text: # pylint: disable-msg=E1101,W0612
  
  import operator
  from datetime import datetime
  
  import pytest
  
  from numpy import nan
  import numpy as np
  import pandas as pd
  
  from pandas import (Series, DataFrame, bdate_range,
                      isna, compat, _np_version_under1p12)
  from pandas.tseries.offsets import BDay
  import pandas.util.testing as tm
  import pandas.util._test_decorators as td
  from pandas.compat import range
  from pandas.core.reshape.util import cartesian_product
  
  import pandas.core.sparse.frame as spf
  
  from pandas._libs.sparse import BlockIndex, IntIndex
  from pandas.core.sparse.api import SparseSeries
  from pandas.tests.series.test_api import SharedWithSparse
  
  
  def _test_data1():
      # nan-based
      arr = np.arange(20, dtype=float)
      index = np.arange(20)
      arr[:2] = nan
      arr[5:10] = nan
      arr[-3:] = nan
  
      return arr, index
  
  
  def _test_data2():
      # nan-based
      arr = np.arange(15, dtype=float)
      index = np.arange(15)
      arr[7:12] = nan
      arr[-1:] = nan
      return arr, index
  
  
  def _test_data1_zero():
      # zero-based
      arr, index = _test_data1()
      arr[np.isnan(arr)] = 0
      return arr, index
  
  
  def _test_data2_zero():
      # zero-based
      arr, index = _test_data2()
      arr[np.isnan(arr)] = 0
      return arr, index
  
  
  class TestSparseSeries(SharedWithSparse):
  
      series_klass = SparseSeries
      # SharedWithSparse tests use generic, series_klass-agnostic assertion
      _assert_series_equal = staticmethod(tm.assert_sp_series_equal)
  
      def setup_method(self, method):
          arr, index = _test_data1()
  
          date_index = bdate_range('1/1/2011', periods=len(index))
  
          self.bseries = SparseSeries(arr, index=index, kind='block',
                                      name='bseries')
          self.ts = self.bseries
  
          self.btseries = SparseSeries(arr, index=date_index, kind='block')
  
          self.iseries = SparseSeries(arr, index=index, kind='integer',
                                      name='iseries')
  
          arr, index = _test_data2()
          self.bseries2 = SparseSeries(arr, index=index, kind='block')
          self.iseries2 = SparseSeries(arr, index=index, kind='integer')
  
          arr, index = _test_data1_zero()
          self.zbseries = SparseSeries(arr, index=index, kind='block',
                                       fill_value=0, name='zbseries')
          self.ziseries = SparseSeries(arr, index=index, kind='integer',
                                       fill_value=0)
  
          arr, index = _test_data2_zero()
          self.zbseries2 = SparseSeries(arr, index=index, kind='block',
                                        fill_value=0)
          self.ziseries2 = SparseSeries(arr, index=index, kind='integer',
                                        fill_value=0)
  
      def test_constructor_dict_input(self):
          # gh-16905
          constructor_dict = {1: 1.}
          index = [0, 1, 2]
  
          # Series with index passed in
          series = pd.Series(constructor_dict)
          expected = SparseSeries(series, index=index)
  
          result = SparseSeries(constructor_dict, index=index)
          tm.assert_sp_series_equal(result, expected)
  
          # Series with index and dictionary with no index
          expected = SparseSeries(series)
  
          result = SparseSeries(constructor_dict)
          tm.assert_sp_series_equal(result, expected)
  
      def test_constructor_dtype(self):
          arr = SparseSeries([np.nan, 1, 2, np.nan])
          assert arr.dtype == np.float64
          assert np.isnan(arr.fill_value)
  
          arr = SparseSeries([np.nan, 1, 2, np.nan], fill_value=0)
          assert arr.dtype == np.float64
          assert arr.fill_value == 0
  
          arr = SparseSeries([0, 1, 2, 4], dtype=np.int64, fill_value=np.nan)
          assert arr.dtype == np.int64
          assert np.isnan(arr.fill_value)
  
          arr = SparseSeries([0, 1, 2, 4], dtype=np.int64)
          assert arr.dtype == np.int64
          assert arr.fill_value == 0
  
          arr = SparseSeries([0, 1, 2, 4], fill_value=0, dtype=np.int64)
          assert arr.dtype == np.int64
          assert arr.fill_value == 0
  
      def test_iteration_and_str(self):
          [x for x in self.bseries]
          str(self.bseries)
  
      def test_construct_DataFrame_with_sp_series(self):
          # it works!
          df = DataFrame({'col': self.bseries})
  
          # printing & access
          df.iloc[:1]
          df['col']
          df.dtypes
          str(df)
  
          tm.assert_sp_series_equal(df['col'], self.bseries, check_names=False)
  
          result = df.iloc[:, 0]
          tm.assert_sp_series_equal(result, self.bseries, check_names=False)
  
          # blocking
          expected = Series({'col': 'float64:sparse'})
          result = df.ftypes
          tm.assert_series_equal(expected, result)
  
      def test_constructor_preserve_attr(self):
          arr = pd.SparseArray([1, 0, 3, 0], dtype=np.int64, fill_value=0)
          assert arr.dtype == np.int64
          assert arr.fill_value == 0
  
          s = pd.SparseSeries(arr, name='x')
          assert s.dtype == np.int64
          assert s.fill_value == 0
  
      def test_series_density(self):
          # GH2803
          ts = Series(np.random.randn(10))
          ts[2:-2] = nan
          sts = ts.to_sparse()
          density = sts.density  # don't die
          assert density == 4 / 10.0
  
      def test_sparse_to_dense(self):
          arr, index = _test_data1()
          series = self.bseries.to_dense()
          tm.assert_series_equal(series, Series(arr, name='bseries'))
  
          # see gh-14647
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              series = self.bseries.to_dense(sparse_only=True)
  
          indexer = np.isfinite(arr)
          exp = Series(arr[indexer], index=index[indexer], name='bseries')
          tm.assert_series_equal(series, exp)
  
          series = self.iseries.to_dense()
          tm.assert_series_equal(series, Series(arr, name='iseries'))
  
          arr, index = _test_data1_zero()
          series = self.zbseries.to_dense()
          tm.assert_series_equal(series, Series(arr, name='zbseries'))
  
          series = self.ziseries.to_dense()
          tm.assert_series_equal(series, Series(arr))
  
      def test_to_dense_fill_value(self):
          s = pd.Series([1, np.nan, np.nan, 3, np.nan])
          res = SparseSeries(s).to_dense()
          tm.assert_series_equal(res, s)
  
          res = SparseSeries(s, fill_value=0).to_dense()
          tm.assert_series_equal(res, s)
  
          s = pd.Series([1, np.nan, 0, 3, 0])
          res = SparseSeries(s, fill_value=0).to_dense()
          tm.assert_series_equal(res, s)
  
          res = SparseSeries(s, fill_value=0).to_dense()
          tm.assert_series_equal(res, s)
  
          s = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan])
          res = SparseSeries(s).to_dense()
          tm.assert_series_equal(res, s)
  
          s = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan])
          res = SparseSeries(s, fill_value=0).to_dense()
          tm.assert_series_equal(res, s)
  
      def test_dense_to_sparse(self):
          series = self.bseries.to_dense()
          bseries = series.to_sparse(kind='block')
          iseries = series.to_sparse(kind='integer')
          tm.assert_sp_series_equal(bseries, self.bseries)
          tm.assert_sp_series_equal(iseries, self.iseries, check_names=False)
          assert iseries.name == self.bseries.name
  
          assert len(series) == len(bseries)
          assert len(series) == len(iseries)
          assert series.shape == bseries.shape
          assert series.shape == iseries.shape
  
