""" pickle compat """ from __future__ import annotations import pickle from typing import ( TYPE_CHECKING, Any, ) import warnings from pandas.compat import pickle_compat as pc from pandas.util._decorators import doc from pandas.core.shared_docs import _shared_docs from pandas.io.common import get_handle if TYPE_CHECKING: from pandas._typing import ( CompressionOptions, FilePath, ReadPickleBuffer, StorageOptions, WriteBuffer, ) from pandas import ( DataFrame, Series, ) @doc( storage_options=_shared_docs["storage_options"], compression_options=_shared_docs["compression_options"] % "filepath_or_buffer", ) def to_pickle( obj: Any, filepath_or_buffer: FilePath | WriteBuffer[bytes], compression: CompressionOptions = "infer", protocol: int = pickle.HIGHEST_PROTOCOL, storage_options: StorageOptions | None = None, ) -> None: """ 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} .. versionadded:: 1.2.0 .. [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 """ # noqa: E501 if protocol < 0: protocol = pickle.HIGHEST_PROTOCOL with get_handle( filepath_or_buffer, "wb", compression=compression, is_text=False, storage_options=storage_options, ) as handles: # letting pickle write directly to the buffer is more memory-efficient pickle.dump(obj, handles.handle, protocol=protocol) @doc( storage_options=_shared_docs["storage_options"], decompression_options=_shared_docs["decompression_options"] % "filepath_or_buffer", ) def read_pickle( filepath_or_buffer: FilePath | ReadPickleBuffer, compression: CompressionOptions = "infer", storage_options: StorageOptions | None = None, ) -> DataFrame | Series: """ 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} .. versionadded:: 1.2.0 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 """ excs_to_catch = (AttributeError, ImportError, ModuleNotFoundError, TypeError) with get_handle( filepath_or_buffer, "rb", compression=compression, is_text=False, storage_options=storage_options, ) as handles: # 1) try standard library Pickle # 2) try pickle_compat (older pandas version) to handle subclass changes # 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError try: # TypeError for Cython complaints about object.__new__ vs Tick.__new__ try: with warnings.catch_warnings(record=True): # We want to silence any warnings about, e.g. moved modules. warnings.simplefilter("ignore", Warning) return pickle.load(handles.handle) except excs_to_catch: # e.g. # "No module named 'pandas.core.sparse.series'" # "Can't get attribute '__nat_unpickle' on