text: from __future__ import annotations
  
  import collections
  from datetime import timedelta
  import functools
  import gc
  import json
  import operator
  import pickle
  import re
  from typing import (
      TYPE_CHECKING,
      Any,
      AnyStr,
      Callable,
      Hashable,
      Literal,
      Mapping,
      Sequence,
      cast,
      final,
      overload,
  )
  import warnings
  import weakref
  
  import numpy as np
  
  from pandas._config import config
  
  from pandas._libs import lib
  from pandas._libs.tslibs import (
      Period,
      Tick,
      Timestamp,
      to_offset,
  )
  from pandas._typing import (
      Axis,
      CompressionOptions,
      Dtype,
      DtypeArg,
      DtypeObj,
      FilePathOrBuffer,
      FrameOrSeries,
      IndexKeyFunc,
      IndexLabel,
      JSONSerializable,
      Level,
      Manager,
      RandomState,
      Renamer,
      StorageOptions,
      T,
      TimedeltaConvertibleTypes,
      TimestampConvertibleTypes,
      ValueKeyFunc,
      npt,
  )
  from pandas.compat._optional import import_optional_dependency
  from pandas.compat.numpy import function as nv
  from pandas.errors import (
      AbstractMethodError,
      InvalidIndexError,
  )
  from pandas.util._decorators import (
      doc,
      rewrite_axis_style_signature,
  )
  from pandas.util._exceptions import find_stack_level
  from pandas.util._validators import (
      validate_ascending,
      validate_bool_kwarg,
      validate_fillna_kwargs,
  )
  
  from pandas.core.dtypes.common import (
      ensure_object,
      ensure_platform_int,
      ensure_str,
      is_bool,
      is_bool_dtype,
      is_datetime64_any_dtype,
      is_datetime64tz_dtype,
      is_dict_like,
      is_dtype_equal,
      is_extension_array_dtype,
      is_float,
      is_list_like,
      is_number,
      is_numeric_dtype,
      is_object_dtype,
      is_re_compilable,
      is_scalar,
      is_timedelta64_dtype,
      pandas_dtype,
  )
  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 (
      arraylike,
      indexing,
      missing,
      nanops,
  )
  import pandas.core.algorithms as algos
  from pandas.core.arrays import ExtensionArray
  from pandas.core.base import PandasObject
  import pandas.core.common as com
  from pandas.core.construction import (
      create_series_with_explicit_dtype,
      extract_array,
  )
  from pandas.core.describe import describe_ndframe
  from pandas.core.flags import Flags
  from pandas.core.indexes import base as ibase
  from pandas.core.indexes.api import (
      DatetimeIndex,
      Index,
      MultiIndex,
      PeriodIndex,
      RangeIndex,
      ensure_index,
  )
  from pandas.core.internals import (
      ArrayManager,
      BlockManager,
      SingleArrayManager,
  )
  from pandas.core.internals.construction import mgr_to_mgr
  from pandas.core.missing import find_valid_index
  from pandas.core.ops import align_method_FRAME
  from pandas.core.reshape.concat import concat
  import pandas.core.sample as sample
  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 import format as fmt
  from pandas.io.formats.format import (
      DataFrameFormatter,
      DataFrameRenderer,
  )
  from pandas.io.formats.printing import pprint_thing
  
  if TYPE_CHECKING:
  
      from pandas._libs.tslibs import BaseOffset
  
      from pandas.core.frame import DataFrame
      from pandas.core.resample import Resampler
      from pandas.core.series import Series
      from pandas.core.window.indexers import BaseIndexer
  
  # 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": "int or labels for object",
      "args_transpose": "axes to permute (int or label for object)",
      "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""",
      "replace_iloc": """
      This differs from updating with ``.loc`` or ``.iloc``, which require
      you to specify a location to update with some value.""",
  }
  
  
  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",
          "_subtyp",
          "_name",
          "_index",
          "_default_kind",
          "_default_fill_value",
          "_metadata",
          "__array_struct__",
          "__array_interface__",
          "_flags",
      ]
      _internal_names_set: set[str] = set(_internal_names)
      _accessors: set[str] = set()
      _hidden_attrs: frozenset[str] = frozenset(
          ["_AXIS_NAMES", "_AXIS_NUMBERS", "get_values", "tshift"]
      )
      _metadata: list[str] = []
      _is_copy: weakref.ReferenceType[NDFrame] | None = None
      _mgr: Manager
      _attrs: dict[Hashable, Any]
      _typ: str
  
      # ----------------------------------------------------------------------
      # Constructors
  
      def __init__(
          self,
          data: Manager,
          copy: bool_t = False,
          attrs: Mapping[Hashable, Any] | None = None,
      ):
          # copy kwarg is retained for mypy compat, is not used
  
          object.__setattr__(self, "_is_copy", None)
          object.__setattr__(self, "_mgr", data)
          object.__setattr__(self, "_item_cache", {})
          if attrs is None:
              attrs = {}
          else:
              attrs = dict(attrs)
          object.__setattr__(self, "_attrs", attrs)
          object.__setattr__(self, "_flags", Flags(self, allows_duplicate_labels=True))
  
      @classmethod
      def _init_mgr(
          cls,
          mgr: Manager,
          axes,
          dtype: Dtype | 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 is_dtype_equal(mgr.blocks[0].values.dtype, dtype)
              ):
                  pass
              else:
                  mgr = mgr.astype(dtype=dtype)
          return mgr
  
      @classmethod
      def _from_mgr(cls, mgr: Manager):
          """
          Fastpath to create a new DataFrame/Series from just a BlockManager/ArrayManager.
  
          Notes
          -----
          Skips setting `_flags` attribute; caller is responsible for doing so.
          """
          obj = cls.__new__(cls)
          object.__setattr__(obj, "_is_copy", None)
          object.__setattr__(obj, "_mgr", mgr)
          object.__setattr__(obj, "_item_cache", {})
          object.__setattr__(obj, "_attrs", {})
          return obj
  
      def _as_manager(
          self: FrameOrSeries, typ: str, copy: bool_t = True
      ) -> FrameOrSeries:
          """
          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.
          """
          new_mgr: Manager
          new_mgr = mgr_to_mgr(self._mgr, typ=typ, copy=copy)
          # fastpath of passing a manager doesn't check the option/manager class
          return self._constructor(new_mgr).__finalize__(self)
  
      # ----------------------------------------------------------------------
      # attrs and flags
  
      @property
      def attrs(self) -> dict[Hashable, Any]:
          """
          Dictionary of global attributes of this dataset.
  
          .. warning::
  
             attrs is experimental and may change without warning.
  
          See Also
          --------
          DataFrame.flags : Global flags applying to this object.
          """
          if self._attrs is None:
              self._attrs = {}
          return self._attrs
  
      @attrs.setter
      def attrs(self, value: Mapping[Hashable, Any]) -> None:
          self._attrs = dict(value)
  
      @final
      @property
      def flags(self) -> Flags:
          """
          Get the properties associated with this pandas object.
  
          The available flags are
  
          * :attr:`Flags.allows_duplicate_labels`
  
          See Also
          --------
          Flags : Flags that apply to pandas objects.
          DataFrame.attrs : Global metadata applying to this dataset.
  
          Notes
          -----
          "Flags" differ from "metadata". Flags reflect properties of the
          pandas object (the Series or DataFrame). Metadata refer to properties
          of the dataset, and should be stored in :attr:`DataFrame.attrs`.
  
          Examples
          --------
          >>> df = pd.DataFrame({"A": [1, 2]})
          >>> df.flags
          <Flags(allows_duplicate_labels=True)>
  
          Flags can be get or set using ``.``
  
          >>> df.flags.allows_duplicate_labels
          True
          >>> df.flags.allows_duplicate_labels = False
  
          Or by slicing with a key
  
          >>> df.flags["allows_duplicate_labels"]
          False
          >>> df.flags["allows_duplicate_labels"] = True
          """
          return self._flags
  
      @final
      def set_flags(
          self: FrameOrSeries,
          *,
          copy: bool_t = False,
          allows_duplicate_labels: bool_t | None = None,
      ) -> FrameOrSeries:
          """
          Return a new object with updated flags.
  
          Parameters
          ----------
          allows_duplicate_labels : bool, optional
              Whether the returned object allows duplicate labels.
  
          Returns
          -------
          Series or DataFrame
              The same type as the caller.
  
          See Also
          --------
          DataFrame.attrs : Global metadata applying to this dataset.
          DataFrame.flags : Global flags applying to this object.
  
          Notes
          -----
          This method returns a new object that's a view on the same data
          as the input. Mutating the input or the output values will be reflected
          in the other.
  
          This method is intended to be used in method chains.
  
          "Flags" differ from "metadata". Flags reflect properties of the
          pandas object (the Series or DataFrame). Metadata refer to properties
          of the dataset, and should be stored in :attr:`DataFrame.attrs`.
  
          Examples
          --------
          >>> df = pd.DataFrame({"A": [1, 2]})
          >>> df.flags.allows_duplicate_labels
          True
          >>> df2 = df.set_flags(allows_duplicate_labels=False)
          >>> df2.flags.allows_duplicate_labels
          False
          """
          df = self.copy(deep=copy)
          if allows_duplicate_labels is not None:
              df.flags["allows_duplicate_labels"] = allows_duplicate_labels
          return df
  
      @final
      @classmethod
      def _validate_dtype(cls, dtype) -> DtypeObj | None:
          """validate the passed dtype"""
          if dtype is not None:
              dtype = pandas_dtype(dtype)
  
              # a compound dtype
              if dtype.kind == "V":
                  raise NotImplementedError(
                      "compound dtypes are not implemented "
                      f"in the {cls.__name__} constructor"
                  )
  
          return dtype
  
      # ----------------------------------------------------------------------
      # Construction
  
      @property
      def _constructor(self: FrameOrSeries) -> type[FrameOrSeries]:
          """
          Used when a manipulation result has the same dimensions as the
          original.
          """
          raise AbstractMethodError(self)
  
      # ----------------------------------------------------------------------
      # Internals
  
      @final
      @property
      def _data(self):
          # GH#33054 retained because some downstream packages uses this,
          #  e.g. fastparquet
          return self._mgr
  
      # ----------------------------------------------------------------------
      # Axis
      _stat_axis_number = 0
      _stat_axis_name = "index"
      _AXIS_ORDERS: list[str]
      _AXIS_TO_AXIS_NUMBER: dict[Axis, int] = {0: 0, "index": 0, "rows": 0}
      _AXIS_REVERSED: bool_t
      _info_axis_number: int
      _info_axis_name: str
      _AXIS_LEN: int
  
      @property
      def _AXIS_NUMBERS(self) -> dict[str, int]:
          """.. deprecated:: 1.1.0"""
          level = self.ndim + 1
          warnings.warn(
              "_AXIS_NUMBERS has been deprecated.", FutureWarning, stacklevel=level
          )
          return {"index": 0}
  
      @property
      def _AXIS_NAMES(self) -> dict[int, str]:
          """.. deprecated:: 1.1.0"""
          level = self.ndim + 1
          warnings.warn(
              "_AXIS_NAMES has been deprecated.", FutureWarning, stacklevel=level
          )
          return {0: "index"}
  
      @final
      def _construct_axes_dict(self, axes=None, **kwargs):
          """Return an axes dictionary for myself."""
          d = {a: self._get_axis(a) for a in (axes or self._AXIS_ORDERS)}
          d.update(kwargs)
          return d
  
      @final
      @classmethod
      def _construct_axes_from_arguments(
          cls, args, kwargs, require_all: bool_t = False, sentinel=None
      ):
          """
          Construct and returns axes if supplied in args/kwargs.
  
          If require_all, raise if all axis arguments are not supplied
          return a tuple of (axes, kwargs).
  
          sentinel specifies the default parameter when an axis is not
          supplied; useful to distinguish when a user explicitly passes None
          in scenarios where None has special meaning.
          """
          # construct the args
          args = list(args)
          for a in cls._AXIS_ORDERS:
  
              # look for a argument by position
              if a not in kwargs:
                  try:
                      kwargs[a] = args.pop(0)
                  except IndexError as err:
                      if require_all:
                          raise TypeError(
                              "not enough/duplicate arguments specified!"
                          ) from err
  
          axes = {a: kwargs.pop(a, sentinel) for a in cls._AXIS_ORDERS}
          return axes, kwargs
  
      @final
      @classmethod
      def _get_axis_number(cls, axis: Axis) -> int:
          try:
              return cls._AXIS_TO_AXIS_NUMBER[axis]
          except KeyError:
              raise ValueError(f"No axis named {axis} for object type {cls.__name__}")
  
      @final
      @classmethod
      def _get_axis_name(cls, axis: Axis) -> str:
          axis_number = cls._get_axis_number(axis)
          return cls._AXIS_ORDERS[axis_number]
  
      @final
      def _get_axis(self, axis: Axis) -> Index:
          axis_number = self._get_axis_number(axis)
          assert axis_number in {0, 1}
          return self.index if axis_number == 0 else self.columns
  
      @final
      @classmethod
      def _get_block_manager_axis(cls, axis: Axis) -> int:
          """Map the axis to the block_manager axis."""
          axis = cls._get_axis_number(axis)
          if cls._AXIS_REVERSED:
              m = cls._AXIS_LEN - 1
              return m - axis
          return axis
  
      @final
      def _get_axis_resolvers(self, axis: str) -> dict[str, Series | MultiIndex]:
          # index or columns
          axis_index = getattr(self, axis)
          d = {}
          prefix = axis[0]
  
          for i, name in enumerate(axis_index.names):
              if name is not None:
                  key = level = name
              else:
                  # prefix with 'i' or 'c' depending on the input axis
                  # e.g., you must do ilevel_0 for the 0th level of an unnamed
                  # multiiindex
                  key = f"{prefix}level_{i}"
                  level = i
  
              level_values = axis_index.get_level_values(level)
              s = level_values.to_series()
              s.index = axis_index
              d[key] = s
  
          # put the index/columns itself in the dict
          if isinstance(axis_index, MultiIndex):
              dindex = axis_index
          else:
              dindex = axis_index.to_series()
  
          d[axis] = dindex
          return d
  
      @final
      def _get_index_resolvers(self) -> dict[Hashable, Series | MultiIndex]:
          from pandas.core.computation.parsing import clean_column_name
  
          d: dict[str, Series | MultiIndex] = {}
          for axis_name in self._AXIS_ORDERS:
              d.update(self._get_axis_resolvers(axis_name))
  
          return {clean_column_name(k): v for k, v in d.items() if not isinstance(k, int)}
  
      @final
      def _get_cleaned_column_resolvers(self) -> dict[Hashable, Series]:
          """
          Return the special character free column resolvers of a dataframe.
  
          Column names with special characters are 'cleaned up' so that they can
          be referred to by backtick quoting.
          Used in :meth:`DataFrame.eval`.
          """
          from pandas.core.computation.parsing import clean_column_name
  
          if isinstance(self, ABCSeries):
              return {clean_column_name(self.name): self}
  
          return {
              clean_column_name(k): v for k, v in self.items() if not isinstance(k, int)
          }
  
      @property
      def _info_axis(self) -> Index:
          return getattr(self, self._info_axis_name)
  
      @property
      def _stat_axis(self) -> Index:
          return getattr(self, self._stat_axis_name)
  
      @property
      def shape(self) -> tuple[int, ...]:
          """
          Return a tuple of axis dimensions
          """
          return tuple(len(self._get_axis(a)) for a in self._AXIS_ORDERS)
  
      @property
      def axes(self) -> list[Index]:
          """
          Return index label(s) of the internal NDFrame
          """
          # we do it this way because if we have reversed axes, then
          # the block manager shows then reversed
          return [self._get_axis(a) for a in self._AXIS_ORDERS]
  
      @property
      def ndim(self) -> int:
          """
          Return an int representing the number of axes / array dimensions.
  
          Return 1 if Series. Otherwise return 2 if DataFrame.
  
          See Also
          --------
          ndarray.ndim : Number of array dimensions.
  
          Examples
          --------
          >>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
          >>> s.ndim
          1
  
          >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
          >>> df.ndim
          2
          """
          return self._mgr.ndim
  
      @property
      def size(self) -> int:
          """
          Return an int representing the number of elements in this object.
  
          Return the number of rows if Series. Otherwise return the number of
          rows times number of columns if DataFrame.
  
          See Also
          --------
          ndarray.size : Number of elements in the array.
  
          Examples
          --------
          >>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
          >>> s.size
          3
  
          >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
          >>> df.size
          4
          """
          return np.prod(self.shape)
  
      @overload
      def set_axis(
          self: FrameOrSeries, labels, axis: Axis = ..., inplace: Literal[False] = ...
      ) -> FrameOrSeries:
          ...
  
      @overload
      def set_axis(
          self: FrameOrSeries, labels, axis: Axis, inplace: Literal[True]
      ) -> None:
          ...
  
      @overload
      def set_axis(self: FrameOrSeries, labels, *, inplace: Literal[True]) -> None:
          ...
  
      @overload
      def set_axis(
          self: FrameOrSeries, labels, axis: Axis = ..., inplace: bool_t = ...
      ) -> FrameOrSeries | None:
          ...
  
      def set_axis(self, labels, axis: Axis = 0, inplace: bool_t = False):
          """
          Assign desired index to given axis.
  
          Indexes for%(extended_summary_sub)s row labels can be changed by assigning
          a list-like or Index.
  
          Parameters
          ----------
          labels : list-like, Index
              The values for the new index.
  
          axis : %(axes_single_arg)s, default 0
              The axis to update. The value 0 identifies the rows%(axis_description_sub)s.
  
          inplace : bool, default False
              Whether to return a new %(klass)s instance.
  
          Returns
          -------
          renamed : %(klass)s or None
              An object of type %(klass)s or None if ``inplace=True``.
  
          See Also
          --------
          %(klass)s.rename_axis : Alter the name of the index%(see_also_sub)s.
          """
          self._check_inplace_and_allows_duplicate_labels(inplace)
          return self._set_axis_nocheck(labels, axis, inplace)
  
      @final
      def _set_axis_nocheck(self, labels, axis: Axis, inplace: bool_t):
          # NDFrame.rename with inplace=False calls set_axis(inplace=True) on a copy.
          if inplace:
              setattr(self, self._get_axis_name(axis), labels)
          else:
              obj = self.copy()
              obj.set_axis(labels, axis=axis, inplace=True)
              return obj
  
      def _set_axis(self, axis: int, labels: Index) -> None:
          labels = ensure_index(labels)
          self._mgr.set_axis(axis, labels)
          self._clear_item_cache()
  
      @final
      def swapaxes(self: FrameOrSeries, axis1, axis2, copy=True) -> FrameOrSeries:
          """
          Interchange axes and swap values axes appropriately.
  
          Returns
          -------
          y : same as input
          """
          i = self._get_axis_number(axis1)
          j = self._get_axis_number(axis2)
  
          if i == j:
              if copy:
                  return self.copy()
              return self
  
          mapping = {i: j, j: i}
  
          new_axes = (self._get_axis(mapping.get(k, k)) for k in range(self._AXIS_LEN))
          new_values = self.values.swapaxes(i, j)
          if copy:
              new_values = new_values.copy()
  
          # ignore needed because of NDFrame constructor is different than
          # DataFrame/Series constructors.
          return self._constructor(
              # error: Argument 1 to "NDFrame" has incompatible type "ndarray"; expected
              # "Union[ArrayManager, BlockManager]"
              # error: Argument 2 to "NDFrame" has incompatible type "*Generator[Index,
              # None, None]"; expected "bool" [arg-type]
              # error: Argument 2 to "NDFrame" has incompatible type "*Generator[Index,
              # None, None]"; expected "Optional[Mapping[Hashable, Any]]"
              new_values,  # type: ignore[arg-type]
              *new_axes,  # type: ignore[arg-type]
          ).__finalize__(self, method="swapaxes")
  
      @final
      @doc(klass=_shared_doc_kwargs["klass"])
      def droplevel(self: FrameOrSeries, level, axis=0) -> FrameOrSeries:
          """
          Return {klass} with requested index / column level(s) removed.
  
          Parameters
          ----------
          level : int, str, or list-like
              If a string is given, must be the name of a level
              If list-like, elements must be names or positional indexes
              of levels.
  
          axis : {{0 or 'index', 1 or 'columns'}}, default 0
              Axis along which the level(s) is removed:
  
              * 0 or 'index': remove level(s) in column.
              * 1 or 'columns': remove level(s) in row.
  
          Returns
          -------
          {klass}
              {klass} with requested index / column level(s) removed.
  
          Examples
          --------
          >>> df = pd.DataFrame([
          ...     [1, 2, 3, 4],
          ...     [5, 6, 7, 8],
          ...     [9, 10, 11, 12]
          ... ]).set_index([0, 1]).rename_axis(['a', 'b'])
  
          >>> df.columns = pd.MultiIndex.from_tuples([
          ...     ('c', 'e'), ('d', 'f')
          ... ], names=['level_1', 'level_2'])
  
          >>> df
          level_1   c   d
          level_2   e   f
          a b
          1 2      3   4
          5 6      7   8
          9 10    11  12
  
          >>> df.droplevel('a')
          level_1   c   d
          level_2   e   f
          b
          2        3   4
          6        7   8
          10      11  12
  
          >>> df.droplevel('level_2', axis=1)
          level_1   c   d
          a b
          1 2      3   4
          5 6      7   8
          9 10    11  12
          """
          labels = self._get_axis(axis)
          new_labels = labels.droplevel(level)
          return self.set_axis(new_labels, axis=axis, inplace=False)
  
      def pop(self, item: Hashable) -> Series | Any:
          result = self[item]
          del self[item]
  
          return result
  
      @final
      def squeeze(self, axis=None):
          """
          Squeeze 1 dimensional axis objects into scalars.
  
          Series or DataFrames with a single element are squeezed to a scalar.
          DataFrames with a single column or a single row are squeezed to a
          Series. Otherwise the object is unchanged.
  
          This method is most useful when you don't know if your
          object is a Series or DataFrame, but you do know it has just a single
          column. In that case you can safely call `squeeze` to ensure you have a
          Series.
  
          Parameters
          ----------
          axis : {0 or 'index', 1 or 'columns', None}, default None
              A specific axis to squeeze. By default, all length-1 axes are
              squeezed.
  
          Returns
          -------
          DataFrame, Series, or scalar
              The projection after squeezing `axis` or all the axes.
  
          See Also
          --------
          Series.iloc : Integer-location based indexing for selecting scalars.
          DataFrame.iloc : Integer-location based indexing for selecting Series.
          Series.to_frame : Inverse of DataFrame.squeeze for a
              single-column DataFrame.
  
          Examples
          --------
          >>> primes = pd.Series([2, 3, 5, 7])
  
          Slicing might produce a Series with a single value:
  
          >>> even_primes = primes[primes % 2 == 0]
          >>> even_primes
          0    2
          dtype: int64
  
          >>> even_primes.squeeze()
          2
  
          Squeezing objects with more than one value in every axis does nothing:
  
          >>> odd_primes = primes[primes % 2 == 1]
          >>> odd_primes
          1    3
          2    5
          3    7
          dtype: int64
  
          >>> odd_primes.squeeze()
          1    3
          2    5
          3    7
          dtype: int64
  
          Squeezing is even more effective when used with DataFrames.
  
          >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
          >>> df
             a  b
          0  1  2
          1  3  4
  
          Slicing a single column will produce a DataFrame with the columns
          having only one value:
  
          >>> df_a = df[['a']]
          >>> df_a
             a
          0  1
          1  3
  
          So the columns can be squeezed down, resulting in a Series:
  
          >>> df_a.squeeze('columns')
          0    1
          1    3
          Name: a, dtype: int64
  
          Slicing a single row from a single column will produce a single
          scalar DataFrame:
  
          >>> df_0a = df.loc[df.index < 1, ['a']]
          >>> df_0a
             a
          0  1
  
          Squeezing the rows produces a single scalar Series:
  
          >>> df_0a.squeeze('rows')
          a    1
          Name: 0, dtype: int64
  
          Squeezing all axes will project directly into a scalar:
  
          >>> df_0a.squeeze()
          1
          """
          axis = range(self._AXIS_LEN) if axis is None else (self._get_axis_number(axis),)
          return self.iloc[
              tuple(
                  0 if i in axis and len(a) == 1 else slice(None)
                  for i, a in enumerate(self.axes)
              )
          ]
  
      # ----------------------------------------------------------------------
      # Rename
  
      def rename(
          self: FrameOrSeries,
          mapper: Renamer | None = None,
          *,
          index: Renamer | None = None,
          columns: Renamer | None = None,
          axis: Axis | None = None,
          copy: bool_t = True,
          inplace: bool_t = False,
          level: Level | None = None,
          errors: str = "ignore",
      ) -> FrameOrSeries | None:
          """
          Alter axes input function or functions. Function / dict values must be
          unique (1-to-1). Labels not contained in a dict / Series will be left
          as-is. Extra labels listed don't throw an error. Alternatively, change
          ``Series.name`` with a scalar value (Series only).
  
          Parameters
          ----------
          %(axes)s : scalar, list-like, dict-like or function, optional
              Scalar or list-like will alter the ``Series.name`` attribute,
              and raise on DataFrame.
              dict-like or functions are transformations to apply to
              that axis' values
          copy : bool, default True
              Also copy underlying data.
          inplace : bool, default False
              Whether to return a new {klass}. If True then value of copy is
              ignored.
          level : int or level name, default None
              In case of a MultiIndex, only rename labels in the specified
              level.
          errors : {'ignore', 'raise'}, default 'ignore'
              If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
              or `columns` contains labels that are not present in the Index
              being transformed.
              If 'ignore', existing keys will be renamed and extra keys will be
              ignored.
  
          Returns
          -------
          renamed : {klass} (new object)
  
          Raises
          ------
          KeyError
              If any of the labels is not found in the selected axis and
              "errors='raise'".
  
          See Also
          --------
          NDFrame.rename_axis
  
          Examples
          --------
          >>> s = pd.Series([1, 2, 3])
          >>> s
          0    1
          1    2
          2    3
          dtype: int64
          >>> s.rename("my_name") # scalar, changes Series.name
          0    1
          1    2
          2    3
          Name: my_name, dtype: int64
          >>> s.rename(lambda x: x ** 2)  # function, changes labels
          0    1
          1    2
          4    3
          dtype: int64
          >>> s.rename({1: 3, 2: 5})  # mapping, changes labels
          0    1
          3    2
          5    3
          dtype: int64
  
          Since ``DataFrame`` doesn't have a ``.name`` attribute,
          only mapping-type arguments are allowed.
  
          >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
          >>> df.rename(2)
          Traceback (most recent call last):
          ...
          TypeError: 'int' object is not callable
  
          ``DataFrame.rename`` supports two calling conventions
  
          * ``(index=index_mapper, columns=columns_mapper, ...)``
          * ``(mapper, axis={'index', 'columns'}, ...)``
  
          We *highly* recommend using keyword arguments to clarify your
          intent.
  
          >>> df.rename(index=str, columns={"A": "a", "B": "c"})
             a  c
          0  1  4
          1  2  5
          2  3  6
  
          >>> df.rename(index=str, columns={"A": "a", "C": "c"})
             a  B
          0  1  4
          1  2  5
          2  3  6
  
          Using axis-style parameters
  
          >>> df.rename(str.lower, axis='columns')
             a  b
          0  1  4
          1  2  5
          2  3  6
  
          >>> df.rename({1: 2, 2: 4}, axis='index')
             A  B
          0  1  4
          2  2  5
          4  3  6
  
          See the :ref:`user guide <basics.rename>` for more.
          """
          if mapper is None and index is None and columns is None:
              raise TypeError("must pass an index to rename")
  
          if index is not None or columns is not None:
              if axis is not None:
                  raise TypeError(
                      "Cannot specify both 'axis' and any of 'index' or 'columns'"
                  )
              elif mapper is not None:
                  raise TypeError(
                      "Cannot specify both 'mapper' and any of 'index' or 'columns'"
                  )
          else:
              # use the mapper argument
              if axis and self._get_axis_number(axis) == 1:
                  columns = mapper
              else:
                  index = mapper
  
          self._check_inplace_and_allows_duplicate_labels(inplace)
          result = self if inplace else self.copy(deep=copy)
  
          for axis_no, replacements in enumerate((index, columns)):
              if replacements is None:
                  continue
  
              ax = self._get_axis(axis_no)
              f = com.get_rename_function(replacements)
  
              if level is not None:
                  level = ax._get_level_number(level)
  
              # GH 13473
              if not callable(replacements):
                  indexer = ax.get_indexer_for(replacements)
                  if errors == "raise" and len(indexer[indexer == -1]):
                      missing_labels = [
                          label
                          for index, label in enumerate(replacements)
                          if indexer[index] == -1
                      ]
                      raise KeyError(f"{missing_labels} not found in axis")
  
              new_index = ax._transform_index(f, level=level)
              result._set_axis_nocheck(new_index, axis=axis_no, inplace=True)
              result._clear_item_cache()
  
          if inplace:
              self._update_inplace(result)
              return None
          else:
              return result.__finalize__(self, method="rename")
  
      @rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)])
      def rename_axis(self, mapper=lib.no_default, **kwargs):
          """
          Set the name of the axis for the index or columns.
  
          Parameters
          ----------
          mapper : scalar, list-like, optional
              Value to set the axis name attribute.
          index, columns : scalar, list-like, dict-like or function, optional
              A scalar, list-like, dict-like or functions transformations to
              apply to that axis' values.
              Note that the ``columns`` parameter is not allowed if the
              object is a Series. This parameter only apply for DataFrame
              type objects.
  
              Use either ``mapper`` and ``axis`` to
              specify the axis to target with ``mapper``, or ``index``
              and/or ``columns``.
          axis : {0 or 'index', 1 or 'columns'}, default 0
              The axis to rename.
          copy : bool, default True
              Also copy underlying data.
          inplace : bool, default False
              Modifies the object directly, instead of creating a new Series
              or DataFrame.
  
          Returns
          -------
          Series, DataFrame, or None
              The same type as the caller or None if ``inplace=True``.
  
          See Also
          --------
          Series.rename : Alter Series index labels or name.
          DataFrame.rename : Alter DataFrame index labels or name.
          Index.rename : Set new names on index.
  
          Notes
          -----
          ``DataFrame.rename_axis`` supports two calling conventions
  
          * ``(index=index_mapper, columns=columns_mapper, ...)``
          * ``(mapper, axis={'index', 'columns'}, ...)``
  
          The first calling convention will only modify the names of
          the index and/or the names of the Index object that is the columns.
          In this case, the parameter ``copy`` is ignored.
  
          The second calling convention will modify the names of the
          corresponding index if mapper is a list or a scalar.
          However, if mapper is dict-like or a function, it will use the
          deprecated behavior of modifying the axis *labels*.
  
          We *highly* recommend using keyword arguments to clarify your
          intent.
  
          Examples
          --------
          **Series**
  
          >>> s = pd.Series(["dog", "cat", "monkey"])
          >>> s
          0       dog
          1       cat
          2    monkey
          dtype: object
          >>> s.rename_axis("animal")
          animal
          0    dog
          1    cat
          2    monkey
          dtype: object
  
          **DataFrame**
  
          >>> df = pd.DataFrame({"num_legs": [4, 4, 2],
          ...                    "num_arms": [0, 0, 2]},
          ...                   ["dog", "cat", "monkey"])
          >>> df
                  num_legs  num_arms
          dog            4         0
          cat            4         0
          monkey         2         2
          >>> df = df.rename_axis("animal")
          >>> df
                  num_legs  num_arms
          animal
          dog            4         0
          cat            4         0
          monkey         2         2
          >>> df = df.rename_axis("limbs", axis="columns")
          >>> df
          limbs   num_legs  num_arms
          animal
          dog            4         0
          cat            4         0
          monkey         2         2
  
          **MultiIndex**
  
          >>> df.index = pd.MultiIndex.from_product([['mammal'],
          ...                                        ['dog', 'cat', 'monkey']],
          ...                                       names=['type', 'name'])
          >>> df
          limbs          num_legs  num_arms
          type   name
          mammal dog            4         0
                 cat            4         0
                 monkey         2         2
  
          >>> df.rename_axis(index={'type': 'class'})
          limbs          num_legs  num_arms
          class  name
          mammal dog            4         0
                 cat            4         0
                 monkey         2         2
  
          >>> df.rename_axis(columns=str.upper)
          LIMBS          num_legs  num_arms
          type   name
          mammal dog            4         0
                 cat            4         0
                 monkey         2         2
          """
          axes, kwargs = self._construct_axes_from_arguments(
              (), kwargs, sentinel=lib.no_default
          )
          copy = kwargs.pop("copy", True)
          inplace = kwargs.pop("inplace", False)
          axis = kwargs.pop("axis", 0)
          if axis is not None:
              axis = self._get_axis_number(axis)
  
          if kwargs:
              raise TypeError(
                  "rename_axis() got an unexpected keyword "
                  f'argument "{list(kwargs.keys())[0]}"'
              )
  
          inplace = validate_bool_kwarg(inplace, "inplace")
  
          if mapper is not lib.no_default:
              # Use v0.23 behavior if a scalar or list
              non_mapper = is_scalar(mapper) or (
                  is_list_like(mapper) and not is_dict_like(mapper)
              )
              if non_mapper:
                  return self._set_axis_name(mapper, axis=axis, inplace=inplace)
              else:
                  raise ValueError("Use `.rename` to alter labels with a mapper.")
          else:
              # Use new behavior.  Means that index and/or columns
              # is specified
              result = self if inplace else self.copy(deep=copy)
  
              for axis in range(self._AXIS_LEN):
                  v = axes.get(self._get_axis_name(axis))
                  if v is lib.no_default:
                      continue
                  non_mapper = is_scalar(v) or (is_list_like(v) and not is_dict_like(v))
                  if non_mapper:
                      newnames = v
                  else:
                      f = com.get_rename_function(v)
                      curnames = self._get_axis(axis).names
                      newnames = [f(name) for name in curnames]
                  result._set_axis_name(newnames, axis=axis, inplace=True)
              if not inplace:
                  return result
  
      @final
      def _set_axis_name(self, name, axis=0, inplace=False):
          """
          Set the name(s) of the axis.
  
          Parameters
          ----------
          name : str or list of str
              Name(s) to set.
          axis : {0 or 'index', 1 or 'columns'}, default 0
              The axis to set the label. The value 0 or 'index' specifies index,
              and the value 1 or 'columns' specifies columns.
          inplace : bool, default False
              If `True`, do operation inplace and return None.
  
          Returns
          -------
          Series, DataFrame, or None
              The same type as the caller or `None` if `inplace` is `True`.
  
          See Also
          --------
          DataFrame.rename : Alter the axis labels of :class:`DataFrame`.
          Series.rename : Alter the index labels or set the index name
              of :class:`Series`.
          Index.rename : Set the name of :class:`Index` or :class:`MultiIndex`.
  
          Examples
          --------
          >>> df = pd.DataFrame({"num_legs": [4, 4, 2]},
          ...                   ["dog", "cat", "monkey"])
          >>> df
                  num_legs
          dog            4
          cat            4
          monkey         2
          >>> df._set_axis_name("animal")
                  num_legs
          animal
          dog            4
          cat            4
          monkey         2
          >>> df.index = pd.MultiIndex.from_product(
          ...                [["mammal"], ['dog', 'cat', 'monkey']])
          >>> df._set_axis_name(["type", "name"])
                         num_legs
          type   name
          mammal dog        4
                 cat        4
                 monkey     2
          """
          axis = self._get_axis_number(axis)
          idx = self._get_axis(axis).set_names(name)
  
          inplace = validate_bool_kwarg(inplace, "inplace")
          renamed = self if inplace else self.copy()
          renamed.set_axis(idx, axis=axis, inplace=True)
          if not inplace:
              return renamed
  
      # ----------------------------------------------------------------------
      # Comparison Methods
  
      @final
      def _indexed_same(self, other) -> bool_t:
          return all(
              self._get_axis(a).equals(other._get_axis(a)) for a in self._AXIS_ORDERS
          )
  
      @final
      def equals(self, other: object) -> bool_t:
          """
          Test whether two objects contain the same elements.
  
          This function allows two Series or DataFrames to be compared against
          each other to see if they have the same shape and elements. NaNs in
          the same location are considered equal.
  
          The row/column index do not need to have the same type, as long
          as the values are considered equal. Corresponding columns must be of
          the same dtype.
  
          Parameters
          ----------
          other : Series or DataFrame
              The other Series or DataFrame to be compared with the first.
  
          Returns
          -------
          bool
              True if all elements are the same in both objects, False
              otherwise.
  
          See Also
          --------
          Series.eq : Compare two Series objects of the same length
              and return a Series where each element is True if the element
              in each Series is equal, False otherwise.
          DataFrame.eq : Compare two DataFrame objects of the same shape and
              return a DataFrame where each element is True if the respective
              element in each DataFrame is equal, False otherwise.
          testing.assert_series_equal : Raises an AssertionError if left and
              right are not equal. Provides an easy interface to ignore
              inequality in dtypes, indexes and precision among others.
          testing.assert_frame_equal : Like assert_series_equal, but targets
              DataFrames.
          numpy.array_equal : Return True if two arrays have the same shape
              and elements, False otherwise.
  
          Examples
          --------
          >>> df = pd.DataFrame({1: [10], 2: [20]})
          >>> df
              1   2
          0  10  20
  
          DataFrames df and exactly_equal have the same types and values for
          their elements and column labels, which will return True.
  
          >>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
          >>> exactly_equal
              1   2
          0  10  20
          >>> df.equals(exactly_equal)
          True
  
          DataFrames df and different_column_type have the same element
          types and values, but have different types for the column labels,
          which will still return True.
  
          >>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
          >>> different_column_type
             1.0  2.0
          0   10   20
          >>> df.equals(different_column_type)
          True
  
          DataFrames df and different_data_type have different types for the
          same values for their elements, and will return False even though
          their column labels are the same values and types.
  
          >>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
          >>> different_data_type
                1     2
          0  10.0  20.0
          >>> df.equals(different_data_type)
          False
          """
          if not (isinstance(other, type(self)) or isinstance(self, type(other))):
              return False
          other = cast(NDFrame, other)
          return self._mgr.equals(other._mgr)
  
      # -------------------------------------------------------------------------
      # Unary Methods
  
      @final
      def __neg__(self):
          values = self._values
          if is_bool_dtype(values):
              arr = operator.inv(values)
          elif (
              is_numeric_dtype(values)
              or is_timedelta64_dtype(values)
              or is_object_dtype(values)
          ):
              arr = operator.neg(values)
          else:
              raise TypeError(f"Unary negative expects numeric dtype, not {values.dtype}")
          return self.__array_wrap__(arr)
  
      @final
      def __pos__(self):
          values = self._values
          if is_bool_dtype(values):
              arr = values
          elif (
              is_numeric_dtype(values)
              or is_timedelta64_dtype(values)
              or is_object_dtype(values)
          ):
              arr = operator.pos(values)
          else:
              raise TypeError(
                  "Unary plus expects bool, numeric, timedelta, "
                  f"or object dtype, not {values.dtype}"
              )
          return self.__array_wrap__(arr)
  
      @final
      def __invert__(self):
          if not self.size:
              # inv fails with 0 len
              return self
  
          new_data = self._mgr.apply(operator.invert)
          return self._constructor(new_data).__finalize__(self, method="__invert__")
  
      @final
      def __nonzero__(self):
          raise ValueError(
              f"The truth value of a {type(self).__name__} is ambiguous. "
              "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
          )
  
      __bool__ = __nonzero__
  
      @final
      def bool(self):
          """
          Return the bool of a single element Series or DataFrame.
  
          This must be a boolean scalar value, either True or False. It will raise a
          ValueError if the Series or DataFrame does not have exactly 1 element, or that
          element is not boolean (integer values 0 and 1 will also raise an exception).
  
          Returns
          -------
          bool
              The value in the Series or DataFrame.
  
          See Also
          --------
          Series.astype : Change the data type of a Series, including to boolean.
          DataFrame.astype : Change the data type of a DataFrame, including to boolean.
          numpy.bool_ : NumPy boolean data type, used by pandas for boolean values.
  
          Examples
          --------
          The method will only work for single element objects with a boolean value:
  
          >>> pd.Series([True]).bool()
          True
          >>> pd.Series([False]).bool()
          False
  
          >>> pd.DataFrame({'col': [True]}).bool()
          True
          >>> pd.DataFrame({'col': [False]}).bool()
          False
          """
          v = self.squeeze()
          if isinstance(v, (bool, np.bool_)):
              return bool(v)
          elif is_scalar(v):
              raise ValueError(
                  "bool cannot act on a non-boolean single element "
                  f"{type(self).__name__}"
              )
  
          self.__nonzero__()
  
      @final
      def __abs__(self: FrameOrSeries) -> FrameOrSeries:
          return self.abs()
  
      @final
      def __round__(self: FrameOrSeries, decimals: int = 0) -> FrameOrSeries:
          return self.round(decimals)
  
      # -------------------------------------------------------------------------
      # Label or Level Combination Helpers
      #
      # A collection of helper methods for DataFrame/Series operations that
      # accept a combination of column/index labels and levels.  All such
      # operations should utilize/extend these methods when possible so that we
      # have consistent precedence and validation logic throughout the library.
  
