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211 lines
6.4 KiB
Python
211 lines
6.4 KiB
Python
""" pickle compat """
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from __future__ import annotations
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import pickle
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from typing import (
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TYPE_CHECKING,
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Any,
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)
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import warnings
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from pandas.compat import pickle_compat as pc
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from pandas.util._decorators import doc
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from pandas.core.shared_docs import _shared_docs
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from pandas.io.common import get_handle
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if TYPE_CHECKING:
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from pandas._typing import (
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CompressionOptions,
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FilePath,
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ReadPickleBuffer,
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StorageOptions,
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WriteBuffer,
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)
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from pandas import (
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DataFrame,
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Series,
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)
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@doc(
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storage_options=_shared_docs["storage_options"],
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compression_options=_shared_docs["compression_options"] % "filepath_or_buffer",
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)
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def to_pickle(
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obj: Any,
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filepath_or_buffer: FilePath | WriteBuffer[bytes],
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compression: CompressionOptions = "infer",
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protocol: int = pickle.HIGHEST_PROTOCOL,
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storage_options: StorageOptions | None = None,
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) -> None:
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"""
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Pickle (serialize) object to file.
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Parameters
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----------
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obj : any object
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Any python object.
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filepath_or_buffer : str, path object, or file-like object
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String, path object (implementing ``os.PathLike[str]``), or file-like
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object implementing a binary ``write()`` function.
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Also accepts URL. URL has to be of S3 or GCS.
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{compression_options}
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.. versionchanged:: 1.4.0 Zstandard support.
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protocol : int
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Int which indicates which protocol should be used by the pickler,
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default HIGHEST_PROTOCOL (see [1], paragraph 12.1.2). The possible
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values for this parameter depend on the version of Python. For Python
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2.x, possible values are 0, 1, 2. For Python>=3.0, 3 is a valid value.
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For Python >= 3.4, 4 is a valid value. A negative value for the
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protocol parameter is equivalent to setting its value to
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HIGHEST_PROTOCOL.
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{storage_options}
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.. [1] https://docs.python.org/3/library/pickle.html
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See Also
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--------
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read_pickle : Load pickled pandas object (or any object) from file.
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DataFrame.to_hdf : Write DataFrame to an HDF5 file.
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DataFrame.to_sql : Write DataFrame to a SQL database.
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DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
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Examples
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--------
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>>> original_df = pd.DataFrame({{"foo": range(5), "bar": range(5, 10)}}) # doctest: +SKIP
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>>> original_df # doctest: +SKIP
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foo bar
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0 0 5
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1 1 6
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2 2 7
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3 3 8
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4 4 9
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>>> pd.to_pickle(original_df, "./dummy.pkl") # doctest: +SKIP
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>>> unpickled_df = pd.read_pickle("./dummy.pkl") # doctest: +SKIP
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>>> unpickled_df # doctest: +SKIP
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foo bar
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0 0 5
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1 1 6
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2 2 7
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3 3 8
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4 4 9
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""" # noqa: E501
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if protocol < 0:
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protocol = pickle.HIGHEST_PROTOCOL
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with get_handle(
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filepath_or_buffer,
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"wb",
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compression=compression,
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is_text=False,
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storage_options=storage_options,
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) as handles:
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# letting pickle write directly to the buffer is more memory-efficient
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pickle.dump(obj, handles.handle, protocol=protocol)
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@doc(
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storage_options=_shared_docs["storage_options"],
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decompression_options=_shared_docs["decompression_options"] % "filepath_or_buffer",
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)
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def read_pickle(
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filepath_or_buffer: FilePath | ReadPickleBuffer,
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compression: CompressionOptions = "infer",
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storage_options: StorageOptions | None = None,
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) -> DataFrame | Series:
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"""
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Load pickled pandas object (or any object) from file.
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.. warning::
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Loading pickled data received from untrusted sources can be
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unsafe. See `here <https://docs.python.org/3/library/pickle.html>`__.
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Parameters
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----------
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filepath_or_buffer : str, path object, or file-like object
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String, path object (implementing ``os.PathLike[str]``), or file-like
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object implementing a binary ``readlines()`` function.
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Also accepts URL. URL is not limited to S3 and GCS.
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{decompression_options}
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.. versionchanged:: 1.4.0 Zstandard support.
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{storage_options}
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Returns
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-------
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same type as object stored in file
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See Also
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--------
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DataFrame.to_pickle : Pickle (serialize) DataFrame object to file.
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Series.to_pickle : Pickle (serialize) Series object to file.
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read_hdf : Read HDF5 file into a DataFrame.
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read_sql : Read SQL query or database table into a DataFrame.
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read_parquet : Load a parquet object, returning a DataFrame.
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Notes
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-----
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read_pickle is only guaranteed to be backwards compatible to pandas 0.20.3
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provided the object was serialized with to_pickle.
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Examples
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--------
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>>> original_df = pd.DataFrame(
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... {{"foo": range(5), "bar": range(5, 10)}}
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... ) # doctest: +SKIP
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>>> original_df # doctest: +SKIP
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foo bar
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0 0 5
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1 1 6
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2 2 7
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3 3 8
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4 4 9
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>>> pd.to_pickle(original_df, "./dummy.pkl") # doctest: +SKIP
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>>> unpickled_df = pd.read_pickle("./dummy.pkl") # doctest: +SKIP
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>>> unpickled_df # doctest: +SKIP
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foo bar
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0 0 5
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1 1 6
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2 2 7
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3 3 8
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4 4 9
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"""
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excs_to_catch = (AttributeError, ImportError, ModuleNotFoundError, TypeError)
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with get_handle(
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filepath_or_buffer,
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"rb",
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compression=compression,
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is_text=False,
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storage_options=storage_options,
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) as handles:
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# 1) try standard library Pickle
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# 2) try pickle_compat (older pandas version) to handle subclass changes
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# 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError
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try:
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# TypeError for Cython complaints about object.__new__ vs Tick.__new__
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try:
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with warnings.catch_warnings(record=True):
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# We want to silence any warnings about, e.g. moved modules.
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warnings.simplefilter("ignore", Warning)
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return pickle.load(handles.handle)
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except excs_to_catch:
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# e.g.
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# "No module named 'pandas.core.sparse.series'"
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# "Can't get attribute '__nat_unpickle' on <module 'pandas._libs.tslib"
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return pc.load(handles.handle, encoding=None)
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except UnicodeDecodeError:
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# e.g. can occur for files written in py27; see GH#28645 and GH#31988
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return pc.load(handles.handle, encoding="latin-1")
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