text: import operator
  import re
  import warnings
  
  import numpy as np
  import pytest
  
  from pandas._libs.sparse import IntIndex
  import pandas.util._test_decorators as td
  
  import pandas as pd
  from pandas import isna
  import pandas._testing as tm
  from pandas.core.api import Int64Index
  from pandas.core.arrays.sparse import (
      SparseArray,
      SparseDtype,
  )
  
  
  class TestSparseArray:
      def setup_method(self, method):
          self.arr_data = np.array([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6])
          self.arr = SparseArray(self.arr_data)
          self.zarr = SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0)
  
      def test_constructor_dtype(self):
          arr = SparseArray([np.nan, 1, 2, np.nan])
          assert arr.dtype == SparseDtype(np.float64, np.nan)
          assert arr.dtype.subtype == np.float64
          assert np.isnan(arr.fill_value)
  
          arr = SparseArray([np.nan, 1, 2, np.nan], fill_value=0)
          assert arr.dtype == SparseDtype(np.float64, 0)
          assert arr.fill_value == 0
  
          arr = SparseArray([0, 1, 2, 4], dtype=np.float64)
          assert arr.dtype == SparseDtype(np.float64, np.nan)
          assert np.isnan(arr.fill_value)
  
          arr = SparseArray([0, 1, 2, 4], dtype=np.int64)
          assert arr.dtype == SparseDtype(np.int64, 0)
          assert arr.fill_value == 0
  
          arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=np.int64)
          assert arr.dtype == SparseDtype(np.int64, 0)
          assert arr.fill_value == 0
  
          arr = SparseArray([0, 1, 2, 4], dtype=None)
          assert arr.dtype == SparseDtype(np.int64, 0)
          assert arr.fill_value == 0
  
          arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=None)
          assert arr.dtype == SparseDtype(np.int64, 0)
          assert arr.fill_value == 0
  
      def test_constructor_dtype_str(self):
          result = SparseArray([1, 2, 3], dtype="int")
          expected = SparseArray([1, 2, 3], dtype=int)
          tm.assert_sp_array_equal(result, expected)
  
      def test_constructor_sparse_dtype(self):
          result = SparseArray([1, 0, 0, 1], dtype=SparseDtype("int64", -1))
          expected = SparseArray([1, 0, 0, 1], fill_value=-1, dtype=np.int64)
          tm.assert_sp_array_equal(result, expected)
          assert result.sp_values.dtype == np.dtype("int64")
  
      def test_constructor_sparse_dtype_str(self):
          result = SparseArray([1, 0, 0, 1], dtype="Sparse[int32]")
          expected = SparseArray([1, 0, 0, 1], dtype=np.int32)
          tm.assert_sp_array_equal(result, expected)
          assert result.sp_values.dtype == np.dtype("int32")
  
      def test_constructor_object_dtype(self):
          # GH 11856
          arr = SparseArray(["A", "A", np.nan, "B"], dtype=object)
          assert arr.dtype == SparseDtype(object)
          assert np.isnan(arr.fill_value)
  
          arr = SparseArray(["A", "A", np.nan, "B"], dtype=object, fill_value="A")
          assert arr.dtype == SparseDtype(object, "A")
          assert arr.fill_value == "A"
  
          # GH 17574
          data = [False, 0, 100.0, 0.0]
          arr = SparseArray(data, dtype=object, fill_value=False)
          assert arr.dtype == SparseDtype(object, False)
          assert arr.fill_value is False
          arr_expected = np.array(data, dtype=object)
          it = (type(x) == type(y) and x == y for x, y in zip(arr, arr_expected))
          assert np.fromiter(it, dtype=np.bool_).all()
  
      @pytest.mark.parametrize("dtype", [SparseDtype(int, 0), int])
      def test_constructor_na_dtype(self, dtype):
          with pytest.raises(ValueError, match="Cannot convert"):
              SparseArray([0, 1, np.nan], dtype=dtype)
  
      def test_constructor_warns_when_losing_timezone(self):
          # GH#32501 warn when losing timezone information
          dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific")
  
          expected = SparseArray(np.asarray(dti, dtype="datetime64[ns]"))
  
          with tm.assert_produces_warning(UserWarning):
              result = SparseArray(dti)
  
          tm.assert_sp_array_equal(result, expected)
  
          with tm.assert_produces_warning(UserWarning):
              result = SparseArray(pd.Series(dti))
  
          tm.assert_sp_array_equal(result, expected)
  
      def test_constructor_spindex_dtype(self):
          arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]))
          # XXX: Behavior change: specifying SparseIndex no longer changes the
          # fill_value
          expected = SparseArray([0, 1, 2, 0], kind="integer")
          tm.assert_sp_array_equal(arr, expected)
          assert arr.dtype == SparseDtype(np.int64)
          assert arr.fill_value == 0
  
          arr = SparseArray(
              data=[1, 2, 3],
              sparse_index=IntIndex(4, [1, 2, 3]),
              dtype=np.int64,
              fill_value=0,
          )
          exp = SparseArray([0, 1, 2, 3], dtype=np.int64, fill_value=0)
          tm.assert_sp_array_equal(arr, exp)
          assert arr.dtype == SparseDtype(np.int64)
          assert arr.fill_value == 0
  
          arr = SparseArray(
              data=[1, 2], sparse_index=IntIndex(4, [1, 2]), fill_value=0, dtype=np.int64
          )
          exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=np.int64)
          tm.assert_sp_array_equal(arr, exp)
          assert arr.dtype == SparseDtype(np.int64)
          assert arr.fill_value == 0
  
          arr = SparseArray(
              data=[1, 2, 3],
              sparse_index=IntIndex(4, [1, 2, 3]),
              dtype=None,
              fill_value=0,
          )
          exp = SparseArray([0, 1, 2, 3], dtype=None)
          tm.assert_sp_array_equal(arr, exp)
          assert arr.dtype == SparseDtype(np.int64)
          assert arr.fill_value == 0
  
      @pytest.mark.parametrize("sparse_index", [None, IntIndex(1, [0])])
      def test_constructor_spindex_dtype_scalar(self, sparse_index):
          # scalar input
          arr = SparseArray(data=1, sparse_index=sparse_index, dtype=None)
          exp = SparseArray([1], dtype=None)
          tm.assert_sp_array_equal(arr, exp)
          assert arr.dtype == SparseDtype(np.int64)
          assert arr.fill_value == 0
  
          arr = SparseArray(data=1, sparse_index=IntIndex(1, [0]), dtype=None)
          exp = SparseArray([1], dtype=None)
          tm.assert_sp_array_equal(arr, exp)
          assert arr.dtype == SparseDtype(np.int64)
          assert arr.fill_value == 0
  
      def test_constructor_spindex_dtype_scalar_broadcasts(self):
          arr = SparseArray(
              data=[1, 2], sparse_index=IntIndex(4, [1, 2]), fill_value=0, dtype=None
          )
          exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=None)
          tm.assert_sp_array_equal(arr, exp)
          assert arr.dtype == SparseDtype(np.int64)
          assert arr.fill_value == 0
  