      @final
      def _is_level_reference(self, key, axis=0):
          """
          Test whether a key is a level reference for a given axis.
  
          To be considered a level reference, `key` must be a string that:
            - (axis=0): Matches the name of an index level and does NOT match
              a column label.
            - (axis=1): Matches the name of a column level and does NOT match
              an index label.
  
          Parameters
          ----------
          key : str
              Potential level name for the given axis
          axis : int, default 0
              Axis that levels are associated with (0 for index, 1 for columns)
  
          Returns
          -------
          is_level : bool
          """
          axis = self._get_axis_number(axis)
  
          return (
              key is not None
              and is_hashable(key)
              and key in self.axes[axis].names
              and not self._is_label_reference(key, axis=axis)
          )
  
      @final
      def _is_label_reference(self, key, axis=0) -> bool_t:
          """
          Test whether a key is a label reference for a given axis.
  
          To be considered a label reference, `key` must be a string that:
            - (axis=0): Matches a column label
            - (axis=1): Matches an index label
  
          Parameters
          ----------
          key : str
              Potential label name
          axis : int, default 0
              Axis perpendicular to the axis that labels are associated with
              (0 means search for column labels, 1 means search for index labels)
  
          Returns
          -------
          is_label: bool
          """
          axis = self._get_axis_number(axis)
          other_axes = (ax for ax in range(self._AXIS_LEN) if ax != axis)
  
          return (
              key is not None
              and is_hashable(key)
              and any(key in self.axes[ax] for ax in other_axes)
          )
  
      @final
      def _is_label_or_level_reference(self, key: str, axis: int = 0) -> bool_t:
          """
          Test whether a key is a label or level reference for a given axis.
  
          To be considered either a label or a level reference, `key` must be a
          string that:
            - (axis=0): Matches a column label or an index level
            - (axis=1): Matches an index label or a column level
  
          Parameters
          ----------
          key : str
              Potential label or level name
          axis : int, default 0
              Axis that levels are associated with (0 for index, 1 for columns)
  
          Returns
          -------
          bool
          """
          return self._is_level_reference(key, axis=axis) or self._is_label_reference(
              key, axis=axis
          )
  
      @final
      def _check_label_or_level_ambiguity(self, key, axis: int = 0) -> None:
          """
          Check whether `key` is ambiguous.
  
          By ambiguous, we mean that it matches both a level of the input
          `axis` and a label of the other axis.
  
          Parameters
          ----------
          key : str or object
              Label or level name.
          axis : int, default 0
              Axis that levels are associated with (0 for index, 1 for columns).
  
          Raises
          ------
          ValueError: `key` is ambiguous
          """
          axis = self._get_axis_number(axis)
          other_axes = (ax for ax in range(self._AXIS_LEN) if ax != axis)
  
          if (
              key is not None
              and is_hashable(key)
              and key in self.axes[axis].names
              and any(key in self.axes[ax] for ax in other_axes)
          ):
  
              # Build an informative and grammatical warning
              level_article, level_type = (
                  ("an", "index") if axis == 0 else ("a", "column")
              )
  
              label_article, label_type = (
                  ("a", "column") if axis == 0 else ("an", "index")
              )
  
              msg = (
                  f"'{key}' is both {level_article} {level_type} level and "
                  f"{label_article} {label_type} label, which is ambiguous."
              )
              raise ValueError(msg)
  
      @final
      def _get_label_or_level_values(self, key: str, axis: int = 0) -> np.ndarray:
          """
          Return a 1-D array of values associated with `key`, a label or level
          from the given `axis`.
  
          Retrieval logic:
            - (axis=0): Return column values if `key` matches a column label.
              Otherwise return index level values if `key` matches an index
              level.
            - (axis=1): Return row values if `key` matches an index label.
              Otherwise return column level values if 'key' matches a column
              level
  
          Parameters
          ----------
          key : str
              Label or level name.
          axis : int, default 0
              Axis that levels are associated with (0 for index, 1 for columns)
  
          Returns
          -------
          values : np.ndarray
  
          Raises
          ------
          KeyError
              if `key` matches neither a label nor a level
          ValueError
              if `key` matches multiple labels
          FutureWarning
              if `key` is ambiguous. This will become an ambiguity error in a
              future version
          """
          axis = self._get_axis_number(axis)
          other_axes = [ax for ax in range(self._AXIS_LEN) if ax != axis]
  
          if self._is_label_reference(key, axis=axis):
              self._check_label_or_level_ambiguity(key, axis=axis)
              values = self.xs(key, axis=other_axes[0])._values
          elif self._is_level_reference(key, axis=axis):
              values = self.axes[axis].get_level_values(key)._values
          else:
              raise KeyError(key)
  
          # Check for duplicates
          if values.ndim > 1:
  
              if other_axes and isinstance(self._get_axis(other_axes[0]), MultiIndex):
                  multi_message = (
                      "\n"
                      "For a multi-index, the label must be a "
                      "tuple with elements corresponding to each level."
                  )
              else:
                  multi_message = ""
  
              label_axis_name = "column" if axis == 0 else "index"
              raise ValueError(
                  f"The {label_axis_name} label '{key}' is not unique.{multi_message}"
              )
  
          return values
  
      @final
      def _drop_labels_or_levels(self, keys, axis: int = 0):
          """
          Drop labels and/or levels for the given `axis`.
  
          For each key in `keys`:
            - (axis=0): If key matches a column label then drop the column.
              Otherwise if key matches an index level then drop the level.
            - (axis=1): If key matches an index label then drop the row.
              Otherwise if key matches a column level then drop the level.
  
          Parameters
          ----------
          keys : str or list of str
              labels or levels to drop
          axis : int, default 0
              Axis that levels are associated with (0 for index, 1 for columns)
  
          Returns
          -------
          dropped: DataFrame
  
          Raises
          ------
          ValueError
              if any `keys` match neither a label nor a level
          """
          axis = self._get_axis_number(axis)
  
          # Validate keys
          keys = com.maybe_make_list(keys)
          invalid_keys = [
              k for k in keys if not self._is_label_or_level_reference(k, axis=axis)
          ]
  
          if invalid_keys:
              raise ValueError(
                  "The following keys are not valid labels or "
                  f"levels for axis {axis}: {invalid_keys}"
              )
  
          # Compute levels and labels to drop
          levels_to_drop = [k for k in keys if self._is_level_reference(k, axis=axis)]
  
          labels_to_drop = [k for k in keys if not self._is_level_reference(k, axis=axis)]
  
          # Perform copy upfront and then use inplace operations below.
          # This ensures that we always perform exactly one copy.
          # ``copy`` and/or ``inplace`` options could be added in the future.
          dropped = self.copy()
  
          if axis == 0:
              # Handle dropping index levels
              if levels_to_drop:
                  dropped.reset_index(levels_to_drop, drop=True, inplace=True)
  
              # Handle dropping columns labels
              if labels_to_drop:
                  dropped.drop(labels_to_drop, axis=1, inplace=True)
          else:
              # Handle dropping column levels
              if levels_to_drop:
                  if isinstance(dropped.columns, MultiIndex):
                      # Drop the specified levels from the MultiIndex
                      dropped.columns = dropped.columns.droplevel(levels_to_drop)
                  else:
                      # Drop the last level of Index by replacing with
                      # a RangeIndex
                      dropped.columns = RangeIndex(dropped.columns.size)
  
              # Handle dropping index labels
              if labels_to_drop:
                  dropped.drop(labels_to_drop, axis=0, inplace=True)
  
          return dropped
  
      # ----------------------------------------------------------------------
      # Iteration
  
      # https://github.com/python/typeshed/issues/2148#issuecomment-520783318
      # Incompatible types in assignment (expression has type "None", base class
      # "object" defined the type as "Callable[[object], int]")
      __hash__: None  # type: ignore[assignment]
  
      def __iter__(self):
          """
          Iterate over info axis.
  
          Returns
          -------
          iterator
              Info axis as iterator.
          """
          return iter(self._info_axis)
  
      # can we get a better explanation of this?
      def keys(self):
          """
          Get the 'info axis' (see Indexing for more).
  
          This is index for Series, columns for DataFrame.
  
          Returns
          -------
          Index
              Info axis.
          """
          return self._info_axis
  
      def items(self):
          """
          Iterate over (label, values) on info axis
  
          This is index for Series and columns for DataFrame.
  
          Returns
          -------
          Generator
          """
          for h in self._info_axis:
              yield h, self[h]
  
      @doc(items)
      def iteritems(self):
          return self.items()
  
      def __len__(self) -> int:
          """Returns length of info axis"""
          return len(self._info_axis)
  
      @final
      def __contains__(self, key) -> bool_t:
          """True if the key is in the info axis"""
          return key in self._info_axis
  
      @property
      def empty(self) -> bool_t:
          """
          Indicator whether DataFrame is empty.
  
          True if DataFrame is entirely empty (no items), meaning any of the
          axes are of length 0.
  
          Returns
          -------
          bool
              If DataFrame is empty, return True, if not return False.
  
          See Also
          --------
          Series.dropna : Return series without null values.
          DataFrame.dropna : Return DataFrame with labels on given axis omitted
              where (all or any) data are missing.
  
          Notes
          -----
          If DataFrame contains only NaNs, it is still not considered empty. See
          the example below.
  
          Examples
          --------
          An example of an actual empty DataFrame. Notice the index is empty:
  
          >>> df_empty = pd.DataFrame({'A' : []})
          >>> df_empty
          Empty DataFrame
          Columns: [A]
          Index: []
          >>> df_empty.empty
          True
  
          If we only have NaNs in our DataFrame, it is not considered empty! We
          will need to drop the NaNs to make the DataFrame empty:
  
          >>> df = pd.DataFrame({'A' : [np.nan]})
          >>> df
              A
          0 NaN
          >>> df.empty
          False
          >>> df.dropna().empty
          True
          """
          return any(len(self._get_axis(a)) == 0 for a in self._AXIS_ORDERS)
  
      # ----------------------------------------------------------------------
      # Array Interface
  
      # This is also set in IndexOpsMixin
      # GH#23114 Ensure ndarray.__op__(DataFrame) returns NotImplemented
      __array_priority__ = 1000
  
      def __array__(self, dtype: npt.DTypeLike | None = None) -> np.ndarray:
          return np.asarray(self._values, dtype=dtype)
  
      def __array_wrap__(
          self,
          result: np.ndarray,
          context: tuple[Callable, tuple[Any, ...], int] | None = None,
      ):
          """
          Gets called after a ufunc and other functions.
  
          Parameters
          ----------
          result: np.ndarray
              The result of the ufunc or other function called on the NumPy array
              returned by __array__
          context: tuple of (func, tuple, int)
              This parameter is returned by ufuncs as a 3-element tuple: (name of the
              ufunc, arguments of the ufunc, domain of the ufunc), but is not set by
              other numpy functions.q
  
          Notes
          -----
          Series implements __array_ufunc_ so this not called for ufunc on Series.
          """
          res = lib.item_from_zerodim(result)
          if is_scalar(res):
              # e.g. we get here with np.ptp(series)
              # ptp also requires the item_from_zerodim
              return res
          d = self._construct_axes_dict(self._AXIS_ORDERS, copy=False)
          # error: Argument 1 to "NDFrame" has incompatible type "ndarray";
          # expected "BlockManager"
          return self._constructor(res, **d).__finalize__(  # type: ignore[arg-type]
              self, method="__array_wrap__"
          )
  
      def __array_ufunc__(
          self, ufunc: np.ufunc, method: str, *inputs: Any, **kwargs: Any
      ):
          return arraylike.array_ufunc(self, ufunc, method, *inputs, **kwargs)
  
      # ideally we would define this to avoid the getattr checks, but
      # is slower
      # @property
      # def __array_interface__(self):
      #    """ provide numpy array interface method """
      #    values = self.values
      #    return dict(typestr=values.dtype.str,shape=values.shape,data=values)
  
      # ----------------------------------------------------------------------
      # Picklability
  
      @final
      def __getstate__(self) -> dict[str, Any]:
          meta = {k: getattr(self, k, None) for k in self._metadata}
          return {
              "_mgr": self._mgr,
              "_typ": self._typ,
              "_metadata": self._metadata,
              "attrs": self.attrs,
              "_flags": {k: self.flags[k] for k in self.flags._keys},
              **meta,
          }
  
      @final
      def __setstate__(self, state):
          if isinstance(state, BlockManager):
              self._mgr = state
          elif isinstance(state, dict):
              if "_data" in state and "_mgr" not in state:
                  # compat for older pickles
                  state["_mgr"] = state.pop("_data")
              typ = state.get("_typ")
              if typ is not None:
                  attrs = state.get("_attrs", {})
                  object.__setattr__(self, "_attrs", attrs)
                  flags = state.get("_flags", {"allows_duplicate_labels": True})
                  object.__setattr__(self, "_flags", Flags(self, **flags))
  
                  # set in the order of internal names
                  # to avoid definitional recursion
                  # e.g. say fill_value needing _mgr to be
                  # defined
                  meta = set(self._internal_names + self._metadata)
                  for k in list(meta):
                      if k in state and k != "_flags":
                          v = state[k]
                          object.__setattr__(self, k, v)
  
                  for k, v in state.items():
                      if k not in meta:
                          object.__setattr__(self, k, v)
  
              else:
                  raise NotImplementedError("Pre-0.12 pickles are no longer supported")
          elif len(state) == 2:
              raise NotImplementedError("Pre-0.12 pickles are no longer supported")
  
          self._item_cache = {}
  
      # ----------------------------------------------------------------------
      # Rendering Methods
  
      def __repr__(self) -> str:
          # string representation based upon iterating over self
          # (since, by definition, `PandasContainers` are iterable)
          prepr = f"[{','.join(map(pprint_thing, self))}]"
          return f"{type(self).__name__}({prepr})"
  
      @final
      def _repr_latex_(self):
          """
          Returns a LaTeX representation for a particular object.
          Mainly for use with nbconvert (jupyter notebook conversion to pdf).
          """
          if config.get_option("display.latex.repr"):
              return self.to_latex()
          else:
              return None
  
      @final
      def _repr_data_resource_(self):
          """
          Not a real Jupyter special repr method, but we use the same
          naming convention.
          """
          if config.get_option("display.html.table_schema"):
              data = self.head(config.get_option("display.max_rows"))
  
              as_json = data.to_json(orient="table")
              as_json = cast(str, as_json)
              return json.loads(as_json, object_pairs_hook=collections.OrderedDict)
  
      # ----------------------------------------------------------------------
      # I/O Methods
  
      @final
      @doc(klass="object", storage_options=_shared_docs["storage_options"])
      def to_excel(
          self,
          excel_writer,
          sheet_name: str = "Sheet1",
          na_rep: str = "",
          float_format: str | None = None,
          columns=None,
          header=True,
          index=True,
          index_label=None,
          startrow=0,
          startcol=0,
          engine=None,
          merge_cells=True,
          encoding=None,
          inf_rep="inf",
          verbose=True,
          freeze_panes=None,
          storage_options: StorageOptions = None,
      ) -> None:
          """
          Write {klass} to an Excel sheet.
  
          To write a single {klass} to an Excel .xlsx file it is only necessary to
          specify a target file name. To write to multiple sheets it is necessary to
          create an `ExcelWriter` object with a target file name, and specify a sheet
          in the file to write to.
  
          Multiple sheets may be written to by specifying unique `sheet_name`.
          With all data written to the file it is necessary to save the changes.
          Note that creating an `ExcelWriter` object with a file name that already
          exists will result in the contents of the existing file being erased.
  
          Parameters
          ----------
          excel_writer : path-like, file-like, or ExcelWriter object
              File path or existing ExcelWriter.
          sheet_name : str, default 'Sheet1'
              Name of sheet which will contain DataFrame.
          na_rep : str, default ''
              Missing data representation.
          float_format : str, optional
              Format string for floating point numbers. For example
              ``float_format="%.2f"`` will format 0.1234 to 0.12.
          columns : sequence or list of str, optional
              Columns to write.
          header : bool or list of str, default True
              Write out the column names. If a list of string is given it is
              assumed to be aliases for the column names.
          index : bool, default True
              Write row names (index).
          index_label : str or sequence, optional
              Column label for index column(s) if desired. If not specified, and
              `header` and `index` are True, then the index names are used. A
              sequence should be given if the DataFrame uses MultiIndex.
          startrow : int, default 0
              Upper left cell row to dump data frame.
          startcol : int, default 0
              Upper left cell column to dump data frame.
          engine : str, optional
              Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set this
              via the options ``io.excel.xlsx.writer``, ``io.excel.xls.writer``, and
              ``io.excel.xlsm.writer``.
  
              .. deprecated:: 1.2.0
  
                  As the `xlwt <https://pypi.org/project/xlwt/>`__ package is no longer
                  maintained, the ``xlwt`` engine will be removed in a future version
                  of pandas.
  
          merge_cells : bool, default True
              Write MultiIndex and Hierarchical Rows as merged cells.
          encoding : str, optional
              Encoding of the resulting excel file. Only necessary for xlwt,
              other writers support unicode natively.
          inf_rep : str, default 'inf'
              Representation for infinity (there is no native representation for
              infinity in Excel).
          verbose : bool, default True
              Display more information in the error logs.
          freeze_panes : tuple of int (length 2), optional
              Specifies the one-based bottommost row and rightmost column that
              is to be frozen.
          {storage_options}
  
              .. versionadded:: 1.2.0
  
          See Also
          --------
          to_csv : Write DataFrame to a comma-separated values (csv) file.
          ExcelWriter : Class for writing DataFrame objects into excel sheets.
          read_excel : Read an Excel file into a pandas DataFrame.
          read_csv : Read a comma-separated values (csv) file into DataFrame.
  
          Notes
          -----
          For compatibility with :meth:`~DataFrame.to_csv`,
          to_excel serializes lists and dicts to strings before writing.
  
          Once a workbook has been saved it is not possible to write further
          data without rewriting the whole workbook.
  
          Examples
          --------
  
          Create, write to and save a workbook:
  
          >>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']],
          ...                    index=['row 1', 'row 2'],
          ...                    columns=['col 1', 'col 2'])
          >>> df1.to_excel("output.xlsx")  # doctest: +SKIP
  
          To specify the sheet name:
  
          >>> df1.to_excel("output.xlsx",
          ...              sheet_name='Sheet_name_1')  # doctest: +SKIP
  
          If you wish to write to more than one sheet in the workbook, it is
          necessary to specify an ExcelWriter object:
  
          >>> df2 = df1.copy()
          >>> with pd.ExcelWriter('output.xlsx') as writer:  # doctest: +SKIP
          ...     df1.to_excel(writer, sheet_name='Sheet_name_1')
          ...     df2.to_excel(writer, sheet_name='Sheet_name_2')
  
          ExcelWriter can also be used to append to an existing Excel file:
  
          >>> with pd.ExcelWriter('output.xlsx',
          ...                     mode='a') as writer:  # doctest: +SKIP
          ...     df.to_excel(writer, sheet_name='Sheet_name_3')
  
          To set the library that is used to write the Excel file,
          you can pass the `engine` keyword (the default engine is
          automatically chosen depending on the file extension):
  
          >>> df1.to_excel('output1.xlsx', engine='xlsxwriter')  # doctest: +SKIP
          """
  
          df = self if isinstance(self, ABCDataFrame) else self.to_frame()
  
          from pandas.io.formats.excel import ExcelFormatter
  
          formatter = ExcelFormatter(
              df,
              na_rep=na_rep,
              cols=columns,
              header=header,
              float_format=float_format,
              index=index,
              index_label=index_label,
              merge_cells=merge_cells,
              inf_rep=inf_rep,
          )
          formatter.write(
              excel_writer,
              sheet_name=sheet_name,
              startrow=startrow,
              startcol=startcol,
              freeze_panes=freeze_panes,
              engine=engine,
              storage_options=storage_options,
          )
  
      @final
      @doc(storage_options=_shared_docs["storage_options"])
      def to_json(
          self,
          path_or_buf: FilePathOrBuffer | None = None,
          orient: str | None = None,
          date_format: str | None = None,
          double_precision: int = 10,
          force_ascii: bool_t = True,
          date_unit: str = "ms",
          default_handler: Callable[[Any], JSONSerializable] | None = None,
          lines: bool_t = False,
          compression: CompressionOptions = "infer",
          index: bool_t = True,
          indent: int | None = None,
          storage_options: StorageOptions = None,
      ) -> str | None:
          """
          Convert the object to a JSON string.
  
          Note NaN's and None will be converted to null and datetime objects
          will be converted to UNIX timestamps.
  
          Parameters
          ----------
          path_or_buf : str or file handle, optional
              File path or object. If not specified, the result is returned as
              a string.
          orient : str
              Indication of expected JSON string format.
  
              * Series:
  
                  - default is 'index'
                  - allowed values are: {{'split', 'records', 'index', 'table'}}.
  
              * DataFrame:
  
                  - default is 'columns'
                  - allowed values are: {{'split', 'records', 'index', 'columns',
                    'values', 'table'}}.
  
              * The format of the JSON string:
  
                  - 'split' : dict like {{'index' -> [index], 'columns' -> [columns],
                    'data' -> [values]}}
                  - 'records' : list like [{{column -> value}}, ... , {{column -> value}}]
                  - 'index' : dict like {{index -> {{column -> value}}}}
                  - 'columns' : dict like {{column -> {{index -> value}}}}
                  - 'values' : just the values array
                  - 'table' : dict like {{'schema': {{schema}}, 'data': {{data}}}}
  
                  Describing the data, where data component is like ``orient='records'``.
  
          date_format : {{None, 'epoch', 'iso'}}
              Type of date conversion. 'epoch' = epoch milliseconds,
              'iso' = ISO8601. The default depends on the `orient`. For
              ``orient='table'``, the default is 'iso'. For all other orients,
              the default is 'epoch'.
          double_precision : int, default 10
              The number of decimal places to use when encoding
              floating point values.
          force_ascii : bool, default True
              Force encoded string to be ASCII.
          date_unit : str, default 'ms' (milliseconds)
              The time unit to encode to, governs timestamp and ISO8601
              precision.  One of 's', 'ms', 'us', 'ns' for second, millisecond,
              microsecond, and nanosecond respectively.
          default_handler : callable, default None
              Handler to call if object cannot otherwise be converted to a
              suitable format for JSON. Should receive a single argument which is
              the object to convert and return a serialisable object.
          lines : bool, default False
              If 'orient' is 'records' write out line-delimited json format. Will
              throw ValueError if incorrect 'orient' since others are not
              list-like.
  
          compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}
  
              A string representing the compression to use in the output file,
              only used when the first argument is a filename. By default, the
              compression is inferred from the filename.
          index : bool, default True
              Whether to include the index values in the JSON string. Not
              including the index (``index=False``) is only supported when
              orient is 'split' or 'table'.
          indent : int, optional
             Length of whitespace used to indent each record.
  
             .. versionadded:: 1.0.0
  
          {storage_options}
  
              .. versionadded:: 1.2.0
  
          Returns
          -------
          None or str
              If path_or_buf is None, returns the resulting json format as a
              string. Otherwise returns None.
  
          See Also
          --------
          read_json : Convert a JSON string to pandas object.
  
          Notes
          -----
          The behavior of ``indent=0`` varies from the stdlib, which does not
          indent the output but does insert newlines. Currently, ``indent=0``
          and the default ``indent=None`` are equivalent in pandas, though this
          may change in a future release.
  
          ``orient='table'`` contains a 'pandas_version' field under 'schema'.
          This stores the version of `pandas` used in the latest revision of the
          schema.
  
          Examples
          --------
          >>> import json
          >>> df = pd.DataFrame(
          ...     [["a", "b"], ["c", "d"]],
          ...     index=["row 1", "row 2"],
          ...     columns=["col 1", "col 2"],
          ... )
  
          >>> result = df.to_json(orient="split")
          >>> parsed = json.loads(result)
          >>> json.dumps(parsed, indent=4)  # doctest: +SKIP
          {{
              "columns": [
                  "col 1",
                  "col 2"
              ],
              "index": [
                  "row 1",
                  "row 2"
              ],
              "data": [
                  [
                      "a",
                      "b"
                  ],
                  [
                      "c",
                      "d"
                  ]
              ]
          }}
  
          Encoding/decoding a Dataframe using ``'records'`` formatted JSON.
          Note that index labels are not preserved with this encoding.
  
          >>> result = df.to_json(orient="records")
          >>> parsed = json.loads(result)
          >>> json.dumps(parsed, indent=4)  # doctest: +SKIP
          [
              {{
                  "col 1": "a",
                  "col 2": "b"
              }},
              {{
                  "col 1": "c",
                  "col 2": "d"
              }}
          ]
  
          Encoding/decoding a Dataframe using ``'index'`` formatted JSON:
  
          >>> result = df.to_json(orient="index")
          >>> parsed = json.loads(result)
          >>> json.dumps(parsed, indent=4)  # doctest: +SKIP
          {{
              "row 1": {{
                  "col 1": "a",
                  "col 2": "b"
              }},
              "row 2": {{
                  "col 1": "c",
                  "col 2": "d"
              }}
          }}
  
          Encoding/decoding a Dataframe using ``'columns'`` formatted JSON:
  
          >>> result = df.to_json(orient="columns")
          >>> parsed = json.loads(result)
          >>> json.dumps(parsed, indent=4)  # doctest: +SKIP
          {{
              "col 1": {{
                  "row 1": "a",
                  "row 2": "c"
              }},
              "col 2": {{
                  "row 1": "b",
                  "row 2": "d"
              }}
          }}
  
          Encoding/decoding a Dataframe using ``'values'`` formatted JSON:
  
          >>> result = df.to_json(orient="values")
          >>> parsed = json.loads(result)
          >>> json.dumps(parsed, indent=4)  # doctest: +SKIP
          [
              [
                  "a",
                  "b"
              ],
              [
                  "c",
                  "d"
              ]
          ]
  
          Encoding with Table Schema:
  
          >>> result = df.to_json(orient="table")
          >>> parsed = json.loads(result)
          >>> json.dumps(parsed, indent=4)  # doctest: +SKIP
          {{
              "schema": {{
                  "fields": [
                      {{
                          "name": "index",
                          "type": "string"
                      }},
                      {{
                          "name": "col 1",
                          "type": "string"
                      }},
                      {{
                          "name": "col 2",
                          "type": "string"
                      }}
                  ],
                  "primaryKey": [
                      "index"
                  ],
                  "pandas_version": "0.20.0"
              }},
              "data": [
                  {{
                      "index": "row 1",
                      "col 1": "a",
                      "col 2": "b"
                  }},
                  {{
                      "index": "row 2",
                      "col 1": "c",
                      "col 2": "d"
                  }}
              ]
          }}
          """
          from pandas.io import json
  
          if date_format is None and orient == "table":
              date_format = "iso"
          elif date_format is None:
              date_format = "epoch"
  
          config.is_nonnegative_int(indent)
          indent = indent or 0
  
          return json.to_json(
              path_or_buf=path_or_buf,
              obj=self,
              orient=orient,
              date_format=date_format,
              double_precision=double_precision,
              force_ascii=force_ascii,
              date_unit=date_unit,
              default_handler=default_handler,
              lines=lines,
              compression=compression,
              index=index,
              indent=indent,
              storage_options=storage_options,
          )
  
      @final
      def to_hdf(
          self,
          path_or_buf,
          key: str,
          mode: str = "a",
          complevel: int | None = None,
          complib: str | None = None,
          append: bool_t = False,
          format: str | None = None,
          index: bool_t = True,
          min_itemsize: int | dict[str, int] | None = None,
          nan_rep=None,
          dropna: bool_t | None = None,
          data_columns: bool_t | list[str] | None = None,
          errors: str = "strict",
          encoding: str = "UTF-8",
      ) -> None:
          """
          Write the contained data to an HDF5 file using HDFStore.
  
          Hierarchical Data Format (HDF) is self-describing, allowing an
          application to interpret the structure and contents of a file with
          no outside information. One HDF file can hold a mix of related objects
          which can be accessed as a group or as individual objects.
  
          In order to add another DataFrame or Series to an existing HDF file
          please use append mode and a different a key.
  
          .. warning::
  
             One can store a subclass of ``DataFrame`` or ``Series`` to HDF5,
             but the type of the subclass is lost upon storing.
  
          For more information see the :ref:`user guide <io.hdf5>`.
  
          Parameters
          ----------
          path_or_buf : str or pandas.HDFStore
              File path or HDFStore object.
          key : str
              Identifier for the group in the store.
          mode : {'a', 'w', 'r+'}, default 'a'
              Mode to open file:
  
              - 'w': write, a new file is created (an existing file with
                the same name would be deleted).
              - 'a': append, an existing file is opened for reading and
                writing, and if the file does not exist it is created.
              - 'r+': similar to 'a', but the file must already exist.
          complevel : {0-9}, optional
              Specifies a compression level for data.
              A value of 0 disables compression.
          complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
              Specifies the compression library to be used.
              As of v0.20.2 these additional compressors for Blosc are supported
              (default if no compressor specified: 'blosc:blosclz'):
              {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
              'blosc:zlib', 'blosc:zstd'}.
              Specifying a compression library which is not available issues
              a ValueError.
          append : bool, default False
              For Table formats, append the input data to the existing.
          format : {'fixed', 'table', None}, default 'fixed'
              Possible values:
  
              - 'fixed': Fixed format. Fast writing/reading. Not-appendable,
                nor searchable.
              - 'table': Table format. Write as a PyTables Table structure
                which may perform worse but allow more flexible operations
                like searching / selecting subsets of the data.
              - If None, pd.get_option('io.hdf.default_format') is checked,
                followed by fallback to "fixed"
          errors : str, default 'strict'
              Specifies how encoding and decoding errors are to be handled.
              See the errors argument for :func:`open` for a full list
              of options.
          encoding : str, default "UTF-8"
          min_itemsize : dict or int, optional
              Map column names to minimum string sizes for columns.
          nan_rep : Any, optional
              How to represent null values as str.
              Not allowed with append=True.
          data_columns : list of columns or True, optional
              List of columns to create as indexed data columns for on-disk
              queries, or True to use all columns. By default only the axes
              of the object are indexed. See :ref:`io.hdf5-query-data-columns`.
              Applicable only to format='table'.
  
          See Also
          --------
          read_hdf : Read from HDF file.
          DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
          DataFrame.to_sql : Write to a SQL table.
          DataFrame.to_feather : Write out feather-format for DataFrames.
          DataFrame.to_csv : Write out to a csv file.
  
          Examples
          --------
          >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
          ...                   index=['a', 'b', 'c'])
          >>> df.to_hdf('data.h5', key='df', mode='w')
  
          We can add another object to the same file:
  
          >>> s = pd.Series([1, 2, 3, 4])
          >>> s.to_hdf('data.h5', key='s')
  
          Reading from HDF file:
  
          >>> pd.read_hdf('data.h5', 'df')
          A  B
          a  1  4
          b  2  5
          c  3  6
          >>> pd.read_hdf('data.h5', 's')
          0    1
          1    2
          2    3
          3    4
          dtype: int64
  
          Deleting file with data:
  
          >>> import os
          >>> os.remove('data.h5')
          """
          from pandas.io import pytables
  
          pytables.to_hdf(
              path_or_buf,
              key,
              self,
              mode=mode,
              complevel=complevel,
              complib=complib,
              append=append,
              format=format,
              index=index,
              min_itemsize=min_itemsize,
              nan_rep=nan_rep,
              dropna=dropna,
              data_columns=data_columns,
              errors=errors,
              encoding=encoding,
          )
  
      @final
      def to_sql(
          self,
          name: str,
          con,
          schema=None,
          if_exists: str = "fail",
          index: bool_t = True,
          index_label=None,
          chunksize=None,
          dtype: DtypeArg | None = None,
          method=None,
      ) -> None:
          """
          Write records stored in a DataFrame to a SQL database.
  
          Databases supported by SQLAlchemy [1]_ are supported. Tables can be
          newly created, appended to, or overwritten.
  
          Parameters
          ----------
          name : str
              Name of SQL table.
          con : sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection
              Using SQLAlchemy makes it possible to use any DB supported by that
              library. Legacy support is provided for sqlite3.Connection objects. The user
              is responsible for engine disposal and connection closure for the SQLAlchemy
              connectable See `here \
                  <https://docs.sqlalchemy.org/en/13/core/connections.html>`_.
  
          schema : str, optional
              Specify the schema (if database flavor supports this). If None, use
              default schema.
          if_exists : {'fail', 'replace', 'append'}, default 'fail'
              How to behave if the table already exists.
  
              * fail: Raise a ValueError.
              * replace: Drop the table before inserting new values.
              * append: Insert new values to the existing table.
  
          index : bool, default True
              Write DataFrame index as a column. Uses `index_label` as the column
              name in the table.
          index_label : str or sequence, default None
              Column label for index column(s). If None is given (default) and
              `index` is True, then the index names are used.
              A sequence should be given if the DataFrame uses MultiIndex.
          chunksize : int, optional
              Specify the number of rows in each batch to be written at a time.
              By default, all rows will be written at once.
          dtype : dict or scalar, optional
              Specifying the datatype for columns. If a dictionary is used, the
              keys should be the column names and the values should be the
              SQLAlchemy types or strings for the sqlite3 legacy mode. If a
              scalar is provided, it will be applied to all columns.
          method : {None, 'multi', callable}, optional
              Controls the SQL insertion clause used:
  
              * None : Uses standard SQL ``INSERT`` clause (one per row).
              * 'multi': Pass multiple values in a single ``INSERT`` clause.
              * callable with signature ``(pd_table, conn, keys, data_iter)``.
  
              Details and a sample callable implementation can be found in the
              section :ref:`insert method <io.sql.method>`.
  
          Raises
          ------
          ValueError
              When the table already exists and `if_exists` is 'fail' (the
              default).
  
          See Also
          --------
          read_sql : Read a DataFrame from a table.
  
          Notes
          -----
          Timezone aware datetime columns will be written as
          ``Timestamp with timezone`` type with SQLAlchemy if supported by the
          database. Otherwise, the datetimes will be stored as timezone unaware
          timestamps local to the original timezone.
  
          References
          ----------
          .. [1] https://docs.sqlalchemy.org
          .. [2] https://www.python.org/dev/peps/pep-0249/
  
          Examples
          --------
          Create an in-memory SQLite database.
  
          >>> from sqlalchemy import create_engine
          >>> engine = create_engine('sqlite://', echo=False)
  
          Create a table from scratch with 3 rows.
  
          >>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
          >>> df
               name
          0  User 1
          1  User 2
          2  User 3
  
          >>> df.to_sql('users', con=engine)
          >>> engine.execute("SELECT * FROM users").fetchall()
          [(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]
  
          An `sqlalchemy.engine.Connection` can also be passed to `con`:
  
          >>> with engine.begin() as connection:
          ...     df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
          ...     df1.to_sql('users', con=connection, if_exists='append')
  
          This is allowed to support operations that require that the same
          DBAPI connection is used for the entire operation.
  
          >>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
          >>> df2.to_sql('users', con=engine, if_exists='append')
          >>> engine.execute("SELECT * FROM users").fetchall()
          [(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
           (0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
           (1, 'User 7')]
  
          Overwrite the table with just ``df2``.
  
          >>> df2.to_sql('users', con=engine, if_exists='replace',
          ...            index_label='id')
          >>> engine.execute("SELECT * FROM users").fetchall()
          [(0, 'User 6'), (1, 'User 7')]
  
          Specify the dtype (especially useful for integers with missing values).
          Notice that while pandas is forced to store the data as floating point,
          the database supports nullable integers. When fetching the data with
          Python, we get back integer scalars.
  
          >>> df = pd.DataFrame({"A": [1, None, 2]})
          >>> df
               A
          0  1.0
          1  NaN
          2  2.0
  
          >>> from sqlalchemy.types import Integer
          >>> df.to_sql('integers', con=engine, index=False,
          ...           dtype={"A": Integer()})
  
          >>> engine.execute("SELECT * FROM integers").fetchall()
          [(1,), (None,), (2,)]
          """
          from pandas.io import sql
  
          sql.to_sql(
              self,
              name,
              con,
              schema=schema,
              if_exists=if_exists,
              index=index,
              index_label=index_label,
              chunksize=chunksize,
              dtype=dtype,
              method=method,
          )
  
      @final
      @doc(storage_options=_shared_docs["storage_options"])
      def to_pickle(
          self,
          path,
          compression: CompressionOptions = "infer",
          protocol: int = pickle.HIGHEST_PROTOCOL,
          storage_options: StorageOptions = None,
      ) -> None:
          """
          Pickle (serialize) object to file.
  
          Parameters
          ----------
          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.
              Compression mode may be any of the following possible
              values: {{‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}}. If compression
              mode is ‘infer’ and path_or_buf is path-like, then detect
              compression mode from the following extensions:
              ‘.gz’, ‘.bz2’, ‘.zip’ or ‘.xz’. (otherwise no compression).
              If dict given and mode is ‘zip’ or inferred as ‘zip’, other entries
              passed as additional compression options.
          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 are 0, 1, 2, 3, 4, 5. 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.
  
          {storage_options}
  
              .. versionadded:: 1.2.0
  
          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
          >>> original_df.to_pickle("./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")
          """
          from pandas.io.pickle import to_pickle
  
          to_pickle(
              self,
              path,
              compression=compression,
              protocol=protocol,
              storage_options=storage_options,
          )
  
      @final
      def to_clipboard(
          self, excel: bool_t = True, sep: str | None = None, **kwargs
      ) -> None:
          r"""
          Copy object to the system clipboard.
  
          Write a text representation of object to the system clipboard.
          This can be pasted into Excel, for example.
  
          Parameters
          ----------
          excel : bool, default True
              Produce output in a csv format for easy pasting into excel.
  
              - True, use the provided separator for csv pasting.
              - False, write a string representation of the object to the clipboard.
  
          sep : str, default ``'\t'``
              Field delimiter.
          **kwargs
              These parameters will be passed to DataFrame.to_csv.
  
          See Also
          --------
          DataFrame.to_csv : Write a DataFrame to a comma-separated values
              (csv) file.
          read_clipboard : Read text from clipboard and pass to read_csv.
  
          Notes
          -----
          Requirements for your platform.
  
            - Linux : `xclip`, or `xsel` (with `PyQt4` modules)
            - Windows : none
            - macOS : none
  
          Examples
          --------
          Copy the contents of a DataFrame to the clipboard.
  
          >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
  
          >>> df.to_clipboard(sep=',')  # doctest: +SKIP
          ... # Wrote the following to the system clipboard:
          ... # ,A,B,C
          ... # 0,1,2,3
          ... # 1,4,5,6
  
          We can omit the index by passing the keyword `index` and setting
          it to false.
  
          >>> df.to_clipboard(sep=',', index=False)  # doctest: +SKIP
          ... # Wrote the following to the system clipboard:
          ... # A,B,C
          ... # 1,2,3
          ... # 4,5,6
          """
          from pandas.io import clipboards
  
          clipboards.to_clipboard(self, excel=excel, sep=sep, **kwargs)
  
      @final
      def to_xarray(self):
          """
          Return an xarray object from the pandas object.
  
          Returns
          -------
          xarray.DataArray or xarray.Dataset
              Data in the pandas structure converted to Dataset if the object is
              a DataFrame, or a DataArray if the object is a Series.
  
