EÀÓEÐEr^ c
|
óÐ t |d¬« }t |t t f« rt |« dk( r|d }t |t
|
« r| j ||||||| ¬« S t |t t f« s t d« t |« s t d« ||d }|D
|
cg c] }
|
| j |
|
« c}
|
| j |« } d} t j | |fgt |« « D ]d \ } }
|
| t d|
|
d « | j st d
|
| j d « | | j } K| j | k7 s[t d « D cg c] }t |t" « s| }}|D ch c] } | j$ d d c} j' « fd }t) | | ||| ||||| ¬«
|
}|j+ d¬« S c c}
|
w c c}w c c} w )aÙ
|
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
|
rk rl r )r rr r© r§ r¨ rª r« r¬ zkeys must be a list/tuplez keys must have a non-zero lengthNzInvalid table [ú]zobject [z>] is not a table, and cannot be used in all select as multiplez,all tables must have exactly the same nrows!c ó D cg c] }|j || |¬« }}t |d¬« j « S c c}w )N©rr r© r§ r¨ F)ÚaxisÚverify_integrity)r* r5 Ú _consolidate)r+ r, r- r| ÚobjsrD r© Útblss r\ r. z)HDFStore.select_as_multiple.<locals>.func? sR ø ð
|
öàð V¨W¸FÈÕOðDð ô $ T¸EÔB×OÑOÓQÐ Qùò s A r/ T)Ú coordinates)ru rW rn ro rq rf r¹ r´ r r8 Ú itertoolsÚchainÚziprï Úis_tableÚpathnamer0 r Únon_index_axesÚpopr3 r4 )r× r rr Úselectorr© r§ r¨ rª r« r¬ Úkr[ r0 r| ÚxÚ_tblsr. r5 rD rH s ` @@r\ Úselect_as_multiplezHDFStore.select_as_multipleá s ú ôV U°Ô2Ü dT¤5MÔ *¬s°4«y¸Aª~Ø7DÜ dCÔ Ø;;ØØØ ØØØ!Ø#Ø%ð ó ð ô $¤¤u Ô.ÜÐ 7Ó8Ð 8ä4yÜ Ð ?Ó@Ð @à Ð ØAwHð -1Ö1 q Õ"Ò1Ø OOHÓ %ð Ü OO a¨ ] O´S¸¸t³_ÓEò QDAqØyÜ °°°1Ð 5Ó6Ð6Ø::Ü Ø qzzlð +)ð )óð ð
|
}ØØEÓ!Ü Ð!OÓPÐPð Qð !Ö9q¤J¨q´%Õ$8Ð9Ð9ð 16Ö6¨1× Ñ Ñ# AÓ&Ò6×:Ñ:Ó<ö Rô Ø Ø Ø ØØØØØ Ø Ø!ô
|
ð }}¨}Ó.Ð.ùòg 2ùò* :ùò 7s Â%GÅ!G Å7G ÆG#c ó |t d« xs d}| j |« }| j ||||||||| |
|
| | | |¬« y)a¬
|
Store object in HDFStore.
|
|
Parameters
|
----------
|
key : str
|
value : {Series, DataFrame}
|
format : 'fixed(f)|table(t)', default is 'fixed'
|
Format to use when storing object in HDFStore. Value can be one of:
|
|
``'fixed'``
|
Fixed format. Fast writing/reading. Not-appendable, nor searchable.
|
``'table'``
|
Table format. Write as a PyTables Table structure which may perform
|
worse but allow more flexible operations like searching / selecting
|
subsets of the data.
|
index : bool, default True
|
Write DataFrame index as a column.
|
append : bool, default False
|
This will force Table format, append the input data to the existing.
|
data_columns : list of columns or True, default None
|
List of columns to create as data columns, or True to use all columns.
|
See `here
|
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
|
encoding : str, default None
|
Provide an encoding for strings.
|
track_times : bool, default True
|
Parameter is propagated to 'create_table' method of 'PyTables'.
|
If set to False it enables to have the same h5 files (same hashes)
|
independent on creation time.
|
dropna : bool, default False, optional
|
Remove missing values.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
|
>>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP
|
>>> store.put('data', df) # doctest: +SKIP
|
Núio.hdf.default_formatry ) r r r r r r r r rc r Ú track_timesr )r Ú_validate_formatÚ_write_to_group)r× r r r r r r r r r r rc r rX r s r\ r z HDFStore.putZ sg ðp >Ü Ð 7Ó8ÒC¸GFØ×&Ñ& vÓ.Ø × Ñ Ø Ø ØØØØØ Ø%ØØ%Ø ØØ#Øð õ
|
r^ c ó¾ t |d¬« } | j |« }t j |||« r j j d¬« yj s t d« |j |||¬« S # t $ r t $ r t $ rB}| t d« || j |« }||j d¬« Y d}~yY d}~·d}~ww xY w) 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
|
|
rk rl Nz5trying to remove a node with a non-None where clause!T©Ú recursivez7can only remove with where on objects written as tablesr7 ) ru r8 rï ÚAssertionErrorÚ Exceptionr rô Ú _f_removeÚcomÚall_nonerÅ rM Údelete)r× r rr r§ r¨ r[ Úerrrõ s r\ rë zHDFStore.remove¦ sé ô* U°Ô2ð Ø Ó$Aô. <<u dÔ +Ø GG× Ñ ¨Ð Õ -ð ::Ü ØMóð ð 88 %¨u¸48Ó@Ð @øô? ò à Ü ò à Üò ð Ð Ü ØKóàðð
|
== Ó%DØРبÔ.Üô ûð ús A? Á?C Â3CÃC c ó¾ | t d« | t d« }|t d« xs d}| j |« }| j |||||||||
|
| | | ||||¬« y)a|
|
Append to Table in file.
|
|
Node must already exist and be Table format.
|
|
Parameters
|
----------
|
key : str
|
value : {Series, DataFrame}
|
format : 'table' is the default
|
Format to use when storing object in HDFStore. Value can be one of:
|
|
``'table'``
|
Table format. Write as a PyTables Table structure which may perform
|
worse but allow more flexible operations like searching / selecting
|
subsets of the data.
|
index : bool, default True
|
Write DataFrame index as a column.
|
append : bool, default True
|
Append the input data to the existing.
|
data_columns : list of columns, or True, default None
|
List of columns to create as indexed data columns for on-disk
|
queries, or True to use all columns. By default only the axes
|
of the object are indexed. See `here
|
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#query-via-data-columns>`__.
|
min_itemsize : dict of columns that specify minimum str sizes
|
nan_rep : str to use as str nan representation
|
chunksize : size to chunk the writing
|
expectedrows : expected TOTAL row size of this table
|
encoding : default None, provide an encoding for str
|
dropna : bool, default False, optional
|
Do not write an ALL nan row to the store settable
|
by the option 'io.hdf.dropna_table'.
