text: """
  SparseArray data structure
  """
  from __future__ import annotations
  
  from collections import abc
  import numbers
  import operator
  from typing import (
      TYPE_CHECKING,
      Any,
      Callable,
      Literal,
      Sequence,
      TypeVar,
      cast,
      overload,
  )
  import warnings
  
  import numpy as np
  
  from pandas._libs import lib
  import pandas._libs.sparse as splib
  from pandas._libs.sparse import (
      BlockIndex,
      IntIndex,
      SparseIndex,
  )
  from pandas._libs.tslibs import NaT
  from pandas._typing import (
      ArrayLike,
      AstypeArg,
      Dtype,
      NpDtype,
      PositionalIndexer,
      Scalar,
      ScalarIndexer,
      SequenceIndexer,
      npt,
  )
  from pandas.compat.numpy import function as nv
  from pandas.errors import PerformanceWarning
  from pandas.util._exceptions import find_stack_level
  
  from pandas.core.dtypes.cast import (
      astype_nansafe,
      construct_1d_arraylike_from_scalar,
      find_common_type,
      maybe_box_datetimelike,
  )
  from pandas.core.dtypes.common import (
      is_array_like,
      is_bool_dtype,
      is_datetime64_any_dtype,
      is_datetime64tz_dtype,
      is_dtype_equal,
      is_integer,
      is_object_dtype,
      is_scalar,
      is_string_dtype,
      pandas_dtype,
  )
  from pandas.core.dtypes.generic import (
      ABCIndex,
      ABCSeries,
  )
  from pandas.core.dtypes.missing import (
      isna,
      na_value_for_dtype,
      notna,
  )
  
  import pandas.core.algorithms as algos
  from pandas.core.arraylike import OpsMixin
  from pandas.core.arrays import ExtensionArray
  from pandas.core.arrays.sparse.dtype import SparseDtype
  from pandas.core.base import PandasObject
  import pandas.core.common as com
  from pandas.core.construction import (
      extract_array,
      sanitize_array,
  )
  from pandas.core.indexers import check_array_indexer
  from pandas.core.missing import interpolate_2d
  from pandas.core.nanops import check_below_min_count
  import pandas.core.ops as ops
  
  import pandas.io.formats.printing as printing
  
  # See https://github.com/python/typing/issues/684
  if TYPE_CHECKING:
      from enum import Enum
  
      class ellipsis(Enum):
          Ellipsis = "..."
  
      Ellipsis = ellipsis.Ellipsis
  
      from scipy.sparse import spmatrix
  
      from pandas._typing import (
          FillnaOptions,
          NumpySorter,
      )
  
      SparseIndexKind = Literal["integer", "block"]
  
      from pandas import Series
  
  else:
      ellipsis = type(Ellipsis)
  
  
  # ----------------------------------------------------------------------------
  # Array
  
  SparseArrayT = TypeVar("SparseArrayT", bound="SparseArray")
  
  _sparray_doc_kwargs = {"klass": "SparseArray"}
  
  
  def _get_fill(arr: SparseArray) -> np.ndarray:
      """
      Create a 0-dim ndarray containing the fill value
  
      Parameters
      ----------
      arr : SparseArray
  
      Returns
      -------
      fill_value : ndarray
          0-dim ndarray with just the fill value.
  
      Notes
      -----
      coerce fill_value to arr dtype if possible
      int64 SparseArray can have NaN as fill_value if there is no missing
      """
      try:
          return np.asarray(arr.fill_value, dtype=arr.dtype.subtype)
      except ValueError:
          return np.asarray(arr.fill_value)
  
  
  def _sparse_array_op(
      left: SparseArray, right: SparseArray, op: Callable, name: str
  ) -> SparseArray:
      """
      Perform a binary operation between two arrays.
  
      Parameters
      ----------
      left : Union[SparseArray, ndarray]
      right : Union[SparseArray, ndarray]
      op : Callable
          The binary operation to perform
      name str
          Name of the callable.
  
      Returns
      -------
      SparseArray
      """
      if name.startswith("__"):
          # For lookups in _libs.sparse we need non-dunder op name
          name = name[2:-2]
  
      # dtype used to find corresponding sparse method
      ltype = left.dtype.subtype
      rtype = right.dtype.subtype
  
      if not is_dtype_equal(ltype, rtype):
          subtype = find_common_type([ltype, rtype])
          ltype = SparseDtype(subtype, left.fill_value)
          rtype = SparseDtype(subtype, right.fill_value)
  
          # TODO(GH-23092): pass copy=False. Need to fix astype_nansafe
          left = left.astype(ltype)
          right = right.astype(rtype)
          dtype = ltype.subtype
      else:
          dtype = ltype
  
      # dtype the result must have
      result_dtype = None
  
      if left.sp_index.ngaps == 0 or right.sp_index.ngaps == 0:
          with np.errstate(all="ignore"):
              result = op(left.to_dense(), right.to_dense())
              fill = op(_get_fill(left), _get_fill(right))
  
          if left.sp_index.ngaps == 0:
              index = left.sp_index
          else:
              index = right.sp_index
      elif left.sp_index.equals(right.sp_index):
          with np.errstate(all="ignore"):
              result = op(left.sp_values, right.sp_values)
              fill = op(_get_fill(left), _get_fill(right))
          index = left.sp_index
      else:
          if name[0] == "r":
              left, right = right, left
              name = name[1:]
  
          if name in ("and", "or", "xor") and dtype == "bool":
              opname = f"sparse_{name}_uint8"
              # to make template simple, cast here
              left_sp_values = left.sp_values.view(np.uint8)
              right_sp_values = right.sp_values.view(np.uint8)
              result_dtype = bool
          else:
              opname = f"sparse_{name}_{dtype}"
              left_sp_values = left.sp_values
              right_sp_values = right.sp_values
  
          sparse_op = getattr(splib, opname)
  
          with np.errstate(all="ignore"):
              result, index, fill = sparse_op(
                  left_sp_values,
                  left.sp_index,
                  left.fill_value,
                  right_sp_values,
                  right.sp_index,
                  right.fill_value,
              )
  
      if result_dtype is None:
          result_dtype = result.dtype
  
      return _wrap_result(name, result, index, fill, dtype=result_dtype)
  
  
  def _wrap_result(
      name: str, data, sparse_index, fill_value, dtype: Dtype | None = None
  ) -> SparseArray:
      """
      wrap op result to have correct dtype
      """
      if name.startswith("__"):
          # e.g. __eq__ --> eq
          name = name[2:-2]
  
      if name in ("eq", "ne", "lt", "gt", "le", "ge"):
          dtype = bool
  
      fill_value = lib.item_from_zerodim(fill_value)
  
      if is_bool_dtype(dtype):
          # fill_value may be np.bool_
          fill_value = bool(fill_value)
      return SparseArray(
          data, sparse_index=sparse_index, fill_value=fill_value, dtype=dtype
      )
  
  
  class SparseArray(OpsMixin, PandasObject, ExtensionArray):
      """
      An ExtensionArray for storing sparse data.
  