          See Also
          --------
          DataFrame.to_hdf : Write DataFrame to an HDF5 file.
          DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
  
          Notes
          -----
          See the `xarray docs <https://xarray.pydata.org/en/stable/>`__
  
          Examples
          --------
          >>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),
          ...                    ('parrot', 'bird', 24.0, 2),
          ...                    ('lion', 'mammal', 80.5, 4),
          ...                    ('monkey', 'mammal', np.nan, 4)],
          ...                   columns=['name', 'class', 'max_speed',
          ...                            'num_legs'])
          >>> df
               name   class  max_speed  num_legs
          0  falcon    bird      389.0         2
          1  parrot    bird       24.0         2
          2    lion  mammal       80.5         4
          3  monkey  mammal        NaN         4
  
          >>> df.to_xarray()
          <xarray.Dataset>
          Dimensions:    (index: 4)
          Coordinates:
            * index      (index) int64 0 1 2 3
          Data variables:
              name       (index) object 'falcon' 'parrot' 'lion' 'monkey'
              class      (index) object 'bird' 'bird' 'mammal' 'mammal'
              max_speed  (index) float64 389.0 24.0 80.5 nan
              num_legs   (index) int64 2 2 4 4
  
          >>> df['max_speed'].to_xarray()
          <xarray.DataArray 'max_speed' (index: 4)>
          array([389. ,  24. ,  80.5,   nan])
          Coordinates:
            * index    (index) int64 0 1 2 3
  
          >>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
          ...                         '2018-01-02', '2018-01-02'])
          >>> df_multiindex = pd.DataFrame({'date': dates,
          ...                               'animal': ['falcon', 'parrot',
          ...                                          'falcon', 'parrot'],
          ...                               'speed': [350, 18, 361, 15]})
          >>> df_multiindex = df_multiindex.set_index(['date', 'animal'])
  
          >>> df_multiindex
                             speed
          date       animal
          2018-01-01 falcon    350
                     parrot     18
          2018-01-02 falcon    361
                     parrot     15
  
          >>> df_multiindex.to_xarray()
          <xarray.Dataset>
          Dimensions:  (animal: 2, date: 2)
          Coordinates:
            * date     (date) datetime64[ns] 2018-01-01 2018-01-02
            * animal   (animal) object 'falcon' 'parrot'
          Data variables:
              speed    (date, animal) int64 350 18 361 15
          """
          xarray = import_optional_dependency("xarray")
  
          if self.ndim == 1:
              return xarray.DataArray.from_series(self)
          else:
              return xarray.Dataset.from_dataframe(self)
  
      @final
      @doc(returns=fmt.return_docstring)
      def to_latex(
          self,
          buf=None,
          columns=None,
          col_space=None,
          header=True,
          index=True,
          na_rep="NaN",
          formatters=None,
          float_format=None,
          sparsify=None,
          index_names=True,
          bold_rows=False,
          column_format=None,
          longtable=None,
          escape=None,
          encoding=None,
          decimal=".",
          multicolumn=None,
          multicolumn_format=None,
          multirow=None,
          caption=None,
          label=None,
          position=None,
      ):
          r"""
          Render object to a LaTeX tabular, longtable, or nested table/tabular.
  
          Requires ``\usepackage{{booktabs}}``.  The output can be copy/pasted
          into a main LaTeX document or read from an external file
          with ``\input{{table.tex}}``.
  
          .. versionchanged:: 1.0.0
             Added caption and label arguments.
  
          .. versionchanged:: 1.2.0
             Added position argument, changed meaning of caption argument.
  
          Parameters
          ----------
          buf : str, Path or StringIO-like, optional, default None
              Buffer to write to. If None, the output is returned as a string.
          columns : list of label, optional
              The subset of columns to write. Writes all columns by default.
          col_space : int, optional
              The minimum width of each column.
          header : bool or list of str, default True
              Write out the column names. If a list of strings is given,
              it is assumed to be aliases for the column names.
          index : bool, default True
              Write row names (index).
          na_rep : str, default 'NaN'
              Missing data representation.
          formatters : list of functions or dict of {{str: function}}, optional
              Formatter functions to apply to columns' elements by position or
              name. The result of each function must be a unicode string.
              List must be of length equal to the number of columns.
          float_format : one-parameter function or str, optional, default None
              Formatter for floating point numbers. For example
              ``float_format="%.2f"`` and ``float_format="{{:0.2f}}".format`` will
              both result in 0.1234 being formatted as 0.12.
          sparsify : bool, optional
              Set to False for a DataFrame with a hierarchical index to print
              every multiindex key at each row. By default, the value will be
              read from the config module.
          index_names : bool, default True
              Prints the names of the indexes.
          bold_rows : bool, default False
              Make the row labels bold in the output.
          column_format : str, optional
              The columns format as specified in `LaTeX table format
              <https://en.wikibooks.org/wiki/LaTeX/Tables>`__ e.g. 'rcl' for 3
              columns. By default, 'l' will be used for all columns except
              columns of numbers, which default to 'r'.
          longtable : bool, optional
              By default, the value will be read from the pandas config
              module. Use a longtable environment instead of tabular. Requires
              adding a \usepackage{{longtable}} to your LaTeX preamble.
          escape : bool, optional
              By default, the value will be read from the pandas config
              module. When set to False prevents from escaping latex special
              characters in column names.
          encoding : str, optional
              A string representing the encoding to use in the output file,
              defaults to 'utf-8'.
          decimal : str, default '.'
              Character recognized as decimal separator, e.g. ',' in Europe.
          multicolumn : bool, default True
              Use \multicolumn to enhance MultiIndex columns.
              The default will be read from the config module.
          multicolumn_format : str, default 'l'
              The alignment for multicolumns, similar to `column_format`
              The default will be read from the config module.
          multirow : bool, default False
              Use \multirow to enhance MultiIndex rows. Requires adding a
              \usepackage{{multirow}} to your LaTeX preamble. Will print
              centered labels (instead of top-aligned) across the contained
              rows, separating groups via clines. The default will be read
              from the pandas config module.
          caption : str or tuple, optional
              Tuple (full_caption, short_caption),
              which results in ``\caption[short_caption]{{full_caption}}``;
              if a single string is passed, no short caption will be set.
  
              .. versionadded:: 1.0.0
  
              .. versionchanged:: 1.2.0
                 Optionally allow caption to be a tuple ``(full_caption, short_caption)``.
  
          label : str, optional
              The LaTeX label to be placed inside ``\label{{}}`` in the output.
              This is used with ``\ref{{}}`` in the main ``.tex`` file.
  
              .. versionadded:: 1.0.0
          position : str, optional
              The LaTeX positional argument for tables, to be placed after
              ``\begin{{}}`` in the output.
  
              .. versionadded:: 1.2.0
          {returns}
          See Also
          --------
          DataFrame.to_string : Render a DataFrame to a console-friendly
              tabular output.
          DataFrame.to_html : Render a DataFrame as an HTML table.
  
          Examples
          --------
          >>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'],
          ...                   mask=['red', 'purple'],
          ...                   weapon=['sai', 'bo staff']))
          >>> print(df.to_latex(index=False))  # doctest: +NORMALIZE_WHITESPACE
          \begin{{tabular}}{{lll}}
           \toprule
                 name &    mask &    weapon \\
           \midrule
              Raphael &     red &       sai \\
            Donatello &  purple &  bo staff \\
          \bottomrule
          \end{{tabular}}
          """
          # Get defaults from the pandas config
          if self.ndim == 1:
              self = self.to_frame()
          if longtable is None:
              longtable = config.get_option("display.latex.longtable")
          if escape is None:
              escape = config.get_option("display.latex.escape")
          if multicolumn is None:
              multicolumn = config.get_option("display.latex.multicolumn")
          if multicolumn_format is None:
              multicolumn_format = config.get_option("display.latex.multicolumn_format")
          if multirow is None:
              multirow = config.get_option("display.latex.multirow")
  
          self = cast("DataFrame", self)
          formatter = DataFrameFormatter(
              self,
              columns=columns,
              col_space=col_space,
              na_rep=na_rep,
              header=header,
              index=index,
              formatters=formatters,
              float_format=float_format,
              bold_rows=bold_rows,
              sparsify=sparsify,
              index_names=index_names,
              escape=escape,
              decimal=decimal,
          )
          return DataFrameRenderer(formatter).to_latex(
              buf=buf,
              column_format=column_format,
              longtable=longtable,
              encoding=encoding,
              multicolumn=multicolumn,
              multicolumn_format=multicolumn_format,
              multirow=multirow,
              caption=caption,
              label=label,
              position=position,
          )
  
      @final
      @doc(storage_options=_shared_docs["storage_options"])
      def to_csv(
          self,
          path_or_buf: FilePathOrBuffer[AnyStr] | None = None,
          sep: str = ",",
          na_rep: str = "",
          float_format: str | None = None,
          columns: Sequence[Hashable] | None = None,
          header: bool_t | list[str] = True,
          index: bool_t = True,
          index_label: IndexLabel | None = None,
          mode: str = "w",
          encoding: str | None = None,
          compression: CompressionOptions = "infer",
          quoting: int | None = None,
          quotechar: str = '"',
          line_terminator: str | None = None,
          chunksize: int | None = None,
          date_format: str | None = None,
          doublequote: bool_t = True,
          escapechar: str | None = None,
          decimal: str = ".",
          errors: str = "strict",
          storage_options: StorageOptions = None,
      ) -> str | None:
          r"""
          Write object to a comma-separated values (csv) file.
  
          Parameters
          ----------
          path_or_buf : str or file handle, default None
              File path or object, if None is provided the result is returned as
              a string.  If a non-binary file object is passed, it should be opened
              with `newline=''`, disabling universal newlines. If a binary
              file object is passed, `mode` might need to contain a `'b'`.
  
              .. versionchanged:: 1.2.0
  
                 Support for binary file objects was introduced.
  
          sep : str, default ','
              String of length 1. Field delimiter for the output file.
          na_rep : str, default ''
              Missing data representation.
          float_format : str, default None
              Format string for floating point numbers.
          columns : sequence, optional
              Columns to write.
          header : bool or list of str, default True
              Write out the column names. If a list of strings is given it is
              assumed to be aliases for the column names.
          index : bool, default True
              Write row names (index).
          index_label : str or sequence, or False, default None
              Column label for index column(s) if desired. If None is given, and
              `header` and `index` are True, then the index names are used. A
              sequence should be given if the object uses MultiIndex. If
              False do not print fields for index names. Use index_label=False
              for easier importing in R.
          mode : str
              Python write mode, default 'w'.
          encoding : str, optional
              A string representing the encoding to use in the output file,
              defaults to 'utf-8'. `encoding` is not supported if `path_or_buf`
              is a non-binary file object.
          compression : str or dict, default 'infer'
              If str, represents compression mode. If dict, value at 'method' is
              the compression mode. Compression mode may be any of the following
              possible values: {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}. If
              compression mode is 'infer' and `path_or_buf` is path-like, then
              detect compression mode from the following extensions: '.gz',
              '.bz2', '.zip' or '.xz'. (otherwise no compression). If dict given
              and mode is one of {{'zip', 'gzip', 'bz2'}}, or inferred as
              one of the above, other entries passed as
              additional compression options.
  
              .. versionchanged:: 1.0.0
  
                 May now be a dict with key 'method' as compression mode
                 and other entries as additional compression options if
                 compression mode is 'zip'.
  
              .. versionchanged:: 1.1.0
  
                 Passing compression options as keys in dict is
                 supported for compression modes 'gzip' and 'bz2'
                 as well as 'zip'.
  
              .. versionchanged:: 1.2.0
  
                  Compression is supported for binary file objects.
  
              .. versionchanged:: 1.2.0
  
                  Previous versions forwarded dict entries for 'gzip' to
                  `gzip.open` instead of `gzip.GzipFile` which prevented
                  setting `mtime`.
  
          quoting : optional constant from csv module
              Defaults to csv.QUOTE_MINIMAL. If you have set a `float_format`
              then floats are converted to strings and thus csv.QUOTE_NONNUMERIC
              will treat them as non-numeric.
          quotechar : str, default '\"'
              String of length 1. Character used to quote fields.
          line_terminator : str, optional
              The newline character or character sequence to use in the output
              file. Defaults to `os.linesep`, which depends on the OS in which
              this method is called ('\\n' for linux, '\\r\\n' for Windows, i.e.).
          chunksize : int or None
              Rows to write at a time.
          date_format : str, default None
              Format string for datetime objects.
          doublequote : bool, default True
              Control quoting of `quotechar` inside a field.
          escapechar : str, default None
              String of length 1. Character used to escape `sep` and `quotechar`
              when appropriate.
          decimal : str, default '.'
              Character recognized as decimal separator. E.g. use ',' for
              European data.
          errors : str, default 'strict'
              Specifies how encoding and decoding errors are to be handled.
              See the errors argument for :func:`open` for a full list
              of options.
  
              .. versionadded:: 1.1.0
  
          {storage_options}
  
              .. versionadded:: 1.2.0
  
          Returns
          -------
          None or str
              If path_or_buf is None, returns the resulting csv format as a
              string. Otherwise returns None.
  
          See Also
          --------
          read_csv : Load a CSV file into a DataFrame.
          to_excel : Write DataFrame to an Excel file.
  
          Examples
          --------
          >>> df = pd.DataFrame({{'name': ['Raphael', 'Donatello'],
          ...                    'mask': ['red', 'purple'],
          ...                    'weapon': ['sai', 'bo staff']}})
          >>> df.to_csv(index=False)
          'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'
  
          Create 'out.zip' containing 'out.csv'
  
          >>> compression_opts = dict(method='zip',
          ...                         archive_name='out.csv')  # doctest: +SKIP
          >>> df.to_csv('out.zip', index=False,
          ...           compression=compression_opts)  # doctest: +SKIP
          """
          df = self if isinstance(self, ABCDataFrame) else self.to_frame()
  
          formatter = DataFrameFormatter(
              frame=df,
              header=header,
              index=index,
              na_rep=na_rep,
              float_format=float_format,
              decimal=decimal,
          )
  
          return DataFrameRenderer(formatter).to_csv(
              path_or_buf,
              line_terminator=line_terminator,
              sep=sep,
              encoding=encoding,
              errors=errors,
              compression=compression,
              quoting=quoting,
              columns=columns,
              index_label=index_label,
              mode=mode,
              chunksize=chunksize,
              quotechar=quotechar,
              date_format=date_format,
              doublequote=doublequote,
              escapechar=escapechar,
              storage_options=storage_options,
          )
  
      # ----------------------------------------------------------------------
      # Lookup Caching
  
      def _reset_cacher(self) -> None:
          """
          Reset the cacher.
          """
          raise AbstractMethodError(self)
  
      def _maybe_update_cacher(
          self, clear: bool_t = False, verify_is_copy: bool_t = True
      ) -> None:
          """
          See if we need to update our parent cacher if clear, then clear our
          cache.
  
          Parameters
          ----------
          clear : bool, default False
              Clear the item cache.
          verify_is_copy : bool, default True
              Provide is_copy checks.
          """
  
          if verify_is_copy:
              self._check_setitem_copy(t="referent")
  
          if clear:
              self._clear_item_cache()
  
      def _clear_item_cache(self) -> None:
          raise AbstractMethodError(self)
  
      # ----------------------------------------------------------------------
      # Indexing Methods
  
      def take(
          self: FrameOrSeries, indices, axis=0, is_copy: bool_t | None = None, **kwargs
      ) -> FrameOrSeries:
          """
          Return the elements in the given *positional* indices along an axis.
  
          This means that we are not indexing according to actual values in
          the index attribute of the object. We are indexing according to the
          actual position of the element in the object.
  
          Parameters
          ----------
          indices : array-like
              An array of ints indicating which positions to take.
          axis : {0 or 'index', 1 or 'columns', None}, default 0
              The axis on which to select elements. ``0`` means that we are
              selecting rows, ``1`` means that we are selecting columns.
          is_copy : bool
              Before pandas 1.0, ``is_copy=False`` can be specified to ensure
              that the return value is an actual copy. Starting with pandas 1.0,
              ``take`` always returns a copy, and the keyword is therefore
              deprecated.
  
              .. deprecated:: 1.0.0
          **kwargs
              For compatibility with :meth:`numpy.take`. Has no effect on the
              output.
  
          Returns
          -------
          taken : same type as caller
              An array-like containing the elements taken from the object.
  
          See Also
          --------
          DataFrame.loc : Select a subset of a DataFrame by labels.
          DataFrame.iloc : Select a subset of a DataFrame by positions.
          numpy.take : Take elements from an array along an axis.
  
          Examples
          --------
          >>> df = pd.DataFrame([('falcon', 'bird', 389.0),
          ...                    ('parrot', 'bird', 24.0),
          ...                    ('lion', 'mammal', 80.5),
          ...                    ('monkey', 'mammal', np.nan)],
          ...                   columns=['name', 'class', 'max_speed'],
          ...                   index=[0, 2, 3, 1])
          >>> df
               name   class  max_speed
          0  falcon    bird      389.0
          2  parrot    bird       24.0
          3    lion  mammal       80.5
          1  monkey  mammal        NaN
  
          Take elements at positions 0 and 3 along the axis 0 (default).
  
          Note how the actual indices selected (0 and 1) do not correspond to
          our selected indices 0 and 3. That's because we are selecting the 0th
          and 3rd rows, not rows whose indices equal 0 and 3.
  
          >>> df.take([0, 3])
               name   class  max_speed
          0  falcon    bird      389.0
          1  monkey  mammal        NaN
  
          Take elements at indices 1 and 2 along the axis 1 (column selection).
  
          >>> df.take([1, 2], axis=1)
              class  max_speed
          0    bird      389.0
          2    bird       24.0
          3  mammal       80.5
          1  mammal        NaN
  
          We may take elements using negative integers for positive indices,
          starting from the end of the object, just like with Python lists.
  
          >>> df.take([-1, -2])
               name   class  max_speed
          1  monkey  mammal        NaN
          3    lion  mammal       80.5
          """
          if is_copy is not None:
              warnings.warn(
                  "is_copy is deprecated and will be removed in a future version. "
                  "'take' always returns a copy, so there is no need to specify this.",
                  FutureWarning,
                  stacklevel=2,
              )
  
          nv.validate_take((), kwargs)
  
          self._consolidate_inplace()
  
          new_data = self._mgr.take(
              indices, axis=self._get_block_manager_axis(axis), verify=True
          )
          return self._constructor(new_data).__finalize__(self, method="take")
  
      def _take_with_is_copy(self: FrameOrSeries, indices, axis=0) -> FrameOrSeries:
          """
          Internal version of the `take` method that sets the `_is_copy`
          attribute to keep track of the parent dataframe (using in indexing
          for the SettingWithCopyWarning).
  
          See the docstring of `take` for full explanation of the parameters.
          """
          result = self.take(indices=indices, axis=axis)
          # Maybe set copy if we didn't actually change the index.
          if not result._get_axis(axis).equals(self._get_axis(axis)):
              result._set_is_copy(self)
          return result
  
      @final
      def xs(self, key, axis=0, level=None, drop_level: bool_t = True):
          """
          Return cross-section from the Series/DataFrame.
  
          This method takes a `key` argument to select data at a particular
          level of a MultiIndex.
  
          Parameters
          ----------
          key : label or tuple of label
              Label contained in the index, or partially in a MultiIndex.
          axis : {0 or 'index', 1 or 'columns'}, default 0
              Axis to retrieve cross-section on.
          level : object, defaults to first n levels (n=1 or len(key))
              In case of a key partially contained in a MultiIndex, indicate
              which levels are used. Levels can be referred by label or position.
          drop_level : bool, default True
              If False, returns object with same levels as self.
  
          Returns
          -------
          Series or DataFrame
              Cross-section from the original Series or DataFrame
              corresponding to the selected index levels.
  
          See Also
          --------
          DataFrame.loc : Access a group of rows and columns
              by label(s) or a boolean array.
          DataFrame.iloc : Purely integer-location based indexing
              for selection by position.
  
          Notes
          -----
          `xs` can not be used to set values.
  
          MultiIndex Slicers is a generic way to get/set values on
          any level or levels.
          It is a superset of `xs` functionality, see
          :ref:`MultiIndex Slicers <advanced.mi_slicers>`.
  
          Examples
          --------
          >>> d = {'num_legs': [4, 4, 2, 2],
          ...      'num_wings': [0, 0, 2, 2],
          ...      'class': ['mammal', 'mammal', 'mammal', 'bird'],
          ...      'animal': ['cat', 'dog', 'bat', 'penguin'],
          ...      'locomotion': ['walks', 'walks', 'flies', 'walks']}
          >>> df = pd.DataFrame(data=d)
          >>> df = df.set_index(['class', 'animal', 'locomotion'])
          >>> df
                                     num_legs  num_wings
          class  animal  locomotion
          mammal cat     walks              4          0
                 dog     walks              4          0
                 bat     flies              2          2
          bird   penguin walks              2          2
  
          Get values at specified index
  
          >>> df.xs('mammal')
                             num_legs  num_wings
          animal locomotion
          cat    walks              4          0
          dog    walks              4          0
          bat    flies              2          2
  
          Get values at several indexes
  
          >>> df.xs(('mammal', 'dog'))
                      num_legs  num_wings
          locomotion
          walks              4          0
  
          Get values at specified index and level
  
          >>> df.xs('cat', level=1)
                             num_legs  num_wings
          class  locomotion
          mammal walks              4          0
  
          Get values at several indexes and levels
  
          >>> df.xs(('bird', 'walks'),
          ...       level=[0, 'locomotion'])
                   num_legs  num_wings
          animal
          penguin         2          2
  
          Get values at specified column and axis
  
          >>> df.xs('num_wings', axis=1)
          class   animal   locomotion
          mammal  cat      walks         0
                  dog      walks         0
                  bat      flies         2
          bird    penguin  walks         2
          Name: num_wings, dtype: int64
          """
          axis = self._get_axis_number(axis)
          labels = self._get_axis(axis)
  
          if isinstance(key, list):
              warnings.warn(
                  "Passing lists as key for xs is deprecated and will be removed in a "
                  "future version. Pass key as a tuple instead.",
                  FutureWarning,
                  stacklevel=2,
              )
  
          if level is not None:
              if not isinstance(labels, MultiIndex):
                  raise TypeError("Index must be a MultiIndex")
              loc, new_ax = labels.get_loc_level(key, level=level, drop_level=drop_level)
  
              # create the tuple of the indexer
              _indexer = [slice(None)] * self.ndim
              _indexer[axis] = loc
              indexer = tuple(_indexer)
  
              result = self.iloc[indexer]
              setattr(result, result._get_axis_name(axis), new_ax)
              return result
  
          if axis == 1:
              if drop_level:
                  return self[key]
              index = self.columns
          else:
              index = self.index
  
          self._consolidate_inplace()
  
          if isinstance(index, MultiIndex):
              loc, new_index = index._get_loc_level(key, level=0)
              if not drop_level:
                  if lib.is_integer(loc):
                      new_index = index[loc : loc + 1]
                  else:
                      new_index = index[loc]
          else:
              loc = index.get_loc(key)
  
              if isinstance(loc, np.ndarray):
                  if loc.dtype == np.bool_:
                      (inds,) = loc.nonzero()
                      return self._take_with_is_copy(inds, axis=axis)
                  else:
                      return self._take_with_is_copy(loc, axis=axis)
  
              if not is_scalar(loc):
                  new_index = index[loc]
  
          if is_scalar(loc) and axis == 0:
              # In this case loc should be an integer
              if self.ndim == 1:
                  # if we encounter an array-like and we only have 1 dim
                  # that means that their are list/ndarrays inside the Series!
                  # so just return them (GH 6394)
                  return self._values[loc]
  
              new_values = self._mgr.fast_xs(loc)
  
              result = self._constructor_sliced(
                  new_values,
                  index=self.columns,
                  name=self.index[loc],
                  dtype=new_values.dtype,
              )
          elif is_scalar(loc):
              result = self.iloc[:, slice(loc, loc + 1)]
          elif axis == 1:
              result = self.iloc[:, loc]
          else:
              result = self.iloc[loc]
              result.index = new_index
  
          # this could be a view
          # but only in a single-dtyped view sliceable case
          result._set_is_copy(self, copy=not result._is_view)
          return result
  
      def __getitem__(self, item):
          raise AbstractMethodError(self)
  
      def _slice(self: FrameOrSeries, slobj: slice, axis=0) -> FrameOrSeries:
          """
          Construct a slice of this container.
  
          Slicing with this method is *always* positional.
          """
          assert isinstance(slobj, slice), type(slobj)
          axis = self._get_block_manager_axis(axis)
          result = self._constructor(self._mgr.get_slice(slobj, axis=axis))
          result = result.__finalize__(self)
  
          # this could be a view
          # but only in a single-dtyped view sliceable case
          is_copy = axis != 0 or result._is_view
          result._set_is_copy(self, copy=is_copy)
          return result
  
      @final
      def _set_is_copy(self, ref: FrameOrSeries, copy: bool_t = True) -> None:
          if not copy:
              self._is_copy = None
          else:
              assert ref is not None
              self._is_copy = weakref.ref(ref)
  
      def _check_is_chained_assignment_possible(self) -> bool_t:
          """
          Check if we are a view, have a cacher, and are of mixed type.
          If so, then force a setitem_copy check.
  
          Should be called just near setting a value
  
          Will return a boolean if it we are a view and are cached, but a
          single-dtype meaning that the cacher should be updated following
          setting.
          """
          if self._is_copy:
              self._check_setitem_copy(t="referent")
          return False
  
      @final
      def _check_setitem_copy(self, t="setting", force=False):
          """
  
          Parameters
          ----------
          t : str, the type of setting error
          force : bool, default False
             If True, then force showing an error.
  
          validate if we are doing a setitem on a chained copy.
  
          It is technically possible to figure out that we are setting on
          a copy even WITH a multi-dtyped pandas object. In other words, some
          blocks may be views while other are not. Currently _is_view will ALWAYS
          return False for multi-blocks to avoid having to handle this case.
  
          df = DataFrame(np.arange(0,9), columns=['count'])
          df['group'] = 'b'
  
          # This technically need not raise SettingWithCopy if both are view
          # (which is not # generally guaranteed but is usually True.  However,
          # this is in general not a good practice and we recommend using .loc.
          df.iloc[0:5]['group'] = 'a'
  
          """
          # return early if the check is not needed
          if not (force or self._is_copy):
              return
  
          value = config.get_option("mode.chained_assignment")
          if value is None:
              return
  
          # see if the copy is not actually referred; if so, then dissolve
          # the copy weakref
          if self._is_copy is not None and not isinstance(self._is_copy, str):
              r = self._is_copy()
              if not gc.get_referents(r) or (r is not None and r.shape == self.shape):
                  self._is_copy = None
                  return
  
          # a custom message
          if isinstance(self._is_copy, str):
              t = self._is_copy
  
          elif t == "referent":
              t = (
                  "\n"
                  "A value is trying to be set on a copy of a slice from a "
                  "DataFrame\n\n"
                  "See the caveats in the documentation: "
                  "https://pandas.pydata.org/pandas-docs/stable/user_guide/"
                  "indexing.html#returning-a-view-versus-a-copy"
              )
  
          else:
              t = (
                  "\n"
                  "A value is trying to be set on a copy of a slice from a "
                  "DataFrame.\n"
                  "Try using .loc[row_indexer,col_indexer] = value "
                  "instead\n\nSee the caveats in the documentation: "
                  "https://pandas.pydata.org/pandas-docs/stable/user_guide/"
                  "indexing.html#returning-a-view-versus-a-copy"
              )
  
          if value == "raise":
              raise com.SettingWithCopyError(t)
          elif value == "warn":
              warnings.warn(t, com.SettingWithCopyWarning, stacklevel=find_stack_level())
  
      def __delitem__(self, key) -> None:
          """
          Delete item
          """
          deleted = False
  
          maybe_shortcut = False
          if self.ndim == 2 and isinstance(self.columns, MultiIndex):
              try:
                  maybe_shortcut = key not in self.columns._engine
              except TypeError:
                  pass
  
          if maybe_shortcut:
              # Allow shorthand to delete all columns whose first len(key)
              # elements match key:
              if not isinstance(key, tuple):
                  key = (key,)
              for col in self.columns:
                  if isinstance(col, tuple) and col[: len(key)] == key:
                      del self[col]
                      deleted = True
          if not deleted:
              # If the above loop ran and didn't delete anything because
              # there was no match, this call should raise the appropriate
              # exception:
              loc = self.axes[-1].get_loc(key)
              self._mgr = self._mgr.idelete(loc)
  
          # delete from the caches
          try:
              del self._item_cache[key]
          except KeyError:
              pass
  
      # ----------------------------------------------------------------------
      # Unsorted
  
      @final
      def _check_inplace_and_allows_duplicate_labels(self, inplace):
          if inplace and not self.flags.allows_duplicate_labels:
              raise ValueError(
                  "Cannot specify 'inplace=True' when "
                  "'self.flags.allows_duplicate_labels' is False."
              )
  
      @final
      def get(self, key, default=None):
          """
          Get item from object for given key (ex: DataFrame column).
  
          Returns default value if not found.
  
          Parameters
          ----------
          key : object
  
          Returns
          -------
          value : same type as items contained in object
          """
          try:
              return self[key]
          except (KeyError, ValueError, IndexError):
              return default
  
      @final
      @property
      def _is_view(self) -> bool_t:
          """Return boolean indicating if self is view of another array"""
          return self._mgr.is_view
  
      @final
      def reindex_like(
          self: FrameOrSeries,
          other,
          method: str | None = None,
          copy: bool_t = True,
          limit=None,
          tolerance=None,
      ) -> FrameOrSeries:
          """
          Return an object with matching indices as other object.
  
          Conform the object to the same index on all axes. Optional
          filling logic, placing NaN in locations having no value
          in the previous index. A new object is produced unless the
          new index is equivalent to the current one and copy=False.
  
          Parameters
          ----------
          other : Object of the same data type
              Its row and column indices are used to define the new indices
              of this object.
          method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}
              Method to use for filling holes in reindexed DataFrame.
              Please note: this is only applicable to DataFrames/Series with a
              monotonically increasing/decreasing index.
  
              * None (default): don't fill gaps
              * pad / ffill: propagate last valid observation forward to next
                valid
              * backfill / bfill: use next valid observation to fill gap
              * nearest: use nearest valid observations to fill gap.
  
          copy : bool, default True
              Return a new object, even if the passed indexes are the same.
          limit : int, default None
              Maximum number of consecutive labels to fill for inexact matches.
          tolerance : optional
              Maximum distance between original and new labels for inexact
              matches. The values of the index at the matching locations must
              satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
  
              Tolerance may be a scalar value, which applies the same tolerance
              to all values, or list-like, which applies variable tolerance per
              element. List-like includes list, tuple, array, Series, and must be
              the same size as the index and its dtype must exactly match the
              index's type.
  
          Returns
          -------
          Series or DataFrame
              Same type as caller, but with changed indices on each axis.
  
          See Also
          --------
          DataFrame.set_index : Set row labels.
          DataFrame.reset_index : Remove row labels or move them to new columns.
          DataFrame.reindex : Change to new indices or expand indices.
  
          Notes
          -----
          Same as calling
          ``.reindex(index=other.index, columns=other.columns,...)``.
  
          Examples
          --------
          >>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],
          ...                     [31, 87.8, 'high'],
          ...                     [22, 71.6, 'medium'],
          ...                     [35, 95, 'medium']],
          ...                    columns=['temp_celsius', 'temp_fahrenheit',
          ...                             'windspeed'],
          ...                    index=pd.date_range(start='2014-02-12',
          ...                                        end='2014-02-15', freq='D'))
  
          >>> df1
                      temp_celsius  temp_fahrenheit windspeed
          2014-02-12          24.3             75.7      high
          2014-02-13          31.0             87.8      high
          2014-02-14          22.0             71.6    medium
          2014-02-15          35.0             95.0    medium
  
          >>> df2 = pd.DataFrame([[28, 'low'],
          ...                     [30, 'low'],
          ...                     [35.1, 'medium']],
          ...                    columns=['temp_celsius', 'windspeed'],
          ...                    index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
          ...                                            '2014-02-15']))
  
          >>> df2
                      temp_celsius windspeed
          2014-02-12          28.0       low
          2014-02-13          30.0       low
          2014-02-15          35.1    medium
  
          >>> df2.reindex_like(df1)
                      temp_celsius  temp_fahrenheit windspeed
          2014-02-12          28.0              NaN       low
          2014-02-13          30.0              NaN       low
          2014-02-14           NaN              NaN       NaN
          2014-02-15          35.1              NaN    medium
          """
          d = other._construct_axes_dict(
              axes=self._AXIS_ORDERS,
              method=method,
              copy=copy,
              limit=limit,
              tolerance=tolerance,
          )
  
          return self.reindex(**d)
  
      def drop(
          self,
          labels=None,
          axis=0,
          index=None,
          columns=None,
          level=None,
          inplace: bool_t = False,
          errors: str = "raise",
      ):
  
          inplace = validate_bool_kwarg(inplace, "inplace")
  
          if labels is not None:
              if index is not None or columns is not None:
                  raise ValueError("Cannot specify both 'labels' and 'index'/'columns'")
              axis_name = self._get_axis_name(axis)
              axes = {axis_name: labels}
          elif index is not None or columns is not None:
              axes, _ = self._construct_axes_from_arguments((index, columns), {})
          else:
              raise ValueError(
                  "Need to specify at least one of 'labels', 'index' or 'columns'"
              )
  
          obj = self
  
          for axis, labels in axes.items():
              if labels is not None:
                  obj = obj._drop_axis(labels, axis, level=level, errors=errors)
  
          if inplace:
              self._update_inplace(obj)
          else:
              return obj
  
      @final
      def _drop_axis(
          self: FrameOrSeries, labels, axis, level=None, errors: str = "raise"
      ) -> FrameOrSeries:
          """
          Drop labels from specified axis. Used in the ``drop`` method
          internally.
  
          Parameters
          ----------
          labels : single label or list-like
          axis : int or axis name
          level : int or level name, default None
              For MultiIndex
          errors : {'ignore', 'raise'}, default 'raise'
              If 'ignore', suppress error and existing labels are dropped.
  
          """
          axis = self._get_axis_number(axis)
          axis_name = self._get_axis_name(axis)
          axis = self._get_axis(axis)
  
          if axis.is_unique:
              if level is not None:
                  if not isinstance(axis, MultiIndex):
                      raise AssertionError("axis must be a MultiIndex")
                  new_axis = axis.drop(labels, level=level, errors=errors)
              else:
                  new_axis = axis.drop(labels, errors=errors)
              result = self.reindex(**{axis_name: new_axis})
  
          # Case for non-unique axis
          else:
              is_tuple_labels = is_nested_list_like(labels) or isinstance(labels, tuple)
              labels = ensure_object(com.index_labels_to_array(labels))
              if level is not None:
                  if not isinstance(axis, MultiIndex):
                      raise AssertionError("axis must be a MultiIndex")
                  indexer = ~axis.get_level_values(level).isin(labels)
  
                  # GH 18561 MultiIndex.drop should raise if label is absent
                  if errors == "raise" and indexer.all():
                      raise KeyError(f"{labels} not found in axis")
              elif (
                  isinstance(axis, MultiIndex)
                  and labels.dtype == "object"
                  and not is_tuple_labels
              ):
                  # Set level to zero in case of MultiIndex and label is string,
                  #  because isin can't handle strings for MultiIndexes GH#36293
                  # In case of tuples we get dtype object but have to use isin GH#42771
                  indexer = ~axis.get_level_values(0).isin(labels)
              else:
                  indexer = ~axis.isin(labels)
                  # Check if label doesn't exist along axis
                  labels_missing = (axis.get_indexer_for(labels) == -1).any()
                  if errors == "raise" and labels_missing:
                      raise KeyError(f"{labels} not found in axis")
  
              slicer = [slice(None)] * self.ndim
              slicer[self._get_axis_number(axis_name)] = indexer
  
              result = self.loc[tuple(slicer)]
  
          return result
  
      @final
      def _update_inplace(self, result, verify_is_copy: bool_t = True) -> None:
          """
          Replace self internals with result.
  
          Parameters
          ----------
          result : same type as self
          verify_is_copy : bool, default True
              Provide is_copy checks.
          """
          # NOTE: This does *not* call __finalize__ and that's an explicit
          # decision that we may revisit in the future.
          self._reset_cache()
          self._clear_item_cache()
          self._mgr = result._mgr
          self._maybe_update_cacher(verify_is_copy=verify_is_copy)
  
      @final
      def add_prefix(self: FrameOrSeries, prefix: str) -> FrameOrSeries:
          """
          Prefix labels with string `prefix`.
  
          For Series, the row labels are prefixed.
          For DataFrame, the column labels are prefixed.
  
          Parameters
          ----------
          prefix : str
              The string to add before each label.
  
          Returns
          -------
          Series or DataFrame
              New Series or DataFrame with updated labels.
  
          See Also
          --------
          Series.add_suffix: Suffix row labels with string `suffix`.
          DataFrame.add_suffix: Suffix column labels with string `suffix`.
  
          Examples
          --------
          >>> s = pd.Series([1, 2, 3, 4])
          >>> s
          0    1
          1    2
          2    3
          3    4
          dtype: int64
  
          >>> s.add_prefix('item_')
          item_0    1
          item_1    2
          item_2    3
          item_3    4
          dtype: int64
  
          >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
          >>> df
             A  B
          0  1  3
          1  2  4
          2  3  5
          3  4  6
  
          >>> df.add_prefix('col_')
               col_A  col_B
          0       1       3
          1       2       4
          2       3       5
          3       4       6
          """
          f = functools.partial("{prefix}{}".format, prefix=prefix)
  
          mapper = {self._info_axis_name: f}
          # error: Incompatible return value type (got "Optional[FrameOrSeries]",
          # expected "FrameOrSeries")
          # error: Argument 1 to "rename" of "NDFrame" has incompatible type
          # "**Dict[str, partial[str]]"; expected "Union[str, int, None]"
          return self.rename(**mapper)  # type: ignore[return-value, arg-type]
  
      @final
      def add_suffix(self: FrameOrSeries, suffix: str) -> FrameOrSeries:
          """
          Suffix labels with string `suffix`.
  
          For Series, the row labels are suffixed.
          For DataFrame, the column labels are suffixed.
  
          Parameters
          ----------
          suffix : str
              The string to add after each label.
  
          Returns
          -------
          Series or DataFrame
              New Series or DataFrame with updated labels.
  
          See Also
          --------
          Series.add_prefix: Prefix row labels with string `prefix`.
          DataFrame.add_prefix: Prefix column labels with string `prefix`.
  
          Examples
          --------
          >>> s = pd.Series([1, 2, 3, 4])
          >>> s
          0    1
          1    2
          2    3
          3    4
          dtype: int64
  
          >>> s.add_suffix('_item')
          0_item    1
          1_item    2
          2_item    3
          3_item    4
          dtype: int64
  
          >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
          >>> df
             A  B
          0  1  3
          1  2  4
          2  3  5
          3  4  6
  
          >>> df.add_suffix('_col')
               A_col  B_col
          0       1       3
          1       2       4
          2       3       5
          3       4       6
          """
          f = functools.partial("{}{suffix}".format, suffix=suffix)
  
          mapper = {self._info_axis_name: f}
          # error: Incompatible return value type (got "Optional[FrameOrSeries]",
          # expected "FrameOrSeries")
          # error: Argument 1 to "rename" of "NDFrame" has incompatible type
          # "**Dict[str, partial[str]]"; expected "Union[str, int, None]"
          return self.rename(**mapper)  # type: ignore[return-value, arg-type]
  
      def sort_values(
          self,
          axis=0,
          ascending=True,
          inplace: bool_t = False,
          kind: str = "quicksort",
          na_position: str = "last",
          ignore_index: bool_t = False,
          key: ValueKeyFunc = None,
      ):
          """
          Sort by the values along either axis.
  
          Parameters
          ----------%(optional_by)s
          axis : %(axes_single_arg)s, default 0
               Axis to be sorted.
          ascending : bool or list of bool, default True
               Sort ascending vs. descending. Specify list for multiple sort
               orders.  If this is a list of bools, must match the length of
               the by.
          inplace : bool, default False
               If True, perform operation in-place.
          kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
               Choice of sorting algorithm. See also :func:`numpy.sort` for more
               information. `mergesort` and `stable` are the only stable algorithms. For
               DataFrames, this option is only applied when sorting on a single
               column or label.
          na_position : {'first', 'last'}, default 'last'
               Puts NaNs at the beginning if `first`; `last` puts NaNs at the
               end.
          ignore_index : bool, default False
               If True, the resulting axis will be labeled 0, 1, …, n - 1.
  
               .. versionadded:: 1.0.0
  
          key : callable, optional
              Apply the key function to the values
              before sorting. This is similar to the `key` argument in the
              builtin :meth:`sorted` function, with the notable difference that
              this `key` function should be *vectorized*. It should expect a
              ``Series`` and return a Series with the same shape as the input.
              It will be applied to each column in `by` independently.
  
              .. versionadded:: 1.1.0
  
          Returns
          -------
          DataFrame or None
              DataFrame with sorted values or None if ``inplace=True``.
  
          See Also
          --------
          DataFrame.sort_index : Sort a DataFrame by the index.
          Series.sort_values : Similar method for a Series.
  