|
|
Notes
|
-----
|
Does *not* check if data being appended overlaps with existing
|
data in the table, so be careful
|
|
Examples
|
--------
|
>>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
|
>>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP
|
>>> store.put('data', df1, format='table') # doctest: +SKIP
|
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=['A', 'B'])
|
>>> store.append('data', df2) # doctest: +SKIP
|
>>> store.close() # doctest: +SKIP
|
A B
|
0 1 2
|
1 3 4
|
0 5 6
|
1 7 8
|
Nz>columns is not a supported keyword in append, try data_columnszio.hdf.dropna_tablerW rz )r Úaxesr r r r r r r« Ú expectedrowsr r rc r )r´ r rY rZ )r× r r r rf r r r r r© r r r« rg r r rc r s r\ r zHDFStore.appendß s ðR Ð ÜØPóð ð >Ü Ð 5Ó6FØ >Ü Ð 7Ó8ÒC¸GFØ×&Ñ& vÓ.Ø × Ñ Ø Ø ØØØØØØ Ø%ØØ Ø%ØØ%Ø Øð! õ
|
r^ c ó | t d« t |t « s t d« ||vr t d« t t t t j « « t t t « « z
|
« « }d} g }
|
|j « D ]( \ } } | | t d« | } |
|
j | « * | Wj | } | j t |
|
« « }t! | j# |« « }| j% |« || < ||| }|rKfd|j' « D « }t |« }|D ] }|j) |« } j* | |j- dd« }|j « D ]e \ } } | |k( r|nd}j/ | |¬« }|)|j « D ci c] \ }}|| v s || c}}nd} | j0 | |f||d |¤ g yc c}}w )
|
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<append_to_multiple can only have one value in d that is Nonec 3 ó\ K | ]# }| j d ¬« j % yw)Úall)ÚhowN)r r )Ú.0Úcolsr s r\ ú <genexpr>z.HDFStore.append_to_multiple.<locals>.<genexpr> s) øè ø ÒO¸DE$K×&Ñ&¨5Ð&Ó1×7Õ7ÑOùs ),r ©rD )r r )r´ rW Údictr Únextr ÚsetÚrangeÚndimÚ _AXES_MAPrð r Úextendrf Ú
|
differencer. ÚsortedÚ get_indexerÚtakeÚvaluesÚ intersectionÚlocrP Úreindexr )r× Údr rQ r rf r r¼ rD Ú
|
remain_keyÚ remain_valuesrR ÚvÚorderedÚorddÚidxsÚ valid_indexr r ÚdcÚvalr Úfiltereds ` r\ Úappend_to_multiplezHDFStore.append_to_multipleE s4 ø ð> Ð ÜðBóð ô
|
!TÔ"Ü ð)óð ð
|
1Ñ Ü ØOóð ô
|
DU 5§:¡:Ó .Ó/´#´iÄÀUÃ Ñ6LÓ2MÑMÓNÓOð
|
Ø ØGGIò (DAqØyØ Ð)Ü$ØVóð ð
|
à ×$Ñ$ QÕ'ð (ð Ð !Øjj Ñ&GØ×%Ñ%¤e¨MÓ&:Ó;DÜ'×-Ñ-¨dÓ3Ó4DØ #LL¨Ó .AjMð Ð Ø X;Lñ ÛOÀAÇHÁHÃJÔODÜ t*KØ ò >Ø )× 6Ñ 6°uÓ = ð >àIIkÑ*Eà zz .°$Ó7 ð GGIò RDAqØ!" h¢ °DBð -- ¨-Ó-Cð Ð+ð 1=×0BÑ0BÓ0D×Q¡ eÈÈqÊeÔQàð ð
|
DKK3Ð Q¨R¸hÑ QÈ&Ó Qñ Rùó Rs Ç
|
G<ÇG<c ó t « | j |« }|yt |t « s t d« |j |||¬« y)aà
|
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)r© ÚoptlevelÚkind)r r8 rW r r´ Ú create_index)r× r r© r r r[ s r\ Úcreate_table_indexzHDFStore.create_table_index¦ sG ô> Ø OOCÓ Ø 9Ø ä!UÔ#ÜÐ OÓPÐ PØ w°ÀÕEr^ c ó² t « | j « | j J t J | j j « D cg c] }t |t j j « sYt |j dd« s@t |dd« s3t |t j j « r|j dk7 r| c}S c c}w )a
|
Return a list of all the top-level nodes.
|
|
Each node returned is not a pandas storage object.
|
|
Returns
|
-------
|
list
|
List of objects.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
|
>>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP
|
>>> store.put('data', df) # doctest: +SKIP
|
>>> print(store.groups()) # doctest: +SKIP
|
>>> store.close() # doctest: +SKIP
|
[/data (Group) ''
|
children := ['axis0' (Array), 'axis1' (Array), 'block0_values' (Array),
|
'block0_items' (Array)]]
|
NÚ pandas_typerz ) r rß rÊ r Ú walk_groupsrW ÚlinkÚLinkÚgetattrÚ_v_attrsrz r rÄ r s r\ r¶ zHDFStore.groupsÎ s± ô, Ø ×ÑÔ Ø||Ð'Ð'Ð'ÜÐ%Ð%Ð%ð \\×-Ñ-Ó/ö
|
àä q¤*§/¡/×"6Ñ"6Ô7äAJJ¨ °tÔ<Ü q '¨4Ô0Ü" 1¤j×&6Ñ&6×&<Ñ&<Ô=À!Ç)Á)ÈwÒBVò ò
|
ð
|
ùò
|
s Á BCc # óL K t « | j « | j J t J | j j |« D ]Ò }t |j dd« g }g }|j j « D ]w }t |j dd« }|At |t j j « sA|j |j « ]|j |j « y |j j d« ||f Ô yw)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`.
|
|
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
|
Nr r )r rß rÊ r r r r Ú _v_childrenr{ rW rÅ ÚGroupr rÄ r¸ Úrstrip)r× rr r r¶ ÚleavesÚchildr s r\ Úwalkz HDFStore.walkõ sü è ø ôN Ø ×ÑÔ Ø||Ð'Ð'Ð'ÜÐ%Ð%Ð%à ×)Ñ)¨%Ó0ò >AÜqzz =°$Ó7ÐCØàFØFØ ×-Ñ-Ó/ò 1Ü % e§n¡n°mÀTÓ J Ø Ð&Ü! %¬×)9Ñ)9×)?Ñ)?Õ@Ø e§m¡mÕ4àMM %§-¡-Õ0ð 1ð ==×'Ñ'¨Ó,¨f°fÐ=Ó =ñ >ùs CD$ÃA D$c óf | j « |j d« sd|z }| j J t J | j j | j
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|« }t |t j « sJ t |« « |S # t j j $ r Y yw xY w)z9return the node with the key or None if it does not existr N) rß Ú
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exceptionsÚNoSuchNodeErrorrW rK rð )r× r rõ s r\ rô zHDFStore.get_node1 s¢ à ×ÑÔ Ø~~cÔ"Ø)Cà||Ð'Ð'Ð'ÜÐ%Ð%Ð%ð Ø<<×(Ñ(¨¯©°CÓ8Dô $¤
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§¡Ô0Ð<´$°t³*Ó<Ð0Ø øô ×$Ñ$×4Ñ4ò Ùð ús ¾&B  B0Â/B0c ó | j |« }|t d| d« | j |« }|j « |S )z<return the storer object for a key, raise if not in the filer$ r% )rô rï r1 r2 )r× r rÅ r[ s r\ r8 zHDFStore.get_storerA sH à cÓ"Ø =ÜÐ -¨c¨U°,Ð?Ó@Ð @à × Ñ Ó &Ø Ør^ c ó: t |||||¬« } |t | j « « }t |t t f« s|g}|D ]Ì }
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|
Copy the existing store to a new file, updating in place.
|
|
Parameters
|
----------
|
propindexes : bool, default True
|
Restore indexes in copied file.
|
keys : list, optional
|
List of keys to include in the copy (defaults to all).
|
overwrite : bool, default True
|
Whether to overwrite (remove and replace) existing nodes in the new store.
|
mode, complib, complevel, fletcher32 same as in HDFStore.__init__
|
|
Returns
|
-------
|
open file handle of the new store
|
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ð <Ü Ó$DÜ$¤¬ Ô.Ø6DØò @AØ Ó"AØ}Ø >Ù Ø!×(Ñ(¨Ô+à{{ 1~Ü a¤Ô'Ø.3EÙ"Ø12·±Ö H¨A¸1¿<»< §£Ð HÐ HØ ×$Ñ$ØØ Ø #Ü%,¨Q°ÀÓ%EØ!"§¡ð %õ ð MM ! T°A·J±JMÕ?ð) @ð, Ðùò !Is Â(DÂ:Dc óX t | j « }t | « d| d}| j r¢t | j « « }t |« rwg }g }|D ]\ } | j |« }|F|j t |j xs |« « |j t |xs d« « ^ |t d||« z }|S |dz }|S |d z }|S # t $ r t $ r;}|j |« t |« } |j d| d« Y d}~Çd}~ww xY w)
|
a
|
Print detailed information on the store.
|
|
Returns
|
-------
|
str
|
|
Examples
|
--------
|
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
|
>>> store = pd.HDFStore("store.h5", 'w') # doctest: +SKIP
|
>>> store.put('data', df) # doctest: +SKIP
|
>>> print(store.info()) # doctest: +SKIP
|
>>> store.close() # doctest: +SKIP
|
<class 'pandas.io.pytables.HDFStore'>
|
File path: store.h5
|
/data frame (shape->[2,2])
|
rú rû Nzinvalid_HDFStore nodez[invalid_HDFStore node: rA é ÚEmptyzFile is CLOSED) rD rÑ rð r® rx r rq r8 r rN r^ r_ rC )
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|
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Parameters
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----------
|
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)
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stop : the passed stop value (default is None)
|
iterator : bool, default False
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Whether to use the default iterator.