      Parameters
      ----------
      data : array-like or scalar
          A dense array of values to store in the SparseArray. This may contain
          `fill_value`.
      sparse_index : SparseIndex, optional
      index : Index
  
          .. deprecated:: 1.4.0
              Use a function like `np.full` to construct an array with the desired
              repeats of the scalar value instead.
  
      fill_value : scalar, optional
          Elements in `data` that are `fill_value` are not stored in the
          SparseArray. For memory savings, this should be the most common value
          in `data`. By default, `fill_value` depends on the dtype of `data`:
  
          =========== ==========
          data.dtype  na_value
          =========== ==========
          float       ``np.nan``
          int         ``0``
          bool        False
          datetime64  ``pd.NaT``
          timedelta64 ``pd.NaT``
          =========== ==========
  
          The fill value is potentially specified in three ways. In order of
          precedence, these are
  
          1. The `fill_value` argument
          2. ``dtype.fill_value`` if `fill_value` is None and `dtype` is
             a ``SparseDtype``
          3. ``data.dtype.fill_value`` if `fill_value` is None and `dtype`
             is not a ``SparseDtype`` and `data` is a ``SparseArray``.
  
      kind : {'integer', 'block'}, default 'integer'
          The type of storage for sparse locations.
  
          * 'block': Stores a `block` and `block_length` for each
            contiguous *span* of sparse values. This is best when
            sparse data tends to be clumped together, with large
            regions of ``fill-value`` values between sparse values.
          * 'integer': uses an integer to store the location of
            each sparse value.
  
      dtype : np.dtype or SparseDtype, optional
          The dtype to use for the SparseArray. For numpy dtypes, this
          determines the dtype of ``self.sp_values``. For SparseDtype,
          this determines ``self.sp_values`` and ``self.fill_value``.
      copy : bool, default False
          Whether to explicitly copy the incoming `data` array.
  
      Attributes
      ----------
      None
  
      Methods
      -------
      None
  
      Examples
      --------
      >>> from pandas.arrays import SparseArray
      >>> arr = SparseArray([0, 0, 1, 2])
      >>> arr
      [0, 0, 1, 2]
      Fill: 0
      IntIndex
      Indices: array([2, 3], dtype=int32)
      """
  
      _subtyp = "sparse_array"  # register ABCSparseArray
      _hidden_attrs = PandasObject._hidden_attrs | frozenset(["get_values"])
      _sparse_index: SparseIndex
  
      def __init__(
          self,
          data,
          sparse_index=None,
          index=None,
          fill_value=None,
          kind: SparseIndexKind = "integer",
          dtype: Dtype | None = None,
          copy: bool = False,
      ):
  
          if fill_value is None and isinstance(dtype, SparseDtype):
              fill_value = dtype.fill_value
  
          if isinstance(data, type(self)):
              # disable normal inference on dtype, sparse_index, & fill_value
              if sparse_index is None:
                  sparse_index = data.sp_index
              if fill_value is None:
                  fill_value = data.fill_value
              if dtype is None:
                  dtype = data.dtype
              # TODO: make kind=None, and use data.kind?
              data = data.sp_values
  
          # Handle use-provided dtype
          if isinstance(dtype, str):
              # Two options: dtype='int', regular numpy dtype
              # or dtype='Sparse[int]', a sparse dtype
              try:
                  dtype = SparseDtype.construct_from_string(dtype)
              except TypeError:
                  dtype = pandas_dtype(dtype)
  
          if isinstance(dtype, SparseDtype):
              if fill_value is None:
                  fill_value = dtype.fill_value
              dtype = dtype.subtype
  
          if index is not None:
              warnings.warn(
                  "The index argument has been deprecated and will be "
                  "removed in a future version. Use a function like np.full "
                  "to construct an array with the desired repeats of the "
                  "scalar value instead.\n\n",
                  FutureWarning,
                  stacklevel=find_stack_level(),
              )
  
          if index is not None and not is_scalar(data):
              raise Exception("must only pass scalars with an index")
  
          if is_scalar(data):
              if index is not None and data is None:
                  data = np.nan
  
              if index is not None:
                  npoints = len(index)
              elif sparse_index is None:
                  npoints = 1
              else:
                  npoints = sparse_index.length
  
              data = construct_1d_arraylike_from_scalar(data, npoints, dtype=None)
              dtype = data.dtype
  
          if dtype is not None:
              dtype = pandas_dtype(dtype)
  
          # TODO: disentangle the fill_value dtype inference from
          # dtype inference
          if data is None:
              # TODO: What should the empty dtype be? Object or float?
  
              # error: Argument "dtype" to "array" has incompatible type
              # "Union[ExtensionDtype, dtype[Any], None]"; expected "Union[dtype[Any],
              # None, type, _SupportsDType, str, Union[Tuple[Any, int], Tuple[Any,
              # Union[int, Sequence[int]]], List[Any], _DTypeDict, Tuple[Any, Any]]]"
              data = np.array([], dtype=dtype)  # type: ignore[arg-type]
  
          if not is_array_like(data):
              try:
                  # probably shared code in sanitize_series
  
                  data = sanitize_array(data, index=None)
              except ValueError:
                  # NumPy may raise a ValueError on data like [1, []]
                  # we retry with object dtype here.
                  if dtype is None:
                      dtype = object
                      data = np.atleast_1d(np.asarray(data, dtype=dtype))
                  else:
                      raise
  
          if copy:
              # TODO: avoid double copy when dtype forces cast.
              data = data.copy()
  
          if fill_value is None:
              fill_value_dtype = data.dtype if dtype is None else dtype
              if fill_value_dtype is None:
                  fill_value = np.nan
              else:
                  fill_value = na_value_for_dtype(fill_value_dtype)
  
