code: """
  Data structure for 1-dimensional cross-sectional and time series data
  """
  from __future__ import annotations
  
  from io import StringIO
  from shutil import get_terminal_size
  from textwrap import dedent
  from typing import (
      IO,
      TYPE_CHECKING,
      Any,
      Callable,
      Hashable,
      Iterable,
      List,
      Optional,
      Tuple,
      Type,
      Union,
  )
  import warnings
  
  import numpy as np
  
  from pandas._config import get_option
  
  from pandas._libs import lib, properties, reshape, tslibs
  from pandas._libs.lib import no_default
  from pandas._typing import (
      AggFuncType,
      ArrayLike,
      Axis,
      Dtype,
      DtypeObj,
      FrameOrSeriesUnion,
      IndexKeyFunc,
      NpDtype,
      StorageOptions,
      ValueKeyFunc,
  )
  from pandas.compat.numpy import function as nv
  from pandas.errors import InvalidIndexError
  from pandas.util._decorators import Appender, Substitution, doc
  from pandas.util._validators import validate_bool_kwarg, validate_percentile
  
  from pandas.core.dtypes.cast import (
      convert_dtypes,
      maybe_cast_to_extension_array,
      validate_numeric_casting,
  )
  from pandas.core.dtypes.common import (
      ensure_platform_int,
      is_bool,
      is_categorical_dtype,
      is_dict_like,
      is_extension_array_dtype,
      is_integer,
      is_iterator,
      is_list_like,
      is_object_dtype,
      is_scalar,
      validate_all_hashable,
  )
  from pandas.core.dtypes.generic import ABCDataFrame
  from pandas.core.dtypes.inference import is_hashable
  from pandas.core.dtypes.missing import (
      isna,
      na_value_for_dtype,
      notna,
      remove_na_arraylike,
  )
  
  from pandas.core import algorithms, base, generic, missing, nanops, ops
  from pandas.core.accessor import CachedAccessor
  from pandas.core.aggregation import transform
  from pandas.core.apply import series_apply
  from pandas.core.arrays import ExtensionArray
  from pandas.core.arrays.categorical import CategoricalAccessor
  from pandas.core.arrays.sparse import SparseAccessor
  import pandas.core.common as com
  from pandas.core.construction import (
      create_series_with_explicit_dtype,
      extract_array,
      is_empty_data,
      sanitize_array,
  )
  from pandas.core.generic import NDFrame
  from pandas.core.indexers import deprecate_ndim_indexing, unpack_1tuple
  from pandas.core.indexes.accessors import CombinedDatetimelikeProperties
  from pandas.core.indexes.api import (
      CategoricalIndex,
      Float64Index,
      Index,
      MultiIndex,
      ensure_index,
  )
  import pandas.core.indexes.base as ibase
  from pandas.core.indexes.datetimes import DatetimeIndex
  from pandas.core.indexes.period import PeriodIndex
  from pandas.core.indexes.timedeltas import TimedeltaIndex
  from pandas.core.indexing import check_bool_indexer
  from pandas.core.internals import SingleBlockManager
  from pandas.core.internals.construction import sanitize_index
  from pandas.core.shared_docs import _shared_docs
  from pandas.core.sorting import ensure_key_mapped, nargsort
  from pandas.core.strings import StringMethods
  from pandas.core.tools.datetimes import to_datetime
  
  import pandas.io.formats.format as fmt
  import pandas.plotting
  
  if TYPE_CHECKING:
      from pandas._typing import TimedeltaConvertibleTypes, TimestampConvertibleTypes
  
      from pandas.core.frame import DataFrame
      from pandas.core.groupby.generic import SeriesGroupBy
      from pandas.core.resample import Resampler
  
  __all__ = ["Series"]
  
  _shared_doc_kwargs = {
      "axes": "index",
      "klass": "Series",
      "axes_single_arg": "{0 or 'index'}",
      "axis": """axis : {0 or 'index'}
          Parameter needed for compatibility with DataFrame.""",
      "inplace": """inplace : boolean, default False
          If True, performs operation inplace and returns None.""",
      "unique": "np.ndarray",
      "duplicated": "Series",
      "optional_by": "",
      "optional_mapper": "",
      "optional_labels": "",
      "optional_axis": "",
      "replace_iloc": """
      This differs from updating with ``.loc`` or ``.iloc``, which require
      you to specify a location to update with some value.""",
  }
  
  
  def _coerce_method(converter):
      """
      Install the scalar coercion methods.
      """
  
      def wrapper(self):
          if len(self) == 1:
              return converter(self.iloc[0])
          raise TypeError(f"cannot convert the series to {converter}")
  
      wrapper.__name__ = f"__{converter.__name__}__"
      return wrapper
  
  
  # ----------------------------------------------------------------------
  # Series class
  
  
  class Series(base.IndexOpsMixin, generic.NDFrame):
      """
      One-dimensional ndarray with axis labels (including time series).
  
      Labels need not be unique but must be a hashable type. The object
      supports both integer- and label-based indexing and provides a host of
      methods for performing operations involving the index. Statistical
      methods from ndarray have been overridden to automatically exclude
      missing data (currently represented as NaN).
  
      Operations between Series (+, -, /, *, **) align values based on their
      associated index values-- they need not be the same length. The result
      index will be the sorted union of the two indexes.
  
      Parameters
      ----------
      data : array-like, Iterable, dict, or scalar value
          Contains data stored in Series. If data is a dict, argument order is
          maintained.
      index : array-like or Index (1d)
          Values must be hashable and have the same length as `data`.
          Non-unique index values are allowed. Will default to
          RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like
          and index is None, then the values in the index are used to
          reindex the Series after it is created using the keys in the data.
      dtype : str, numpy.dtype, or ExtensionDtype, optional
          Data type for the output Series. If not specified, this will be
          inferred from `data`.
          See the :ref:`user guide <basics.dtypes>` for more usages.
      name : str, optional
          The name to give to the Series.
      copy : bool, default False
          Copy input data.
      """
  
      _typ = "series"
      _HANDLED_TYPES = (Index, ExtensionArray, np.ndarray)
  
      _name: Hashable
      _metadata: List[str] = ["name"]
      _internal_names_set = {"index"} | generic.NDFrame._internal_names_set
      _accessors = {"dt", "cat", "str", "sparse"}
      _hidden_attrs = (
          base.IndexOpsMixin._hidden_attrs
          | generic.NDFrame._hidden_attrs
          | frozenset(["compress", "ptp"])
      )
  
      # Override cache_readonly bc Series is mutable
      hasnans = property(
          base.IndexOpsMixin.hasnans.func, doc=base.IndexOpsMixin.hasnans.__doc__
      )
      __hash__ = generic.NDFrame.__hash__
      _mgr: SingleBlockManager
      div: Callable[[Series, Any], Series]
      rdiv: Callable[[Series, Any], Series]
  
      # ----------------------------------------------------------------------
      # Constructors
  
      def __init__(
          self,
          data=None,
          index=None,
          dtype: Optional[Dtype] = None,
          name=None,
          copy: bool = False,
          fastpath: bool = False,
      ):
  
          if (
              isinstance(data, SingleBlockManager)
              and index is None
              and dtype is None
              and copy is False
          ):
              # GH#33357 called with just the SingleBlockManager
              NDFrame.__init__(self, data)
              self.name = name
              return
  
          # we are called internally, so short-circuit
          if fastpath:
  
              # data is an ndarray, index is defined
              if not isinstance(data, SingleBlockManager):
                  data = SingleBlockManager.from_array(data, index)
              if copy:
                  data = data.copy()
              if index is None:
                  index = data.index
  
          else:
  
              name = ibase.maybe_extract_name(name, data, type(self))
  
              if is_empty_data(data) and dtype is None:
                  # gh-17261
                  warnings.warn(
                      "The default dtype for empty Series will be 'object' instead "
                      "of 'float64' in a future version. Specify a dtype explicitly "
                      "to silence this warning.",
                      DeprecationWarning,
                      stacklevel=2,
                  )
                  # uncomment the line below when removing the DeprecationWarning
                  # dtype = np.dtype(object)
  
              if index is not None:
                  index = ensure_index(index)
  
              if data is None:
                  data = {}
              if dtype is not None:
                  dtype = self._validate_dtype(dtype)
  
              if isinstance(data, MultiIndex):
                  raise NotImplementedError(
                      "initializing a Series from a MultiIndex is not supported"
                  )
              elif isinstance(data, Index):
  
                  if dtype is not None:
                      # astype copies
                      data = data.astype(dtype)
                  else:
                      # GH#24096 we need to ensure the index remains immutable
                      data = data._values.copy()
                  copy = False
  
              elif isinstance(data, np.ndarray):
                  if len(data.dtype):
                      # GH#13296 we are dealing with a compound dtype, which
                      #  should be treated as 2D
                      raise ValueError(
                          "Cannot construct a Series from an ndarray with "
                          "compound dtype.  Use DataFrame instead."
                      )
              elif isinstance(data, Series):
                  if index is None:
                      index = data.index
                  else:
                      data = data.reindex(index, copy=copy)
                      copy = False
                  data = data._mgr
              elif is_dict_like(data):
                  data, index = self._init_dict(data, index, dtype)
                  dtype = None
                  copy = False
              elif isinstance(data, SingleBlockManager):
                  if index is None:
                      index = data.index
                  elif not data.index.equals(index) or copy:
                      # GH#19275 SingleBlockManager input should only be called
                      # internally
                      raise AssertionError(
                          "Cannot pass both SingleBlockManager "
                          "`data` argument and a different "
                          "`index` argument. `copy` must be False."
                      )
  
              elif is_extension_array_dtype(data):
                  pass
              elif isinstance(data, (set, frozenset)):
                  raise TypeError(f"'{type(data).__name__}' type is unordered")
              else:
                  data = com.maybe_iterable_to_list(data)
  
              if index is None:
                  if not is_list_like(data):
                      data = [data]
                  index = ibase.default_index(len(data))
              elif is_list_like(data):
                  sanitize_index(data, index)
  
              # create/copy the manager
              if isinstance(data, SingleBlockManager):
                  if dtype is not None:
                      data = data.astype(dtype=dtype, errors="ignore", copy=copy)
                  elif copy:
                      data = data.copy()
              else:
                  data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
  
                  data = SingleBlockManager.from_array(data, index)
  
          generic.NDFrame.__init__(self, data)
          self.name = name
          self._set_axis(0, index, fastpath=True)
  
      def _init_dict(self, data, index=None, dtype: Optional[Dtype] = None):
          """
          Derive the "_mgr" and "index" attributes of a new Series from a
          dictionary input.
  
          Parameters
          ----------
          data : dict or dict-like
              Data used to populate the new Series.
          index : Index or index-like, default None
              Index for the new Series: if None, use dict keys.
          dtype : dtype, default None
              The dtype for the new Series: if None, infer from data.
  
          Returns
          -------
          _data : BlockManager for the new Series
          index : index for the new Series
          """
          # Looking for NaN in dict doesn't work ({np.nan : 1}[float('nan')]
          # raises KeyError), so we iterate the entire dict, and align
          if data:
              # GH:34717, issue was using zip to extract key and values from data.
              # using generators in effects the performance.
              # Below is the new way of extracting the keys and values
  
              keys = tuple(data.keys())
              values = list(data.values())  # Generating list of values- faster way
          elif index is not None:
              # fastpath for Series(data=None). Just use broadcasting a scalar
              # instead of reindexing.
              values = na_value_for_dtype(dtype)
              keys = index
          else:
              keys, values = (), []
  
          # Input is now list-like, so rely on "standard" construction:
  
          # TODO: passing np.float64 to not break anything yet. See GH-17261
          s = create_series_with_explicit_dtype(
              values, index=keys, dtype=dtype, dtype_if_empty=np.float64
          )
  
          # Now we just make sure the order is respected, if any
          if data and index is not None:
              s = s.reindex(index, copy=False)
          return s._mgr, s.index
  
      # ----------------------------------------------------------------------
  
      @property
      def _constructor(self) -> Type[Series]:
          return Series
  
      @property
      def _constructor_expanddim(self) -> Type[DataFrame]:
          from pandas.core.frame import DataFrame
  
          return DataFrame
  
      # types
      @property
      def _can_hold_na(self) -> bool:
          return self._mgr._can_hold_na
  
      _index = None
  
      def _set_axis(self, axis: int, labels, fastpath: bool = False) -> None:
          """
          Override generic, we want to set the _typ here.
  
          This is called from the cython code when we set the `index` attribute
          directly, e.g. `series.index = [1, 2, 3]`.
          """
          if not fastpath:
              labels = ensure_index(labels)
  
          if labels._is_all_dates:
              deep_labels = labels
              if isinstance(labels, CategoricalIndex):
                  deep_labels = labels.categories
  
              if not isinstance(
                  deep_labels, (DatetimeIndex, PeriodIndex, TimedeltaIndex)
              ):
                  try:
                      labels = DatetimeIndex(labels)
                      # need to set here because we changed the index
                      if fastpath:
                          self._mgr.set_axis(axis, labels)
                  except (tslibs.OutOfBoundsDatetime, ValueError):
                      # labels may exceeds datetime bounds,
                      # or not be a DatetimeIndex
                      pass
  
          object.__setattr__(self, "_index", labels)
          if not fastpath:
              # The ensure_index call above ensures we have an Index object
              self._mgr.set_axis(axis, labels)
  
      # ndarray compatibility
      @property
      def dtype(self) -> DtypeObj:
          """
          Return the dtype object of the underlying data.
          """
          return self._mgr.dtype
  
      @property
      def dtypes(self) -> DtypeObj:
          """
          Return the dtype object of the underlying data.
          """
          # DataFrame compatibility
          return self.dtype
  
      @property
      def name(self) -> Hashable:
          """
          Return the name of the Series.
  
          The name of a Series becomes its index or column name if it is used
          to form a DataFrame. It is also used whenever displaying the Series
          using the interpreter.
  
          Returns
          -------
          label (hashable object)
              The name of the Series, also the column name if part of a DataFrame.
  
          See Also
          --------
          Series.rename : Sets the Series name when given a scalar input.
          Index.name : Corresponding Index property.
  
          Examples
          --------
          The Series name can be set initially when calling the constructor.
  
          >>> s = pd.Series([1, 2, 3], dtype=np.int64, name='Numbers')
          >>> s
          0    1
          1    2
          2    3
          Name: Numbers, dtype: int64
          >>> s.name = "Integers"
          >>> s
          0    1
          1    2
          2    3
          Name: Integers, dtype: int64
  
          The name of a Series within a DataFrame is its column name.
  
          >>> df = pd.DataFrame([[1, 2], [3, 4], [5, 6]],
          ...                   columns=["Odd Numbers", "Even Numbers"])
          >>> df
             Odd Numbers  Even Numbers
          0            1             2
          1            3             4
          2            5             6
          >>> df["Even Numbers"].name
          'Even Numbers'
          """
          return self._name
  
      @name.setter
      def name(self, value: Hashable) -> None:
          validate_all_hashable(value, error_name=f"{type(self).__name__}.name")
          object.__setattr__(self, "_name", value)
  
      @property
      def values(self):
          """
          Return Series as ndarray or ndarray-like depending on the dtype.
  
          .. warning::
  
             We recommend using :attr:`Series.array` or
             :meth:`Series.to_numpy`, depending on whether you need
             a reference to the underlying data or a NumPy array.
  
          Returns
          -------
          numpy.ndarray or ndarray-like
  
          See Also
          --------
          Series.array : Reference to the underlying data.
          Series.to_numpy : A NumPy array representing the underlying data.
  
          Examples
          --------
          >>> pd.Series([1, 2, 3]).values
          array([1, 2, 3])
  
          >>> pd.Series(list('aabc')).values
          array(['a', 'a', 'b', 'c'], dtype=object)
  
          >>> pd.Series(list('aabc')).astype('category').values
          ['a', 'a', 'b', 'c']
          Categories (3, object): ['a', 'b', 'c']
  
          Timezone aware datetime data is converted to UTC:
  
          >>> pd.Series(pd.date_range('20130101', periods=3,
          ...                         tz='US/Eastern')).values
          array(['2013-01-01T05:00:00.000000000',
                 '2013-01-02T05:00:00.000000000',
                 '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')
          """
          return self._mgr.external_values()
  
      @property
      def _values(self):
          """
          Return the internal repr of this data (defined by Block.interval_values).
          This are the values as stored in the Block (ndarray or ExtensionArray
          depending on the Block class), with datetime64[ns] and timedelta64[ns]
          wrapped in ExtensionArrays to match Index._values behavior.
  
