Ë
|
nñúhY\ ã óV d Z ddlmZ ddlZddlZddlZddlmZmZm Z ddl
|
Z
|
ddl
|
m Z m Z ddl mZ ddlmZ ddlmZ dd lmZ dd
|
lmZ dd lmZ dd lmZ dd lm Z m Z ddl m Z ddl m!Z! ddl"m#Z#m$Z$m%Z%m&Z&m'Z' erddl(m)Z)m*Z*m+Z+m,Z,m-Z- d dZ. d d!dZ/ G d d« Z0 G d de0« Z1 G d de0« Z2 ee d ¬« d" d#d « Z3 ee d ¬« d ddejh ejh ddf d$d « Z5y)%z parquet compat é )Ú annotationsN)Ú TYPE_CHECKINGÚAnyÚLiteral)Úcatch_warningsÚfilterwarnings)Ú _get_option)Úlib)Úimport_optional_dependency©ÚAbstractMethodError)Údoc)Úfind_stack_level)Úcheck_dtype_backend)Ú DataFrameÚ
|
get_option)Ú _shared_docs)Úarrow_table_to_pandas)Ú IOHandlesÚ
|
get_handleÚ is_fsspec_urlÚis_urlÚstringify_path)Ú DtypeBackendÚFilePathÚ
|
ReadBufferÚStorageOptionsÚ WriteBufferÚBaseImplc ó$ | dk( r t d« } | dk( r,t t g}d}|D ] } |« c S t d| « | dk( r
|
t « S | dk( r
|
t « S t d « # t $ r}|dt |« z z }Y d}~dd}~ww xY w)
|
zreturn our implementationÚautozio.parquet.engineÚ z
|
- NzÉUnable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
|
A suitable version of pyarrow or fastparquet is required for parquet support.
|
Trying to import the above resulted in these errors:ÚpyarrowÚ fastparquetz.engine must be one of 'pyarrow', 'fastparquet')r Ú PyArrowImplÚFastParquetImplÚ ImportErrorÚstrÚ
|
ValueError)ÚengineÚengine_classesÚ
|
error_msgsÚ engine_classÚerrs úDH:\Change_password\venv_build\Lib\site-packages\pandas/io/parquet.pyÚ
|
get_enginer0 4 s½ à ÒÜÐ /Ó0à Òä%¤Ð7à
|
Ø *ò 1Lð 1Ù#~Ò%ð 1ô ð Cð lð ó
|
ð
|
ð ÒÜ}Ð Ø =Ò Ü Ó Ð ä
|
ÐEÓ
|
FÐFøô% ò 1Øg¬¨C«Ñ 0Ñ0
|
ûð 1ús ªA+Á+ BÁ4B
|
Â
|
BÚstorage_optionsc ó, t | « }|t dd¬« }t dd¬« }|#t ||j « r |rOt d« |!t ||j
|
j « rn!t dt |« j « t |« rk|i|5t d« }t d« } |j j | « \ }}|Mt d« } |j j |fi |xs i ¤\ }}n |rt! |« r|d k7 r t d
|
« d} |sN|sLt |t" « r<t$ j& j) |« s t+ ||d |¬ « } d}| j, }|| |fS # t |j f$ r Y ½w xY w) zFile handling for PyArrow.Nz
|
pyarrow.fsÚignore)ÚerrorsÚfsspecz8storage_options not supported with a pyarrow FileSystem.z9filesystem must be a pyarrow or fsspec FileSystem, not a r# Úrbz8storage_options passed with buffer, or non-supported URLF©Úis_textr1 )r r Ú
|
isinstanceÚ
|
FileSystemÚNotImplementedErrorÚspecÚAbstractFileSystemr) ÚtypeÚ__name__r Úfrom_uriÚ TypeErrorÚ ArrowInvalidÚcoreÚ url_to_fsr r( ÚosÚpathÚisdirr Úhandle)
|
rF Úfsr1 ÚmodeÚis_dirÚpath_or_handleÚpa_fsr5 ÚpaÚhandless
|
r/ Ú_get_path_or_handlerP V s° ô $ DÓ)NØ ~Ü*¨<ÀÔIÜ+¨H¸XÔFØ Ð ¤¨B°×0@Ñ0@Ô!AÙ Ü)ØNóð ð Ð ¤J¨r°6·;±;×3QÑ3QÔ$RØ ä ðÜ b×*Ñ*Ð+ð-óð ô ^Ô$¨¨Ø Ð "Ü+¨IÓ6BÜ.¨|Ó<Eð Ø%*×%5Ñ%5×%>Ñ%>¸tÓ%DÑ"Nð :Ü/°Ó9FØ!6 §¡×!6Ñ!6Ø ñ"Ø#2Ò#8°bñ"Ñ Bñ