          if isinstance(data, type(self)) and sparse_index is None:
              sparse_index = data._sparse_index
              # error: Argument "dtype" to "asarray" has incompatible type
              # "Union[ExtensionDtype, dtype[Any], Type[object], None]"; expected
              # "Union[dtype[Any], None, type, _SupportsDType, str, Union[Tuple[Any, int],
              # Tuple[Any, Union[int, Sequence[int]]], List[Any], _DTypeDict, Tuple[Any,
              # Any]]]"
              sparse_values = np.asarray(
                  data.sp_values, dtype=dtype  # type: ignore[arg-type]
              )
          elif sparse_index is None:
              data = extract_array(data, extract_numpy=True)
              if not isinstance(data, np.ndarray):
                  # EA
                  if is_datetime64tz_dtype(data.dtype):
                      warnings.warn(
                          f"Creating SparseArray from {data.dtype} data "
                          "loses timezone information. Cast to object before "
                          "sparse to retain timezone information.",
                          UserWarning,
                          stacklevel=2,
                      )
                      data = np.asarray(data, dtype="datetime64[ns]")
                      if fill_value is NaT:
                          fill_value = np.datetime64("NaT", "ns")
                  data = np.asarray(data)
              sparse_values, sparse_index, fill_value = make_sparse(
                  # error: Argument "dtype" to "make_sparse" has incompatible type
                  # "Union[ExtensionDtype, dtype[Any], Type[object], None]"; expected
                  # "Union[str, dtype[Any], None]"
                  data,
                  kind=kind,
                  fill_value=fill_value,
                  dtype=dtype,  # type: ignore[arg-type]
              )
          else:
              # error: Argument "dtype" to "asarray" has incompatible type
              # "Union[ExtensionDtype, dtype[Any], Type[object], None]"; expected
              # "Union[dtype[Any], None, type, _SupportsDType, str, Union[Tuple[Any, int],
              # Tuple[Any, Union[int, Sequence[int]]], List[Any], _DTypeDict, Tuple[Any,
              # Any]]]"
              sparse_values = np.asarray(data, dtype=dtype)  # type: ignore[arg-type]
              if len(sparse_values) != sparse_index.npoints:
                  raise AssertionError(
                      f"Non array-like type {type(sparse_values)} must "
                      "have the same length as the index"
                  )
          self._sparse_index = sparse_index
          self._sparse_values = sparse_values
          self._dtype = SparseDtype(sparse_values.dtype, fill_value)
  
      @classmethod
      def _simple_new(
          cls: type[SparseArrayT],
          sparse_array: np.ndarray,
          sparse_index: SparseIndex,
          dtype: SparseDtype,
      ) -> SparseArrayT:
          new = object.__new__(cls)
          new._sparse_index = sparse_index
          new._sparse_values = sparse_array
          new._dtype = dtype
          return new
  
      @classmethod
      def from_spmatrix(cls: type[SparseArrayT], data: spmatrix) -> SparseArrayT:
          """
          Create a SparseArray from a scipy.sparse matrix.
  
          .. versionadded:: 0.25.0
  
          Parameters
          ----------
          data : scipy.sparse.sp_matrix
              This should be a SciPy sparse matrix where the size
              of the second dimension is 1. In other words, a
              sparse matrix with a single column.
  
          Returns
          -------
          SparseArray
  
          Examples
          --------
          >>> import scipy.sparse
          >>> mat = scipy.sparse.coo_matrix((4, 1))
          >>> pd.arrays.SparseArray.from_spmatrix(mat)
          [0.0, 0.0, 0.0, 0.0]
          Fill: 0.0
          IntIndex
          Indices: array([], dtype=int32)
          """
          length, ncol = data.shape
  
          if ncol != 1:
              raise ValueError(f"'data' must have a single column, not '{ncol}'")
  
          # our sparse index classes require that the positions be strictly
          # increasing. So we need to sort loc, and arr accordingly.
          data = data.tocsc()
          data.sort_indices()
          arr = data.data
          idx = data.indices
  
          zero = np.array(0, dtype=arr.dtype).item()
          dtype = SparseDtype(arr.dtype, zero)
          index = IntIndex(length, idx)
  
          return cls._simple_new(arr, index, dtype)
  
      def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
          fill_value = self.fill_value
  
          if self.sp_index.ngaps == 0:
              # Compat for na dtype and int values.
              return self.sp_values
          if dtype is None:
              # Can NumPy represent this type?
              # If not, `np.result_type` will raise. We catch that
              # and return object.
              if is_datetime64_any_dtype(self.sp_values.dtype):
                  # However, we *do* special-case the common case of
                  # a datetime64 with pandas NaT.
                  if fill_value is NaT:
                      # Can't put pd.NaT in a datetime64[ns]
                      fill_value = np.datetime64("NaT")
              try:
                  dtype = np.result_type(self.sp_values.dtype, type(fill_value))
              except TypeError:
                  dtype = object
  
          out = np.full(self.shape, fill_value, dtype=dtype)
          out[self.sp_index.to_int_index().indices] = self.sp_values
          return out
  
      def __setitem__(self, key, value):
          # I suppose we could allow setting of non-fill_value elements.
          # TODO(SparseArray.__setitem__): remove special cases in
          # ExtensionBlock.where
          msg = "SparseArray does not support item assignment via setitem"
          raise TypeError(msg)
  
      @classmethod
      def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy: bool = False):
          return cls(scalars, dtype=dtype)
  
      @classmethod
      def _from_factorized(cls, values, original):
          return cls(values, dtype=original.dtype)
  
      # ------------------------------------------------------------------------
      # Data
      # ------------------------------------------------------------------------
      @property
      def sp_index(self) -> SparseIndex:
          """
          The SparseIndex containing the location of non- ``fill_value`` points.
          """
          return self._sparse_index
  
      @property
      def sp_values(self) -> np.ndarray:
          """
          An ndarray containing the non- ``fill_value`` values.
  
          Examples
          --------
          >>> s = SparseArray([0, 0, 1, 0, 2], fill_value=0)
          >>> s.sp_values
          array([1, 2])
          """
          return self._sparse_values
  
      @property
      def dtype(self) -> SparseDtype:
          return self._dtype
  
      @property
      def fill_value(self):
          """
          Elements in `data` that are `fill_value` are not stored.
  
          For memory savings, this should be the most common value in the array.
          """
          return self.dtype.fill_value
  
      @fill_value.setter
      def fill_value(self, value):
          self._dtype = SparseDtype(self.dtype.subtype, value)
  
      @property
      def kind(self) -> SparseIndexKind:
          """
          The kind of sparse index for this array. One of {'integer', 'block'}.
          """
          if isinstance(self.sp_index, IntIndex):
              return "integer"
          else:
              return "block"
  
      @property
      def _valid_sp_values(self) -> np.ndarray:
          sp_vals = self.sp_values
          mask = notna(sp_vals)
          return sp_vals[mask]
  
      def __len__(self) -> int:
          return self.sp_index.length
  
      @property
      def _null_fill_value(self) -> bool:
          return self._dtype._is_na_fill_value
  
      def _fill_value_matches(self, fill_value) -> bool:
          if self._null_fill_value:
              return isna(fill_value)
          else:
              return self.fill_value == fill_value
  
      @property
      def nbytes(self) -> int:
          return self.sp_values.nbytes + self.sp_index.nbytes
  
      @property
      def density(self) -> float:
          """
          The percent of non- ``fill_value`` points, as decimal.
  
          Examples
          --------
          >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)
          >>> s.density
          0.6
          """
          return self.sp_index.npoints / self.sp_index.length
  
      @property
      def npoints(self) -> int:
          """
          The number of non- ``fill_value`` points.
  