          Differs from the public ``.values`` for certain data types, because of
          historical backwards compatibility of the public attribute (e.g. period
          returns object ndarray and datetimetz a datetime64[ns] ndarray for
          ``.values`` while it returns an ExtensionArray for ``._values`` in those
          cases).
  
          Differs from ``.array`` in that this still returns the numpy array if
          the Block is backed by a numpy array (except for datetime64 and
          timedelta64 dtypes), while ``.array`` ensures to always return an
          ExtensionArray.
  
          Overview:
  
          dtype       | values        | _values       | array         |
          ----------- | ------------- | ------------- | ------------- |
          Numeric     | ndarray       | ndarray       | PandasArray   |
          Category    | Categorical   | Categorical   | Categorical   |
          dt64[ns]    | ndarray[M8ns] | DatetimeArray | DatetimeArray |
          dt64[ns tz] | ndarray[M8ns] | DatetimeArray | DatetimeArray |
          td64[ns]    | ndarray[m8ns] | TimedeltaArray| ndarray[m8ns] |
          Period      | ndarray[obj]  | PeriodArray   | PeriodArray   |
          Nullable    | EA            | EA            | EA            |
  
          """
          return self._mgr.internal_values()
  
      # error: Decorated property not supported
      @Appender(base.IndexOpsMixin.array.__doc__)  # type: ignore[misc]
      @property
      def array(self) -> ExtensionArray:
          return self._mgr._block.array_values()
  
      # ops
      def ravel(self, order="C"):
          """
          Return the flattened underlying data as an ndarray.
  
          Returns
          -------
          numpy.ndarray or ndarray-like
              Flattened data of the Series.
  
          See Also
          --------
          numpy.ndarray.ravel : Return a flattened array.
          """
          return self._values.ravel(order=order)
  
      def __len__(self) -> int:
          """
          Return the length of the Series.
          """
          return len(self._mgr)
  
      def view(self, dtype: Optional[Dtype] = None) -> Series:
          """
          Create a new view of the Series.
  
          This function will return a new Series with a view of the same
          underlying values in memory, optionally reinterpreted with a new data
          type. The new data type must preserve the same size in bytes as to not
          cause index misalignment.
  
          Parameters
          ----------
          dtype : data type
              Data type object or one of their string representations.
  
          Returns
          -------
          Series
              A new Series object as a view of the same data in memory.
  
          See Also
          --------
          numpy.ndarray.view : Equivalent numpy function to create a new view of
              the same data in memory.
  
          Notes
          -----
          Series are instantiated with ``dtype=float64`` by default. While
          ``numpy.ndarray.view()`` will return a view with the same data type as
          the original array, ``Series.view()`` (without specified dtype)
          will try using ``float64`` and may fail if the original data type size
          in bytes is not the same.
  
          Examples
          --------
          >>> s = pd.Series([-2, -1, 0, 1, 2], dtype='int8')
          >>> s
          0   -2
          1   -1
          2    0
          3    1
          4    2
          dtype: int8
  
          The 8 bit signed integer representation of `-1` is `0b11111111`, but
          the same bytes represent 255 if read as an 8 bit unsigned integer:
  
          >>> us = s.view('uint8')
          >>> us
          0    254
          1    255
          2      0
          3      1
          4      2
          dtype: uint8
  
          The views share the same underlying values:
  
          >>> us[0] = 128
          >>> s
          0   -128
          1     -1
          2      0
          3      1
          4      2
          dtype: int8
          """
          return self._constructor(
              self._values.view(dtype), index=self.index
          ).__finalize__(self, method="view")
  
      # ----------------------------------------------------------------------
      # NDArray Compat
      _HANDLED_TYPES = (Index, ExtensionArray, np.ndarray)
  
      def __array__(self, dtype: Optional[NpDtype] = None) -> np.ndarray:
          """
          Return the values as a NumPy array.
  
          Users should not call this directly. Rather, it is invoked by
          :func:`numpy.array` and :func:`numpy.asarray`.
  
          Parameters
          ----------
          dtype : str or numpy.dtype, optional
              The dtype to use for the resulting NumPy array. By default,
              the dtype is inferred from the data.
  
          Returns
          -------
          numpy.ndarray
              The values in the series converted to a :class:`numpy.ndarray`
              with the specified `dtype`.
  
          See Also
          --------
          array : Create a new array from data.
          Series.array : Zero-copy view to the array backing the Series.
          Series.to_numpy : Series method for similar behavior.
  
          Examples
          --------
          >>> ser = pd.Series([1, 2, 3])
          >>> np.asarray(ser)
          array([1, 2, 3])
  
          For timezone-aware data, the timezones may be retained with
          ``dtype='object'``
  
          >>> tzser = pd.Series(pd.date_range('2000', periods=2, tz="CET"))
          >>> np.asarray(tzser, dtype="object")
          array([Timestamp('2000-01-01 00:00:00+0100', tz='CET', freq='D'),
                 Timestamp('2000-01-02 00:00:00+0100', tz='CET', freq='D')],
                dtype=object)
  
          Or the values may be localized to UTC and the tzinfo discarded with
          ``dtype='datetime64[ns]'``
  
          >>> np.asarray(tzser, dtype="datetime64[ns]")  # doctest: +ELLIPSIS
          array(['1999-12-31T23:00:00.000000000', ...],
                dtype='datetime64[ns]')
          """
          return np.asarray(self.array, dtype)
  
      # ----------------------------------------------------------------------
      # Unary Methods
  
      # coercion
      __float__ = _coerce_method(float)
      __long__ = _coerce_method(int)
      __int__ = _coerce_method(int)
  
      # ----------------------------------------------------------------------
  
      # indexers
      @property
      def axes(self) -> List[Index]:
          """
          Return a list of the row axis labels.
          """
          return [self.index]
  
      # ----------------------------------------------------------------------
      # Indexing Methods
  
      @Appender(generic.NDFrame.take.__doc__)
      def take(self, indices, axis=0, is_copy=None, **kwargs) -> Series:
          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)
  
          indices = ensure_platform_int(indices)
          new_index = self.index.take(indices)
          new_values = self._values.take(indices)
  
          result = self._constructor(new_values, index=new_index, fastpath=True)
          return result.__finalize__(self, method="take")
  
      def _take_with_is_copy(self, indices, axis=0):
          """
          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). For Series this does the same
          as the public take (it never sets `_is_copy`).
  
          See the docstring of `take` for full explanation of the parameters.
          """
          return self.take(indices=indices, axis=axis)
  
      def _ixs(self, i: int, axis: int = 0):
          """
          Return the i-th value or values in the Series by location.
  
          Parameters
          ----------
          i : int
  
          Returns
          -------
          scalar (int) or Series (slice, sequence)
          """
          return self._values[i]
  
      def _slice(self, slobj: slice, axis: int = 0) -> Series:
          # axis kwarg is retained for compat with NDFrame method
          #  _slice is *always* positional
          return self._get_values(slobj)
  
      def __getitem__(self, key):
          key = com.apply_if_callable(key, self)
  
          if key is Ellipsis:
              return self
  
          key_is_scalar = is_scalar(key)
          if isinstance(key, (list, tuple)):
              key = unpack_1tuple(key)
  
          if is_integer(key) and self.index._should_fallback_to_positional():
              return self._values[key]
  
          elif key_is_scalar:
              return self._get_value(key)
  
          if is_hashable(key):
              # Otherwise index.get_value will raise InvalidIndexError
              try:
                  # For labels that don't resolve as scalars like tuples and frozensets
                  result = self._get_value(key)
  
                  return result
  
              except (KeyError, TypeError):
                  if isinstance(key, tuple) and isinstance(self.index, MultiIndex):
                      # We still have the corner case where a tuple is a key
                      # in the first level of our MultiIndex
                      return self._get_values_tuple(key)
  
          if is_iterator(key):
              key = list(key)
  
          if com.is_bool_indexer(key):
              key = check_bool_indexer(self.index, key)
              key = np.asarray(key, dtype=bool)
              return self._get_values(key)
  
          return self._get_with(key)
  
      def _get_with(self, key):
          # other: fancy integer or otherwise
          if isinstance(key, slice):
              # _convert_slice_indexer to determine if this slice is positional
              #  or label based, and if the latter, convert to positional
              slobj = self.index._convert_slice_indexer(key, kind="getitem")
              return self._slice(slobj)
          elif isinstance(key, ABCDataFrame):
              raise TypeError(
                  "Indexing a Series with DataFrame is not "
                  "supported, use the appropriate DataFrame column"
              )
          elif isinstance(key, tuple):
              return self._get_values_tuple(key)
  
          elif not is_list_like(key):
              # e.g. scalars that aren't recognized by lib.is_scalar, GH#32684
              return self.loc[key]
  
          if not isinstance(key, (list, np.ndarray, ExtensionArray, Series, Index)):
              key = list(key)
  
          if isinstance(key, Index):
              key_type = key.inferred_type
          else:
              key_type = lib.infer_dtype(key, skipna=False)
  
          # Note: The key_type == "boolean" case should be caught by the
          #  com.is_bool_indexer check in __getitem__
          if key_type == "integer":
              # We need to decide whether to treat this as a positional indexer
              #  (i.e. self.iloc) or label-based (i.e. self.loc)
              if not self.index._should_fallback_to_positional():
                  return self.loc[key]
              else:
                  return self.iloc[key]
  
          # handle the dup indexing case GH#4246
          return self.loc[key]
  
      def _get_values_tuple(self, key):
          # mpl hackaround
          if com.any_none(*key):
              result = self._get_values(key)
              deprecate_ndim_indexing(result, stacklevel=5)
              return result
  
          if not isinstance(self.index, MultiIndex):
              raise KeyError("key of type tuple not found and not a MultiIndex")
  
          # If key is contained, would have returned by now
          indexer, new_index = self.index.get_loc_level(key)
          return self._constructor(self._values[indexer], index=new_index).__finalize__(
              self
          )
  
      def _get_values(self, indexer):
          try:
              return self._constructor(self._mgr.get_slice(indexer)).__finalize__(self)
          except ValueError:
              # mpl compat if we look up e.g. ser[:, np.newaxis];
              #  see tests.series.timeseries.test_mpl_compat_hack
              # the asarray is needed to avoid returning a 2D DatetimeArray
              return np.asarray(self._values[indexer])
  
      def _get_value(self, label, takeable: bool = False):
          """
          Quickly retrieve single value at passed index label.
  
          Parameters
          ----------
          label : object
          takeable : interpret the index as indexers, default False
  
          Returns
          -------
          scalar value
          """
          if takeable:
              return self._values[label]
  
          # Similar to Index.get_value, but we do not fall back to positional
          loc = self.index.get_loc(label)
          return self.index._get_values_for_loc(self, loc, label)
  
      def __setitem__(self, key, value):
          key = com.apply_if_callable(key, self)
          cacher_needs_updating = self._check_is_chained_assignment_possible()
  
          if key is Ellipsis:
              key = slice(None)
  
          try:
              self._set_with_engine(key, value)
          except (KeyError, ValueError):
              values = self._values
              if is_integer(key) and self.index.inferred_type != "integer":
                  # positional setter
                  values[key] = value
              else:
                  # GH#12862 adding a new key to the Series
                  self.loc[key] = value
  
          except TypeError as err:
              if isinstance(key, tuple) and not isinstance(self.index, MultiIndex):
                  raise KeyError(
                      "key of type tuple not found and not a MultiIndex"
                  ) from err
  
              if com.is_bool_indexer(key):
                  key = check_bool_indexer(self.index, key)
                  key = np.asarray(key, dtype=bool)
                  try:
                      self._where(~key, value, inplace=True)
                  except InvalidIndexError:
                      self.iloc[key] = value
                  return
  
              else:
                  self._set_with(key, value)
  
          if cacher_needs_updating:
              self._maybe_update_cacher()
  
      def _set_with_engine(self, key, value):
          # fails with AttributeError for IntervalIndex
          loc = self.index._engine.get_loc(key)
          validate_numeric_casting(self.dtype, value)
          self._values[loc] = value
  
      def _set_with(self, key, value):
          # other: fancy integer or otherwise
          if isinstance(key, slice):
              indexer = self.index._convert_slice_indexer(key, kind="getitem")
              return self._set_values(indexer, value)
  
          else:
              assert not isinstance(key, tuple)
  
              if is_scalar(key):
                  key = [key]
  
              if isinstance(key, Index):
                  key_type = key.inferred_type
                  key = key._values
              else:
                  key_type = lib.infer_dtype(key, skipna=False)
  
              # Note: key_type == "boolean" should not occur because that
              #  should be caught by the is_bool_indexer check in __setitem__
              if key_type == "integer":
                  if not self.index._should_fallback_to_positional():
                      self._set_labels(key, value)
                  else:
                      self._set_values(key, value)
              else:
                  self.loc[key] = value
  
      def _set_labels(self, key, value):
          key = com.asarray_tuplesafe(key)
          indexer: np.ndarray = self.index.get_indexer(key)
          mask = indexer == -1
          if mask.any():
              raise KeyError(f"{key[mask]} not in index")
          self._set_values(indexer, value)
  
      def _set_values(self, key, value):
          if isinstance(key, Series):
              key = key._values
          self._mgr = self._mgr.setitem(  # type: ignore[assignment]
              indexer=key, value=value
          )
          self._maybe_update_cacher()
  
      def _set_value(self, label, value, takeable: bool = False):
          """
          Quickly set single value at passed label.
  
          If label is not contained, a new object is created with the label
          placed at the end of the result index.
  
          Parameters
          ----------
          label : object
              Partial indexing with MultiIndex not allowed.
          value : object
              Scalar value.
          takeable : interpret the index as indexers, default False
          """
          try:
              if takeable:
                  self._values[label] = value
              else:
                  loc = self.index.get_loc(label)
                  validate_numeric_casting(self.dtype, value)
                  self._values[loc] = value
          except KeyError:
  
              # set using a non-recursive method
              self.loc[label] = value
  
      # ----------------------------------------------------------------------
      # Unsorted
  
      @property
      def _is_mixed_type(self):
          return False
  
      def repeat(self, repeats, axis=None) -> Series:
          """
          Repeat elements of a Series.
  
          Returns a new Series where each element of the current Series
          is repeated consecutively a given number of times.
  
          Parameters
          ----------
          repeats : int or array of ints
              The number of repetitions for each element. This should be a
              non-negative integer. Repeating 0 times will return an empty
              Series.
          axis : None
              Must be ``None``. Has no effect but is accepted for compatibility
              with numpy.
  
          Returns
          -------
          Series
              Newly created Series with repeated elements.
  
          See Also
          --------
          Index.repeat : Equivalent function for Index.
          numpy.repeat : Similar method for :class:`numpy.ndarray`.
  
          Examples
          --------
          >>> s = pd.Series(['a', 'b', 'c'])
          >>> s
          0    a
          1    b
          2    c
          dtype: object
          >>> s.repeat(2)
          0    a
          0    a
          1    b
          1    b
          2    c
          2    c
          dtype: object
          >>> s.repeat([1, 2, 3])
          0    a
          1    b
          1    b
          2    c
          2    c
          2    c
          dtype: object
          """
          nv.validate_repeat((), {"axis": axis})
          new_index = self.index.repeat(repeats)
          new_values = self._values.repeat(repeats)
          return self._constructor(new_values, index=new_index).__finalize__(
              self, method="repeat"
          )
  
      def reset_index(self, level=None, drop=False, name=None, inplace=False):
          """
          Generate a new DataFrame or Series with the index reset.
  
          This is useful when the index needs to be treated as a column, or
          when the index is meaningless and needs to be reset to the default
          before another operation.
  
          Parameters
          ----------
          level : int, str, tuple, or list, default optional
              For a Series with a MultiIndex, only remove the specified levels
              from the index. Removes all levels by default.
          drop : bool, default False
              Just reset the index, without inserting it as a column in
              the new DataFrame.
          name : object, optional
              The name to use for the column containing the original Series
              values. Uses ``self.name`` by default. This argument is ignored
              when `drop` is True.
          inplace : bool, default False
              Modify the Series in place (do not create a new object).
  
          Returns
          -------
          Series or DataFrame or None
              When `drop` is False (the default), a DataFrame is returned.
              The newly created columns will come first in the DataFrame,
              followed by the original Series values.
              When `drop` is True, a `Series` is returned.
              In either case, if ``inplace=True``, no value is returned.
  
          See Also
          --------
          DataFrame.reset_index: Analogous function for DataFrame.
  
          Examples
          --------
          >>> s = pd.Series([1, 2, 3, 4], name='foo',
          ...               index=pd.Index(['a', 'b', 'c', 'd'], name='idx'))
  
          Generate a DataFrame with default index.
  