          # non-NaN fill value
          series = self.zbseries.to_dense()
          zbseries = series.to_sparse(kind='block', fill_value=0)
          ziseries = series.to_sparse(kind='integer', fill_value=0)
          tm.assert_sp_series_equal(zbseries, self.zbseries)
          tm.assert_sp_series_equal(ziseries, self.ziseries, check_names=False)
          assert ziseries.name == self.zbseries.name
  
          assert len(series) == len(zbseries)
          assert len(series) == len(ziseries)
          assert series.shape == zbseries.shape
          assert series.shape == ziseries.shape
  
      def test_to_dense_preserve_name(self):
          assert (self.bseries.name is not None)
          result = self.bseries.to_dense()
          assert result.name == self.bseries.name
  
      def test_constructor(self):
          # test setup guys
          assert np.isnan(self.bseries.fill_value)
          assert isinstance(self.bseries.sp_index, BlockIndex)
          assert np.isnan(self.iseries.fill_value)
          assert isinstance(self.iseries.sp_index, IntIndex)
  
          assert self.zbseries.fill_value == 0
          tm.assert_numpy_array_equal(self.zbseries.values.values,
                                      self.bseries.to_dense().fillna(0).values)
  
          # pass SparseSeries
          def _check_const(sparse, name):
              # use passed series name
              result = SparseSeries(sparse)
              tm.assert_sp_series_equal(result, sparse)
              assert sparse.name == name
              assert result.name == name
  
              # use passed name
              result = SparseSeries(sparse, name='x')
              tm.assert_sp_series_equal(result, sparse, check_names=False)
              assert result.name == 'x'
  
          _check_const(self.bseries, 'bseries')
          _check_const(self.iseries, 'iseries')
          _check_const(self.zbseries, 'zbseries')
  
          # Sparse time series works
          date_index = bdate_range('1/1/2000', periods=len(self.bseries))
          s5 = SparseSeries(self.bseries, index=date_index)
          assert isinstance(s5, SparseSeries)
  
          # pass Series
          bseries2 = SparseSeries(self.bseries.to_dense())
          tm.assert_numpy_array_equal(self.bseries.sp_values, bseries2.sp_values)
  
          # pass dict?
  
          # don't copy the data by default
          values = np.ones(self.bseries.npoints)
          sp = SparseSeries(values, sparse_index=self.bseries.sp_index)
          sp.sp_values[:5] = 97
          assert values[0] == 97
  
          assert len(sp) == 20
          assert sp.shape == (20, )
  
          # but can make it copy!
          sp = SparseSeries(values, sparse_index=self.bseries.sp_index,
                            copy=True)
          sp.sp_values[:5] = 100
          assert values[0] == 97
  
          assert len(sp) == 20
          assert sp.shape == (20, )
  
      def test_constructor_scalar(self):
          data = 5
          sp = SparseSeries(data, np.arange(100))
          sp = sp.reindex(np.arange(200))
          assert (sp.loc[:99] == data).all()
          assert isna(sp.loc[100:]).all()
  
          data = np.nan
          sp = SparseSeries(data, np.arange(100))
          assert len(sp) == 100
          assert sp.shape == (100, )
  
      def test_constructor_ndarray(self):
          pass
  
      def test_constructor_nonnan(self):
          arr = [0, 0, 0, nan, nan]
          sp_series = SparseSeries(arr, fill_value=0)
          tm.assert_numpy_array_equal(sp_series.values.values, np.array(arr))
          assert len(sp_series) == 5
          assert sp_series.shape == (5, )
  
      def test_constructor_empty(self):
          # see gh-9272
          sp = SparseSeries()
          assert len(sp.index) == 0
          assert sp.shape == (0, )
  
      def test_copy_astype(self):
          cop = self.bseries.astype(np.float64)
          assert cop is not self.bseries
          assert cop.sp_index is self.bseries.sp_index
          assert cop.dtype == np.float64
  
          cop2 = self.iseries.copy()
  
          tm.assert_sp_series_equal(cop, self.bseries)
          tm.assert_sp_series_equal(cop2, self.iseries)
  
          # test that data is copied
          cop[:5] = 97
          assert cop.sp_values[0] == 97
          assert self.bseries.sp_values[0] != 97
  
          # correct fill value
          zbcop = self.zbseries.copy()
          zicop = self.ziseries.copy()
  
          tm.assert_sp_series_equal(zbcop, self.zbseries)
          tm.assert_sp_series_equal(zicop, self.ziseries)
  
          # no deep copy
          view = self.bseries.copy(deep=False)
          view.sp_values[:5] = 5
          assert (self.bseries.sp_values[:5] == 5).all()
  
      def test_shape(self):
          # see gh-10452
          assert self.bseries.shape == (20, )
          assert self.btseries.shape == (20, )
          assert self.iseries.shape == (20, )
  
          assert self.bseries2.shape == (15, )
          assert self.iseries2.shape == (15, )
  
          assert self.zbseries2.shape == (15, )
          assert self.ziseries2.shape == (15, )
  
      def test_astype(self):
          with pytest.raises(ValueError):
              self.bseries.astype(np.int64)
  
      def test_astype_all(self):
          orig = pd.Series(np.array([1, 2, 3]))
          s = SparseSeries(orig)
  
          types = [np.float64, np.float32, np.int64,
                   np.int32, np.int16, np.int8]
          for typ in types:
              res = s.astype(typ)
              assert res.dtype == typ
              tm.assert_series_equal(res.to_dense(), orig.astype(typ))
  
      def test_kind(self):
          assert self.bseries.kind == 'block'
          assert self.iseries.kind == 'integer'
  
      def test_to_frame(self):
          # GH 9850
          s = pd.SparseSeries([1, 2, 0, nan, 4, nan, 0], name='x')
          exp = pd.SparseDataFrame({'x': [1, 2, 0, nan, 4, nan, 0]})
          tm.assert_sp_frame_equal(s.to_frame(), exp)
  
          exp = pd.SparseDataFrame({'y': [1, 2, 0, nan, 4, nan, 0]})
          tm.assert_sp_frame_equal(s.to_frame(name='y'), exp)
  
          s = pd.SparseSeries([1, 2, 0, nan, 4, nan, 0], name='x', fill_value=0)
          exp = pd.SparseDataFrame({'x': [1, 2, 0, nan, 4, nan, 0]},
                                   default_fill_value=0)
  
          tm.assert_sp_frame_equal(s.to_frame(), exp)
          exp = pd.DataFrame({'y': [1, 2, 0, nan, 4, nan, 0]})
          tm.assert_frame_equal(s.to_frame(name='y').to_dense(), exp)
  
      def test_pickle(self):
          def _test_roundtrip(series):
              unpickled = tm.round_trip_pickle(series)
              tm.assert_sp_series_equal(series, unpickled)
              tm.assert_series_equal(series.to_dense(), unpickled.to_dense())
  
          self._check_all(_test_roundtrip)
  
      def _check_all(self, check_func):
          check_func(self.bseries)
          check_func(self.iseries)
          check_func(self.zbseries)
          check_func(self.ziseries)
  
      def test_getitem(self):
          def _check_getitem(sp, dense):
              for idx, val in compat.iteritems(dense):
                  tm.assert_almost_equal(val, sp[idx])
  
              for i in range(len(dense)):
                  tm.assert_almost_equal(sp[i], dense[i])
                  # j = np.float64(i)
                  # assert_almost_equal(sp[j], dense[j])
  