|
¤&¨ Ô"8¸DÀDºLô ÐSÓTÐTàGá ÙÜ ~¤sÔ +Ü nÔ-ô
|
Ø D¨%À ô
|
ð Ø Ø 7 BÐ &Ð&øô7 rÐ/ò Ùð ús Â7 E; Å;FÆFc ó0 e Zd Zedd« ZddZdddZy) r c ó: t | t « s t d« y )Nz+to_parquet only supports IO with DataFrames)r9 r r) )Údfs r/ Úvalidate_dataframezBaseImpl.validate_dataframe s ä"iÔ(Ü Ð JÓKÐ Kð )ó c ó t | « ©Nr )ÚselfrS rF Ú compressionÚkwargss r/ ÚwritezBaseImpl.write ó Ü! $Ó'Ð'rU Nc ó t | « rW r )rX rF ÚcolumnsrZ s r/ Úreadz BaseImpl.read r\ rU )rS r ÚreturnÚNone)rS r rW )r` r )r? Ú
|
__module__Ú __qualname__Ú staticmethodrT r[ r_ © rU r/ r r s ØòLó ðLó(õ(rU c óz e Zd ZddZ d ddZdddej ddf d dZy)
|
r% c ó< t dd¬« dd l}dd l}|| _ y )Nr# z(pyarrow is required for parquet support.©Úextrar )r Úpyarrow.parquetÚ(pandas.core.arrays.arrow.extension_typesÚapi)rX r# Úpandass r/ Ú__init__zPyArrowImpl.__init__£ s! Ü"Ø Ð Gõ
|
ó ó 8àrU Nc ó¤ | j |« d|j dd « i} ||| d< | j j j |fi | ¤}
|
|j
|
rNdt j |j
|
« i} |
|
j j } i | ¥| ¥} |
|
j | « }
|
t |||d|d u¬« \ }}}t |t j « rmt |d« rat |j t" t$ f« rAt |j t$ « r|j j' « }n |j } |- | j j( j* |
|
|f|||d|¤ n+ | j j( j, |
|
|f||d|¤ ||j/ « y y # ||j/ « w w xY w) NÚschemaÚpreserve_indexÚ PANDAS_ATTRSÚwb)r1 rJ rK Úname)rY Úpartition_colsÚ
|
filesystem)rY rv )rT Úpoprl ÚTableÚ from_pandasÚattrsÚjsonÚdumpsrp ÚmetadataÚreplace_schema_metadatarP r9 ÚioÚBufferedWriterÚhasattrrt r( ÚbytesÚdecodeÚparquetÚwrite_to_datasetÚ write_tableÚclose)rX rS rF rY Úindexr1 ru rv rZ Úfrom_pandas_kwargsÚtableÚ df_metadataÚexisting_metadataÚmerged_metadatarL rO s r/ r[ zPyArrowImpl.write® sÕ ð × Ñ Ô#à.6¸¿
|
¹
|
À8ÈTÓ8RÐ-SÐØ Ð Ø38Ð Ð /Ñ 0à*×*Ñ*¨2ÑDÐ1CÑDà 88Ø)¬4¯:©:°b·h±hÓ+?Ð@KØ %§ ¡ × 5Ñ 5Ð Ø BÐ!2Ð B°kÐ BOØ×1Ñ1°/ÓBEä.AØ Ø Ø +ØØ!¨Ð-ô /
|
Ñ+ ô ~¤r×'8Ñ'8Ô 9ܨÔ/Ü>×.Ñ.´´e° Ô=ä.×-Ñ-¬uÔ5Ø!/×!4Ñ!4×!;Ñ!;Ó!=à!/×!4Ñ!4ð Ø Ð)à1× Ñ ×1Ñ1ØØ"ðð !,Ø#1Ø )ñ ð ó ð -× Ñ ×,Ñ,ØØ"ðð !,Ø )ñ ð
|
ò ð Ð"Ø ð #øwÐ"Ø ð #ús Å AF: Æ:GFc ó d|d< i } t dd¬« }
|
|
|
dk( rd| d< t |||d¬« \ } } } | j j j | f|||d |¤} t « 5 t d
|
d t « t | || ¬ « }d d d « |
|
dk( rj dd ¬« }| j j rKd| j j v r3| j j d }t j |« _ | | j « S S # 1 sw Y xY w# | | j « w w xY w)NTÚuse_pandas_metadatazmode.data_manager)ÚsilentÚarrayÚ split_blocksr6 )r1 rJ )r^ rv Úfiltersr3 zmake_block is deprecated)Ú dtype_backendÚto_pandas_kwargsF)Úcopys PANDAS_ATTRS)r rP rl r Ú
|
read_tabler r ÚDeprecationWarningr Ú _as_managerrp r} r{ Úloadsrz r )rX rF r^ r Úuse_nullable_dtypesr r1 rv rZ r ÚmanagerrL rO Úpa_tableÚresultr s r/ r_ zPyArrowImpl.readð sa ð )-Ð$Ñ%à Ðä Ð 1¸$Ô?Ø gÒ Ø/3Ð ^Ñ ,Ü.AØ Ø Ø +Øô /