          Examples
          --------
          >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)
          >>> s.npoints
          3
          """
          return self.sp_index.npoints
  
      def isna(self):
          # If null fill value, we want SparseDtype[bool, true]
          # to preserve the same memory usage.
          dtype = SparseDtype(bool, self._null_fill_value)
          return type(self)._simple_new(isna(self.sp_values), self.sp_index, dtype)
  
      def fillna(
          self: SparseArrayT,
          value=None,
          method: FillnaOptions | None = None,
          limit: int | None = None,
      ) -> SparseArrayT:
          """
          Fill missing values with `value`.
  
          Parameters
          ----------
          value : scalar, optional
          method : str, optional
  
              .. warning::
  
                 Using 'method' will result in high memory use,
                 as all `fill_value` methods will be converted to
                 an in-memory ndarray
  
          limit : int, optional
  
          Returns
          -------
          SparseArray
  
          Notes
          -----
          When `value` is specified, the result's ``fill_value`` depends on
          ``self.fill_value``. The goal is to maintain low-memory use.
  
          If ``self.fill_value`` is NA, the result dtype will be
          ``SparseDtype(self.dtype, fill_value=value)``. This will preserve
          amount of memory used before and after filling.
  
          When ``self.fill_value`` is not NA, the result dtype will be
          ``self.dtype``. Again, this preserves the amount of memory used.
          """
          if (method is None and value is None) or (
              method is not None and value is not None
          ):
              raise ValueError("Must specify one of 'method' or 'value'.")
  
          elif method is not None:
              msg = "fillna with 'method' requires high memory usage."
              warnings.warn(msg, PerformanceWarning)
              filled = interpolate_2d(np.asarray(self), method=method, limit=limit)
              return type(self)(filled, fill_value=self.fill_value)
  
          else:
              new_values = np.where(isna(self.sp_values), value, self.sp_values)
  
              if self._null_fill_value:
                  # This is essentially just updating the dtype.
                  new_dtype = SparseDtype(self.dtype.subtype, fill_value=value)
              else:
                  new_dtype = self.dtype
  
          return self._simple_new(new_values, self._sparse_index, new_dtype)
  
      def shift(self: SparseArrayT, periods: int = 1, fill_value=None) -> SparseArrayT:
  
          if not len(self) or periods == 0:
              return self.copy()
  
          if isna(fill_value):
              fill_value = self.dtype.na_value
  
          subtype = np.result_type(fill_value, self.dtype.subtype)
  
          if subtype != self.dtype.subtype:
              # just coerce up front
              arr = self.astype(SparseDtype(subtype, self.fill_value))
          else:
              arr = self
  
          empty = self._from_sequence(
              [fill_value] * min(abs(periods), len(self)), dtype=arr.dtype
          )
  
          if periods > 0:
              a = empty
              b = arr[:-periods]
          else:
              a = arr[abs(periods) :]
              b = empty
          return arr._concat_same_type([a, b])
  
      def _first_fill_value_loc(self):
          """
          Get the location of the first missing value.
  
          Returns
          -------
          int
          """
          if len(self) == 0 or self.sp_index.npoints == len(self):
              return -1
  
          indices = self.sp_index.to_int_index().indices
          if not len(indices) or indices[0] > 0:
              return 0
  
          diff = indices[1:] - indices[:-1]
          return np.searchsorted(diff, 2) + 1
  
      def unique(self: SparseArrayT) -> SparseArrayT:
          uniques = list(algos.unique(self.sp_values))
          fill_loc = self._first_fill_value_loc()
          if fill_loc >= 0:
              uniques.insert(fill_loc, self.fill_value)
          return type(self)._from_sequence(uniques, dtype=self.dtype)
  
      def _values_for_factorize(self):
          # Still override this for hash_pandas_object
          return np.asarray(self), self.fill_value
  
      def factorize(self, na_sentinel: int = -1) -> tuple[np.ndarray, SparseArray]:
          # Currently, ExtensionArray.factorize -> Tuple[ndarray, EA]
          # The sparsity on this is backwards from what Sparse would want. Want
          # ExtensionArray.factorize -> Tuple[EA, EA]
          # Given that we have to return a dense array of codes, why bother
          # implementing an efficient factorize?
          codes, uniques = algos.factorize(np.asarray(self), na_sentinel=na_sentinel)
          uniques_sp = SparseArray(uniques, dtype=self.dtype)
          return codes, uniques_sp
  
      def value_counts(self, dropna: bool = True) -> Series:
          """
          Returns a Series containing counts of unique values.
  
          Parameters
          ----------
          dropna : bool, default True
              Don't include counts of NaN, even if NaN is in sp_values.
  
          Returns
          -------
          counts : Series
          """
          from pandas import (
              Index,
              Series,
          )
  
          keys, counts = algos.value_counts_arraylike(self.sp_values, dropna=dropna)
          fcounts = self.sp_index.ngaps
          if fcounts > 0 and (not self._null_fill_value or not dropna):
              mask = isna(keys) if self._null_fill_value else keys == self.fill_value
              if mask.any():
                  counts[mask] += fcounts
              else:
                  keys = np.insert(keys, 0, self.fill_value)
                  counts = np.insert(counts, 0, fcounts)
  
          if not isinstance(keys, ABCIndex):
              keys = Index(keys)
          return Series(counts, index=keys)
  
      # --------
      # Indexing
      # --------
      @overload
      def __getitem__(self, key: ScalarIndexer) -> Any:
          ...
  
      @overload
      def __getitem__(
          self: SparseArrayT,
          key: SequenceIndexer | tuple[int | ellipsis, ...],
      ) -> SparseArrayT:
          ...
  
      def __getitem__(
          self: SparseArrayT,
          key: PositionalIndexer | tuple[int | ellipsis, ...],
      ) -> SparseArrayT | Any:
  
          if isinstance(key, tuple):
              if len(key) > 1:
                  if key[0] is Ellipsis:
                      key = key[1:]
                  elif key[-1] is Ellipsis:
                      key = key[:-1]
              if len(key) > 1:
                  raise IndexError("too many indices for array.")
              if key[0] is Ellipsis:
                  raise ValueError("Cannot slice with Ellipsis")
              key = key[0]
  
          if is_integer(key):
              return self._get_val_at(key)
          elif isinstance(key, tuple):
              data_slice = self.to_dense()[key]
          elif isinstance(key, slice):
              # special case to preserve dtypes
              if key == slice(None):
                  return self.copy()
              # TODO: this logic is surely elsewhere
              # TODO: this could be more efficient
              indices = np.arange(len(self), dtype=np.int32)[key]
              return self.take(indices)
          else:
              # TODO: I think we can avoid densifying when masking a
              # boolean SparseArray with another. Need to look at the
              # key's fill_value for True / False, and then do an intersection
              # on the indices of the sp_values.
              if isinstance(key, SparseArray):
                  if is_bool_dtype(key):
                      key = key.to_dense()
                  else:
                      key = np.asarray(key)
  
