abaqus 调用外部模块pandas的方法， 这里是abaqus2024的方法，亲测好用

# pyright: reportPropertyTypeMismatch=false from __future__ import annotations import collections from copy import deepcopy import datetime as dt from functools import partial import gc from json import loads import operator import pickle import re import sys from typing import ( TYPE_CHECKING, Any, Callable, ClassVar, Literal, NoReturn, cast, final, overload, ) import warnings import weakref import numpy as np from pandas._config import ( config, using_copy_on_write, warn_copy_on_write, ) from pandas._libs import lib from pandas._libs.lib import is_range_indexer from pandas._libs.tslibs import ( Period, Tick, Timestamp, to_offset, ) from pandas._libs.tslibs.dtypes import freq_to_period_freqstr from pandas._typing import ( AlignJoin, AnyArrayLike, ArrayLike, Axes, Axis, AxisInt, CompressionOptions, DtypeArg, DtypeBackend, DtypeObj, FilePath, FillnaOptions, FloatFormatType, FormattersType, Frequency, IgnoreRaise, IndexKeyFunc, IndexLabel, InterpolateOptions, IntervalClosedType, JSONSerializable, Level, Manager, NaPosition, NDFrameT, OpenFileErrors, RandomState, ReindexMethod, Renamer, Scalar, Self, SequenceNotStr, SortKind, StorageOptions, Suffixes, T, TimeAmbiguous, TimedeltaConvertibleTypes, TimeNonexistent, TimestampConvertibleTypes, TimeUnit, ValueKeyFunc, WriteBuffer, WriteExcelBuffer, npt, ) from pandas.compat import PYPY from pandas.compat._constants import REF_COUNT from pandas.compat._optional import import_optional_dependency from pandas.compat.numpy import function as nv from pandas.errors import ( AbstractMethodError, ChainedAssignmentError, InvalidIndexError, SettingWithCopyError, SettingWithCopyWarning, _chained_assignment_method_msg, _chained_assignment_warning_method_msg, _check_cacher, ) from pandas.util._decorators import ( deprecate_nonkeyword_arguments, doc, ) from pandas.util._exceptions import find_stack_level from pandas.util._validators import ( check_dtype_backend, validate_ascending, validate_bool_kwarg, validate_fillna_kwargs, validate_inclusive, ) from pandas.core.dtypes.astype import astype_is_view from pandas.core.dtypes.common import ( ensure_object, ensure_platform_int, ensure_str, is_bool, is_bool_dtype, is_dict_like, is_extension_array_dtype, is_list_like, is_number, is_numeric_dtype, is_re_compilable, is_scalar, pandas_dtype, ) from pandas.core.dtypes.dtypes import ( DatetimeTZDtype, ExtensionDtype, ) from pandas.core.dtypes.generic import ( ABCDataFrame, ABCSeries, ) from pandas.core.dtypes.inference import ( is_hashable, is_nested_list_like, ) from pandas.core.dtypes.missing import ( isna, notna, ) from pandas.core import ( algorithms as algos, arraylike, common, indexing, missing, nanops, sample, ) from pandas.core.array_algos.replace import should_use_regex from pandas.core.arrays import ExtensionArray from pandas.core.base import PandasObject from pandas.core.construction import extract_array from pandas.core.flags import Flags from pandas.core.indexes.api import ( DatetimeIndex, Index, MultiIndex, PeriodIndex, RangeIndex, default_index, ensure_index, ) from pandas.core.internals import ( ArrayManager, BlockManager, SingleArrayManager, ) from pandas.core.internals.construction import ( mgr_to_mgr, ndarray_to_mgr, ) from pandas.core.methods.describe import describe_ndframe from pandas.core.missing import ( clean_fill_method, clean_reindex_fill_method, find_valid_index, ) from pandas.core.reshape.concat import concat from pandas.core.shared_docs import _shared_docs from pandas.core.sorting import get_indexer_indexer from pandas.core.window import ( Expanding, ExponentialMovingWindow, Rolling, Window, ) from pandas.io.formats.format import ( DataFrameFormatter, DataFrameRenderer, ) from pandas.io.formats.printing import pprint_thing if TYPE_CHECKING: from collections.abc import ( Hashable, Iterator, Mapping, Sequence, ) from pandas._libs.tslibs import BaseOffset from pandas import ( DataFrame, ExcelWriter, HDFStore, Series, ) from pandas.core.indexers.objects import BaseIndexer from pandas.core.resample import Resampler # goal is to be able to define the docs close to function, while still being # able to share _shared_docs = {**_shared_docs} _shared_doc_kwargs = { "axes": "keywords for axes", "klass": "Series/DataFrame", "axes_single_arg": "{0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame", # noqa: E501 "inplace": """ inplace : bool, default False If True, performs operation inplace and returns None.""", "optional_by": """ by : str or list of str Name or list of names to sort by""", } bool_t = bool # Need alias because NDFrame has def bool: class NDFrame(PandasObject, indexing.IndexingMixin): """ N-dimensional analogue of DataFrame. Store multi-dimensional in a size-mutable, labeled data structure Parameters ---------- data : BlockManager axes : list copy : bool, default False """ _internal_names: list[str] = [ "_mgr", "_cacher", "_item_cache", "_cache", "_is_copy", "_name", "_metadata", "_flags", ] _internal_names_set: set[str] = set(_internal_names) _accessors: set[str] = set() _hidden_attrs: frozenset[str] = frozenset([]) _metadata: list[str] = [] _is_copy: weakref.ReferenceType[NDFrame] | str | None = None _mgr: Manager _attrs: dict[Hashable, Any] _typ: str # ---------------------------------------------------------------------- # Constructors def __init__(self, data: Manager) -> None: object.__setattr__(self, "_is_copy", None) object.__setattr__(self, "_mgr", data) object.__setattr__(self, "_item_cache", {}) object.__setattr__(self, "_attrs", {}) object.__setattr__(self, "_flags", Flags(self, allows_duplicate_labels=True)) @final @classmethod def _init_mgr( cls, mgr: Manager, axes: dict[Literal["index", "columns"], Axes | None], dtype: DtypeObj | None = None, copy: bool_t = False, ) -> Manager: """passed a manager and a axes dict""" for a, axe in axes.items(): if axe is not None: axe = ensure_index(axe) bm_axis = cls._get_block_manager_axis(a) mgr = mgr.reindex_axis(axe, axis=bm_axis) # make a copy if explicitly requested if copy: mgr = mgr.copy() if dtype is not None: # avoid further copies if we can if ( isinstance(mgr, BlockManager) and len(mgr.blocks) == 1 and mgr.blocks[0].values.dtype == dtype ): pass else: mgr = mgr.astype(dtype=dtype) return mgr @final def _as_manager(self, typ: str, copy: bool_t = True) -> Self: """ Private helper function to create a DataFrame with specific manager. Parameters ---------- typ : {"block", "array"} copy : bool, default True Only controls whether the conversion from Block->ArrayManager copies the 1D arrays (to ensure proper/contiguous memory layout). Returns ------- DataFrame New DataFrame using specified manager type. Is not guaranteed to be a copy or not. """