|
Ñ+ ð Ø2txx×'Ñ'×2Ñ2Ø ðà Ø%Ø ñ ð
|
ñ Hô Ó!ñ
|
Ü Ø Ø.Ü&ôô
|
/Ø Ø"/Ø%5ô÷
|
ð 'Ò!Ø ×+Ñ+¨G¸%Ð+Ó@à×'Ò'Ø" h§o¡o×&>Ñ&>Ñ>Ø"*§/¡/×":Ñ":¸?Ñ"KKÜ#'§:¡:¨kÓ#:FLØàÐ"Ø ð #÷+
|
ð
|
ûð* Ð"Ø ð #ús$ ³5D* Á( D ÂBD* Ä D'Ä#D* Ä*D?©r` ra ©ÚsnappyNNNN)rS r rF z FilePath | WriteBuffer[bytes]rY ú
|
str | Noner ú bool | Noner1 úStorageOptions | Noneru úlist[str] | Noner` ra )r Úboolr ú DtypeBackend | lib.NoDefaultr1 r¤ r` r )r? rb rc rn r[ r
|
Ú
|
no_defaultr_ re rU r/ r% r% ¢ s¯ ó ð #+Ø !Ø15Ø+/Øð@ à ð@ ð ,ð@ ð ð @ ð
|
ð @ ð /ð @ ð )ð@ ð
|
ó@ ðJ ØØ$)Ø69·n±nØ15Øð7 ð
|
"ð 7 ð 4ð 7 ð /ð7 ð
|
ô7 rU r% c óN e Zd ZddZ d ddZ d d dZy)
|
r& c ó, t dd¬« }|| _ y )Nr$ z,fastparquet is required for parquet support.rh )r rl )rX r$ s r/ rn zFastParquetImpl.__init__+ s ô 1Ø Ð!Oô
|
ð rU Nc ó | j |« d|v r | t d« d|v r|j d« }|d|d< | t d« t |« }t |« rt d« fd|d< n r t d « t d
|
¬ « 5 | j j ||f|||d |¤ d d d « y # 1 sw Y y xY w) NÚ partition_onzYCannot use both partition_on and partition_cols. Use partition_cols for partitioning dataÚhiveÚ file_schemeú9filesystem is not implemented for the fastparquet engine.r5 c óP j | dfi xs i ¤j « S )Nrs )Úopen)rF Ú_r5 r1 s r/ ú<lambda>z'FastParquetImpl.write.<locals>.<lambda>V s. ø °+°&·+±+Ødñ3Ø .Ò 4°"ñ3çdfð rU Ú open_withz?storage_options passed with file object or non-fsspec file pathT)Úrecord)rY Ú write_indexr¬ )
|
rT r) rw r; r r r r rl r[ )
|
rX rS rF rY r ru r1 rv rZ r5 s
|
` @r/ r[ zFastParquetImpl.write3 s ù ð × Ñ Ô#à VÑ #¨Ð(BÜ ðKóð ð VÑ #Ø #ZZ¨Ó 7Nà Ð %Ø$*F=Ñ !à Ð !Ü%ØKóð ô
|
dÓ#Ü Ô Ü/°Ó9Fô#F;Ò ñ Ü ØQóð ô 4Ô (ñ Ø DHHNNØØð ð (Ø !Ø +ñ ð ò ÷ ÷ ñ ús Â#B>Â>Cc óÄ i }|j dd« }|j dt j « } d|d< |r t d« | t j ur t d« | t d« t |« }d }
|
t |« r1t d« } | j |d fi |xs i ¤j |d
|
< nJt |t « r:t j j |« st |d d|¬ « }
|
|
|
j } | j" j$ |fi |¤} | j& d ||d |¤|
|
|
|
j) « S S # |
|
|
|
j) « w w xY w)Nr Fr Ú pandas_nullszNThe 'use_nullable_dtypes' argument is not supported for the fastparquet enginezHThe 'dtype_backend' argument is not supported for the fastparquet enginer¯ r5 r6 rI r7 )r^ r re )rw r
|
r¨ r) r; r r r r± rI r9 r( rE rF rG r rH rl Ú ParquetFileÚ to_pandasr ) rX rF r^ r r1 rv rZ Úparquet_kwargsr r rO r5 Ú parquet_files r/ r_ zFastParquetImpl.readh sm ð *,Ø $jjÐ)>ÀÓ FÐØ
|
|
?´C·N±NÓC à).~Ñ&Ù Ü ð%óð 𠤧¡Ñ .Ü ð%óð ð Ð !Ü%ØKóð ô dÓ#ØÜ Ô Ü/°Ó9Fà#. 6§;¡;¨t°TÑ#U¸oÒ>SÐQSÑ#U×#XÑ#XN4Ò Ü cÔ "¬2¯7©7¯=©=¸Ô+>ô !Ød E¸?ôGð >>Dð Ø/488×/Ñ/°ÑG¸ÑGLØ)<×)Ñ)ÐU°'À7ÑUÈfÑUàÐ"Ø ð #øwÐ"Ø ð #ús Ä1E