          >>> s.reset_index()
            idx  foo
          0   a    1
          1   b    2
          2   c    3
          3   d    4
  
          To specify the name of the new column use `name`.
  
          >>> s.reset_index(name='values')
            idx  values
          0   a       1
          1   b       2
          2   c       3
          3   d       4
  
          To generate a new Series with the default set `drop` to True.
  
          >>> s.reset_index(drop=True)
          0    1
          1    2
          2    3
          3    4
          Name: foo, dtype: int64
  
          To update the Series in place, without generating a new one
          set `inplace` to True. Note that it also requires ``drop=True``.
  
          >>> s.reset_index(inplace=True, drop=True)
          >>> s
          0    1
          1    2
          2    3
          3    4
          Name: foo, dtype: int64
  
          The `level` parameter is interesting for Series with a multi-level
          index.
  
          >>> arrays = [np.array(['bar', 'bar', 'baz', 'baz']),
          ...           np.array(['one', 'two', 'one', 'two'])]
          >>> s2 = pd.Series(
          ...     range(4), name='foo',
          ...     index=pd.MultiIndex.from_arrays(arrays,
          ...                                     names=['a', 'b']))
  
          To remove a specific level from the Index, use `level`.
  
          >>> s2.reset_index(level='a')
                 a  foo
          b
          one  bar    0
          two  bar    1
          one  baz    2
          two  baz    3
  
          If `level` is not set, all levels are removed from the Index.
  
          >>> s2.reset_index()
               a    b  foo
          0  bar  one    0
          1  bar  two    1
          2  baz  one    2
          3  baz  two    3
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
          if drop:
              new_index = ibase.default_index(len(self))
              if level is not None:
                  if not isinstance(level, (tuple, list)):
                      level = [level]
                  level = [self.index._get_level_number(lev) for lev in level]
                  if len(level) < self.index.nlevels:
                      new_index = self.index.droplevel(level)
  
              if inplace:
                  self.index = new_index
                  # set name if it was passed, otherwise, keep the previous name
                  self.name = name or self.name
              else:
                  return self._constructor(
                      self._values.copy(), index=new_index
                  ).__finalize__(self, method="reset_index")
          elif inplace:
              raise TypeError(
                  "Cannot reset_index inplace on a Series to create a DataFrame"
              )
          else:
              df = self.to_frame(name)
              return df.reset_index(level=level, drop=drop)
  
      # ----------------------------------------------------------------------
      # Rendering Methods
  
      def __repr__(self) -> str:
          """
          Return a string representation for a particular Series.
          """
          buf = StringIO("")
          width, height = get_terminal_size()
          max_rows = (
              height
              if get_option("display.max_rows") == 0
              else get_option("display.max_rows")
          )
          min_rows = (
              height
              if get_option("display.max_rows") == 0
              else get_option("display.min_rows")
          )
          show_dimensions = get_option("display.show_dimensions")
  
          self.to_string(
              buf=buf,
              name=self.name,
              dtype=self.dtype,
              min_rows=min_rows,
              max_rows=max_rows,
              length=show_dimensions,
          )
          return buf.getvalue()
  
      def to_string(
          self,
          buf=None,
          na_rep="NaN",
          float_format=None,
          header=True,
          index=True,
          length=False,
          dtype=False,
          name=False,
          max_rows=None,
          min_rows=None,
      ):
          """
          Render a string representation of the Series.
  
          Parameters
          ----------
          buf : StringIO-like, optional
              Buffer to write to.
          na_rep : str, optional
              String representation of NaN to use, default 'NaN'.
          float_format : one-parameter function, optional
              Formatter function to apply to columns' elements if they are
              floats, default None.
          header : bool, default True
              Add the Series header (index name).
          index : bool, optional
              Add index (row) labels, default True.
          length : bool, default False
              Add the Series length.
          dtype : bool, default False
              Add the Series dtype.
          name : bool, default False
              Add the Series name if not None.
          max_rows : int, optional
              Maximum number of rows to show before truncating. If None, show
              all.
          min_rows : int, optional
              The number of rows to display in a truncated repr (when number
              of rows is above `max_rows`).
  
          Returns
          -------
          str or None
              String representation of Series if ``buf=None``, otherwise None.
          """
          formatter = fmt.SeriesFormatter(
              self,
              name=name,
              length=length,
              header=header,
              index=index,
              dtype=dtype,
              na_rep=na_rep,
              float_format=float_format,
              min_rows=min_rows,
              max_rows=max_rows,
          )
          result = formatter.to_string()
  
          # catch contract violations
          if not isinstance(result, str):
              raise AssertionError(
                  "result must be of type str, type "
                  f"of result is {repr(type(result).__name__)}"
              )
  
          if buf is None:
              return result
          else:
              try:
                  buf.write(result)
              except AttributeError:
                  with open(buf, "w") as f:
                      f.write(result)
  
      @doc(
          klass=_shared_doc_kwargs["klass"],
          storage_options=generic._shared_docs["storage_options"],
          examples=dedent(
              """
              Examples
              --------
              >>> s = pd.Series(["elk", "pig", "dog", "quetzal"], name="animal")
              >>> print(s.to_markdown())
              |    | animal   |
              |---:|:---------|
              |  0 | elk      |
              |  1 | pig      |
              |  2 | dog      |
              |  3 | quetzal  |
              """
          ),
      )
      def to_markdown(
          self,
          buf: Optional[IO[str]] = None,
          mode: str = "wt",
          index: bool = True,
          storage_options: StorageOptions = None,
          **kwargs,
      ) -> Optional[str]:
          """
          Print {klass} in Markdown-friendly format.
  
          .. versionadded:: 1.0.0
  
          Parameters
          ----------
          buf : str, Path or StringIO-like, optional, default None
              Buffer to write to. If None, the output is returned as a string.
          mode : str, optional
              Mode in which file is opened, "wt" by default.
          index : bool, optional, default True
              Add index (row) labels.
  
              .. versionadded:: 1.1.0
          {storage_options}
  
              .. versionadded:: 1.2.0
  
          **kwargs
              These parameters will be passed to `tabulate \
                  <https://pypi.org/project/tabulate>`_.
  
          Returns
          -------
          str
              {klass} in Markdown-friendly format.
  
          Notes
          -----
          Requires the `tabulate <https://pypi.org/project/tabulate>`_ package.
  
          Examples
          --------
          >>> s = pd.Series(["elk", "pig", "dog", "quetzal"], name="animal")
          >>> print(s.to_markdown())
          |    | animal   |
          |---:|:---------|
          |  0 | elk      |
          |  1 | pig      |
          |  2 | dog      |
          |  3 | quetzal  |
  
          Output markdown with a tabulate option.
  
          >>> print(s.to_markdown(tablefmt="grid"))
          +----+----------+
          |    | animal   |
          +====+==========+
          |  0 | elk      |
          +----+----------+
          |  1 | pig      |
          +----+----------+
          |  2 | dog      |
          +----+----------+
          |  3 | quetzal  |
          +----+----------+
          """
          return self.to_frame().to_markdown(
              buf, mode, index, storage_options=storage_options, **kwargs
          )
  
      # ----------------------------------------------------------------------
  
      def items(self) -> Iterable[Tuple[Hashable, Any]]:
          """
          Lazily iterate over (index, value) tuples.
  
          This method returns an iterable tuple (index, value). This is
          convenient if you want to create a lazy iterator.
  
          Returns
          -------
          iterable
              Iterable of tuples containing the (index, value) pairs from a
              Series.
  
          See Also
          --------
          DataFrame.items : Iterate over (column name, Series) pairs.
          DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs.
  
          Examples
          --------
          >>> s = pd.Series(['A', 'B', 'C'])
          >>> for index, value in s.items():
          ...     print(f"Index : {index}, Value : {value}")
          Index : 0, Value : A
          Index : 1, Value : B
          Index : 2, Value : C
          """
          return zip(iter(self.index), iter(self))
  
      @Appender(items.__doc__)
      def iteritems(self) -> Iterable[Tuple[Hashable, Any]]:
          return self.items()
  
      # ----------------------------------------------------------------------
      # Misc public methods
  
      def keys(self) -> Index:
          """
          Return alias for index.
  
          Returns
          -------
          Index
              Index of the Series.
          """
          return self.index
  
      def to_dict(self, into=dict):
          """
          Convert Series to {label -> value} dict or dict-like object.
  
          Parameters
          ----------
          into : class, default dict
              The collections.abc.Mapping subclass to use as the return
              object. Can be the actual class or an empty
              instance of the mapping type you want.  If you want a
              collections.defaultdict, you must pass it initialized.
  
          Returns
          -------
          collections.abc.Mapping
              Key-value representation of Series.
  
          Examples
          --------
          >>> s = pd.Series([1, 2, 3, 4])
          >>> s.to_dict()
          {0: 1, 1: 2, 2: 3, 3: 4}
          >>> from collections import OrderedDict, defaultdict
          >>> s.to_dict(OrderedDict)
          OrderedDict([(0, 1), (1, 2), (2, 3), (3, 4)])
          >>> dd = defaultdict(list)
          >>> s.to_dict(dd)
          defaultdict(<class 'list'>, {0: 1, 1: 2, 2: 3, 3: 4})
          """
          # GH16122
          into_c = com.standardize_mapping(into)
          return into_c(self.items())
  
      def to_frame(self, name=None) -> DataFrame:
          """
          Convert Series to DataFrame.
  
          Parameters
          ----------
          name : object, default None
              The passed name should substitute for the series name (if it has
              one).
  
          Returns
          -------
          DataFrame
              DataFrame representation of Series.
  
          Examples
          --------
          >>> s = pd.Series(["a", "b", "c"],
          ...               name="vals")
          >>> s.to_frame()
            vals
          0    a
          1    b
          2    c
          """
          if name is None:
              df = self._constructor_expanddim(self)
          else:
              df = self._constructor_expanddim({name: self})
  
          return df
  
      def _set_name(self, name, inplace=False) -> Series:
          """
          Set the Series name.
  
          Parameters
          ----------
          name : str
          inplace : bool
              Whether to modify `self` directly or return a copy.
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
          ser = self if inplace else self.copy()
          ser.name = name
          return ser
  
      @Appender(
          """
  Examples
  --------
  >>> ser = pd.Series([390., 350., 30., 20.],
  ...                 index=['Falcon', 'Falcon', 'Parrot', 'Parrot'], name="Max Speed")
  >>> ser
  Falcon    390.0
  Falcon    350.0
  Parrot     30.0
  Parrot     20.0
  Name: Max Speed, dtype: float64
  >>> ser.groupby(["a", "b", "a", "b"]).mean()
  a    210.0
  b    185.0
  Name: Max Speed, dtype: float64
  >>> ser.groupby(level=0).mean()
  Falcon    370.0
  Parrot     25.0
  Name: Max Speed, dtype: float64
  >>> ser.groupby(ser > 100).mean()
  Max Speed
  False     25.0
  True     370.0
  Name: Max Speed, dtype: float64
  
  **Grouping by Indexes**
  
  We can groupby different levels of a hierarchical index
  using the `level` parameter:
  
  >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
  ...           ['Captive', 'Wild', 'Captive', 'Wild']]
  >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
  >>> ser = pd.Series([390., 350., 30., 20.], index=index, name="Max Speed")
  >>> ser
  Animal  Type
  Falcon  Captive    390.0
          Wild       350.0
  Parrot  Captive     30.0
          Wild        20.0
  Name: Max Speed, dtype: float64
  >>> ser.groupby(level=0).mean()
  Animal
  Falcon    370.0
  Parrot     25.0
  Name: Max Speed, dtype: float64
  >>> ser.groupby(level="Type").mean()
  Type
  Captive    210.0
  Wild       185.0
  Name: Max Speed, dtype: float64
  
  We can also choose to include `NA` in group keys or not by defining
  `dropna` parameter, the default setting is `True`:
  
  >>> ser = pd.Series([1, 2, 3, 3], index=["a", 'a', 'b', np.nan])
  >>> ser.groupby(level=0).sum()
  a    3
  b    3
  dtype: int64
  
  >>> ser.groupby(level=0, dropna=False).sum()
  a    3
  b    3
  NaN  3
  dtype: int64
  
  >>> arrays = ['Falcon', 'Falcon', 'Parrot', 'Parrot']
  >>> ser = pd.Series([390., 350., 30., 20.], index=arrays, name="Max Speed")
  >>> ser.groupby(["a", "b", "a", np.nan]).mean()
  a    210.0
  b    350.0
  Name: Max Speed, dtype: float64
  
  >>> ser.groupby(["a", "b", "a", np.nan], dropna=False).mean()
  a    210.0
  b    350.0
  NaN   20.0
  Name: Max Speed, dtype: float64
  """
      )
      @Appender(generic._shared_docs["groupby"] % _shared_doc_kwargs)
      def groupby(
          self,
          by=None,
          axis=0,
          level=None,
          as_index: bool = True,
          sort: bool = True,
          group_keys: bool = True,
          squeeze: bool = no_default,
          observed: bool = False,
          dropna: bool = True,
      ) -> SeriesGroupBy:
          from pandas.core.groupby.generic import SeriesGroupBy
  
          if squeeze is not no_default:
              warnings.warn(
                  (
                      "The `squeeze` parameter is deprecated and "
                      "will be removed in a future version."
                  ),
                  FutureWarning,
                  stacklevel=2,
              )
          else:
              squeeze = False
  
          if level is None and by is None:
              raise TypeError("You have to supply one of 'by' and 'level'")
          axis = self._get_axis_number(axis)
  
          return SeriesGroupBy(
              obj=self,
              keys=by,
              axis=axis,
              level=level,
              as_index=as_index,
              sort=sort,
              group_keys=group_keys,
              squeeze=squeeze,
              observed=observed,
              dropna=dropna,
          )
  
      # ----------------------------------------------------------------------
      # Statistics, overridden ndarray methods
  
      # TODO: integrate bottleneck
  
      def count(self, level=None):
          """
          Return number of non-NA/null observations in the Series.
  
          Parameters
          ----------
          level : int or level name, default None
              If the axis is a MultiIndex (hierarchical), count along a
              particular level, collapsing into a smaller Series.
  
          Returns
          -------
          int or Series (if level specified)
              Number of non-null values in the Series.
  
          See Also
          --------
          DataFrame.count : Count non-NA cells for each column or row.
  
          Examples
          --------
          >>> s = pd.Series([0.0, 1.0, np.nan])
          >>> s.count()
          2
          """
          if level is None:
              return notna(self.array).sum()
          elif not isinstance(self.index, MultiIndex):
              raise ValueError("Series.count level is only valid with a MultiIndex")
  
          index = self.index
          assert isinstance(index, MultiIndex)  # for mypy
  
          if isinstance(level, str):
              level = index._get_level_number(level)
  
          lev = index.levels[level]
          level_codes = np.array(index.codes[level], subok=False, copy=True)
  
          mask = level_codes == -1
          if mask.any():
              level_codes[mask] = cnt = len(lev)
              lev = lev.insert(cnt, lev._na_value)
  
          obs = level_codes[notna(self._values)]
          out = np.bincount(obs, minlength=len(lev) or None)
          return self._constructor(out, index=lev, dtype="int64").__finalize__(
              self, method="count"
          )
  
      def mode(self, dropna=True) -> Series:
          """
          Return the mode(s) of the Series.
  
          The mode is the value that appears most often. There can be multiple modes.
  
          Always returns Series even if only one value is returned.
  
          Parameters
          ----------
          dropna : bool, default True
              Don't consider counts of NaN/NaT.
  
              .. versionadded:: 0.24.0
  
          Returns
          -------
          Series
              Modes of the Series in sorted order.
          """
          # TODO: Add option for bins like value_counts()
          return algorithms.mode(self, dropna=dropna)
  
      def unique(self):
          """
          Return unique values of Series object.
  
          Uniques are returned in order of appearance. Hash table-based unique,
          therefore does NOT sort.
  
          Returns
          -------
          ndarray or ExtensionArray
              The unique values returned as a NumPy array. See Notes.
  
          See Also
          --------
          unique : Top-level unique method for any 1-d array-like object.
          Index.unique : Return Index with unique values from an Index object.
  
          Notes
          -----
          Returns the unique values as a NumPy array. In case of an
          extension-array backed Series, a new
          :class:`~api.extensions.ExtensionArray` of that type with just
          the unique values is returned. This includes
  
              * Categorical
              * Period
              * Datetime with Timezone
              * Interval
              * Sparse
              * IntegerNA
  
          See Examples section.
  
