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""" 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}
.. [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 <https://docs.python.org/3/library/pickle.html>`__.
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
"""
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 <module 'pandas._libs.tslib"
return pc.load(handles.handle, encoding=None)
except UnicodeDecodeError:
# e.g. can occur for files written in py27; see GH#28645 and GH#31988
return pc.load(handles.handle, encoding="latin-1")
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