          Examples
          --------
          >>> df = pd.DataFrame({
          ...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
          ...     'col2': [2, 1, 9, 8, 7, 4],
          ...     'col3': [0, 1, 9, 4, 2, 3],
          ...     'col4': ['a', 'B', 'c', 'D', 'e', 'F']
          ... })
          >>> df
            col1  col2  col3 col4
          0    A     2     0    a
          1    A     1     1    B
          2    B     9     9    c
          3  NaN     8     4    D
          4    D     7     2    e
          5    C     4     3    F
  
          Sort by col1
  
          >>> df.sort_values(by=['col1'])
            col1  col2  col3 col4
          0    A     2     0    a
          1    A     1     1    B
          2    B     9     9    c
          5    C     4     3    F
          4    D     7     2    e
          3  NaN     8     4    D
  
          Sort by multiple columns
  
          >>> df.sort_values(by=['col1', 'col2'])
            col1  col2  col3 col4
          1    A     1     1    B
          0    A     2     0    a
          2    B     9     9    c
          5    C     4     3    F
          4    D     7     2    e
          3  NaN     8     4    D
  
          Sort Descending
  
          >>> df.sort_values(by='col1', ascending=False)
            col1  col2  col3 col4
          4    D     7     2    e
          5    C     4     3    F
          2    B     9     9    c
          0    A     2     0    a
          1    A     1     1    B
          3  NaN     8     4    D
  
          Putting NAs first
  
          >>> df.sort_values(by='col1', ascending=False, na_position='first')
            col1  col2  col3 col4
          3  NaN     8     4    D
          4    D     7     2    e
          5    C     4     3    F
          2    B     9     9    c
          0    A     2     0    a
          1    A     1     1    B
  
          Sorting with a key function
  
          >>> df.sort_values(by='col4', key=lambda col: col.str.lower())
             col1  col2  col3 col4
          0    A     2     0    a
          1    A     1     1    B
          2    B     9     9    c
          3  NaN     8     4    D
          4    D     7     2    e
          5    C     4     3    F
  
          Natural sort with the key argument,
          using the `natsort <https://github.com/SethMMorton/natsort>` package.
  
          >>> df = pd.DataFrame({
          ...    "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
          ...    "value": [10, 20, 30, 40, 50]
          ... })
          >>> df
              time  value
          0    0hr     10
          1  128hr     20
          2   72hr     30
          3   48hr     40
          4   96hr     50
          >>> from natsort import index_natsorted
          >>> df.sort_values(
          ...    by="time",
          ...    key=lambda x: np.argsort(index_natsorted(df["time"]))
          ... )
              time  value
          0    0hr     10
          3   48hr     40
          2   72hr     30
          4   96hr     50
          1  128hr     20
          """
          raise AbstractMethodError(self)
  
      def sort_index(
          self,
          axis=0,
          level=None,
          ascending: bool_t | int | Sequence[bool_t | int] = True,
          inplace: bool_t = False,
          kind: str = "quicksort",
          na_position: str = "last",
          sort_remaining: bool_t = True,
          ignore_index: bool_t = False,
          key: IndexKeyFunc = None,
      ):
  
          inplace = validate_bool_kwarg(inplace, "inplace")
          axis = self._get_axis_number(axis)
          ascending = validate_ascending(ascending)
  
          target = self._get_axis(axis)
  
          indexer = get_indexer_indexer(
              target, level, ascending, kind, na_position, sort_remaining, key
          )
  
          if indexer is None:
              if inplace:
                  return
              else:
                  return self.copy()
  
          baxis = self._get_block_manager_axis(axis)
          new_data = self._mgr.take(indexer, axis=baxis, verify=False)
  
          # reconstruct axis if needed
          new_data.set_axis(baxis, new_data.axes[baxis]._sort_levels_monotonic())
  
          if ignore_index:
              axis = 1 if isinstance(self, ABCDataFrame) else 0
              new_data.set_axis(axis, ibase.default_index(len(indexer)))
  
          result = self._constructor(new_data)
  
          if inplace:
              return self._update_inplace(result)
          else:
              return result.__finalize__(self, method="sort_index")
  
      @doc(
          klass=_shared_doc_kwargs["klass"],
          axes=_shared_doc_kwargs["axes"],
          optional_labels="",
          optional_axis="",
      )
      def reindex(self: FrameOrSeries, *args, **kwargs) -> FrameOrSeries:
          """
          Conform {klass} to new index with optional filling logic.
  
          Places NA/NaN in locations having no value in the previous index. A new object
          is produced unless the new index is equivalent to the current one and
          ``copy=False``.
  
          Parameters
          ----------
          {optional_labels}
          {axes} : array-like, optional
              New labels / index to conform to, should be specified using
              keywords. Preferably an Index object to avoid duplicating data.
          {optional_axis}
          method : {{None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}}
              Method to use for filling holes in reindexed DataFrame.
              Please note: this is only applicable to DataFrames/Series with a
              monotonically increasing/decreasing index.
  
              * None (default): don't fill gaps
              * pad / ffill: Propagate last valid observation forward to next
                valid.
              * backfill / bfill: Use next valid observation to fill gap.
              * nearest: Use nearest valid observations to fill gap.
  
          copy : bool, default True
              Return a new object, even if the passed indexes are the same.
          level : int or name
              Broadcast across a level, matching Index values on the
              passed MultiIndex level.
          fill_value : scalar, default np.NaN
              Value to use for missing values. Defaults to NaN, but can be any
              "compatible" value.
          limit : int, default None
              Maximum number of consecutive elements to forward or backward fill.
          tolerance : optional
              Maximum distance between original and new labels for inexact
              matches. The values of the index at the matching locations most
              satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
  
              Tolerance may be a scalar value, which applies the same tolerance
              to all values, or list-like, which applies variable tolerance per
              element. List-like includes list, tuple, array, Series, and must be
              the same size as the index and its dtype must exactly match the
              index's type.
  
          Returns
          -------
          {klass} with changed index.
  
          See Also
          --------
          DataFrame.set_index : Set row labels.
          DataFrame.reset_index : Remove row labels or move them to new columns.
          DataFrame.reindex_like : Change to same indices as other DataFrame.
  
          Examples
          --------
          ``DataFrame.reindex`` supports two calling conventions
  
          * ``(index=index_labels, columns=column_labels, ...)``
          * ``(labels, axis={{'index', 'columns'}}, ...)``
  
          We *highly* recommend using keyword arguments to clarify your
          intent.
  
          Create a dataframe with some fictional data.
  
          >>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
          >>> df = pd.DataFrame({{'http_status': [200, 200, 404, 404, 301],
          ...                   'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]}},
          ...                   index=index)
          >>> df
                     http_status  response_time
          Firefox            200           0.04
          Chrome             200           0.02
          Safari             404           0.07
          IE10               404           0.08
          Konqueror          301           1.00
  
          Create a new index and reindex the dataframe. By default
          values in the new index that do not have corresponding
          records in the dataframe are assigned ``NaN``.
  
          >>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
          ...              'Chrome']
          >>> df.reindex(new_index)
                         http_status  response_time
          Safari               404.0           0.07
          Iceweasel              NaN            NaN
          Comodo Dragon          NaN            NaN
          IE10                 404.0           0.08
          Chrome               200.0           0.02
  
          We can fill in the missing values by passing a value to
          the keyword ``fill_value``. Because the index is not monotonically
          increasing or decreasing, we cannot use arguments to the keyword
          ``method`` to fill the ``NaN`` values.
  
          >>> df.reindex(new_index, fill_value=0)
                         http_status  response_time
          Safari                 404           0.07
          Iceweasel                0           0.00
          Comodo Dragon            0           0.00
          IE10                   404           0.08
          Chrome                 200           0.02
  
          >>> df.reindex(new_index, fill_value='missing')
                        http_status response_time
          Safari                404          0.07
          Iceweasel         missing       missing
          Comodo Dragon     missing       missing
          IE10                  404          0.08
          Chrome                200          0.02
  
          We can also reindex the columns.
  
          >>> df.reindex(columns=['http_status', 'user_agent'])
                     http_status  user_agent
          Firefox            200         NaN
          Chrome             200         NaN
          Safari             404         NaN
          IE10               404         NaN
          Konqueror          301         NaN
  
          Or we can use "axis-style" keyword arguments
  
          >>> df.reindex(['http_status', 'user_agent'], axis="columns")
                     http_status  user_agent
          Firefox            200         NaN
          Chrome             200         NaN
          Safari             404         NaN
          IE10               404         NaN
          Konqueror          301         NaN
  
          To further illustrate the filling functionality in
          ``reindex``, we will create a dataframe with a
          monotonically increasing index (for example, a sequence
          of dates).
  
          >>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
          >>> df2 = pd.DataFrame({{"prices": [100, 101, np.nan, 100, 89, 88]}},
          ...                    index=date_index)
          >>> df2
                      prices
          2010-01-01   100.0
          2010-01-02   101.0
          2010-01-03     NaN
          2010-01-04   100.0
          2010-01-05    89.0
          2010-01-06    88.0
  
          Suppose we decide to expand the dataframe to cover a wider
          date range.
  
          >>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
          >>> df2.reindex(date_index2)
                      prices
          2009-12-29     NaN
          2009-12-30     NaN
          2009-12-31     NaN
          2010-01-01   100.0
          2010-01-02   101.0
          2010-01-03     NaN
          2010-01-04   100.0
          2010-01-05    89.0
          2010-01-06    88.0
          2010-01-07     NaN
  
          The index entries that did not have a value in the original data frame
          (for example, '2009-12-29') are by default filled with ``NaN``.
          If desired, we can fill in the missing values using one of several
          options.
  
          For example, to back-propagate the last valid value to fill the ``NaN``
          values, pass ``bfill`` as an argument to the ``method`` keyword.
  
          >>> df2.reindex(date_index2, method='bfill')
                      prices
          2009-12-29   100.0
          2009-12-30   100.0
          2009-12-31   100.0
          2010-01-01   100.0
          2010-01-02   101.0
          2010-01-03     NaN
          2010-01-04   100.0
          2010-01-05    89.0
          2010-01-06    88.0
          2010-01-07     NaN
  
          Please note that the ``NaN`` value present in the original dataframe
          (at index value 2010-01-03) will not be filled by any of the
          value propagation schemes. This is because filling while reindexing
          does not look at dataframe values, but only compares the original and
          desired indexes. If you do want to fill in the ``NaN`` values present
          in the original dataframe, use the ``fillna()`` method.
  
          See the :ref:`user guide <basics.reindexing>` for more.
          """
          # TODO: Decide if we care about having different examples for different
          # kinds
  
          # construct the args
          axes, kwargs = self._construct_axes_from_arguments(args, kwargs)
          method = missing.clean_reindex_fill_method(kwargs.pop("method", None))
          level = kwargs.pop("level", None)
          copy = kwargs.pop("copy", True)
          limit = kwargs.pop("limit", None)
          tolerance = kwargs.pop("tolerance", None)
          fill_value = kwargs.pop("fill_value", None)
  
          # Series.reindex doesn't use / need the axis kwarg
          # We pop and ignore it here, to make writing Series/Frame generic code
          # easier
          kwargs.pop("axis", None)
  
          if kwargs:
              raise TypeError(
                  "reindex() got an unexpected keyword "
                  f'argument "{list(kwargs.keys())[0]}"'
              )
  
          self._consolidate_inplace()
  
          # if all axes that are requested to reindex are equal, then only copy
          # if indicated must have index names equal here as well as values
          if all(
              self._get_axis(axis).identical(ax)
              for axis, ax in axes.items()
              if ax is not None
          ):
              if copy:
                  return self.copy()
              return self
  
          # check if we are a multi reindex
          if self._needs_reindex_multi(axes, method, level):
              return self._reindex_multi(axes, copy, fill_value)
  
          # perform the reindex on the axes
          return self._reindex_axes(
              axes, level, limit, tolerance, method, fill_value, copy
          ).__finalize__(self, method="reindex")
  
      @final
      def _reindex_axes(
          self: FrameOrSeries, axes, level, limit, tolerance, method, fill_value, copy
      ) -> FrameOrSeries:
          """Perform the reindex for all the axes."""
          obj = self
          for a in self._AXIS_ORDERS:
              labels = axes[a]
              if labels is None:
                  continue
  
              ax = self._get_axis(a)
              new_index, indexer = ax.reindex(
                  labels, level=level, limit=limit, tolerance=tolerance, method=method
              )
  
              axis = self._get_axis_number(a)
              obj = obj._reindex_with_indexers(
                  {axis: [new_index, indexer]},
                  fill_value=fill_value,
                  copy=copy,
                  allow_dups=False,
              )
              # If we've made a copy once, no need to make another one
              copy = False
  
          return obj
  
      def _needs_reindex_multi(self, axes, method, level) -> bool_t:
          """Check if we do need a multi reindex."""
          return (
              (com.count_not_none(*axes.values()) == self._AXIS_LEN)
              and method is None
              and level is None
              and not self._is_mixed_type
          )
  
      def _reindex_multi(self, axes, copy, fill_value):
          raise AbstractMethodError(self)
  
      @final
      def _reindex_with_indexers(
          self: FrameOrSeries,
          reindexers,
          fill_value=None,
          copy: bool_t = False,
          allow_dups: bool_t = False,
      ) -> FrameOrSeries:
          """allow_dups indicates an internal call here"""
          # reindex doing multiple operations on different axes if indicated
          new_data = self._mgr
          for axis in sorted(reindexers.keys()):
              index, indexer = reindexers[axis]
              baxis = self._get_block_manager_axis(axis)
  
              if index is None:
                  continue
  
              index = ensure_index(index)
              if indexer is not None:
                  indexer = ensure_platform_int(indexer)
  
              # TODO: speed up on homogeneous DataFrame objects
              new_data = new_data.reindex_indexer(
                  index,
                  indexer,
                  axis=baxis,
                  fill_value=fill_value,
                  allow_dups=allow_dups,
                  copy=copy,
              )
              # If we've made a copy once, no need to make another one
              copy = False
  
          if copy and new_data is self._mgr:
              new_data = new_data.copy()
  
          return self._constructor(new_data).__finalize__(self)
  
      def filter(
          self: FrameOrSeries,
          items=None,
          like: str | None = None,
          regex: str | None = None,
          axis=None,
      ) -> FrameOrSeries:
          """
          Subset the dataframe rows or columns according to the specified index labels.
  
          Note that this routine does not filter a dataframe on its
          contents. The filter is applied to the labels of the index.
  
          Parameters
          ----------
          items : list-like
              Keep labels from axis which are in items.
          like : str
              Keep labels from axis for which "like in label == True".
          regex : str (regular expression)
              Keep labels from axis for which re.search(regex, label) == True.
          axis : {0 or ‘index’, 1 or ‘columns’, None}, default None
              The axis to filter on, expressed either as an index (int)
              or axis name (str). By default this is the info axis,
              'index' for Series, 'columns' for DataFrame.
  
          Returns
          -------
          same type as input object
  
          See Also
          --------
          DataFrame.loc : Access a group of rows and columns
              by label(s) or a boolean array.
  
          Notes
          -----
          The ``items``, ``like``, and ``regex`` parameters are
          enforced to be mutually exclusive.
  
          ``axis`` defaults to the info axis that is used when indexing
          with ``[]``.
  
          Examples
          --------
          >>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),
          ...                   index=['mouse', 'rabbit'],
          ...                   columns=['one', 'two', 'three'])
          >>> df
                  one  two  three
          mouse     1    2      3
          rabbit    4    5      6
  
          >>> # select columns by name
          >>> df.filter(items=['one', 'three'])
                   one  three
          mouse     1      3
          rabbit    4      6
  
          >>> # select columns by regular expression
          >>> df.filter(regex='e$', axis=1)
                   one  three
          mouse     1      3
          rabbit    4      6
  
          >>> # select rows containing 'bbi'
          >>> df.filter(like='bbi', axis=0)
                   one  two  three
          rabbit    4    5      6
          """
          nkw = com.count_not_none(items, like, regex)
          if nkw > 1:
              raise TypeError(
                  "Keyword arguments `items`, `like`, or `regex` "
                  "are mutually exclusive"
              )
  
          if axis is None:
              axis = self._info_axis_name
          labels = self._get_axis(axis)
  
          if items is not None:
              name = self._get_axis_name(axis)
              return self.reindex(**{name: [r for r in items if r in labels]})
          elif like:
  
              def f(x) -> bool_t:
                  assert like is not None  # needed for mypy
                  return like in ensure_str(x)
  
              values = labels.map(f)
              return self.loc(axis=axis)[values]
          elif regex:
  
              def f(x) -> bool_t:
                  return matcher.search(ensure_str(x)) is not None
  
              matcher = re.compile(regex)
              values = labels.map(f)
              return self.loc(axis=axis)[values]
          else:
              raise TypeError("Must pass either `items`, `like`, or `regex`")
  
      @final
      def head(self: FrameOrSeries, n: int = 5) -> FrameOrSeries:
          """
          Return the first `n` rows.
  
          This function returns the first `n` rows for the object based
          on position. It is useful for quickly testing if your object
          has the right type of data in it.
  
          For negative values of `n`, this function returns all rows except
          the last `n` rows, equivalent to ``df[:-n]``.
  
          Parameters
          ----------
          n : int, default 5
              Number of rows to select.
  
          Returns
          -------
          same type as caller
              The first `n` rows of the caller object.
  
          See Also
          --------
          DataFrame.tail: Returns the last `n` rows.
  
          Examples
          --------
          >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
          ...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
          >>> df
                animal
          0  alligator
          1        bee
          2     falcon
          3       lion
          4     monkey
          5     parrot
          6      shark
          7      whale
          8      zebra
  
          Viewing the first 5 lines
  
          >>> df.head()
                animal
          0  alligator
          1        bee
          2     falcon
          3       lion
          4     monkey
  
          Viewing the first `n` lines (three in this case)
  
          >>> df.head(3)
                animal
          0  alligator
          1        bee
          2     falcon
  
          For negative values of `n`
  
          >>> df.head(-3)
                animal
          0  alligator
          1        bee
          2     falcon
          3       lion
          4     monkey
          5     parrot
          """
          return self.iloc[:n]
  
      @final
      def tail(self: FrameOrSeries, n: int = 5) -> FrameOrSeries:
          """
          Return the last `n` rows.
  
          This function returns last `n` rows from the object based on
          position. It is useful for quickly verifying data, for example,
          after sorting or appending rows.
  
          For negative values of `n`, this function returns all rows except
          the first `n` rows, equivalent to ``df[n:]``.
  
          Parameters
          ----------
          n : int, default 5
              Number of rows to select.
  
          Returns
          -------
          type of caller
              The last `n` rows of the caller object.
  
          See Also
          --------
          DataFrame.head : The first `n` rows of the caller object.
  
          Examples
          --------
          >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
          ...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
          >>> df
                animal
          0  alligator
          1        bee
          2     falcon
          3       lion
          4     monkey
          5     parrot
          6      shark
          7      whale
          8      zebra
  
          Viewing the last 5 lines
  
          >>> df.tail()
             animal
          4  monkey
          5  parrot
          6   shark
          7   whale
          8   zebra
  
          Viewing the last `n` lines (three in this case)
  
          >>> df.tail(3)
            animal
          6  shark
          7  whale
          8  zebra
  
          For negative values of `n`
  
          >>> df.tail(-3)
             animal
          3    lion
          4  monkey
          5  parrot
          6   shark
          7   whale
          8   zebra
          """
          if n == 0:
              return self.iloc[0:0]
          return self.iloc[-n:]
  
      @final
      def sample(
          self: FrameOrSeries,
          n: int | None = None,
          frac: float | None = None,
          replace: bool_t = False,
          weights=None,
          random_state: RandomState | None = None,
          axis: Axis | None = None,
          ignore_index: bool_t = False,
      ) -> FrameOrSeries:
          """
          Return a random sample of items from an axis of object.
  
          You can use `random_state` for reproducibility.
  
          Parameters
          ----------
          n : int, optional
              Number of items from axis to return. Cannot be used with `frac`.
              Default = 1 if `frac` = None.
          frac : float, optional
              Fraction of axis items to return. Cannot be used with `n`.
          replace : bool, default False
              Allow or disallow sampling of the same row more than once.
          weights : str or ndarray-like, optional
              Default 'None' results in equal probability weighting.
              If passed a Series, will align with target object on index. Index
              values in weights not found in sampled object will be ignored and
              index values in sampled object not in weights will be assigned
              weights of zero.
              If called on a DataFrame, will accept the name of a column
              when axis = 0.
              Unless weights are a Series, weights must be same length as axis
              being sampled.
              If weights do not sum to 1, they will be normalized to sum to 1.
              Missing values in the weights column will be treated as zero.
              Infinite values not allowed.
          random_state : int, array-like, BitGenerator, np.random.RandomState,
              np.random.Generator, optional. If int, array-like, or BitGenerator, seed for
              random number generator. If np.random.RandomState or np.random.Generator,
              use as given.
  
              .. versionchanged:: 1.1.0
  
                  array-like and BitGenerator object now passed to np.random.RandomState()
                  as seed
  
              .. versionchanged:: 1.4.0
  
                  np.random.Generator objects now accepted
  
          axis : {0 or ‘index’, 1 or ‘columns’, None}, default None
              Axis to sample. Accepts axis number or name. Default is stat axis
              for given data type (0 for Series and DataFrames).
          ignore_index : bool, default False
              If True, the resulting index will be labeled 0, 1, …, n - 1.
  
              .. versionadded:: 1.3.0
  
          Returns
          -------
          Series or DataFrame
              A new object of same type as caller containing `n` items randomly
              sampled from the caller object.
  
          See Also
          --------
          DataFrameGroupBy.sample: Generates random samples from each group of a
              DataFrame object.
          SeriesGroupBy.sample: Generates random samples from each group of a
              Series object.
          numpy.random.choice: Generates a random sample from a given 1-D numpy
              array.
  
          Notes
          -----
          If `frac` > 1, `replacement` should be set to `True`.
  
          Examples
          --------
          >>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
          ...                    'num_wings': [2, 0, 0, 0],
          ...                    'num_specimen_seen': [10, 2, 1, 8]},
          ...                   index=['falcon', 'dog', 'spider', 'fish'])
          >>> df
                  num_legs  num_wings  num_specimen_seen
          falcon         2          2                 10
          dog            4          0                  2
          spider         8          0                  1
          fish           0          0                  8
  
          Extract 3 random elements from the ``Series`` ``df['num_legs']``:
          Note that we use `random_state` to ensure the reproducibility of
          the examples.
  
          >>> df['num_legs'].sample(n=3, random_state=1)
          fish      0
          spider    8
          falcon    2
          Name: num_legs, dtype: int64
  
          A random 50% sample of the ``DataFrame`` with replacement:
  
          >>> df.sample(frac=0.5, replace=True, random_state=1)
                num_legs  num_wings  num_specimen_seen
          dog          4          0                  2
          fish         0          0                  8
  
          An upsample sample of the ``DataFrame`` with replacement:
          Note that `replace` parameter has to be `True` for `frac` parameter > 1.
  
          >>> df.sample(frac=2, replace=True, random_state=1)
                  num_legs  num_wings  num_specimen_seen
          dog            4          0                  2
          fish           0          0                  8
          falcon         2          2                 10
          falcon         2          2                 10
          fish           0          0                  8
          dog            4          0                  2
          fish           0          0                  8
          dog            4          0                  2
  
          Using a DataFrame column as weights. Rows with larger value in the
          `num_specimen_seen` column are more likely to be sampled.
  
          >>> df.sample(n=2, weights='num_specimen_seen', random_state=1)
                  num_legs  num_wings  num_specimen_seen
          falcon         2          2                 10
          fish           0          0                  8
          """
          if axis is None:
              axis = self._stat_axis_number
  
          axis = self._get_axis_number(axis)
          obj_len = self.shape[axis]
  
          # Process random_state argument
          rs = com.random_state(random_state)
  
          size = sample.process_sampling_size(n, frac, replace)
          if size is None:
              assert frac is not None
              size = round(frac * obj_len)
  
          if weights is not None:
              weights = sample.preprocess_weights(self, weights, axis)
  
          sampled_indices = sample.sample(obj_len, size, replace, weights, rs)
          result = self.take(sampled_indices, axis=axis)
  
          if ignore_index:
              result.index = ibase.default_index(len(result))
  
          return result
  
      @final
      @doc(klass=_shared_doc_kwargs["klass"])
      def pipe(
          self,
          func: Callable[..., T] | tuple[Callable[..., T], str],
          *args,
          **kwargs,
      ) -> T:
          r"""
          Apply func(self, \*args, \*\*kwargs).
  
          Parameters
          ----------
          func : function
              Function to apply to the {klass}.
              ``args``, and ``kwargs`` are passed into ``func``.
              Alternatively a ``(callable, data_keyword)`` tuple where
              ``data_keyword`` is a string indicating the keyword of
              ``callable`` that expects the {klass}.
          args : iterable, optional
              Positional arguments passed into ``func``.
          kwargs : mapping, optional
              A dictionary of keyword arguments passed into ``func``.
  
          Returns
          -------
          object : the return type of ``func``.
  
          See Also
          --------
          DataFrame.apply : Apply a function along input axis of DataFrame.
          DataFrame.applymap : Apply a function elementwise on a whole DataFrame.
          Series.map : Apply a mapping correspondence on a
              :class:`~pandas.Series`.
  
          Notes
          -----
          Use ``.pipe`` when chaining together functions that expect
          Series, DataFrames or GroupBy objects. Instead of writing
  
          >>> func(g(h(df), arg1=a), arg2=b, arg3=c)  # doctest: +SKIP
  
          You can write
  
          >>> (df.pipe(h)
          ...    .pipe(g, arg1=a)
          ...    .pipe(func, arg2=b, arg3=c)
          ... )  # doctest: +SKIP
  
          If you have a function that takes the data as (say) the second
          argument, pass a tuple indicating which keyword expects the
          data. For example, suppose ``f`` takes its data as ``arg2``:
  
          >>> (df.pipe(h)
          ...    .pipe(g, arg1=a)
          ...    .pipe((func, 'arg2'), arg1=a, arg3=c)
          ...  )  # doctest: +SKIP
          """
          return com.pipe(self, func, *args, **kwargs)
  
      # ----------------------------------------------------------------------
      # Attribute access
  
      @final
      def __finalize__(
          self: FrameOrSeries, other, method: str | None = None, **kwargs
      ) -> FrameOrSeries:
          """
          Propagate metadata from other to self.
  
          Parameters
          ----------
          other : the object from which to get the attributes that we are going
              to propagate
          method : str, optional
              A passed method name providing context on where ``__finalize__``
              was called.
  
              .. warning::
  
                 The value passed as `method` are not currently considered
                 stable across pandas releases.
          """
          if isinstance(other, NDFrame):
              for name in other.attrs:
                  self.attrs[name] = other.attrs[name]
  
              self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
              # For subclasses using _metadata.
              for name in set(self._metadata) & set(other._metadata):
                  assert isinstance(name, str)
                  object.__setattr__(self, name, getattr(other, name, None))
  
          if method == "concat":
              allows_duplicate_labels = all(
                  x.flags.allows_duplicate_labels for x in other.objs
              )
              self.flags.allows_duplicate_labels = allows_duplicate_labels
  
          return self
  
      def __getattr__(self, name: str):
          """
          After regular attribute access, try looking up the name
          This allows simpler access to columns for interactive use.
          """
          # Note: obj.x will always call obj.__getattribute__('x') prior to
          # calling obj.__getattr__('x').
          if (
              name not in self._internal_names_set
              and name not in self._metadata
              and name not in self._accessors
              and self._info_axis._can_hold_identifiers_and_holds_name(name)
          ):
              return self[name]
          return object.__getattribute__(self, name)
  
      def __setattr__(self, name: str, value) -> None:
          """
          After regular attribute access, try setting the name
          This allows simpler access to columns for interactive use.
          """
          # first try regular attribute access via __getattribute__, so that
          # e.g. ``obj.x`` and ``obj.x = 4`` will always reference/modify
          # the same attribute.
  
          try:
              object.__getattribute__(self, name)
              return object.__setattr__(self, name, value)
          except AttributeError:
              pass
  
          # if this fails, go on to more involved attribute setting
          # (note that this matches __getattr__, above).
          if name in self._internal_names_set:
              object.__setattr__(self, name, value)
          elif name in self._metadata:
              object.__setattr__(self, name, value)
          else:
              try:
                  existing = getattr(self, name)
                  if isinstance(existing, Index):
                      object.__setattr__(self, name, value)
                  elif name in self._info_axis:
                      self[name] = value
                  else:
                      object.__setattr__(self, name, value)
              except (AttributeError, TypeError):
                  if isinstance(self, ABCDataFrame) and (is_list_like(value)):
                      warnings.warn(
                          "Pandas doesn't allow columns to be "
                          "created via a new attribute name - see "
                          "https://pandas.pydata.org/pandas-docs/"
                          "stable/indexing.html#attribute-access",
                          stacklevel=2,
                      )
                  object.__setattr__(self, name, value)
  
      @final
      def _dir_additions(self) -> set[str]:
          """
          add the string-like attributes from the info_axis.
          If info_axis is a MultiIndex, its first level values are used.
          """
          additions = super()._dir_additions()
          if self._info_axis._can_hold_strings:
              additions.update(self._info_axis._dir_additions_for_owner)
          return additions
  
      # ----------------------------------------------------------------------
      # Consolidation of internals
  
      @final
      def _protect_consolidate(self, f):
          """
          Consolidate _mgr -- if the blocks have changed, then clear the
          cache
          """
          if isinstance(self._mgr, (ArrayManager, SingleArrayManager)):
              return f()
          blocks_before = len(self._mgr.blocks)
          result = f()
          if len(self._mgr.blocks) != blocks_before:
              self._clear_item_cache()
          return result
  
      @final
      def _consolidate_inplace(self) -> None:
          """Consolidate data in place and return None"""
  
          def f():
              self._mgr = self._mgr.consolidate()
  
          self._protect_consolidate(f)
  
      @final
      def _consolidate(self):
          """
          Compute NDFrame with "consolidated" internals (data of each dtype
          grouped together in a single ndarray).
  
          Returns
          -------
          consolidated : same type as caller
          """
          f = lambda: self._mgr.consolidate()
          cons_data = self._protect_consolidate(f)
          return self._constructor(cons_data).__finalize__(self)
  
      @final
      @property
      def _is_mixed_type(self) -> bool_t:
          if self._mgr.is_single_block:
              return False
  
          if self._mgr.any_extension_types:
              # Even if they have the same dtype, we can't consolidate them,
              #  so we pretend this is "mixed'"
              return True
  
          return self.dtypes.nunique() > 1
  
      @final
      def _check_inplace_setting(self, value) -> bool_t:
          """check whether we allow in-place setting with this type of value"""
          if self._is_mixed_type and not self._mgr.is_numeric_mixed_type:
  
              # allow an actual np.nan thru
              if is_float(value) and np.isnan(value):
                  return True
  
              raise TypeError(
                  "Cannot do inplace boolean setting on "
                  "mixed-types with a non np.nan value"
              )
  
          return True
  
      @final
      def _get_numeric_data(self):
          return self._constructor(self._mgr.get_numeric_data()).__finalize__(self)
  
      @final
      def _get_bool_data(self):
          return self._constructor(self._mgr.get_bool_data()).__finalize__(self)
  
      # ----------------------------------------------------------------------
      # Internal Interface Methods
  
      @property
      def values(self) -> np.ndarray:
          raise AbstractMethodError(self)
  
      @property
      def _values(self) -> np.ndarray:
          """internal implementation"""
          raise AbstractMethodError(self)
  
      @property
      def dtypes(self):
          """
          Return the dtypes in the DataFrame.
  
          This returns a Series with the data type of each column.
          The result's index is the original DataFrame's columns. Columns
          with mixed types are stored with the ``object`` dtype. See
          :ref:`the User Guide <basics.dtypes>` for more.
  
          Returns
          -------
          pandas.Series
              The data type of each column.
  
          Examples
          --------
          >>> df = pd.DataFrame({'float': [1.0],
          ...                    'int': [1],
          ...                    'datetime': [pd.Timestamp('20180310')],
          ...                    'string': ['foo']})
          >>> df.dtypes
          float              float64
          int                  int64
          datetime    datetime64[ns]
          string              object
          dtype: object
          """
          data = self._mgr.get_dtypes()
          return self._constructor_sliced(data, index=self._info_axis, dtype=np.object_)
  
      def astype(
          self: FrameOrSeries, dtype, copy: bool_t = True, errors: str = "raise"
      ) -> FrameOrSeries:
          """
          Cast a pandas object to a specified dtype ``dtype``.
  
          Parameters
          ----------
          dtype : data type, or dict of column name -> data type
              Use a numpy.dtype or Python type to cast entire pandas object to
              the same type. Alternatively, use {col: dtype, ...}, where col is a
              column label and dtype is a numpy.dtype or Python type to cast one
              or more of the DataFrame's columns to column-specific types.
          copy : bool, default True
              Return a copy when ``copy=True`` (be very careful setting
              ``copy=False`` as changes to values then may propagate to other
              pandas objects).
          errors : {'raise', 'ignore'}, default 'raise'
              Control raising of exceptions on invalid data for provided dtype.
  
              - ``raise`` : allow exceptions to be raised
              - ``ignore`` : suppress exceptions. On error return original object.
  
          Returns
          -------
          casted : same type as caller
  
          See Also
          --------
          to_datetime : Convert argument to datetime.
          to_timedelta : Convert argument to timedelta.
          to_numeric : Convert argument to a numeric type.
          numpy.ndarray.astype : Cast a numpy array to a specified type.
  
          Notes
          -----
          .. deprecated:: 1.3.0
  
              Using ``astype`` to convert from timezone-naive dtype to
              timezone-aware dtype is deprecated and will raise in a
              future version.  Use :meth:`Series.dt.tz_localize` instead.
  
          Examples
          --------
          Create a DataFrame:
  
          >>> d = {'col1': [1, 2], 'col2': [3, 4]}
          >>> df = pd.DataFrame(data=d)
          >>> df.dtypes
          col1    int64
          col2    int64
          dtype: object
  
          Cast all columns to int32:
  
          >>> df.astype('int32').dtypes
          col1    int32
          col2    int32
          dtype: object
  
          Cast col1 to int32 using a dictionary:
  
          >>> df.astype({'col1': 'int32'}).dtypes
          col1    int32
          col2    int64
          dtype: object
  
          Create a series:
  
          >>> ser = pd.Series([1, 2], dtype='int32')
          >>> ser
          0    1
          1    2
          dtype: int32
          >>> ser.astype('int64')
          0    1
          1    2
          dtype: int64
  
          Convert to categorical type:
  
          >>> ser.astype('category')
          0    1
          1    2
          dtype: category
          Categories (2, int64): [1, 2]
  
          Convert to ordered categorical type with custom ordering:
  
          >>> from pandas.api.types import CategoricalDtype
          >>> cat_dtype = CategoricalDtype(
          ...     categories=[2, 1], ordered=True)
          >>> ser.astype(cat_dtype)
          0    1
          1    2
          dtype: category
          Categories (2, int64): [2 < 1]
  
          Note that using ``copy=False`` and changing data on a new
          pandas object may propagate changes:
  
          >>> s1 = pd.Series([1, 2])
          >>> s2 = s1.astype('int64', copy=False)
          >>> s2[0] = 10
          >>> s1  # note that s1[0] has changed too
          0    10
          1     2
          dtype: int64
  
          Create a series of dates:
  
          >>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
          >>> ser_date
          0   2020-01-01
          1   2020-01-02
          2   2020-01-03
          dtype: datetime64[ns]
          """
          if is_dict_like(dtype):
              if self.ndim == 1:  # i.e. Series
                  if len(dtype) > 1 or self.name not in dtype:
                      raise KeyError(
                          "Only the Series name can be used for "
                          "the key in Series dtype mappings."
                      )
                  new_type = dtype[self.name]
                  return self.astype(new_type, copy, errors)
  
              for col_name in dtype.keys():
                  if col_name not in self:
                      raise KeyError(
                          "Only a column name can be used for the "
                          "key in a dtype mappings argument."
                      )
              results = []
              for col_name, col in self.items():
                  if col_name in dtype:
                      results.append(
                          col.astype(dtype=dtype[col_name], copy=copy, errors=errors)
                      )
                  else:
                      results.append(col.copy() if copy else col)
  
          elif is_extension_array_dtype(dtype) and self.ndim > 1:
              # GH 18099/22869: columnwise conversion to extension dtype
              # GH 24704: use iloc to handle duplicate column names
              # TODO(EA2D): special case not needed with 2D EAs
              results = [
                  self.iloc[:, i].astype(dtype, copy=copy)
                  for i in range(len(self.columns))
              ]
  
          else:
              # else, only a single dtype is given
              new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
              return self._constructor(new_data).__finalize__(self, method="astype")
  
          # GH 33113: handle empty frame or series
          if not results:
              return self.copy()
  
          # GH 19920: retain column metadata after concat
          result = concat(results, axis=1, copy=False)
          result.columns = self.columns
          return result
  
      @final
      def copy(self: FrameOrSeries, deep: bool_t = True) -> FrameOrSeries:
          """
          Make a copy of this object's indices and data.
  
          When ``deep=True`` (default), a new object will be created with a
          copy of the calling object's data and indices. Modifications to
          the data or indices of the copy will not be reflected in the
          original object (see notes below).
  
          When ``deep=False``, a new object will be created without copying
          the calling object's data or index (only references to the data
          and index are copied). Any changes to the data of the original
          will be reflected in the shallow copy (and vice versa).
  
          Parameters
          ----------
          deep : bool, default True
              Make a deep copy, including a copy of the data and the indices.
              With ``deep=False`` neither the indices nor the data are copied.
  
          Returns
          -------
          copy : Series or DataFrame
              Object type matches caller.
  
          Notes
          -----
          When ``deep=True``, data is copied but actual Python objects
          will not be copied recursively, only the reference to the object.
          This is in contrast to `copy.deepcopy` in the Standard Library,
          which recursively copies object data (see examples below).
  
          While ``Index`` objects are copied when ``deep=True``, the underlying
          numpy array is not copied for performance reasons. Since ``Index`` is
          immutable, the underlying data can be safely shared and a copy
          is not needed.
  
          Examples
          --------
          >>> s = pd.Series([1, 2], index=["a", "b"])
          >>> s
          a    1
          b    2
          dtype: int64
  
          >>> s_copy = s.copy()
          >>> s_copy
          a    1
          b    2
          dtype: int64
  
          **Shallow copy versus default (deep) copy:**
  
          >>> s = pd.Series([1, 2], index=["a", "b"])
          >>> deep = s.copy()
          >>> shallow = s.copy(deep=False)
  
          Shallow copy shares data and index with original.
  
          >>> s is shallow
          False
          >>> s.values is shallow.values and s.index is shallow.index
          True
  
          Deep copy has own copy of data and index.
  
          >>> s is deep
          False
          >>> s.values is deep.values or s.index is deep.index
          False
  
          Updates to the data shared by shallow copy and original is reflected
          in both; deep copy remains unchanged.
  
          >>> s[0] = 3
          >>> shallow[1] = 4
          >>> s
          a    3
          b    4
          dtype: int64
          >>> shallow
          a    3
          b    4
          dtype: int64
          >>> deep
          a    1
          b    2
          dtype: int64
  
          Note that when copying an object containing Python objects, a deep copy
          will copy the data, but will not do so recursively. Updating a nested
          data object will be reflected in the deep copy.
  
          >>> s = pd.Series([[1, 2], [3, 4]])
          >>> deep = s.copy()
          >>> s[0][0] = 10
          >>> s
          0    [10, 2]
          1     [3, 4]
          dtype: object
          >>> deep
          0    [10, 2]
          1     [3, 4]
          dtype: object
          """
          data = self._mgr.copy(deep=deep)
          self._clear_item_cache()
          return self._constructor(data).__finalize__(self, method="copy")
  
      @final
      def __copy__(self: FrameOrSeries, deep: bool_t = True) -> FrameOrSeries:
          return self.copy(deep=deep)
  
      @final
      def __deepcopy__(self: FrameOrSeries, memo=None) -> FrameOrSeries:
          """
          Parameters
          ----------
          memo, default None
              Standard signature. Unused
          """
          return self.copy(deep=True)
  
      @final
      def _convert(
          self: FrameOrSeries,
          datetime: bool_t = False,
          numeric: bool_t = False,
          timedelta: bool_t = False,
      ) -> FrameOrSeries:
          """
          Attempt to infer better dtype for object columns
  
          Parameters
          ----------
          datetime : bool, default False
              If True, convert to date where possible.
          numeric : bool, default False
              If True, attempt to convert to numbers (including strings), with
              unconvertible values becoming NaN.
          timedelta : bool, default False
              If True, convert to timedelta where possible.
  
          Returns
          -------
          converted : same as input object
          """
          validate_bool_kwarg(datetime, "datetime")
          validate_bool_kwarg(numeric, "numeric")
          validate_bool_kwarg(timedelta, "timedelta")
          return self._constructor(
              self._mgr.convert(
                  datetime=datetime,
                  numeric=numeric,
                  timedelta=timedelta,
                  copy=True,
              )
          ).__finalize__(self)
  
      @final
      def infer_objects(self: FrameOrSeries) -> FrameOrSeries:
          """
          Attempt to infer better dtypes for object columns.
  