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Gð 5Ù" 6Ñ4¨VÑ4Lô* ! ¨t¯w©wÓ7Ø ~Ð-Ð-øô+ "ò à/Ò)Ü Ð 4Ô5ÜH×%Ñ%Ð&>Ô?Ý á &Øñ ä%¨hÄÇÁÔHð ð ñ ð ô ûô ò 5ð Ñ Ø!%vÙ" 6Ñ4¨VÑ4Lð 5ús Ä E Å GÅ AF$Æ$ GÇGc ó | j S )zreturn the values©r{ rÜ s r\ Ú take_datazIndexCol.take_data¨ s à{{Ðr^ c ó. | j j S r` )rz r rÜ s r\ ÚattrszIndexCol.attrs¬ ó àzz×"Ñ"Ð"r^ c ó. | j j S r` ©rz Ú descriptionrÜ s r\ rW zIndexCol.description° ó àzz×%Ñ%Ð%r^ c óD t | j | j d« S )z!return my current col descriptionN)r rW r rÜ s r\ Úcolz IndexCol.col´ s ô t×'Ñ'¨¯©°TÓ:Ð:r^ c ó | j S ©zreturn my cython valuesrP rÜ s r\ ÚcvalueszIndexCol.cvalues¹ s ð {{Ðr^ c ó, t | j « S r` )r r{ rÜ s r\ r zIndexCol.__iter__¾ s ÜDKKÓ Ð r^ c ó t | j « dk( rst |t « r|j | j
|
« }|E| j j |k r+t « j || j ¬« | _ yyyy)z
|
maybe set a string col itemsize:
|
min_itemsize can be an integer or a dict with this columns name
|
with an integer size
|
ÚstringN)r r ) r] r rW rp rå rh r r r Ú StringColr )r× r s r\ Úmaybe_set_sizezIndexCol.maybe_set_sizeÁ su ô 499Ó %¨Ò 1Ü,¬Ô-Ø +× /Ñ /°· ± Ó : àÐ'¨D¯H©H×,=Ñ,=À Ò,LÜ"9×.Ñ.¸ È$Ï(É(Ð.ÓSð -MÐ'ð 2r^ c ó y r` rÍ rÜ s r\ Úvalidate_nameszIndexCol.validate_namesÎ ó Ø r^ c óÌ |j | _ | j « | j |« | j |« | j |« | j « y r` )rz Ú validate_colÚ validate_attrÚvalidate_metadataÚwrite_metadataÚset_attr)r× Úhandlerr s r\ Úvalidate_and_setzIndexCol.validate_and_setÑ sL Ø ]]
|
Ø ×ÑÔØ ×Ñ6Ô"Ø × Ñ wÔ'Ø ×ÑGÔ$Ø r^ c óô t | j « dk( r`| j }|R| | j }|j |k r)t d| d| j
|
d|j d« |j S y)z:validate this column: return the compared against itemsizer` Nz#Trying to store a string with len [z] in [z)] column but
|
this column has a limit of [zC]!
|
Consider using min_itemsize to preset the sizes on these columns)r] r rZ r r r )r× r Úcs r\ rg zIndexCol.validate_colÙ s ô 499Ó %¨Ò 1ØAØ}ØÐ#Ø #}}HØ:: Ò(Ü$Ø=¸h¸Zð H Ø JJ<ð ( Ø JJ<ð (<ð<óð ð zzÐ!àr^ c ó¨ |rPt | j | j d « }|,|| j k7 r t d| d| j d« y y y )Nzincompatible kind in col [ú - rA )r rS r r r´ )r× r Ú existing_kinds r\ rh zIndexCol.validate_attrì s] á Ü # D§J¡J°·±ÀÓ EMØ Ð(¨]¸d¿i¹iÒ-GÜ Ø0° °¸sÀ4Ç9Á9À+ÈQÐOóð ð .HÐ(ð r^ c ó | j D ]º }t | |d« }|j | j i « }|j |« }||v rp|n||k7 ri|dv rCt
|
|||fz }t j |t t « ¬« d||< t | |d« t d| j d| d| d| d « ||¶|||< ¼ y) z
|
set/update the info for this indexable with the key/value
|
if there is a conflict raise/warn as needed
|
N)r r ©Ú
|
stacklevelzinvalid info for [z] for [z], existing_value [z ] conflicts with new value [rA ) Ú _info_fieldsr Ú
|
setdefaultrh rå rw ÚwarningsÚwarnr r Úsetattrr )r× r± r r ÚidxÚexisting_valueÚwss r\ Ú update_infozIndexCol.update_infoõ s÷ ð
|
×$Ñ$ò !CÜD # tÓ,EØ// $§)¡)¨RÓ0Cà WW S\NØczeÐ /°NÀeÒ4KàÐ0Ñ0Ü/°3¸ÈÐ2NÑNBÜ MMØÔ 4ÔAQÓASõð
|
$CHÜD # tÕ,ô %Ø,¨T¯Y©Y¨K°w¸s¸eð D+Ø+9Ð*:ð ;&Ø&+ W¨Að/óð ð
|
Ð" nÑ&@Ø Cñ1 !r^ c óv |j | j « }| | j j |« yy)z!set my state from the passed infoN)rå rh Ú__dict__Úupdate)r× r± r{ s r\ Úset_infozIndexCol.set_info s0 àhhtyyÓ!Ø ?Ø MM× Ñ Õ %ð r^ c óZ t | j | j | j « y)z set the kind for this columnN)rz rS r r rÜ s r\ rk zIndexCol.set_attr s ä
|
|
DNN¨D¯I©IÕ6r^ c ó² | j dk( rH| j }|j | j « }| |t ||dd¬« s t d« yyyy)z:validate that kind=category does not change the categoriesÚcategoryNT©Ú
|
strict_nanÚ dtype_equalzEcannot append a categorical with different categories to the existing)rÁ r Ú read_metadatar r+ r )r× rl Ú new_metadataÚ cur_metadatas r\ ri zIndexCol.validate_metadata sp à 99
|
Ò "Ø ==LØ"×0Ñ0°·±Ó<Là Ð(Ø Ð,Ü(Ø ,¸4ÈTõô !ð;óð ð ð -ð )ð #r^ c ój | j '|j | j | j « yy)zset the meta dataN)r rj r )r× rl s r\ rj zIndexCol.write_metadata/ s) à ==Ð $Ø × "Ñ " 4§:¡:¨t¯}©}Õ =ð %r^ ) NNNNNNNNNNNNN)rh rf r rø ræ rç rê rè )r rë ræ rç ©r* Úobjectræ r rî )r{ ú
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np.ndarrayrc rf r rf ræ z3tuple[np.ndarray, np.ndarray] | tuple[Index, Index]r r` rí )rl ÚAppendableTabler r ræ rç )r r ræ rç )rl r ræ rç )"rñ rú rû rü r
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rý r rv rÙ rþ r r r rý r/ r1 r¤ rN rQ rS rW rZ r] r rb rd rm rg rh r~ r rk ri rj rÍ r^ r\ r r ÿ sq
ñ ð !OTÓ Ø "ÐtÓ"Ú/Lð
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Ø Ø Ø Ø Ø Ø Ø ØØØØ Øð )+àð)+ð ð )+ð
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ó!)+ðV ò!ó ð!ð ò#ó ð#ó"ó
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ó
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ó&ð ò?ó ð?ð>.Ø ð>.Ø58ð>.ØBEð>.à <ó>.ò@ð ñ#ó ð#ð ñ&ó ð&ð ñ;ó ð;ð ñó ðó!ô Tó óóó&ó !ó>&ó 7óô">r^ r c óB e Zd ZdZedd« Z ddZddZy) ÚGenericIndexColz:an index which is not represented in the data of the tablec ó y©NFrÍ rÜ s r\ r¤ zGenericIndexCol.is_indexed8 ó àr^ c ó t |t j « sJ t |« « t t |« « }||fS )zÛ
|
Convert the data from this selection to the appropriate pandas type.