      @pytest.mark.parametrize(
          "data, fill_value",
          [
              (np.array([1, 2]), 0),
              (np.array([1.0, 2.0]), np.nan),
              ([True, False], False),
              ([pd.Timestamp("2017-01-01")], pd.NaT),
          ],
      )
      def test_constructor_inferred_fill_value(self, data, fill_value):
          result = SparseArray(data).fill_value
  
          if isna(fill_value):
              assert isna(result)
          else:
              assert result == fill_value
  
      @pytest.mark.parametrize("format", ["coo", "csc", "csr"])
      @pytest.mark.parametrize("size", [0, 10])
      @td.skip_if_no_scipy
      def test_from_spmatrix(self, size, format):
          import scipy.sparse
  
          mat = scipy.sparse.random(size, 1, density=0.5, format=format)
          result = SparseArray.from_spmatrix(mat)
  
          result = np.asarray(result)
          expected = mat.toarray().ravel()
          tm.assert_numpy_array_equal(result, expected)
  
      @pytest.mark.parametrize("format", ["coo", "csc", "csr"])
      @td.skip_if_no_scipy
      def test_from_spmatrix_including_explicit_zero(self, format):
          import scipy.sparse
  
          mat = scipy.sparse.random(10, 1, density=0.5, format=format)
          mat.data[0] = 0
          result = SparseArray.from_spmatrix(mat)
  
          result = np.asarray(result)
          expected = mat.toarray().ravel()
          tm.assert_numpy_array_equal(result, expected)
  
      @td.skip_if_no_scipy
      def test_from_spmatrix_raises(self):
          import scipy.sparse
  
          mat = scipy.sparse.eye(5, 4, format="csc")
  
          with pytest.raises(ValueError, match="not '4'"):
              SparseArray.from_spmatrix(mat)
  
      @pytest.mark.parametrize(
          "scalar,dtype",
          [
              (False, SparseDtype(bool, False)),
              (0.0, SparseDtype("float64", 0)),
              (1, SparseDtype("int64", 1)),
              ("z", SparseDtype("object", "z")),
          ],
      )
      def test_scalar_with_index_infer_dtype(self, scalar, dtype):
          # GH 19163
          with tm.assert_produces_warning(
              FutureWarning, match="The index argument has been deprecated"
          ):
              arr = SparseArray(scalar, index=[1, 2, 3], fill_value=scalar)
          exp = SparseArray([scalar, scalar, scalar], fill_value=scalar)
  
          tm.assert_sp_array_equal(arr, exp)
  
          assert arr.dtype == dtype
          assert exp.dtype == dtype
  
      def test_get_item(self):
  
          assert np.isnan(self.arr[1])
          assert self.arr[2] == 1
          assert self.arr[7] == 5
  
          assert self.zarr[0] == 0
          assert self.zarr[2] == 1
          assert self.zarr[7] == 5
  
          errmsg = "must be an integer between -10 and 10"
  
          with pytest.raises(IndexError, match=errmsg):
              self.arr[11]
  
          with pytest.raises(IndexError, match=errmsg):
              self.arr[-11]
  
          assert self.arr[-1] == self.arr[len(self.arr) - 1]
  
      def test_take_scalar_raises(self):
          msg = "'indices' must be an array, not a scalar '2'."
          with pytest.raises(ValueError, match=msg):
              self.arr.take(2)
  
      def test_take(self):
          exp = SparseArray(np.take(self.arr_data, [2, 3]))
          tm.assert_sp_array_equal(self.arr.take([2, 3]), exp)
  
          exp = SparseArray(np.take(self.arr_data, [0, 1, 2]))
          tm.assert_sp_array_equal(self.arr.take([0, 1, 2]), exp)
  
      def test_take_all_empty(self):
          a = pd.array([0, 0], dtype=SparseDtype("int64"))
          result = a.take([0, 1], allow_fill=True, fill_value=np.nan)
          tm.assert_sp_array_equal(a, result)
  
      def test_take_fill_value(self):
          data = np.array([1, np.nan, 0, 3, 0])
          sparse = SparseArray(data, fill_value=0)
  
          exp = SparseArray(np.take(data, [0]), fill_value=0)
          tm.assert_sp_array_equal(sparse.take([0]), exp)
  
          exp = SparseArray(np.take(data, [1, 3, 4]), fill_value=0)
          tm.assert_sp_array_equal(sparse.take([1, 3, 4]), exp)
  
      def test_take_negative(self):
          exp = SparseArray(np.take(self.arr_data, [-1]))
          tm.assert_sp_array_equal(self.arr.take([-1]), exp)
  
          exp = SparseArray(np.take(self.arr_data, [-4, -3, -2]))
          tm.assert_sp_array_equal(self.arr.take([-4, -3, -2]), exp)
  
      @pytest.mark.parametrize("fill_value", [0, None, np.nan])
      def test_shift_fill_value(self, fill_value):
          # GH #24128
          sparse = SparseArray(np.array([1, 0, 0, 3, 0]), fill_value=8.0)
          res = sparse.shift(1, fill_value=fill_value)
          if isna(fill_value):
              fill_value = res.dtype.na_value
          exp = SparseArray(np.array([fill_value, 1, 0, 0, 3]), fill_value=8.0)
          tm.assert_sp_array_equal(res, exp)
  
      def test_bad_take(self):
          with pytest.raises(IndexError, match="bounds"):
              self.arr.take([11])
  
      def test_take_filling(self):
          # similar tests as GH 12631
          sparse = SparseArray([np.nan, np.nan, 1, np.nan, 4])
          result = sparse.take(np.array([1, 0, -1]))
          expected = SparseArray([np.nan, np.nan, 4])
          tm.assert_sp_array_equal(result, expected)
  
          # XXX: test change: fill_value=True -> allow_fill=True
          result = sparse.take(np.array([1, 0, -1]), allow_fill=True)
          expected = SparseArray([np.nan, np.nan, np.nan])
          tm.assert_sp_array_equal(result, expected)
  
          # allow_fill=False
          result = sparse.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
          expected = SparseArray([np.nan, np.nan, 4])
          tm.assert_sp_array_equal(result, expected)
  
          msg = "Invalid value in 'indices'"
          with pytest.raises(ValueError, match=msg):
              sparse.take(np.array([1, 0, -2]), allow_fill=True)
  
          with pytest.raises(ValueError, match=msg):
              sparse.take(np.array([1, 0, -5]), allow_fill=True)
  
          msg = "out of bounds value in 'indices'"
          with pytest.raises(IndexError, match=msg):
              sparse.take(np.array([1, -6]))
          with pytest.raises(IndexError, match=msg):
              sparse.take(np.array([1, 5]))
          with pytest.raises(IndexError, match=msg):
              sparse.take(np.array([1, 5]), allow_fill=True)
  
      def test_take_filling_fill_value(self):
          # same tests as GH 12631
          sparse = SparseArray([np.nan, 0, 1, 0, 4], fill_value=0)
          result = sparse.take(np.array([1, 0, -1]))
          expected = SparseArray([0, np.nan, 4], fill_value=0)
          tm.assert_sp_array_equal(result, expected)
  
