206 lines
6.1 KiB
Python
206 lines
6.1 KiB
Python
|
""" pickle compat """
|
||
|
|
import warnings
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from numpy.lib.format import read_array, write_array
|
||
|
|
from pandas.compat import BytesIO, cPickle as pkl, pickle_compat as pc, PY3
|
||
|
|
from pandas.core.dtypes.common import is_datetime64_dtype, _NS_DTYPE
|
||
|
|
from pandas.io.common import _get_handle, _infer_compression, _stringify_path
|
||
|
|
|
||
|
|
|
||
|
|
def to_pickle(obj, path, compression='infer', protocol=pkl.HIGHEST_PROTOCOL):
|
||
|
|
"""
|
||
|
|
Pickle (serialize) object to file.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
obj : any object
|
||
|
|
Any python object.
|
||
|
|
path : str
|
||
|
|
File path where the pickled object will be stored.
|
||
|
|
compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
|
||
|
|
A string representing the compression to use in the output file. By
|
||
|
|
default, infers from the file extension in specified path.
|
||
|
|
|
||
|
|
.. versionadded:: 0.20.0
|
||
|
|
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.
|
||
|
|
|
||
|
|
.. [1] https://docs.python.org/3/library/pickle.html
|
||
|
|
.. versionadded:: 0.21.0
|
||
|
|
|
||
|
|
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")
|
||
|
|
"""
|
||
|
|
path = _stringify_path(path)
|
||
|
|
inferred_compression = _infer_compression(path, compression)
|
||
|
|
f, fh = _get_handle(path, 'wb',
|
||
|
|
compression=inferred_compression,
|
||
|
|
is_text=False)
|
||
|
|
if protocol < 0:
|
||
|
|
protocol = pkl.HIGHEST_PROTOCOL
|
||
|
|
try:
|
||
|
|
f.write(pkl.dumps(obj, protocol=protocol))
|
||
|
|
finally:
|
||
|
|
for _f in fh:
|
||
|
|
_f.close()
|
||
|
|
|
||
|
|
|
||
|
|
def read_pickle(path, compression='infer'):
|
||
|
|
"""
|
||
|
|
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
|
||
|
|
----------
|
||
|
|
path : str
|
||
|
|
File path where the pickled object will be loaded.
|
||
|
|
compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
|
||
|
|
For on-the-fly decompression of on-disk data. If 'infer', then use
|
||
|
|
gzip, bz2, xz or zip if path ends in '.gz', '.bz2', '.xz',
|
||
|
|
or '.zip' respectively, and no decompression otherwise.
|
||
|
|
Set to None for no decompression.
|
||
|
|
|
||
|
|
.. versionadded:: 0.20.0
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
unpickled : type of 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.
|
||
|
|
|
||
|
|
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")
|
||
|
|
"""
|
||
|
|
path = _stringify_path(path)
|
||
|
|
inferred_compression = _infer_compression(path, compression)
|
||
|
|
|
||
|
|
def read_wrapper(func):
|
||
|
|
# wrapper file handle open/close operation
|
||
|
|
f, fh = _get_handle(path, 'rb',
|
||
|
|
compression=inferred_compression,
|
||
|
|
is_text=False)
|
||
|
|
try:
|
||
|
|
return func(f)
|
||
|
|
finally:
|
||
|
|
for _f in fh:
|
||
|
|
_f.close()
|
||
|
|
|
||
|
|
def try_read(path, encoding=None):
|
||
|
|
# try with cPickle
|
||
|
|
# try with current pickle, if we have a Type Error then
|
||
|
|
# try with the compat pickle to handle subclass changes
|
||
|
|
# pass encoding only if its not None as py2 doesn't handle
|
||
|
|
# the param
|
||
|
|
|
||
|
|
# cpickle
|
||
|
|
# GH 6899
|
||
|
|
try:
|
||
|
|
with warnings.catch_warnings(record=True):
|
||
|
|
# We want to silencce any warnings about, e.g. moved modules.
|
||
|
|
return read_wrapper(lambda f: pkl.load(f))
|
||
|
|
except Exception:
|
||
|
|
# reg/patched pickle
|
||
|
|
try:
|
||
|
|
return read_wrapper(
|
||
|
|
lambda f: pc.load(f, encoding=encoding, compat=False))
|
||
|
|
# compat pickle
|
||
|
|
except:
|
||
|
|
return read_wrapper(
|
||
|
|
lambda f: pc.load(f, encoding=encoding, compat=True))
|
||
|
|
try:
|
||
|
|
return try_read(path)
|
||
|
|
except:
|
||
|
|
if PY3:
|
||
|
|
return try_read(path, encoding='latin1')
|
||
|
|
raise
|
||
|
|
|
||
|
|
|
||
|
|
# compat with sparse pickle / unpickle
|
||
|
|
|
||
|
|
|
||
|
|
def _pickle_array(arr):
|
||
|
|
arr = arr.view(np.ndarray)
|
||
|
|
|
||
|
|
buf = BytesIO()
|
||
|
|
write_array(buf, arr)
|
||
|
|
|
||
|
|
return buf.getvalue()
|
||
|
|
|
||
|
|
|
||
|
|
def _unpickle_array(bytes):
|
||
|
|
arr = read_array(BytesIO(bytes))
|
||
|
|
|
||
|
|
# All datetimes should be stored as M8[ns]. When unpickling with
|
||
|
|
# numpy1.6, it will read these as M8[us]. So this ensures all
|
||
|
|
# datetime64 types are read as MS[ns]
|
||
|
|
if is_datetime64_dtype(arr):
|
||
|
|
arr = arr.view(_NS_DTYPE)
|
||
|
|
|
||
|
|
return arr
|