              key = check_array_indexer(self, key)
  
              if com.is_bool_indexer(key):
                  # mypy doesn't know we have an array here
                  key = cast(np.ndarray, key)
                  return self.take(np.arange(len(key), dtype=np.int32)[key])
              elif hasattr(key, "__len__"):
                  return self.take(key)
              else:
                  raise ValueError(f"Cannot slice with '{key}'")
  
          return type(self)(data_slice, kind=self.kind)
  
      def _get_val_at(self, loc):
          n = len(self)
          if loc < 0:
              loc += n
  
          if loc >= n or loc < 0:
              raise IndexError("Out of bounds access")
  
          sp_loc = self.sp_index.lookup(loc)
          if sp_loc == -1:
              return self.fill_value
          else:
              val = self.sp_values[sp_loc]
              val = maybe_box_datetimelike(val, self.sp_values.dtype)
              return val
  
      def take(
          self: SparseArrayT, indices, *, allow_fill: bool = False, fill_value=None
      ) -> SparseArrayT:
          if is_scalar(indices):
              raise ValueError(f"'indices' must be an array, not a scalar '{indices}'.")
          indices = np.asarray(indices, dtype=np.int32)
  
          dtype = None
          if indices.size == 0:
              result = np.array([], dtype="object")
              dtype = self.dtype
          elif allow_fill:
              result = self._take_with_fill(indices, fill_value=fill_value)
          else:
              return self._take_without_fill(indices)
  
          return type(self)(
              result, fill_value=self.fill_value, kind=self.kind, dtype=dtype
          )
  
      def _take_with_fill(self, indices, fill_value=None) -> np.ndarray:
          if fill_value is None:
              fill_value = self.dtype.na_value
  
          if indices.min() < -1:
              raise ValueError(
                  "Invalid value in 'indices'. Must be between -1 "
                  "and the length of the array."
              )
  
          if indices.max() >= len(self):
              raise IndexError("out of bounds value in 'indices'.")
  
          if len(self) == 0:
              # Empty... Allow taking only if all empty
              if (indices == -1).all():
                  dtype = np.result_type(self.sp_values, type(fill_value))
                  taken = np.empty_like(indices, dtype=dtype)
                  taken.fill(fill_value)
                  return taken
              else:
                  raise IndexError("cannot do a non-empty take from an empty axes.")
  
          # sp_indexer may be -1 for two reasons
          # 1.) we took for an index of -1 (new)
          # 2.) we took a value that was self.fill_value (old)
          sp_indexer = self.sp_index.lookup_array(indices)
          new_fill_indices = indices == -1
          old_fill_indices = (sp_indexer == -1) & ~new_fill_indices
  
          if self.sp_index.npoints == 0 and old_fill_indices.all():
              # We've looked up all valid points on an all-sparse array.
              taken = np.full(
                  sp_indexer.shape, fill_value=self.fill_value, dtype=self.dtype.subtype
              )
  
          elif self.sp_index.npoints == 0:
              # Avoid taking from the empty self.sp_values
              _dtype = np.result_type(self.dtype.subtype, type(fill_value))
              taken = np.full(sp_indexer.shape, fill_value=fill_value, dtype=_dtype)
          else:
              taken = self.sp_values.take(sp_indexer)
  
              # Fill in two steps.
              # Old fill values
              # New fill values
              # potentially coercing to a new dtype at each stage.
  
              m0 = sp_indexer[old_fill_indices] < 0
              m1 = sp_indexer[new_fill_indices] < 0
  
              result_type = taken.dtype
  
              if m0.any():
                  result_type = np.result_type(result_type, type(self.fill_value))
                  taken = taken.astype(result_type)
                  taken[old_fill_indices] = self.fill_value
  
              if m1.any():
                  result_type = np.result_type(result_type, type(fill_value))
                  taken = taken.astype(result_type)
                  taken[new_fill_indices] = fill_value
  
          return taken
  
      def _take_without_fill(self: SparseArrayT, indices) -> SparseArrayT:
          to_shift = indices < 0
  
          n = len(self)
  
          if (indices.max() >= n) or (indices.min() < -n):
              if n == 0:
                  raise IndexError("cannot do a non-empty take from an empty axes.")
              else:
                  raise IndexError("out of bounds value in 'indices'.")
  
          if to_shift.any():
              indices = indices.copy()
              indices[to_shift] += n
  
          sp_indexer = self.sp_index.lookup_array(indices)
          value_mask = sp_indexer != -1
          new_sp_values = self.sp_values[sp_indexer[value_mask]]
  
          value_indices = np.flatnonzero(value_mask).astype(np.int32, copy=False)
  
          new_sp_index = make_sparse_index(len(indices), value_indices, kind=self.kind)
          return type(self)._simple_new(new_sp_values, new_sp_index, dtype=self.dtype)
  
      def searchsorted(
          self,
          v: ArrayLike | object,
          side: Literal["left", "right"] = "left",
          sorter: NumpySorter = None,
      ) -> npt.NDArray[np.intp] | np.intp:
  
          msg = "searchsorted requires high memory usage."
          warnings.warn(msg, PerformanceWarning, stacklevel=2)
          if not is_scalar(v):
              v = np.asarray(v)
          v = np.asarray(v)
          return np.asarray(self, dtype=self.dtype.subtype).searchsorted(v, side, sorter)
  
      def copy(self: SparseArrayT) -> SparseArrayT:
          values = self.sp_values.copy()
          return self._simple_new(values, self.sp_index, self.dtype)
  
      @classmethod
      def _concat_same_type(
          cls: type[SparseArrayT], to_concat: Sequence[SparseArrayT]
      ) -> SparseArrayT:
          fill_value = to_concat[0].fill_value
  
          values = []
          length = 0
  
          if to_concat:
              sp_kind = to_concat[0].kind
          else:
              sp_kind = "integer"
  
          sp_index: SparseIndex
          if sp_kind == "integer":
              indices = []
  
              for arr in to_concat:
                  int_idx = arr.sp_index.to_int_index().indices.copy()
                  int_idx += length  # TODO: wraparound
                  length += arr.sp_index.length
  
                  values.append(arr.sp_values)
                  indices.append(int_idx)
  
              data = np.concatenate(values)
              indices_arr = np.concatenate(indices)
              sp_index = IntIndex(length, indices_arr)
  
          else:
              # when concatenating block indices, we don't claim that you'll
              # get an identical index as concatenating the values and then
              # creating a new index. We don't want to spend the time trying
              # to merge blocks across arrays in `to_concat`, so the resulting
              # BlockIndex may have more blocks.
              blengths = []
              blocs = []
  
              for arr in to_concat:
                  block_idx = arr.sp_index.to_block_index()
  
                  values.append(arr.sp_values)
                  blocs.append(block_idx.blocs.copy() + length)
                  blengths.append(block_idx.blengths)
                  length += arr.sp_index.length
  
              data = np.concatenate(values)
              blocs_arr = np.concatenate(blocs)
              blengths_arr = np.concatenate(blengths)
  
              sp_index = BlockIndex(length, blocs_arr, blengths_arr)
  
          return cls(data, sparse_index=sp_index, fill_value=fill_value)
  
      def astype(self, dtype: AstypeArg | None = None, copy: bool = True):
          """
          Change the dtype of a SparseArray.
  