          # fill_value
          result = sparse.take(np.array([1, 0, -1]), allow_fill=True)
          # XXX: behavior change.
          # the old way of filling self.fill_value doesn't follow EA rules.
          # It's supposed to be self.dtype.na_value (nan in this case)
          expected = SparseArray([0, np.nan, np.nan], fill_value=0)
          tm.assert_sp_array_equal(result, expected)
  
          # allow_fill=False
          result = sparse.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
          expected = SparseArray([0, np.nan, 4], fill_value=0)
          tm.assert_sp_array_equal(result, expected)
  
          msg = "Invalid value in 'indices'."
          with pytest.raises(ValueError, match=msg):
              sparse.take(np.array([1, 0, -2]), allow_fill=True)
          with pytest.raises(ValueError, match=msg):
              sparse.take(np.array([1, 0, -5]), allow_fill=True)
  
          msg = "out of bounds value in 'indices'"
          with pytest.raises(IndexError, match=msg):
              sparse.take(np.array([1, -6]))
          with pytest.raises(IndexError, match=msg):
              sparse.take(np.array([1, 5]))
          with pytest.raises(IndexError, match=msg):
              sparse.take(np.array([1, 5]), fill_value=True)
  
      @pytest.mark.parametrize("kind", ["block", "integer"])
      def test_take_filling_all_nan(self, kind):
          sparse = SparseArray([np.nan, np.nan, np.nan, np.nan, np.nan], kind=kind)
          result = sparse.take(np.array([1, 0, -1]))
          expected = SparseArray([np.nan, np.nan, np.nan], kind=kind)
          tm.assert_sp_array_equal(result, expected)
  
          result = sparse.take(np.array([1, 0, -1]), fill_value=True)
          expected = SparseArray([np.nan, np.nan, np.nan], kind=kind)
          tm.assert_sp_array_equal(result, expected)
  
          msg = "out of bounds value in 'indices'"
          with pytest.raises(IndexError, match=msg):
              sparse.take(np.array([1, -6]))
          with pytest.raises(IndexError, match=msg):
              sparse.take(np.array([1, 5]))
          with pytest.raises(IndexError, match=msg):
              sparse.take(np.array([1, 5]), fill_value=True)
  
      def test_set_item(self):
          def setitem():
              self.arr[5] = 3
  
          def setslice():
              self.arr[1:5] = 2
  
          with pytest.raises(TypeError, match="assignment via setitem"):
              setitem()
  
          with pytest.raises(TypeError, match="assignment via setitem"):
              setslice()
  
      def test_constructor_from_too_large_array(self):
          with pytest.raises(TypeError, match="expected dimension <= 1 data"):
              SparseArray(np.arange(10).reshape((2, 5)))
  
      def test_constructor_from_sparse(self):
          res = SparseArray(self.zarr)
          assert res.fill_value == 0
          tm.assert_almost_equal(res.sp_values, self.zarr.sp_values)
  
      def test_constructor_copy(self):
          cp = SparseArray(self.arr, copy=True)
          cp.sp_values[:3] = 0
          assert not (self.arr.sp_values[:3] == 0).any()
  
          not_copy = SparseArray(self.arr)
          not_copy.sp_values[:3] = 0
          assert (self.arr.sp_values[:3] == 0).all()
  
      def test_constructor_bool(self):
          # GH 10648
          data = np.array([False, False, True, True, False, False])
          arr = SparseArray(data, fill_value=False, dtype=bool)
  
          assert arr.dtype == SparseDtype(bool)
          tm.assert_numpy_array_equal(arr.sp_values, np.array([True, True]))
          # Behavior change: np.asarray densifies.
          # tm.assert_numpy_array_equal(arr.sp_values, np.asarray(arr))
          tm.assert_numpy_array_equal(arr.sp_index.indices, np.array([2, 3], np.int32))
  
          dense = arr.to_dense()
          assert dense.dtype == bool
          tm.assert_numpy_array_equal(dense, data)
  
      def test_constructor_bool_fill_value(self):
          arr = SparseArray([True, False, True], dtype=None)
          assert arr.dtype == SparseDtype(np.bool_)
          assert not arr.fill_value
  
          arr = SparseArray([True, False, True], dtype=np.bool_)
          assert arr.dtype == SparseDtype(np.bool_)
          assert not arr.fill_value
  
          arr = SparseArray([True, False, True], dtype=np.bool_, fill_value=True)
          assert arr.dtype == SparseDtype(np.bool_, True)
          assert arr.fill_value
  
      def test_constructor_float32(self):
          # GH 10648
          data = np.array([1.0, np.nan, 3], dtype=np.float32)
          arr = SparseArray(data, dtype=np.float32)
  
          assert arr.dtype == SparseDtype(np.float32)
          tm.assert_numpy_array_equal(arr.sp_values, np.array([1, 3], dtype=np.float32))
          # Behavior change: np.asarray densifies.
          # tm.assert_numpy_array_equal(arr.sp_values, np.asarray(arr))
          tm.assert_numpy_array_equal(
              arr.sp_index.indices, np.array([0, 2], dtype=np.int32)
          )
  
          dense = arr.to_dense()
          assert dense.dtype == np.float32
          tm.assert_numpy_array_equal(dense, data)
  
      def test_astype(self):
          # float -> float
          arr = SparseArray([None, None, 0, 2])
          result = arr.astype("Sparse[float32]")
          expected = SparseArray([None, None, 0, 2], dtype=np.dtype("float32"))
          tm.assert_sp_array_equal(result, expected)
  
          dtype = SparseDtype("float64", fill_value=0)
          result = arr.astype(dtype)
          expected = SparseArray._simple_new(
              np.array([0.0, 2.0], dtype=dtype.subtype), IntIndex(4, [2, 3]), dtype
          )
          tm.assert_sp_array_equal(result, expected)
  
          dtype = SparseDtype("int64", 0)
          result = arr.astype(dtype)
          expected = SparseArray._simple_new(
              np.array([0, 2], dtype=np.int64), IntIndex(4, [2, 3]), dtype
          )
          tm.assert_sp_array_equal(result, expected)
  
          arr = SparseArray([0, np.nan, 0, 1], fill_value=0)
          with pytest.raises(ValueError, match="NA"):
              arr.astype("Sparse[i8]")
  
      def test_astype_bool(self):
          a = SparseArray([1, 0, 0, 1], dtype=SparseDtype(int, 0))
          result = a.astype(bool)
          expected = SparseArray([True, 0, 0, True], dtype=SparseDtype(bool, 0))
          tm.assert_sp_array_equal(result, expected)
  