          Attempts soft conversion of object-dtyped
          columns, leaving non-object and unconvertible
          columns unchanged. The inference rules are the
          same as during normal Series/DataFrame construction.
  
          Returns
          -------
          converted : same type as input object
  
          See Also
          --------
          to_datetime : Convert argument to datetime.
          to_timedelta : Convert argument to timedelta.
          to_numeric : Convert argument to numeric type.
          convert_dtypes : Convert argument to best possible dtype.
  
          Examples
          --------
          >>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})
          >>> df = df.iloc[1:]
          >>> df
             A
          1  1
          2  2
          3  3
  
          >>> df.dtypes
          A    object
          dtype: object
  
          >>> df.infer_objects().dtypes
          A    int64
          dtype: object
          """
          # numeric=False necessary to only soft convert;
          # python objects will still be converted to
          # native numpy numeric types
          return self._constructor(
              self._mgr.convert(datetime=True, numeric=False, timedelta=True, copy=True)
          ).__finalize__(self, method="infer_objects")
  
      @final
      def convert_dtypes(
          self: FrameOrSeries,
          infer_objects: bool_t = True,
          convert_string: bool_t = True,
          convert_integer: bool_t = True,
          convert_boolean: bool_t = True,
          convert_floating: bool_t = True,
      ) -> FrameOrSeries:
          """
          Convert columns to best possible dtypes using dtypes supporting ``pd.NA``.
  
          .. versionadded:: 1.0.0
  
          Parameters
          ----------
          infer_objects : bool, default True
              Whether object dtypes should be converted to the best possible types.
          convert_string : bool, default True
              Whether object dtypes should be converted to ``StringDtype()``.
          convert_integer : bool, default True
              Whether, if possible, conversion can be done to integer extension types.
          convert_boolean : bool, defaults True
              Whether object dtypes should be converted to ``BooleanDtypes()``.
          convert_floating : bool, defaults True
              Whether, if possible, conversion can be done to floating extension types.
              If `convert_integer` is also True, preference will be give to integer
              dtypes if the floats can be faithfully casted to integers.
  
              .. versionadded:: 1.2.0
  
          Returns
          -------
          Series or DataFrame
              Copy of input object with new dtype.
  
          See Also
          --------
          infer_objects : Infer dtypes of objects.
          to_datetime : Convert argument to datetime.
          to_timedelta : Convert argument to timedelta.
          to_numeric : Convert argument to a numeric type.
  
          Notes
          -----
          By default, ``convert_dtypes`` will attempt to convert a Series (or each
          Series in a DataFrame) to dtypes that support ``pd.NA``. By using the options
          ``convert_string``, ``convert_integer``, ``convert_boolean`` and
          ``convert_boolean``, it is possible to turn off individual conversions
          to ``StringDtype``, the integer extension types, ``BooleanDtype``
          or floating extension types, respectively.
  
          For object-dtyped columns, if ``infer_objects`` is ``True``, use the inference
          rules as during normal Series/DataFrame construction.  Then, if possible,
          convert to ``StringDtype``, ``BooleanDtype`` or an appropriate integer
          or floating extension type, otherwise leave as ``object``.
  
          If the dtype is integer, convert to an appropriate integer extension type.
  
          If the dtype is numeric, and consists of all integers, convert to an
          appropriate integer extension type. Otherwise, convert to an
          appropriate floating extension type.
  
          .. versionchanged:: 1.2
              Starting with pandas 1.2, this method also converts float columns
              to the nullable floating extension type.
  
          In the future, as new dtypes are added that support ``pd.NA``, the results
          of this method will change to support those new dtypes.
  
          Examples
          --------
          >>> df = pd.DataFrame(
          ...     {
          ...         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
          ...         "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
          ...         "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
          ...         "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
          ...         "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
          ...         "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
          ...     }
          ... )
  
          Start with a DataFrame with default dtypes.
  
          >>> df
             a  b      c    d     e      f
          0  1  x   True    h  10.0    NaN
          1  2  y  False    i   NaN  100.5
          2  3  z    NaN  NaN  20.0  200.0
  
          >>> df.dtypes
          a      int32
          b     object
          c     object
          d     object
          e    float64
          f    float64
          dtype: object
  
          Convert the DataFrame to use best possible dtypes.
  
          >>> dfn = df.convert_dtypes()
          >>> dfn
             a  b      c     d     e      f
          0  1  x   True     h    10   <NA>
          1  2  y  False     i  <NA>  100.5
          2  3  z   <NA>  <NA>    20  200.0
  
          >>> dfn.dtypes
          a      Int32
          b     string
          c    boolean
          d     string
          e      Int64
          f    Float64
          dtype: object
  
          Start with a Series of strings and missing data represented by ``np.nan``.
  
          >>> s = pd.Series(["a", "b", np.nan])
          >>> s
          0      a
          1      b
          2    NaN
          dtype: object
  
          Obtain a Series with dtype ``StringDtype``.
  
          >>> s.convert_dtypes()
          0       a
          1       b
          2    <NA>
          dtype: string
          """
          if self.ndim == 1:
              return self._convert_dtypes(
                  infer_objects,
                  convert_string,
                  convert_integer,
                  convert_boolean,
                  convert_floating,
              )
          else:
              results = [
                  col._convert_dtypes(
                      infer_objects,
                      convert_string,
                      convert_integer,
                      convert_boolean,
                      convert_floating,
                  )
                  for col_name, col in self.items()
              ]
              if len(results) > 0:
                  return concat(results, axis=1, copy=False)
              else:
                  return self.copy()
  
      # ----------------------------------------------------------------------
      # Filling NA's
  
      @doc(**_shared_doc_kwargs)
      def fillna(
          self: FrameOrSeries,
          value=None,
          method=None,
          axis=None,
          inplace: bool_t = False,
          limit=None,
          downcast=None,
      ) -> FrameOrSeries | None:
          """
          Fill NA/NaN values using the specified method.
  
          Parameters
          ----------
          value : scalar, dict, Series, or DataFrame
              Value to use to fill holes (e.g. 0), alternately a
              dict/Series/DataFrame of values specifying which value to use for
              each index (for a Series) or column (for a DataFrame).  Values not
              in the dict/Series/DataFrame will not be filled. This value cannot
              be a list.
          method : {{'backfill', 'bfill', 'pad', 'ffill', None}}, default None
              Method to use for filling holes in reindexed Series
              pad / ffill: propagate last valid observation forward to next valid
              backfill / bfill: use next valid observation to fill gap.
          axis : {axes_single_arg}
              Axis along which to fill missing values.
          inplace : bool, default False
              If True, fill in-place. Note: this will modify any
              other views on this object (e.g., a no-copy slice for a column in a
              DataFrame).
          limit : int, default None
              If method is specified, this is the maximum number of consecutive
              NaN values to forward/backward fill. In other words, if there is
              a gap with more than this number of consecutive NaNs, it will only
              be partially filled. If method is not specified, this is the
              maximum number of entries along the entire axis where NaNs will be
              filled. Must be greater than 0 if not None.
          downcast : dict, default is None
              A dict of item->dtype of what to downcast if possible,
              or the string 'infer' which will try to downcast to an appropriate
              equal type (e.g. float64 to int64 if possible).
  
          Returns
          -------
          {klass} or None
              Object with missing values filled or None if ``inplace=True``.
  
          See Also
          --------
          interpolate : Fill NaN values using interpolation.
          reindex : Conform object to new index.
          asfreq : Convert TimeSeries to specified frequency.
  
          Examples
          --------
          >>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
          ...                    [3, 4, np.nan, 1],
          ...                    [np.nan, np.nan, np.nan, 5],
          ...                    [np.nan, 3, np.nan, 4]],
          ...                   columns=list("ABCD"))
          >>> df
               A    B   C  D
          0  NaN  2.0 NaN  0
          1  3.0  4.0 NaN  1
          2  NaN  NaN NaN  5
          3  NaN  3.0 NaN  4
  
          Replace all NaN elements with 0s.
  
          >>> df.fillna(0)
              A   B   C   D
          0   0.0 2.0 0.0 0
          1   3.0 4.0 0.0 1
          2   0.0 0.0 0.0 5
          3   0.0 3.0 0.0 4
  
          We can also propagate non-null values forward or backward.
  
          >>> df.fillna(method="ffill")
              A   B   C   D
          0   NaN 2.0 NaN 0
          1   3.0 4.0 NaN 1
          2   3.0 4.0 NaN 5
          3   3.0 3.0 NaN 4
  
          Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1,
          2, and 3 respectively.
  
          >>> values = {{"A": 0, "B": 1, "C": 2, "D": 3}}
          >>> df.fillna(value=values)
              A   B   C   D
          0   0.0 2.0 2.0 0
          1   3.0 4.0 2.0 1
          2   0.0 1.0 2.0 5
          3   0.0 3.0 2.0 4
  
          Only replace the first NaN element.
  
          >>> df.fillna(value=values, limit=1)
              A   B   C   D
          0   0.0 2.0 2.0 0
          1   3.0 4.0 NaN 1
          2   NaN 1.0 NaN 5
          3   NaN 3.0 NaN 4
  
          When filling using a DataFrame, replacement happens along
          the same column names and same indices
  
          >>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
          >>> df.fillna(df2)
              A   B   C   D
          0   0.0 2.0 0.0 0
          1   3.0 4.0 0.0 1
          2   0.0 0.0 0.0 5
          3   0.0 3.0 0.0 4
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
          value, method = validate_fillna_kwargs(value, method)
  
          self._consolidate_inplace()
  
          # set the default here, so functions examining the signaure
          # can detect if something was set (e.g. in groupby) (GH9221)
          if axis is None:
              axis = 0
          axis = self._get_axis_number(axis)
  
          if value is None:
              if not self._mgr.is_single_block and axis == 1:
                  if inplace:
                      raise NotImplementedError()
                  result = self.T.fillna(method=method, limit=limit).T
  
                  # need to downcast here because of all of the transposes
                  result._mgr = result._mgr.downcast()
  
                  return result
  
              new_data = self._mgr.interpolate(
                  method=method,
                  axis=axis,
                  limit=limit,
                  inplace=inplace,
                  coerce=True,
                  downcast=downcast,
              )
          else:
              if self.ndim == 1:
                  if isinstance(value, (dict, ABCSeries)):
                      value = create_series_with_explicit_dtype(
                          value, dtype_if_empty=object
                      )
                      value = value.reindex(self.index, copy=False)
                      value = value._values
                  elif not is_list_like(value):
                      pass
                  else:
                      raise TypeError(
                          '"value" parameter must be a scalar, dict '
                          "or Series, but you passed a "
                          f'"{type(value).__name__}"'
                      )
  
                  new_data = self._mgr.fillna(
                      value=value, limit=limit, inplace=inplace, downcast=downcast
                  )
  
              elif isinstance(value, (dict, ABCSeries)):
                  if axis == 1:
                      raise NotImplementedError(
                          "Currently only can fill "
                          "with dict/Series column "
                          "by column"
                      )
  
                  result = self if inplace else self.copy()
                  is_dict = isinstance(downcast, dict)
                  for k, v in value.items():
                      if k not in result:
                          continue
                      obj = result[k]
                      downcast_k = downcast if not is_dict else downcast.get(k)
                      obj.fillna(v, limit=limit, inplace=True, downcast=downcast_k)
                  return result if not inplace else None
  
              elif not is_list_like(value):
                  new_data = self._mgr.fillna(
                      value=value, limit=limit, inplace=inplace, downcast=downcast
                  )
              elif isinstance(value, ABCDataFrame) and self.ndim == 2:
                  new_data = self.where(self.notna(), value)._data
              else:
                  raise ValueError(f"invalid fill value with a {type(value)}")
  
          result = self._constructor(new_data)
          if inplace:
              return self._update_inplace(result)
          else:
              return result.__finalize__(self, method="fillna")
  
      @doc(klass=_shared_doc_kwargs["klass"])
      def ffill(
          self: FrameOrSeries,
          axis: None | Axis = None,
          inplace: bool_t = False,
          limit: None | int = None,
          downcast=None,
      ) -> FrameOrSeries | None:
          """
          Synonym for :meth:`DataFrame.fillna` with ``method='ffill'``.
  
          Returns
          -------
          {klass} or None
              Object with missing values filled or None if ``inplace=True``.
          """
          return self.fillna(
              method="ffill", axis=axis, inplace=inplace, limit=limit, downcast=downcast
          )
  
      pad = ffill
  
      @doc(klass=_shared_doc_kwargs["klass"])
      def bfill(
          self: FrameOrSeries,
          axis: None | Axis = None,
          inplace: bool_t = False,
          limit: None | int = None,
          downcast=None,
      ) -> FrameOrSeries | None:
          """
          Synonym for :meth:`DataFrame.fillna` with ``method='bfill'``.
  
          Returns
          -------
          {klass} or None
              Object with missing values filled or None if ``inplace=True``.
          """
          return self.fillna(
              method="bfill", axis=axis, inplace=inplace, limit=limit, downcast=downcast
          )
  
      backfill = bfill
  
      @doc(
          _shared_docs["replace"],
          klass=_shared_doc_kwargs["klass"],
          inplace=_shared_doc_kwargs["inplace"],
          replace_iloc=_shared_doc_kwargs["replace_iloc"],
      )
      def replace(
          self,
          to_replace=None,
          value=None,
          inplace: bool_t = False,
          limit: int | None = None,
          regex=False,
          method="pad",
      ):
          if not (
              is_scalar(to_replace)
              or is_re_compilable(to_replace)
              or is_list_like(to_replace)
          ):
              raise TypeError(
                  "Expecting 'to_replace' to be either a scalar, array-like, "
                  "dict or None, got invalid type "
                  f"{repr(type(to_replace).__name__)}"
              )
  
          inplace = validate_bool_kwarg(inplace, "inplace")
          if not is_bool(regex) and to_replace is not None:
              raise ValueError("'to_replace' must be 'None' if 'regex' is not a bool")
  
          self._consolidate_inplace()
  
          if value is None:
              # passing a single value that is scalar like
              # when value is None (GH5319), for compat
              if not is_dict_like(to_replace) and not is_dict_like(regex):
                  to_replace = [to_replace]
  
              if isinstance(to_replace, (tuple, list)):
                  if isinstance(self, ABCDataFrame):
                      return self.apply(
                          self._constructor_sliced._replace_single,
                          args=(to_replace, method, inplace, limit),
                      )
                  self = cast("Series", self)
                  return self._replace_single(to_replace, method, inplace, limit)
  
              if not is_dict_like(to_replace):
                  if not is_dict_like(regex):
                      raise TypeError(
                          'If "to_replace" and "value" are both None '
                          'and "to_replace" is not a list, then '
                          "regex must be a mapping"
                      )
                  to_replace = regex
                  regex = True
  
              items = list(to_replace.items())
              if items:
                  keys, values = zip(*items)
              else:
                  keys, values = ([], [])
  
              are_mappings = [is_dict_like(v) for v in values]
  
              if any(are_mappings):
                  if not all(are_mappings):
                      raise TypeError(
                          "If a nested mapping is passed, all values "
                          "of the top level mapping must be mappings"
                      )
                  # passed a nested dict/Series
                  to_rep_dict = {}
                  value_dict = {}
  
                  for k, v in items:
                      keys, values = list(zip(*v.items())) or ([], [])
  
                      to_rep_dict[k] = list(keys)
                      value_dict[k] = list(values)
  
                  to_replace, value = to_rep_dict, value_dict
              else:
                  to_replace, value = keys, values
  
              return self.replace(
                  to_replace, value, inplace=inplace, limit=limit, regex=regex
              )
          else:
  
              # need a non-zero len on all axes
              if not self.size:
                  if inplace:
                      return
                  return self.copy()
  
              if is_dict_like(to_replace):
                  if is_dict_like(value):  # {'A' : NA} -> {'A' : 0}
                      # Note: Checking below for `in foo.keys()` instead of
                      #  `in foo` is needed for when we have a Series and not dict
                      mapping = {
                          col: (to_replace[col], value[col])
                          for col in to_replace.keys()
                          if col in value.keys() and col in self
                      }
                      return self._replace_columnwise(mapping, inplace, regex)
  
                  # {'A': NA} -> 0
                  elif not is_list_like(value):
                      # Operate column-wise
                      if self.ndim == 1:
                          raise ValueError(
                              "Series.replace cannot use dict-like to_replace "
                              "and non-None value"
                          )
                      mapping = {
                          col: (to_rep, value) for col, to_rep in to_replace.items()
                      }
                      return self._replace_columnwise(mapping, inplace, regex)
                  else:
                      raise TypeError("value argument must be scalar, dict, or Series")
  
              elif is_list_like(to_replace):
                  if not is_list_like(value):
                      # e.g. to_replace = [NA, ''] and value is 0,
                      #  so we replace NA with 0 and then replace '' with 0
                      value = [value] * len(to_replace)
  
                  # e.g. we have to_replace = [NA, ''] and value = [0, 'missing']
                  if len(to_replace) != len(value):
                      raise ValueError(
                          f"Replacement lists must match in length. "
                          f"Expecting {len(to_replace)} got {len(value)} "
                      )
                  new_data = self._mgr.replace_list(
                      src_list=to_replace,
                      dest_list=value,
                      inplace=inplace,
                      regex=regex,
                  )
  
              elif to_replace is None:
                  if not (
                      is_re_compilable(regex)
                      or is_list_like(regex)
                      or is_dict_like(regex)
                  ):
                      raise TypeError(
                          f"'regex' must be a string or a compiled regular expression "
                          f"or a list or dict of strings or regular expressions, "
                          f"you passed a {repr(type(regex).__name__)}"
                      )
                  return self.replace(
                      regex, value, inplace=inplace, limit=limit, regex=True
                  )
              else:
  
                  # dest iterable dict-like
                  if is_dict_like(value):  # NA -> {'A' : 0, 'B' : -1}
                      # Operate column-wise
                      if self.ndim == 1:
                          raise ValueError(
                              "Series.replace cannot use dict-value and "
                              "non-None to_replace"
                          )
                      mapping = {col: (to_replace, val) for col, val in value.items()}
                      return self._replace_columnwise(mapping, inplace, regex)
  
                  elif not is_list_like(value):  # NA -> 0
                      new_data = self._mgr.replace(
                          to_replace=to_replace, value=value, inplace=inplace, regex=regex
                      )
                  else:
                      raise TypeError(
                          f'Invalid "to_replace" type: {repr(type(to_replace).__name__)}'
                      )
  
          result = self._constructor(new_data)
          if inplace:
              return self._update_inplace(result)
          else:
              return result.__finalize__(self, method="replace")
  
      def interpolate(
          self: FrameOrSeries,
          method: str = "linear",
          axis: Axis = 0,
          limit: int | None = None,
          inplace: bool_t = False,
          limit_direction: str | None = None,
          limit_area: str | None = None,
          downcast: str | None = None,
          **kwargs,
      ) -> FrameOrSeries | None:
          """
          Fill NaN values using an interpolation method.
  
          Please note that only ``method='linear'`` is supported for
          DataFrame/Series with a MultiIndex.
  
          Parameters
          ----------
          method : str, default 'linear'
              Interpolation technique to use. One of:
  
              * 'linear': Ignore the index and treat the values as equally
                spaced. This is the only method supported on MultiIndexes.
              * 'time': Works on daily and higher resolution data to interpolate
                given length of interval.
              * 'index', 'values': use the actual numerical values of the index.
              * 'pad': Fill in NaNs using existing values.
              * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'spline',
                'barycentric', 'polynomial': Passed to
                `scipy.interpolate.interp1d`. These methods use the numerical
                values of the index.  Both 'polynomial' and 'spline' require that
                you also specify an `order` (int), e.g.
                ``df.interpolate(method='polynomial', order=5)``.
              * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima',
                'cubicspline': Wrappers around the SciPy interpolation methods of
                similar names. See `Notes`.
              * 'from_derivatives': Refers to
                `scipy.interpolate.BPoly.from_derivatives` which
                replaces 'piecewise_polynomial' interpolation method in
                scipy 0.18.
  
          axis : {{0 or 'index', 1 or 'columns', None}}, default None
              Axis to interpolate along.
          limit : int, optional
              Maximum number of consecutive NaNs to fill. Must be greater than
              0.
          inplace : bool, default False
              Update the data in place if possible.
          limit_direction : {{'forward', 'backward', 'both'}}, Optional
              Consecutive NaNs will be filled in this direction.
  
              If limit is specified:
                  * If 'method' is 'pad' or 'ffill', 'limit_direction' must be 'forward'.
                  * If 'method' is 'backfill' or 'bfill', 'limit_direction' must be
                    'backwards'.
  
              If 'limit' is not specified:
                  * If 'method' is 'backfill' or 'bfill', the default is 'backward'
                  * else the default is 'forward'
  
              .. versionchanged:: 1.1.0
                  raises ValueError if `limit_direction` is 'forward' or 'both' and
                      method is 'backfill' or 'bfill'.
                  raises ValueError if `limit_direction` is 'backward' or 'both' and
                      method is 'pad' or 'ffill'.
  
          limit_area : {{`None`, 'inside', 'outside'}}, default None
              If limit is specified, consecutive NaNs will be filled with this
              restriction.
  
              * ``None``: No fill restriction.
              * 'inside': Only fill NaNs surrounded by valid values
                (interpolate).
              * 'outside': Only fill NaNs outside valid values (extrapolate).
  
          downcast : optional, 'infer' or None, defaults to None
              Downcast dtypes if possible.
          ``**kwargs`` : optional
              Keyword arguments to pass on to the interpolating function.
  
          Returns
          -------
          Series or DataFrame or None
              Returns the same object type as the caller, interpolated at
              some or all ``NaN`` values or None if ``inplace=True``.
  
          See Also
          --------
          fillna : Fill missing values using different methods.
          scipy.interpolate.Akima1DInterpolator : Piecewise cubic polynomials
              (Akima interpolator).
          scipy.interpolate.BPoly.from_derivatives : Piecewise polynomial in the
              Bernstein basis.
          scipy.interpolate.interp1d : Interpolate a 1-D function.
          scipy.interpolate.KroghInterpolator : Interpolate polynomial (Krogh
              interpolator).
          scipy.interpolate.PchipInterpolator : PCHIP 1-d monotonic cubic
              interpolation.
          scipy.interpolate.CubicSpline : Cubic spline data interpolator.
  
          Notes
          -----
          The 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima'
          methods are wrappers around the respective SciPy implementations of
          similar names. These use the actual numerical values of the index.
          For more information on their behavior, see the
          `SciPy documentation
          <https://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__
          and `SciPy tutorial
          <https://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html>`__.
  
          Examples
          --------
          Filling in ``NaN`` in a :class:`~pandas.Series` via linear
          interpolation.
  
          >>> s = pd.Series([0, 1, np.nan, 3])
          >>> s
          0    0.0
          1    1.0
          2    NaN
          3    3.0
          dtype: float64
          >>> s.interpolate()
          0    0.0
          1    1.0
          2    2.0
          3    3.0
          dtype: float64
  
          Filling in ``NaN`` in a Series by padding, but filling at most two
          consecutive ``NaN`` at a time.
  
          >>> s = pd.Series([np.nan, "single_one", np.nan,
          ...                "fill_two_more", np.nan, np.nan, np.nan,
          ...                4.71, np.nan])
          >>> s
          0              NaN
          1       single_one
          2              NaN
          3    fill_two_more
          4              NaN
          5              NaN
          6              NaN
          7             4.71
          8              NaN
          dtype: object
          >>> s.interpolate(method='pad', limit=2)
          0              NaN
          1       single_one
          2       single_one
          3    fill_two_more
          4    fill_two_more
          5    fill_two_more
          6              NaN
          7             4.71
          8             4.71
          dtype: object
  
          Filling in ``NaN`` in a Series via polynomial interpolation or splines:
          Both 'polynomial' and 'spline' methods require that you also specify
          an ``order`` (int).
  
          >>> s = pd.Series([0, 2, np.nan, 8])
          >>> s.interpolate(method='polynomial', order=2)
          0    0.000000
          1    2.000000
          2    4.666667
          3    8.000000
          dtype: float64
  
          Fill the DataFrame forward (that is, going down) along each column
          using linear interpolation.
  
          Note how the last entry in column 'a' is interpolated differently,
          because there is no entry after it to use for interpolation.
          Note how the first entry in column 'b' remains ``NaN``, because there
          is no entry before it to use for interpolation.
  
          >>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0),
          ...                    (np.nan, 2.0, np.nan, np.nan),
          ...                    (2.0, 3.0, np.nan, 9.0),
          ...                    (np.nan, 4.0, -4.0, 16.0)],
          ...                   columns=list('abcd'))
          >>> df
               a    b    c     d
          0  0.0  NaN -1.0   1.0
          1  NaN  2.0  NaN   NaN
          2  2.0  3.0  NaN   9.0
          3  NaN  4.0 -4.0  16.0
          >>> df.interpolate(method='linear', limit_direction='forward', axis=0)
               a    b    c     d
          0  0.0  NaN -1.0   1.0
          1  1.0  2.0 -2.0   5.0
          2  2.0  3.0 -3.0   9.0
          3  2.0  4.0 -4.0  16.0
  
          Using polynomial interpolation.
  
          >>> df['d'].interpolate(method='polynomial', order=2)
          0     1.0
          1     4.0
          2     9.0
          3    16.0
          Name: d, dtype: float64
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
  
          axis = self._get_axis_number(axis)
  
          fillna_methods = ["ffill", "bfill", "pad", "backfill"]
          should_transpose = axis == 1 and method not in fillna_methods
  
          obj = self.T if should_transpose else self
  
          if obj.empty:
              return self.copy()
  
          if method not in fillna_methods:
              axis = self._info_axis_number
  
          if isinstance(obj.index, MultiIndex) and method != "linear":
              raise ValueError(
                  "Only `method=linear` interpolation is supported on MultiIndexes."
              )
  
          # Set `limit_direction` depending on `method`
          if limit_direction is None:
              limit_direction = (
                  "backward" if method in ("backfill", "bfill") else "forward"
              )
          else:
              if method in ("pad", "ffill") and limit_direction != "forward":
                  raise ValueError(
                      f"`limit_direction` must be 'forward' for method `{method}`"
                  )
              if method in ("backfill", "bfill") and limit_direction != "backward":
                  raise ValueError(
                      f"`limit_direction` must be 'backward' for method `{method}`"
                  )
  
          if obj.ndim == 2 and np.all(obj.dtypes == np.dtype("object")):
              raise TypeError(
                  "Cannot interpolate with all object-dtype columns "
                  "in the DataFrame. Try setting at least one "
                  "column to a numeric dtype."
              )
  
          # create/use the index
          if method == "linear":
              # prior default
              index = np.arange(len(obj.index))
              index = Index(index)
          else:
              index = obj.index
              methods = {"index", "values", "nearest", "time"}
              is_numeric_or_datetime = (
                  is_numeric_dtype(index.dtype)
                  or is_datetime64_any_dtype(index.dtype)
                  or is_timedelta64_dtype(index.dtype)
              )
              if method not in methods and not is_numeric_or_datetime:
                  raise ValueError(
                      "Index column must be numeric or datetime type when "
                      f"using {method} method other than linear. "
                      "Try setting a numeric or datetime index column before "
                      "interpolating."
                  )
  
          if isna(index).any():
              raise NotImplementedError(
                  "Interpolation with NaNs in the index "
                  "has not been implemented. Try filling "
                  "those NaNs before interpolating."
              )
          new_data = obj._mgr.interpolate(
              method=method,
              axis=axis,
              index=index,
              limit=limit,
              limit_direction=limit_direction,
              limit_area=limit_area,
              inplace=inplace,
              downcast=downcast,
              **kwargs,
          )
  
          result = self._constructor(new_data)
          if should_transpose:
              result = result.T
          if inplace:
              return self._update_inplace(result)
          else:
              return result.__finalize__(self, method="interpolate")
  
      # ----------------------------------------------------------------------
      # Timeseries methods Methods
  
      @final
      def asof(self, where, subset=None):
          """
          Return the last row(s) without any NaNs before `where`.
  
          The last row (for each element in `where`, if list) without any
          NaN is taken.
          In case of a :class:`~pandas.DataFrame`, the last row without NaN
          considering only the subset of columns (if not `None`)
  
          If there is no good value, NaN is returned for a Series or
          a Series of NaN values for a DataFrame
  
          Parameters
          ----------
          where : date or array-like of dates
              Date(s) before which the last row(s) are returned.
          subset : str or array-like of str, default `None`
              For DataFrame, if not `None`, only use these columns to
              check for NaNs.
  
          Returns
          -------
          scalar, Series, or DataFrame
  
              The return can be:
  
              * scalar : when `self` is a Series and `where` is a scalar
              * Series: when `self` is a Series and `where` is an array-like,
                or when `self` is a DataFrame and `where` is a scalar
              * DataFrame : when `self` is a DataFrame and `where` is an
                array-like
  
              Return scalar, Series, or DataFrame.
  
          See Also
          --------
          merge_asof : Perform an asof merge. Similar to left join.
  
          Notes
          -----
          Dates are assumed to be sorted. Raises if this is not the case.
  
          Examples
          --------
          A Series and a scalar `where`.
  
          >>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
          >>> s
          10    1.0
          20    2.0
          30    NaN
          40    4.0
          dtype: float64
  
          >>> s.asof(20)
          2.0
  
          For a sequence `where`, a Series is returned. The first value is
          NaN, because the first element of `where` is before the first
          index value.
  
          >>> s.asof([5, 20])
          5     NaN
          20    2.0
          dtype: float64
  
          Missing values are not considered. The following is ``2.0``, not
          NaN, even though NaN is at the index location for ``30``.
  
          >>> s.asof(30)
          2.0
  
          Take all columns into consideration
  
          >>> df = pd.DataFrame({'a': [10, 20, 30, 40, 50],
          ...                    'b': [None, None, None, None, 500]},
          ...                   index=pd.DatetimeIndex(['2018-02-27 09:01:00',
          ...                                           '2018-02-27 09:02:00',
          ...                                           '2018-02-27 09:03:00',
          ...                                           '2018-02-27 09:04:00',
          ...                                           '2018-02-27 09:05:00']))
          >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
          ...                           '2018-02-27 09:04:30']))
                                a   b
          2018-02-27 09:03:30 NaN NaN
          2018-02-27 09:04:30 NaN NaN
  
          Take a single column into consideration
  
          >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
          ...                           '2018-02-27 09:04:30']),
          ...         subset=['a'])
                                   a   b
          2018-02-27 09:03:30   30.0 NaN
          2018-02-27 09:04:30   40.0 NaN
          """
          if isinstance(where, str):
              where = Timestamp(where)
  
          if not self.index.is_monotonic:
              raise ValueError("asof requires a sorted index")
  
          is_series = isinstance(self, ABCSeries)
          if is_series:
              if subset is not None:
                  raise ValueError("subset is not valid for Series")
          else:
              if subset is None:
                  subset = self.columns
              if not is_list_like(subset):
                  subset = [subset]
  
          is_list = is_list_like(where)
          if not is_list:
              start = self.index[0]
              if isinstance(self.index, PeriodIndex):
                  where = Period(where, freq=self.index.freq)
  
              if where < start:
                  if not is_series:
                      return self._constructor_sliced(
                          index=self.columns, name=where, dtype=np.float64
                      )
                  return np.nan
  
              # It's always much faster to use a *while* loop here for
              # Series than pre-computing all the NAs. However a
              # *while* loop is extremely expensive for DataFrame
              # so we later pre-compute all the NAs and use the same
              # code path whether *where* is a scalar or list.
              # See PR: https://github.com/pandas-dev/pandas/pull/14476
              if is_series:
                  loc = self.index.searchsorted(where, side="right")
                  if loc > 0:
                      loc -= 1
  
                  values = self._values
                  while loc > 0 and isna(values[loc]):
                      loc -= 1
                  return values[loc]
  
          if not isinstance(where, Index):
              where = Index(where) if is_list else Index([where])
  
          nulls = self.isna() if is_series else self[subset].isna().any(1)
          if nulls.all():
              if is_series:
                  self = cast("Series", self)
                  return self._constructor(np.nan, index=where, name=self.name)
              elif is_list:
                  self = cast("DataFrame", self)
                  return self._constructor(np.nan, index=where, columns=self.columns)
              else:
                  self = cast("DataFrame", self)
                  return self._constructor_sliced(
                      np.nan, index=self.columns, name=where[0]
                  )
  
          locs = self.index.asof_locs(where, ~(nulls._values))
  
          # mask the missing
          missing = locs == -1
          data = self.take(locs)
          data.index = where
          data.loc[missing] = np.nan
          return data if is_list else data.iloc[-1]
  
      # ----------------------------------------------------------------------
      # Action Methods
  
      @doc(klass=_shared_doc_kwargs["klass"])
      def isna(self: FrameOrSeries) -> FrameOrSeries:
          """
          Detect missing values.
  
          Return a boolean same-sized object indicating if the values are NA.
          NA values, such as None or :attr:`numpy.NaN`, gets mapped to True
          values.
          Everything else gets mapped to False values. Characters such as empty
          strings ``''`` or :attr:`numpy.inf` are not considered NA values
          (unless you set ``pandas.options.mode.use_inf_as_na = True``).
  
          Returns
          -------
          {klass}
              Mask of bool values for each element in {klass} that
              indicates whether an element is an NA value.
  
          See Also
          --------
          {klass}.isnull : Alias of isna.
          {klass}.notna : Boolean inverse of isna.
          {klass}.dropna : Omit axes labels with missing values.
          isna : Top-level isna.
  
          Examples
          --------
          Show which entries in a DataFrame are NA.
  
          >>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
          ...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
          ...                          pd.Timestamp('1940-04-25')],
          ...                    name=['Alfred', 'Batman', ''],
          ...                    toy=[None, 'Batmobile', 'Joker']))
          >>> df
             age       born    name        toy
          0  5.0        NaT  Alfred       None
          1  6.0 1939-05-27  Batman  Batmobile
          2  NaN 1940-04-25              Joker
  
          >>> df.isna()
               age   born   name    toy
          0  False   True  False   True
          1  False  False  False  False
          2   True  False  False  False
  
          Show which entries in a Series are NA.
  
          >>> ser = pd.Series([5, 6, np.NaN])
          >>> ser
          0    5.0
          1    6.0
          2    NaN
          dtype: float64
  
          >>> ser.isna()
          0    False
          1    False
          2     True
          dtype: bool
          """
          return isna(self).__finalize__(self, method="isna")
  
      @doc(isna, klass=_shared_doc_kwargs["klass"])
      def isnull(self: FrameOrSeries) -> FrameOrSeries:
          return isna(self).__finalize__(self, method="isnull")
  
      @doc(klass=_shared_doc_kwargs["klass"])
      def notna(self: FrameOrSeries) -> FrameOrSeries:
          """
          Detect existing (non-missing) values.
  
          Return a boolean same-sized object indicating if the values are not NA.
          Non-missing values get mapped to True. Characters such as empty
          strings ``''`` or :attr:`numpy.inf` are not considered NA values
          (unless you set ``pandas.options.mode.use_inf_as_na = True``).
          NA values, such as None or :attr:`numpy.NaN`, get mapped to False
          values.
  
          Returns
          -------
          {klass}
              Mask of bool values for each element in {klass} that
              indicates whether an element is not an NA value.
  
          See Also
          --------
          {klass}.notnull : Alias of notna.
          {klass}.isna : Boolean inverse of notna.
          {klass}.dropna : Omit axes labels with missing values.
          notna : Top-level notna.
  
          Examples
          --------
          Show which entries in a DataFrame are not NA.
  
          >>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
          ...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
          ...                          pd.Timestamp('1940-04-25')],
          ...                    name=['Alfred', 'Batman', ''],
          ...                    toy=[None, 'Batmobile', 'Joker']))
          >>> df
             age       born    name        toy
          0  5.0        NaT  Alfred       None
          1  6.0 1939-05-27  Batman  Batmobile
          2  NaN 1940-04-25              Joker
  
          >>> df.notna()
               age   born  name    toy
          0   True  False  True  False
          1   True   True  True   True
          2  False   True  True   True
  
          Show which entries in a Series are not NA.
  
          >>> ser = pd.Series([5, 6, np.NaN])
          >>> ser
          0    5.0
          1    6.0
          2    NaN
          dtype: float64
  
          >>> ser.notna()
          0     True
          1     True
          2    False
          dtype: bool
          """
          return notna(self).__finalize__(self, method="notna")
  
      @doc(notna, klass=_shared_doc_kwargs["klass"])
      def notnull(self: FrameOrSeries) -> FrameOrSeries:
          return notna(self).__finalize__(self, method="notnull")
  
      @final
      def _clip_with_scalar(self, lower, upper, inplace: bool_t = False):
          if (lower is not None and np.any(isna(lower))) or (
              upper is not None and np.any(isna(upper))
          ):
              raise ValueError("Cannot use an NA value as a clip threshold")
  
          result = self
          mask = isna(self._values)
  
          with np.errstate(all="ignore"):
              if upper is not None:
                  subset = self <= upper
                  result = result.where(subset, upper, axis=None, inplace=False)
              if lower is not None:
                  subset = self >= lower
                  result = result.where(subset, lower, axis=None, inplace=False)
  
          if np.any(mask):
              result[mask] = np.nan
  
          if inplace:
              return self._update_inplace(result)
          else:
              return result
  
      @final
      def _clip_with_one_bound(self, threshold, method, axis, inplace):
  
          if axis is not None:
              axis = self._get_axis_number(axis)
  
          # method is self.le for upper bound and self.ge for lower bound
          if is_scalar(threshold) and is_number(threshold):
              if method.__name__ == "le":
                  return self._clip_with_scalar(None, threshold, inplace=inplace)
              return self._clip_with_scalar(threshold, None, inplace=inplace)
  
          # GH #15390
          # In order for where method to work, the threshold must
          # be transformed to NDFrame from other array like structure.
          if (not isinstance(threshold, ABCSeries)) and is_list_like(threshold):
              if isinstance(self, ABCSeries):
                  threshold = self._constructor(threshold, index=self.index)
              else:
                  threshold = align_method_FRAME(self, threshold, axis, flex=None)[1]
  
          # GH 40420
          # Treat missing thresholds as no bounds, not clipping the values
          if is_list_like(threshold):
              fill_value = np.inf if method.__name__ == "le" else -np.inf
              threshold_inf = threshold.fillna(fill_value)
          else:
              threshold_inf = threshold
  
          subset = method(threshold_inf, axis=axis) | isna(self)
  
          # GH 40420
          return self.where(subset, threshold, axis=axis, inplace=inplace)
  
      def clip(
          self: FrameOrSeries,
          lower=None,
          upper=None,
          axis: Axis | None = None,
          inplace: bool_t = False,
          *args,
          **kwargs,
      ) -> FrameOrSeries | None:
          """
          Trim values at input threshold(s).
  
          Assigns values outside boundary to boundary values. Thresholds
          can be singular values or array like, and in the latter case
          the clipping is performed element-wise in the specified axis.
  
          Parameters
          ----------
          lower : float or array-like, default None
              Minimum threshold value. All values below this
              threshold will be set to it. A missing
              threshold (e.g `NA`) will not clip the value.
          upper : float or array-like, default None
              Maximum threshold value. All values above this
              threshold will be set to it. A missing
              threshold (e.g `NA`) will not clip the value.
          axis : int or str axis name, optional
              Align object with lower and upper along the given axis.
          inplace : bool, default False
              Whether to perform the operation in place on the data.
          *args, **kwargs
              Additional keywords have no effect but might be accepted
              for compatibility with numpy.
  
          Returns
          -------
          Series or DataFrame or None
              Same type as calling object with the values outside the
              clip boundaries replaced or None if ``inplace=True``.
  
          See Also
          --------
          Series.clip : Trim values at input threshold in series.
          DataFrame.clip : Trim values at input threshold in dataframe.
          numpy.clip : Clip (limit) the values in an array.
  