|
Å
|
E r r )rS r rY z*Literal['snappy', 'gzip', 'brotli'] | Noner1 r¤ r` ra )NNNN)r1 r¤ r` r )r? rb rc rn r[ r_ re rU r/ r& r& * sn ó ð CKØØØ15Øð3à ð3ð @ð 3ð /ð3ð
|
ó3ðp ØØ15Øð 0 ð
|
/ð 0 ð
|
ô0 rU r& )r1 r! c ó t |t « r|g}t |« } |t j « n|}
|
| j
|
| |
|
f|||||d|¤ |,t |
|
t j « sJ |
|
j « S y)a
|
Write a DataFrame to the parquet format.
|
|
Parameters
|
----------
|
df : DataFrame
|
path : str, path object, file-like object, or None, default None
|
String, path object (implementing ``os.PathLike[str]``), or file-like
|
object implementing a binary ``write()`` function. If None, the result is
|
returned as bytes. If a string, it will be used as Root Directory path
|
when writing a partitioned dataset. The engine fastparquet does not
|
accept file-like objects.
|
engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
|
Parquet library to use. If 'auto', then the option
|
``io.parquet.engine`` is used. The default ``io.parquet.engine``
|
behavior is to try 'pyarrow', falling back to 'fastparquet' if
|
'pyarrow' is unavailable.
|
|
When using the ``'pyarrow'`` engine and no storage options are provided
|
and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec``
|
(e.g. "s3://"), then the ``pyarrow.fs`` filesystem is attempted first.
|
Use the filesystem keyword with an instantiated fsspec filesystem
|
if you wish to use its implementation.
|
compression : {{'snappy', 'gzip', 'brotli', 'lz4', 'zstd', None}},
|
default 'snappy'. Name of the compression to use. Use ``None``
|
for no compression.
|
index : bool, default None
|
If ``True``, include the dataframe's index(es) in the file output. If
|
``False``, they will not be written to the file.
|
If ``None``, similar to ``True`` the dataframe's index(es)
|
will be saved. However, instead of being saved as values,
|
the RangeIndex will be stored as a range in the metadata so it
|
doesn't require much space and is faster. Other indexes will
|
be included as columns in the file output.
|
partition_cols : str or list, optional, default None
|
Column names by which to partition the dataset.
|
Columns are partitioned in the order they are given.
|
Must be None if path is not a string.
|
{storage_options}
|
|
filesystem : fsspec or pyarrow filesystem, default None
|
Filesystem object to use when reading the parquet file. Only implemented
|
for ``engine="pyarrow"``.