|
|
Parameters
|
----------
|
values : np.ndarray
|
nan_rep : str
|
encoding : str
|
errors : str
|
)rW rX rC rð r1 rq )r× r{ r rc r r s r\ rN zGenericIndexCol.convert< s9 ô &¤"§*¡*Ô-Ð;¬t°F«|Ó;Ð-ä3v;Ó'Øe|Ðr^ c ó y r` rÍ rÜ s r\ rk zGenericIndexCol.set_attrN re r^ Nrî )r{ r rc rf r rf ræ ztuple[Index, Index]rí )rñ rú rû rü rþ r¤ rN rk rÍ r^ r\ r r 5 sA ÙDà òó ðð Ø ð Ø58ð ØBEð à ó ô$ r^ r c ó< e Zd ZdZdZdZddgZ d d fd Zedd« Z edd« Z
|
ddZ dd Z dd
|
Z d Zed d « Zed « Zed d« Zed d« Zed « Zed « Zed « Zed « Zd dZd dZd dZ xZS )!ÚDataCola3
|
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
|
Fr r c óV t | ||||||||| |
|
| ¬« | | _ | | _ y )N) rh r{ r r r r r r rz rÁ r )ÚsuperrÙ r> r¨ )r× rh r{ r r r r r r rz rÁ r r> r¨ Ú __class__s r\ rÙ zDataCol.__init__c sH ø ô ÑØØØØØØØØØØØ ð ô
|
ð
|
Ø r^ c ó | j dS )NÚ_dtyperg rÜ s r\ Ú
|
dtype_attrzDataCol.dtype_attr s à))FÐ#Ð#r^ c ó | j dS )NÚ_metarg rÜ s r\ Ú meta_attrzDataCol.meta_attr r r^ c
|
ó t t t | j | j | j
|
| j | j f« « }dj t g d¢|« D cg c] \ }}| d| c}}« S c c}}w )Nr )rh r r> r Úshaper )
|
ro r rD rh r r> r r¤ r! rL r" s r\ rý zDataCol.__repr__ s ÜÜ Ü tyy¨$¯*©*°d·j±jÀ$Ç)Á)ÈTÏZÉZÐ Xó ó
|
ð
|
xxô #&Ò&QÐSWÓ"X÷ á Cð %r%Ò!ó ó
|
ð
|
ùó r$ c ó0 t fddD « « S )r& c 3 óT K | ] }t |d « t |d « k( ! y wr` r( r) s r\ rn z!DataCol.__eq__.<locals>.<genexpr> r+ r, )rh r r> r r- r. s ``r\ r/ zDataCol.__eq__ s ù äô
|
à6ô
|
ó
|
ð
|
r^ c ó |J | j J t |« \ }}|| _ || _ t |« | _ y r` )r> Ú_get_data_and_dtype_namer¨ Ú_dtype_to_kindr )r× r¨ Ú
|
dtype_names r\ Úset_datazDataCol.set_data sG ØÐ Ð Ð ØzzÐ!Ð!Ð!ä3°DÓ9Ñjà Ø
|
Ü" :Ó. r^ c ó | j S )zreturn the data©r¨ rÜ s r\ rQ zDataCol.take_data© s àyyÐr^ c ó¤ |j }|j }|j }|j dk( rd|j f}t |t « r5|j }| j ||j j ¬« }|S t j |d« st |t « r| j |« }|S t j |d« r| j |« }|S t |« r t! « j# ||d ¬« }|S t% |« r| j' ||« }|S | j ||j ¬« }|S )zW
|
Get an appropriately typed and shaped pytables.Col object for values.
|
rk ©r r5 Úmr ©r r¤ )r> r r¤ rt ÚsizerW r7 ÚcodesÚ get_atom_datarh r rF r( Úget_atom_datetime64Úget_atom_timedelta64r# r Ú
|
ComplexColr% Úget_atom_string)rÑ r{ r> r r¤ r³ Úatoms r\ Ú _get_atomzDataCol._get_atom s< ð
|
ð >>à Ø ;;!Ò ð Ð$Eä fkÔ *ØLLEØ×$Ñ$ U°·±×1AÑ1AÐ$ÓBDð ô __U CÔ (¬J°u¼oÔ,NØ×*Ñ*¨5Ó1Dð ô __U CÔ (Ø×+Ñ+¨EÓ2Dð ô eÔ $Ü9×'Ñ'°ÀÀqÁÐ'ÓJDð ô UÔ #Ø×&Ñ& u¨hÓ7Dð ð ×$Ñ$ U°·±Ð$Ó<Dà r^ c ó> t « j ||d ¬« S )Nr r± ©r ra ©rÑ r¤ r s r\ r¸ zDataCol.get_atom_stringÍ s äy×"Ñ"¨H¸EÀ!¹HÐ"ÓEÐEr^ c ó¶ |j d« r |dd }d| d}n)|j d« rd}n|j « }| d}t t « |« S )z0return the PyTables column class for this columnÚuinté NÚUIntrI ÚperiodÚInt64Col)r Ú
|
capitalizer r )rÑ r Úk4Úcol_nameÚkcaps r\ Úget_atom_coltypezDataCol.get_atom_coltypeÑ s_ ð ??6Ô "ØabBØ bT ~HØ __XÔ &à!Hà??Ó$DØ s|Häwy (Ó+Ð+r^ c ó: | j |¬« |d ¬« S )Nr¯ r ©r¤ ©rÈ ©rÑ r¤ r s r\ r´ zDataCol.get_atom_dataà s! à.s×#Ñ#¨Ð#Ó.°U¸1±XÔ>Ð>r^ c ó< t « j |d ¬« S ©Nr rÊ ©r rà ©rÑ r¤ s r\ rµ zDataCol.get_atom_datetime64ä ó äy×!Ñ!¨¨a©Ð!Ó1Ð1r^ c ó< t « j |d ¬« S rÎ rÏ rÐ s r\ r¶ z DataCol.get_atom_timedelta64è rÑ r^ c ó0 t | j dd « S )Nr¤ )r r¨ rÜ s r\ r¤ z DataCol.shapeì s ätyy '¨4Ó0Ð0r^ c ó | j S r\ r rÜ s r\ r] zDataCol.cvaluesð s ð yyÐr^ c ó |r
t | j | j d« }|#|t | j « k7 r t d« t | j | j d« }||| j k7 r t d« yyy)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!)r rS r rn r{ r r r> )r× r Úexisting_fieldsÚexisting_dtypes r\ rh zDataCol.validate_attrõ s á Ü % d§j¡j°$·.±.À$Ó GOØ Ð*¨Ä$ÀtÇ{Á{ÓBSÒ/SÜ Ð!WÓXÐXä $ T§Z¡Z°· ± À$Ó GNØ Ð)¨nÀÇ
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Á
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Ò.JÜ ØVóð ð /KÐ)ð r^ c óÌ t |t j « sJ t |« « |j j
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|| j }| j J | j t |« \ }}t |« }n|}| j }| j }t |t j « sJ t | j « }| j } | j }
|
| j } |J t |« } | j! d« rt# || d¬« }n | dk( rt j$ |d¬« }n| dk( r> t j$ |D cg c] } t' j( | « c} t* ¬« }n¿|dk( r¦| }|j1 « }| t3 g t j4 ¬« }n\t7 |« }|j9 « rA|| }||d k7 xx |j; t< « j? « j@ z cc< tC jD |||
|
d
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¬ « }n |j; | d