          Examples
          --------
          >>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]}
          >>> df = pd.DataFrame(data)
          >>> df
             col_0  col_1
          0      9     -2
          1     -3     -7
          2      0      6
          3     -1      8
          4      5     -5
  
          Clips per column using lower and upper thresholds:
  
          >>> df.clip(-4, 6)
             col_0  col_1
          0      6     -2
          1     -3     -4
          2      0      6
          3     -1      6
          4      5     -4
  
          Clips using specific lower and upper thresholds per column element:
  
          >>> t = pd.Series([2, -4, -1, 6, 3])
          >>> t
          0    2
          1   -4
          2   -1
          3    6
          4    3
          dtype: int64
  
          >>> df.clip(t, t + 4, axis=0)
             col_0  col_1
          0      6      2
          1     -3     -4
          2      0      3
          3      6      8
          4      5      3
  
          Clips using specific lower threshold per column element, with missing values:
  
          >>> t = pd.Series([2, -4, np.NaN, 6, 3])
          >>> t
          0    2.0
          1   -4.0
          2    NaN
          3    6.0
          4    3.0
          dtype: float64
  
          >>> df.clip(t, axis=0)
          col_0  col_1
          0      9      2
          1     -3     -4
          2      0      6
          3      6      8
          4      5      3
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
  
          axis = nv.validate_clip_with_axis(axis, args, kwargs)
          if axis is not None:
              axis = self._get_axis_number(axis)
  
          # GH 17276
          # numpy doesn't like NaN as a clip value
          # so ignore
          # GH 19992
          # numpy doesn't drop a list-like bound containing NaN
          isna_lower = isna(lower)
          if not is_list_like(lower):
              if np.any(isna_lower):
                  lower = None
          elif np.all(isna_lower):
              lower = None
          isna_upper = isna(upper)
          if not is_list_like(upper):
              if np.any(isna_upper):
                  upper = None
          elif np.all(isna_upper):
              upper = None
  
          # GH 2747 (arguments were reversed)
          if (
              lower is not None
              and upper is not None
              and is_scalar(lower)
              and is_scalar(upper)
          ):
              lower, upper = min(lower, upper), max(lower, upper)
  
          # fast-path for scalars
          if (lower is None or (is_scalar(lower) and is_number(lower))) and (
              upper is None or (is_scalar(upper) and is_number(upper))
          ):
              return self._clip_with_scalar(lower, upper, inplace=inplace)
  
          result = self
          if lower is not None:
              result = result._clip_with_one_bound(
                  lower, method=self.ge, axis=axis, inplace=inplace
              )
          if upper is not None:
              if inplace:
                  result = self
              result = result._clip_with_one_bound(
                  upper, method=self.le, axis=axis, inplace=inplace
              )
  
          return result
  
      @doc(**_shared_doc_kwargs)
      def asfreq(
          self: FrameOrSeries,
          freq,
          method=None,
          how: str | None = None,
          normalize: bool_t = False,
          fill_value=None,
      ) -> FrameOrSeries:
          """
          Convert time series to specified frequency.
  
          Returns the original data conformed to a new index with the specified
          frequency.
  
          If the index of this {klass} is a :class:`~pandas.PeriodIndex`, the new index
          is the result of transforming the original index with
          :meth:`PeriodIndex.asfreq <pandas.PeriodIndex.asfreq>` (so the original index
          will map one-to-one to the new index).
  
          Otherwise, the new index will be equivalent to ``pd.date_range(start, end,
          freq=freq)`` where ``start`` and ``end`` are, respectively, the first and
          last entries in the original index (see :func:`pandas.date_range`). The
          values corresponding to any timesteps in the new index which were not present
          in the original index will be null (``NaN``), unless a method for filling
          such unknowns is provided (see the ``method`` parameter below).
  
          The :meth:`resample` method is more appropriate if an operation on each group of
          timesteps (such as an aggregate) is necessary to represent the data at the new
          frequency.
  
          Parameters
          ----------
          freq : DateOffset or str
              Frequency DateOffset or string.
          method : {{'backfill'/'bfill', 'pad'/'ffill'}}, default None
              Method to use for filling holes in reindexed Series (note this
              does not fill NaNs that already were present):
  
              * 'pad' / 'ffill': propagate last valid observation forward to next
                valid
              * 'backfill' / 'bfill': use NEXT valid observation to fill.
          how : {{'start', 'end'}}, default end
              For PeriodIndex only (see PeriodIndex.asfreq).
          normalize : bool, default False
              Whether to reset output index to midnight.
          fill_value : scalar, optional
              Value to use for missing values, applied during upsampling (note
              this does not fill NaNs that already were present).
  
          Returns
          -------
          {klass}
              {klass} object reindexed to the specified frequency.
  
          See Also
          --------
          reindex : Conform DataFrame to new index with optional filling logic.
  
          Notes
          -----
          To learn more about the frequency strings, please see `this link
          <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
  
          Examples
          --------
          Start by creating a series with 4 one minute timestamps.
  
          >>> index = pd.date_range('1/1/2000', periods=4, freq='T')
          >>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
          >>> df = pd.DataFrame({{'s': series}})
          >>> df
                                 s
          2000-01-01 00:00:00    0.0
          2000-01-01 00:01:00    NaN
          2000-01-01 00:02:00    2.0
          2000-01-01 00:03:00    3.0
  
          Upsample the series into 30 second bins.
  
          >>> df.asfreq(freq='30S')
                                 s
          2000-01-01 00:00:00    0.0
          2000-01-01 00:00:30    NaN
          2000-01-01 00:01:00    NaN
          2000-01-01 00:01:30    NaN
          2000-01-01 00:02:00    2.0
          2000-01-01 00:02:30    NaN
          2000-01-01 00:03:00    3.0
  
          Upsample again, providing a ``fill value``.
  
          >>> df.asfreq(freq='30S', fill_value=9.0)
                                 s
          2000-01-01 00:00:00    0.0
          2000-01-01 00:00:30    9.0
          2000-01-01 00:01:00    NaN
          2000-01-01 00:01:30    9.0
          2000-01-01 00:02:00    2.0
          2000-01-01 00:02:30    9.0
          2000-01-01 00:03:00    3.0
  
          Upsample again, providing a ``method``.
  
          >>> df.asfreq(freq='30S', method='bfill')
                                 s
          2000-01-01 00:00:00    0.0
          2000-01-01 00:00:30    NaN
          2000-01-01 00:01:00    NaN
          2000-01-01 00:01:30    2.0
          2000-01-01 00:02:00    2.0
          2000-01-01 00:02:30    3.0
          2000-01-01 00:03:00    3.0
          """
          from pandas.core.resample import asfreq
  
          return asfreq(
              self,
              freq,
              method=method,
              how=how,
              normalize=normalize,
              fill_value=fill_value,
          )
  
      @final
      def at_time(
          self: FrameOrSeries, time, asof: bool_t = False, axis=None
      ) -> FrameOrSeries:
          """
          Select values at particular time of day (e.g., 9:30AM).
  
          Parameters
          ----------
          time : datetime.time or str
          axis : {0 or 'index', 1 or 'columns'}, default 0
  
          Returns
          -------
          Series or DataFrame
  
          Raises
          ------
          TypeError
              If the index is not  a :class:`DatetimeIndex`
  
          See Also
          --------
          between_time : Select values between particular times of the day.
          first : Select initial periods of time series based on a date offset.
          last : Select final periods of time series based on a date offset.
          DatetimeIndex.indexer_at_time : Get just the index locations for
              values at particular time of the day.
  
          Examples
          --------
          >>> i = pd.date_range('2018-04-09', periods=4, freq='12H')
          >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
          >>> ts
                               A
          2018-04-09 00:00:00  1
          2018-04-09 12:00:00  2
          2018-04-10 00:00:00  3
          2018-04-10 12:00:00  4
  
          >>> ts.at_time('12:00')
                               A
          2018-04-09 12:00:00  2
          2018-04-10 12:00:00  4
          """
          if axis is None:
              axis = self._stat_axis_number
          axis = self._get_axis_number(axis)
  
          index = self._get_axis(axis)
  
          if not isinstance(index, DatetimeIndex):
              raise TypeError("Index must be DatetimeIndex")
  
          indexer = index.indexer_at_time(time, asof=asof)
          return self._take_with_is_copy(indexer, axis=axis)
  
      @final
      def between_time(
          self: FrameOrSeries,
          start_time,
          end_time,
          include_start: bool_t = True,
          include_end: bool_t = True,
          axis=None,
      ) -> FrameOrSeries:
          """
          Select values between particular times of the day (e.g., 9:00-9:30 AM).
  
          By setting ``start_time`` to be later than ``end_time``,
          you can get the times that are *not* between the two times.
  
          Parameters
          ----------
          start_time : datetime.time or str
              Initial time as a time filter limit.
          end_time : datetime.time or str
              End time as a time filter limit.
          include_start : bool, default True
              Whether the start time needs to be included in the result.
          include_end : bool, default True
              Whether the end time needs to be included in the result.
          axis : {0 or 'index', 1 or 'columns'}, default 0
              Determine range time on index or columns value.
  
          Returns
          -------
          Series or DataFrame
              Data from the original object filtered to the specified dates range.
  
          Raises
          ------
          TypeError
              If the index is not  a :class:`DatetimeIndex`
  
          See Also
          --------
          at_time : Select values at a particular time of the day.
          first : Select initial periods of time series based on a date offset.
          last : Select final periods of time series based on a date offset.
          DatetimeIndex.indexer_between_time : Get just the index locations for
              values between particular times of the day.
  
          Examples
          --------
          >>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')
          >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
          >>> ts
                               A
          2018-04-09 00:00:00  1
          2018-04-10 00:20:00  2
          2018-04-11 00:40:00  3
          2018-04-12 01:00:00  4
  
          >>> ts.between_time('0:15', '0:45')
                               A
          2018-04-10 00:20:00  2
          2018-04-11 00:40:00  3
  
          You get the times that are *not* between two times by setting
          ``start_time`` later than ``end_time``:
  
          >>> ts.between_time('0:45', '0:15')
                               A
          2018-04-09 00:00:00  1
          2018-04-12 01:00:00  4
          """
          if axis is None:
              axis = self._stat_axis_number
          axis = self._get_axis_number(axis)
  
          index = self._get_axis(axis)
          if not isinstance(index, DatetimeIndex):
              raise TypeError("Index must be DatetimeIndex")
  
          indexer = index.indexer_between_time(
              start_time, end_time, include_start=include_start, include_end=include_end
          )
          return self._take_with_is_copy(indexer, axis=axis)
  
      @doc(**_shared_doc_kwargs)
      def resample(
          self,
          rule,
          axis=0,
          closed: str | None = None,
          label: str | None = None,
          convention: str = "start",
          kind: str | None = None,
          loffset=None,
          base: int | None = None,
          on=None,
          level=None,
          origin: str | TimestampConvertibleTypes = "start_day",
          offset: TimedeltaConvertibleTypes | None = None,
      ) -> Resampler:
          """
          Resample time-series data.
  
          Convenience method for frequency conversion and resampling of time series.
          The object must have a datetime-like index (`DatetimeIndex`, `PeriodIndex`,
          or `TimedeltaIndex`), or the caller must pass the label of a datetime-like
          series/index to the ``on``/``level`` keyword parameter.
  
          Parameters
          ----------
          rule : DateOffset, Timedelta or str
              The offset string or object representing target conversion.
          axis : {{0 or 'index', 1 or 'columns'}}, default 0
              Which axis to use for up- or down-sampling. For `Series` this
              will default to 0, i.e. along the rows. Must be
              `DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex`.
          closed : {{'right', 'left'}}, default None
              Which side of bin interval is closed. The default is 'left'
              for all frequency offsets except for 'M', 'A', 'Q', 'BM',
              'BA', 'BQ', and 'W' which all have a default of 'right'.
          label : {{'right', 'left'}}, default None
              Which bin edge label to label bucket with. The default is 'left'
              for all frequency offsets except for 'M', 'A', 'Q', 'BM',
              'BA', 'BQ', and 'W' which all have a default of 'right'.
          convention : {{'start', 'end', 's', 'e'}}, default 'start'
              For `PeriodIndex` only, controls whether to use the start or
              end of `rule`.
          kind : {{'timestamp', 'period'}}, optional, default None
              Pass 'timestamp' to convert the resulting index to a
              `DateTimeIndex` or 'period' to convert it to a `PeriodIndex`.
              By default the input representation is retained.
          loffset : timedelta, default None
              Adjust the resampled time labels.
  
              .. deprecated:: 1.1.0
                  You should add the loffset to the `df.index` after the resample.
                  See below.
  
          base : int, default 0
              For frequencies that evenly subdivide 1 day, the "origin" of the
              aggregated intervals. For example, for '5min' frequency, base could
              range from 0 through 4. Defaults to 0.
  
              .. deprecated:: 1.1.0
                  The new arguments that you should use are 'offset' or 'origin'.
  
          on : str, optional
              For a DataFrame, column to use instead of index for resampling.
              Column must be datetime-like.
          level : str or int, optional
              For a MultiIndex, level (name or number) to use for
              resampling. `level` must be datetime-like.
          origin : {{'epoch', 'start', 'start_day', 'end', 'end_day'}}, Timestamp
              or str, default 'start_day'
              The timestamp on which to adjust the grouping. The timezone of origin
              must match the timezone of the index.
              If a timestamp is not used, these values are also supported:
  
              - 'epoch': `origin` is 1970-01-01
              - 'start': `origin` is the first value of the timeseries
              - 'start_day': `origin` is the first day at midnight of the timeseries
  
              .. versionadded:: 1.1.0
  
              - 'end': `origin` is the last value of the timeseries
              - 'end_day': `origin` is the ceiling midnight of the last day
  
              .. versionadded:: 1.3.0
  
          offset : Timedelta or str, default is None
              An offset timedelta added to the origin.
  
              .. versionadded:: 1.1.0
  
          Returns
          -------
          pandas.core.Resampler
              :class:`~pandas.core.Resampler` object.
  
          See Also
          --------
          Series.resample : Resample a Series.
          DataFrame.resample : Resample a DataFrame.
          groupby : Group {klass} by mapping, function, label, or list of labels.
          asfreq : Reindex a {klass} with the given frequency without grouping.
  
          Notes
          -----
          See the `user guide
          <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling>`__
          for more.
  
          To learn more about the offset strings, please see `this link
          <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects>`__.
  
          Examples
          --------
          Start by creating a series with 9 one minute timestamps.
  
          >>> index = pd.date_range('1/1/2000', periods=9, freq='T')
          >>> series = pd.Series(range(9), index=index)
          >>> series
          2000-01-01 00:00:00    0
          2000-01-01 00:01:00    1
          2000-01-01 00:02:00    2
          2000-01-01 00:03:00    3
          2000-01-01 00:04:00    4
          2000-01-01 00:05:00    5
          2000-01-01 00:06:00    6
          2000-01-01 00:07:00    7
          2000-01-01 00:08:00    8
          Freq: T, dtype: int64
  
          Downsample the series into 3 minute bins and sum the values
          of the timestamps falling into a bin.
  
          >>> series.resample('3T').sum()
          2000-01-01 00:00:00     3
          2000-01-01 00:03:00    12
          2000-01-01 00:06:00    21
          Freq: 3T, dtype: int64
  
          Downsample the series into 3 minute bins as above, but label each
          bin using the right edge instead of the left. Please note that the
          value in the bucket used as the label is not included in the bucket,
          which it labels. For example, in the original series the
          bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed
          value in the resampled bucket with the label ``2000-01-01 00:03:00``
          does not include 3 (if it did, the summed value would be 6, not 3).
          To include this value close the right side of the bin interval as
          illustrated in the example below this one.
  
          >>> series.resample('3T', label='right').sum()
          2000-01-01 00:03:00     3
          2000-01-01 00:06:00    12
          2000-01-01 00:09:00    21
          Freq: 3T, dtype: int64
  
          Downsample the series into 3 minute bins as above, but close the right
          side of the bin interval.
  
          >>> series.resample('3T', label='right', closed='right').sum()
          2000-01-01 00:00:00     0
          2000-01-01 00:03:00     6
          2000-01-01 00:06:00    15
          2000-01-01 00:09:00    15
          Freq: 3T, dtype: int64
  
          Upsample the series into 30 second bins.
  
          >>> series.resample('30S').asfreq()[0:5]   # Select first 5 rows
          2000-01-01 00:00:00   0.0
          2000-01-01 00:00:30   NaN
          2000-01-01 00:01:00   1.0
          2000-01-01 00:01:30   NaN
          2000-01-01 00:02:00   2.0
          Freq: 30S, dtype: float64
  
          Upsample the series into 30 second bins and fill the ``NaN``
          values using the ``pad`` method.
  
          >>> series.resample('30S').pad()[0:5]
          2000-01-01 00:00:00    0
          2000-01-01 00:00:30    0
          2000-01-01 00:01:00    1
          2000-01-01 00:01:30    1
          2000-01-01 00:02:00    2
          Freq: 30S, dtype: int64
  
          Upsample the series into 30 second bins and fill the
          ``NaN`` values using the ``bfill`` method.
  
          >>> series.resample('30S').bfill()[0:5]
          2000-01-01 00:00:00    0
          2000-01-01 00:00:30    1
          2000-01-01 00:01:00    1
          2000-01-01 00:01:30    2
          2000-01-01 00:02:00    2
          Freq: 30S, dtype: int64
  
          Pass a custom function via ``apply``
  
          >>> def custom_resampler(arraylike):
          ...     return np.sum(arraylike) + 5
          ...
          >>> series.resample('3T').apply(custom_resampler)
          2000-01-01 00:00:00     8
          2000-01-01 00:03:00    17
          2000-01-01 00:06:00    26
          Freq: 3T, dtype: int64
  
          For a Series with a PeriodIndex, the keyword `convention` can be
          used to control whether to use the start or end of `rule`.
  
          Resample a year by quarter using 'start' `convention`. Values are
          assigned to the first quarter of the period.
  
          >>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01',
          ...                                             freq='A',
          ...                                             periods=2))
          >>> s
          2012    1
          2013    2
          Freq: A-DEC, dtype: int64
          >>> s.resample('Q', convention='start').asfreq()
          2012Q1    1.0
          2012Q2    NaN
          2012Q3    NaN
          2012Q4    NaN
          2013Q1    2.0
          2013Q2    NaN
          2013Q3    NaN
          2013Q4    NaN
          Freq: Q-DEC, dtype: float64
  
          Resample quarters by month using 'end' `convention`. Values are
          assigned to the last month of the period.
  
          >>> q = pd.Series([1, 2, 3, 4], index=pd.period_range('2018-01-01',
          ...                                                   freq='Q',
          ...                                                   periods=4))
          >>> q
          2018Q1    1
          2018Q2    2
          2018Q3    3
          2018Q4    4
          Freq: Q-DEC, dtype: int64
          >>> q.resample('M', convention='end').asfreq()
          2018-03    1.0
          2018-04    NaN
          2018-05    NaN
          2018-06    2.0
          2018-07    NaN
          2018-08    NaN
          2018-09    3.0
          2018-10    NaN
          2018-11    NaN
          2018-12    4.0
          Freq: M, dtype: float64
  
          For DataFrame objects, the keyword `on` can be used to specify the
          column instead of the index for resampling.
  
          >>> d = {{'price': [10, 11, 9, 13, 14, 18, 17, 19],
          ...      'volume': [50, 60, 40, 100, 50, 100, 40, 50]}}
          >>> df = pd.DataFrame(d)
          >>> df['week_starting'] = pd.date_range('01/01/2018',
          ...                                     periods=8,
          ...                                     freq='W')
          >>> df
             price  volume week_starting
          0     10      50    2018-01-07
          1     11      60    2018-01-14
          2      9      40    2018-01-21
          3     13     100    2018-01-28
          4     14      50    2018-02-04
          5     18     100    2018-02-11
          6     17      40    2018-02-18
          7     19      50    2018-02-25
          >>> df.resample('M', on='week_starting').mean()
                         price  volume
          week_starting
          2018-01-31     10.75    62.5
          2018-02-28     17.00    60.0
  
          For a DataFrame with MultiIndex, the keyword `level` can be used to
          specify on which level the resampling needs to take place.
  
          >>> days = pd.date_range('1/1/2000', periods=4, freq='D')
          >>> d2 = {{'price': [10, 11, 9, 13, 14, 18, 17, 19],
          ...       'volume': [50, 60, 40, 100, 50, 100, 40, 50]}}
          >>> df2 = pd.DataFrame(
          ...     d2,
          ...     index=pd.MultiIndex.from_product(
          ...         [days, ['morning', 'afternoon']]
          ...     )
          ... )
          >>> df2
                                price  volume
          2000-01-01 morning       10      50
                     afternoon     11      60
          2000-01-02 morning        9      40
                     afternoon     13     100
          2000-01-03 morning       14      50
                     afternoon     18     100
          2000-01-04 morning       17      40
                     afternoon     19      50
          >>> df2.resample('D', level=0).sum()
                      price  volume
          2000-01-01     21     110
          2000-01-02     22     140
          2000-01-03     32     150
          2000-01-04     36      90
  
          If you want to adjust the start of the bins based on a fixed timestamp:
  
          >>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
          >>> rng = pd.date_range(start, end, freq='7min')
          >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
          >>> ts
          2000-10-01 23:30:00     0
          2000-10-01 23:37:00     3
          2000-10-01 23:44:00     6
          2000-10-01 23:51:00     9
          2000-10-01 23:58:00    12
          2000-10-02 00:05:00    15
          2000-10-02 00:12:00    18
          2000-10-02 00:19:00    21
          2000-10-02 00:26:00    24
          Freq: 7T, dtype: int64
  
          >>> ts.resample('17min').sum()
          2000-10-01 23:14:00     0
          2000-10-01 23:31:00     9
          2000-10-01 23:48:00    21
          2000-10-02 00:05:00    54
          2000-10-02 00:22:00    24
          Freq: 17T, dtype: int64
  
          >>> ts.resample('17min', origin='epoch').sum()
          2000-10-01 23:18:00     0
          2000-10-01 23:35:00    18
          2000-10-01 23:52:00    27
          2000-10-02 00:09:00    39
          2000-10-02 00:26:00    24
          Freq: 17T, dtype: int64
  
          >>> ts.resample('17min', origin='2000-01-01').sum()
          2000-10-01 23:24:00     3
          2000-10-01 23:41:00    15
          2000-10-01 23:58:00    45
          2000-10-02 00:15:00    45
          Freq: 17T, dtype: int64
  
          If you want to adjust the start of the bins with an `offset` Timedelta, the two
          following lines are equivalent:
  
          >>> ts.resample('17min', origin='start').sum()
          2000-10-01 23:30:00     9
          2000-10-01 23:47:00    21
          2000-10-02 00:04:00    54
          2000-10-02 00:21:00    24
          Freq: 17T, dtype: int64
  
          >>> ts.resample('17min', offset='23h30min').sum()
          2000-10-01 23:30:00     9
          2000-10-01 23:47:00    21
          2000-10-02 00:04:00    54
          2000-10-02 00:21:00    24
          Freq: 17T, dtype: int64
  
          If you want to take the largest Timestamp as the end of the bins:
  
          >>> ts.resample('17min', origin='end').sum()
          2000-10-01 23:35:00     0
          2000-10-01 23:52:00    18
          2000-10-02 00:09:00    27
          2000-10-02 00:26:00    63
          Freq: 17T, dtype: int64
  
          In contrast with the `start_day`, you can use `end_day` to take the ceiling
          midnight of the largest Timestamp as the end of the bins and drop the bins
          not containing data:
  
          >>> ts.resample('17min', origin='end_day').sum()
          2000-10-01 23:38:00     3
          2000-10-01 23:55:00    15
          2000-10-02 00:12:00    45
          2000-10-02 00:29:00    45
          Freq: 17T, dtype: int64
  
          To replace the use of the deprecated `base` argument, you can now use `offset`,
          in this example it is equivalent to have `base=2`:
  
          >>> ts.resample('17min', offset='2min').sum()
          2000-10-01 23:16:00     0
          2000-10-01 23:33:00     9
          2000-10-01 23:50:00    36
          2000-10-02 00:07:00    39
          2000-10-02 00:24:00    24
          Freq: 17T, dtype: int64
  
          To replace the use of the deprecated `loffset` argument:
  
          >>> from pandas.tseries.frequencies import to_offset
          >>> loffset = '19min'
          >>> ts_out = ts.resample('17min').sum()
          >>> ts_out.index = ts_out.index + to_offset(loffset)
          >>> ts_out
          2000-10-01 23:33:00     0
          2000-10-01 23:50:00     9
          2000-10-02 00:07:00    21
          2000-10-02 00:24:00    54
          2000-10-02 00:41:00    24
          Freq: 17T, dtype: int64
          """
          from pandas.core.resample import get_resampler
  
          axis = self._get_axis_number(axis)
          return get_resampler(
              self,
              freq=rule,
              label=label,
              closed=closed,
              axis=axis,
              kind=kind,
              loffset=loffset,
              convention=convention,
              base=base,
              key=on,
              level=level,
              origin=origin,
              offset=offset,
          )
  
      @final
      def first(self: FrameOrSeries, offset) -> FrameOrSeries:
          """
          Select initial periods of time series data based on a date offset.
  
          When having a DataFrame with dates as index, this function can
          select the first few rows based on a date offset.
  
          Parameters
          ----------
          offset : str, DateOffset or dateutil.relativedelta
              The offset length of the data that will be selected. For instance,
              '1M' will display all the rows having their index within the first month.
  
          Returns
          -------
          Series or DataFrame
              A subset of the caller.
  
          Raises
          ------
          TypeError
              If the index is not  a :class:`DatetimeIndex`
  
          See Also
          --------
          last : Select final periods of time series based on a date offset.
          at_time : Select values at a particular time of the day.
          between_time : Select values between particular times of the day.
  
          Examples
          --------
          >>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
          >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
          >>> ts
                      A
          2018-04-09  1
          2018-04-11  2
          2018-04-13  3
          2018-04-15  4
  
          Get the rows for the first 3 days:
  
          >>> ts.first('3D')
                      A
          2018-04-09  1
          2018-04-11  2
  
          Notice the data for 3 first calendar days were returned, not the first
          3 days observed in the dataset, and therefore data for 2018-04-13 was
          not returned.
          """
          if not isinstance(self.index, DatetimeIndex):
              raise TypeError("'first' only supports a DatetimeIndex index")
  
          if len(self.index) == 0:
              return self
  
          offset = to_offset(offset)
          if not isinstance(offset, Tick) and offset.is_on_offset(self.index[0]):
              # GH#29623 if first value is end of period, remove offset with n = 1
              #  before adding the real offset
              end_date = end = self.index[0] - offset.base + offset
          else:
              end_date = end = self.index[0] + offset
  
          # Tick-like, e.g. 3 weeks
          if isinstance(offset, Tick) and end_date in self.index:
              end = self.index.searchsorted(end_date, side="left")
              return self.iloc[:end]
  
          return self.loc[:end]
  
      @final
      def last(self: FrameOrSeries, offset) -> FrameOrSeries:
          """
          Select final periods of time series data based on a date offset.
  
          For a DataFrame with a sorted DatetimeIndex, this function
          selects the last few rows based on a date offset.
  
          Parameters
          ----------
          offset : str, DateOffset, dateutil.relativedelta
              The offset length of the data that will be selected. For instance,
              '3D' will display all the rows having their index within the last 3 days.
  
          Returns
          -------
          Series or DataFrame
              A subset of the caller.
  
          Raises
          ------
          TypeError
              If the index is not  a :class:`DatetimeIndex`
  
          See Also
          --------
          first : Select initial periods of time series based on a date offset.
          at_time : Select values at a particular time of the day.
          between_time : Select values between particular times of the day.
  
          Examples
          --------
          >>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
          >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
          >>> ts
                      A
          2018-04-09  1
          2018-04-11  2
          2018-04-13  3
          2018-04-15  4
  
          Get the rows for the last 3 days:
  
          >>> ts.last('3D')
                      A
          2018-04-13  3
          2018-04-15  4
  
          Notice the data for 3 last calendar days were returned, not the last
          3 observed days in the dataset, and therefore data for 2018-04-11 was
          not returned.
          """
          if not isinstance(self.index, DatetimeIndex):
              raise TypeError("'last' only supports a DatetimeIndex index")
  
          if len(self.index) == 0:
              return self
  
          offset = to_offset(offset)
  
          start_date = self.index[-1] - offset
          start = self.index.searchsorted(start_date, side="right")
          # error: Slice index must be an integer or None
          return self.iloc[start:]  # type: ignore[misc]
  
      @final
      def rank(
          self: FrameOrSeries,
          axis=0,
          method: str = "average",
          numeric_only: bool_t | None = None,
          na_option: str = "keep",
          ascending: bool_t = True,
          pct: bool_t = False,
      ) -> FrameOrSeries:
          """
          Compute numerical data ranks (1 through n) along axis.
  
          By default, equal values are assigned a rank that is the average of the
          ranks of those values.
  
          Parameters
          ----------
          axis : {0 or 'index', 1 or 'columns'}, default 0
              Index to direct ranking.
          method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
              How to rank the group of records that have the same value (i.e. ties):
  
              * average: average rank of the group
              * min: lowest rank in the group
              * max: highest rank in the group
              * first: ranks assigned in order they appear in the array
              * dense: like 'min', but rank always increases by 1 between groups.
  
          numeric_only : bool, optional
              For DataFrame objects, rank only numeric columns if set to True.
          na_option : {'keep', 'top', 'bottom'}, default 'keep'
              How to rank NaN values:
  
              * keep: assign NaN rank to NaN values
              * top: assign lowest rank to NaN values
              * bottom: assign highest rank to NaN values
  
          ascending : bool, default True
              Whether or not the elements should be ranked in ascending order.
          pct : bool, default False
              Whether or not to display the returned rankings in percentile
              form.
  
          Returns
          -------
          same type as caller
              Return a Series or DataFrame with data ranks as values.
  
          See Also
          --------
          core.groupby.GroupBy.rank : Rank of values within each group.
  
          Examples
          --------
          >>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',
          ...                                    'spider', 'snake'],
          ...                         'Number_legs': [4, 2, 4, 8, np.nan]})
          >>> df
              Animal  Number_legs
          0      cat          4.0
          1  penguin          2.0
          2      dog          4.0
          3   spider          8.0
          4    snake          NaN
  
          The following example shows how the method behaves with the above
          parameters:
  
          * default_rank: this is the default behaviour obtained without using
            any parameter.
          * max_rank: setting ``method = 'max'`` the records that have the
            same values are ranked using the highest rank (e.g.: since 'cat'
            and 'dog' are both in the 2nd and 3rd position, rank 3 is assigned.)
          * NA_bottom: choosing ``na_option = 'bottom'``, if there are records
            with NaN values they are placed at the bottom of the ranking.
          * pct_rank: when setting ``pct = True``, the ranking is expressed as
            percentile rank.
  
          >>> df['default_rank'] = df['Number_legs'].rank()
          >>> df['max_rank'] = df['Number_legs'].rank(method='max')
          >>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
          >>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
          >>> df
              Animal  Number_legs  default_rank  max_rank  NA_bottom  pct_rank
          0      cat          4.0           2.5       3.0        2.5     0.625
          1  penguin          2.0           1.0       1.0        1.0     0.250
          2      dog          4.0           2.5       3.0        2.5     0.625
          3   spider          8.0           4.0       4.0        4.0     1.000
          4    snake          NaN           NaN       NaN        5.0       NaN
          """
          axis = self._get_axis_number(axis)
  
          if na_option not in {"keep", "top", "bottom"}:
              msg = "na_option must be one of 'keep', 'top', or 'bottom'"
              raise ValueError(msg)
  
          def ranker(data):
              ranks = algos.rank(
                  data.values,
                  axis=axis,
                  method=method,
                  ascending=ascending,
                  na_option=na_option,
                  pct=pct,
              )
              # error: Argument 1 to "NDFrame" has incompatible type "ndarray"; expected
              # "Union[ArrayManager, BlockManager]"
              ranks_obj = self._constructor(
                  ranks, **data._construct_axes_dict()  # type: ignore[arg-type]
              )
              return ranks_obj.__finalize__(self, method="rank")
  
          # if numeric_only is None, and we can't get anything, we try with
          # numeric_only=True
          if numeric_only is None:
              try:
                  return ranker(self)
              except TypeError:
                  numeric_only = True
  
          if numeric_only:
              data = self._get_numeric_data()
          else:
              data = self
  
          return ranker(data)
  
      @doc(_shared_docs["compare"], klass=_shared_doc_kwargs["klass"])
      def compare(
          self,
          other,
          align_axis: Axis = 1,
          keep_shape: bool_t = False,
          keep_equal: bool_t = False,
      ):
          from pandas.core.reshape.concat import concat
  
          if type(self) is not type(other):
              cls_self, cls_other = type(self).__name__, type(other).__name__
              raise TypeError(
                  f"can only compare '{cls_self}' (not '{cls_other}') with '{cls_self}'"
              )
  
          mask = ~((self == other) | (self.isna() & other.isna()))
          keys = ["self", "other"]
  
          if not keep_equal:
              self = self.where(mask)
              other = other.where(mask)
  
          if not keep_shape:
              if isinstance(self, ABCDataFrame):
                  cmask = mask.any()
                  rmask = mask.any(axis=1)
                  self = self.loc[rmask, cmask]
                  other = other.loc[rmask, cmask]
              else:
                  self = self[mask]
                  other = other[mask]
  
          if align_axis in (1, "columns"):  # This is needed for Series
              axis = 1
          else:
              axis = self._get_axis_number(align_axis)
  
          diff = concat([self, other], axis=axis, keys=keys)
  
          if axis >= self.ndim:
              # No need to reorganize data if stacking on new axis
              # This currently applies for stacking two Series on columns
              return diff
  
          ax = diff._get_axis(axis)
          ax_names = np.array(ax.names)
  
          # set index names to positions to avoid confusion
          ax.names = np.arange(len(ax_names))
  
          # bring self-other to inner level
          order = list(range(1, ax.nlevels)) + [0]
          if isinstance(diff, ABCDataFrame):
              diff = diff.reorder_levels(order, axis=axis)
          else:
              diff = diff.reorder_levels(order)
  
          # restore the index names in order
          diff._get_axis(axis=axis).names = ax_names[order]
  
          # reorder axis to keep things organized
          indices = (
              np.arange(diff.shape[axis]).reshape([2, diff.shape[axis] // 2]).T.flatten()
          )
          diff = diff.take(indices, axis=axis)
  
          return diff
  
      @doc(**_shared_doc_kwargs)
      def align(
          self,
          other,
          join="outer",
          axis=None,
          level=None,
          copy=True,
          fill_value=None,
          method=None,
          limit=None,
          fill_axis=0,
          broadcast_axis=None,
      ):
          """
          Align two objects on their axes with the specified join method.
  
          Join method is specified for each axis Index.
  
          Parameters
          ----------
          other : DataFrame or Series
          join : {{'outer', 'inner', 'left', 'right'}}, default 'outer'
          axis : allowed axis of the other object, default None
              Align on index (0), columns (1), or both (None).
          level : int or level name, default None
              Broadcast across a level, matching Index values on the
              passed MultiIndex level.
          copy : bool, default True
              Always returns new objects. If copy=False and no reindexing is
              required then original objects are returned.
          fill_value : scalar, default np.NaN
              Value to use for missing values. Defaults to NaN, but can be any
              "compatible" value.
          method : {{'backfill', 'bfill', 'pad', 'ffill', None}}, default None
              Method to use for filling holes in reindexed Series:
  
              - pad / ffill: propagate last valid observation forward to next valid.
              - backfill / bfill: use NEXT valid observation to fill gap.
  
          limit : int, default None
              If method is specified, this is the maximum number of consecutive
              NaN values to forward/backward fill. In other words, if there is
              a gap with more than this number of consecutive NaNs, it will only
              be partially filled. If method is not specified, this is the
              maximum number of entries along the entire axis where NaNs will be
              filled. Must be greater than 0 if not None.
          fill_axis : {axes_single_arg}, default 0
              Filling axis, method and limit.
          broadcast_axis : {axes_single_arg}, default None
              Broadcast values along this axis, if aligning two objects of
              different dimensions.
  
          Returns
          -------
          (left, right) : ({klass}, type of other)
              Aligned objects.
          """
  
          method = missing.clean_fill_method(method)
  
          if broadcast_axis == 1 and self.ndim != other.ndim:
              if isinstance(self, ABCSeries):
                  # this means other is a DataFrame, and we need to broadcast
                  # self
                  cons = self._constructor_expanddim
                  df = cons(
                      {c: self for c in other.columns}, **other._construct_axes_dict()
                  )
                  return df._align_frame(
                      other,
                      join=join,
                      axis=axis,
                      level=level,
                      copy=copy,
                      fill_value=fill_value,
                      method=method,
                      limit=limit,
                      fill_axis=fill_axis,
                  )
              elif isinstance(other, ABCSeries):
                  # this means self is a DataFrame, and we need to broadcast
                  # other
                  cons = other._constructor_expanddim
                  df = cons(
                      {c: other for c in self.columns}, **self._construct_axes_dict()
                  )
                  return self._align_frame(
                      df,
                      join=join,
                      axis=axis,
                      level=level,
                      copy=copy,
                      fill_value=fill_value,
                      method=method,
                      limit=limit,
                      fill_axis=fill_axis,
                  )
  
          if axis is not None:
              axis = self._get_axis_number(axis)
          if isinstance(other, ABCDataFrame):
              return self._align_frame(
                  other,
                  join=join,
                  axis=axis,
                  level=level,
                  copy=copy,
                  fill_value=fill_value,
                  method=method,
                  limit=limit,
                  fill_axis=fill_axis,
              )
          elif isinstance(other, ABCSeries):
              return self._align_series(
                  other,
                  join=join,
                  axis=axis,
                  level=level,
                  copy=copy,
                  fill_value=fill_value,
                  method=method,
                  limit=limit,
                  fill_axis=fill_axis,
              )
          else:  # pragma: no cover
              raise TypeError(f"unsupported type: {type(other)}")
  
      @final
      def _align_frame(
          self,
          other,
          join="outer",
          axis=None,
          level=None,
          copy: bool_t = True,
          fill_value=None,
          method=None,
          limit=None,
          fill_axis=0,
      ):
          # defaults
          join_index, join_columns = None, None
          ilidx, iridx = None, None
          clidx, cridx = None, None
  
          is_series = isinstance(self, ABCSeries)
  
          if (axis is None or axis == 0) and not self.index.equals(other.index):
              join_index, ilidx, iridx = self.index.join(
                  other.index, how=join, level=level, return_indexers=True
              )
  
          if (
              (axis is None or axis == 1)
              and not is_series
              and not self.columns.equals(other.columns)
          ):
              join_columns, clidx, cridx = self.columns.join(
                  other.columns, how=join, level=level, return_indexers=True
              )
  
          if is_series:
              reindexers = {0: [join_index, ilidx]}
          else:
              reindexers = {0: [join_index, ilidx], 1: [join_columns, clidx]}
  
          left = self._reindex_with_indexers(
              reindexers, copy=copy, fill_value=fill_value, allow_dups=True
          )
          # other must be always DataFrame
          right = other._reindex_with_indexers(
              {0: [join_index, iridx], 1: [join_columns, cridx]},
              copy=copy,
              fill_value=fill_value,
              allow_dups=True,
          )
  
          if method is not None:
              _left = left.fillna(method=method, axis=fill_axis, limit=limit)
              assert _left is not None  # needed for mypy
              left = _left
              right = right.fillna(method=method, axis=fill_axis, limit=limit)
  
          # if DatetimeIndex have different tz, convert to UTC
          left, right = _align_as_utc(left, right, join_index)
  
          return (
              left.__finalize__(self),
              right.__finalize__(other),
          )
  
      @final
      def _align_series(
          self,
          other,
          join="outer",
          axis=None,
          level=None,
          copy: bool_t = True,
          fill_value=None,
          method=None,
          limit=None,
          fill_axis=0,
      ):
  
          is_series = isinstance(self, ABCSeries)
  
          # series/series compat, other must always be a Series
          if is_series:
              if axis:
                  raise ValueError("cannot align series to a series other than axis 0")
  
              # equal
              if self.index.equals(other.index):
                  join_index, lidx, ridx = None, None, None
              else:
                  join_index, lidx, ridx = self.index.join(
                      other.index, how=join, level=level, return_indexers=True
                  )
  
              left = self._reindex_indexer(join_index, lidx, copy)
              right = other._reindex_indexer(join_index, ridx, copy)
  
          else:
              # one has > 1 ndim
              fdata = self._mgr
              if axis in [0, 1]:
                  join_index = self.axes[axis]
                  lidx, ridx = None, None
                  if not join_index.equals(other.index):
                      join_index, lidx, ridx = join_index.join(
                          other.index, how=join, level=level, return_indexers=True
                      )
  
                  if lidx is not None:
                      bm_axis = self._get_block_manager_axis(axis)
                      fdata = fdata.reindex_indexer(join_index, lidx, axis=bm_axis)
  
              else:
                  raise ValueError("Must specify axis=0 or 1")
  
              if copy and fdata is self._mgr:
                  fdata = fdata.copy()
  
              left = self._constructor(fdata)
  
              if ridx is None:
                  right = other
              else:
                  right = other.reindex(join_index, level=level)
  
          # fill
          fill_na = notna(fill_value) or (method is not None)
          if fill_na:
              left = left.fillna(fill_value, method=method, limit=limit, axis=fill_axis)
              right = right.fillna(fill_value, method=method, limit=limit)
  
          # if DatetimeIndex have different tz, convert to UTC
          if is_series or (not is_series and axis == 0):
              left, right = _align_as_utc(left, right, join_index)
  
          return (
              left.__finalize__(self),
              right.__finalize__(other),
          )
  
      @final
      def _where(
          self,
          cond,
          other=np.nan,
          inplace=False,
          axis=None,
          level=None,
          errors="raise",
      ):
          """
          Equivalent to public method `where`, except that `other` is not
          applied as a function even if callable. Used in __setitem__.
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
  
          if axis is not None:
              axis = self._get_axis_number(axis)
  
          # align the cond to same shape as myself
          cond = com.apply_if_callable(cond, self)
          if isinstance(cond, NDFrame):
              cond, _ = cond.align(self, join="right", broadcast_axis=1, copy=False)
          else:
              if not hasattr(cond, "shape"):
                  cond = np.asanyarray(cond)
              if cond.shape != self.shape:
                  raise ValueError("Array conditional must be same shape as self")
              cond = self._constructor(cond, **self._construct_axes_dict())
  
          # make sure we are boolean
          fill_value = bool(inplace)
          cond = cond.fillna(fill_value)
  
          msg = "Boolean array expected for the condition, not {dtype}"
  
          if not cond.empty:
              if not isinstance(cond, ABCDataFrame):
                  # This is a single-dimensional object.
                  if not is_bool_dtype(cond):
                      raise ValueError(msg.format(dtype=cond.dtype))
              else:
                  for dt in cond.dtypes:
                      if not is_bool_dtype(dt):
                          raise ValueError(msg.format(dtype=dt))
          else:
              # GH#21947 we have an empty DataFrame/Series, could be object-dtype
              cond = cond.astype(bool)
  
          cond = -cond if inplace else cond
          cond = cond.reindex(self._info_axis, axis=self._info_axis_number, copy=False)
  
          # try to align with other
          if isinstance(other, NDFrame):
  
              # align with me
              if other.ndim <= self.ndim:
  
                  _, other = self.align(
                      other,
                      join="left",
                      axis=axis,
                      level=level,
                      fill_value=None,
                      copy=False,
                  )
  
                  # if we are NOT aligned, raise as we cannot where index
                  if axis is None and not other._indexed_same(self):
                      raise InvalidIndexError
  
                  elif other.ndim < self.ndim:
                      # TODO(EA2D): avoid object-dtype cast in EA case GH#38729
                      other = other._values
                      if axis == 0:
                          other = np.reshape(other, (-1, 1))
                      elif axis == 1:
                          other = np.reshape(other, (1, -1))
  
                      other = np.broadcast_to(other, self.shape)
  
              # slice me out of the other
              else:
                  raise NotImplementedError(
                      "cannot align with a higher dimensional NDFrame"
                  )
  
          elif not isinstance(other, (MultiIndex, NDFrame)):
              # mainly just catching Index here
              other = extract_array(other, extract_numpy=True)
  
          if isinstance(other, (np.ndarray, ExtensionArray)):
  
              if other.shape != self.shape:
                  if self.ndim != 1:
                      # In the ndim == 1 case we may have
                      #  other length 1, which we treat as scalar (GH#2745, GH#4192)
                      #  or len(other) == icond.sum(), which we treat like
                      #  __setitem__ (GH#3235)
                      raise ValueError(
                          "other must be the same shape as self when an ndarray"
                      )
  
              # we are the same shape, so create an actual object for alignment
              else:
                  # error: Argument 1 to "NDFrame" has incompatible type "ndarray";
                  # expected "BlockManager"
                  other = self._constructor(
                      other, **self._construct_axes_dict()  # type: ignore[arg-type]
                  )
  
          if axis is None:
              axis = 0
  
          if self.ndim == getattr(other, "ndim", 0):
              align = True
          else:
              align = self._get_axis_number(axis) == 1
  
          if inplace:
              # we may have different type blocks come out of putmask, so
              # reconstruct the block manager
  
              self._check_inplace_setting(other)
              new_data = self._mgr.putmask(mask=cond, new=other, align=align)
              result = self._constructor(new_data)
              return self._update_inplace(result)
  
          else:
              new_data = self._mgr.where(
                  other=other,
                  cond=cond,
                  align=align,
                  errors=errors,
              )
              result = self._constructor(new_data)
              return result.__finalize__(self)
  
      @doc(
          klass=_shared_doc_kwargs["klass"],
          cond="True",
          cond_rev="False",
          name="where",
          name_other="mask",
      )
      def where(
          self,
          cond,
          other=np.nan,
          inplace=False,
          axis=None,
          level=None,
          errors="raise",
          try_cast=lib.no_default,
      ):
          """
          Replace values where the condition is {cond_rev}.
  