          # update fill value
          result = a.astype(SparseDtype(bool, False))
          expected = SparseArray(
              [True, False, False, True], dtype=SparseDtype(bool, False)
          )
          tm.assert_sp_array_equal(result, expected)
  
      def test_astype_all(self, any_real_numpy_dtype):
          vals = np.array([1, 2, 3])
          arr = SparseArray(vals, fill_value=1)
          typ = np.dtype(any_real_numpy_dtype)
          res = arr.astype(typ)
          assert res.dtype == SparseDtype(typ, 1)
          assert res.sp_values.dtype == typ
  
          tm.assert_numpy_array_equal(np.asarray(res.to_dense()), vals.astype(typ))
  
      @pytest.mark.parametrize(
          "arr, dtype, expected",
          [
              (
                  SparseArray([0, 1]),
                  "float",
                  SparseArray([0.0, 1.0], dtype=SparseDtype(float, 0.0)),
              ),
              (SparseArray([0, 1]), bool, SparseArray([False, True])),
              (
                  SparseArray([0, 1], fill_value=1),
                  bool,
                  SparseArray([False, True], dtype=SparseDtype(bool, True)),
              ),
              pytest.param(
                  SparseArray([0, 1]),
                  "datetime64[ns]",
                  SparseArray(
                      np.array([0, 1], dtype="datetime64[ns]"),
                      dtype=SparseDtype("datetime64[ns]", pd.Timestamp("1970")),
                  ),
                  marks=[pytest.mark.xfail(reason="NumPy-7619")],
              ),
              (
                  SparseArray([0, 1, 10]),
                  str,
                  SparseArray(["0", "1", "10"], dtype=SparseDtype(str, "0")),
              ),
              (SparseArray(["10", "20"]), float, SparseArray([10.0, 20.0])),
              (
                  SparseArray([0, 1, 0]),
                  object,
                  SparseArray([0, 1, 0], dtype=SparseDtype(object, 0)),
              ),
          ],
      )
      def test_astype_more(self, arr, dtype, expected):
          result = arr.astype(dtype)
          tm.assert_sp_array_equal(result, expected)
  
      def test_astype_nan_raises(self):
          arr = SparseArray([1.0, np.nan])
          with pytest.raises(ValueError, match="Cannot convert non-finite"):
              arr.astype(int)
  
      def test_astype_copy_false(self):
          # GH#34456 bug caused by using .view instead of .astype in astype_nansafe
          arr = SparseArray([1, 2, 3])
  
          result = arr.astype(float, copy=False)
          expected = SparseArray([1.0, 2.0, 3.0], fill_value=0.0)
          tm.assert_sp_array_equal(result, expected)
  
      def test_set_fill_value(self):
          arr = SparseArray([1.0, np.nan, 2.0], fill_value=np.nan)
          arr.fill_value = 2
          assert arr.fill_value == 2
  
          arr = SparseArray([1, 0, 2], fill_value=0, dtype=np.int64)
          arr.fill_value = 2
          assert arr.fill_value == 2
  
          # XXX: this seems fine? You can construct an integer
          # sparsearray with NaN fill value, why not update one?
          # coerces to int
          # msg = "unable to set fill_value 3\\.1 to int64 dtype"
          # with pytest.raises(ValueError, match=msg):
          arr.fill_value = 3.1
          assert arr.fill_value == 3.1
  
          # msg = "unable to set fill_value nan to int64 dtype"
          # with pytest.raises(ValueError, match=msg):
          arr.fill_value = np.nan
          assert np.isnan(arr.fill_value)
  
          arr = SparseArray([True, False, True], fill_value=False, dtype=np.bool_)
          arr.fill_value = True
          assert arr.fill_value
  
          # coerces to bool
          # msg = "unable to set fill_value 0 to bool dtype"
          # with pytest.raises(ValueError, match=msg):
          arr.fill_value = 0
          assert arr.fill_value == 0
  
          # msg = "unable to set fill_value nan to bool dtype"
          # with pytest.raises(ValueError, match=msg):
          arr.fill_value = np.nan
          assert np.isnan(arr.fill_value)
  
      @pytest.mark.parametrize("val", [[1, 2, 3], np.array([1, 2]), (1, 2, 3)])
      def test_set_fill_invalid_non_scalar(self, val):
          arr = SparseArray([True, False, True], fill_value=False, dtype=np.bool_)
          msg = "fill_value must be a scalar"
  
          with pytest.raises(ValueError, match=msg):
              arr.fill_value = val
  
      def test_copy(self):
          arr2 = self.arr.copy()
          assert arr2.sp_values is not self.arr.sp_values
          assert arr2.sp_index is self.arr.sp_index
  
      def test_values_asarray(self):
          tm.assert_almost_equal(self.arr.to_dense(), self.arr_data)
  
      @pytest.mark.parametrize(
          "data,shape,dtype",
          [
              ([0, 0, 0, 0, 0], (5,), None),
              ([], (0,), None),
              ([0], (1,), None),
              (["A", "A", np.nan, "B"], (4,), object),
          ],
      )
      def test_shape(self, data, shape, dtype):
          # GH 21126
          out = SparseArray(data, dtype=dtype)
          assert out.shape == shape
  
      @pytest.mark.parametrize(
          "vals",
          [
              [np.nan, np.nan, np.nan, np.nan, np.nan],
              [1, np.nan, np.nan, 3, np.nan],
              [1, np.nan, 0, 3, 0],
          ],
      )
      @pytest.mark.parametrize("fill_value", [None, 0])
      def test_dense_repr(self, vals, fill_value):
          vals = np.array(vals)
          arr = SparseArray(vals, fill_value=fill_value)
  
          res = arr.to_dense()
          tm.assert_numpy_array_equal(res, vals)
  
          res2 = arr._internal_get_values()
  
          tm.assert_numpy_array_equal(res2, vals)
  
      def test_getitem(self):
          def _checkit(i):
              tm.assert_almost_equal(self.arr[i], self.arr.to_dense()[i])
  
          for i in range(len(self.arr)):
              _checkit(i)
              _checkit(-i)
  
      def test_getitem_arraylike_mask(self):
          arr = SparseArray([0, 1, 2])
          result = arr[[True, False, True]]
          expected = SparseArray([0, 2])
          tm.assert_sp_array_equal(result, expected)
  