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¬ « }t |« dk( rtI ||||¬« }| jJ |fS c c} w # t, $ rE t j$ |D cg c] } t' j. | « nc c} w c} t* ¬« }Y }w xY w# tF $ r |j; d d
|
¬ « }Y w xY w)aR
|
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
|
Ú
|
datetime64T©ÚcoerceÚ timedelta64úm8[ns]©r> r r
éÿÿÿÿF)Ú
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categoriesr Úvalidate©r© ÚOr` ©r rc r )&rW rX rC rð r> rD r r r¨ r© r r] rÁ r r r r rI Úasarrayr Ú fromordinalr r Ú fromtimestampÚravelr. Úfloat64r6 ÚanyÚastyperë ÚcumsumÚ_valuesr7 Ú
|
from_codesr´ Ú_unconvert_string_arrayr{ )r× r{ r rc r Ú convertedrª r rÁ r r r r> r rà r³ Úmasks r\ rN zDataCol.convert
|
s ô &¤"§*¡*Ô-Ð;¬t°F«|Ó;Ð-ð <<× Ñ Ð *ØDJJÑ'FàxxÐ#Ð#Ð#Ø ::Ð ô %=¸VÓ$DÑ !IzÜ! *Ó-Dà IØ JØ99Dä)¤R§Z¡ZÔ0Ð0Ð0ô tyyÓ)Ø==Ø,,Ø WWàÐ%Ð%Ð%ä
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Ó+ð × Ñ LÔ )ä ¨2°dÔ;Ià mÒ #Ü
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9°HÔ=IØ f_ð Ü JJØ2;Ö<¨QT×%Ñ% aÕ(Ò<ÄFô ð ZÒ à!JØ OOÓ%Eð
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Ð!ô
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# 2¬R¯Z©ZÔ 8
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äJÓ'Ø88:Ø!+¨T¨EÑ!2JØ% 2+Ó&¨$¯+©+´cÓ*:×*AÑ*AÓ*C×*KÑ*KÑKÓ&ä#×.Ñ.Ø *°gÈôIð
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>Ø %× ,Ñ ,¨U¸Ð ,Ó ? ô
|
4Ó HÒ ,Ü/Ø 7°XÀfôIð {{IÐ%Ð%ùòW =øä ò Ü JJØ4=Ö>¨qT×'Ñ'¨Õ*Ñ>ùÔ>Äfô ð ûô@ ò >Ø %× ,Ñ ,¨S°uÐ ,Ó = ð >úsB ÅI3 Å I.Å6 I3 È0K É.I3 É3 KÊ J,
|
Ê+KË KË K#Ë"K#c ó" t | j | j | j « t | j | j | j
|
« | j J t | j | j | j « y)z set the data for this columnN)rz rS r r{ r¢ rÁ r> r rÜ s r\ rk zDataCol.set_attrf
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sZ ä
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DNN¨D¯K©KÔ8Ü
|
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DNN¨D¯I©IÔ6ØzzÐ%Ð%Ð%Ü
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DOO¨T¯Z©ZÕ8r^ ) NNNNNNNNNNNN)rh rf r rø r> zDtypeArg | Noneræ rç rè r )r¨ rM ræ rç )r{ rM ræ rI )r rf ræ z type[Col]©r rf ræ rI rí )r{ r rc rf r rf ) rñ rú rû rü r
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r rv rÙ rþ r r¢ rý r/ r« rQ Ú classmethodrº r¸ rÈ r´ rµ r¶ r¤ r] rh rN rk Ú __classcell__©r s @r\ r r R s` ø ñ
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ð OØ ÐØ)Ð$Lð
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Ø Ø Ø Ø Ø ØØØ ØØ!%Ø ð àð ð ð ð ð ð
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õ ð@ ò$ó ð$ð ò#ó ð#ó
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ó
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ó/òð ò ó ð ð> ñFó ðFð ò ,ó ð ,ð ò?ó ð?ð ñ2ó ð2ð ñ2ó ð2ð ñ1ó ð1ð ñó ðó ób&÷H9r^ r c ó^ e Zd ZdZdZd dZed « Zed
|
d« Zed « Z ed « Z
|
y) ÚDataIndexableColz+represent a data column that can be indexedTc ój t t | j « j « s t d« y )Nú-cannot have non-object label DataIndexableCol)r% r. r{ r> r rÜ s r\ rd z DataIndexableCol.validate_namess
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s* Ü u T§[¡[Ó 1× 7Ñ 7Ô8ä Ð LÓMÐ Mð 9r^ c ó6 t « j |¬« S )N)r r¼ r½ s r\ r¸ z DataIndexableCol.get_atom_stringx
|
s äy×"Ñ"¨HÐ"Ó5Ð5r^ c ó0 | j |¬« « S )Nr¯ rË rÌ s r\ r´ z DataIndexableCol.get_atom_data|
|
s à.s×#Ñ#¨Ð#Ó.Ó0Ð0r^ c ó2 t « j « S r` rÏ rÐ s r\ rµ z$DataIndexableCol.get_atom_datetime64
|
ó äy×!Ñ!Ó#Ð#r^ c ó2 t « j « S r` rÏ rÐ s r\ r¶ z%DataIndexableCol.get_atom_timedelta64
|
rþ r^ Nrí ró ) rñ rú rû rü r rd rô r¸ r´ rµ r¶ rÍ r^ r\ rø rø n
|
sa Ù5à ÐóNð
|
ñ6ó ð6ð ò1ó ð1ð ñ$ó ð$ð ñ$ó ñ$r^ rø c ó e Zd ZdZy)ÚGenericDataIndexableColz(represent a generic pytables data columnN)rñ rú rû rü rÍ r^ r\ r r
|
s Ú2r^ r c óÈ e Zd ZU dZded< dZded< ded< ded < d
|
ed < d Zd ed< d) d*dZed+d« Z ed,d« Z
|
ed « Z d-dZ d.dZ d/dZed « Zed « Zed « Zed « Zed0d« Zed+d« Zed « Zd.d Zd.d Zed « Zed+d « Zed! « Zd1d"Zd2d.d$Z d+d%Z d3 d4d&Z d.d'Z d5 d6d(Z y#)7ÚFixedzø
|
represent an object in my store
|
facilitate read/write of various types of objects
|
this is an abstract base class
|
|
Parameters
|
----------
|
parent : HDFStore
|
group : Node
|
The group node where the table resides.
