MhHSrSSKJr SSKrSSKJrJr SSKrSSKJ r SSK J r SSK Jr SSKJr \(aSS KJrJrJrJrJr SS KJrJr \ "\S \S S -S9S\R4S4SSjj5r\ "\S \SS -S9SSSjj5rg)zpickle compat ) annotationsN) TYPE_CHECKINGAny) pickle_compat)doc) _shared_docs) get_handle)CompressionOptionsFilePathReadPickleBufferStorageOptions WriteBuffer) DataFrameSeriesstorage_optionscompression_optionsfilepath_or_buffer)rrinfercUS:a[Rn[USUSUS9n[R"XRUS9 SSS5 g!,(df  g=f)a} Pickle (serialize) object to file. Parameters ---------- obj : any object Any python object. filepath_or_buffer : str, path object, or file-like object String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``write()`` function. Also accepts URL. URL has to be of S3 or GCS. {compression_options} .. versionchanged:: 1.4.0 Zstandard support. protocol : int Int which indicates which protocol should be used by the pickler, default HIGHEST_PROTOCOL (see [1], paragraph 12.1.2). The possible values for this parameter depend on the version of Python. For Python 2.x, possible values are 0, 1, 2. For Python>=3.0, 3 is a valid value. For Python >= 3.4, 4 is a valid value. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL. {storage_options} .. [1] https://docs.python.org/3/library/pickle.html See Also -------- read_pickle : Load pickled pandas object (or any object) from file. DataFrame.to_hdf : Write DataFrame to an HDF5 file. DataFrame.to_sql : Write DataFrame to a SQL database. DataFrame.to_parquet : Write a DataFrame to the binary parquet format. Examples -------- >>> original_df = pd.DataFrame({{"foo": range(5), "bar": range(5, 10)}}) # doctest: +SKIP >>> original_df # doctest: +SKIP foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 >>> pd.to_pickle(original_df, "./dummy.pkl") # doctest: +SKIP >>> unpickled_df = pd.read_pickle("./dummy.pkl") # doctest: +SKIP >>> unpickled_df # doctest: +SKIP foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 rwbF compressionis_textr)protocolN)pickleHIGHEST_PROTOCOLr dumphandle)objrrrrhandless B/var/www/html/env/lib/python3.13/site-packages/pandas/io/pickle.py to_pickler"!sUF!|**  '    C(;   s A  Adecompression_options)rr#cB[[[[4n[ USUSUS9n[ R "SS9 [ R"S[5 [R"UR5sSSS5 sSSS5 $!,(df  Oo=f!Ua+ [R"URSS9ssSSS5 $f=f![a+ [R"URS S9ssSSS5 $f=fSSS5 g!,(df  g=f) a  Load pickled pandas object (or any object) from file. .. warning:: Loading pickled data received from untrusted sources can be unsafe. See `here `__. Parameters ---------- filepath_or_buffer : str, path object, or file-like object String, path object (implementing ``os.PathLike[str]``), or file-like object implementing a binary ``readlines()`` function. Also accepts URL. URL is not limited to S3 and GCS. {decompression_options} .. versionchanged:: 1.4.0 Zstandard support. {storage_options} Returns ------- same type as object stored in file See Also -------- DataFrame.to_pickle : Pickle (serialize) DataFrame object to file. Series.to_pickle : Pickle (serialize) Series object to file. read_hdf : Read HDF5 file into a DataFrame. read_sql : Read SQL query or database table into a DataFrame. read_parquet : Load a parquet object, returning a DataFrame. Notes ----- read_pickle is only guaranteed to be backwards compatible to pandas 0.20.3 provided the object was serialized with to_pickle. Examples -------- >>> original_df = pd.DataFrame( ... {{"foo": range(5), "bar": range(5, 10)}} ... ) # doctest: +SKIP >>> original_df # doctest: +SKIP foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 >>> pd.to_pickle(original_df, "./dummy.pkl") # doctest: +SKIP >>> unpickled_df = pd.read_pickle("./dummy.pkl") # doctest: +SKIP >>> unpickled_df # doctest: +SKIP foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 rbFrT)recordignoreN)encodingzlatin-1)AttributeError ImportErrorModuleNotFoundError TypeErrorr warningscatch_warnings simplefilterWarningrloadrpcUnicodeDecodeError)rrr excs_to_catchr s r! read_pickler5rsL$[2EyQM  '     ? >,,D9))(G<!;;w~~6:9  :9! >www~~==-  $ > " ?777>>I> >3  . ?:   scDB;B 6 B B BDB%C ?C C  C)D7DDD D) rrrzFilePath | WriteBuffer[bytes]rr rintrStorageOptions | NonereturnNone)rN)rzFilePath | ReadPickleBufferrr rr7r8zDataFrame | Series)__doc__ __future__rrtypingrrr- pandas.compatrr2pandas.util._decoratorsrpandas.core.shared_docsrpandas.io.commonr pandas._typingr r r r rpandasrrrr"r5r!rEs" -'0'  !23$%:;>RR'.++-1 J< J<5J<$J< J< + J<  J< J?BVV '.-1\?3\?#\?+\? \? \?rD