      @pytest.mark.parametrize(
          "slc",
          [
              np.s_[:],
              np.s_[1:10],
              np.s_[1:100],
              np.s_[10:1],
              np.s_[:-3],
              np.s_[-5:-4],
              np.s_[:-12],
              np.s_[-12:],
              np.s_[2:],
              np.s_[2::3],
              np.s_[::2],
              np.s_[::-1],
              np.s_[::-2],
              np.s_[1:6:2],
              np.s_[:-6:-2],
          ],
      )
      @pytest.mark.parametrize(
          "as_dense", [[np.nan] * 10, [1] * 10, [np.nan] * 5 + [1] * 5, []]
      )
      def test_getslice(self, slc, as_dense):
          as_dense = np.array(as_dense)
          arr = SparseArray(as_dense)
  
          result = arr[slc]
          expected = SparseArray(as_dense[slc])
  
          tm.assert_sp_array_equal(result, expected)
  
      def test_getslice_tuple(self):
          dense = np.array([np.nan, 0, 3, 4, 0, 5, np.nan, np.nan, 0])
  
          sparse = SparseArray(dense)
          res = sparse[(slice(4, None),)]
          exp = SparseArray(dense[4:])
          tm.assert_sp_array_equal(res, exp)
  
          sparse = SparseArray(dense, fill_value=0)
          res = sparse[(slice(4, None),)]
          exp = SparseArray(dense[4:], fill_value=0)
          tm.assert_sp_array_equal(res, exp)
  
          msg = "too many indices for array"
          with pytest.raises(IndexError, match=msg):
              sparse[4:, :]
  
          with pytest.raises(IndexError, match=msg):
              # check numpy compat
              dense[4:, :]
  
      def test_boolean_slice_empty(self):
          arr = SparseArray([0, 1, 2])
          res = arr[[False, False, False]]
          assert res.dtype == arr.dtype
  
      @pytest.mark.parametrize("op", ["add", "sub", "mul", "truediv", "floordiv", "pow"])
      def test_binary_operators(self, op):
          op = getattr(operator, op)
          data1 = np.random.randn(20)
          data2 = np.random.randn(20)
  
          data1[::2] = np.nan
          data2[::3] = np.nan
  
          arr1 = SparseArray(data1)
          arr2 = SparseArray(data2)
  
          data1[::2] = 3
          data2[::3] = 3
          farr1 = SparseArray(data1, fill_value=3)
          farr2 = SparseArray(data2, fill_value=3)
  
          def _check_op(op, first, second):
              res = op(first, second)
              exp = SparseArray(
                  op(first.to_dense(), second.to_dense()), fill_value=first.fill_value
              )
              assert isinstance(res, SparseArray)
              tm.assert_almost_equal(res.to_dense(), exp.to_dense())
  
              res2 = op(first, second.to_dense())
              assert isinstance(res2, SparseArray)
              tm.assert_sp_array_equal(res, res2)
  
              res3 = op(first.to_dense(), second)
              assert isinstance(res3, SparseArray)
              tm.assert_sp_array_equal(res, res3)
  
              res4 = op(first, 4)
              assert isinstance(res4, SparseArray)
  
              # Ignore this if the actual op raises (e.g. pow).
              try:
                  exp = op(first.to_dense(), 4)
                  exp_fv = op(first.fill_value, 4)
              except ValueError:
                  pass
              else:
                  tm.assert_almost_equal(res4.fill_value, exp_fv)
                  tm.assert_almost_equal(res4.to_dense(), exp)
  
          with np.errstate(all="ignore"):
              for first_arr, second_arr in [(arr1, arr2), (farr1, farr2)]:
                  _check_op(op, first_arr, second_arr)
  
      def test_pickle(self):
          def _check_roundtrip(obj):
              unpickled = tm.round_trip_pickle(obj)
              tm.assert_sp_array_equal(unpickled, obj)
  
          _check_roundtrip(self.arr)
          _check_roundtrip(self.zarr)
  
      def test_generator_warnings(self):
          sp_arr = SparseArray([1, 2, 3])
          with warnings.catch_warnings(record=True) as w:
              warnings.filterwarnings(action="always", category=DeprecationWarning)
              warnings.filterwarnings(action="always", category=PendingDeprecationWarning)
              for _ in sp_arr:
                  pass
              assert len(w) == 0
  
      def test_fillna(self):
          s = SparseArray([1, np.nan, np.nan, 3, np.nan])
          res = s.fillna(-1)
          exp = SparseArray([1, -1, -1, 3, -1], fill_value=-1, dtype=np.float64)
          tm.assert_sp_array_equal(res, exp)
  
          s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0)
          res = s.fillna(-1)
          exp = SparseArray([1, -1, -1, 3, -1], fill_value=0, dtype=np.float64)
          tm.assert_sp_array_equal(res, exp)
  
          s = SparseArray([1, np.nan, 0, 3, 0])
          res = s.fillna(-1)
          exp = SparseArray([1, -1, 0, 3, 0], fill_value=-1, dtype=np.float64)
          tm.assert_sp_array_equal(res, exp)
  
          s = SparseArray([1, np.nan, 0, 3, 0], fill_value=0)
          res = s.fillna(-1)
          exp = SparseArray([1, -1, 0, 3, 0], fill_value=0, dtype=np.float64)
          tm.assert_sp_array_equal(res, exp)
  
          s = SparseArray([np.nan, np.nan, np.nan, np.nan])
          res = s.fillna(-1)
          exp = SparseArray([-1, -1, -1, -1], fill_value=-1, dtype=np.float64)
          tm.assert_sp_array_equal(res, exp)
  
          s = SparseArray([np.nan, np.nan, np.nan, np.nan], fill_value=0)
          res = s.fillna(-1)
          exp = SparseArray([-1, -1, -1, -1], fill_value=0, dtype=np.float64)
          tm.assert_sp_array_equal(res, exp)
  
          # float dtype's fill_value is np.nan, replaced by -1
          s = SparseArray([0.0, 0.0, 0.0, 0.0])
          res = s.fillna(-1)
          exp = SparseArray([0.0, 0.0, 0.0, 0.0], fill_value=-1)
          tm.assert_sp_array_equal(res, exp)
  
          # int dtype shouldn't have missing. No changes.
          s = SparseArray([0, 0, 0, 0])
          assert s.dtype == SparseDtype(np.int64)
          assert s.fill_value == 0
          res = s.fillna(-1)
          tm.assert_sp_array_equal(res, s)
  
          s = SparseArray([0, 0, 0, 0], fill_value=0)
          assert s.dtype == SparseDtype(np.int64)
          assert s.fill_value == 0
          res = s.fillna(-1)
          exp = SparseArray([0, 0, 0, 0], fill_value=0)
          tm.assert_sp_array_equal(res, exp)
  