          Examples
          --------
          >>> pd.Series([2, 1, 3, 3], name='A').unique()
          array([2, 1, 3])
  
          >>> pd.Series([pd.Timestamp('2016-01-01') for _ in range(3)]).unique()
          array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
  
          >>> pd.Series([pd.Timestamp('2016-01-01', tz='US/Eastern')
          ...            for _ in range(3)]).unique()
          <DatetimeArray>
          ['2016-01-01 00:00:00-05:00']
          Length: 1, dtype: datetime64[ns, US/Eastern]
  
          An unordered Categorical will return categories in the order of
          appearance.
  
          >>> pd.Series(pd.Categorical(list('baabc'))).unique()
          ['b', 'a', 'c']
          Categories (3, object): ['b', 'a', 'c']
  
          An ordered Categorical preserves the category ordering.
  
          >>> pd.Series(pd.Categorical(list('baabc'), categories=list('abc'),
          ...                          ordered=True)).unique()
          ['b', 'a', 'c']
          Categories (3, object): ['a' < 'b' < 'c']
          """
          return super().unique()
  
      def drop_duplicates(self, keep="first", inplace=False) -> Optional[Series]:
          """
          Return Series with duplicate values removed.
  
          Parameters
          ----------
          keep : {'first', 'last', ``False``}, default 'first'
              Method to handle dropping duplicates:
  
              - 'first' : Drop duplicates except for the first occurrence.
              - 'last' : Drop duplicates except for the last occurrence.
              - ``False`` : Drop all duplicates.
  
          inplace : bool, default ``False``
              If ``True``, performs operation inplace and returns None.
  
          Returns
          -------
          Series or None
              Series with duplicates dropped or None if ``inplace=True``.
  
          See Also
          --------
          Index.drop_duplicates : Equivalent method on Index.
          DataFrame.drop_duplicates : Equivalent method on DataFrame.
          Series.duplicated : Related method on Series, indicating duplicate
              Series values.
  
          Examples
          --------
          Generate a Series with duplicated entries.
  
          >>> s = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'],
          ...               name='animal')
          >>> s
          0      lama
          1       cow
          2      lama
          3    beetle
          4      lama
          5     hippo
          Name: animal, dtype: object
  
          With the 'keep' parameter, the selection behaviour of duplicated values
          can be changed. The value 'first' keeps the first occurrence for each
          set of duplicated entries. The default value of keep is 'first'.
  
          >>> s.drop_duplicates()
          0      lama
          1       cow
          3    beetle
          5     hippo
          Name: animal, dtype: object
  
          The value 'last' for parameter 'keep' keeps the last occurrence for
          each set of duplicated entries.
  
          >>> s.drop_duplicates(keep='last')
          1       cow
          3    beetle
          4      lama
          5     hippo
          Name: animal, dtype: object
  
          The value ``False`` for parameter 'keep' discards all sets of
          duplicated entries. Setting the value of 'inplace' to ``True`` performs
          the operation inplace and returns ``None``.
  
          >>> s.drop_duplicates(keep=False, inplace=True)
          >>> s
          1       cow
          3    beetle
          5     hippo
          Name: animal, dtype: object
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
          result = super().drop_duplicates(keep=keep)
          if inplace:
              self._update_inplace(result)
              return None
          else:
              return result
  
      def duplicated(self, keep="first") -> Series:
          """
          Indicate duplicate Series values.
  
          Duplicated values are indicated as ``True`` values in the resulting
          Series. Either all duplicates, all except the first or all except the
          last occurrence of duplicates can be indicated.
  
          Parameters
          ----------
          keep : {'first', 'last', False}, default 'first'
              Method to handle dropping duplicates:
  
              - 'first' : Mark duplicates as ``True`` except for the first
                occurrence.
              - 'last' : Mark duplicates as ``True`` except for the last
                occurrence.
              - ``False`` : Mark all duplicates as ``True``.
  
          Returns
          -------
          Series
              Series indicating whether each value has occurred in the
              preceding values.
  
          See Also
          --------
          Index.duplicated : Equivalent method on pandas.Index.
          DataFrame.duplicated : Equivalent method on pandas.DataFrame.
          Series.drop_duplicates : Remove duplicate values from Series.
  
          Examples
          --------
          By default, for each set of duplicated values, the first occurrence is
          set on False and all others on True:
  
          >>> animals = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama'])
          >>> animals.duplicated()
          0    False
          1    False
          2     True
          3    False
          4     True
          dtype: bool
  
          which is equivalent to
  
          >>> animals.duplicated(keep='first')
          0    False
          1    False
          2     True
          3    False
          4     True
          dtype: bool
  
          By using 'last', the last occurrence of each set of duplicated values
          is set on False and all others on True:
  
          >>> animals.duplicated(keep='last')
          0     True
          1    False
          2     True
          3    False
          4    False
          dtype: bool
  
          By setting keep on ``False``, all duplicates are True:
  
          >>> animals.duplicated(keep=False)
          0     True
          1    False
          2     True
          3    False
          4     True
          dtype: bool
          """
          res = base.IndexOpsMixin.duplicated(self, keep=keep)
          result = self._constructor(res, index=self.index)
          return result.__finalize__(self, method="duplicated")
  
      def idxmin(self, axis=0, skipna=True, *args, **kwargs):
          """
          Return the row label of the minimum value.
  
          If multiple values equal the minimum, the first row label with that
          value is returned.
  
          Parameters
          ----------
          axis : int, default 0
              For compatibility with DataFrame.idxmin. Redundant for application
              on Series.
          skipna : bool, default True
              Exclude NA/null values. If the entire Series is NA, the result
              will be NA.
          *args, **kwargs
              Additional arguments and keywords have no effect but might be
              accepted for compatibility with NumPy.
  
          Returns
          -------
          Index
              Label of the minimum value.
  
          Raises
          ------
          ValueError
              If the Series is empty.
  
          See Also
          --------
          numpy.argmin : Return indices of the minimum values
              along the given axis.
          DataFrame.idxmin : Return index of first occurrence of minimum
              over requested axis.
          Series.idxmax : Return index *label* of the first occurrence
              of maximum of values.
  
          Notes
          -----
          This method is the Series version of ``ndarray.argmin``. This method
          returns the label of the minimum, while ``ndarray.argmin`` returns
          the position. To get the position, use ``series.values.argmin()``.
  
          Examples
          --------
          >>> s = pd.Series(data=[1, None, 4, 1],
          ...               index=['A', 'B', 'C', 'D'])
          >>> s
          A    1.0
          B    NaN
          C    4.0
          D    1.0
          dtype: float64
  
          >>> s.idxmin()
          'A'
  
          If `skipna` is False and there is an NA value in the data,
          the function returns ``nan``.
  
          >>> s.idxmin(skipna=False)
          nan
          """
          i = self.argmin(axis, skipna, *args, **kwargs)
          if i == -1:
              return np.nan
          return self.index[i]
  
      def idxmax(self, axis=0, skipna=True, *args, **kwargs):
          """
          Return the row label of the maximum value.
  
          If multiple values equal the maximum, the first row label with that
          value is returned.
  
          Parameters
          ----------
          axis : int, default 0
              For compatibility with DataFrame.idxmax. Redundant for application
              on Series.
          skipna : bool, default True
              Exclude NA/null values. If the entire Series is NA, the result
              will be NA.
          *args, **kwargs
              Additional arguments and keywords have no effect but might be
              accepted for compatibility with NumPy.
  
          Returns
          -------
          Index
              Label of the maximum value.
  
          Raises
          ------
          ValueError
              If the Series is empty.
  
          See Also
          --------
          numpy.argmax : Return indices of the maximum values
              along the given axis.
          DataFrame.idxmax : Return index of first occurrence of maximum
              over requested axis.
          Series.idxmin : Return index *label* of the first occurrence
              of minimum of values.
  
          Notes
          -----
          This method is the Series version of ``ndarray.argmax``. This method
          returns the label of the maximum, while ``ndarray.argmax`` returns
          the position. To get the position, use ``series.values.argmax()``.
  
          Examples
          --------
          >>> s = pd.Series(data=[1, None, 4, 3, 4],
          ...               index=['A', 'B', 'C', 'D', 'E'])
          >>> s
          A    1.0
          B    NaN
          C    4.0
          D    3.0
          E    4.0
          dtype: float64
  
          >>> s.idxmax()
          'C'
  
          If `skipna` is False and there is an NA value in the data,
          the function returns ``nan``.
  
          >>> s.idxmax(skipna=False)
          nan
          """
          i = self.argmax(axis, skipna, *args, **kwargs)
          if i == -1:
              return np.nan
          return self.index[i]
  
      def round(self, decimals=0, *args, **kwargs) -> Series:
          """
          Round each value in a Series to the given number of decimals.
  
          Parameters
          ----------
          decimals : int, default 0
              Number of decimal places to round to. If decimals is negative,
              it specifies the number of positions to the left of the decimal point.
          *args, **kwargs
              Additional arguments and keywords have no effect but might be
              accepted for compatibility with NumPy.
  
          Returns
          -------
          Series
              Rounded values of the Series.
  
          See Also
          --------
          numpy.around : Round values of an np.array.
          DataFrame.round : Round values of a DataFrame.
  
          Examples
          --------
          >>> s = pd.Series([0.1, 1.3, 2.7])
          >>> s.round()
          0    0.0
          1    1.0
          2    3.0
          dtype: float64
          """
          nv.validate_round(args, kwargs)
          result = self._values.round(decimals)
          result = self._constructor(result, index=self.index).__finalize__(
              self, method="round"
          )
  
          return result
  
      def quantile(self, q=0.5, interpolation="linear"):
          """
          Return value at the given quantile.
  
          Parameters
          ----------
          q : float or array-like, default 0.5 (50% quantile)
              The quantile(s) to compute, which can lie in range: 0 <= q <= 1.
          interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
              This optional parameter specifies the interpolation method to use,
              when the desired quantile lies between two data points `i` and `j`:
  
                  * linear: `i + (j - i) * fraction`, where `fraction` is the
                    fractional part of the index surrounded by `i` and `j`.
                  * lower: `i`.
                  * higher: `j`.
                  * nearest: `i` or `j` whichever is nearest.
                  * midpoint: (`i` + `j`) / 2.
  
          Returns
          -------
          float or Series
              If ``q`` is an array, a Series will be returned where the
              index is ``q`` and the values are the quantiles, otherwise
              a float will be returned.
  
          See Also
          --------
          core.window.Rolling.quantile : Calculate the rolling quantile.
          numpy.percentile : Returns the q-th percentile(s) of the array elements.
  
          Examples
          --------
          >>> s = pd.Series([1, 2, 3, 4])
          >>> s.quantile(.5)
          2.5
          >>> s.quantile([.25, .5, .75])
          0.25    1.75
          0.50    2.50
          0.75    3.25
          dtype: float64
          """
          validate_percentile(q)
  
          # We dispatch to DataFrame so that core.internals only has to worry
          #  about 2D cases.
          df = self.to_frame()
  
          result = df.quantile(q=q, interpolation=interpolation, numeric_only=False)
          if result.ndim == 2:
              result = result.iloc[:, 0]
  
          if is_list_like(q):
              result.name = self.name
              return self._constructor(result, index=Float64Index(q), name=self.name)
          else:
              # scalar
              return result.iloc[0]
  
      def corr(self, other, method="pearson", min_periods=None) -> float:
          """
          Compute correlation with `other` Series, excluding missing values.
  
          Parameters
          ----------
          other : Series
              Series with which to compute the correlation.
          method : {'pearson', 'kendall', 'spearman'} or callable
              Method used to compute correlation:
  
              - pearson : Standard correlation coefficient
              - kendall : Kendall Tau correlation coefficient
              - spearman : Spearman rank correlation
              - callable: Callable with input two 1d ndarrays and returning a float.
  
              .. versionadded:: 0.24.0
                  Note that the returned matrix from corr will have 1 along the
                  diagonals and will be symmetric regardless of the callable's
                  behavior.
          min_periods : int, optional
              Minimum number of observations needed to have a valid result.
  
          Returns
          -------
          float
              Correlation with other.
  
          See Also
          --------
          DataFrame.corr : Compute pairwise correlation between columns.
          DataFrame.corrwith : Compute pairwise correlation with another
              DataFrame or Series.
  
          Examples
          --------
          >>> def histogram_intersection(a, b):
          ...     v = np.minimum(a, b).sum().round(decimals=1)
          ...     return v
          >>> s1 = pd.Series([.2, .0, .6, .2])
          >>> s2 = pd.Series([.3, .6, .0, .1])
          >>> s1.corr(s2, method=histogram_intersection)
          0.3
          """
          this, other = self.align(other, join="inner", copy=False)
          if len(this) == 0:
              return np.nan
  
          if method in ["pearson", "spearman", "kendall"] or callable(method):
              return nanops.nancorr(
                  this.values, other.values, method=method, min_periods=min_periods
              )
  
          raise ValueError(
              "method must be either 'pearson', "
              "'spearman', 'kendall', or a callable, "
              f"'{method}' was supplied"
          )
  
      def cov(
          self,
          other: Series,
          min_periods: Optional[int] = None,
          ddof: Optional[int] = 1,
      ) -> float:
          """
          Compute covariance with Series, excluding missing values.
  
          Parameters
          ----------
          other : Series
              Series with which to compute the covariance.
          min_periods : int, optional
              Minimum number of observations needed to have a valid result.
          ddof : int, default 1
              Delta degrees of freedom.  The divisor used in calculations
              is ``N - ddof``, where ``N`` represents the number of elements.
  
              .. versionadded:: 1.1.0
  
          Returns
          -------
          float
              Covariance between Series and other normalized by N-1
              (unbiased estimator).
  
          See Also
          --------
          DataFrame.cov : Compute pairwise covariance of columns.
  
          Examples
          --------
          >>> s1 = pd.Series([0.90010907, 0.13484424, 0.62036035])
          >>> s2 = pd.Series([0.12528585, 0.26962463, 0.51111198])
          >>> s1.cov(s2)
          -0.01685762652715874
          """
          this, other = self.align(other, join="inner", copy=False)
          if len(this) == 0:
              return np.nan
          return nanops.nancov(
              this.values, other.values, min_periods=min_periods, ddof=ddof
          )
  
      @doc(
          klass="Series",
          extra_params="",
          other_klass="DataFrame",
          examples=dedent(
              """
          Difference with previous row
  
          >>> s = pd.Series([1, 1, 2, 3, 5, 8])
          >>> s.diff()
          0    NaN
          1    0.0
          2    1.0
          3    1.0
          4    2.0
          5    3.0
          dtype: float64
  
          Difference with 3rd previous row
  
          >>> s.diff(periods=3)
          0    NaN
          1    NaN
          2    NaN
          3    2.0
          4    4.0
          5    6.0
          dtype: float64
  
          Difference with following row
  
          >>> s.diff(periods=-1)
          0    0.0
          1   -1.0
          2   -1.0
          3   -2.0
          4   -3.0
          5    NaN
          dtype: float64
  
          Overflow in input dtype
  
          >>> s = pd.Series([1, 0], dtype=np.uint8)
          >>> s.diff()
          0      NaN
          1    255.0
          dtype: float64"""
          ),
      )
      def diff(self, periods: int = 1) -> Series:
          """
          First discrete difference of element.
  
          Calculates the difference of a {klass} element compared with another
          element in the {klass} (default is element in previous row).
  
          Parameters
          ----------
          periods : int, default 1
              Periods to shift for calculating difference, accepts negative
              values.
          {extra_params}
          Returns
          -------
          {klass}
              First differences of the Series.
  
          See Also
          --------
          {klass}.pct_change: Percent change over given number of periods.
          {klass}.shift: Shift index by desired number of periods with an
              optional time freq.
          {other_klass}.diff: First discrete difference of object.
  
          Notes
          -----
          For boolean dtypes, this uses :meth:`operator.xor` rather than
          :meth:`operator.sub`.
          The result is calculated according to current dtype in {klass},
          however dtype of the result is always float64.
  
          Examples
          --------
          {examples}
          """
          result = algorithms.diff(self.array, periods)
          return self._constructor(result, index=self.index).__finalize__(
              self, method="diff"
          )
  
      def autocorr(self, lag=1) -> float:
          """
          Compute the lag-N autocorrelation.
  
          This method computes the Pearson correlation between
          the Series and its shifted self.
  
          Parameters
          ----------
          lag : int, default 1
              Number of lags to apply before performing autocorrelation.
  
          Returns
          -------
          float
              The Pearson correlation between self and self.shift(lag).
  