                  # API change 1/6/2012
                  # negative getitem works
                  # for i in xrange(len(dense)):
                  #     assert_almost_equal(sp[-i], dense[-i])
  
          _check_getitem(self.bseries, self.bseries.to_dense())
          _check_getitem(self.btseries, self.btseries.to_dense())
  
          _check_getitem(self.zbseries, self.zbseries.to_dense())
          _check_getitem(self.iseries, self.iseries.to_dense())
          _check_getitem(self.ziseries, self.ziseries.to_dense())
  
          # exception handling
          pytest.raises(Exception, self.bseries.__getitem__,
                        len(self.bseries) + 1)
  
          # index not contained
          pytest.raises(Exception, self.btseries.__getitem__,
                        self.btseries.index[-1] + BDay())
  
      def test_get_get_value(self):
          tm.assert_almost_equal(self.bseries.get(10), self.bseries[10])
          assert self.bseries.get(len(self.bseries) + 1) is None
  
          dt = self.btseries.index[10]
          result = self.btseries.get(dt)
          expected = self.btseries.to_dense()[dt]
          tm.assert_almost_equal(result, expected)
  
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              tm.assert_almost_equal(
                  self.bseries.get_value(10), self.bseries[10])
  
      def test_set_value(self):
  
          idx = self.btseries.index[7]
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              self.btseries.set_value(idx, 0)
          assert self.btseries[idx] == 0
  
          with tm.assert_produces_warning(FutureWarning,
                                          check_stacklevel=False):
              self.iseries.set_value('foobar', 0)
          assert self.iseries.index[-1] == 'foobar'
          assert self.iseries['foobar'] == 0
  
      def test_getitem_slice(self):
          idx = self.bseries.index
          res = self.bseries[::2]
          assert isinstance(res, SparseSeries)
  
          expected = self.bseries.reindex(idx[::2])
          tm.assert_sp_series_equal(res, expected)
  
          res = self.bseries[:5]
          assert isinstance(res, SparseSeries)
          tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:5]))
  
          res = self.bseries[5:]
          tm.assert_sp_series_equal(res, self.bseries.reindex(idx[5:]))
  
          # negative indices
          res = self.bseries[:-3]
          tm.assert_sp_series_equal(res, self.bseries.reindex(idx[:-3]))
  
      def test_take(self):
          def _compare_with_dense(sp):
              dense = sp.to_dense()
  
              def _compare(idx):
                  dense_result = dense.take(idx).values
                  sparse_result = sp.take(idx)
                  assert isinstance(sparse_result, SparseSeries)
                  tm.assert_almost_equal(dense_result,
                                         sparse_result.values.values)
  
              _compare([1., 2., 3., 4., 5., 0.])
              _compare([7, 2, 9, 0, 4])
              _compare([3, 6, 3, 4, 7])
  
          self._check_all(_compare_with_dense)
  
          pytest.raises(Exception, self.bseries.take,
                        [0, len(self.bseries) + 1])
  
          # Corner case
          sp = SparseSeries(np.ones(10) * nan)
          exp = pd.Series(np.repeat(nan, 5))
          tm.assert_series_equal(sp.take([0, 1, 2, 3, 4]), exp)
  
          with tm.assert_produces_warning(FutureWarning):
              sp.take([1, 5], convert=True)
  
          with tm.assert_produces_warning(FutureWarning):
              sp.take([1, 5], convert=False)
  
      def test_numpy_take(self):
          sp = SparseSeries([1.0, 2.0, 3.0])
          indices = [1, 2]
  
          if not _np_version_under1p12:
              tm.assert_series_equal(np.take(sp, indices, axis=0).to_dense(),
                                     np.take(sp.to_dense(), indices, axis=0))
  
              msg = "the 'out' parameter is not supported"
              tm.assert_raises_regex(ValueError, msg, np.take,
                                     sp, indices, out=np.empty(sp.shape))
  
              msg = "the 'mode' parameter is not supported"
              tm.assert_raises_regex(ValueError, msg, np.take,
                                     sp, indices, out=None, mode='clip')
  
      def test_setitem(self):
          self.bseries[5] = 7.
          assert self.bseries[5] == 7.
  
      def test_setslice(self):
          self.bseries[5:10] = 7.
          tm.assert_series_equal(self.bseries[5:10].to_dense(),
                                 Series(7., index=range(5, 10),
                                        name=self.bseries.name))
  
      def test_operators(self):
  
          def _check_op(a, b, op):
              sp_result = op(a, b)
              adense = a.to_dense() if isinstance(a, SparseSeries) else a
              bdense = b.to_dense() if isinstance(b, SparseSeries) else b
              dense_result = op(adense, bdense)
              tm.assert_almost_equal(sp_result.to_dense(), dense_result)
  
          def check(a, b):
              _check_op(a, b, operator.add)
              _check_op(a, b, operator.sub)
              _check_op(a, b, operator.truediv)
              _check_op(a, b, operator.floordiv)
              _check_op(a, b, operator.mul)
  
              _check_op(a, b, lambda x, y: operator.add(y, x))
              _check_op(a, b, lambda x, y: operator.sub(y, x))
              _check_op(a, b, lambda x, y: operator.truediv(y, x))
              _check_op(a, b, lambda x, y: operator.floordiv(y, x))
              _check_op(a, b, lambda x, y: operator.mul(y, x))
  
              # NaN ** 0 = 1 in C?
              # _check_op(a, b, operator.pow)
              # _check_op(a, b, lambda x, y: operator.pow(y, x))
  
          check(self.bseries, self.bseries)
          check(self.iseries, self.iseries)
          check(self.bseries, self.iseries)
  
          check(self.bseries, self.bseries2)
          check(self.bseries, self.iseries2)
          check(self.iseries, self.iseries2)
  
          # scalar value
          check(self.bseries, 5)
  
          # zero-based
          check(self.zbseries, self.zbseries * 2)
          check(self.zbseries, self.zbseries2)
          check(self.ziseries, self.ziseries2)
  
          # with dense
          result = self.bseries + self.bseries.to_dense()
          tm.assert_sp_series_equal(result, self.bseries + self.bseries)
  
      def test_binary_operators(self):
  