          # fill_value can be nan if there is no missing hole.
          # only fill_value will be changed
          s = SparseArray([0, 0, 0, 0], fill_value=np.nan)
          assert s.dtype == SparseDtype(np.int64, fill_value=np.nan)
          assert np.isnan(s.fill_value)
          res = s.fillna(-1)
          exp = SparseArray([0, 0, 0, 0], fill_value=-1)
          tm.assert_sp_array_equal(res, exp)
  
      def test_fillna_overlap(self):
          s = SparseArray([1, np.nan, np.nan, 3, np.nan])
          # filling with existing value doesn't replace existing value with
          # fill_value, i.e. existing 3 remains in sp_values
          res = s.fillna(3)
          exp = np.array([1, 3, 3, 3, 3], dtype=np.float64)
          tm.assert_numpy_array_equal(res.to_dense(), exp)
  
          s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0)
          res = s.fillna(3)
          exp = SparseArray([1, 3, 3, 3, 3], fill_value=0, dtype=np.float64)
          tm.assert_sp_array_equal(res, exp)
  
      def test_nonzero(self):
          # Tests regression #21172.
          sa = SparseArray([float("nan"), float("nan"), 1, 0, 0, 2, 0, 0, 0, 3, 0, 0])
          expected = np.array([2, 5, 9], dtype=np.int32)
          (result,) = sa.nonzero()
          tm.assert_numpy_array_equal(expected, result)
  
          sa = SparseArray([0, 0, 1, 0, 0, 2, 0, 0, 0, 3, 0, 0])
          (result,) = sa.nonzero()
          tm.assert_numpy_array_equal(expected, result)
  
  
  class TestSparseArrayAnalytics:
      @pytest.mark.parametrize(
          "data,pos,neg",
          [
              ([True, True, True], True, False),
              ([1, 2, 1], 1, 0),
              ([1.0, 2.0, 1.0], 1.0, 0.0),
          ],
      )
      def test_all(self, data, pos, neg):
          # GH 17570
          out = SparseArray(data).all()
          assert out
  
          out = SparseArray(data, fill_value=pos).all()
          assert out
  
          data[1] = neg
          out = SparseArray(data).all()
          assert not out
  
          out = SparseArray(data, fill_value=pos).all()
          assert not out
  
      @pytest.mark.parametrize(
          "data,pos,neg",
          [
              ([True, True, True], True, False),
              ([1, 2, 1], 1, 0),
              ([1.0, 2.0, 1.0], 1.0, 0.0),
          ],
      )
      def test_numpy_all(self, data, pos, neg):
          # GH 17570
          out = np.all(SparseArray(data))
          assert out
  
          out = np.all(SparseArray(data, fill_value=pos))
          assert out
  
          data[1] = neg
          out = np.all(SparseArray(data))
          assert not out
  
          out = np.all(SparseArray(data, fill_value=pos))
          assert not out
  
          # raises with a different message on py2.
          msg = "the 'out' parameter is not supported"
          with pytest.raises(ValueError, match=msg):
              np.all(SparseArray(data), out=np.array([]))
  
      @pytest.mark.parametrize(
          "data,pos,neg",
          [
              ([False, True, False], True, False),
              ([0, 2, 0], 2, 0),
              ([0.0, 2.0, 0.0], 2.0, 0.0),
          ],
      )
      def test_any(self, data, pos, neg):
          # GH 17570
          out = SparseArray(data).any()
          assert out
  
          out = SparseArray(data, fill_value=pos).any()
          assert out
  
          data[1] = neg
          out = SparseArray(data).any()
          assert not out
  
          out = SparseArray(data, fill_value=pos).any()
          assert not out
  
      @pytest.mark.parametrize(
          "data,pos,neg",
          [
              ([False, True, False], True, False),
              ([0, 2, 0], 2, 0),
              ([0.0, 2.0, 0.0], 2.0, 0.0),
          ],
      )
      def test_numpy_any(self, data, pos, neg):
          # GH 17570
          out = np.any(SparseArray(data))
          assert out
  
          out = np.any(SparseArray(data, fill_value=pos))
          assert out
  
          data[1] = neg
          out = np.any(SparseArray(data))
          assert not out
  
          out = np.any(SparseArray(data, fill_value=pos))
          assert not out
  
          msg = "the 'out' parameter is not supported"
          with pytest.raises(ValueError, match=msg):
              np.any(SparseArray(data), out=out)
  
      def test_sum(self):
          data = np.arange(10).astype(float)
          out = SparseArray(data).sum()
          assert out == 45.0
  
          data[5] = np.nan
          out = SparseArray(data, fill_value=2).sum()
          assert out == 40.0
  
          out = SparseArray(data, fill_value=np.nan).sum()
          assert out == 40.0
  
      @pytest.mark.parametrize(
          "arr",
          [
              np.array([0, 1, np.nan, 1]),
              np.array([0, 1, 1]),
              np.array([True, True, False]),
          ],
      )
      @pytest.mark.parametrize("fill_value", [0, 1, np.nan, True, False])
      @pytest.mark.parametrize("min_count, expected", [(3, 2), (4, np.nan)])
      def test_sum_min_count(self, arr, fill_value, min_count, expected):
          # https://github.com/pandas-dev/pandas/issues/25777
          sparray = SparseArray(arr, fill_value=fill_value)
          result = sparray.sum(min_count=min_count)
          if np.isnan(expected):
              assert np.isnan(result)
          else:
              assert result == expected
  
      def test_numpy_sum(self):
          data = np.arange(10).astype(float)
          out = np.sum(SparseArray(data))
          assert out == 45.0
  
          data[5] = np.nan
          out = np.sum(SparseArray(data, fill_value=2))
          assert out == 40.0
  
          out = np.sum(SparseArray(data, fill_value=np.nan))
          assert out == 40.0
  
          msg = "the 'dtype' parameter is not supported"
          with pytest.raises(ValueError, match=msg):
              np.sum(SparseArray(data), dtype=np.int64)
  
          msg = "the 'out' parameter is not supported"
          with pytest.raises(ValueError, match=msg):
              np.sum(SparseArray(data), out=out)
  
      @pytest.mark.parametrize(
          "data,expected",
          [
              (
                  np.array([1, 2, 3, 4, 5], dtype=float),  # non-null data
                  SparseArray(np.array([1.0, 3.0, 6.0, 10.0, 15.0])),
              ),
              (
                  np.array([1, 2, np.nan, 4, 5], dtype=float),  # null data
                  SparseArray(np.array([1.0, 3.0, np.nan, 7.0, 12.0])),
              ),
          ],
      )
      @pytest.mark.parametrize("numpy", [True, False])
      def test_cumsum(self, data, expected, numpy):
          cumsum = np.cumsum if numpy else lambda s: s.cumsum()
  
          out = cumsum(SparseArray(data))
          tm.assert_sp_array_equal(out, expected)
  
          out = cumsum(SparseArray(data, fill_value=np.nan))
          tm.assert_sp_array_equal(out, expected)
  
          out = cumsum(SparseArray(data, fill_value=2))
          tm.assert_sp_array_equal(out, expected)
  
          if numpy:  # numpy compatibility checks.
              msg = "the 'dtype' parameter is not supported"
              with pytest.raises(ValueError, match=msg):
                  np.cumsum(SparseArray(data), dtype=np.int64)
  
              msg = "the 'out' parameter is not supported"
              with pytest.raises(ValueError, match=msg):
                  np.cumsum(SparseArray(data), out=out)
          else:
              axis = 1  # SparseArray currently 1-D, so only axis = 0 is valid.
              msg = re.escape(f"axis(={axis}) out of bounds")
              with pytest.raises(ValueError, match=msg):
                  SparseArray(data).cumsum(axis=axis)
  