|
rf Ú pandas_kindry Ú format_typeútype[DataFrame | Series]Úobj_typerë rt r rÈ Fr rM c óò t |t « sJ t |« « t J t |t j « sJ t |« « || _ || _ t |« | _ || _ y r` )
|
rW r rð r rK rÈ rÅ rd rc r )r× rÈ rÅ rc r s r\ rÙ zFixed.__init__¡
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sg ô &¤(Ô+Ð9¬T°&«\Ó9Ð+ÜÐ%Ð%Ð%Ü%¤§ ¡ Ô1Ð>´4¸³;Ó>Ð1Ø Ø
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Ü(¨Ó2 Ø r^ c óv | j d dk xr& | j d dk xr | j d dk S )Nr rk é
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é )ÚversionrÜ s r\ Úis_old_versionzFixed.is_old_version°
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s: à||A !Ñ#ÒU¨¯ © °Q©¸2Ñ(=ÒUÀ$Ç,Á,ÈqÁ/ÐTUÑBUÐUr^ c óè t t | j j dd« « } t d |j d« D « « }t |« dk( r|dz }|S # t $ r d}Y |S w xY w)zcompute and set our versionÚpandas_versionNc 3 ó2 K | ] }t |« y wr` )rë )rl rS s r\ rn z Fixed.version.<locals>.<genexpr>¹
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s è ø Ò? qC FÑ?ùs ú.r )r )r r r )r] r rÅ r ro rß rq r )r× r s r\ r z Fixed.version´
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sx ô "¤'¨$¯*©*×*=Ñ*=Ð?OÐQUÓ"VÓWð ÜÑ?¨G¯M©M¸#Ó,>Ô?Ó?GÜ7|qÒ Ø! D.ð øô ò Ø GØð ús ¬4A" Á" A1Á0A1c óV t t | j j dd « « S )Nr )r] r rÅ r rÜ s r\ r zFixed.pandas_typeÀ
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s ä w t§z¡z×':Ñ':¸MÈ4Ó PÓQÐQr^ c ó | j « | j }|Wt |t t f« r.dj |D cg c] }t |« c}« }d| d}| j dd| dS | j S c c}w )ú(return a pretty representation of myselfr ú[rA ú12.12z (shape->ú))r2 r¤ rW rn ro r! rD r )r× r[ rS Újshapes r\ rý zFixed.__repr__Ä
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s à ÔØ JJØ =Ü!d¤E]Ô+Ø ¸AÖ">°q¤<°¥?Ò">Ó?ØxqMØ×&Ñ& uÐ-¨Y°q°c¸Ð;Ð ;Ø× Ñ Ð ùò #?s ÁBc ó t | j « | j _ t t « | j _ y)z set my pandas type & versionN)rf r rS r Ú_versionr rÜ s r\ rØ zFixed.set_object_infoÏ
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s) ä!$ T×%5Ñ%5Ó!6
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Ô Ü$'¬£M
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Õ!r^ c ó0 t j | « }|S r` râ )r× Únew_selfs r\ r© z
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Fixed.copyÔ
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s Ü99T?Ør^ c ó | j S r` )r0 rÜ s r\ r¤ z Fixed.shapeØ
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rã r^ c ó. | j j S r` ©rÅ r¸ rÜ s r\ rN zFixed.pathnameÜ
|
rX r^ c ó. | j j S r` )rÈ rÊ rÜ s r\ rÊ z Fixed._handleà
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s à{{×"Ñ"Ð"r^ c ó. | j j S r` )rÈ rÕ rÜ s r\ rÕ zFixed._filtersä
|
s à{{×#Ñ#Ð#r^ c ó. | j j S r` )rÈ rÒ rÜ s r\ rÒ zFixed._complevelè
|
s à{{×%Ñ%Ð%r^ c ó. | j j S r` )rÈ rÔ rÜ s r\ rÔ zFixed._fletcher32ì
|
s à{{×&Ñ&Ð&r^ c ó. | j j S r` )rÅ r rÜ s r\ rS z Fixed.attrsð
|
rT r^ c ó y©zset our object attributesNrÍ rÜ s r\ Ú set_attrszFixed.set_attrsô
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d<d « Zd=d Zd Z d>d Z d?d!Z d@d"Z dAd#Z d7d$Z!d7d%Z"dBd7d&Z#d7d'Z$e%d( « Z& dC dDd)Z' dE dFd*Z(e)dGd+« Z*d, Z+ dH dId-Z,e- dJd.« Z.dBdKd/Z/ dLd0Z0 dC dMd1Z1 dC dNd2Z2 xZ3S )Or aa
|
represent a table:
|
facilitate read/write of various types of tables
|
|
Attrs in Table Node
|
-------------------
|
These are attributes that are store in the main table node, they are
|
necessary to recreate these tables when read back in.
|
|
index_axes : a list of tuples of the (original indexing axis and
|
index column)
|
non_index_axes: a list of tuples of the (original index axis and
|
columns on a non-indexing axis)
|
values_axes : a list of the columns which comprise the data of this
|
table
|
data_columns : a list of the columns that we are allowing indexing
|
(these become single columns in values_axes)
|
nan_rep : the string to use for nan representations for string
|
objects
|
levels : the names of levels
|
metadata : the names of the metadata columns
|
Ú
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wide_tablerz rf r r· rk zint | list[Hashable]rz Trn r c ó¨ t | ||||¬« |xs g | _ |xs g | _ |xs g | _ |xs g | _ | xs i | _ |
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index_axesrO Ú values_axesr r± r ) r× rÈ rÅ rc r rÁ rO r r r± r r s r\ rÙ zTable.__init__u s` ø ô Ñ °À&ÐÔIØ$Ò*¨Ø ,Ò 2°ÔØ&Ò,¨"ÔØ (Ò .¨BÔØJB Ø r^ c ó> | j j d« d S )NÚ_r )r· rß rÜ s r\ Útable_type_shortzTable.table_type_short s à×$Ñ$ SÓ)¨!Ñ,Ð,r^ c ó | j « t | j « rdj | j « nd}d| d}d}| j r8dj | j
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ùò ;ùò As Á0DÂ(Dc óL | j D ] }||j k( s|c S y)zreturn the axis for cN)rf rh )r× ro r¦ s r\ rç zTable.__getitem__ s* àò AØAFF{Øð ð r^ c ó
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|y|j | j k7 r&t d|j d| j d« dD ]¼ }t | |d« }t ||d« }||k7 s#t |« D ]x \ }}|| }||k7 s|dk( rO|j |j k7 r6t d|j d d |j d
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|j d « t d | d | d| d« t d | d | d| d« y)z"validate against an existing tableNz'incompatible table_type with existing [rq rA )rÁ rO r r ú Cannot serialize the column [r z%] because its data contents are not [z] but [ú] object dtypezinvalid combination of [z] on appending data [z] vs current table [)r· r´ r ry r r r{ r_ )r× r* ro ÚsvÚovr| ÚsaxÚoaxs r\ rá zTable.validate§ sb à =Ø à × Ñ tÒ .ÜðØ×$Ñ$Ð% S¨¯ © Ð(9¸ð<óð ð
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Á
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È1Á ¸ð OFØFIÇhÁhÀZð P(Ø(+¯© z° ð!Aó# ð ô
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)Ø 6°q°cð : Ø #uÐ$8¸¸¸Qð @ó ð ð ô Ø.¨q¨cÐ1FÀrÀdð K&Ø&( T¨ð,óð ñ/ r^ c ó6 t | j t « S )z@the levels attribute is 1 or a list in the case of a multi-index)rW rz rn rÜ s r\ Úis_multi_indexzTable.is_multi_indexÎ s ô $++¤tÓ,Ð,r^ c óÜ t j |j j « } |j « }t |t « sJ ||fS # t
|
$ r}t d« |d}~ww xY w)ze
|
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)ra Úfill_missing_namesr r{ Ú reset_indexr rW r, )r× rÕ rz Ú reset_objrd s r\ Úvalidate_multiindexzTable.validate_multiindexÓ so ô ×'Ñ'¨¯ © ¯©Ó8ð ØÓ)Iô
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)¤YÔ/Ð/Ð/Ø&Ð Ð øô ò Ü ØTóàð ûð ús «A Á A+Á A&Á&A+c ó t j | j D cg c] }|j j d c}« S c c}w )z-based on our axes, compute the expected nrowsr )rX r rÁ r] r¤ ©r× r| s r\ Únrows_expectedzTable.nrows_expectedä s2 ô ww°D·O±OÖD¨q ¨Ó*ÒDÓEÐEùÒDs Ac ó d| j v S )zhas this table been createdrz r, rÜ s r\ r× zTable.is_existsé s ð $**Ð$Ð$r^ c ó0 t | j dd « S ©Nrz ©r rÅ rÜ s r\ r- zTable.storableî s ätzz 7¨DÓ1Ð1r^ c ó | j S )z,return the table group (this is my storable))r- rÜ s r\ rz z Table.tableò s ð }}Ð r^ c ó. | j j S r` )rz r> rÜ s r\ r> z Table.dtype÷ s àzz× Ñ Ð r^ c ó. | j j S r` rV rÜ s r\ rW zTable.descriptionû rX r^ c óV t j | j | j « S r` )rJ rK rÁ r rÜ s r\ rf z