          # skipping for now #####
          import pytest
          pytest.skip("skipping sparse binary operators test")
  
          def _check_inplace_op(iop, op):
              tmp = self.bseries.copy()
  
              expected = op(tmp, self.bseries)
              iop(tmp, self.bseries)
              tm.assert_sp_series_equal(tmp, expected)
  
          inplace_ops = ['add', 'sub', 'mul', 'truediv', 'floordiv', 'pow']
          for op in inplace_ops:
              _check_inplace_op(getattr(operator, "i%s" % op),
                                getattr(operator, op))
  
      def test_abs(self):
          s = SparseSeries([1, 2, -3], name='x')
          expected = SparseSeries([1, 2, 3], name='x')
          result = s.abs()
          tm.assert_sp_series_equal(result, expected)
          assert result.name == 'x'
  
          result = abs(s)
          tm.assert_sp_series_equal(result, expected)
          assert result.name == 'x'
  
          result = np.abs(s)
          tm.assert_sp_series_equal(result, expected)
          assert result.name == 'x'
  
          s = SparseSeries([1, -2, 2, -3], fill_value=-2, name='x')
          expected = SparseSeries([1, 2, 3], sparse_index=s.sp_index,
                                  fill_value=2, name='x')
          result = s.abs()
          tm.assert_sp_series_equal(result, expected)
          assert result.name == 'x'
  
          result = abs(s)
          tm.assert_sp_series_equal(result, expected)
          assert result.name == 'x'
  
          result = np.abs(s)
          tm.assert_sp_series_equal(result, expected)
          assert result.name == 'x'
  
      def test_reindex(self):
          def _compare_with_series(sps, new_index):
              spsre = sps.reindex(new_index)
  
              series = sps.to_dense()
              seriesre = series.reindex(new_index)
              seriesre = seriesre.to_sparse(fill_value=sps.fill_value)
  
              tm.assert_sp_series_equal(spsre, seriesre)
              tm.assert_series_equal(spsre.to_dense(), seriesre.to_dense())
  
          _compare_with_series(self.bseries, self.bseries.index[::2])
          _compare_with_series(self.bseries, list(self.bseries.index[::2]))
          _compare_with_series(self.bseries, self.bseries.index[:10])
          _compare_with_series(self.bseries, self.bseries.index[5:])
  
          _compare_with_series(self.zbseries, self.zbseries.index[::2])
          _compare_with_series(self.zbseries, self.zbseries.index[:10])
          _compare_with_series(self.zbseries, self.zbseries.index[5:])
  
          # special cases
          same_index = self.bseries.reindex(self.bseries.index)
          tm.assert_sp_series_equal(self.bseries, same_index)
          assert same_index is not self.bseries
  
          # corner cases
          sp = SparseSeries([], index=[])
          # TODO: sp_zero is not used anywhere...remove?
          sp_zero = SparseSeries([], index=[], fill_value=0)  # noqa
          _compare_with_series(sp, np.arange(10))
  
          # with copy=False
          reindexed = self.bseries.reindex(self.bseries.index, copy=True)
          reindexed.sp_values[:] = 1.
          assert (self.bseries.sp_values != 1.).all()
  
          reindexed = self.bseries.reindex(self.bseries.index, copy=False)
          reindexed.sp_values[:] = 1.
          tm.assert_numpy_array_equal(self.bseries.sp_values, np.repeat(1., 10))
  
      def test_sparse_reindex(self):
          length = 10
  
          def _check(values, index1, index2, fill_value):
              first_series = SparseSeries(values, sparse_index=index1,
                                          fill_value=fill_value)
              reindexed = first_series.sparse_reindex(index2)
              assert reindexed.sp_index is index2
  
              int_indices1 = index1.to_int_index().indices
              int_indices2 = index2.to_int_index().indices
  
              expected = Series(values, index=int_indices1)
              expected = expected.reindex(int_indices2).fillna(fill_value)
              tm.assert_almost_equal(expected.values, reindexed.sp_values)
  
              # make sure level argument asserts
              # TODO: expected is not used anywhere...remove?
              expected = expected.reindex(int_indices2).fillna(fill_value)  # noqa
  
          def _check_with_fill_value(values, first, second, fill_value=nan):
              i_index1 = IntIndex(length, first)
              i_index2 = IntIndex(length, second)
  
              b_index1 = i_index1.to_block_index()
              b_index2 = i_index2.to_block_index()
  
              _check(values, i_index1, i_index2, fill_value)
              _check(values, b_index1, b_index2, fill_value)
  
          def _check_all(values, first, second):
              _check_with_fill_value(values, first, second, fill_value=nan)
              _check_with_fill_value(values, first, second, fill_value=0)
  
          index1 = [2, 4, 5, 6, 8, 9]
          values1 = np.arange(6.)
  
          _check_all(values1, index1, [2, 4, 5])
          _check_all(values1, index1, [2, 3, 4, 5, 6, 7, 8, 9])
          _check_all(values1, index1, [0, 1])
          _check_all(values1, index1, [0, 1, 7, 8, 9])
          _check_all(values1, index1, [])
  
          first_series = SparseSeries(values1,
                                      sparse_index=IntIndex(length, index1),
                                      fill_value=nan)
          with tm.assert_raises_regex(TypeError,
                                      'new index must be a SparseIndex'):
              reindexed = first_series.sparse_reindex(0)  # noqa
  
      def test_repr(self):
          # TODO: These aren't used
          bsrepr = repr(self.bseries)  # noqa
          isrepr = repr(self.iseries)  # noqa
  
      def test_iter(self):
          pass
  
      def test_truncate(self):
          pass
  
      def test_fillna(self):
          pass
  
      def test_groupby(self):
          pass
  
      def test_reductions(self):
          def _compare_with_dense(obj, op):
              sparse_result = getattr(obj, op)()
              series = obj.to_dense()
              dense_result = getattr(series, op)()
              assert sparse_result == dense_result
  
          to_compare = ['count', 'sum', 'mean', 'std', 'var', 'skew']
  
          def _compare_all(obj):
              for op in to_compare:
                  _compare_with_dense(obj, op)
  
          _compare_all(self.bseries)
  
          self.bseries.sp_values[5:10] = np.NaN
          _compare_all(self.bseries)
  