          The output will always be a SparseArray. To convert to a dense
          ndarray with a certain dtype, use :meth:`numpy.asarray`.
  
          Parameters
          ----------
          dtype : np.dtype or ExtensionDtype
              For SparseDtype, this changes the dtype of
              ``self.sp_values`` and the ``self.fill_value``.
  
              For other dtypes, this only changes the dtype of
              ``self.sp_values``.
  
          copy : bool, default True
              Whether to ensure a copy is made, even if not necessary.
  
          Returns
          -------
          SparseArray
  
          Examples
          --------
          >>> arr = pd.arrays.SparseArray([0, 0, 1, 2])
          >>> arr
          [0, 0, 1, 2]
          Fill: 0
          IntIndex
          Indices: array([2, 3], dtype=int32)
  
          >>> arr.astype(np.dtype('int32'))
          [0, 0, 1, 2]
          Fill: 0
          IntIndex
          Indices: array([2, 3], dtype=int32)
  
          Using a NumPy dtype with a different kind (e.g. float) will coerce
          just ``self.sp_values``.
  
          >>> arr.astype(np.dtype('float64'))
          ... # doctest: +NORMALIZE_WHITESPACE
          [0.0, 0.0, 1.0, 2.0]
          Fill: 0.0
          IntIndex
          Indices: array([2, 3], dtype=int32)
  
          Use a SparseDtype if you wish to be change the fill value as well.
  
          >>> arr.astype(SparseDtype("float64", fill_value=np.nan))
          ... # doctest: +NORMALIZE_WHITESPACE
          [nan, nan, 1.0, 2.0]
          Fill: nan
          IntIndex
          Indices: array([2, 3], dtype=int32)
          """
          if is_dtype_equal(dtype, self._dtype):
              if not copy:
                  return self
              else:
                  return self.copy()
          dtype = self.dtype.update_dtype(dtype)
          # error: Item "ExtensionDtype" of "Union[ExtensionDtype, str, dtype[Any],
          # Type[str], Type[float], Type[int], Type[complex], Type[bool], Type[object],
          # None]" has no attribute "_subtype_with_str"
          # error: Item "str" of "Union[ExtensionDtype, str, dtype[Any], Type[str],
          # Type[float], Type[int], Type[complex], Type[bool], Type[object], None]" has no
          # attribute "_subtype_with_str"
          # error: Item "dtype[Any]" of "Union[ExtensionDtype, str, dtype[Any], Type[str],
          # Type[float], Type[int], Type[complex], Type[bool], Type[object], None]" has no
          # attribute "_subtype_with_str"
          # error: Item "ABCMeta" of "Union[ExtensionDtype, str, dtype[Any], Type[str],
          # Type[float], Type[int], Type[complex], Type[bool], Type[object], None]" has no
          # attribute "_subtype_with_str"
          # error: Item "type" of "Union[ExtensionDtype, str, dtype[Any], Type[str],
          # Type[float], Type[int], Type[complex], Type[bool], Type[object], None]" has no
          # attribute "_subtype_with_str"
          # error: Item "None" of "Union[ExtensionDtype, str, dtype[Any], Type[str],
          # Type[float], Type[int], Type[complex], Type[bool], Type[object], None]" has no
          # attribute "_subtype_with_str"
          subtype = pandas_dtype(dtype._subtype_with_str)  # type: ignore[union-attr]
          # TODO copy=False is broken for astype_nansafe with int -> float, so cannot
          # passthrough copy keyword: https://github.com/pandas-dev/pandas/issues/34456
          sp_values = astype_nansafe(self.sp_values, subtype, copy=True)
          if sp_values is self.sp_values and copy:
              sp_values = sp_values.copy()
  
          # error: Argument 1 to "_simple_new" of "SparseArray" has incompatible type
          # "ExtensionArray"; expected "ndarray"
          return self._simple_new(
              sp_values, self.sp_index, dtype  # type: ignore[arg-type]
          )
  
      def map(self: SparseArrayT, mapper) -> SparseArrayT:
          """
          Map categories using input correspondence (dict, Series, or function).
  
          Parameters
          ----------
          mapper : dict, Series, callable
              The correspondence from old values to new.
  
          Returns
          -------
          SparseArray
              The output array will have the same density as the input.
              The output fill value will be the result of applying the
              mapping to ``self.fill_value``
  
          Examples
          --------
          >>> arr = pd.arrays.SparseArray([0, 1, 2])
          >>> arr.map(lambda x: x + 10)
          [10, 11, 12]
          Fill: 10
          IntIndex
          Indices: array([1, 2], dtype=int32)
  
          >>> arr.map({0: 10, 1: 11, 2: 12})
          [10, 11, 12]
          Fill: 10
          IntIndex
          Indices: array([1, 2], dtype=int32)
  
          >>> arr.map(pd.Series([10, 11, 12], index=[0, 1, 2]))
          [10, 11, 12]
          Fill: 10
          IntIndex
          Indices: array([1, 2], dtype=int32)
          """
          # this is used in apply.
          # We get hit since we're an "is_extension_type" but regular extension
          # types are not hit. This may be worth adding to the interface.
          if isinstance(mapper, ABCSeries):
              mapper = mapper.to_dict()
  
          if isinstance(mapper, abc.Mapping):
              fill_value = mapper.get(self.fill_value, self.fill_value)
              sp_values = [mapper.get(x, None) for x in self.sp_values]
          else:
              fill_value = mapper(self.fill_value)
              sp_values = [mapper(x) for x in self.sp_values]
  
          return type(self)(sp_values, sparse_index=self.sp_index, fill_value=fill_value)
  
      def to_dense(self) -> np.ndarray:
          """
          Convert SparseArray to a NumPy array.
  