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Table.axesÿ s ät°×0@Ñ0@ÓAÐAr^ c ó: t d | j D « « S )z.the number of total columns in the values axesc 3 óF K | ] }t |j « y wr` )rq r{ )rl r¦ s r\ rn z Table.ncols.<locals>.<genexpr> s è ø Ò; Q3qxx=Ñ;ùs !)Úsumr rÜ s r\ rÇ z Table.ncols s ô Ñ;¨$×*:Ñ*:Ô;Ó;Ð;r^ c ó yr rÍ rÜ s r\ Ú is_transposedzTable.is_transposed r r^ c óò t t j | j D cg c] }t |d « c}| j
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D cg c] }t |j « c}« « S c c}w c c}w )z@return a tuple of my permutated axes, non_indexable at the frontr )ro rJ rK rO rë rÁ rD ©r× r¦ s r\ Údata_orientationzTable.data_orientation sY ô Ü OOØ$(×$7Ñ$7Ö8qQqTÒ8Ø&*§o¡oÖ6 QVVÒ6ó ó
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ð
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ùâ8ùÚ6s £A/ Á A4c ól ddd}| j D cg c] }|j |f }}| j D cg c] \ }}|| df }}}| j D cg c]2 }|j t | j « v s%|j |f4 }}t ||z |z « S c c}w c c}}w c c}w )z<return a dict of the kinds allowable columns for this objectr r© ©r rk N)rÁ r rO r rh rr r rp ) r× Ú
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axis_namesr¦ Úd1rD r{ Úd2r Úd3s r\ Ú
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queryableszTable.queryables s¶ ð ! YÑ/
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ð %)§O¡OÖ 4qqwwlÐ 4Ð 4Ø;?×;NÑ;N× O©<¨4°z$Ñ Ò&Ð OÑ Oà"&×"2Ñ"2ö
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ð
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ô BGbLÓ!Ð!ùò 5ùÛ Oùò
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s B&ºB+Á &B1ÂB1c ól | j D cg c] }|j |j f c}S c c}w )z return a list of my index cols)rÁ rD r rÜ s r\ Ú
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index_colszTable.index_cols$ s) ð ,0¯?©?Ö; aÒ!Ò;Ð;ùÒ;s 1c óT | j D cg c] }|j c}S c c}w )z return a list of my values cols)r r rÜ s r\ Ú values_colszTable.values_cols) s à!%×!1Ñ!1Ö2AÒ2Ð2ùÒ2s %c ó> | j j }| d| dS )z)return the metadata pathname for this keyz/meta/z/metar r' s r\ Ú_get_metadata_pathzTable._get_metadata_path- s$ à
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×&Ñ&Øse 5Ð)Ð)r^ c ó¶ | j j | j |« t |d¬« d| j | j
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Write out a metadata array to the key as a fixed-format Series.
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Parameters
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----------
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key : str
|
values : ndarray
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r^ c ó t t | j dd« |d« *| j j | j |« « S y)z'return the meta data array for this keyrÁ N)r rÅ rÈ r¹ rú ræ s r\ r zTable.read_metadataD sA ä 74:: v¨tÓ4°c¸4Ó @Ð LØ;;×%Ñ% d×&=Ñ&=¸cÓ&BÓCÐ CØr^ c óB t | j « | j _ | j « | j _ | j « | j _ | j
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| j _ | j | j _ | j | j _ | j | j _ | j | j _ | j | j _
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r[ rO Nr r± r rc r r rz )r rS rO r r± r rd rc r] r rz Ú
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indexablesr
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rÁ r rì s r\ r* zTable.get_attrsW sõ ä % d§j¡jÐ2BÀDÓ IÒ OÈRÔÜ # D§J¡J°ÀÓ EÒ KÈÔÜDJJ¨°Ó5Ò;¸ Ü tzz¨9°dÓ; Ü(¬°·±¸ZÈÓ)NÓO Ü%¤g¨d¯j©j¸(ÀHÓ&MÓN Ü&-¨d¯j©j¸(ÀDÓ&IÒ&OÈR Ø&*§o¡oÖK ¸×9JÓ9J1ÒKØ'+§¡ÖP !¸a×>OÓ>OAÒPÕùò LùÚPs Ã5E ÄE Ä!EÄ3Ec óè |k| j r^t dj | j D cg c] }t |« c}« z }t j |t t « ¬« yyyc c}w )r2 Nr rt ) r rv r! r rf rx ry r r )r× rr rS r} s r\ r3 zTable.validate_versionc s\ à Ð Ø×"Ò"Ü(¨3¯8©8ÀTÇ\Á\Ö4RÀ´S¸µVÒ4RÓ+SÑSÜ ØÜ*Ü /Ó 1öð #ð ùâ4Rs A/
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c ó |yt |t « sy| j « }|D ] }|dk( r ||vst d| d« y)z
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validate the min_itemsize doesn't contain items that are not in the
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axes this needs data_columns to be defined
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Nr{ zmin_itemsize has the key [z%] which is not an axis or data_column)rW rp rô r )r× r ÚqrR s r\ Úvalidate_min_itemsizezTable.validate_min_itemsizen sd ð
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Ð Ø Ü,¬Ô-Ø à OOÓ Ø ò AàH}ØØzÜ Ø0°°ð 4"ð "óð ñ r^ c ó` g } j j j t j j « D ]k \ }\ }}t |« } j |« }|dnd}| d}t |d« } t |||| | j ||¬« }
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|j |
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« m t j « t |« fd} |j t j j « D cg c] \ }} | || « c} }« |S c c} }w )z/create/cache the indexables if they don't existNr
r )rh rD r r r rz rÁ r c óf t |t « sJ t }| v rt }t |« }t |j « }t | dd « }t | dd « }t |« }j |« }t | dd « } ||||| | z |j | ||¬«
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}
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rW rf r rø r Ú_maybe_adjust_namer r© r rz )r| ro Úklassr¹ Úadj_namer{ r> r ÚmdrÁ rÕ Úbase_posr Údescr× Ú table_attrss r\ r{ zTable.indexables.<locals>.f¦ sÖ ø Ü a¤Ô%Ð %Ð%ÜEØBwÜ(ä4 Ó#DÜ)¨!¨T¯\©\Ó:Hô [¨X¨J°eÐ*<¸dÓCFÜK¨H¨:°VÐ)<¸dÓCEô " %Ó(Dà×#Ñ# AÓ&Bô ;¨8¨*°EÐ(:¸DÓADáØ ØØ ØØ qLØØjjØØØô Cð Jr^ )rW rz rS ry rö r r r r rr r rq rv rø )r× Ú _indexablesr| rD rh r¹ r rÁ r r Ú index_colr{ ro r
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r r r s ` @@@@r\ rÿ zTable.indexables s) ü ð à× Ñ Øjj×&Ñ& ô
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)¨¯©×)>Ñ)>Ó ?ò *OA|dÜ4 Ó&DØ×#Ñ# DÓ)BØ!# :°TDà & IÜ;¨ °4Ó8Dä ØØØØØØjjØØô Ið × Ñ yÕ )ð% *ô* ×"Ñ"Ó #Ü{Ó#÷! ð ! ðJ ×Ñ´ ¸$¿*¹*×:PÑ:PÓ0Q×R©¨¨1Aa GÓRÔSàÐùó Ss Ä D*
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c
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ó | j « sy|du ry||du r0| j D cg c] }|j s|j }}t |t
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t f« s|g}i }|||d< |||d< | j }|D ]ô }t |j |d« }|°|j r^|j } | j }
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| j } || |k7 r|j « n| |d< ||
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|d< |j r|j j! d« r t# d« |j$ di |¤ Ì|| j& d d v sát) d
|
| d | d | d « yc c}w )aZ
|
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.