      def test_mean(self):
          data = np.arange(10).astype(float)
          out = SparseArray(data).mean()
          assert out == 4.5
  
          data[5] = np.nan
          out = SparseArray(data).mean()
          assert out == 40.0 / 9
  
      def test_numpy_mean(self):
          data = np.arange(10).astype(float)
          out = np.mean(SparseArray(data))
          assert out == 4.5
  
          data[5] = np.nan
          out = np.mean(SparseArray(data))
          assert out == 40.0 / 9
  
          msg = "the 'dtype' parameter is not supported"
          with pytest.raises(ValueError, match=msg):
              np.mean(SparseArray(data), dtype=np.int64)
  
          msg = "the 'out' parameter is not supported"
          with pytest.raises(ValueError, match=msg):
              np.mean(SparseArray(data), out=out)
  
      def test_ufunc(self):
          # GH 13853 make sure ufunc is applied to fill_value
          sparse = SparseArray([1, np.nan, 2, np.nan, -2])
          result = SparseArray([1, np.nan, 2, np.nan, 2])
          tm.assert_sp_array_equal(abs(sparse), result)
          tm.assert_sp_array_equal(np.abs(sparse), result)
  
          sparse = SparseArray([1, -1, 2, -2], fill_value=1)
          result = SparseArray([1, 2, 2], sparse_index=sparse.sp_index, fill_value=1)
          tm.assert_sp_array_equal(abs(sparse), result)
          tm.assert_sp_array_equal(np.abs(sparse), result)
  
          sparse = SparseArray([1, -1, 2, -2], fill_value=-1)
          result = SparseArray([1, 2, 2], sparse_index=sparse.sp_index, fill_value=1)
          tm.assert_sp_array_equal(abs(sparse), result)
          tm.assert_sp_array_equal(np.abs(sparse), result)
  
          sparse = SparseArray([1, np.nan, 2, np.nan, -2])
          result = SparseArray(np.sin([1, np.nan, 2, np.nan, -2]))
          tm.assert_sp_array_equal(np.sin(sparse), result)
  
          sparse = SparseArray([1, -1, 2, -2], fill_value=1)
          result = SparseArray(np.sin([1, -1, 2, -2]), fill_value=np.sin(1))
          tm.assert_sp_array_equal(np.sin(sparse), result)
  
          sparse = SparseArray([1, -1, 0, -2], fill_value=0)
          result = SparseArray(np.sin([1, -1, 0, -2]), fill_value=np.sin(0))
          tm.assert_sp_array_equal(np.sin(sparse), result)
  
      def test_ufunc_args(self):
          # GH 13853 make sure ufunc is applied to fill_value, including its arg
          sparse = SparseArray([1, np.nan, 2, np.nan, -2])
          result = SparseArray([2, np.nan, 3, np.nan, -1])
          tm.assert_sp_array_equal(np.add(sparse, 1), result)
  
          sparse = SparseArray([1, -1, 2, -2], fill_value=1)
          result = SparseArray([2, 0, 3, -1], fill_value=2)
          tm.assert_sp_array_equal(np.add(sparse, 1), result)
  
          sparse = SparseArray([1, -1, 0, -2], fill_value=0)
          result = SparseArray([2, 0, 1, -1], fill_value=1)
          tm.assert_sp_array_equal(np.add(sparse, 1), result)
  
      @pytest.mark.parametrize("fill_value", [0.0, np.nan])
      def test_modf(self, fill_value):
          # https://github.com/pandas-dev/pandas/issues/26946
          sparse = SparseArray([fill_value] * 10 + [1.1, 2.2], fill_value=fill_value)
          r1, r2 = np.modf(sparse)
          e1, e2 = np.modf(np.asarray(sparse))
          tm.assert_sp_array_equal(r1, SparseArray(e1, fill_value=fill_value))
          tm.assert_sp_array_equal(r2, SparseArray(e2, fill_value=fill_value))
  
      def test_nbytes_integer(self):
          arr = SparseArray([1, 0, 0, 0, 2], kind="integer")
          result = arr.nbytes
          # (2 * 8) + 2 * 4
          assert result == 24
  
      def test_nbytes_block(self):
          arr = SparseArray([1, 2, 0, 0, 0], kind="block")
          result = arr.nbytes
          # (2 * 8) + 4 + 4
          # sp_values, blocs, blengths
          assert result == 24
  
      def test_asarray_datetime64(self):
          s = SparseArray(pd.to_datetime(["2012", None, None, "2013"]))
          np.asarray(s)
  
      def test_density(self):
          arr = SparseArray([0, 1])
          assert arr.density == 0.5
  
      def test_npoints(self):
          arr = SparseArray([0, 1])
          assert arr.npoints == 1
  
  
  class TestAccessor:
      @pytest.mark.parametrize("attr", ["npoints", "density", "fill_value", "sp_values"])
      def test_get_attributes(self, attr):
          arr = SparseArray([0, 1])
          ser = pd.Series(arr)
  
          result = getattr(ser.sparse, attr)
          expected = getattr(arr, attr)
          assert result == expected
  
      @td.skip_if_no_scipy
      def test_from_coo(self):
          import scipy.sparse
  
          row = [0, 3, 1, 0]
          col = [0, 3, 1, 2]
          data = [4, 5, 7, 9]
          # TODO(scipy#13585): Remove dtype when scipy is fixed
          # https://github.com/scipy/scipy/issues/13585
          sp_array = scipy.sparse.coo_matrix((data, (row, col)), dtype="int")
          result = pd.Series.sparse.from_coo(sp_array)
  
          index = pd.MultiIndex.from_arrays([[0, 0, 1, 3], [0, 2, 1, 3]])
          expected = pd.Series([4, 9, 7, 5], index=index, dtype="Sparse[int]")
          tm.assert_series_equal(result, expected)
  
      @td.skip_if_no_scipy
      @pytest.mark.parametrize(
          "sort_labels, expected_rows, expected_cols, expected_values_pos",
          [
              (
                  False,
                  [("b", 2), ("a", 2), ("b", 1), ("a", 1)],
                  [("z", 1), ("z", 2), ("x", 2), ("z", 0)],
                  {1: (1, 0), 3: (3, 3)},
              ),
              (
                  True,
                  [("a", 1), ("a", 2), ("b", 1), ("b", 2)],
                  [("x", 2), ("z", 0), ("z", 1), ("z", 2)],
                  {1: (1, 2), 3: (0, 1)},
              ),
          ],
      )
      def test_to_coo(
          self, sort_labels, expected_rows, expected_cols, expected_values_pos
      ):
          import scipy.sparse
  
          values = SparseArray([0, np.nan, 1, 0, None, 3], fill_value=0)
          index = pd.MultiIndex.from_tuples(
              [
                  ("b", 2, "z", 1),
                  ("a", 2, "z", 2),
                  ("a", 2, "z", 1),
                  ("a", 2, "x", 2),
                  ("b", 1, "z", 1),
                  ("a", 1, "z", 0),
              ]
          )
          ss = pd.Series(values, index=index)
  
          expected_A = np.zeros((4, 4))
          for value, (row, col) in expected_values_pos.items():
              expected_A[row, col] = value
  
