""" Support pre-0.12 series pickle compatibility. """ from __future__ import annotations import contextlib import copy import io import pickle as pkl from typing import TYPE_CHECKING import warnings import numpy as np from pandas._libs.arrays import NDArrayBacked from pandas._libs.tslibs import BaseOffset from pandas import Index from pandas.core.arrays import ( DatetimeArray, PeriodArray, TimedeltaArray, ) from pandas.core.internals import BlockManager if TYPE_CHECKING: from pandas import ( DataFrame, Series, ) def load_reduce(self): stack = self.stack args = stack.pop() func = stack[-1] try: stack[-1] = func(*args) return except TypeError as err: # If we have a deprecated function, # try to replace and try again. msg = "_reconstruct: First argument must be a sub-type of ndarray" if msg in str(err): try: cls = args[0] stack[-1] = object.__new__(cls) return except TypeError: pass elif args and isinstance(args[0], type) and issubclass(args[0], BaseOffset): # TypeError: object.__new__(Day) is not safe, use Day.__new__() cls = args[0] stack[-1] = cls.__new__(*args) return elif args and issubclass(args[0], PeriodArray): cls = args[0] stack[-1] = NDArrayBacked.__new__(*args) return raise _sparse_msg = """\ Loading a saved '{cls}' as a {new} with sparse values. '{cls}' is now removed. You should re-save this dataset in its new format. """ class _LoadSparseSeries: # To load a SparseSeries as a Series[Sparse] # https://github.com/python/mypy/issues/1020 # error: Incompatible return type for "__new__" (returns "Series", but must return # a subtype of "_LoadSparseSeries") def __new__(cls) -> Series: # type: ignore[misc] from pandas import Series warnings.warn( _sparse_msg.format(cls="SparseSeries", new="Series"), FutureWarning, stacklevel=6, ) return Series(dtype=object) class _LoadSparseFrame: # To load a SparseDataFrame as a DataFrame[Sparse] # https://github.com/python/mypy/issues/1020 # error: Incompatible return type for "__new__" (returns "DataFrame", but must # return a subtype of "_LoadSparseFrame") def __new__(cls) -> DataFrame: # type: ignore[misc] from pandas import DataFrame warnings.warn( _sparse_msg.format(cls="SparseDataFrame", new="DataFrame"), FutureWarning, stacklevel=6, ) return DataFrame() # If classes are moved, provide compat here. _class_locations_map = { ("pandas.core.sparse.array", "SparseArray"): ("pandas.core.arrays", "SparseArray"), # 15477 ("pandas.core.base", "FrozenNDArray"): ("numpy", "ndarray"), ("pandas.core.indexes.frozen", "FrozenNDArray"): ("numpy", "ndarray"), ("pandas.core.base", "FrozenList"): ("pandas.core.indexes.frozen", "FrozenList"), # 10890 ("pandas.core.series", "TimeSeries"): ("pandas.core.series", "Series"), ("pandas.sparse.series", "SparseTimeSeries"): ( "pandas.core.sparse.series", "SparseSeries", ), # 12588, extensions moving ("pandas._sparse", "BlockIndex"): ("pandas._libs.sparse", "BlockIndex"), ("pandas.tslib", "Timestamp"): ("pandas._libs.tslib", "Timestamp"), # 18543 moving period ("pandas._period", "Period"): ("pandas._libs.tslibs.period", "Period"), ("pandas._libs.period", "Period"): ("pandas._libs.tslibs.period", "Period"), # 18014 moved __nat_unpickle from _libs.tslib-->_libs.tslibs.nattype ("pandas.tslib", "__nat_unpickle"): ( "pandas._libs.tslibs.nattype", "__nat_unpickle", ), ("pandas._libs.tslib", "__nat_unpickle"): ( "pandas._libs.tslibs.nattype", "__nat_unpickle", ), # 15998 top-level dirs moving ("pandas.sparse.array", "SparseArray"): ( "pandas.core.arrays.sparse", "SparseArray", ), ("pandas.sparse.series", "SparseSeries"): ( "pandas.compat.pickle_compat", "_LoadSparseSeries", ), ("pandas.sparse.frame", "SparseDataFrame"): ( "pandas.core.sparse.frame", "_LoadSparseFrame", ), ("pandas.indexes.base", "_new_Index"): ("pandas.core.indexes.base", "_new_Index"), ("pandas.indexes.base", "Index"): ("pandas.core.indexes.base", "Index"), ("pandas.indexes.numeric", "Int64Index"): ( "pandas.core.indexes.numeric", "Int64Index", ), ("pandas.indexes.range", "RangeIndex"): ("pandas.core.indexes.range", "RangeIndex"), ("pandas.indexes.multi", "MultiIndex"): ("pandas.core.indexes.multi", "MultiIndex"), ("pandas.tseries.index", "_new_DatetimeIndex"): ( "pandas.core.indexes.datetimes", "_new_DatetimeIndex", ), ("pandas.tseries.index", "DatetimeIndex"): ( "pandas.core.indexes.datetimes", "DatetimeIndex", ), ("pandas.tseries.period", "PeriodIndex"): ( "pandas.core.indexes.period", "PeriodIndex", ), # 19269, arrays moving ("pandas.core.categorical", "Categorical"): ("pandas.core.arrays", "Categorical"), # 19939, add timedeltaindex, float64index compat from 15998 move ("pandas.tseries.tdi", "TimedeltaIndex"): ( "pandas.core.indexes.timedeltas", "TimedeltaIndex", ), ("pandas.indexes.numeric", "Float64Index"): ( "pandas.core.indexes.numeric", "Float64Index", ), ("pandas.core.sparse.series", "SparseSeries"): ( "pandas.compat.pickle_compat", "_LoadSparseSeries", ), ("pandas.core.sparse.frame", "SparseDataFrame"): ( "pandas.compat.pickle_compat", "_LoadSparseFrame", ), } # our Unpickler sub-class to override methods and some dispatcher # functions for compat and uses a non-public class of the pickle module. class Unpickler(pkl._Unpickler): def find_class(self, module, name): # override superclass key = (module, name) module, name = _class_locations_map.get(key, key) return super().find_class(module, name) Unpickler.dispatch = copy.copy(Unpickler.dispatch) Unpickler.dispatch[pkl.REDUCE[0]] = load_reduce def load_newobj(self): args = self.stack.pop() cls = self.stack[-1] # compat if issubclass(cls, Index): obj = object.__new__(cls) elif issubclass(cls, DatetimeArray) and not args: arr = np.array([], dtype="M8[ns]") obj = cls.__new__(cls, arr, arr.dtype) elif issubclass(cls, TimedeltaArray) and not args: arr = np.array([], dtype="m8[ns]") obj = cls.__new__(cls, arr, arr.dtype) elif cls is BlockManager and not args: obj = cls.__new__(cls, (), [], False) else: obj = cls.__new__(cls, *args) self.stack[-1] = obj Unpickler.dispatch[pkl.NEWOBJ[0]] = load_newobj def load_newobj_ex(self): kwargs = self.stack.pop() args = self.stack.pop() cls = self.stack.pop() # compat if issubclass(cls, Index): obj = object.__new__(cls) else: obj = cls.__new__(cls, *args, **kwargs) self.append(obj) try: Unpickler.dispatch[pkl.NEWOBJ_EX[0]] = load_newobj_ex except (AttributeError, KeyError): pass def load(fh, encoding: str | None = None, is_verbose: bool = False): """ Load a pickle, with a provided encoding, Parameters ---------- fh : a filelike object encoding : an optional encoding is_verbose : show exception output """ try: fh.seek(0) if encoding is not None: up = Unpickler(fh, encoding=encoding) else: up = Unpickler(fh) # "Unpickler" has no attribute "is_verbose" [attr-defined] up.is_verbose = is_verbose # type: ignore[attr-defined] return up.load() except (ValueError, TypeError): raise def loads( bytes_object: bytes, *, fix_imports: bool = True, encoding: str = "ASCII", errors: str = "strict", ): """ Analogous to pickle._loads. """ fd = io.BytesIO(bytes_object) return Unpickler( fd, fix_imports=fix_imports, encoding=encoding, errors=errors ).load() @contextlib.contextmanager def patch_pickle(): """ Temporarily patch pickle to use our unpickler. """ orig_loads = pkl.loads try: setattr(pkl, "loads", loads) yield finally: setattr(pkl, "loads", orig_loads)