+ NV-j#Rt^RIHt^RIt^RIHtHt^RIt^RIH t ^RI H t ^RI H t ]'d^RIHtHtHtHtHt^RIHtHt] !R 4R ]P.R3R R ll4t] !R 4RR Rll4tR#)z pickle compat) annotationsN) TYPE_CHECKINGAny) pickle_compat) set_module) get_handle)CompressionOptionsFilePathReadPickleBufferStorageOptions WriteBuffer) DataFrameSeriespandasinferc 0V^8dQhRRRRRRRRR R R R /#) objrfilepath_or_bufferzFilePath | WriteBuffer[bytes] compressionrprotocolintstorage_optionsStorageOptions | NonereturnNone)formats"a/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/pandas/io/pickle.py __annotate__r!sL^<^< ^<5^<$^< ^< + ^<  ^<cV^8d\Pp\VRVRVR7;_uu_4p\P!WPVR7RRR4R# +'giR#;i)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 : str or dict, default 'infer' For on-the-fly compression of the output data. If 'infer' and 'filepath_or_buffer' is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression). Set to ``None`` for no compression. Can also be a dict with key ``'method'`` set to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and other key-value pairs are forwarded to ``zipfile.ZipFile``, ``gzip.GzipFile``, ``bz2.BZ2File``, ``zstandard.ZstdCompressor``, ``lzma.LZMAFile`` or ``tarfile.TarFile``, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: ``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``. 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 : dict, optional Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to ``urllib.request.Request`` as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more details, and for more examples on storage options refer `here `_. .. [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 wbFris_textr)rN)pickleHIGHEST_PROTOCOLrdumphandle)rrrrrhandless&&&&& r to_pickler* s_h!|**  '     C(;    s #A A. c(V^8dQhRRRRRRRR/#) rrzFilePath | ReadPickleBufferrrrrrzDataFrame | Seriesr)rs"rrrs<lBlB3lB#lB+lB lBr c d\\\\3p\ VRVRVR7;_uu_4p\ P !RR7;_uu_4\ P!R\4\P!VP4uuRRR4uuRRR4# +'giMc;i TdWTPP^4\P!TP4P4uuuRRR4#i;iRRR4R# +'giR#;i)a" Load pickled pandas object (or any object) from file and return unpickled object. .. 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. compression : str or dict, default 'infer' For on-the-fly decompression of on-disk data. If 'infer' and 'filepath_or_buffer' is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression). If using 'zip' or 'tar', the ZIP file must contain only one data file to be read in. Set to ``None`` for no decompression. Can also be a dict with key ``'method'`` set to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and other key-value pairs are forwarded to ``zipfile.ZipFile``, ``gzip.GzipFile``, ``bz2.BZ2File``, ``zstandard.ZstdDecompressor``, ``lzma.LZMAFile`` or ``tarfile.TarFile``, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary: ``compression={'method': 'zstd', 'dict_data': my_compression_dict}``. storage_options : dict, optional Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to ``urllib.request.Request`` as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more details, and for more examples on storage options refer `here `_. Returns ------- object The unpickled pandas object (or any object) that was 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 1.0 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 rbFr#T)recordignoreN)AttributeError ImportErrorModuleNotFoundError TypeErrorrwarningscatch_warnings simplefilterWarningr%loadr(seekr Unpickler)rrr excs_to_catchr)s&&& r read_pickler<sr$[2EyQM  '     B((55%%h8{{7>>265  65 B NN   " **7>>:??A A'   B 6    sMDB3;B B3 B0 +B3/D0B33ADDDD D/ )rN)__doc__ __future__rr%typingrrr4 pandas.compatrpandas.util._decoratorsrpandas.io.commonrpandas._typingrr r r r rr rr&r*r<rr rrDs}" '.'  H'.++-1 ^<^