          Parameters
          ----------
          cond : bool {klass}, array-like, or callable
              Where `cond` is {cond}, keep the original value. Where
              {cond_rev}, replace with corresponding value from `other`.
              If `cond` is callable, it is computed on the {klass} and
              should return boolean {klass} or array. The callable must
              not change input {klass} (though pandas doesn't check it).
          other : scalar, {klass}, or callable
              Entries where `cond` is {cond_rev} are replaced with
              corresponding value from `other`.
              If other is callable, it is computed on the {klass} and
              should return scalar or {klass}. The callable must not
              change input {klass} (though pandas doesn't check it).
          inplace : bool, default False
              Whether to perform the operation in place on the data.
          axis : int, default None
              Alignment axis if needed.
          level : int, default None
              Alignment level if needed.
          errors : str, {{'raise', 'ignore'}}, default 'raise'
              Note that currently this parameter won't affect
              the results and will always coerce to a suitable dtype.
  
              - 'raise' : allow exceptions to be raised.
              - 'ignore' : suppress exceptions. On error return original object.
  
          try_cast : bool, default None
              Try to cast the result back to the input type (if possible).
  
              .. deprecated:: 1.3.0
                  Manually cast back if necessary.
  
          Returns
          -------
          Same type as caller or None if ``inplace=True``.
  
          See Also
          --------
          :func:`DataFrame.{name_other}` : Return an object of same shape as
              self.
  
          Notes
          -----
          The {name} method is an application of the if-then idiom. For each
          element in the calling DataFrame, if ``cond`` is ``{cond}`` the
          element is used; otherwise the corresponding element from the DataFrame
          ``other`` is used.
  
          The signature for :func:`DataFrame.where` differs from
          :func:`numpy.where`. Roughly ``df1.where(m, df2)`` is equivalent to
          ``np.where(m, df1, df2)``.
  
          For further details and examples see the ``{name}`` documentation in
          :ref:`indexing <indexing.where_mask>`.
  
          Examples
          --------
          >>> s = pd.Series(range(5))
          >>> s.where(s > 0)
          0    NaN
          1    1.0
          2    2.0
          3    3.0
          4    4.0
          dtype: float64
          >>> s.mask(s > 0)
          0    0.0
          1    NaN
          2    NaN
          3    NaN
          4    NaN
          dtype: float64
  
          >>> s.where(s > 1, 10)
          0    10
          1    10
          2    2
          3    3
          4    4
          dtype: int64
          >>> s.mask(s > 1, 10)
          0     0
          1     1
          2    10
          3    10
          4    10
          dtype: int64
  
          >>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
          >>> df
             A  B
          0  0  1
          1  2  3
          2  4  5
          3  6  7
          4  8  9
          >>> m = df % 3 == 0
          >>> df.where(m, -df)
             A  B
          0  0 -1
          1 -2  3
          2 -4 -5
          3  6 -7
          4 -8  9
          >>> df.where(m, -df) == np.where(m, df, -df)
                A     B
          0  True  True
          1  True  True
          2  True  True
          3  True  True
          4  True  True
          >>> df.where(m, -df) == df.mask(~m, -df)
                A     B
          0  True  True
          1  True  True
          2  True  True
          3  True  True
          4  True  True
          """
          other = com.apply_if_callable(other, self)
  
          if try_cast is not lib.no_default:
              warnings.warn(
                  "try_cast keyword is deprecated and will be removed in a "
                  "future version",
                  FutureWarning,
                  stacklevel=4,
              )
  
          return self._where(cond, other, inplace, axis, level, errors=errors)
  
      @final
      @doc(
          where,
          klass=_shared_doc_kwargs["klass"],
          cond="False",
          cond_rev="True",
          name="mask",
          name_other="where",
      )
      def mask(
          self,
          cond,
          other=np.nan,
          inplace=False,
          axis=None,
          level=None,
          errors="raise",
          try_cast=lib.no_default,
      ):
  
          inplace = validate_bool_kwarg(inplace, "inplace")
          cond = com.apply_if_callable(cond, self)
  
          if try_cast is not lib.no_default:
              warnings.warn(
                  "try_cast keyword is deprecated and will be removed in a "
                  "future version",
                  FutureWarning,
                  stacklevel=4,
              )
  
          # see gh-21891
          if not hasattr(cond, "__invert__"):
              cond = np.array(cond)
  
          return self.where(
              ~cond,
              other=other,
              inplace=inplace,
              axis=axis,
              level=level,
              errors=errors,
          )
  
      @doc(klass=_shared_doc_kwargs["klass"])
      def shift(
          self: FrameOrSeries, periods=1, freq=None, axis=0, fill_value=None
      ) -> FrameOrSeries:
          """
          Shift index by desired number of periods with an optional time `freq`.
  
          When `freq` is not passed, shift the index without realigning the data.
          If `freq` is passed (in this case, the index must be date or datetime,
          or it will raise a `NotImplementedError`), the index will be
          increased using the periods and the `freq`. `freq` can be inferred
          when specified as "infer" as long as either freq or inferred_freq
          attribute is set in the index.
  
          Parameters
          ----------
          periods : int
              Number of periods to shift. Can be positive or negative.
          freq : DateOffset, tseries.offsets, timedelta, or str, optional
              Offset to use from the tseries module or time rule (e.g. 'EOM').
              If `freq` is specified then the index values are shifted but the
              data is not realigned. That is, use `freq` if you would like to
              extend the index when shifting and preserve the original data.
              If `freq` is specified as "infer" then it will be inferred from
              the freq or inferred_freq attributes of the index. If neither of
              those attributes exist, a ValueError is thrown.
          axis : {{0 or 'index', 1 or 'columns', None}}, default None
              Shift direction.
          fill_value : object, optional
              The scalar value to use for newly introduced missing values.
              the default depends on the dtype of `self`.
              For numeric data, ``np.nan`` is used.
              For datetime, timedelta, or period data, etc. :attr:`NaT` is used.
              For extension dtypes, ``self.dtype.na_value`` is used.
  
              .. versionchanged:: 1.1.0
  
          Returns
          -------
          {klass}
              Copy of input object, shifted.
  
          See Also
          --------
          Index.shift : Shift values of Index.
          DatetimeIndex.shift : Shift values of DatetimeIndex.
          PeriodIndex.shift : Shift values of PeriodIndex.
          tshift : Shift the time index, using the index's frequency if
              available.
  
          Examples
          --------
          >>> df = pd.DataFrame({{"Col1": [10, 20, 15, 30, 45],
          ...                    "Col2": [13, 23, 18, 33, 48],
          ...                    "Col3": [17, 27, 22, 37, 52]}},
          ...                   index=pd.date_range("2020-01-01", "2020-01-05"))
          >>> df
                      Col1  Col2  Col3
          2020-01-01    10    13    17
          2020-01-02    20    23    27
          2020-01-03    15    18    22
          2020-01-04    30    33    37
          2020-01-05    45    48    52
  
          >>> df.shift(periods=3)
                      Col1  Col2  Col3
          2020-01-01   NaN   NaN   NaN
          2020-01-02   NaN   NaN   NaN
          2020-01-03   NaN   NaN   NaN
          2020-01-04  10.0  13.0  17.0
          2020-01-05  20.0  23.0  27.0
  
          >>> df.shift(periods=1, axis="columns")
                      Col1  Col2  Col3
          2020-01-01   NaN    10    13
          2020-01-02   NaN    20    23
          2020-01-03   NaN    15    18
          2020-01-04   NaN    30    33
          2020-01-05   NaN    45    48
  
          >>> df.shift(periods=3, fill_value=0)
                      Col1  Col2  Col3
          2020-01-01     0     0     0
          2020-01-02     0     0     0
          2020-01-03     0     0     0
          2020-01-04    10    13    17
          2020-01-05    20    23    27
  
          >>> df.shift(periods=3, freq="D")
                      Col1  Col2  Col3
          2020-01-04    10    13    17
          2020-01-05    20    23    27
          2020-01-06    15    18    22
          2020-01-07    30    33    37
          2020-01-08    45    48    52
  
          >>> df.shift(periods=3, freq="infer")
                      Col1  Col2  Col3
          2020-01-04    10    13    17
          2020-01-05    20    23    27
          2020-01-06    15    18    22
          2020-01-07    30    33    37
          2020-01-08    45    48    52
          """
          if periods == 0:
              return self.copy()
  
          if freq is None:
              # when freq is None, data is shifted, index is not
              axis = self._get_axis_number(axis)
              new_data = self._mgr.shift(
                  periods=periods, axis=axis, fill_value=fill_value
              )
              return self._constructor(new_data).__finalize__(self, method="shift")
  
          # when freq is given, index is shifted, data is not
          index = self._get_axis(axis)
  
          if freq == "infer":
              freq = getattr(index, "freq", None)
  
              if freq is None:
                  freq = getattr(index, "inferred_freq", None)
  
              if freq is None:
                  msg = "Freq was not set in the index hence cannot be inferred"
                  raise ValueError(msg)
  
          elif isinstance(freq, str):
              freq = to_offset(freq)
  
          if isinstance(index, PeriodIndex):
              orig_freq = to_offset(index.freq)
              if freq != orig_freq:
                  assert orig_freq is not None  # for mypy
                  raise ValueError(
                      f"Given freq {freq.rule_code} does not match "
                      f"PeriodIndex freq {orig_freq.rule_code}"
                  )
              new_ax = index.shift(periods)
          else:
              new_ax = index.shift(periods, freq)
  
          result = self.set_axis(new_ax, axis=axis)
          return result.__finalize__(self, method="shift")
  
      @final
      def slice_shift(self: FrameOrSeries, periods: int = 1, axis=0) -> FrameOrSeries:
          """
          Equivalent to `shift` without copying data.
          The shifted data will not include the dropped periods and the
          shifted axis will be smaller than the original.
  
          .. deprecated:: 1.2.0
              slice_shift is deprecated,
              use DataFrame/Series.shift instead.
  
          Parameters
          ----------
          periods : int
              Number of periods to move, can be positive or negative.
  
          Returns
          -------
          shifted : same type as caller
  
          Notes
          -----
          While the `slice_shift` is faster than `shift`, you may pay for it
          later during alignment.
          """
  
          msg = (
              "The 'slice_shift' method is deprecated "
              "and will be removed in a future version. "
              "You can use DataFrame/Series.shift instead"
          )
          warnings.warn(msg, FutureWarning, stacklevel=2)
  
          if periods == 0:
              return self
  
          if periods > 0:
              vslicer = slice(None, -periods)
              islicer = slice(periods, None)
          else:
              vslicer = slice(-periods, None)
              islicer = slice(None, periods)
  
          new_obj = self._slice(vslicer, axis=axis)
          shifted_axis = self._get_axis(axis)[islicer]
          new_obj.set_axis(shifted_axis, axis=axis, inplace=True)
  
          return new_obj.__finalize__(self, method="slice_shift")
  
      @final
      def tshift(
          self: FrameOrSeries, periods: int = 1, freq=None, axis: Axis = 0
      ) -> FrameOrSeries:
          """
          Shift the time index, using the index's frequency if available.
  
          .. deprecated:: 1.1.0
              Use `shift` instead.
  
          Parameters
          ----------
          periods : int
              Number of periods to move, can be positive or negative.
          freq : DateOffset, timedelta, or str, default None
              Increment to use from the tseries module
              or time rule expressed as a string (e.g. 'EOM').
          axis : {0 or ‘index’, 1 or ‘columns’, None}, default 0
              Corresponds to the axis that contains the Index.
  
          Returns
          -------
          shifted : Series/DataFrame
  
          Notes
          -----
          If freq is not specified then tries to use the freq or inferred_freq
          attributes of the index. If neither of those attributes exist, a
          ValueError is thrown
          """
          warnings.warn(
              (
                  "tshift is deprecated and will be removed in a future version. "
                  "Please use shift instead."
              ),
              FutureWarning,
              stacklevel=2,
          )
  
          if freq is None:
              freq = "infer"
  
          return self.shift(periods, freq, axis)
  
      def truncate(
          self: FrameOrSeries, before=None, after=None, axis=None, copy: bool_t = True
      ) -> FrameOrSeries:
          """
          Truncate a Series or DataFrame before and after some index value.
  
          This is a useful shorthand for boolean indexing based on index
          values above or below certain thresholds.
  
          Parameters
          ----------
          before : date, str, int
              Truncate all rows before this index value.
          after : date, str, int
              Truncate all rows after this index value.
          axis : {0 or 'index', 1 or 'columns'}, optional
              Axis to truncate. Truncates the index (rows) by default.
          copy : bool, default is True,
              Return a copy of the truncated section.
  
          Returns
          -------
          type of caller
              The truncated Series or DataFrame.
  
          See Also
          --------
          DataFrame.loc : Select a subset of a DataFrame by label.
          DataFrame.iloc : Select a subset of a DataFrame by position.
  
          Notes
          -----
          If the index being truncated contains only datetime values,
          `before` and `after` may be specified as strings instead of
          Timestamps.
  
          Examples
          --------
          >>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],
          ...                    'B': ['f', 'g', 'h', 'i', 'j'],
          ...                    'C': ['k', 'l', 'm', 'n', 'o']},
          ...                   index=[1, 2, 3, 4, 5])
          >>> df
             A  B  C
          1  a  f  k
          2  b  g  l
          3  c  h  m
          4  d  i  n
          5  e  j  o
  
          >>> df.truncate(before=2, after=4)
             A  B  C
          2  b  g  l
          3  c  h  m
          4  d  i  n
  
          The columns of a DataFrame can be truncated.
  
          >>> df.truncate(before="A", after="B", axis="columns")
             A  B
          1  a  f
          2  b  g
          3  c  h
          4  d  i
          5  e  j
  
          For Series, only rows can be truncated.
  
          >>> df['A'].truncate(before=2, after=4)
          2    b
          3    c
          4    d
          Name: A, dtype: object
  
          The index values in ``truncate`` can be datetimes or string
          dates.
  
          >>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
          >>> df = pd.DataFrame(index=dates, data={'A': 1})
          >>> df.tail()
                               A
          2016-01-31 23:59:56  1
          2016-01-31 23:59:57  1
          2016-01-31 23:59:58  1
          2016-01-31 23:59:59  1
          2016-02-01 00:00:00  1
  
          >>> df.truncate(before=pd.Timestamp('2016-01-05'),
          ...             after=pd.Timestamp('2016-01-10')).tail()
                               A
          2016-01-09 23:59:56  1
          2016-01-09 23:59:57  1
          2016-01-09 23:59:58  1
          2016-01-09 23:59:59  1
          2016-01-10 00:00:00  1
  
          Because the index is a DatetimeIndex containing only dates, we can
          specify `before` and `after` as strings. They will be coerced to
          Timestamps before truncation.
  
          >>> df.truncate('2016-01-05', '2016-01-10').tail()
                               A
          2016-01-09 23:59:56  1
          2016-01-09 23:59:57  1
          2016-01-09 23:59:58  1
          2016-01-09 23:59:59  1
          2016-01-10 00:00:00  1
  
          Note that ``truncate`` assumes a 0 value for any unspecified time
          component (midnight). This differs from partial string slicing, which
          returns any partially matching dates.
  
          >>> df.loc['2016-01-05':'2016-01-10', :].tail()
                               A
          2016-01-10 23:59:55  1
          2016-01-10 23:59:56  1
          2016-01-10 23:59:57  1
          2016-01-10 23:59:58  1
          2016-01-10 23:59:59  1
          """
          if axis is None:
              axis = self._stat_axis_number
          axis = self._get_axis_number(axis)
          ax = self._get_axis(axis)
  
          # GH 17935
          # Check that index is sorted
          if not ax.is_monotonic_increasing and not ax.is_monotonic_decreasing:
              raise ValueError("truncate requires a sorted index")
  
          # if we have a date index, convert to dates, otherwise
          # treat like a slice
          if ax._is_all_dates:
              from pandas.core.tools.datetimes import to_datetime
  
              before = to_datetime(before)
              after = to_datetime(after)
  
          if before is not None and after is not None and before > after:
              raise ValueError(f"Truncate: {after} must be after {before}")
  
          if len(ax) > 1 and ax.is_monotonic_decreasing and ax.nunique() > 1:
              before, after = after, before
  
          slicer = [slice(None, None)] * self._AXIS_LEN
          slicer[axis] = slice(before, after)
          result = self.loc[tuple(slicer)]
  
          if isinstance(ax, MultiIndex):
              setattr(result, self._get_axis_name(axis), ax.truncate(before, after))
  
          if copy:
              result = result.copy()
  
          return result
  
      @final
      def tz_convert(
          self: FrameOrSeries, tz, axis=0, level=None, copy: bool_t = True
      ) -> FrameOrSeries:
          """
          Convert tz-aware axis to target time zone.
  
          Parameters
          ----------
          tz : str or tzinfo object
          axis : the axis to convert
          level : int, str, default None
              If axis is a MultiIndex, convert a specific level. Otherwise
              must be None.
          copy : bool, default True
              Also make a copy of the underlying data.
  
          Returns
          -------
          {klass}
              Object with time zone converted axis.
  
          Raises
          ------
          TypeError
              If the axis is tz-naive.
          """
          axis = self._get_axis_number(axis)
          ax = self._get_axis(axis)
  
          def _tz_convert(ax, tz):
              if not hasattr(ax, "tz_convert"):
                  if len(ax) > 0:
                      ax_name = self._get_axis_name(axis)
                      raise TypeError(
                          f"{ax_name} is not a valid DatetimeIndex or PeriodIndex"
                      )
                  else:
                      ax = DatetimeIndex([], tz=tz)
              else:
                  ax = ax.tz_convert(tz)
              return ax
  
          # if a level is given it must be a MultiIndex level or
          # equivalent to the axis name
          if isinstance(ax, MultiIndex):
              level = ax._get_level_number(level)
              new_level = _tz_convert(ax.levels[level], tz)
              ax = ax.set_levels(new_level, level=level)
          else:
              if level not in (None, 0, ax.name):
                  raise ValueError(f"The level {level} is not valid")
              ax = _tz_convert(ax, tz)
  
          result = self.copy(deep=copy)
          result = result.set_axis(ax, axis=axis, inplace=False)
          return result.__finalize__(self, method="tz_convert")
  
      @final
      def tz_localize(
          self: FrameOrSeries,
          tz,
          axis=0,
          level=None,
          copy: bool_t = True,
          ambiguous="raise",
          nonexistent: str = "raise",
      ) -> FrameOrSeries:
          """
          Localize tz-naive index of a Series or DataFrame to target time zone.
  
          This operation localizes the Index. To localize the values in a
          timezone-naive Series, use :meth:`Series.dt.tz_localize`.
  
          Parameters
          ----------
          tz : str or tzinfo
          axis : the axis to localize
          level : int, str, default None
              If axis ia a MultiIndex, localize a specific level. Otherwise
              must be None.
          copy : bool, default True
              Also make a copy of the underlying data.
          ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
              When clocks moved backward due to DST, ambiguous times may arise.
              For example in Central European Time (UTC+01), when going from
              03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at
              00:30:00 UTC and at 01:30:00 UTC. In such a situation, the
              `ambiguous` parameter dictates how ambiguous times should be
              handled.
  
              - 'infer' will attempt to infer fall dst-transition hours based on
                order
              - bool-ndarray where True signifies a DST time, False designates
                a non-DST time (note that this flag is only applicable for
                ambiguous times)
              - 'NaT' will return NaT where there are ambiguous times
              - 'raise' will raise an AmbiguousTimeError if there are ambiguous
                times.
          nonexistent : str, default 'raise'
              A nonexistent time does not exist in a particular timezone
              where clocks moved forward due to DST. Valid values are:
  
              - 'shift_forward' will shift the nonexistent time forward to the
                closest existing time
              - 'shift_backward' will shift the nonexistent time backward to the
                closest existing time
              - 'NaT' will return NaT where there are nonexistent times
              - timedelta objects will shift nonexistent times by the timedelta
              - 'raise' will raise an NonExistentTimeError if there are
                nonexistent times.
  
          Returns
          -------
          Series or DataFrame
              Same type as the input.
  
          Raises
          ------
          TypeError
              If the TimeSeries is tz-aware and tz is not None.
  
          Examples
          --------
          Localize local times:
  
          >>> s = pd.Series([1],
          ...               index=pd.DatetimeIndex(['2018-09-15 01:30:00']))
          >>> s.tz_localize('CET')
          2018-09-15 01:30:00+02:00    1
          dtype: int64
  
          Be careful with DST changes. When there is sequential data, pandas
          can infer the DST time:
  
          >>> s = pd.Series(range(7),
          ...               index=pd.DatetimeIndex(['2018-10-28 01:30:00',
          ...                                       '2018-10-28 02:00:00',
          ...                                       '2018-10-28 02:30:00',
          ...                                       '2018-10-28 02:00:00',
          ...                                       '2018-10-28 02:30:00',
          ...                                       '2018-10-28 03:00:00',
          ...                                       '2018-10-28 03:30:00']))
          >>> s.tz_localize('CET', ambiguous='infer')
          2018-10-28 01:30:00+02:00    0
          2018-10-28 02:00:00+02:00    1
          2018-10-28 02:30:00+02:00    2
          2018-10-28 02:00:00+01:00    3
          2018-10-28 02:30:00+01:00    4
          2018-10-28 03:00:00+01:00    5
          2018-10-28 03:30:00+01:00    6
          dtype: int64
  
          In some cases, inferring the DST is impossible. In such cases, you can
          pass an ndarray to the ambiguous parameter to set the DST explicitly
  
          >>> s = pd.Series(range(3),
          ...               index=pd.DatetimeIndex(['2018-10-28 01:20:00',
          ...                                       '2018-10-28 02:36:00',
          ...                                       '2018-10-28 03:46:00']))
          >>> s.tz_localize('CET', ambiguous=np.array([True, True, False]))
          2018-10-28 01:20:00+02:00    0
          2018-10-28 02:36:00+02:00    1
          2018-10-28 03:46:00+01:00    2
          dtype: int64
  
          If the DST transition causes nonexistent times, you can shift these
          dates forward or backward with a timedelta object or `'shift_forward'`
          or `'shift_backward'`.
  
          >>> s = pd.Series(range(2),
          ...               index=pd.DatetimeIndex(['2015-03-29 02:30:00',
          ...                                       '2015-03-29 03:30:00']))
          >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
          2015-03-29 03:00:00+02:00    0
          2015-03-29 03:30:00+02:00    1
          dtype: int64
          >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
          2015-03-29 01:59:59.999999999+01:00    0
          2015-03-29 03:30:00+02:00              1
          dtype: int64
          >>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H'))
          2015-03-29 03:30:00+02:00    0
          2015-03-29 03:30:00+02:00    1
          dtype: int64
          """
          nonexistent_options = ("raise", "NaT", "shift_forward", "shift_backward")
          if nonexistent not in nonexistent_options and not isinstance(
              nonexistent, timedelta
          ):
              raise ValueError(
                  "The nonexistent argument must be one of 'raise', "
                  "'NaT', 'shift_forward', 'shift_backward' or "
                  "a timedelta object"
              )
  
          axis = self._get_axis_number(axis)
          ax = self._get_axis(axis)
  
          def _tz_localize(ax, tz, ambiguous, nonexistent):
              if not hasattr(ax, "tz_localize"):
                  if len(ax) > 0:
                      ax_name = self._get_axis_name(axis)
                      raise TypeError(
                          f"{ax_name} is not a valid DatetimeIndex or PeriodIndex"
                      )
                  else:
                      ax = DatetimeIndex([], tz=tz)
              else:
                  ax = ax.tz_localize(tz, ambiguous=ambiguous, nonexistent=nonexistent)
              return ax
  
          # if a level is given it must be a MultiIndex level or
          # equivalent to the axis name
          if isinstance(ax, MultiIndex):
              level = ax._get_level_number(level)
              new_level = _tz_localize(ax.levels[level], tz, ambiguous, nonexistent)
              ax = ax.set_levels(new_level, level=level)
          else:
              if level not in (None, 0, ax.name):
                  raise ValueError(f"The level {level} is not valid")
              ax = _tz_localize(ax, tz, ambiguous, nonexistent)
  
          result = self.copy(deep=copy)
          result = result.set_axis(ax, axis=axis, inplace=False)
          return result.__finalize__(self, method="tz_localize")
  
      # ----------------------------------------------------------------------
      # Numeric Methods
  
      @final
      def abs(self: FrameOrSeries) -> FrameOrSeries:
          """
          Return a Series/DataFrame with absolute numeric value of each element.
  
          This function only applies to elements that are all numeric.
  
          Returns
          -------
          abs
              Series/DataFrame containing the absolute value of each element.
  
          See Also
          --------
          numpy.absolute : Calculate the absolute value element-wise.
  
          Notes
          -----
          For ``complex`` inputs, ``1.2 + 1j``, the absolute value is
          :math:`\\sqrt{ a^2 + b^2 }`.
  
          Examples
          --------
          Absolute numeric values in a Series.
  
          >>> s = pd.Series([-1.10, 2, -3.33, 4])
          >>> s.abs()
          0    1.10
          1    2.00
          2    3.33
          3    4.00
          dtype: float64
  
          Absolute numeric values in a Series with complex numbers.
  
          >>> s = pd.Series([1.2 + 1j])
          >>> s.abs()
          0    1.56205
          dtype: float64
  
          Absolute numeric values in a Series with a Timedelta element.
  
          >>> s = pd.Series([pd.Timedelta('1 days')])
          >>> s.abs()
          0   1 days
          dtype: timedelta64[ns]
  
          Select rows with data closest to certain value using argsort (from
          `StackOverflow <https://stackoverflow.com/a/17758115>`__).
  
          >>> df = pd.DataFrame({
          ...     'a': [4, 5, 6, 7],
          ...     'b': [10, 20, 30, 40],
          ...     'c': [100, 50, -30, -50]
          ... })
          >>> df
               a    b    c
          0    4   10  100
          1    5   20   50
          2    6   30  -30
          3    7   40  -50
          >>> df.loc[(df.c - 43).abs().argsort()]
               a    b    c
          1    5   20   50
          0    4   10  100
          2    6   30  -30
          3    7   40  -50
          """
          # error: Incompatible return value type (got "ndarray[Any, dtype[Any]]",
          # expected "FrameOrSeries")
          return np.abs(self)  # type: ignore[return-value]
  
      @final
      def describe(
          self: FrameOrSeries,
          percentiles=None,
          include=None,
          exclude=None,
          datetime_is_numeric=False,
      ) -> FrameOrSeries:
          """
          Generate descriptive statistics.
  
          Descriptive statistics include those that summarize the central
          tendency, dispersion and shape of a
          dataset's distribution, excluding ``NaN`` values.
  
          Analyzes both numeric and object series, as well
          as ``DataFrame`` column sets of mixed data types. The output
          will vary depending on what is provided. Refer to the notes
          below for more detail.
  
          Parameters
          ----------
          percentiles : list-like of numbers, optional
              The percentiles to include in the output. All should
              fall between 0 and 1. The default is
              ``[.25, .5, .75]``, which returns the 25th, 50th, and
              75th percentiles.
          include : 'all', list-like of dtypes or None (default), optional
              A white list of data types to include in the result. Ignored
              for ``Series``. Here are the options:
  
              - 'all' : All columns of the input will be included in the output.
              - A list-like of dtypes : Limits the results to the
                provided data types.
                To limit the result to numeric types submit
                ``numpy.number``. To limit it instead to object columns submit
                the ``numpy.object`` data type. Strings
                can also be used in the style of
                ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
                select pandas categorical columns, use ``'category'``
              - None (default) : The result will include all numeric columns.
          exclude : list-like of dtypes or None (default), optional,
              A black list of data types to omit from the result. Ignored
              for ``Series``. Here are the options:
  
              - A list-like of dtypes : Excludes the provided data types
                from the result. To exclude numeric types submit
                ``numpy.number``. To exclude object columns submit the data
                type ``numpy.object``. Strings can also be used in the style of
                ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
                exclude pandas categorical columns, use ``'category'``
              - None (default) : The result will exclude nothing.
          datetime_is_numeric : bool, default False
              Whether to treat datetime dtypes as numeric. This affects statistics
              calculated for the column. For DataFrame input, this also
              controls whether datetime columns are included by default.
  
              .. versionadded:: 1.1.0
  
          Returns
          -------
          Series or DataFrame
              Summary statistics of the Series or Dataframe provided.
  
          See Also
          --------
          DataFrame.count: Count number of non-NA/null observations.
          DataFrame.max: Maximum of the values in the object.
          DataFrame.min: Minimum of the values in the object.
          DataFrame.mean: Mean of the values.
          DataFrame.std: Standard deviation of the observations.
          DataFrame.select_dtypes: Subset of a DataFrame including/excluding
              columns based on their dtype.
  
          Notes
          -----
          For numeric data, the result's index will include ``count``,
          ``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and
          upper percentiles. By default the lower percentile is ``25`` and the
          upper percentile is ``75``. The ``50`` percentile is the
          same as the median.
  
          For object data (e.g. strings or timestamps), the result's index
          will include ``count``, ``unique``, ``top``, and ``freq``. The ``top``
          is the most common value. The ``freq`` is the most common value's
          frequency. Timestamps also include the ``first`` and ``last`` items.
  
          If multiple object values have the highest count, then the
          ``count`` and ``top`` results will be arbitrarily chosen from
          among those with the highest count.
  
          For mixed data types provided via a ``DataFrame``, the default is to
          return only an analysis of numeric columns. If the dataframe consists
          only of object and categorical data without any numeric columns, the
          default is to return an analysis of both the object and categorical
          columns. If ``include='all'`` is provided as an option, the result
          will include a union of attributes of each type.
  
          The `include` and `exclude` parameters can be used to limit
          which columns in a ``DataFrame`` are analyzed for the output.
          The parameters are ignored when analyzing a ``Series``.
  
          Examples
          --------
          Describing a numeric ``Series``.
  
          >>> s = pd.Series([1, 2, 3])
          >>> s.describe()
          count    3.0
          mean     2.0
          std      1.0
          min      1.0
          25%      1.5
          50%      2.0
          75%      2.5
          max      3.0
          dtype: float64
  
          Describing a categorical ``Series``.
  
          >>> s = pd.Series(['a', 'a', 'b', 'c'])
          >>> s.describe()
          count     4
          unique    3
          top       a
          freq      2
          dtype: object
  
          Describing a timestamp ``Series``.
  
          >>> s = pd.Series([
          ...   np.datetime64("2000-01-01"),
          ...   np.datetime64("2010-01-01"),
          ...   np.datetime64("2010-01-01")
          ... ])
          >>> s.describe(datetime_is_numeric=True)
          count                      3
          mean     2006-09-01 08:00:00
          min      2000-01-01 00:00:00
          25%      2004-12-31 12:00:00
          50%      2010-01-01 00:00:00
          75%      2010-01-01 00:00:00
          max      2010-01-01 00:00:00
          dtype: object
  
          Describing a ``DataFrame``. By default only numeric fields
          are returned.
  
          >>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),
          ...                    'numeric': [1, 2, 3],
          ...                    'object': ['a', 'b', 'c']
          ...                   })
          >>> df.describe()
                 numeric
          count      3.0
          mean       2.0
          std        1.0
          min        1.0
          25%        1.5
          50%        2.0
          75%        2.5
          max        3.0
  
          Describing all columns of a ``DataFrame`` regardless of data type.
  
          >>> df.describe(include='all')  # doctest: +SKIP
                 categorical  numeric object
          count            3      3.0      3
          unique           3      NaN      3
          top              f      NaN      a
          freq             1      NaN      1
          mean           NaN      2.0    NaN
          std            NaN      1.0    NaN
          min            NaN      1.0    NaN
          25%            NaN      1.5    NaN
          50%            NaN      2.0    NaN
          75%            NaN      2.5    NaN
          max            NaN      3.0    NaN
  
          Describing a column from a ``DataFrame`` by accessing it as
          an attribute.
  
          >>> df.numeric.describe()
          count    3.0
          mean     2.0
          std      1.0
          min      1.0
          25%      1.5
          50%      2.0
          75%      2.5
          max      3.0
          Name: numeric, dtype: float64
  
          Including only numeric columns in a ``DataFrame`` description.
  
          >>> df.describe(include=[np.number])
                 numeric
          count      3.0
          mean       2.0
          std        1.0
          min        1.0
          25%        1.5
          50%        2.0
          75%        2.5
          max        3.0
  
          Including only string columns in a ``DataFrame`` description.
  
          >>> df.describe(include=[object])  # doctest: +SKIP
                 object
          count       3
          unique      3
          top         a
          freq        1
  
          Including only categorical columns from a ``DataFrame`` description.
  
          >>> df.describe(include=['category'])
                 categorical
          count            3
          unique           3
          top              d
          freq             1
  
          Excluding numeric columns from a ``DataFrame`` description.
  
          >>> df.describe(exclude=[np.number])  # doctest: +SKIP
                 categorical object
          count            3      3
          unique           3      3
          top              f      a
          freq             1      1
  
          Excluding object columns from a ``DataFrame`` description.
  
          >>> df.describe(exclude=[object])  # doctest: +SKIP
                 categorical  numeric
          count            3      3.0
          unique           3      NaN
          top              f      NaN
          freq             1      NaN
          mean           NaN      2.0
          std            NaN      1.0
          min            NaN      1.0
          25%            NaN      1.5
          50%            NaN      2.0
          75%            NaN      2.5
          max            NaN      3.0
          """
          return describe_ndframe(
              obj=self,
              include=include,
              exclude=exclude,
              datetime_is_numeric=datetime_is_numeric,
              percentiles=percentiles,
          )
  
      @final
      def pct_change(
          self: FrameOrSeries,
          periods=1,
          fill_method="pad",
          limit=None,
          freq=None,
          **kwargs,
      ) -> FrameOrSeries:
          """
          Percentage change between the current and a prior element.
  
          Computes the percentage change from the immediately previous row by
          default. This is useful in comparing the percentage of change in a time
          series of elements.
  
          Parameters
          ----------
          periods : int, default 1
              Periods to shift for forming percent change.
          fill_method : str, default 'pad'
              How to handle NAs before computing percent changes.
          limit : int, default None
              The number of consecutive NAs to fill before stopping.
          freq : DateOffset, timedelta, or str, optional
              Increment to use from time series API (e.g. 'M' or BDay()).
          **kwargs
              Additional keyword arguments are passed into
              `DataFrame.shift` or `Series.shift`.
  
          Returns
          -------
          chg : Series or DataFrame
              The same type as the calling object.
  
          See Also
          --------
          Series.diff : Compute the difference of two elements in a Series.
          DataFrame.diff : Compute the difference of two elements in a DataFrame.
          Series.shift : Shift the index by some number of periods.
          DataFrame.shift : Shift the index by some number of periods.
  
          Examples
          --------
          **Series**
  
          >>> s = pd.Series([90, 91, 85])
          >>> s
          0    90
          1    91
          2    85
          dtype: int64
  
          >>> s.pct_change()
          0         NaN
          1    0.011111
          2   -0.065934
          dtype: float64
  
          >>> s.pct_change(periods=2)
          0         NaN
          1         NaN
          2   -0.055556
          dtype: float64
  
          See the percentage change in a Series where filling NAs with last
          valid observation forward to next valid.
  
          >>> s = pd.Series([90, 91, None, 85])
          >>> s
          0    90.0
          1    91.0
          2     NaN
          3    85.0
          dtype: float64
  
          >>> s.pct_change(fill_method='ffill')
          0         NaN
          1    0.011111
          2    0.000000
          3   -0.065934
          dtype: float64
  
          **DataFrame**
  
          Percentage change in French franc, Deutsche Mark, and Italian lira from
          1980-01-01 to 1980-03-01.
  
          >>> df = pd.DataFrame({
          ...     'FR': [4.0405, 4.0963, 4.3149],
          ...     'GR': [1.7246, 1.7482, 1.8519],
          ...     'IT': [804.74, 810.01, 860.13]},
          ...     index=['1980-01-01', '1980-02-01', '1980-03-01'])
          >>> df
                          FR      GR      IT
          1980-01-01  4.0405  1.7246  804.74
          1980-02-01  4.0963  1.7482  810.01
          1980-03-01  4.3149  1.8519  860.13
  
          >>> df.pct_change()
                            FR        GR        IT
          1980-01-01       NaN       NaN       NaN
          1980-02-01  0.013810  0.013684  0.006549
          1980-03-01  0.053365  0.059318  0.061876
  
          Percentage of change in GOOG and APPL stock volume. Shows computing
          the percentage change between columns.
  