          See Also
          --------
          Series.corr : Compute the correlation between two Series.
          Series.shift : Shift index by desired number of periods.
          DataFrame.corr : Compute pairwise correlation of columns.
          DataFrame.corrwith : Compute pairwise correlation between rows or
              columns of two DataFrame objects.
  
          Notes
          -----
          If the Pearson correlation is not well defined return 'NaN'.
  
          Examples
          --------
          >>> s = pd.Series([0.25, 0.5, 0.2, -0.05])
          >>> s.autocorr()  # doctest: +ELLIPSIS
          0.10355...
          >>> s.autocorr(lag=2)  # doctest: +ELLIPSIS
          -0.99999...
  
          If the Pearson correlation is not well defined, then 'NaN' is returned.
  
          >>> s = pd.Series([1, 0, 0, 0])
          >>> s.autocorr()
          nan
          """
          return self.corr(self.shift(lag))
  
      def dot(self, other):
          """
          Compute the dot product between the Series and the columns of other.
  
          This method computes the dot product between the Series and another
          one, or the Series and each columns of a DataFrame, or the Series and
          each columns of an array.
  
          It can also be called using `self @ other` in Python >= 3.5.
  
          Parameters
          ----------
          other : Series, DataFrame or array-like
              The other object to compute the dot product with its columns.
  
          Returns
          -------
          scalar, Series or numpy.ndarray
              Return the dot product of the Series and other if other is a
              Series, the Series of the dot product of Series and each rows of
              other if other is a DataFrame or a numpy.ndarray between the Series
              and each columns of the numpy array.
  
          See Also
          --------
          DataFrame.dot: Compute the matrix product with the DataFrame.
          Series.mul: Multiplication of series and other, element-wise.
  
          Notes
          -----
          The Series and other has to share the same index if other is a Series
          or a DataFrame.
  
          Examples
          --------
          >>> s = pd.Series([0, 1, 2, 3])
          >>> other = pd.Series([-1, 2, -3, 4])
          >>> s.dot(other)
          8
          >>> s @ other
          8
          >>> df = pd.DataFrame([[0, 1], [-2, 3], [4, -5], [6, 7]])
          >>> s.dot(df)
          0    24
          1    14
          dtype: int64
          >>> arr = np.array([[0, 1], [-2, 3], [4, -5], [6, 7]])
          >>> s.dot(arr)
          array([24, 14])
          """
          if isinstance(other, (Series, ABCDataFrame)):
              common = self.index.union(other.index)
              if len(common) > len(self.index) or len(common) > len(other.index):
                  raise ValueError("matrices are not aligned")
  
              left = self.reindex(index=common, copy=False)
              right = other.reindex(index=common, copy=False)
              lvals = left.values
              rvals = right.values
          else:
              lvals = self.values
              rvals = np.asarray(other)
              if lvals.shape[0] != rvals.shape[0]:
                  raise Exception(
                      f"Dot product shape mismatch, {lvals.shape} vs {rvals.shape}"
                  )
  
          if isinstance(other, ABCDataFrame):
              return self._constructor(
                  np.dot(lvals, rvals), index=other.columns
              ).__finalize__(self, method="dot")
          elif isinstance(other, Series):
              return np.dot(lvals, rvals)
          elif isinstance(rvals, np.ndarray):
              return np.dot(lvals, rvals)
          else:  # pragma: no cover
              raise TypeError(f"unsupported type: {type(other)}")
  
      def __matmul__(self, other):
          """
          Matrix multiplication using binary `@` operator in Python>=3.5.
          """
          return self.dot(other)
  
      def __rmatmul__(self, other):
          """
          Matrix multiplication using binary `@` operator in Python>=3.5.
          """
          return self.dot(np.transpose(other))
  
      @doc(base.IndexOpsMixin.searchsorted, klass="Series")
      def searchsorted(self, value, side="left", sorter=None):
          return algorithms.searchsorted(self._values, value, side=side, sorter=sorter)
  
      # -------------------------------------------------------------------
      # Combination
  
      def append(self, to_append, ignore_index=False, verify_integrity=False):
          """
          Concatenate two or more Series.
  
          Parameters
          ----------
          to_append : Series or list/tuple of Series
              Series to append with self.
          ignore_index : bool, default False
              If True, the resulting axis will be labeled 0, 1, …, n - 1.
          verify_integrity : bool, default False
              If True, raise Exception on creating index with duplicates.
  
          Returns
          -------
          Series
              Concatenated Series.
  
          See Also
          --------
          concat : General function to concatenate DataFrame or Series objects.
  
          Notes
          -----
          Iteratively appending to a Series can be more computationally intensive
          than a single concatenate. A better solution is to append values to a
          list and then concatenate the list with the original Series all at
          once.
  
          Examples
          --------
          >>> s1 = pd.Series([1, 2, 3])
          >>> s2 = pd.Series([4, 5, 6])
          >>> s3 = pd.Series([4, 5, 6], index=[3, 4, 5])
          >>> s1.append(s2)
          0    1
          1    2
          2    3
          0    4
          1    5
          2    6
          dtype: int64
  
          >>> s1.append(s3)
          0    1
          1    2
          2    3
          3    4
          4    5
          5    6
          dtype: int64
  
          With `ignore_index` set to True:
  
          >>> s1.append(s2, ignore_index=True)
          0    1
          1    2
          2    3
          3    4
          4    5
          5    6
          dtype: int64
  
          With `verify_integrity` set to True:
  
          >>> s1.append(s2, verify_integrity=True)
          Traceback (most recent call last):
          ...
          ValueError: Indexes have overlapping values: [0, 1, 2]
          """
          from pandas.core.reshape.concat import concat
  
          if isinstance(to_append, (list, tuple)):
              to_concat = [self]
              to_concat.extend(to_append)
          else:
              to_concat = [self, to_append]
          if any(isinstance(x, (ABCDataFrame,)) for x in to_concat[1:]):
              msg = "to_append should be a Series or list/tuple of Series, got DataFrame"
              raise TypeError(msg)
          return concat(
              to_concat, ignore_index=ignore_index, verify_integrity=verify_integrity
          )
  
      def _binop(self, other, func, level=None, fill_value=None):
          """
          Perform generic binary operation with optional fill value.
  
          Parameters
          ----------
          other : Series
          func : binary operator
          fill_value : float or object
              Value to substitute for NA/null values. If both Series are NA in a
              location, the result will be NA regardless of the passed fill value.
          level : int or level name, default None
              Broadcast across a level, matching Index values on the
              passed MultiIndex level.
  
          Returns
          -------
          Series
          """
          if not isinstance(other, Series):
              raise AssertionError("Other operand must be Series")
  
          this = self
  
          if not self.index.equals(other.index):
              this, other = self.align(other, level=level, join="outer", copy=False)
  
          this_vals, other_vals = ops.fill_binop(this.values, other.values, fill_value)
  
          with np.errstate(all="ignore"):
              result = func(this_vals, other_vals)
  
          name = ops.get_op_result_name(self, other)
          return this._construct_result(result, name)
  
      def _construct_result(
          self, result: Union[ArrayLike, Tuple[ArrayLike, ArrayLike]], name: Hashable
      ) -> Union[Series, Tuple[Series, Series]]:
          """
          Construct an appropriately-labelled Series from the result of an op.
  
          Parameters
          ----------
          result : ndarray or ExtensionArray
          name : Label
  
          Returns
          -------
          Series
              In the case of __divmod__ or __rdivmod__, a 2-tuple of Series.
          """
          if isinstance(result, tuple):
              # produced by divmod or rdivmod
  
              res1 = self._construct_result(result[0], name=name)
              res2 = self._construct_result(result[1], name=name)
  
              # GH#33427 assertions to keep mypy happy
              assert isinstance(res1, Series)
              assert isinstance(res2, Series)
              return (res1, res2)
  
          # We do not pass dtype to ensure that the Series constructor
          #  does inference in the case where `result` has object-dtype.
          out = self._constructor(result, index=self.index)
          out = out.__finalize__(self)
  
          # Set the result's name after __finalize__ is called because __finalize__
          #  would set it back to self.name
          out.name = name
          return out
  
      @doc(
          generic._shared_docs["compare"],
          """
  Returns
  -------
  Series or DataFrame
      If axis is 0 or 'index' the result will be a Series.
      The resulting index will be a MultiIndex with 'self' and 'other'
      stacked alternately at the inner level.
  
      If axis is 1 or 'columns' the result will be a DataFrame.
      It will have two columns namely 'self' and 'other'.
  
  See Also
  --------
  DataFrame.compare : Compare with another DataFrame and show differences.
  
  Notes
  -----
  Matching NaNs will not appear as a difference.
  
  Examples
  --------
  >>> s1 = pd.Series(["a", "b", "c", "d", "e"])
  >>> s2 = pd.Series(["a", "a", "c", "b", "e"])
  
  Align the differences on columns
  
  >>> s1.compare(s2)
    self other
  1    b     a
  3    d     b
  
  Stack the differences on indices
  
  >>> s1.compare(s2, align_axis=0)
  1  self     b
     other    a
  3  self     d
     other    b
  dtype: object
  
  Keep all original rows
  
  >>> s1.compare(s2, keep_shape=True)
    self other
  0  NaN   NaN
  1    b     a
  2  NaN   NaN
  3    d     b
  4  NaN   NaN
  
  Keep all original rows and also all original values
  
  >>> s1.compare(s2, keep_shape=True, keep_equal=True)
    self other
  0    a     a
  1    b     a
  2    c     c
  3    d     b
  4    e     e
  """,
          klass=_shared_doc_kwargs["klass"],
      )
      def compare(
          self,
          other: Series,
          align_axis: Axis = 1,
          keep_shape: bool = False,
          keep_equal: bool = False,
      ) -> FrameOrSeriesUnion:
          return super().compare(
              other=other,
              align_axis=align_axis,
              keep_shape=keep_shape,
              keep_equal=keep_equal,
          )
  
      def combine(self, other, func, fill_value=None) -> Series:
          """
          Combine the Series with a Series or scalar according to `func`.
  
          Combine the Series and `other` using `func` to perform elementwise
          selection for combined Series.
          `fill_value` is assumed when value is missing at some index
          from one of the two objects being combined.
  
          Parameters
          ----------
          other : Series or scalar
              The value(s) to be combined with the `Series`.
          func : function
              Function that takes two scalars as inputs and returns an element.
          fill_value : scalar, optional
              The value to assume when an index is missing from
              one Series or the other. The default specifies to use the
              appropriate NaN value for the underlying dtype of the Series.
  
          Returns
          -------
          Series
              The result of combining the Series with the other object.
  
          See Also
          --------
          Series.combine_first : Combine Series values, choosing the calling
              Series' values first.
  
          Examples
          --------
          Consider 2 Datasets ``s1`` and ``s2`` containing
          highest clocked speeds of different birds.
  
          >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0})
          >>> s1
          falcon    330.0
          eagle     160.0
          dtype: float64
          >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0})
          >>> s2
          falcon    345.0
          eagle     200.0
          duck       30.0
          dtype: float64
  
          Now, to combine the two datasets and view the highest speeds
          of the birds across the two datasets
  
          >>> s1.combine(s2, max)
          duck        NaN
          eagle     200.0
          falcon    345.0
          dtype: float64
  
          In the previous example, the resulting value for duck is missing,
          because the maximum of a NaN and a float is a NaN.
          So, in the example, we set ``fill_value=0``,
          so the maximum value returned will be the value from some dataset.
  
          >>> s1.combine(s2, max, fill_value=0)
          duck       30.0
          eagle     200.0
          falcon    345.0
          dtype: float64
          """
          if fill_value is None:
              fill_value = na_value_for_dtype(self.dtype, compat=False)
  
          if isinstance(other, Series):
              # If other is a Series, result is based on union of Series,
              # so do this element by element
              new_index = self.index.union(other.index)
              new_name = ops.get_op_result_name(self, other)
              new_values = []
              for idx in new_index:
                  lv = self.get(idx, fill_value)
                  rv = other.get(idx, fill_value)
                  with np.errstate(all="ignore"):
                      new_values.append(func(lv, rv))
          else:
              # Assume that other is a scalar, so apply the function for
              # each element in the Series
              new_index = self.index
              with np.errstate(all="ignore"):
                  new_values = [func(lv, other) for lv in self._values]
              new_name = self.name
  
          if is_categorical_dtype(self.dtype):
              pass
          elif is_extension_array_dtype(self.dtype):
              # TODO: can we do this for only SparseDtype?
              # The function can return something of any type, so check
              # if the type is compatible with the calling EA.
              new_values = maybe_cast_to_extension_array(type(self._values), new_values)
          return self._constructor(new_values, index=new_index, name=new_name)
  
      def combine_first(self, other) -> Series:
          """
          Combine Series values, choosing the calling Series's values first.
  
          Parameters
          ----------
          other : Series
              The value(s) to be combined with the `Series`.
  
          Returns
          -------
          Series
              The result of combining the Series with the other object.
  
          See Also
          --------
          Series.combine : Perform elementwise operation on two Series
              using a given function.
  
          Notes
          -----
          Result index will be the union of the two indexes.
  
          Examples
          --------
          >>> s1 = pd.Series([1, np.nan])
          >>> s2 = pd.Series([3, 4])
          >>> s1.combine_first(s2)
          0    1.0
          1    4.0
          dtype: float64
          """
          new_index = self.index.union(other.index)
          this = self.reindex(new_index, copy=False)
          other = other.reindex(new_index, copy=False)
          if this.dtype.kind == "M" and other.dtype.kind != "M":
              other = to_datetime(other)
  
          return this.where(notna(this), other)
  
      def update(self, other) -> None:
          """
          Modify Series in place using values from passed Series.
  
          Uses non-NA values from passed Series to make updates. Aligns
          on index.
  
          Parameters
          ----------
          other : Series, or object coercible into Series
  
          Examples
          --------
          >>> s = pd.Series([1, 2, 3])
          >>> s.update(pd.Series([4, 5, 6]))
          >>> s
          0    4
          1    5
          2    6
          dtype: int64
  
          >>> s = pd.Series(['a', 'b', 'c'])
          >>> s.update(pd.Series(['d', 'e'], index=[0, 2]))
          >>> s
          0    d
          1    b
          2    e
          dtype: object
  
          >>> s = pd.Series([1, 2, 3])
          >>> s.update(pd.Series([4, 5, 6, 7, 8]))
          >>> s
          0    4
          1    5
          2    6
          dtype: int64
  
          If ``other`` contains NaNs the corresponding values are not updated
          in the original Series.
  
          >>> s = pd.Series([1, 2, 3])
          >>> s.update(pd.Series([4, np.nan, 6]))
          >>> s
          0    4
          1    2
          2    6
          dtype: int64
  
          ``other`` can also be a non-Series object type
          that is coercible into a Series
  
          >>> s = pd.Series([1, 2, 3])
          >>> s.update([4, np.nan, 6])
          >>> s
          0    4
          1    2
          2    6
          dtype: int64
  
          >>> s = pd.Series([1, 2, 3])
          >>> s.update({1: 9})
          >>> s
          0    1
          1    9
          2    3
          dtype: int64
          """
  
          if not isinstance(other, Series):
              other = Series(other)
  
          other = other.reindex_like(self)
          mask = notna(other)
  
          self._mgr = self._mgr.putmask(mask=mask, new=other)
          self._maybe_update_cacher()
  
      # ----------------------------------------------------------------------
      # Reindexing, sorting
  
      def sort_values(
          self,
          axis=0,
          ascending=True,
          inplace: bool = False,
          kind: str = "quicksort",
          na_position: str = "last",
          ignore_index: bool = False,
          key: ValueKeyFunc = None,
      ):
          """
          Sort by the values.
  
          Sort a Series in ascending or descending order by some
          criterion.
  
          Parameters
          ----------
          axis : {0 or 'index'}, default 0
              Axis to direct sorting. The value 'index' is accepted for
              compatibility with DataFrame.sort_values.
          ascending : bool, default True
              If True, sort values in ascending order, otherwise descending.
          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.
          na_position : {'first' or 'last'}, default 'last'
              Argument 'first' puts NaNs at the beginning, '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
              If not None, apply the key function to the series 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 an array-like.
  
              .. versionadded:: 1.1.0
  
          Returns
          -------
          Series or None
              Series ordered by values or None if ``inplace=True``.
  
          See Also
          --------
          Series.sort_index : Sort by the Series indices.
          DataFrame.sort_values : Sort DataFrame by the values along either axis.
          DataFrame.sort_index : Sort DataFrame by indices.
  
