fr/fr_env/lib/python3.8/site-packages/pandas/io/pickle.py

212 lines
6.9 KiB
Python

""" pickle compat """
import pickle
from typing import Any
import warnings
from pandas._typing import CompressionOptions, FilePathOrBuffer, StorageOptions
from pandas.compat import pickle_compat as pc
from pandas.util._decorators import doc
from pandas.core import generic
from pandas.io.common import get_handle
@doc(storage_options=generic._shared_docs["storage_options"])
def to_pickle(
obj: Any,
filepath_or_buffer: FilePathOrBuffer,
compression: CompressionOptions = "infer",
protocol: int = pickle.HIGHEST_PROTOCOL,
storage_options: StorageOptions = None,
):
"""
Pickle (serialize) object to file.
Parameters
----------
obj : any object
Any python object.
filepath_or_buffer : str, path object or file-like object
File path, URL, or buffer where the pickled object will be stored.
.. versionchanged:: 1.0.0
Accept URL. URL has to be of S3 or GCS.
compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, default 'infer'
If 'infer' and 'path_or_url' is path-like, then detect compression from
the following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
compression) If 'infer' and 'path_or_url' is not path-like, then use
None (= no decompression).
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)}})
>>> original_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> pd.to_pickle(original_df, "./dummy.pkl")
>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> import os
>>> os.remove("./dummy.pkl")
"""
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:
if handles.compression["method"] in ("bz2", "xz") and protocol >= 5:
# some weird TypeError GH#39002 with pickle 5: fallback to letting
# pickle create the entire object and then write it to the buffer.
# "zip" would also be here if pandas.io.common._BytesZipFile
# wouldn't buffer write calls
handles.handle.write(
pickle.dumps(obj, protocol=protocol) # type: ignore[arg-type]
)
else:
# letting pickle write directly to the buffer is more memory-efficient
pickle.dump(
obj, handles.handle, protocol=protocol # type: ignore[arg-type]
)
@doc(storage_options=generic._shared_docs["storage_options"])
def read_pickle(
filepath_or_buffer: FilePathOrBuffer,
compression: CompressionOptions = "infer",
storage_options: StorageOptions = None,
):
"""
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
File path, URL, or buffer where the pickled object will be loaded from.
.. versionchanged:: 1.0.0
Accept URL. URL is not limited to S3 and GCS.
compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, default 'infer'
If 'infer' and 'path_or_url' is path-like, then detect compression from
the following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
compression) If 'infer' and 'path_or_url' is not path-like, then use
None (= no decompression).
{storage_options}
.. versionadded:: 1.2.0
Returns
-------
unpickled : 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.
Examples
--------
>>> original_df = pd.DataFrame({{"foo": range(5), "bar": range(5, 10)}})
>>> original_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> pd.to_pickle(original_df, "./dummy.pkl")
>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> import os
>>> os.remove("./dummy.pkl")
"""
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) # type: ignore[arg-type]
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")