          _compare_all(self.zbseries)
          self.zbseries.sp_values[5:10] = np.NaN
          _compare_all(self.zbseries)
  
          series = self.zbseries.copy()
          series.fill_value = 2
          _compare_all(series)
  
          nonna = Series(np.random.randn(20)).to_sparse()
          _compare_all(nonna)
  
          nonna2 = Series(np.random.randn(20)).to_sparse(fill_value=0)
          _compare_all(nonna2)
  
      def test_dropna(self):
          sp = SparseSeries([0, 0, 0, nan, nan, 5, 6], fill_value=0)
  
          sp_valid = sp.dropna()
  
          expected = sp.to_dense().dropna()
          expected = expected[expected != 0]
          exp_arr = pd.SparseArray(expected.values, fill_value=0, kind='block')
          tm.assert_sp_array_equal(sp_valid.values, exp_arr)
          tm.assert_index_equal(sp_valid.index, expected.index)
          assert len(sp_valid.sp_values) == 2
  
          result = self.bseries.dropna()
          expected = self.bseries.to_dense().dropna()
          assert not isinstance(result, SparseSeries)
          tm.assert_series_equal(result, expected)
  
      def test_homogenize(self):
          def _check_matches(indices, expected):
              data = {}
              for i, idx in enumerate(indices):
                  data[i] = SparseSeries(idx.to_int_index().indices,
                                         sparse_index=idx, fill_value=np.nan)
              # homogenized is only valid with NaN fill values
              homogenized = spf.homogenize(data)
  
              for k, v in compat.iteritems(homogenized):
                  assert (v.sp_index.equals(expected))
  
          indices1 = [BlockIndex(10, [2], [7]), BlockIndex(10, [1, 6], [3, 4]),
                      BlockIndex(10, [0], [10])]
          expected1 = BlockIndex(10, [2, 6], [2, 3])
          _check_matches(indices1, expected1)
  
          indices2 = [BlockIndex(10, [2], [7]), BlockIndex(10, [2], [7])]
          expected2 = indices2[0]
          _check_matches(indices2, expected2)
  
          # must have NaN fill value
          data = {'a': SparseSeries(np.arange(7), sparse_index=expected2,
                                    fill_value=0)}
          with tm.assert_raises_regex(TypeError, "NaN fill value"):
              spf.homogenize(data)
  
      def test_fill_value_corner(self):
          cop = self.zbseries.copy()
          cop.fill_value = 0
          result = self.bseries / cop
  
          assert np.isnan(result.fill_value)
  
          cop2 = self.zbseries.copy()
          cop2.fill_value = 1
          result = cop2 / cop
          # 1 / 0 is inf
          assert np.isinf(result.fill_value)
  
      def test_fill_value_when_combine_const(self):
          # GH12723
          s = SparseSeries([0, 1, np.nan, 3, 4, 5], index=np.arange(6))
  
          exp = s.fillna(0).add(2)
          res = s.add(2, fill_value=0)
          tm.assert_series_equal(res, exp)
  
      def test_shift(self):
          series = SparseSeries([nan, 1., 2., 3., nan, nan], index=np.arange(6))
  
          shifted = series.shift(0)
          assert shifted is not series
          tm.assert_sp_series_equal(shifted, series)
  
          f = lambda s: s.shift(1)
          _dense_series_compare(series, f)
  
          f = lambda s: s.shift(-2)
          _dense_series_compare(series, f)
  
          series = SparseSeries([nan, 1., 2., 3., nan, nan],
                                index=bdate_range('1/1/2000', periods=6))
          f = lambda s: s.shift(2, freq='B')
          _dense_series_compare(series, f)
  
          f = lambda s: s.shift(2, freq=BDay())
          _dense_series_compare(series, f)
  
      def test_shift_nan(self):
          # GH 12908
          orig = pd.Series([np.nan, 2, np.nan, 4, 0, np.nan, 0])
          sparse = orig.to_sparse()
  
          tm.assert_sp_series_equal(sparse.shift(0), orig.shift(0).to_sparse())
          tm.assert_sp_series_equal(sparse.shift(1), orig.shift(1).to_sparse())
          tm.assert_sp_series_equal(sparse.shift(2), orig.shift(2).to_sparse())
          tm.assert_sp_series_equal(sparse.shift(3), orig.shift(3).to_sparse())
  
          tm.assert_sp_series_equal(sparse.shift(-1), orig.shift(-1).to_sparse())
          tm.assert_sp_series_equal(sparse.shift(-2), orig.shift(-2).to_sparse())
          tm.assert_sp_series_equal(sparse.shift(-3), orig.shift(-3).to_sparse())
          tm.assert_sp_series_equal(sparse.shift(-4), orig.shift(-4).to_sparse())
  
          sparse = orig.to_sparse(fill_value=0)
          tm.assert_sp_series_equal(sparse.shift(0),
                                    orig.shift(0).to_sparse(fill_value=0))
          tm.assert_sp_series_equal(sparse.shift(1),
                                    orig.shift(1).to_sparse(fill_value=0))
          tm.assert_sp_series_equal(sparse.shift(2),
                                    orig.shift(2).to_sparse(fill_value=0))
          tm.assert_sp_series_equal(sparse.shift(3),
                                    orig.shift(3).to_sparse(fill_value=0))
  
          tm.assert_sp_series_equal(sparse.shift(-1),
                                    orig.shift(-1).to_sparse(fill_value=0))
          tm.assert_sp_series_equal(sparse.shift(-2),
                                    orig.shift(-2).to_sparse(fill_value=0))
          tm.assert_sp_series_equal(sparse.shift(-3),
                                    orig.shift(-3).to_sparse(fill_value=0))
          tm.assert_sp_series_equal(sparse.shift(-4),
                                    orig.shift(-4).to_sparse(fill_value=0))
  
      def test_shift_dtype(self):
          # GH 12908
          orig = pd.Series([1, 2, 3, 4], dtype=np.int64)
  
          sparse = orig.to_sparse()
          tm.assert_sp_series_equal(sparse.shift(0), orig.shift(0).to_sparse())
  
          sparse = orig.to_sparse(fill_value=np.nan)
          tm.assert_sp_series_equal(sparse.shift(0),
                                    orig.shift(0).to_sparse(fill_value=np.nan))
          # shift(1) or more span changes dtype to float64
          tm.assert_sp_series_equal(sparse.shift(1), orig.shift(1).to_sparse())
          tm.assert_sp_series_equal(sparse.shift(2), orig.shift(2).to_sparse())
          tm.assert_sp_series_equal(sparse.shift(3), orig.shift(3).to_sparse())
  
          tm.assert_sp_series_equal(sparse.shift(-1), orig.shift(-1).to_sparse())
          tm.assert_sp_series_equal(sparse.shift(-2), orig.shift(-2).to_sparse())
          tm.assert_sp_series_equal(sparse.shift(-3), orig.shift(-3).to_sparse())
          tm.assert_sp_series_equal(sparse.shift(-4), orig.shift(-4).to_sparse())
  
      def test_shift_dtype_fill_value(self):
          # GH 12908
          orig = pd.Series([1, 0, 0, 4], dtype=np.int64)
  
          for v in [0, 1, np.nan]:
              sparse = orig.to_sparse(fill_value=v)
  
              tm.assert_sp_series_equal(sparse.shift(0),
                                        orig.shift(0).to_sparse(fill_value=v))
              tm.assert_sp_series_equal(sparse.shift(1),
                                        orig.shift(1).to_sparse(fill_value=v))
              tm.assert_sp_series_equal(sparse.shift(2),
                                        orig.shift(2).to_sparse(fill_value=v))
              tm.assert_sp_series_equal(sparse.shift(3),
                                        orig.shift(3).to_sparse(fill_value=v))
  