          A, rows, cols = ss.sparse.to_coo(
              row_levels=(0, 1), column_levels=(2, 3), sort_labels=sort_labels
          )
          assert isinstance(A, scipy.sparse.coo.coo_matrix)
          tm.assert_numpy_array_equal(A.toarray(), expected_A)
          assert rows == expected_rows
          assert cols == expected_cols
  
      def test_non_sparse_raises(self):
          ser = pd.Series([1, 2, 3])
          with pytest.raises(AttributeError, match=".sparse"):
              ser.sparse.density
  
  
  def test_setting_fill_value_fillna_still_works():
      # This is why letting users update fill_value / dtype is bad
      # astype has the same problem.
      arr = SparseArray([1.0, np.nan, 1.0], fill_value=0.0)
      arr.fill_value = np.nan
      result = arr.isna()
      # Can't do direct comparison, since the sp_index will be different
      # So let's convert to ndarray and check there.
      result = np.asarray(result)
  
      expected = np.array([False, True, False])
      tm.assert_numpy_array_equal(result, expected)
  
  
  def test_setting_fill_value_updates():
      arr = SparseArray([0.0, np.nan], fill_value=0)
      arr.fill_value = np.nan
      # use private constructor to get the index right
      # otherwise both nans would be un-stored.
      expected = SparseArray._simple_new(
          sparse_array=np.array([np.nan]),
          sparse_index=IntIndex(2, [1]),
          dtype=SparseDtype(float, np.nan),
      )
      tm.assert_sp_array_equal(arr, expected)
  
  
  @pytest.mark.parametrize(
      "arr, loc",
      [
          ([None, 1, 2], 0),
          ([0, None, 2], 1),
          ([0, 1, None], 2),
          ([0, 1, 1, None, None], 3),
          ([1, 1, 1, 2], -1),
          ([], -1),
      ],
  )
  def test_first_fill_value_loc(arr, loc):
      result = SparseArray(arr)._first_fill_value_loc()
      assert result == loc
  
  
  @pytest.mark.parametrize(
      "arr", [[1, 2, np.nan, np.nan], [1, np.nan, 2, np.nan], [1, 2, np.nan]]
  )
  @pytest.mark.parametrize("fill_value", [np.nan, 0, 1])
  def test_unique_na_fill(arr, fill_value):
      a = SparseArray(arr, fill_value=fill_value).unique()
      b = pd.Series(arr).unique()
      assert isinstance(a, SparseArray)
      a = np.asarray(a)
      tm.assert_numpy_array_equal(a, b)
  
  
  def test_unique_all_sparse():
      # https://github.com/pandas-dev/pandas/issues/23168
      arr = SparseArray([0, 0])
      result = arr.unique()
      expected = SparseArray([0])
      tm.assert_sp_array_equal(result, expected)
  
  
  def test_map():
      arr = SparseArray([0, 1, 2])
      expected = SparseArray([10, 11, 12], fill_value=10)
  
      # dict
      result = arr.map({0: 10, 1: 11, 2: 12})
      tm.assert_sp_array_equal(result, expected)
  
      # series
      result = arr.map(pd.Series({0: 10, 1: 11, 2: 12}))
      tm.assert_sp_array_equal(result, expected)
  
      # function
      result = arr.map(pd.Series({0: 10, 1: 11, 2: 12}))
      expected = SparseArray([10, 11, 12], fill_value=10)
      tm.assert_sp_array_equal(result, expected)
  
  
  def test_map_missing():
      arr = SparseArray([0, 1, 2])
      expected = SparseArray([10, 11, None], fill_value=10)
  
      result = arr.map({0: 10, 1: 11})
      tm.assert_sp_array_equal(result, expected)
  
  
  @pytest.mark.parametrize("fill_value", [np.nan, 1])
  def test_dropna(fill_value):
      # GH-28287
      arr = SparseArray([np.nan, 1], fill_value=fill_value)
      exp = SparseArray([1.0], fill_value=fill_value)
      tm.assert_sp_array_equal(arr.dropna(), exp)
  
      df = pd.DataFrame({"a": [0, 1], "b": arr})
      expected_df = pd.DataFrame({"a": [1], "b": exp}, index=Int64Index([1]))
      tm.assert_equal(df.dropna(), expected_df)
  
  
  def test_drop_duplicates_fill_value():
      # GH 11726
      df = pd.DataFrame(np.zeros((5, 5))).apply(lambda x: SparseArray(x, fill_value=0))
      result = df.drop_duplicates()
      expected = pd.DataFrame({i: SparseArray([0.0], fill_value=0) for i in range(5)})
      tm.assert_frame_equal(result, expected)
  
  
  class TestMinMax:
      @pytest.mark.parametrize(
          "raw_data,max_expected,min_expected",
          [
              (np.arange(5.0), [4], [0]),
              (-np.arange(5.0), [0], [-4]),
              (np.array([0, 1, 2, np.nan, 4]), [4], [0]),
              (np.array([np.nan] * 5), [np.nan], [np.nan]),
              (np.array([]), [np.nan], [np.nan]),
          ],
      )
      def test_nan_fill_value(self, raw_data, max_expected, min_expected):
          max_result = SparseArray(raw_data).max()
          min_result = SparseArray(raw_data).min()
          assert max_result in max_expected
          assert min_result in min_expected
  
      @pytest.mark.parametrize(
          "fill_value,max_expected,min_expected",
          [
              (100, 100, 0),
              (-100, 1, -100),
          ],
      )
      def test_fill_value(self, fill_value, max_expected, min_expected):
          arr = SparseArray(
              np.array([fill_value, 0, 1]), dtype=SparseDtype("int", fill_value)
          )
          max_result = arr.max()
          assert max_result == max_expected
  
          min_result = arr.min()
          assert min_result == min_expected
  
      @pytest.mark.parametrize("func", ["min", "max"])
      @pytest.mark.parametrize("data", [np.array([]), np.array([np.nan, np.nan])])
      @pytest.mark.parametrize(
          "dtype,expected",
          [
              (SparseDtype(np.float64, np.nan), np.nan),
              (SparseDtype(np.float64, 5.0), np.nan),
              (SparseDtype("datetime64[ns]", pd.NaT), pd.NaT),
              (SparseDtype("datetime64[ns]", pd.to_datetime("2018-05-05")), pd.NaT),
          ],
      )
      def test_na_value_if_no_valid_values(self, func, data, dtype, expected):
          arr = SparseArray(data, dtype=dtype)
          result = getattr(arr, func)()
          if expected == pd.NaT:
              assert result == pd.NaT
          else:
              assert np.isnan(result)
  