          >>> df = pd.DataFrame({
          ...     '2016': [1769950, 30586265],
          ...     '2015': [1500923, 40912316],
          ...     '2014': [1371819, 41403351]},
          ...     index=['GOOG', 'APPL'])
          >>> df
                    2016      2015      2014
          GOOG   1769950   1500923   1371819
          APPL  30586265  40912316  41403351
  
          >>> df.pct_change(axis='columns', periods=-1)
                    2016      2015  2014
          GOOG  0.179241  0.094112   NaN
          APPL -0.252395 -0.011860   NaN
          """
          axis = self._get_axis_number(kwargs.pop("axis", self._stat_axis_name))
          if fill_method is None:
              data = self
          else:
              _data = self.fillna(method=fill_method, axis=axis, limit=limit)
              assert _data is not None  # needed for mypy
              data = _data
  
          shifted = data.shift(periods=periods, freq=freq, axis=axis, **kwargs)
          # Unsupported left operand type for / ("FrameOrSeries")
          rs = data / shifted - 1  # type: ignore[operator]
          if freq is not None:
              # Shift method is implemented differently when freq is not None
              # We want to restore the original index
              rs = rs.loc[~rs.index.duplicated()]
              rs = rs.reindex_like(data)
          return rs
  
      @final
      def _agg_by_level(self, name, axis=0, level=0, skipna=True, **kwargs):
          if axis is None:
              raise ValueError("Must specify 'axis' when aggregating by level.")
          grouped = self.groupby(level=level, axis=axis, sort=False)
          if hasattr(grouped, name) and skipna:
              return getattr(grouped, name)(**kwargs)
          axis = self._get_axis_number(axis)
          method = getattr(type(self), name)
          applyf = lambda x: method(x, axis=axis, skipna=skipna, **kwargs)
          return grouped.aggregate(applyf)
  
      @final
      def _logical_func(
          self, name: str, func, axis=0, bool_only=None, skipna=True, level=None, **kwargs
      ):
          nv.validate_logical_func((), kwargs, fname=name)
          if level is not None:
              warnings.warn(
                  "Using the level keyword in DataFrame and Series aggregations is "
                  "deprecated and will be removed in a future version. Use groupby "
                  "instead. df.any(level=1) should use df.groupby(level=1).any()",
                  FutureWarning,
                  stacklevel=4,
              )
              if bool_only is not None:
                  raise NotImplementedError(
                      "Option bool_only is not implemented with option level."
                  )
              return self._agg_by_level(name, axis=axis, level=level, skipna=skipna)
  
          if self.ndim > 1 and axis is None:
              # Reduce along one dimension then the other, to simplify DataFrame._reduce
              res = self._logical_func(
                  name, func, axis=0, bool_only=bool_only, skipna=skipna, **kwargs
              )
              return res._logical_func(name, func, skipna=skipna, **kwargs)
  
          return self._reduce(
              func,
              name=name,
              axis=axis,
              skipna=skipna,
              numeric_only=bool_only,
              filter_type="bool",
          )
  
      def any(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
          return self._logical_func(
              "any", nanops.nanany, axis, bool_only, skipna, level, **kwargs
          )
  
      def all(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
          return self._logical_func(
              "all", nanops.nanall, axis, bool_only, skipna, level, **kwargs
          )
  
      @final
      def _accum_func(self, name: str, func, axis=None, skipna=True, *args, **kwargs):
          skipna = nv.validate_cum_func_with_skipna(skipna, args, kwargs, name)
          if axis is None:
              axis = self._stat_axis_number
          else:
              axis = self._get_axis_number(axis)
  
          if axis == 1:
              return self.T._accum_func(
                  name, func, axis=0, skipna=skipna, *args, **kwargs
              ).T
  
          def block_accum_func(blk_values):
              values = blk_values.T if hasattr(blk_values, "T") else blk_values
  
              result = nanops.na_accum_func(values, func, skipna=skipna)
  
              result = result.T if hasattr(result, "T") else result
              return result
  
          result = self._mgr.apply(block_accum_func)
  
          return self._constructor(result).__finalize__(self, method=name)
  
      def cummax(self, axis=None, skipna=True, *args, **kwargs):
          return self._accum_func(
              "cummax", np.maximum.accumulate, axis, skipna, *args, **kwargs
          )
  
      def cummin(self, axis=None, skipna=True, *args, **kwargs):
          return self._accum_func(
              "cummin", np.minimum.accumulate, axis, skipna, *args, **kwargs
          )
  
      def cumsum(self, axis=None, skipna=True, *args, **kwargs):
          return self._accum_func("cumsum", np.cumsum, axis, skipna, *args, **kwargs)
  
      def cumprod(self, axis=None, skipna=True, *args, **kwargs):
          return self._accum_func("cumprod", np.cumprod, axis, skipna, *args, **kwargs)
  
      @final
      def _stat_function_ddof(
          self,
          name: str,
          func,
          axis=None,
          skipna=None,
          level=None,
          ddof=1,
          numeric_only=None,
          **kwargs,
      ):
          nv.validate_stat_ddof_func((), kwargs, fname=name)
          if skipna is None:
              skipna = True
          if axis is None:
              axis = self._stat_axis_number
          if level is not None:
              warnings.warn(
                  "Using the level keyword in DataFrame and Series aggregations is "
                  "deprecated and will be removed in a future version. Use groupby "
                  "instead. df.var(level=1) should use df.groupby(level=1).var().",
                  FutureWarning,
                  stacklevel=4,
              )
              return self._agg_by_level(
                  name, axis=axis, level=level, skipna=skipna, ddof=ddof
              )
          return self._reduce(
              func, name, axis=axis, numeric_only=numeric_only, skipna=skipna, ddof=ddof
          )
  
      def sem(
          self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs
      ):
          return self._stat_function_ddof(
              "sem", nanops.nansem, axis, skipna, level, ddof, numeric_only, **kwargs
          )
  
      def var(
          self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs
      ):
          return self._stat_function_ddof(
              "var", nanops.nanvar, axis, skipna, level, ddof, numeric_only, **kwargs
          )
  
      def std(
          self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs
      ):
          return self._stat_function_ddof(
              "std", nanops.nanstd, axis, skipna, level, ddof, numeric_only, **kwargs
          )
  
      @final
      def _stat_function(
          self,
          name: str,
          func,
          axis=None,
          skipna=None,
          level=None,
          numeric_only=None,
          **kwargs,
      ):
          if name == "median":
              nv.validate_median((), kwargs)
          else:
              nv.validate_stat_func((), kwargs, fname=name)
          if skipna is None:
              skipna = True
          if axis is None:
              axis = self._stat_axis_number
          if level is not None:
              warnings.warn(
                  "Using the level keyword in DataFrame and Series aggregations is "
                  "deprecated and will be removed in a future version. Use groupby "
                  "instead. df.median(level=1) should use df.groupby(level=1).median().",
                  FutureWarning,
                  stacklevel=4,
              )
              return self._agg_by_level(
                  name, axis=axis, level=level, skipna=skipna, numeric_only=numeric_only
              )
          return self._reduce(
              func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
          )
  
      def min(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
          return self._stat_function(
              "min", nanops.nanmin, axis, skipna, level, numeric_only, **kwargs
          )
  
      def max(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
          return self._stat_function(
              "max", nanops.nanmax, axis, skipna, level, numeric_only, **kwargs
          )
  
      def mean(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
          return self._stat_function(
              "mean", nanops.nanmean, axis, skipna, level, numeric_only, **kwargs
          )
  
      def median(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
          return self._stat_function(
              "median", nanops.nanmedian, axis, skipna, level, numeric_only, **kwargs
          )
  
      def skew(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
          return self._stat_function(
              "skew", nanops.nanskew, axis, skipna, level, numeric_only, **kwargs
          )
  
      def kurt(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
          return self._stat_function(
              "kurt", nanops.nankurt, axis, skipna, level, numeric_only, **kwargs
          )
  
      kurtosis = kurt
  
      @final
      def _min_count_stat_function(
          self,
          name: str,
          func,
          axis=None,
          skipna=None,
          level=None,
          numeric_only=None,
          min_count=0,
          **kwargs,
      ):
          if name == "sum":
              nv.validate_sum((), kwargs)
          elif name == "prod":
              nv.validate_prod((), kwargs)
          else:
              nv.validate_stat_func((), kwargs, fname=name)
          if skipna is None:
              skipna = True
          if axis is None:
              axis = self._stat_axis_number
          if level is not None:
              warnings.warn(
                  "Using the level keyword in DataFrame and Series aggregations is "
                  "deprecated and will be removed in a future version. Use groupby "
                  "instead. df.sum(level=1) should use df.groupby(level=1).sum().",
                  FutureWarning,
                  stacklevel=4,
              )
              return self._agg_by_level(
                  name,
                  axis=axis,
                  level=level,
                  skipna=skipna,
                  min_count=min_count,
                  numeric_only=numeric_only,
              )
          return self._reduce(
              func,
              name=name,
              axis=axis,
              skipna=skipna,
              numeric_only=numeric_only,
              min_count=min_count,
          )
  
      def sum(
          self,
          axis=None,
          skipna=None,
          level=None,
          numeric_only=None,
          min_count=0,
          **kwargs,
      ):
          return self._min_count_stat_function(
              "sum", nanops.nansum, axis, skipna, level, numeric_only, min_count, **kwargs
          )
  
      def prod(
          self,
          axis=None,
          skipna=None,
          level=None,
          numeric_only=None,
          min_count=0,
          **kwargs,
      ):
          return self._min_count_stat_function(
              "prod",
              nanops.nanprod,
              axis,
              skipna,
              level,
              numeric_only,
              min_count,
              **kwargs,
          )
  
      product = prod
  
      def mad(self, axis=None, skipna=None, level=None):
          """
          {desc}
  
          Parameters
          ----------
          axis : {axis_descr}
              Axis for the function to be applied on.
          skipna : bool, default None
              Exclude NA/null values when computing the result.
          level : int or level name, default None
              If the axis is a MultiIndex (hierarchical), count along a
              particular level, collapsing into a {name1}.
  
          Returns
          -------
          {name1} or {name2} (if level specified)\
          {see_also}\
          {examples}
          """
          if skipna is None:
              skipna = True
          if axis is None:
              axis = self._stat_axis_number
          if level is not None:
              warnings.warn(
                  "Using the level keyword in DataFrame and Series aggregations is "
                  "deprecated and will be removed in a future version. Use groupby "
                  "instead. df.mad(level=1) should use df.groupby(level=1).mad()",
                  FutureWarning,
                  stacklevel=3,
              )
              return self._agg_by_level("mad", axis=axis, level=level, skipna=skipna)
  
          data = self._get_numeric_data()
          if axis == 0:
              demeaned = data - data.mean(axis=0)
          else:
              demeaned = data.sub(data.mean(axis=1), axis=0)
          return np.abs(demeaned).mean(axis=axis, skipna=skipna)
  
      @classmethod
      def _add_numeric_operations(cls):
          """
          Add the operations to the cls; evaluate the doc strings again
          """
          axis_descr, name1, name2 = _doc_params(cls)
  
          @doc(
              _bool_doc,
              desc=_any_desc,
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              see_also=_any_see_also,
              examples=_any_examples,
              empty_value=False,
          )
          def any(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
              return NDFrame.any(self, axis, bool_only, skipna, level, **kwargs)
  
          setattr(cls, "any", any)
  
          @doc(
              _bool_doc,
              desc=_all_desc,
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              see_also=_all_see_also,
              examples=_all_examples,
              empty_value=True,
          )
          def all(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
              return NDFrame.all(self, axis, bool_only, skipna, level, **kwargs)
  
          setattr(cls, "all", all)
  
          # error: Argument 1 to "doc" has incompatible type "Optional[str]"; expected
          # "Union[str, Callable[..., Any]]"
          @doc(
              NDFrame.mad.__doc__,  # type: ignore[arg-type]
              desc="Return the mean absolute deviation of the values "
              "over the requested axis.",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              see_also="",
              examples="",
          )
          def mad(self, axis=None, skipna=None, level=None):
              return NDFrame.mad(self, axis, skipna, level)
  
          setattr(cls, "mad", mad)
  
          @doc(
              _num_ddof_doc,
              desc="Return unbiased standard error of the mean over requested "
              "axis.\n\nNormalized by N-1 by default. This can be changed "
              "using the ddof argument",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              notes="",
          )
          def sem(
              self,
              axis=None,
              skipna=None,
              level=None,
              ddof=1,
              numeric_only=None,
              **kwargs,
          ):
              return NDFrame.sem(self, axis, skipna, level, ddof, numeric_only, **kwargs)
  
          setattr(cls, "sem", sem)
  
          @doc(
              _num_ddof_doc,
              desc="Return unbiased variance over requested axis.\n\nNormalized by "
              "N-1 by default. This can be changed using the ddof argument",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              notes="",
          )
          def var(
              self,
              axis=None,
              skipna=None,
              level=None,
              ddof=1,
              numeric_only=None,
              **kwargs,
          ):
              return NDFrame.var(self, axis, skipna, level, ddof, numeric_only, **kwargs)
  
          setattr(cls, "var", var)
  
          @doc(
              _num_ddof_doc,
              desc="Return sample standard deviation over requested axis."
              "\n\nNormalized by N-1 by default. This can be changed using the "
              "ddof argument",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              notes=_std_notes,
          )
          def std(
              self,
              axis=None,
              skipna=None,
              level=None,
              ddof=1,
              numeric_only=None,
              **kwargs,
          ):
              return NDFrame.std(self, axis, skipna, level, ddof, numeric_only, **kwargs)
  
          setattr(cls, "std", std)
  
          @doc(
              _cnum_doc,
              desc="minimum",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              accum_func_name="min",
              examples=_cummin_examples,
          )
          def cummin(self, axis=None, skipna=True, *args, **kwargs):
              return NDFrame.cummin(self, axis, skipna, *args, **kwargs)
  
          setattr(cls, "cummin", cummin)
  
          @doc(
              _cnum_doc,
              desc="maximum",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              accum_func_name="max",
              examples=_cummax_examples,
          )
          def cummax(self, axis=None, skipna=True, *args, **kwargs):
              return NDFrame.cummax(self, axis, skipna, *args, **kwargs)
  
          setattr(cls, "cummax", cummax)
  
          @doc(
              _cnum_doc,
              desc="sum",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              accum_func_name="sum",
              examples=_cumsum_examples,
          )
          def cumsum(self, axis=None, skipna=True, *args, **kwargs):
              return NDFrame.cumsum(self, axis, skipna, *args, **kwargs)
  
          setattr(cls, "cumsum", cumsum)
  
          @doc(
              _cnum_doc,
              desc="product",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              accum_func_name="prod",
              examples=_cumprod_examples,
          )
          def cumprod(self, axis=None, skipna=True, *args, **kwargs):
              return NDFrame.cumprod(self, axis, skipna, *args, **kwargs)
  
          setattr(cls, "cumprod", cumprod)
  
          @doc(
              _num_doc,
              desc="Return the sum of the values over the requested axis.\n\n"
              "This is equivalent to the method ``numpy.sum``.",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              min_count=_min_count_stub,
              see_also=_stat_func_see_also,
              examples=_sum_examples,
          )
          def sum(
              self,
              axis=None,
              skipna=None,
              level=None,
              numeric_only=None,
              min_count=0,
              **kwargs,
          ):
              return NDFrame.sum(
                  self, axis, skipna, level, numeric_only, min_count, **kwargs
              )
  
          setattr(cls, "sum", sum)
  
          @doc(
              _num_doc,
              desc="Return the product of the values over the requested axis.",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              min_count=_min_count_stub,
              see_also=_stat_func_see_also,
              examples=_prod_examples,
          )
          def prod(
              self,
              axis=None,
              skipna=None,
              level=None,
              numeric_only=None,
              min_count=0,
              **kwargs,
          ):
              return NDFrame.prod(
                  self, axis, skipna, level, numeric_only, min_count, **kwargs
              )
  
          setattr(cls, "prod", prod)
          cls.product = prod
  
          @doc(
              _num_doc,
              desc="Return the mean of the values over the requested axis.",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              min_count="",
              see_also="",
              examples="",
          )
          def mean(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
              return NDFrame.mean(self, axis, skipna, level, numeric_only, **kwargs)
  
          setattr(cls, "mean", mean)
  
          @doc(
              _num_doc,
              desc="Return unbiased skew over requested axis.\n\nNormalized by N-1.",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              min_count="",
              see_also="",
              examples="",
          )
          def skew(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
              return NDFrame.skew(self, axis, skipna, level, numeric_only, **kwargs)
  
          setattr(cls, "skew", skew)
  
          @doc(
              _num_doc,
              desc="Return unbiased kurtosis over requested axis.\n\n"
              "Kurtosis obtained using Fisher's definition of\n"
              "kurtosis (kurtosis of normal == 0.0). Normalized "
              "by N-1.",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              min_count="",
              see_also="",
              examples="",
          )
          def kurt(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
              return NDFrame.kurt(self, axis, skipna, level, numeric_only, **kwargs)
  
          setattr(cls, "kurt", kurt)
          cls.kurtosis = kurt
  
          @doc(
              _num_doc,
              desc="Return the median of the values over the requested axis.",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              min_count="",
              see_also="",
              examples="",
          )
          def median(
              self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs
          ):
              return NDFrame.median(self, axis, skipna, level, numeric_only, **kwargs)
  
          setattr(cls, "median", median)
  
          @doc(
              _num_doc,
              desc="Return the maximum of the values over the requested axis.\n\n"
              "If you want the *index* of the maximum, use ``idxmax``. This is "
              "the equivalent of the ``numpy.ndarray`` method ``argmax``.",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              min_count="",
              see_also=_stat_func_see_also,
              examples=_max_examples,
          )
          def max(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
              return NDFrame.max(self, axis, skipna, level, numeric_only, **kwargs)
  
          setattr(cls, "max", max)
  
          @doc(
              _num_doc,
              desc="Return the minimum of the values over the requested axis.\n\n"
              "If you want the *index* of the minimum, use ``idxmin``. This is "
              "the equivalent of the ``numpy.ndarray`` method ``argmin``.",
              name1=name1,
              name2=name2,
              axis_descr=axis_descr,
              min_count="",
              see_also=_stat_func_see_also,
              examples=_min_examples,
          )
          def min(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
              return NDFrame.min(self, axis, skipna, level, numeric_only, **kwargs)
  
          setattr(cls, "min", min)
  
      @final
      @doc(Rolling)
      def rolling(
          self,
          window: int | timedelta | BaseOffset | BaseIndexer,
          min_periods: int | None = None,
          center: bool_t = False,
          win_type: str | None = None,
          on: str | None = None,
          axis: Axis = 0,
          closed: str | None = None,
          method: str = "single",
      ):
          axis = self._get_axis_number(axis)
  
          if win_type is not None:
              return Window(
                  self,
                  window=window,
                  min_periods=min_periods,
                  center=center,
                  win_type=win_type,
                  on=on,
                  axis=axis,
                  closed=closed,
                  method=method,
              )
  
          return Rolling(
              self,
              window=window,
              min_periods=min_periods,
              center=center,
              win_type=win_type,
              on=on,
              axis=axis,
              closed=closed,
              method=method,
          )
  
      @final
      @doc(Expanding)
      def expanding(
          self,
          min_periods: int = 1,
          center: bool_t | None = None,
          axis: Axis = 0,
          method: str = "single",
      ) -> Expanding:
          axis = self._get_axis_number(axis)
          if center is not None:
              warnings.warn(
                  "The `center` argument on `expanding` will be removed in the future",
                  FutureWarning,
                  stacklevel=2,
              )
          else:
              center = False
  
          return Expanding(
              self, min_periods=min_periods, center=center, axis=axis, method=method
          )
  
      @final
      @doc(ExponentialMovingWindow)
      def ewm(
          self,
          com: float | None = None,
          span: float | None = None,
          halflife: float | TimedeltaConvertibleTypes | None = None,
          alpha: float | None = None,
          min_periods: int | None = 0,
          adjust: bool_t = True,
          ignore_na: bool_t = False,
          axis: Axis = 0,
          times: str | np.ndarray | FrameOrSeries | None = None,
          method: str = "single",
      ) -> ExponentialMovingWindow:
          axis = self._get_axis_number(axis)
          # error: Value of type variable "FrameOrSeries" of "ExponentialMovingWindow"
          # cannot be "object"
          return ExponentialMovingWindow(  # type: ignore[type-var]
              self,
              com=com,
              span=span,
              halflife=halflife,
              alpha=alpha,
              min_periods=min_periods,
              adjust=adjust,
              ignore_na=ignore_na,
              axis=axis,
              times=times,
              method=method,
          )
  
      # ----------------------------------------------------------------------
      # Arithmetic Methods
  
      @final
      def _inplace_method(self, other, op):
          """
          Wrap arithmetic method to operate inplace.
          """
          result = op(self, other)
  
          if (
              self.ndim == 1
              and result._indexed_same(self)
              and is_dtype_equal(result.dtype, self.dtype)
          ):
              # GH#36498 this inplace op can _actually_ be inplace.
              self._values[:] = result._values
              return self
  
          # Delete cacher
          self._reset_cacher()
  
          # this makes sure that we are aligned like the input
          # we are updating inplace so we want to ignore is_copy
          self._update_inplace(
              result.reindex_like(self, copy=False), verify_is_copy=False
          )
          return self
  
      def __iadd__(self, other):
          # error: Unsupported left operand type for + ("Type[NDFrame]")
          return self._inplace_method(other, type(self).__add__)  # type: ignore[operator]
  
      def __isub__(self, other):
          # error: Unsupported left operand type for - ("Type[NDFrame]")
          return self._inplace_method(other, type(self).__sub__)  # type: ignore[operator]
  
      def __imul__(self, other):
          # error: Unsupported left operand type for * ("Type[NDFrame]")
          return self._inplace_method(other, type(self).__mul__)  # type: ignore[operator]
  
      def __itruediv__(self, other):
          # error: Unsupported left operand type for / ("Type[NDFrame]")
          return self._inplace_method(
              other, type(self).__truediv__  # type: ignore[operator]
          )
  
      def __ifloordiv__(self, other):
          # error: Unsupported left operand type for // ("Type[NDFrame]")
          return self._inplace_method(
              other, type(self).__floordiv__  # type: ignore[operator]
          )
  
      def __imod__(self, other):
          # error: Unsupported left operand type for % ("Type[NDFrame]")
          return self._inplace_method(other, type(self).__mod__)  # type: ignore[operator]
  
      def __ipow__(self, other):
          # error: Unsupported left operand type for ** ("Type[NDFrame]")
          return self._inplace_method(other, type(self).__pow__)  # type: ignore[operator]
  
      def __iand__(self, other):
          # error: Unsupported left operand type for & ("Type[NDFrame]")
          return self._inplace_method(other, type(self).__and__)  # type: ignore[operator]
  
      def __ior__(self, other):
          # error: Unsupported left operand type for | ("Type[NDFrame]")
          return self._inplace_method(other, type(self).__or__)  # type: ignore[operator]
  
      def __ixor__(self, other):
          # error: Unsupported left operand type for ^ ("Type[NDFrame]")
          return self._inplace_method(other, type(self).__xor__)  # type: ignore[operator]
  
      # ----------------------------------------------------------------------
      # Misc methods
  
      @final
      def _find_valid_index(self, *, how: str) -> Hashable | None:
          """
          Retrieves the index of the first valid value.
  
          Parameters
          ----------
          how : {'first', 'last'}
              Use this parameter to change between the first or last valid index.
  
          Returns
          -------
          idx_first_valid : type of index
          """
          idxpos = find_valid_index(self._values, how=how)
          if idxpos is None:
              return None
          return self.index[idxpos]
  
      @final
      @doc(position="first", klass=_shared_doc_kwargs["klass"])
      def first_valid_index(self) -> Hashable | None:
          """
          Return index for {position} non-NA value or None, if no NA value is found.
  
          Returns
          -------
          scalar : type of index
  
          Notes
          -----
          If all elements are non-NA/null, returns None.
          Also returns None for empty {klass}.
          """
          return self._find_valid_index(how="first")
  
      @final
      @doc(first_valid_index, position="last", klass=_shared_doc_kwargs["klass"])
      def last_valid_index(self) -> Hashable | None:
          return self._find_valid_index(how="last")
  
  
  def _doc_params(cls):
      """Return a tuple of the doc params."""
      axis_descr = (
          f"{{{', '.join([f'{a} ({i})' for i, a in enumerate(cls._AXIS_ORDERS)])}}}"
      )
      name = cls._constructor_sliced.__name__ if cls._AXIS_LEN > 1 else "scalar"
      name2 = cls.__name__
      return axis_descr, name, name2
  
  
  _num_doc = """
  {desc}
  
  Parameters
  ----------
  axis : {axis_descr}
      Axis for the function to be applied on.
  skipna : bool, default True
      Exclude NA/null values when computing the result.
  level : int or level name, default None
      If the axis is a MultiIndex (hierarchical), count along a
      particular level, collapsing into a {name1}.
  numeric_only : bool, default None
      Include only float, int, boolean columns. If None, will attempt to use
      everything, then use only numeric data. Not implemented for Series.
  {min_count}\
  **kwargs
      Additional keyword arguments to be passed to the function.
  
  Returns
  -------
  {name1} or {name2} (if level specified)\
  {see_also}\
  {examples}
  """
  
  _num_ddof_doc = """
  {desc}
  
  Parameters
  ----------
  axis : {axis_descr}
  skipna : bool, default True
      Exclude NA/null values. If an entire row/column is NA, the result
      will be NA.
  level : int or level name, default None
      If the axis is a MultiIndex (hierarchical), count along a
      particular level, collapsing into a {name1}.
  ddof : int, default 1
      Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
      where N represents the number of elements.
  numeric_only : bool, default None
      Include only float, int, boolean columns. If None, will attempt to use
      everything, then use only numeric data. Not implemented for Series.
  
  Returns
  -------
  {name1} or {name2} (if level specified) \
  {notes}
  """
  
  _std_notes = """
  
  Notes
  -----
  To have the same behaviour as `numpy.std`, use `ddof=0` (instead of the
  default `ddof=1`)"""
  
  _bool_doc = """
  {desc}
  
  Parameters
  ----------
  axis : {{0 or 'index', 1 or 'columns', None}}, default 0
      Indicate which axis or axes should be reduced.
  
      * 0 / 'index' : reduce the index, return a Series whose index is the
        original column labels.
      * 1 / 'columns' : reduce the columns, return a Series whose index is the
        original index.
      * None : reduce all axes, return a scalar.
  
  bool_only : bool, default None
      Include only boolean columns. If None, will attempt to use everything,
      then use only boolean data. Not implemented for Series.
  skipna : bool, default True
      Exclude NA/null values. If the entire row/column is NA and skipna is
      True, then the result will be {empty_value}, as for an empty row/column.
      If skipna is False, then NA are treated as True, because these are not
      equal to zero.
  level : int or level name, default None
      If the axis is a MultiIndex (hierarchical), count along a
      particular level, collapsing into a {name1}.
  **kwargs : any, default None
      Additional keywords have no effect but might be accepted for
      compatibility with NumPy.
  
  Returns
  -------
  {name1} or {name2}
      If level is specified, then, {name2} is returned; otherwise, {name1}
      is returned.
  
  {see_also}
  {examples}"""
  
  _all_desc = """\
  Return whether all elements are True, potentially over an axis.
  
  Returns True unless there at least one element within a series or
  along a Dataframe axis that is False or equivalent (e.g. zero or
  empty)."""
  
  _all_examples = """\
  Examples
  --------
  **Series**
  
  >>> pd.Series([True, True]).all()
  True
  >>> pd.Series([True, False]).all()
  False
  >>> pd.Series([], dtype="float64").all()
  True
  >>> pd.Series([np.nan]).all()
  True
  >>> pd.Series([np.nan]).all(skipna=False)
  True
  
  **DataFrames**
  
  Create a dataframe from a dictionary.
  
  >>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
  >>> df
     col1   col2
  0  True   True
  1  True  False
  
  Default behaviour checks if column-wise values all return True.
  
  >>> df.all()
  col1     True
  col2    False
  dtype: bool
  
  Specify ``axis='columns'`` to check if row-wise values all return True.
  
  >>> df.all(axis='columns')
  0     True
  1    False
  dtype: bool
  
  Or ``axis=None`` for whether every value is True.
  
  >>> df.all(axis=None)
  False
  """
  
  _all_see_also = """\
  See Also
  --------
  Series.all : Return True if all elements are True.
  DataFrame.any : Return True if one (or more) elements are True.
  """
  
  _cnum_doc = """
  Return cumulative {desc} over a DataFrame or Series axis.
  
  Returns a DataFrame or Series of the same size containing the cumulative
  {desc}.
  
  Parameters
  ----------
  axis : {{0 or 'index', 1 or 'columns'}}, default 0
      The index or the name of the axis. 0 is equivalent to None or 'index'.
  skipna : bool, default True
      Exclude NA/null values. If an entire row/column is NA, the result
      will be NA.
  *args, **kwargs
      Additional keywords have no effect but might be accepted for
      compatibility with NumPy.
  
  Returns
  -------
  {name1} or {name2}
      Return cumulative {desc} of {name1} or {name2}.
  
  See Also
  --------
  core.window.Expanding.{accum_func_name} : Similar functionality
      but ignores ``NaN`` values.
  {name2}.{accum_func_name} : Return the {desc} over
      {name2} axis.
  {name2}.cummax : Return cumulative maximum over {name2} axis.
  {name2}.cummin : Return cumulative minimum over {name2} axis.
  {name2}.cumsum : Return cumulative sum over {name2} axis.
  {name2}.cumprod : Return cumulative product over {name2} axis.
  
  {examples}"""
  
  _cummin_examples = """\
  Examples
  --------
  **Series**
  
  >>> s = pd.Series([2, np.nan, 5, -1, 0])
  >>> s
  0    2.0
  1    NaN
  2    5.0
  3   -1.0
  4    0.0
  dtype: float64
  
  By default, NA values are ignored.
  
  >>> s.cummin()
  0    2.0
  1    NaN
  2    2.0
  3   -1.0
  4   -1.0
  dtype: float64
  
  To include NA values in the operation, use ``skipna=False``
  
  >>> s.cummin(skipna=False)
  0    2.0
  1    NaN
  2    NaN
  3    NaN
  4    NaN
  dtype: float64
  
  **DataFrame**
  
  >>> df = pd.DataFrame([[2.0, 1.0],
  ...                    [3.0, np.nan],
  ...                    [1.0, 0.0]],
  ...                    columns=list('AB'))
  >>> df
       A    B
  0  2.0  1.0
  1  3.0  NaN
  2  1.0  0.0
  
  By default, iterates over rows and finds the minimum
  in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
  
  >>> df.cummin()
       A    B
  0  2.0  1.0
  1  2.0  NaN
  2  1.0  0.0
  
  To iterate over columns and find the minimum in each row,
  use ``axis=1``
  
  >>> df.cummin(axis=1)
       A    B
  0  2.0  1.0
  1  3.0  NaN
  2  1.0  0.0
  """
  
  _cumsum_examples = """\
  Examples
  --------
  **Series**
  
  >>> s = pd.Series([2, np.nan, 5, -1, 0])
  >>> s
  0    2.0
  1    NaN
  2    5.0
  3   -1.0
  4    0.0
  dtype: float64
  
  By default, NA values are ignored.
  
  >>> s.cumsum()
  0    2.0
  1    NaN
  2    7.0
  3    6.0
  4    6.0
  dtype: float64
  
  To include NA values in the operation, use ``skipna=False``
  
  >>> s.cumsum(skipna=False)
  0    2.0
  1    NaN
  2    NaN
  3    NaN
  4    NaN
  dtype: float64
  
  **DataFrame**
  
  >>> df = pd.DataFrame([[2.0, 1.0],
  ...                    [3.0, np.nan],
  ...                    [1.0, 0.0]],
  ...                    columns=list('AB'))
  >>> df
       A    B
  0  2.0  1.0
  1  3.0  NaN
  2  1.0  0.0
  
  By default, iterates over rows and finds the sum
  in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
  
  >>> df.cumsum()
       A    B
  0  2.0  1.0
  1  5.0  NaN
  2  6.0  1.0
  
  To iterate over columns and find the sum in each row,
  use ``axis=1``
  
  >>> df.cumsum(axis=1)
       A    B
  0  2.0  3.0
  1  3.0  NaN
  2  1.0  1.0
  """
  
  _cumprod_examples = """\
  Examples
  --------
  **Series**
  
  >>> s = pd.Series([2, np.nan, 5, -1, 0])
  >>> s
  0    2.0
  1    NaN
  2    5.0
  3   -1.0
  4    0.0
  dtype: float64
  
  By default, NA values are ignored.
  
  >>> s.cumprod()
  0     2.0
  1     NaN
  2    10.0
  3   -10.0
  4    -0.0
  dtype: float64
  
  To include NA values in the operation, use ``skipna=False``
  
  >>> s.cumprod(skipna=False)
  0    2.0
  1    NaN
  2    NaN
  3    NaN
  4    NaN
  dtype: float64
  
  **DataFrame**
  
  >>> df = pd.DataFrame([[2.0, 1.0],
  ...                    [3.0, np.nan],
  ...                    [1.0, 0.0]],
  ...                    columns=list('AB'))
  >>> df
       A    B
  0  2.0  1.0
  1  3.0  NaN
  2  1.0  0.0
  
  By default, iterates over rows and finds the product
  in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
  
  >>> df.cumprod()
       A    B
  0  2.0  1.0
  1  6.0  NaN
  2  6.0  0.0
  
  To iterate over columns and find the product in each row,
  use ``axis=1``
  
  >>> df.cumprod(axis=1)
       A    B
  0  2.0  2.0
  1  3.0  NaN
  2  1.0  0.0
  """
  
  _cummax_examples = """\
  Examples
  --------
  **Series**
  
  >>> s = pd.Series([2, np.nan, 5, -1, 0])
  >>> s
  0    2.0
  1    NaN
  2    5.0
  3   -1.0
  4    0.0
  dtype: float64
  
  By default, NA values are ignored.
  
  >>> s.cummax()
  0    2.0
  1    NaN
  2    5.0
  3    5.0
  4    5.0
  dtype: float64
  
  To include NA values in the operation, use ``skipna=False``
  
  >>> s.cummax(skipna=False)
  0    2.0
  1    NaN
  2    NaN
  3    NaN
  4    NaN
  dtype: float64
  
  **DataFrame**
  
  >>> df = pd.DataFrame([[2.0, 1.0],
  ...                    [3.0, np.nan],
  ...                    [1.0, 0.0]],
  ...                    columns=list('AB'))
  >>> df
       A    B
  0  2.0  1.0
  1  3.0  NaN
  2  1.0  0.0
  
  By default, iterates over rows and finds the maximum
  in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
  
  >>> df.cummax()
       A    B
  0  2.0  1.0
  1  3.0  NaN
  2  3.0  1.0
  
  To iterate over columns and find the maximum in each row,
  use ``axis=1``
  
  >>> df.cummax(axis=1)
       A    B
  0  2.0  2.0
  1  3.0  NaN
  2  1.0  1.0
  """
  
  _any_see_also = """\
  See Also
  --------
  numpy.any : Numpy version of this method.
  Series.any : Return whether any element is True.
  Series.all : Return whether all elements are True.
  DataFrame.any : Return whether any element is True over requested axis.
  DataFrame.all : Return whether all elements are True over requested axis.
  """
  
  _any_desc = """\
  Return whether any element is True, potentially over an axis.
  
  Returns False unless there is at least one element within a series or
  along a Dataframe axis that is True or equivalent (e.g. non-zero or
  non-empty)."""
  
  _any_examples = """\
  Examples
  --------
  **Series**
  
  For Series input, the output is a scalar indicating whether any element
  is True.
  
  >>> pd.Series([False, False]).any()
  False
  >>> pd.Series([True, False]).any()
  True
  >>> pd.Series([], dtype="float64").any()
  False
  >>> pd.Series([np.nan]).any()
  False
  >>> pd.Series([np.nan]).any(skipna=False)
  True
  
  **DataFrame**
  
  Whether each column contains at least one True element (the default).
  
  >>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
  >>> df
     A  B  C
  0  1  0  0
  1  2  2  0
  
  >>> df.any()
  A     True
  B     True
  C    False
  dtype: bool
  
  Aggregating over the columns.
  
  >>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
  >>> df
         A  B
  0   True  1
  1  False  2
  
  >>> df.any(axis='columns')
  0    True
  1    True
  dtype: bool
  
  >>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
  >>> df
         A  B
  0   True  1
  1  False  0
  
  >>> df.any(axis='columns')
  0    True
  1    False
  dtype: bool
  
  Aggregating over the entire DataFrame with ``axis=None``.
  
  >>> df.any(axis=None)
  True
  
  `any` for an empty DataFrame is an empty Series.
  
  >>> pd.DataFrame([]).any()
  Series([], dtype: bool)
  """
  
  _shared_docs[
      "stat_func_example"
  ] = """
  
  Examples
  --------
  >>> idx = pd.MultiIndex.from_arrays([
  ...     ['warm', 'warm', 'cold', 'cold'],
  ...     ['dog', 'falcon', 'fish', 'spider']],
  ...     names=['blooded', 'animal'])
  >>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
  >>> s
  blooded  animal
  warm     dog       4
           falcon    2
  cold     fish      0
           spider    8
  Name: legs, dtype: int64
  
  >>> s.{stat_func}()
  {default_output}"""
  
  _sum_examples = _shared_docs["stat_func_example"].format(
      stat_func="sum", verb="Sum", default_output=14, level_output_0=6, level_output_1=8
  )
  
  _sum_examples += """
  
  By default, the sum of an empty or all-NA Series is ``0``.
  
  >>> pd.Series([], dtype="float64").sum()  # min_count=0 is the default
  0.0
  
  This can be controlled with the ``min_count`` parameter. For example, if
  you'd like the sum of an empty series to be NaN, pass ``min_count=1``.
  
  >>> pd.Series([], dtype="float64").sum(min_count=1)
  nan
  
  Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
  empty series identically.
  
  >>> pd.Series([np.nan]).sum()
  0.0
  
  >>> pd.Series([np.nan]).sum(min_count=1)
  nan"""
  
  _max_examples = _shared_docs["stat_func_example"].format(
      stat_func="max", verb="Max", default_output=8, level_output_0=4, level_output_1=8
  )
  
  _min_examples = _shared_docs["stat_func_example"].format(
      stat_func="min", verb="Min", default_output=0, level_output_0=2, level_output_1=0
  )
  
  _stat_func_see_also = """
  
  See Also
  --------
  Series.sum : Return the sum.
  Series.min : Return the minimum.
  Series.max : Return the maximum.
  Series.idxmin : Return the index of the minimum.
  Series.idxmax : Return the index of the maximum.
  DataFrame.sum : Return the sum over the requested axis.
  DataFrame.min : Return the minimum over the requested axis.
  DataFrame.max : Return the maximum over the requested axis.
  DataFrame.idxmin : Return the index of the minimum over the requested axis.
  DataFrame.idxmax : Return the index of the maximum over the requested axis."""
  
  _prod_examples = """
  
  Examples
  --------
  By default, the product of an empty or all-NA Series is ``1``
  
  >>> pd.Series([], dtype="float64").prod()
  1.0
  
  This can be controlled with the ``min_count`` parameter
  
  >>> pd.Series([], dtype="float64").prod(min_count=1)
  nan
  
  Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
  empty series identically.
  
  >>> pd.Series([np.nan]).prod()
  1.0
  
  >>> pd.Series([np.nan]).prod(min_count=1)
  nan"""
  
  _min_count_stub = """\
  min_count : int, default 0
      The required number of valid values to perform the operation. If fewer than
      ``min_count`` non-NA values are present the result will be NA.
  """
  
  
  def _align_as_utc(
      left: FrameOrSeries, right: FrameOrSeries, join_index: Index | None
  ) -> tuple[FrameOrSeries, FrameOrSeries]:
      """
      If we are aligning timezone-aware DatetimeIndexes and the timezones
      do not match, convert both to UTC.
      """
      if is_datetime64tz_dtype(left.index.dtype):
          if left.index.tz != right.index.tz:
              if join_index is not None:
                  # GH#33671 ensure we don't change the index on
                  #  our original Series (NB: by default deep=False)
                  left = left.copy()
                  right = right.copy()
                  left.index = join_index
                  right.index = join_index
  
      return left, right
  