          Returns
          -------
          arr : NumPy array
          """
          return np.asarray(self, dtype=self.sp_values.dtype)
  
      _internal_get_values = to_dense
  
      # ------------------------------------------------------------------------
      # IO
      # ------------------------------------------------------------------------
      def __setstate__(self, state):
          """Necessary for making this object picklable"""
          if isinstance(state, tuple):
              # Compat for pandas < 0.24.0
              nd_state, (fill_value, sp_index) = state
              sparse_values = np.array([])
              sparse_values.__setstate__(nd_state)
  
              self._sparse_values = sparse_values
              self._sparse_index = sp_index
              self._dtype = SparseDtype(sparse_values.dtype, fill_value)
          else:
              self.__dict__.update(state)
  
      def nonzero(self):
          if self.fill_value == 0:
              return (self.sp_index.to_int_index().indices,)
          else:
              return (self.sp_index.to_int_index().indices[self.sp_values != 0],)
  
      # ------------------------------------------------------------------------
      # Reductions
      # ------------------------------------------------------------------------
  
      def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
          method = getattr(self, name, None)
  
          if method is None:
              raise TypeError(f"cannot perform {name} with type {self.dtype}")
  
          if skipna:
              arr = self
          else:
              arr = self.dropna()
  
          # we don't support these kwargs.
          # They should only be present when called via pandas, so do it here.
          # instead of in `any` / `all` (which will raise if they're present,
          # thanks to nv.validate
          kwargs.pop("filter_type", None)
          kwargs.pop("numeric_only", None)
          kwargs.pop("op", None)
          return getattr(arr, name)(**kwargs)
  
      def all(self, axis=None, *args, **kwargs):
          """
          Tests whether all elements evaluate True
  
          Returns
          -------
          all : bool
  
          See Also
          --------
          numpy.all
          """
          nv.validate_all(args, kwargs)
  
          values = self.sp_values
  
          if len(values) != len(self) and not np.all(self.fill_value):
              return False
  
          return values.all()
  
      def any(self, axis=0, *args, **kwargs):
          """
          Tests whether at least one of elements evaluate True
  
          Returns
          -------
          any : bool
  
          See Also
          --------
          numpy.any
          """
          nv.validate_any(args, kwargs)
  
          values = self.sp_values
  
          if len(values) != len(self) and np.any(self.fill_value):
              return True
  
          return values.any().item()
  
      def sum(self, axis: int = 0, min_count: int = 0, *args, **kwargs) -> Scalar:
          """
          Sum of non-NA/null values
  
          Parameters
          ----------
          axis : int, default 0
              Not Used. NumPy compatibility.
          min_count : int, default 0
              The required number of valid values to perform the summation. If fewer
              than ``min_count`` valid values are present, the result will be the missing
              value indicator for subarray type.
          *args, **kwargs
              Not Used. NumPy compatibility.
  
          Returns
          -------
          scalar
          """
          nv.validate_sum(args, kwargs)
          valid_vals = self._valid_sp_values
          sp_sum = valid_vals.sum()
          if self._null_fill_value:
              if check_below_min_count(valid_vals.shape, None, min_count):
                  return na_value_for_dtype(self.dtype.subtype, compat=False)
              return sp_sum
          else:
              nsparse = self.sp_index.ngaps
              if check_below_min_count(valid_vals.shape, None, min_count - nsparse):
                  return na_value_for_dtype(self.dtype.subtype, compat=False)
              return sp_sum + self.fill_value * nsparse
  
      def cumsum(self, axis: int = 0, *args, **kwargs) -> SparseArray:
          """
          Cumulative sum of non-NA/null values.
  
          When performing the cumulative summation, any non-NA/null values will
          be skipped. The resulting SparseArray will preserve the locations of
          NaN values, but the fill value will be `np.nan` regardless.
  
          Parameters
          ----------
          axis : int or None
              Axis over which to perform the cumulative summation. If None,
              perform cumulative summation over flattened array.
  
          Returns
          -------
          cumsum : SparseArray
          """
          nv.validate_cumsum(args, kwargs)
  
          if axis is not None and axis >= self.ndim:  # Mimic ndarray behaviour.
              raise ValueError(f"axis(={axis}) out of bounds")
  
          if not self._null_fill_value:
              return SparseArray(self.to_dense()).cumsum()
  
          return SparseArray(
              self.sp_values.cumsum(),
              sparse_index=self.sp_index,
              fill_value=self.fill_value,
          )
  
      def mean(self, axis=0, *args, **kwargs):
          """
          Mean of non-NA/null values
  
          Returns
          -------
          mean : float
          """
          nv.validate_mean(args, kwargs)
          valid_vals = self._valid_sp_values
          sp_sum = valid_vals.sum()
          ct = len(valid_vals)
  
          if self._null_fill_value:
              return sp_sum / ct
          else:
              nsparse = self.sp_index.ngaps
              return (sp_sum + self.fill_value * nsparse) / (ct + nsparse)
  
      def max(self, axis: int = 0, *args, **kwargs) -> Scalar:
          """
          Max of non-NA/null values
  
          Parameters
          ----------
          axis : int, default 0
              Not Used. NumPy compatibility.
          *args, **kwargs
              Not Used. NumPy compatibility.
  
          Returns
          -------
          scalar
          """
          nv.validate_max(args, kwargs)
          return self._min_max("max")
  
      def min(self, axis: int = 0, *args, **kwargs) -> Scalar:
          """
          Min of non-NA/null values
  
          Parameters
          ----------
          axis : int, default 0
              Not Used. NumPy compatibility.
          *args, **kwargs
              Not Used. NumPy compatibility.
  
          Returns
          -------
          scalar
          """
          nv.validate_min(args, kwargs)
          return self._min_max("min")
  
      def _min_max(self, kind: Literal["min", "max"]) -> Scalar:
          """
          Min/max of non-NA/null values
  
          Parameters
          ----------
          kind : {"min", "max"}
  
          Returns
          -------
          scalar
          """
          valid_vals = self._valid_sp_values
          has_nonnull_fill_vals = not self._null_fill_value and self.sp_index.ngaps > 0
          if len(valid_vals) > 0:
              sp_min_max = getattr(valid_vals, kind)()
  
              # If a non-null fill value is currently present, it might be the min/max
              if has_nonnull_fill_vals:
                  func = max if kind == "max" else min
                  return func(sp_min_max, self.fill_value)
              else:
                  return sp_min_max
          elif has_nonnull_fill_vals:
              return self.fill_value
          else:
              return na_value_for_dtype(self.dtype.subtype)
  
      # ------------------------------------------------------------------------
      # Ufuncs
      # ------------------------------------------------------------------------
  
      _HANDLED_TYPES = (np.ndarray, numbers.Number)
  
      def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
          out = kwargs.get("out", ())
  
          for x in inputs + out:
              if not isinstance(x, self._HANDLED_TYPES + (SparseArray,)):
                  return NotImplemented
  