              tm.assert_sp_series_equal(sparse.shift(-1),
                                        orig.shift(-1).to_sparse(fill_value=v))
              tm.assert_sp_series_equal(sparse.shift(-2),
                                        orig.shift(-2).to_sparse(fill_value=v))
              tm.assert_sp_series_equal(sparse.shift(-3),
                                        orig.shift(-3).to_sparse(fill_value=v))
              tm.assert_sp_series_equal(sparse.shift(-4),
                                        orig.shift(-4).to_sparse(fill_value=v))
  
      def test_combine_first(self):
          s = self.bseries
  
          result = s[::2].combine_first(s)
          result2 = s[::2].combine_first(s.to_dense())
  
          expected = s[::2].to_dense().combine_first(s.to_dense())
          expected = expected.to_sparse(fill_value=s.fill_value)
  
          tm.assert_sp_series_equal(result, result2)
          tm.assert_sp_series_equal(result, expected)
  
  
  class TestSparseHandlingMultiIndexes(object):
  
      def setup_method(self, method):
          miindex = pd.MultiIndex.from_product(
              [["x", "y"], ["10", "20"]], names=['row-foo', 'row-bar'])
          micol = pd.MultiIndex.from_product(
              [['a', 'b', 'c'], ["1", "2"]], names=['col-foo', 'col-bar'])
          dense_multiindex_frame = pd.DataFrame(
              index=miindex, columns=micol).sort_index().sort_index(axis=1)
          self.dense_multiindex_frame = dense_multiindex_frame.fillna(value=3.14)
  
      def test_to_sparse_preserve_multiindex_names_columns(self):
          sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse()
          sparse_multiindex_frame = sparse_multiindex_frame.copy()
          tm.assert_index_equal(sparse_multiindex_frame.columns,
                                self.dense_multiindex_frame.columns)
  
      def test_round_trip_preserve_multiindex_names(self):
          sparse_multiindex_frame = self.dense_multiindex_frame.to_sparse()
          round_trip_multiindex_frame = sparse_multiindex_frame.to_dense()
          tm.assert_frame_equal(self.dense_multiindex_frame,
                                round_trip_multiindex_frame,
                                check_column_type=True,
                                check_names=True)
  
  
  @td.skip_if_no_scipy
  class TestSparseSeriesScipyInteraction(object):
      # Issue 8048: add SparseSeries coo methods
  
      def setup_method(self, method):
          import scipy.sparse
          # SparseSeries inputs used in tests, the tests rely on the order
          self.sparse_series = []
          s = pd.Series([3.0, nan, 1.0, 2.0, nan, nan])
          s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),
                                               (1, 2, 'a', 1),
                                               (1, 1, 'b', 0),
                                               (1, 1, 'b', 1),
                                               (2, 1, 'b', 0),
                                               (2, 1, 'b', 1)],
                                              names=['A', 'B', 'C', 'D'])
          self.sparse_series.append(s.to_sparse())
  
          ss = self.sparse_series[0].copy()
          ss.index.names = [3, 0, 1, 2]
          self.sparse_series.append(ss)
  
          ss = pd.Series([
              nan
          ] * 12, index=cartesian_product((range(3), range(4)))).to_sparse()
          for k, v in zip([(0, 0), (1, 2), (1, 3)], [3.0, 1.0, 2.0]):
              ss[k] = v
          self.sparse_series.append(ss)
  
          # results used in tests
          self.coo_matrices = []
          self.coo_matrices.append(scipy.sparse.coo_matrix(
              ([3.0, 1.0, 2.0], ([0, 1, 1], [0, 2, 3])), shape=(3, 4)))
          self.coo_matrices.append(scipy.sparse.coo_matrix(
              ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4)))
          self.coo_matrices.append(scipy.sparse.coo_matrix(
              ([3.0, 1.0, 2.0], ([0, 1, 1], [0, 0, 1])), shape=(3, 2)))
          self.ils = [[(1, 2), (1, 1), (2, 1)], [(1, 1), (1, 2), (2, 1)],
                      [(1, 2, 'a'), (1, 1, 'b'), (2, 1, 'b')]]
          self.jls = [[('a', 0), ('a', 1), ('b', 0), ('b', 1)], [0, 1]]
  
      def test_to_coo_text_names_integer_row_levels_nosort(self):
          ss = self.sparse_series[0]
          kwargs = {'row_levels': [0, 1], 'column_levels': [2, 3]}
          result = (self.coo_matrices[0], self.ils[0], self.jls[0])
          self._run_test(ss, kwargs, result)
  
      def test_to_coo_text_names_integer_row_levels_sort(self):
          ss = self.sparse_series[0]
          kwargs = {'row_levels': [0, 1],
                    'column_levels': [2, 3],
                    'sort_labels': True}
          result = (self.coo_matrices[1], self.ils[1], self.jls[0])
          self._run_test(ss, kwargs, result)
  
      def test_to_coo_text_names_text_row_levels_nosort_col_level_single(self):
          ss = self.sparse_series[0]
          kwargs = {'row_levels': ['A', 'B', 'C'],
                    'column_levels': ['D'],
                    'sort_labels': False}
          result = (self.coo_matrices[2], self.ils[2], self.jls[1])
          self._run_test(ss, kwargs, result)
  
      def test_to_coo_integer_names_integer_row_levels_nosort(self):
          ss = self.sparse_series[1]
          kwargs = {'row_levels': [3, 0], 'column_levels': [1, 2]}
          result = (self.coo_matrices[0], self.ils[0], self.jls[0])
          self._run_test(ss, kwargs, result)
  
      def test_to_coo_text_names_text_row_levels_nosort(self):
          ss = self.sparse_series[0]
          kwargs = {'row_levels': ['A', 'B'], 'column_levels': ['C', 'D']}
          result = (self.coo_matrices[0], self.ils[0], self.jls[0])
          self._run_test(ss, kwargs, result)
  
      def test_to_coo_bad_partition_nonnull_intersection(self):
          ss = self.sparse_series[0]
          pytest.raises(ValueError, ss.to_coo, ['A', 'B', 'C'], ['C', 'D'])
  
      def test_to_coo_bad_partition_small_union(self):
          ss = self.sparse_series[0]
          pytest.raises(ValueError, ss.to_coo, ['A'], ['C', 'D'])
  
      def test_to_coo_nlevels_less_than_two(self):
          ss = self.sparse_series[0]
          ss.index = np.arange(len(ss.index))
          pytest.raises(ValueError, ss.to_coo)
  
      def test_to_coo_bad_ilevel(self):
          ss = self.sparse_series[0]
          pytest.raises(KeyError, ss.to_coo, ['A', 'B'], ['C', 'D', 'E'])
  
      def test_to_coo_duplicate_index_entries(self):
          ss = pd.concat([self.sparse_series[0],
                          self.sparse_series[0]]).to_sparse()
          pytest.raises(ValueError, ss.to_coo, ['A', 'B'], ['C', 'D'])
  
      def test_from_coo_dense_index(self):
          ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=True)
          check = self.sparse_series[2]
          tm.assert_sp_series_equal(ss, check)
  
      def test_from_coo_nodense_index(self):
          ss = SparseSeries.from_coo(self.coo_matrices[0], dense_index=False)
          check = self.sparse_series[2]
          check = check.dropna().to_sparse()
          tm.assert_sp_series_equal(ss, check)
  