|
|
.. versionadded:: 2.1.0
|
|
kwargs
|
Additional keyword arguments passed to the engine
|
|
Returns
|
-------
|
bytes if no path argument is provided else None
|
N)rY r ru r1 rv )r9 r( r0 r ÚBytesIOr[ Úgetvalue) rS rF r* rY r r1 ru rv rZ ÚimplÚ path_or_bufs r/ Ú
|
to_parquetr s ôB .¤#Ô&Ø(Ð)Ü fÓ DàAEÀ ´·±´ ÐSWKàDJJØ
|
Øð ð ØØ%Ø'Ø ñ ð ò ð |Ü+¤r§z¡zÔ2Ð2Ð2Ø×#Ñ#Ó%Ð%àrU c
|
óê t |« } |t j ur0d}
|
|du r|
|
dz }
|
t j |
|
t
|
t « ¬« nd}t |« | j | f||||||d|¤S )a¢
|
Load a parquet object from the file path, returning a DataFrame.
|
|
Parameters
|
----------
|
path : str, path object or file-like object
|
String, path object (implementing ``os.PathLike[str]``), or file-like
|
object implementing a binary ``read()`` function.
|
The string could be a URL. Valid URL schemes include http, ftp, s3,
|
gs, and file. For file URLs, a host is expected. A local file could be:
|
``file://localhost/path/to/table.parquet``.
|
A file URL can also be a path to a directory that contains multiple
|
partitioned parquet files. Both pyarrow and fastparquet support
|
paths to directories as well as file URLs. A directory path could be:
|
``file://localhost/path/to/tables`` or ``s3://bucket/partition_dir``.
|
engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
|
Parquet library to use. If 'auto', then the option
|
``io.parquet.engine`` is used. The default ``io.parquet.engine``
|
behavior is to try 'pyarrow', falling back to 'fastparquet' if
|
'pyarrow' is unavailable.
|
|
When using the ``'pyarrow'`` engine and no storage options are provided
|
and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec``
|
(e.g. "s3://"), then the ``pyarrow.fs`` filesystem is attempted first.
|
Use the filesystem keyword with an instantiated fsspec filesystem
|
if you wish to use its implementation.
|
columns : list, default=None
|
If not None, only these columns will be read from the file.
|
{storage_options}
|
|
.. versionadded:: 1.3.0
|
|
use_nullable_dtypes : bool, default False
|
If True, use dtypes that use ``pd.NA`` as missing value indicator
|
for the resulting DataFrame. (only applicable for the ``pyarrow``
|
engine)
|
As new dtypes are added that support ``pd.NA`` in the future, the
|
output with this option will change to use those dtypes.
|
Note: this is an experimental option, and behaviour (e.g. additional
|
support dtypes) may change without notice.
|
|
.. deprecated:: 2.0
|
|
dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
|
Back-end data type applied to the resultant :class:`DataFrame`
|
(still experimental). Behaviour is as follows:
|
|
* ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
|
(default).
|
* ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
|
DataFrame.
|
|
.. versionadded:: 2.0
|
|
filesystem : fsspec or pyarrow filesystem, default None
|
Filesystem object to use when reading the parquet file. Only implemented
|
for ``engine="pyarrow"``.
|
|
.. versionadded:: 2.1.0
|
|
filters : List[Tuple] or List[List[Tuple]], default None
|
To filter out data.
|
Filter syntax: [[(column, op, val), ...],...]
|
where op is [==, =, >, >=, <, <=, !=, in, not in]
|
The innermost tuples are transposed into a set of filters applied
|
through an `AND` operation.
|
The outer list combines these sets of filters through an `OR`
|
operation.
|
A single list of tuples can also be used, meaning that no `OR`
|
operation between set of filters is to be conducted.
|
|
Using this argument will NOT result in row-wise filtering of the final
|
partitions unless ``engine="pyarrow"`` is also specified. For
|
other engines, filtering is only performed at the partition level, that is,
|
to prevent the loading of some row-groups and/or files.
|
|
.. versionadded:: 2.1.0
|
|
**kwargs
|
Any additional kwargs are passed to the engine.
|
|
Returns
|
-------
|
DataFrame
|
|
See Also
|
--------
|
DataFrame.to_parquet : Create a parquet object that serializes a DataFrame.