          Examples
          --------
          >>> s = pd.Series([np.nan, 1, 3, 10, 5])
          >>> s
          0     NaN
          1     1.0
          2     3.0
          3     10.0
          4     5.0
          dtype: float64
  
          Sort values ascending order (default behaviour)
  
          >>> s.sort_values(ascending=True)
          1     1.0
          2     3.0
          4     5.0
          3    10.0
          0     NaN
          dtype: float64
  
          Sort values descending order
  
          >>> s.sort_values(ascending=False)
          3    10.0
          4     5.0
          2     3.0
          1     1.0
          0     NaN
          dtype: float64
  
          Sort values inplace
  
          >>> s.sort_values(ascending=False, inplace=True)
          >>> s
          3    10.0
          4     5.0
          2     3.0
          1     1.0
          0     NaN
          dtype: float64
  
          Sort values putting NAs first
  
          >>> s.sort_values(na_position='first')
          0     NaN
          1     1.0
          2     3.0
          4     5.0
          3    10.0
          dtype: float64
  
          Sort a series of strings
  
          >>> s = pd.Series(['z', 'b', 'd', 'a', 'c'])
          >>> s
          0    z
          1    b
          2    d
          3    a
          4    c
          dtype: object
  
          >>> s.sort_values()
          3    a
          1    b
          4    c
          2    d
          0    z
          dtype: object
  
          Sort using a key function. Your `key` function will be
          given the ``Series`` of values and should return an array-like.
  
          >>> s = pd.Series(['a', 'B', 'c', 'D', 'e'])
          >>> s.sort_values()
          1    B
          3    D
          0    a
          2    c
          4    e
          dtype: object
          >>> s.sort_values(key=lambda x: x.str.lower())
          0    a
          1    B
          2    c
          3    D
          4    e
          dtype: object
  
          NumPy ufuncs work well here. For example, we can
          sort by the ``sin`` of the value
  
          >>> s = pd.Series([-4, -2, 0, 2, 4])
          >>> s.sort_values(key=np.sin)
          1   -2
          4    4
          2    0
          0   -4
          3    2
          dtype: int64
  
          More complicated user-defined functions can be used,
          as long as they expect a Series and return an array-like
  
          >>> s.sort_values(key=lambda x: (np.tan(x.cumsum())))
          0   -4
          3    2
          4    4
          1   -2
          2    0
          dtype: int64
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
          # Validate the axis parameter
          self._get_axis_number(axis)
  
          # GH 5856/5853
          if inplace and self._is_cached:
              raise ValueError(
                  "This Series is a view of some other array, to "
                  "sort in-place you must create a copy"
              )
  
          if is_list_like(ascending):
              if len(ascending) != 1:
                  raise ValueError(
                      f"Length of ascending ({len(ascending)}) must be 1 for Series"
                  )
              ascending = ascending[0]
  
          if not is_bool(ascending):
              raise ValueError("ascending must be boolean")
  
          if na_position not in ["first", "last"]:
              raise ValueError(f"invalid na_position: {na_position}")
  
          # GH 35922. Make sorting stable by leveraging nargsort
          values_to_sort = ensure_key_mapped(self, key)._values if key else self._values
          sorted_index = nargsort(values_to_sort, kind, ascending, na_position)
  
          result = self._constructor(
              self._values[sorted_index], index=self.index[sorted_index]
          )
  
          if ignore_index:
              result.index = ibase.default_index(len(sorted_index))
  
          if inplace:
              self._update_inplace(result)
          else:
              return result.__finalize__(self, method="sort_values")
  
      def sort_index(
          self,
          axis=0,
          level=None,
          ascending: bool = True,
          inplace: bool = False,
          kind: str = "quicksort",
          na_position: str = "last",
          sort_remaining: bool = True,
          ignore_index: bool = False,
          key: IndexKeyFunc = None,
      ):
          """
          Sort Series by index labels.
  
          Returns a new Series sorted by label if `inplace` argument is
          ``False``, otherwise updates the original series and returns None.
  
          Parameters
          ----------
          axis : int, default 0
              Axis to direct sorting. This can only be 0 for Series.
          level : int, optional
              If not None, sort on values in specified index level(s).
          ascending : bool or list of bools, default True
              Sort ascending vs. descending. When the index is a MultiIndex the
              sort direction can be controlled for each level individually.
          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'
              If 'first' puts NaNs at the beginning, 'last' puts NaNs at the end.
              Not implemented for MultiIndex.
          sort_remaining : bool, default True
              If True and sorting by level and index is multilevel, sort by other
              levels too (in order) after sorting by specified level.
          ignore_index : bool, default False
              If True, the resulting axis will be labeled 0, 1, …, n - 1.
  
              .. versionadded:: 1.0.0
  
          key : callable, optional
              If not None, apply the key function to the index 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 an
              ``Index`` and return an ``Index`` of the same shape.
  
              .. versionadded:: 1.1.0
  
          Returns
          -------
          Series or None
              The original Series sorted by the labels or None if ``inplace=True``.
  
          See Also
          --------
          DataFrame.sort_index: Sort DataFrame by the index.
          DataFrame.sort_values: Sort DataFrame by the value.
          Series.sort_values : Sort Series by the value.
  
          Examples
          --------
          >>> s = pd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, 4])
          >>> s.sort_index()
          1    c
          2    b
          3    a
          4    d
          dtype: object
  
          Sort Descending
  
          >>> s.sort_index(ascending=False)
          4    d
          3    a
          2    b
          1    c
          dtype: object
  
          Sort Inplace
  
          >>> s.sort_index(inplace=True)
          >>> s
          1    c
          2    b
          3    a
          4    d
          dtype: object
  
          By default NaNs are put at the end, but use `na_position` to place
          them at the beginning
  
          >>> s = pd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, np.nan])
          >>> s.sort_index(na_position='first')
          NaN     d
           1.0    c
           2.0    b
           3.0    a
          dtype: object
  
          Specify index level to sort
  
          >>> arrays = [np.array(['qux', 'qux', 'foo', 'foo',
          ...                     'baz', 'baz', 'bar', 'bar']),
          ...           np.array(['two', 'one', 'two', 'one',
          ...                     'two', 'one', 'two', 'one'])]
          >>> s = pd.Series([1, 2, 3, 4, 5, 6, 7, 8], index=arrays)
          >>> s.sort_index(level=1)
          bar  one    8
          baz  one    6
          foo  one    4
          qux  one    2
          bar  two    7
          baz  two    5
          foo  two    3
          qux  two    1
          dtype: int64
  
          Does not sort by remaining levels when sorting by levels
  
          >>> s.sort_index(level=1, sort_remaining=False)
          qux  one    2
          foo  one    4
          baz  one    6
          bar  one    8
          qux  two    1
          foo  two    3
          baz  two    5
          bar  two    7
          dtype: int64
  
          Apply a key function before sorting
  
          >>> s = pd.Series([1, 2, 3, 4], index=['A', 'b', 'C', 'd'])
          >>> s.sort_index(key=lambda x : x.str.lower())
          A    1
          b    2
          C    3
          d    4
          dtype: int64
          """
  
          return super().sort_index(
              axis,
              level,
              ascending,
              inplace,
              kind,
              na_position,
              sort_remaining,
              ignore_index,
              key,
          )
  
      def argsort(self, axis=0, kind="quicksort", order=None) -> Series:
          """
          Return the integer indices that would sort the Series values.
  
          Override ndarray.argsort. Argsorts the value, omitting NA/null values,
          and places the result in the same locations as the non-NA values.
  
          Parameters
          ----------
          axis : {0 or "index"}
              Has no effect but is accepted for compatibility with numpy.
          kind : {'mergesort', 'quicksort', 'heapsort', 'stable'}, default 'quicksort'
              Choice of sorting algorithm. See :func:`numpy.sort` for more
              information. 'mergesort' and 'stable' are the only stable algorithms.
          order : None
              Has no effect but is accepted for compatibility with numpy.
  
          Returns
          -------
          Series
              Positions of values within the sort order with -1 indicating
              nan values.
  
          See Also
          --------
          numpy.ndarray.argsort : Returns the indices that would sort this array.
          """
          values = self._values
          mask = isna(values)
  
          if mask.any():
              result = Series(-1, index=self.index, name=self.name, dtype="int64")
              notmask = ~mask
              result[notmask] = np.argsort(values[notmask], kind=kind)
              return self._constructor(result, index=self.index).__finalize__(
                  self, method="argsort"
              )
          else:
              return self._constructor(
                  np.argsort(values, kind=kind), index=self.index, dtype="int64"
              ).__finalize__(self, method="argsort")
  
      def nlargest(self, n=5, keep="first") -> Series:
          """
          Return the largest `n` elements.
  
          Parameters
          ----------
          n : int, default 5
              Return this many descending sorted values.
          keep : {'first', 'last', 'all'}, default 'first'
              When there are duplicate values that cannot all fit in a
              Series of `n` elements:
  
              - ``first`` : return the first `n` occurrences in order
                  of appearance.
              - ``last`` : return the last `n` occurrences in reverse
                  order of appearance.
              - ``all`` : keep all occurrences. This can result in a Series of
                  size larger than `n`.
  
          Returns
          -------
          Series
              The `n` largest values in the Series, sorted in decreasing order.
  
          See Also
          --------
          Series.nsmallest: Get the `n` smallest elements.
          Series.sort_values: Sort Series by values.
          Series.head: Return the first `n` rows.
  
          Notes
          -----
          Faster than ``.sort_values(ascending=False).head(n)`` for small `n`
          relative to the size of the ``Series`` object.
  
          Examples
          --------
          >>> countries_population = {"Italy": 59000000, "France": 65000000,
          ...                         "Malta": 434000, "Maldives": 434000,
          ...                         "Brunei": 434000, "Iceland": 337000,
          ...                         "Nauru": 11300, "Tuvalu": 11300,
          ...                         "Anguilla": 11300, "Montserrat": 5200}
          >>> s = pd.Series(countries_population)
          >>> s
          Italy       59000000
          France      65000000
          Malta         434000
          Maldives      434000
          Brunei        434000
          Iceland       337000
          Nauru          11300
          Tuvalu         11300
          Anguilla       11300
          Montserrat      5200
          dtype: int64
  
          The `n` largest elements where ``n=5`` by default.
  
          >>> s.nlargest()
          France      65000000
          Italy       59000000
          Malta         434000
          Maldives      434000
          Brunei        434000
          dtype: int64
  
          The `n` largest elements where ``n=3``. Default `keep` value is 'first'
          so Malta will be kept.
  
          >>> s.nlargest(3)
          France    65000000
          Italy     59000000
          Malta       434000
          dtype: int64
  
          The `n` largest elements where ``n=3`` and keeping the last duplicates.
          Brunei will be kept since it is the last with value 434000 based on
          the index order.
  
          >>> s.nlargest(3, keep='last')
          France      65000000
          Italy       59000000
          Brunei        434000
          dtype: int64
  
          The `n` largest elements where ``n=3`` with all duplicates kept. Note
          that the returned Series has five elements due to the three duplicates.
  
          >>> s.nlargest(3, keep='all')
          France      65000000
          Italy       59000000
          Malta         434000
          Maldives      434000
          Brunei        434000
          dtype: int64
          """
          return algorithms.SelectNSeries(self, n=n, keep=keep).nlargest()
  
      def nsmallest(self, n=5, keep="first") -> Series:
          """
          Return the smallest `n` elements.
  
          Parameters
          ----------
          n : int, default 5
              Return this many ascending sorted values.
          keep : {'first', 'last', 'all'}, default 'first'
              When there are duplicate values that cannot all fit in a
              Series of `n` elements:
  
              - ``first`` : return the first `n` occurrences in order
                  of appearance.
              - ``last`` : return the last `n` occurrences in reverse
                  order of appearance.
              - ``all`` : keep all occurrences. This can result in a Series of
                  size larger than `n`.
  
          Returns
          -------
          Series
              The `n` smallest values in the Series, sorted in increasing order.
  
          See Also
          --------
          Series.nlargest: Get the `n` largest elements.
          Series.sort_values: Sort Series by values.
          Series.head: Return the first `n` rows.
  
          Notes
          -----
          Faster than ``.sort_values().head(n)`` for small `n` relative to
          the size of the ``Series`` object.
  
          Examples
          --------
          >>> countries_population = {"Italy": 59000000, "France": 65000000,
          ...                         "Brunei": 434000, "Malta": 434000,
          ...                         "Maldives": 434000, "Iceland": 337000,
          ...                         "Nauru": 11300, "Tuvalu": 11300,
          ...                         "Anguilla": 11300, "Montserrat": 5200}
          >>> s = pd.Series(countries_population)
          >>> s
          Italy       59000000
          France      65000000
          Brunei        434000
          Malta         434000
          Maldives      434000
          Iceland       337000
          Nauru          11300
          Tuvalu         11300
          Anguilla       11300
          Montserrat      5200
          dtype: int64
  
          The `n` smallest elements where ``n=5`` by default.
  
          >>> s.nsmallest()
          Montserrat    5200
          Nauru        11300
          Tuvalu       11300
          Anguilla     11300
          Iceland     337000
          dtype: int64
  
          The `n` smallest elements where ``n=3``. Default `keep` value is
          'first' so Nauru and Tuvalu will be kept.
  
          >>> s.nsmallest(3)
          Montserrat   5200
          Nauru       11300
          Tuvalu      11300
          dtype: int64
  
          The `n` smallest elements where ``n=3`` and keeping the last
          duplicates. Anguilla and Tuvalu will be kept since they are the last
          with value 11300 based on the index order.
  
          >>> s.nsmallest(3, keep='last')
          Montserrat   5200
          Anguilla    11300
          Tuvalu      11300
          dtype: int64
  
          The `n` smallest elements where ``n=3`` with all duplicates kept. Note
          that the returned Series has four elements due to the three duplicates.
  
          >>> s.nsmallest(3, keep='all')
          Montserrat   5200
          Nauru       11300
          Tuvalu      11300
          Anguilla    11300
          dtype: int64
          """
          return algorithms.SelectNSeries(self, n=n, keep=keep).nsmallest()
  
      def swaplevel(self, i=-2, j=-1, copy=True) -> Series:
          """
          Swap levels i and j in a :class:`MultiIndex`.
  
          Default is to swap the two innermost levels of the index.
  
          Parameters
          ----------
          i, j : int, str
              Level of the indices to be swapped. Can pass level name as string.
          copy : bool, default True
              Whether to copy underlying data.
  
          Returns
          -------
          Series
              Series with levels swapped in MultiIndex.
          """
          assert isinstance(self.index, MultiIndex)
          new_index = self.index.swaplevel(i, j)
          return self._constructor(self._values, index=new_index, copy=copy).__finalize__(
              self, method="swaplevel"
          )
  
      def reorder_levels(self, order) -> Series:
          """
          Rearrange index levels using input order.
  
          May not drop or duplicate levels.
  
          Parameters
          ----------
          order : list of int representing new level order
              Reference level by number or key.
  
          Returns
          -------
          type of caller (new object)
          """
          if not isinstance(self.index, MultiIndex):  # pragma: no cover
              raise Exception("Can only reorder levels on a hierarchical axis.")
  
          result = self.copy()
          assert isinstance(result.index, MultiIndex)
          result.index = result.index.reorder_levels(order)
          return result
  
      def explode(self, ignore_index: bool = False) -> Series:
          """
          Transform each element of a list-like to a row.
  
          .. versionadded:: 0.25.0
  
          Parameters
          ----------
          ignore_index : bool, default False
              If True, the resulting index will be labeled 0, 1, …, n - 1.
  
              .. versionadded:: 1.1.0
  
          Returns
          -------
          Series
              Exploded lists to rows; index will be duplicated for these rows.
  
          See Also
          --------
          Series.str.split : Split string values on specified separator.
          Series.unstack : Unstack, a.k.a. pivot, Series with MultiIndex
              to produce DataFrame.
          DataFrame.melt : Unpivot a DataFrame from wide format to long format.
          DataFrame.explode : Explode a DataFrame from list-like
              columns to long format.
  
          Notes
          -----
          This routine will explode list-likes including lists, tuples, sets,
          Series, and np.ndarray. The result dtype of the subset rows will
          be object. Scalars will be returned unchanged, and empty list-likes will
          result in a np.nan for that row. In addition, the ordering of elements in
          the output will be non-deterministic when exploding sets.
  