      def test_from_coo_long_repr(self):
          # GH 13114
          # test it doesn't raise error. Formatting is tested in test_format
          import scipy.sparse
  
          sparse = SparseSeries.from_coo(scipy.sparse.rand(350, 18))
          repr(sparse)
  
      def _run_test(self, ss, kwargs, check):
          results = ss.to_coo(**kwargs)
          self._check_results_to_coo(results, check)
          # for every test, also test symmetry property (transpose), switch
          # row_levels and column_levels
          d = kwargs.copy()
          d['row_levels'] = kwargs['column_levels']
          d['column_levels'] = kwargs['row_levels']
          results = ss.to_coo(**d)
          results = (results[0].T, results[2], results[1])
          self._check_results_to_coo(results, check)
  
      def _check_results_to_coo(self, results, check):
          (A, il, jl) = results
          (A_result, il_result, jl_result) = check
          # convert to dense and compare
          tm.assert_numpy_array_equal(A.todense(), A_result.todense())
          # or compare directly as difference of sparse
          # assert(abs(A - A_result).max() < 1e-12) # max is failing in python
          # 2.6
          assert il == il_result
          assert jl == jl_result
  
      def test_concat(self):
          val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan])
          val2 = np.array([3, np.nan, 4, 0, 0])
  
          for kind in ['integer', 'block']:
              sparse1 = pd.SparseSeries(val1, name='x', kind=kind)
              sparse2 = pd.SparseSeries(val2, name='y', kind=kind)
  
              res = pd.concat([sparse1, sparse2])
              exp = pd.concat([pd.Series(val1), pd.Series(val2)])
              exp = pd.SparseSeries(exp, kind=kind)
              tm.assert_sp_series_equal(res, exp)
  
              sparse1 = pd.SparseSeries(val1, fill_value=0, name='x', kind=kind)
              sparse2 = pd.SparseSeries(val2, fill_value=0, name='y', kind=kind)
  
              res = pd.concat([sparse1, sparse2])
              exp = pd.concat([pd.Series(val1), pd.Series(val2)])
              exp = pd.SparseSeries(exp, fill_value=0, kind=kind)
              tm.assert_sp_series_equal(res, exp)
  
      def test_concat_axis1(self):
          val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan])
          val2 = np.array([3, np.nan, 4, 0, 0])
  
          sparse1 = pd.SparseSeries(val1, name='x')
          sparse2 = pd.SparseSeries(val2, name='y')
  
          res = pd.concat([sparse1, sparse2], axis=1)
          exp = pd.concat([pd.Series(val1, name='x'),
                           pd.Series(val2, name='y')], axis=1)
          exp = pd.SparseDataFrame(exp)
          tm.assert_sp_frame_equal(res, exp)
  
      def test_concat_different_fill(self):
          val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan])
          val2 = np.array([3, np.nan, 4, 0, 0])
  
          for kind in ['integer', 'block']:
              sparse1 = pd.SparseSeries(val1, name='x', kind=kind)
              sparse2 = pd.SparseSeries(val2, name='y', kind=kind, fill_value=0)
  
              res = pd.concat([sparse1, sparse2])
              exp = pd.concat([pd.Series(val1), pd.Series(val2)])
              exp = pd.SparseSeries(exp, kind=kind)
              tm.assert_sp_series_equal(res, exp)
  
              res = pd.concat([sparse2, sparse1])
              exp = pd.concat([pd.Series(val2), pd.Series(val1)])
              exp = pd.SparseSeries(exp, kind=kind, fill_value=0)
              tm.assert_sp_series_equal(res, exp)
  
      def test_concat_axis1_different_fill(self):
          val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan])
          val2 = np.array([3, np.nan, 4, 0, 0])
  
          sparse1 = pd.SparseSeries(val1, name='x')
          sparse2 = pd.SparseSeries(val2, name='y', fill_value=0)
  
          res = pd.concat([sparse1, sparse2], axis=1)
          exp = pd.concat([pd.Series(val1, name='x'),
                           pd.Series(val2, name='y')], axis=1)
          assert isinstance(res, pd.SparseDataFrame)
          tm.assert_frame_equal(res.to_dense(), exp)
  
      def test_concat_different_kind(self):
          val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan])
          val2 = np.array([3, np.nan, 4, 0, 0])
  
          sparse1 = pd.SparseSeries(val1, name='x', kind='integer')
          sparse2 = pd.SparseSeries(val2, name='y', kind='block', fill_value=0)
  
          res = pd.concat([sparse1, sparse2])
          exp = pd.concat([pd.Series(val1), pd.Series(val2)])
          exp = pd.SparseSeries(exp, kind='integer')
          tm.assert_sp_series_equal(res, exp)
  
          res = pd.concat([sparse2, sparse1])
          exp = pd.concat([pd.Series(val2), pd.Series(val1)])
          exp = pd.SparseSeries(exp, kind='block', fill_value=0)
          tm.assert_sp_series_equal(res, exp)
  
      def test_concat_sparse_dense(self):
          # use first input's fill_value
          val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan])
          val2 = np.array([3, np.nan, 4, 0, 0])
  
          for kind in ['integer', 'block']:
              sparse = pd.SparseSeries(val1, name='x', kind=kind)
              dense = pd.Series(val2, name='y')
  
              res = pd.concat([sparse, dense])
              exp = pd.concat([pd.Series(val1), dense])
              exp = pd.SparseSeries(exp, kind=kind)
              tm.assert_sp_series_equal(res, exp)
  
              res = pd.concat([dense, sparse, dense])
              exp = pd.concat([dense, pd.Series(val1), dense])
              exp = pd.SparseSeries(exp, kind=kind)
              tm.assert_sp_series_equal(res, exp)
  
              sparse = pd.SparseSeries(val1, name='x', kind=kind, fill_value=0)
              dense = pd.Series(val2, name='y')
  
              res = pd.concat([sparse, dense])
              exp = pd.concat([pd.Series(val1), dense])
              exp = pd.SparseSeries(exp, kind=kind, fill_value=0)
              tm.assert_sp_series_equal(res, exp)
  
              res = pd.concat([dense, sparse, dense])
              exp = pd.concat([dense, pd.Series(val1), dense])
              exp = pd.SparseSeries(exp, kind=kind, fill_value=0)
              tm.assert_sp_series_equal(res, exp)
  
      def test_value_counts(self):
          vals = [1, 2, nan, 0, nan, 1, 2, nan, nan, 1, 2, 0, 1, 1]
          dense = pd.Series(vals, name='xx')
  
          sparse = pd.SparseSeries(vals, name='xx')
          tm.assert_series_equal(sparse.value_counts(),
                                 dense.value_counts())
          tm.assert_series_equal(sparse.value_counts(dropna=False),
                                 dense.value_counts(dropna=False))
  
          sparse = pd.SparseSeries(vals, name='xx', fill_value=0)
          tm.assert_series_equal(sparse.value_counts(),
                                 dense.value_counts())
          tm.assert_series_equal(sparse.value_counts(dropna=False),
                                 dense.value_counts(dropna=False))
  
      def test_value_counts_dup(self):
          vals = [1, 2, nan, 0, nan, 1, 2, nan, nan, 1, 2, 0, 1, 1]
  