|
|
Examples
|
--------
|
>>> original_df = pd.DataFrame(
|
... {{"foo": range(5), "bar": range(5, 10)}}
|
... )
|
>>> original_df
|
foo bar
|
0 0 5
|
1 1 6
|
2 2 7
|
3 3 8
|
4 4 9
|
>>> df_parquet_bytes = original_df.to_parquet()
|
>>> from io import BytesIO
|
>>> restored_df = pd.read_parquet(BytesIO(df_parquet_bytes))
|
>>> restored_df
|
foo bar
|
0 0 5
|
1 1 6
|
2 2 7
|
3 3 8
|
4 4 9
|
>>> restored_df.equals(original_df)
|
True
|
>>> restored_bar = pd.read_parquet(BytesIO(df_parquet_bytes), columns=["bar"])
|
>>> restored_bar
|
bar
|
0 5
|
1 6
|
2 7
|
3 8
|
4 9
|
>>> restored_bar.equals(original_df[['bar']])
|
True
|
|
The function uses `kwargs` that are passed directly to the engine.
|
In the following example, we use the `filters` argument of the pyarrow
|
engine to filter the rows of the DataFrame.
|
|
Since `pyarrow` is the default engine, we can omit the `engine` argument.
|
Note that the `filters` argument is implemented by the `pyarrow` engine,
|
which can benefit from multithreading and also potentially be more
|
economical in terms of memory.
|
|
>>> sel = [("foo", ">", 2)]
|
>>> restored_part = pd.read_parquet(BytesIO(df_parquet_bytes), filters=sel)
|
>>> restored_part
|
foo bar
|
0 3 8
|
1 4 9
|
zYThe argument 'use_nullable_dtypes' is deprecated and will be removed in a future version.TzFUse dtype_backend='numpy_nullable' instead of use_nullable_dtype=True.)Ú
|
stacklevelF)r^ r r1 r r rv ) r0 r
|
r¨ ÚwarningsÚwarnÚ FutureWarningr r r_ ) rF r* r^ r1 r r rv r rZ rÀ Úmsgs r/ Ú read_parquetrÉ ô s ôr fÓ Dà¤#§.¡.Ñ0ð #ð ð $Ñ &Ø ØXñ Cô c=Ô5EÓ5GÖHà #ÐÜ Ô&à 499Ø ð àØØ'Ø /Ø#Ø ñ ð ñ ð rU )r* r( r` r )Nr6 F) rF z1FilePath | ReadBuffer[bytes] | WriteBuffer[bytes]rI r r1 r¤ rJ r( rK r¦ r` zVtuple[FilePath | ReadBuffer[bytes] | WriteBuffer[bytes], IOHandles[bytes] | None, Any])Nr! r¡ NNNN)rS r rF z$FilePath | WriteBuffer[bytes] | Noner* r( rY r¢ r r£ r1 r¤ ru r¥ rv r r` z bytes | None)rF z FilePath | ReadBuffer[bytes]r* r( r^ r¥ r1 r¤ r zbool | lib.NoDefaultr r§ rv r r z&list[tuple] | list[list[tuple]] | Noner` r )6Ú__doc__Ú
|
__future__r r r{ rE Útypingr r r rÅ r r Úpandas._config.configr Ú pandas._libsr
|
Úpandas.compat._optionalr Ú pandas.errorsr Úpandas.util._decoratorsr Úpandas.util._exceptionsr Úpandas.util._validatorsr rm r r Úpandas.core.shared_docsr Úpandas.io._utilr Úpandas.io.commonr r r r r Úpandas._typingr r r r r r0 rP r r% r& r r¨ rÉ re rU r/ ú<module>rØ s ðÙ Ý "ã Û Û ÷ñ ó
|
÷õ
|
.å Ý >Ý -Ý 'Ý 4Ý 7÷õ 1å 1÷õ ñ ÷õ ó GðJ .2ØØð <'Ø
|
;ð<'à ð<'ð +ð<'ð ð <'ð
|
ð <'ð ó <'÷~
|
(ñ
|
(ôE (ô E ôPn hô n ñb \Ð"3Ñ4Ô5ð 26ØØ &Ø Ø-1Ø'+ØðUØðUà
|
.ðUð ðUð ð Uð
|
ð Uð +ð Uð %ðUð ðUð òUó 6ðUñp \Ð"3Ñ4Ô5ð Ø $Ø-1Ø03·±Ø25·.±.ØØ6:ðqØ
|
&ðqà ðqð ðqð +ð qð
|
.ð qð 0ð qð ðqð 4ðqð òqó 6ñqrU
|