          Examples
          --------
          >>> s = pd.Series([[1, 2, 3], 'foo', [], [3, 4]])
          >>> s
          0    [1, 2, 3]
          1          foo
          2           []
          3       [3, 4]
          dtype: object
  
          >>> s.explode()
          0      1
          0      2
          0      3
          1    foo
          2    NaN
          3      3
          3      4
          dtype: object
          """
          if not len(self) or not is_object_dtype(self):
              return self.copy()
  
          values, counts = reshape.explode(np.asarray(self.array))
  
          if ignore_index:
              index = ibase.default_index(len(values))
          else:
              index = self.index.repeat(counts)
  
          return self._constructor(values, index=index, name=self.name)
  
      def unstack(self, level=-1, fill_value=None):
          """
          Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.
  
          Parameters
          ----------
          level : int, str, or list of these, default last level
              Level(s) to unstack, can pass level name.
          fill_value : scalar value, default None
              Value to use when replacing NaN values.
  
          Returns
          -------
          DataFrame
              Unstacked Series.
  
          Examples
          --------
          >>> s = pd.Series([1, 2, 3, 4],
          ...               index=pd.MultiIndex.from_product([['one', 'two'],
          ...                                                 ['a', 'b']]))
          >>> s
          one  a    1
               b    2
          two  a    3
               b    4
          dtype: int64
  
          >>> s.unstack(level=-1)
               a  b
          one  1  2
          two  3  4
  
          >>> s.unstack(level=0)
             one  two
          a    1    3
          b    2    4
          """
          from pandas.core.reshape.reshape import unstack
  
          return unstack(self, level, fill_value)
  
      # ----------------------------------------------------------------------
      # function application
  
      def map(self, arg, na_action=None) -> Series:
          """
          Map values of Series according to input correspondence.
  
          Used for substituting each value in a Series with another value,
          that may be derived from a function, a ``dict`` or
          a :class:`Series`.
  
          Parameters
          ----------
          arg : function, collections.abc.Mapping subclass or Series
              Mapping correspondence.
          na_action : {None, 'ignore'}, default None
              If 'ignore', propagate NaN values, without passing them to the
              mapping correspondence.
  
          Returns
          -------
          Series
              Same index as caller.
  
          See Also
          --------
          Series.apply : For applying more complex functions on a Series.
          DataFrame.apply : Apply a function row-/column-wise.
          DataFrame.applymap : Apply a function elementwise on a whole DataFrame.
  
          Notes
          -----
          When ``arg`` is a dictionary, values in Series that are not in the
          dictionary (as keys) are converted to ``NaN``. However, if the
          dictionary is a ``dict`` subclass that defines ``__missing__`` (i.e.
          provides a method for default values), then this default is used
          rather than ``NaN``.
  
          Examples
          --------
          >>> s = pd.Series(['cat', 'dog', np.nan, 'rabbit'])
          >>> s
          0      cat
          1      dog
          2      NaN
          3   rabbit
          dtype: object
  
          ``map`` accepts a ``dict`` or a ``Series``. Values that are not found
          in the ``dict`` are converted to ``NaN``, unless the dict has a default
          value (e.g. ``defaultdict``):
  
          >>> s.map({'cat': 'kitten', 'dog': 'puppy'})
          0   kitten
          1    puppy
          2      NaN
          3      NaN
          dtype: object
  
          It also accepts a function:
  
          >>> s.map('I am a {}'.format)
          0       I am a cat
          1       I am a dog
          2       I am a nan
          3    I am a rabbit
          dtype: object
  
          To avoid applying the function to missing values (and keep them as
          ``NaN``) ``na_action='ignore'`` can be used:
  
          >>> s.map('I am a {}'.format, na_action='ignore')
          0     I am a cat
          1     I am a dog
          2            NaN
          3  I am a rabbit
          dtype: object
          """
          new_values = super()._map_values(arg, na_action=na_action)
          return self._constructor(new_values, index=self.index).__finalize__(
              self, method="map"
          )
  
      def _gotitem(self, key, ndim, subset=None) -> Series:
          """
          Sub-classes to define. Return a sliced object.
  
          Parameters
          ----------
          key : string / list of selections
          ndim : 1,2
              Requested ndim of result.
          subset : object, default None
              Subset to act on.
          """
          return self
  
      _agg_see_also_doc = dedent(
          """
      See Also
      --------
      Series.apply : Invoke function on a Series.
      Series.transform : Transform function producing a Series with like indexes.
      """
      )
  
      _agg_examples_doc = dedent(
          """
      Examples
      --------
      >>> s = pd.Series([1, 2, 3, 4])
      >>> s
      0    1
      1    2
      2    3
      3    4
      dtype: int64
  
      >>> s.agg('min')
      1
  
      >>> s.agg(['min', 'max'])
      min   1
      max   4
      dtype: int64
      """
      )
  
      @doc(
          generic._shared_docs["aggregate"],
          klass=_shared_doc_kwargs["klass"],
          axis=_shared_doc_kwargs["axis"],
          see_also=_agg_see_also_doc,
          examples=_agg_examples_doc,
      )
      def aggregate(self, func=None, axis=0, *args, **kwargs):
          # Validate the axis parameter
          self._get_axis_number(axis)
  
          # if func is None, will switch to user-provided "named aggregation" kwargs
          if func is None:
              func = dict(kwargs.items())
  
          op = series_apply(self, func, args=args, kwds=kwargs)
          result, how = op.agg()
          if result is None:
  
              # we can be called from an inner function which
              # passes this meta-data
              kwargs.pop("_axis", None)
              kwargs.pop("_level", None)
  
              # try a regular apply, this evaluates lambdas
              # row-by-row; however if the lambda is expected a Series
              # expression, e.g.: lambda x: x-x.quantile(0.25)
              # this will fail, so we can try a vectorized evaluation
  
              # we cannot FIRST try the vectorized evaluation, because
              # then .agg and .apply would have different semantics if the
              # operation is actually defined on the Series, e.g. str
              try:
                  result = self.apply(func, *args, **kwargs)
              except (ValueError, AttributeError, TypeError):
                  result = func(self, *args, **kwargs)
  
          return result
  
      agg = aggregate
  
      @doc(
          _shared_docs["transform"],
          klass=_shared_doc_kwargs["klass"],
          axis=_shared_doc_kwargs["axis"],
      )
      def transform(
          self, func: AggFuncType, axis: Axis = 0, *args, **kwargs
      ) -> FrameOrSeriesUnion:
          return transform(self, func, axis, *args, **kwargs)
  
      def apply(
          self,
          func: AggFuncType,
          convert_dtype: bool = True,
          args: Tuple[Any, ...] = (),
          **kwds,
      ) -> FrameOrSeriesUnion:
          """
          Invoke function on values of Series.
  
          Can be ufunc (a NumPy function that applies to the entire Series)
          or a Python function that only works on single values.
  
          Parameters
          ----------
          func : function
              Python function or NumPy ufunc to apply.
          convert_dtype : bool, default True
              Try to find better dtype for elementwise function results. If
              False, leave as dtype=object.
          args : tuple
              Positional arguments passed to func after the series value.
          **kwds
              Additional keyword arguments passed to func.
  
          Returns
          -------
          Series or DataFrame
              If func returns a Series object the result will be a DataFrame.
  
          See Also
          --------
          Series.map: For element-wise operations.
          Series.agg: Only perform aggregating type operations.
          Series.transform: Only perform transforming type operations.
  
          Examples
          --------
          Create a series with typical summer temperatures for each city.
  
          >>> s = pd.Series([20, 21, 12],
          ...               index=['London', 'New York', 'Helsinki'])
          >>> s
          London      20
          New York    21
          Helsinki    12
          dtype: int64
  
          Square the values by defining a function and passing it as an
          argument to ``apply()``.
  
          >>> def square(x):
          ...     return x ** 2
          >>> s.apply(square)
          London      400
          New York    441
          Helsinki    144
          dtype: int64
  
          Square the values by passing an anonymous function as an
          argument to ``apply()``.
  
          >>> s.apply(lambda x: x ** 2)
          London      400
          New York    441
          Helsinki    144
          dtype: int64
  
          Define a custom function that needs additional positional
          arguments and pass these additional arguments using the
          ``args`` keyword.
  
          >>> def subtract_custom_value(x, custom_value):
          ...     return x - custom_value
  
          >>> s.apply(subtract_custom_value, args=(5,))
          London      15
          New York    16
          Helsinki     7
          dtype: int64
  
          Define a custom function that takes keyword arguments
          and pass these arguments to ``apply``.
  
          >>> def add_custom_values(x, **kwargs):
          ...     for month in kwargs:
          ...         x += kwargs[month]
          ...     return x
  
          >>> s.apply(add_custom_values, june=30, july=20, august=25)
          London      95
          New York    96
          Helsinki    87
          dtype: int64
  
          Use a function from the Numpy library.
  
          >>> s.apply(np.log)
          London      2.995732
          New York    3.044522
          Helsinki    2.484907
          dtype: float64
          """
          op = series_apply(self, func, convert_dtype, args, kwds)
          return op.apply()
  
      def _reduce(
          self,
          op,
          name: str,
          *,
          axis=0,
          skipna=True,
          numeric_only=None,
          filter_type=None,
          **kwds,
      ):
          """
          Perform a reduction operation.
  
          If we have an ndarray as a value, then simply perform the operation,
          otherwise delegate to the object.
          """
          delegate = self._values
  
          if axis is not None:
              self._get_axis_number(axis)
  
          if isinstance(delegate, ExtensionArray):
              # dispatch to ExtensionArray interface
              return delegate._reduce(name, skipna=skipna, **kwds)
  
          else:
              # dispatch to numpy arrays
              if numeric_only:
                  raise NotImplementedError(
                      f"Series.{name} does not implement numeric_only."
                  )
              with np.errstate(all="ignore"):
                  return op(delegate, skipna=skipna, **kwds)
  
      def _reindex_indexer(self, new_index, indexer, copy):
          if indexer is None:
              if copy:
                  return self.copy()
              return self
  
          new_values = algorithms.take_1d(
              self._values, indexer, allow_fill=True, fill_value=None
          )
          return self._constructor(new_values, index=new_index)
  
      def _needs_reindex_multi(self, axes, method, level):
          """
          Check if we do need a multi reindex; this is for compat with
          higher dims.
          """
          return False
  
      @doc(
          NDFrame.align,
          klass=_shared_doc_kwargs["klass"],
          axes_single_arg=_shared_doc_kwargs["axes_single_arg"],
      )
      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,
      ):
          return super().align(
              other,
              join=join,
              axis=axis,
              level=level,
              copy=copy,
              fill_value=fill_value,
              method=method,
              limit=limit,
              fill_axis=fill_axis,
              broadcast_axis=broadcast_axis,
          )
  
      def rename(
          self,
          index=None,
          *,
          axis=None,
          copy=True,
          inplace=False,
          level=None,
          errors="ignore",
      ):
          """
          Alter Series index labels or name.
  
          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.
  
          See the :ref:`user guide <basics.rename>` for more.
  
          Parameters
          ----------
          axis : {0 or "index"}
              Unused. Accepted for compatibility with DataFrame method only.
          index : scalar, hashable sequence, dict-like or function, optional
              Functions or dict-like are transformations to apply to
              the index.
              Scalar or hashable sequence-like will alter the ``Series.name``
              attribute.
  
          **kwargs
              Additional keyword arguments passed to the function. Only the
              "inplace" keyword is used.
  
          Returns
          -------
          Series or None
              Series with index labels or name altered or None if ``inplace=True``.
  
          See Also
          --------
          DataFrame.rename : Corresponding DataFrame method.
          Series.rename_axis : Set the name of the 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
          """
          if callable(index) or is_dict_like(index):
              return super().rename(
                  index, copy=copy, inplace=inplace, level=level, errors=errors
              )
          else:
              return self._set_name(index, inplace=inplace)
  
      @Appender(
          """
          Examples
          --------
          >>> s = pd.Series([1, 2, 3])
          >>> s
          0    1
          1    2
          2    3
          dtype: int64
  
          >>> s.set_axis(['a', 'b', 'c'], axis=0)
          a    1
          b    2
          c    3
          dtype: int64
      """
      )
      @Substitution(
          **_shared_doc_kwargs,
          extended_summary_sub="",
          axis_description_sub="",
          see_also_sub="",
      )
      @Appender(generic.NDFrame.set_axis.__doc__)
      def set_axis(self, labels, axis: Axis = 0, inplace: bool = False):
          return super().set_axis(labels, axis=axis, inplace=inplace)
  
      @doc(
          NDFrame.reindex,
          klass=_shared_doc_kwargs["klass"],
          axes=_shared_doc_kwargs["axes"],
          optional_labels=_shared_doc_kwargs["optional_labels"],
          optional_axis=_shared_doc_kwargs["optional_axis"],
      )
      def reindex(self, index=None, **kwargs):
          return super().reindex(index=index, **kwargs)
  
      def drop(
          self,
          labels=None,
          axis=0,
          index=None,
          columns=None,
          level=None,
          inplace=False,
          errors="raise",
      ) -> Series:
          """
          Return Series with specified index labels removed.
  
          Remove elements of a Series based on specifying the index labels.
          When using a multi-index, labels on different levels can be removed
          by specifying the level.
  
          Parameters
          ----------
          labels : single label or list-like
              Index labels to drop.
          axis : 0, default 0
              Redundant for application on Series.
          index : single label or list-like
              Redundant for application on Series, but 'index' can be used instead
              of 'labels'.
          columns : single label or list-like
              No change is made to the Series; use 'index' or 'labels' instead.
          level : int or level name, optional
              For MultiIndex, level for which the labels will be removed.
          inplace : bool, default False
              If True, do operation inplace and return None.
          errors : {'ignore', 'raise'}, default 'raise'
              If 'ignore', suppress error and only existing labels are dropped.
  
          Returns
          -------
          Series or None
              Series with specified index labels removed or None if ``inplace=True``.
  
          Raises
          ------
          KeyError
              If none of the labels are found in the index.
  
          See Also
          --------
          Series.reindex : Return only specified index labels of Series.
          Series.dropna : Return series without null values.
          Series.drop_duplicates : Return Series with duplicate values removed.
          DataFrame.drop : Drop specified labels from rows or columns.
  
          Examples
          --------
          >>> s = pd.Series(data=np.arange(3), index=['A', 'B', 'C'])
          >>> s
          A  0
          B  1
          C  2
          dtype: int64
  
          Drop labels B en C
  
          >>> s.drop(labels=['B', 'C'])
          A  0
          dtype: int64
  
          Drop 2nd level label in MultiIndex Series
  
          >>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
          ...                              ['speed', 'weight', 'length']],
          ...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
          ...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
          >>> s = pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
          ...               index=midx)
          >>> s
          lama    speed      45.0
                  weight    200.0
                  length      1.2
          cow     speed      30.0
                  weight    250.0
                  length      1.5
          falcon  speed     320.0
                  weight      1.0
                  length      0.3
          dtype: float64
  
          >>> s.drop(labels='weight', level=1)
          lama    speed      45.0
                  length      1.2
          cow     speed      30.0
                  length      1.5
          falcon  speed     320.0
                  length      0.3
          dtype: float64
          """
          return super().drop(
              labels=labels,
              axis=axis,
              index=index,
              columns=columns,
              level=level,
              inplace=inplace,
              errors=errors,
          )
  
      @doc(NDFrame.fillna, **_shared_doc_kwargs)
      def fillna(
          self,
          value=None,
          method=None,
          axis=None,
          inplace=False,
          limit=None,
          downcast=None,
      ) -> Optional[Series]:
          return super().fillna(
              value=value,
              method=method,
              axis=axis,
              inplace=inplace,
              limit=limit,
              downcast=downcast,
          )
  
      def pop(self, item: Hashable) -> Any:
          """
          Return item and drops from series. Raise KeyError if not found.
  
          Parameters
          ----------
          item : label
              Index of the element that needs to be removed.
  
          Returns
          -------
          Value that is popped from series.
  
          Examples
          --------
          >>> ser = pd.Series([1,2,3])
  
          >>> ser.pop(0)
          1
  
          >>> ser
          1    2
          2    3
          dtype: int64
          """
          return super().pop(item=item)
  
      @doc(
          NDFrame.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=False,
          limit=None,
          regex=False,
          method="pad",
      ):
          return super().replace(
              to_replace=to_replace,
              value=value,
              inplace=inplace,
              limit=limit,
              regex=regex,
              method=method,
          )
  
      def _replace_single(self, to_replace, method: str, inplace: bool, limit):
          """
          Replaces values in a Series using the fill method specified when no
          replacement value is given in the replace method
          """
  
          orig_dtype = self.dtype
          result = self if inplace else self.copy()
          fill_f = missing.get_fill_func(method)
  
          mask = missing.mask_missing(result.values, to_replace)
          values = fill_f(result.values, limit=limit, mask=mask)
  
          if values.dtype == orig_dtype and inplace:
              return
  
          result = self._constructor(values, index=self.index, dtype=self.dtype)
          result = result.__finalize__(self)
  
          if inplace:
              self._update_inplace(result)
              return
  
          return result
  
      @doc(NDFrame.shift, klass=_shared_doc_kwargs["klass"])
      def shift(self, periods=1, freq=None, axis=0, fill_value=None) -> Series:
          return super().shift(
              periods=periods, freq=freq, axis=axis, fill_value=fill_value
          )
  
      def memory_usage(self, index=True, deep=False):
          """
          Return the memory usage of the Series.
  