          # numeric op may cause sp_values to include the same value as
          # fill_value
          dense = pd.Series(vals, name='xx') / 0.
          sparse = pd.SparseSeries(vals, name='xx') / 0.
          tm.assert_series_equal(sparse.value_counts(),
                                 dense.value_counts())
          tm.assert_series_equal(sparse.value_counts(dropna=False),
                                 dense.value_counts(dropna=False))
  
          vals = [1, 2, 0, 0, 0, 1, 2, 0, 0, 1, 2, 0, 1, 1]
  
          dense = pd.Series(vals, name='xx') * 0.
          sparse = pd.SparseSeries(vals, name='xx') * 0.
          tm.assert_series_equal(sparse.value_counts(),
                                 dense.value_counts())
          tm.assert_series_equal(sparse.value_counts(dropna=False),
                                 dense.value_counts(dropna=False))
  
      def test_value_counts_int(self):
          vals = [1, 2, 0, 1, 2, 1, 2, 0, 1, 1]
          dense = pd.Series(vals, name='xx')
  
          # fill_value is np.nan, but should not be included in the result
          sparse = pd.SparseSeries(vals, name='xx')
          tm.assert_series_equal(sparse.value_counts(),
                                 dense.value_counts())
          tm.assert_series_equal(sparse.value_counts(dropna=False),
                                 dense.value_counts(dropna=False))
  
          sparse = pd.SparseSeries(vals, name='xx', fill_value=0)
          tm.assert_series_equal(sparse.value_counts(),
                                 dense.value_counts())
          tm.assert_series_equal(sparse.value_counts(dropna=False),
                                 dense.value_counts(dropna=False))
  
      def test_isna(self):
          # GH 8276
          s = pd.SparseSeries([np.nan, np.nan, 1, 2, np.nan], name='xxx')
  
          res = s.isna()
          exp = pd.SparseSeries([True, True, False, False, True], name='xxx',
                                fill_value=True)
          tm.assert_sp_series_equal(res, exp)
  
          # if fill_value is not nan, True can be included in sp_values
          s = pd.SparseSeries([np.nan, 0., 1., 2., 0.], name='xxx',
                              fill_value=0.)
          res = s.isna()
          assert isinstance(res, pd.SparseSeries)
          exp = pd.Series([True, False, False, False, False], name='xxx')
          tm.assert_series_equal(res.to_dense(), exp)
  
      def test_notna(self):
          # GH 8276
          s = pd.SparseSeries([np.nan, np.nan, 1, 2, np.nan], name='xxx')
  
          res = s.notna()
          exp = pd.SparseSeries([False, False, True, True, False], name='xxx',
                                fill_value=False)
          tm.assert_sp_series_equal(res, exp)
  
          # if fill_value is not nan, True can be included in sp_values
          s = pd.SparseSeries([np.nan, 0., 1., 2., 0.], name='xxx',
                              fill_value=0.)
          res = s.notna()
          assert isinstance(res, pd.SparseSeries)
          exp = pd.Series([False, True, True, True, True], name='xxx')
          tm.assert_series_equal(res.to_dense(), exp)
  
  
  def _dense_series_compare(s, f):
      result = f(s)
      assert (isinstance(result, SparseSeries))
      dense_result = f(s.to_dense())
      tm.assert_series_equal(result.to_dense(), dense_result)
  
  
  class TestSparseSeriesAnalytics(object):
  
      def setup_method(self, method):
          arr, index = _test_data1()
          self.bseries = SparseSeries(arr, index=index, kind='block',
                                      name='bseries')
  
          arr, index = _test_data1_zero()
          self.zbseries = SparseSeries(arr, index=index, kind='block',
                                       fill_value=0, name='zbseries')
  
      def test_cumsum(self):
          result = self.bseries.cumsum()
          expected = SparseSeries(self.bseries.to_dense().cumsum())
          tm.assert_sp_series_equal(result, expected)
  
          result = self.zbseries.cumsum()
          expected = self.zbseries.to_dense().cumsum()
          tm.assert_series_equal(result, expected)
  
          axis = 1  # Series is 1-D, so only axis = 0 is valid.
          msg = "No axis named {axis}".format(axis=axis)
          with tm.assert_raises_regex(ValueError, msg):
              self.bseries.cumsum(axis=axis)
  
      def test_numpy_cumsum(self):
          result = np.cumsum(self.bseries)
          expected = SparseSeries(self.bseries.to_dense().cumsum())
          tm.assert_sp_series_equal(result, expected)
  
          result = np.cumsum(self.zbseries)
          expected = self.zbseries.to_dense().cumsum()
          tm.assert_series_equal(result, expected)
  
          msg = "the 'dtype' parameter is not supported"
          tm.assert_raises_regex(ValueError, msg, np.cumsum,
                                 self.bseries, dtype=np.int64)
  
          msg = "the 'out' parameter is not supported"
          tm.assert_raises_regex(ValueError, msg, np.cumsum,
                                 self.zbseries, out=result)
  
      def test_numpy_func_call(self):
          # no exception should be raised even though
          # numpy passes in 'axis=None' or `axis=-1'
          funcs = ['sum', 'cumsum', 'var', 'mean',
                   'prod', 'cumprod', 'std', 'argsort',
                   'min', 'max']
          for func in funcs:
              for series in ('bseries', 'zbseries'):
                  getattr(np, func)(getattr(self, series))
  
      def test_deprecated_numpy_func_call(self):
          # NOTE: These should be add to the 'test_numpy_func_call' test above
          # once the behavior of argmin/argmax is corrected.
          funcs = ['argmin', 'argmax']
          for func in funcs:
              for series in ('bseries', 'zbseries'):
                  with tm.assert_produces_warning(FutureWarning,
                                                  check_stacklevel=False):
                      getattr(np, func)(getattr(self, series))
  
                  with tm.assert_produces_warning(FutureWarning,
                                                  check_stacklevel=False):
                      getattr(getattr(self, series), func)()
  
      def test_deprecated_reindex_axis(self):
          # https://github.com/pandas-dev/pandas/issues/17833
          with tm.assert_produces_warning(FutureWarning) as m:
              self.bseries.reindex_axis([0, 1, 2])
          assert 'reindex' in str(m[0].message)
  
  
  @pytest.mark.parametrize(
      'datetime_type', (np.datetime64,
                        pd.Timestamp,
                        lambda x: datetime.strptime(x, '%Y-%m-%d')))
  def test_constructor_dict_datetime64_index(datetime_type):
      # GH 9456
      dates = ['1984-02-19', '1988-11-06', '1989-12-03', '1990-03-15']
      values = [42544017.198965244, 1234565, 40512335.181958228, -1]
  
      result = SparseSeries(dict(zip(map(datetime_type, dates), values)))
      expected = SparseSeries(values, map(pd.Timestamp, dates))
  
      tm.assert_sp_series_equal(result, expected)
  