          # for binary ops, use our custom dunder methods
          result = ops.maybe_dispatch_ufunc_to_dunder_op(
              self, ufunc, method, *inputs, **kwargs
          )
          if result is not NotImplemented:
              return result
  
          if len(inputs) == 1:
              # No alignment necessary.
              sp_values = getattr(ufunc, method)(self.sp_values, **kwargs)
              fill_value = getattr(ufunc, method)(self.fill_value, **kwargs)
  
              if isinstance(sp_values, tuple):
                  # multiple outputs. e.g. modf
                  arrays = tuple(
                      self._simple_new(
                          sp_value, self.sp_index, SparseDtype(sp_value.dtype, fv)
                      )
                      for sp_value, fv in zip(sp_values, fill_value)
                  )
                  return arrays
              elif is_scalar(sp_values):
                  # e.g. reductions
                  return sp_values
  
              return self._simple_new(
                  sp_values, self.sp_index, SparseDtype(sp_values.dtype, fill_value)
              )
  
          result = getattr(ufunc, method)(*(np.asarray(x) for x in inputs), **kwargs)
          if out:
              if len(out) == 1:
                  out = out[0]
              return out
  
          if type(result) is tuple:
              return tuple(type(self)(x) for x in result)
          elif method == "at":
              # no return value
              return None
          else:
              return type(self)(result)
  
      def __abs__(self):
          return np.abs(self)
  
      # ------------------------------------------------------------------------
      # Ops
      # ------------------------------------------------------------------------
  
      def _arith_method(self, other, op):
          op_name = op.__name__
  
          if isinstance(other, SparseArray):
              return _sparse_array_op(self, other, op, op_name)
  
          elif is_scalar(other):
              with np.errstate(all="ignore"):
                  fill = op(_get_fill(self), np.asarray(other))
                  result = op(self.sp_values, other)
  
              if op_name == "divmod":
                  left, right = result
                  lfill, rfill = fill
                  return (
                      _wrap_result(op_name, left, self.sp_index, lfill),
                      _wrap_result(op_name, right, self.sp_index, rfill),
                  )
  
              return _wrap_result(op_name, result, self.sp_index, fill)
  
          else:
              other = np.asarray(other)
              with np.errstate(all="ignore"):
                  # TODO: look into _wrap_result
                  if len(self) != len(other):
                      raise AssertionError(
                          f"length mismatch: {len(self)} vs. {len(other)}"
                      )
                  if not isinstance(other, SparseArray):
                      dtype = getattr(other, "dtype", None)
                      other = SparseArray(other, fill_value=self.fill_value, dtype=dtype)
                  return _sparse_array_op(self, other, op, op_name)
  
      def _cmp_method(self, other, op) -> SparseArray:
          if not is_scalar(other) and not isinstance(other, type(self)):
              # convert list-like to ndarray
              other = np.asarray(other)
  
          if isinstance(other, np.ndarray):
              # TODO: make this more flexible than just ndarray...
              if len(self) != len(other):
                  raise AssertionError(f"length mismatch: {len(self)} vs. {len(other)}")
              other = SparseArray(other, fill_value=self.fill_value)
  
          if isinstance(other, SparseArray):
              op_name = op.__name__.strip("_")
              return _sparse_array_op(self, other, op, op_name)
          else:
              with np.errstate(all="ignore"):
                  fill_value = op(self.fill_value, other)
                  result = op(self.sp_values, other)
  
              return type(self)(
                  result,
                  sparse_index=self.sp_index,
                  fill_value=fill_value,
                  dtype=np.bool_,
              )
  
      _logical_method = _cmp_method
  
      def _unary_method(self, op) -> SparseArray:
          fill_value = op(np.array(self.fill_value)).item()
          values = op(self.sp_values)
          dtype = SparseDtype(values.dtype, fill_value)
          return type(self)._simple_new(values, self.sp_index, dtype)
  
      def __pos__(self) -> SparseArray:
          return self._unary_method(operator.pos)
  
      def __neg__(self) -> SparseArray:
          return self._unary_method(operator.neg)
  
      def __invert__(self) -> SparseArray:
          return self._unary_method(operator.invert)
  
      # ----------
      # Formatting
      # -----------
      def __repr__(self) -> str:
          pp_str = printing.pprint_thing(self)
          pp_fill = printing.pprint_thing(self.fill_value)
          pp_index = printing.pprint_thing(self.sp_index)
          return f"{pp_str}\nFill: {pp_fill}\n{pp_index}"
  
      def _formatter(self, boxed=False):
          # Defer to the formatter from the GenericArrayFormatter calling us.
          # This will infer the correct formatter from the dtype of the values.
          return None
  
  
  def make_sparse(
      arr: np.ndarray,
      kind: SparseIndexKind = "block",
      fill_value=None,
      dtype: NpDtype | None = None,
  ):
      """
      Convert ndarray to sparse format
  
      Parameters
      ----------
      arr : ndarray
      kind : {'block', 'integer'}
      fill_value : NaN or another value
      dtype : np.dtype, optional
      copy : bool, default False
  
      Returns
      -------
      (sparse_values, index, fill_value) : (ndarray, SparseIndex, Scalar)
      """
      assert isinstance(arr, np.ndarray)
  
      if arr.ndim > 1:
          raise TypeError("expected dimension <= 1 data")
  
      if fill_value is None:
          fill_value = na_value_for_dtype(arr.dtype)
  
      if isna(fill_value):
          mask = notna(arr)
      else:
          # cast to object comparison to be safe
          if is_string_dtype(arr.dtype):
              arr = arr.astype(object)
  
          if is_object_dtype(arr.dtype):
              # element-wise equality check method in numpy doesn't treat
              # each element type, eg. 0, 0.0, and False are treated as
              # same. So we have to check the both of its type and value.
              mask = splib.make_mask_object_ndarray(arr, fill_value)
          else:
              mask = arr != fill_value
  
      length = len(arr)
      if length != len(mask):
          # the arr is a SparseArray
          indices = mask.sp_index.indices
      else:
          indices = mask.nonzero()[0].astype(np.int32)
  
      index = make_sparse_index(length, indices, kind)
      sparsified_values = arr[mask]
      if dtype is not None:
          # error: Argument "dtype" to "astype_nansafe" has incompatible type "Union[str,
          # dtype[Any]]"; expected "Union[dtype[Any], ExtensionDtype]"
          sparsified_values = astype_nansafe(
              sparsified_values, dtype=dtype  # type: ignore[arg-type]
          )
      # TODO: copy
      return sparsified_values, index, fill_value
  
  
  @overload
  def make_sparse_index(length: int, indices, kind: Literal["block"]) -> BlockIndex:
      ...
  
  
  @overload
  def make_sparse_index(length: int, indices, kind: Literal["integer"]) -> IntIndex:
      ...
  
  
  def make_sparse_index(length: int, indices, kind: SparseIndexKind) -> SparseIndex:
      index: SparseIndex
      if kind == "block" or isinstance(kind, BlockIndex):
          locs, lens = splib.get_blocks(indices)
          index = BlockIndex(length, locs, lens)
      elif kind == "integer" or isinstance(kind, IntIndex):
          index = IntIndex(length, indices)
      else:  # pragma: no cover
          raise ValueError("must be block or integer type")
      return index
  