|
NFTr r ÚcomplexzíColumns 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.r rk zcolumn 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.rÍ )r2 rf r r rW ro rn rz r rm r¤ r r r Ú remove_indexrð r r´ r rO r ) r× r© r r r¦ Úkwrz ro r r Ú cur_optlevelÚcur_kinds r\ r zTable.create_indexÏ s¦ ð< Ô Ø Ø eÑ Ø ð ?g¨oØ(,¯ © ÖI 1°Q×5HÓ5HqwwÐIGÐIÜ'¤E¬4 =Ô1Ø iGà Ø Ð Ø %BzNØ Ð Ø BvJà
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«E<½E<c ó( t | |||¬« }|j « }g }| j D ]a }|j | j « |j || j | j | j ¬« }|j |« c |S )a
|
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]]
|
r7 rä )
|
Ú Selectionr¹ rf r r± rN r rc r r ) r× rr r§ r¨ Ú selectionr{ r r¦ Úress r\ Ú
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_read_axeszTable._read_axes% s ô" d¨%°uÀ4ÔH Ø×!Ñ!Ó#ààò AØ JJtyyÔ !Ø))ØØ Ø Ø{{ð ó Cð NN3Õ ð ð r^ c ó |S )z return the data for this objrÍ ©rÑ rÕ r^ s r\ Ú
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get_objectzTable.get_objectG s ð
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r^ c óÐ t |« sg S |d \ }}| j j |i « }|j d« dk( r|rt d| d| « |du r t |« }n|g }t |t « rLt |« }t |« }|j |j « D cg c] }|dk7 r||vr| c}« |D cg c] } | |v s| c} S c c}w c c} w )zd
|
take the input data_columns and min_itemize and create a data
|
columns spec
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r rð r/ z"cannot use a multi-index on axis [z] with data_columns Tr{ )
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rq r± rå r rn rW rp rr rv r )
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r× r r rO rD Ú axis_labelsr± Úexisting_data_columnsrR ro s
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r\ Úvalidate_data_columnszTable.validate_data_columnsL s ô
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j1 | i « }t |j2 « |d< t |« j4 |d< | j7 | | f« d } |j$ | }|j9 | « }t; ||| j< | j> « }| |_ |jA d« |jC |
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« |jE |« |g}t |« }|d k( sJ t | « d k( sJ | D ] }tG ||d |d « } |j d k( }| jI ||| « }| jK ||« jM « }| jO || | | jP |« \ }}g }tS tU ||« « D ]è \ }\ }} tV } d} |r=t | « d k( r/| d |v r(tX } | d } | t | tZ « s t! d« | r|r | jP | } nd} | xs d| }!ta |!|jb | ||| j< | j> | ¬« }"te |!| jf « }#| ji |"« }$tk |"jl jn « }%d}&tq |"dd« ts |"jt « }&dx}'x}(})t |"jl tv « r<|"jx })d}'t+ jz |"j| « j « }(n/t |jl t « rt[ |jl « }'t |"« \ }*}+ | |#|!t | « |$||%|&|)|'|(|+|*¬« },|,jC |
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}.t | d« r| j |._E |.j |« |r| r|.j | « |.S c c}w c c}w # t\ t^ f$ r"} t! d| d| jP d« | d} ~ ww xY wc c},w )a0
|
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->r½ rA Nr TFr rk z<currently only support ndim-1 indexers in an AppendableTablerH c 3 ó, K | ] }|vs| y wr` rÍ )rl rS rf s r\ rn z%Table._create_axes.<locals>.<genexpr>» s øè ø Ò6¨°ª 1Ñ6ùs rï r r{ rð rú z Incompatible appended table [z]with existing table [Ú values_block_)Ú existing_colr r rc r r© r r
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rÈ rÅ rc r rÁ rO r r r± r rz )HrW r, rÅ rÄ r´ rð Ú_get_axis_numberr2 rÁ rD rn r r r± rt rq r rq rf rO r+ rX r= rx rw r{ rñ r Ú_get_axis_namerp rc r r r~ rb Ú _reindex_axisr r rF Ú_get_blocks_and_itemsr ry rL r rø rf Ú
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IndexErrorrï Ú _maybe_convert_for_string_atomr{ r r rº r© r> rh r rr r r' r rå rà rè r3 r¨ r rÈ r3 rz r rá )/r× rf rÕ rá r r r rÅ r¦ Ú table_existsÚnew_infoÚnew_non_index_axesr{ Ú append_axisÚindexerÚ
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exist_axisr± Ú axis_nameÚ new_indexÚnew_index_axesÚjr^ r» rº r± Úvaxesr| r¼ Úb_itemsr rh r$ rd Únew_nameÚdata_convertedr r r r rÁ r r r¨ rª rZ ÚdcsÚ new_tables/ ` r\ Ú _create_axeszTable._create_axesr s7 ø ô@ #yÔ)ØJJ×&Ñ&EÜØAÀ%Àð IÜ s) Að'óð ð <Ø3Dð 26Ö6¨A×$Ñ$ QÕ'Ò6ð ??Ô Ø LØ$(§O¡OÖ4qAFFÒ4DÜ × 1Ñ 1Ó2LØllGð !Là99àyyA~Ð ~ä t9 A Ò %Ü ØNóð ð
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select coordinates (row numbers) from a table; return the
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return a single column from the table, generally only indexables
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a write-once read-many table: this format DOES NOT ALLOW appending to a
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that allows for searching the data on disk
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read the indices and the indexing array, calculate offset rows and return
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write in a format that we can search later on (but cannot append
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(e.g. a CArray) create an indexing table so that we can search
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we form the data into a 2-d including indexes,values,mask write chunk-by-chunk
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Parameters
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----------
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rows : an empty memory space where we are putting the chunk
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indexes : an array of the indexes
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mask : an array of the masks
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values : an array of the values
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coerce the values to a DatetimeIndex if tz is set
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preserve the input shape if possible
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Parameters
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values : ndarray or Index
|
tz : str or tzinfo
|
coerce : if we do not have a passed timezone, coerce to M8[ns] ndarray
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Nrg rL úM8[ns]rÞ )
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Ä0EÅEc ó t |j t « r|j « }|j t k7 r|S t t j |« }|j j }t j |d¬« } | dk( r t d« | dk( r t d« | dk( s|dk( s|S t |« }
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j | j « } t | j « « }t dt j |« « }t j | d| ¬« } | S )a
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Take a string-like that is object dtype and coerce to a fixed size string type.
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|
Parameters
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----------
|
data : np.ndarray[object]
|
encoding : str
|
errors : str
|
Handler for encoding errors.
|
|
Returns
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-------
|
np.ndarray[fixed-length-string]
|
Fr )r© r> rk ÚSrÞ )rq r2 rè rf Úencoderí rw r¤ r! r[ Ú
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libwritersÚmax_len_string_arrayrX rå )r¨ rc r Úensuredr s r\ rÌ rÌ q s ô 4yä 4::< e°8Ô <ß S &Ó)ß WWWTZZÓ(ð ô DJJLÓ)GÜ1j×5Ñ5°gÓ>Ó?Hä ::d A h Z .Ô 1DØ Kr^ c ó* | j }t j | j « t ¬« } t | « r©t j t | « « }d| }t | d t « rKt | d¬« j j ||d¬« }|j « } d| j _ n(| j# |d¬« j# t d¬« } |d }t j$ | |« | j' |« S )
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a*
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Inverse of _convert_string_array.
|
|
Parameters
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----------
|
data : np.ndarray[fixed-length-string]
|
nan_rep : the storage repr of NaN
|
encoding : str
|
errors : str
|
Handler for encoding errors.
|
|
Returns
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|
np.ndarray[object]
|
Decoded data.
|
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Prior to 0.10.1, we named values blocks like: values_block_0 an the
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name values_0, adjust the given name if necessary.
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Parameters
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|
name : str
|
version : Tuple[int, int, int]
|
|
Returns
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|
str
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Find the "kind" string describing the given dtype name.
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hÒ Øð Kð
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Convert the passed data into a storable form and a dtype string.
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Carries out a selection operation on a tables.Table object.
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Parameters
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----------
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table : a Table object
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