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dashboard/venv/lib/python3.12/site-packages/pandas/io/pickle.py
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dashboard/venv/lib/python3.12/site-packages/pandas/io/pickle.py
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"""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
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from pandas.util._decorators import set_module
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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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@set_module("pandas")
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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 : str or dict, default 'infer'
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For on-the-fly compression of the output data. If 'infer' and
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'filepath_or_buffer' is path-like, then detect compression from the
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following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar',
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'.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression).
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Set to ``None`` for no compression.
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Can also be a dict with key ``'method'`` set
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to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``,
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``'tar'``} and other key-value pairs are forwarded to
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``zipfile.ZipFile``, ``gzip.GzipFile``,
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``bz2.BZ2File``, ``zstandard.ZstdCompressor``, ``lzma.LZMAFile`` or
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``tarfile.TarFile``, respectively.
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As an example, the following could be passed for faster compression
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and to create a reproducible gzip archive:
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``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``.
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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 : dict, optional
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Extra options that make sense for a particular storage connection, e.g.
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host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
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are forwarded to ``urllib.request.Request`` as header options. For other
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URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
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forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
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details, and for more examples on storage options refer `here
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<https://pandas.pydata.org/docs/user_guide/io.html?
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highlight=storage_options#reading-writing-remote-files>`_.
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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(
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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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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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@set_module("pandas")
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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 and return unpickled object.
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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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compression : str or dict, default 'infer'
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For on-the-fly decompression of on-disk data. If 'infer' and
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'filepath_or_buffer' is path-like, then detect compression from the
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following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar',
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'.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression).
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If using 'zip' or 'tar', the ZIP file must contain only one data file
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to be read in.
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Set to ``None`` for no decompression.
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Can also be a dict with key ``'method'`` set
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to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``,
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``'tar'``} and other key-value pairs are forwarded to
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``zipfile.ZipFile``, ``gzip.GzipFile``,
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``bz2.BZ2File``, ``zstandard.ZstdDecompressor``, ``lzma.LZMAFile`` or
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``tarfile.TarFile``, respectively.
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As an example, the following could be passed for Zstandard decompression
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using a custom compression dictionary:
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``compression={'method': 'zstd', 'dict_data': my_compression_dict}``.
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storage_options : dict, optional
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Extra options that make sense for a particular storage connection, e.g.
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host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
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are forwarded to ``urllib.request.Request`` as header options. For other
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URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
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forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
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details, and for more examples on storage options refer `here
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<https://pandas.pydata.org/docs/user_guide/io.html?
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highlight=storage_options#reading-writing-remote-files>`_.
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Returns
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-------
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object
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The unpickled pandas object (or any object) that was 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 1.0
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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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# TypeError for Cython complaints about object.__new__ vs Tick.__new__
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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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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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handles.handle.seek(0)
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return pickle_compat.Unpickler(handles.handle).load()
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