          The memory usage can optionally include the contribution of
          the index and of elements of `object` dtype.
  
          Parameters
          ----------
          index : bool, default True
              Specifies whether to include the memory usage of the Series index.
          deep : bool, default False
              If True, introspect the data deeply by interrogating
              `object` dtypes for system-level memory consumption, and include
              it in the returned value.
  
          Returns
          -------
          int
              Bytes of memory consumed.
  
          See Also
          --------
          numpy.ndarray.nbytes : Total bytes consumed by the elements of the
              array.
          DataFrame.memory_usage : Bytes consumed by a DataFrame.
  
          Examples
          --------
          >>> s = pd.Series(range(3))
          >>> s.memory_usage()
          152
  
          Not including the index gives the size of the rest of the data, which
          is necessarily smaller:
  
          >>> s.memory_usage(index=False)
          24
  
          The memory footprint of `object` values is ignored by default:
  
          >>> s = pd.Series(["a", "b"])
          >>> s.values
          array(['a', 'b'], dtype=object)
          >>> s.memory_usage()
          144
          >>> s.memory_usage(deep=True)
          244
          """
          v = super().memory_usage(deep=deep)
          if index:
              v += self.index.memory_usage(deep=deep)
          return v
  
      def isin(self, values) -> Series:
          """
          Whether elements in Series are contained in `values`.
  
          Return a boolean Series showing whether each element in the Series
          matches an element in the passed sequence of `values` exactly.
  
          Parameters
          ----------
          values : set or list-like
              The sequence of values to test. Passing in a single string will
              raise a ``TypeError``. Instead, turn a single string into a
              list of one element.
  
          Returns
          -------
          Series
              Series of booleans indicating if each element is in values.
  
          Raises
          ------
          TypeError
            * If `values` is a string
  
          See Also
          --------
          DataFrame.isin : Equivalent method on DataFrame.
  
          Examples
          --------
          >>> s = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama',
          ...                'hippo'], name='animal')
          >>> s.isin(['cow', 'lama'])
          0     True
          1     True
          2     True
          3    False
          4     True
          5    False
          Name: animal, dtype: bool
  
          Passing a single string as ``s.isin('lama')`` will raise an error. Use
          a list of one element instead:
  
          >>> s.isin(['lama'])
          0     True
          1    False
          2     True
          3    False
          4     True
          5    False
          Name: animal, dtype: bool
  
          Strings and integers are distinct and are therefore not comparable:
  
          >>> pd.Series([1]).isin(['1'])
          0    False
          dtype: bool
          >>> pd.Series([1.1]).isin(['1.1'])
          0    False
          dtype: bool
          """
          result = algorithms.isin(self._values, values)
          return self._constructor(result, index=self.index).__finalize__(
              self, method="isin"
          )
  
      def between(self, left, right, inclusive=True) -> Series:
          """
          Return boolean Series equivalent to left <= series <= right.
  
          This function returns a boolean vector containing `True` wherever the
          corresponding Series element is between the boundary values `left` and
          `right`. NA values are treated as `False`.
  
          Parameters
          ----------
          left : scalar or list-like
              Left boundary.
          right : scalar or list-like
              Right boundary.
          inclusive : bool, default True
              Include boundaries.
  
          Returns
          -------
          Series
              Series representing whether each element is between left and
              right (inclusive).
  
          See Also
          --------
          Series.gt : Greater than of series and other.
          Series.lt : Less than of series and other.
  
          Notes
          -----
          This function is equivalent to ``(left <= ser) & (ser <= right)``
  
          Examples
          --------
          >>> s = pd.Series([2, 0, 4, 8, np.nan])
  
          Boundary values are included by default:
  
          >>> s.between(1, 4)
          0     True
          1    False
          2     True
          3    False
          4    False
          dtype: bool
  
          With `inclusive` set to ``False`` boundary values are excluded:
  
          >>> s.between(1, 4, inclusive=False)
          0     True
          1    False
          2    False
          3    False
          4    False
          dtype: bool
  
          `left` and `right` can be any scalar value:
  
          >>> s = pd.Series(['Alice', 'Bob', 'Carol', 'Eve'])
          >>> s.between('Anna', 'Daniel')
          0    False
          1     True
          2     True
          3    False
          dtype: bool
          """
          if inclusive:
              lmask = self >= left
              rmask = self <= right
          else:
              lmask = self > left
              rmask = self < right
  
          return lmask & rmask
  
      # ----------------------------------------------------------------------
      # Convert to types that support pd.NA
  
      def _convert_dtypes(
          self,
          infer_objects: bool = True,
          convert_string: bool = True,
          convert_integer: bool = True,
          convert_boolean: bool = True,
          convert_floating: bool = True,
      ) -> Series:
          input_series = self
          if infer_objects:
              input_series = input_series.infer_objects()
              if is_object_dtype(input_series):
                  input_series = input_series.copy()
  
          if convert_string or convert_integer or convert_boolean or convert_floating:
              inferred_dtype = convert_dtypes(
                  input_series._values,
                  convert_string,
                  convert_integer,
                  convert_boolean,
                  convert_floating,
              )
              try:
                  result = input_series.astype(inferred_dtype)
              except TypeError:
                  result = input_series.copy()
          else:
              result = input_series.copy()
          return result
  
      @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
      def isna(self) -> Series:
          return generic.NDFrame.isna(self)
  
      @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
      def isnull(self) -> Series:
          return super().isnull()
  
      @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
      def notna(self) -> Series:
          return super().notna()
  
      @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
      def notnull(self) -> Series:
          return super().notnull()
  
      def dropna(self, axis=0, inplace=False, how=None):
          """
          Return a new Series with missing values removed.
  
          See the :ref:`User Guide <missing_data>` for more on which values are
          considered missing, and how to work with missing data.
  
          Parameters
          ----------
          axis : {0 or 'index'}, default 0
              There is only one axis to drop values from.
          inplace : bool, default False
              If True, do operation inplace and return None.
          how : str, optional
              Not in use. Kept for compatibility.
  
          Returns
          -------
          Series or None
              Series with NA entries dropped from it or None if ``inplace=True``.
  
          See Also
          --------
          Series.isna: Indicate missing values.
          Series.notna : Indicate existing (non-missing) values.
          Series.fillna : Replace missing values.
          DataFrame.dropna : Drop rows or columns which contain NA values.
          Index.dropna : Drop missing indices.
  
          Examples
          --------
          >>> ser = pd.Series([1., 2., np.nan])
          >>> ser
          0    1.0
          1    2.0
          2    NaN
          dtype: float64
  
          Drop NA values from a Series.
  
          >>> ser.dropna()
          0    1.0
          1    2.0
          dtype: float64
  
          Keep the Series with valid entries in the same variable.
  
          >>> ser.dropna(inplace=True)
          >>> ser
          0    1.0
          1    2.0
          dtype: float64
  
          Empty strings are not considered NA values. ``None`` is considered an
          NA value.
  
          >>> ser = pd.Series([np.NaN, 2, pd.NaT, '', None, 'I stay'])
          >>> ser
          0       NaN
          1         2
          2       NaT
          3
          4      None
          5    I stay
          dtype: object
          >>> ser.dropna()
          1         2
          3
          5    I stay
          dtype: object
          """
          inplace = validate_bool_kwarg(inplace, "inplace")
          # Validate the axis parameter
          self._get_axis_number(axis or 0)
  
          if self._can_hold_na:
              result = remove_na_arraylike(self)
              if inplace:
                  self._update_inplace(result)
              else:
                  return result
          else:
              if inplace:
                  # do nothing
                  pass
              else:
                  return self.copy()
  
      # ----------------------------------------------------------------------
      # Time series-oriented methods
  
      @doc(NDFrame.asfreq, **_shared_doc_kwargs)
      def asfreq(
          self,
          freq,
          method=None,
          how: Optional[str] = None,
          normalize: bool = False,
          fill_value=None,
      ) -> Series:
          return super().asfreq(
              freq=freq,
              method=method,
              how=how,
              normalize=normalize,
              fill_value=fill_value,
          )
  
      @doc(NDFrame.resample, **_shared_doc_kwargs)
      def resample(
          self,
          rule,
          axis=0,
          closed: Optional[str] = None,
          label: Optional[str] = None,
          convention: str = "start",
          kind: Optional[str] = None,
          loffset=None,
          base: Optional[int] = None,
          on=None,
          level=None,
          origin: Union[str, TimestampConvertibleTypes] = "start_day",
          offset: Optional[TimedeltaConvertibleTypes] = None,
      ) -> Resampler:
          return super().resample(
              rule=rule,
              axis=axis,
              closed=closed,
              label=label,
              convention=convention,
              kind=kind,
              loffset=loffset,
              base=base,
              on=on,
              level=level,
              origin=origin,
              offset=offset,
          )
  
      def to_timestamp(self, freq=None, how="start", copy=True) -> Series:
          """
          Cast to DatetimeIndex of Timestamps, at *beginning* of period.
  
          Parameters
          ----------
          freq : str, default frequency of PeriodIndex
              Desired frequency.
          how : {'s', 'e', 'start', 'end'}
              Convention for converting period to timestamp; start of period
              vs. end.
          copy : bool, default True
              Whether or not to return a copy.
  
          Returns
          -------
          Series with DatetimeIndex
          """
          new_values = self._values
          if copy:
              new_values = new_values.copy()
  
          if not isinstance(self.index, PeriodIndex):
              raise TypeError(f"unsupported Type {type(self.index).__name__}")
          new_index = self.index.to_timestamp(freq=freq, how=how)
          return self._constructor(new_values, index=new_index).__finalize__(
              self, method="to_timestamp"
          )
  
      def to_period(self, freq=None, copy=True) -> Series:
          """
          Convert Series from DatetimeIndex to PeriodIndex.
  
          Parameters
          ----------
          freq : str, default None
              Frequency associated with the PeriodIndex.
          copy : bool, default True
              Whether or not to return a copy.
  
          Returns
          -------
          Series
              Series with index converted to PeriodIndex.
          """
          new_values = self._values
          if copy:
              new_values = new_values.copy()
  
          if not isinstance(self.index, DatetimeIndex):
              raise TypeError(f"unsupported Type {type(self.index).__name__}")
          new_index = self.index.to_period(freq=freq)
          return self._constructor(new_values, index=new_index).__finalize__(
              self, method="to_period"
          )
  
      # ----------------------------------------------------------------------
      # Add index
      _AXIS_ORDERS = ["index"]
      _AXIS_REVERSED = False
      _AXIS_LEN = len(_AXIS_ORDERS)
      _info_axis_number = 0
      _info_axis_name = "index"
  
      index: Index = properties.AxisProperty(
          axis=0, doc="The index (axis labels) of the Series."
      )
  
      # ----------------------------------------------------------------------
      # Accessor Methods
      # ----------------------------------------------------------------------
      str = CachedAccessor("str", StringMethods)
      dt = CachedAccessor("dt", CombinedDatetimelikeProperties)
      cat = CachedAccessor("cat", CategoricalAccessor)
      plot = CachedAccessor("plot", pandas.plotting.PlotAccessor)
      sparse = CachedAccessor("sparse", SparseAccessor)
  
      # ----------------------------------------------------------------------
      # Add plotting methods to Series
      hist = pandas.plotting.hist_series
  
      # ----------------------------------------------------------------------
      # Template-Based Arithmetic/Comparison Methods
  
      def _cmp_method(self, other, op):
          res_name = ops.get_op_result_name(self, other)
  
          if isinstance(other, Series) and not self._indexed_same(other):
              raise ValueError("Can only compare identically-labeled Series objects")
  
          lvalues = extract_array(self, extract_numpy=True)
          rvalues = extract_array(other, extract_numpy=True)
  
          res_values = ops.comparison_op(lvalues, rvalues, op)
  
          return self._construct_result(res_values, name=res_name)
  
      def _logical_method(self, other, op):
          res_name = ops.get_op_result_name(self, other)
          self, other = ops.align_method_SERIES(self, other, align_asobject=True)
  
          lvalues = extract_array(self, extract_numpy=True)
          rvalues = extract_array(other, extract_numpy=True)
  
          res_values = ops.logical_op(lvalues, rvalues, op)
          return self._construct_result(res_values, name=res_name)
  
      def _arith_method(self, other, op):
          res_name = ops.get_op_result_name(self, other)
          self, other = ops.align_method_SERIES(self, other)
  
          lvalues = extract_array(self, extract_numpy=True)
          rvalues = extract_array(other, extract_numpy=True)
          result = ops.arithmetic_op(lvalues, rvalues, op)
  
          return self._construct_result(result, name=res_name)
  
  
  Series._add_numeric_operations()
  
  # Add arithmetic!
  ops.add_flex_arithmetic_methods(Series)
  
apis: pandas.core.reshape.concat.concat numpy.asarray pandas.core.nanops.nancorr numpy.transpose pandas.core.dtypes.missing.na_value_for_dtype pandas.core.generic.NDFrame.isna pandas._libs.lib.infer_dtype pandas.core.indexing.check_bool_indexer pandas.core.dtypes.common.is_scalar pandas.util._decorators.Appender pandas.core.dtypes.cast.convert_dtypes pandas.core.dtypes.inference.is_hashable pandas.core.algorithms.diff numpy.errstate pandas.core.reshape.reshape.unstack numpy.array numpy.dot pandas.core.indexers.unpack_1tuple pandas.core.dtypes.common.is_integer pandas.core.apply.series_apply pandas.core.common.any_none pandas.compat.numpy.function.validate_round pandas.core.missing.get_fill_func pandas.core.algorithms.take_1d pandas.core.indexers.deprecate_ndim_indexing pandas._libs.properties.AxisProperty pandas.core.aggregation.transform pandas.core.ops.fill_binop pandas.core.dtypes.common.ensure_platform_int pandas.core.dtypes.missing.isna pandas.core.common.maybe_iterable_to_list pandas.core.internals.SingleBlockManager.from_array pandas.core.dtypes.cast.validate_numeric_casting pandas.core.dtypes.common.is_object_dtype pandas.core.construction.sanitize_array pandas.core.dtypes.common.is_dict_like numpy.argsort pandas.util._decorators.doc pandas.core.construction.extract_array pandas.core.dtypes.common.is_list_like pandas.core.dtypes.common.is_categorical_dtype pandas.core.tools.datetimes.to_datetime pandas.core.construction.create_series_with_explicit_dtype pandas.core.ops.arithmetic_op pandas.core.dtypes.missing.remove_na_arraylike pandas.core.common.asarray_tuplesafe pandas.util._validators.validate_percentile pandas.core.base.IndexOpsMixin.duplicated pandas.core.common.apply_if_callable pandas.core.internals.construction.sanitize_index pandas.core.groupby.generic.SeriesGroupBy pandas.core.dtypes.common.is_bool pandas.core.ops.add_flex_arithmetic_methods pandas.core.missing.mask_missing pandas.core.ops.get_op_result_name pandas.core.ops.logical_op pandas.core.algorithms.searchsorted pandas.core.sorting.nargsort pandas.core.ops.comparison_op pandas.util._validators.validate_bool_kwarg pandas.core.indexes.api.Float64Index pandas.core.algorithms.SelectNSeries pandas.core.construction.is_empty_data pandas.core.accessor.CachedAccessor pandas.compat.numpy.function.validate_repeat pandas.core.ops.align_method_SERIES pandas.core.indexes.api.ensure_index pandas.core.sorting.ensure_key_mapped pandas.core.dtypes.common.is_iterator pandas.core.nanops.nancov pandas.core.dtypes.missing.notna pandas.core.generic.NDFrame.__init__ pandas._config.get_option pandas.core.common.is_bool_indexer pandas.core.algorithms.isin pandas.core.indexes.datetimes.DatetimeIndex pandas.io.formats.format.SeriesFormatter pandas.core.dtypes.common.is_extension_array_dtype pandas.util._decorators.Substitution pandas.core.common.standardize_mapping pandas.core.algorithms.mode pandas.compat.numpy.function.validate_take