Python testing.assert_sp_series_equal方法代码示例

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
def test_getitem(self):
        orig = self.orig
        sparse = self.sparse

        assert sparse[0] == 1
        assert np.isnan(sparse[1])
        assert sparse[3] == 3

        result = sparse[[1, 3, 4]]
        exp = orig[[1, 3, 4]].to_sparse()
        tm.assert_sp_series_equal(result, exp)

        # dense array
        result = sparse[orig % 2 == 1]
        exp = orig[orig % 2 == 1].to_sparse()
        tm.assert_sp_series_equal(result, exp)

        # sparse array (actuary it coerces to normal Series)
        result = sparse[sparse % 2 == 1]
        exp = orig[orig % 2 == 1].to_sparse()
        tm.assert_sp_series_equal(result, exp)

        # sparse array
        result = sparse[pd.SparseArray(sparse % 2 == 1, dtype=bool)]
        tm.assert_sp_series_equal(result, exp) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
def test_loc_index(self):
        orig = pd.Series([1, np.nan, np.nan, 3, np.nan], index=list('ABCDE'))
        sparse = orig.to_sparse()

        assert sparse.loc['A'] == 1
        assert np.isnan(sparse.loc['B'])

        result = sparse.loc[['A', 'C', 'D']]
        exp = orig.loc[['A', 'C', 'D']].to_sparse()
        tm.assert_sp_series_equal(result, exp)

        # dense array
        result = sparse.loc[orig % 2 == 1]
        exp = orig.loc[orig % 2 == 1].to_sparse()
        tm.assert_sp_series_equal(result, exp)

        # sparse array (actuary it coerces to normal Series)
        result = sparse.loc[sparse % 2 == 1]
        exp = orig.loc[orig % 2 == 1].to_sparse()
        tm.assert_sp_series_equal(result, exp)

        # sparse array
        result = sparse[pd.SparseArray(sparse % 2 == 1, dtype=bool)]
        tm.assert_sp_series_equal(result, exp) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
def test_loc_index_fill_value(self):
        orig = pd.Series([1, np.nan, 0, 3, 0], index=list('ABCDE'))
        sparse = orig.to_sparse(fill_value=0)

        assert sparse.loc['A'] == 1
        assert np.isnan(sparse.loc['B'])

        result = sparse.loc[['A', 'C', 'D']]
        exp = orig.loc[['A', 'C', 'D']].to_sparse(fill_value=0)
        tm.assert_sp_series_equal(result, exp)

        # dense array
        result = sparse.loc[orig % 2 == 1]
        exp = orig.loc[orig % 2 == 1].to_sparse(fill_value=0)
        tm.assert_sp_series_equal(result, exp)

        # sparse array (actuary it coerces to normal Series)
        result = sparse.loc[sparse % 2 == 1]
        exp = orig.loc[orig % 2 == 1].to_sparse(fill_value=0)
        tm.assert_sp_series_equal(result, exp) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
def test_iloc(self):
        orig = self.orig
        sparse = self.sparse

        assert sparse.iloc[3] == 3
        assert np.isnan(sparse.iloc[2])

        result = sparse.iloc[[1, 3, 4]]
        exp = orig.iloc[[1, 3, 4]].to_sparse()
        tm.assert_sp_series_equal(result, exp)

        result = sparse.iloc[[1, -2, -4]]
        exp = orig.iloc[[1, -2, -4]].to_sparse()
        tm.assert_sp_series_equal(result, exp)

        with pytest.raises(IndexError):
            sparse.iloc[[1, 3, 5]] 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
def test_reindex(self):
        orig = pd.Series([1, np.nan, np.nan, 3, np.nan],
                         index=list('ABCDE'))
        sparse = orig.to_sparse()

        res = sparse.reindex(['A', 'E', 'C', 'D'])
        exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse()
        tm.assert_sp_series_equal(res, exp)

        # all missing & fill_value
        res = sparse.reindex(['B', 'E', 'C'])
        exp = orig.reindex(['B', 'E', 'C']).to_sparse()
        tm.assert_sp_series_equal(res, exp)

        orig = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan],
                         index=list('ABCDE'))
        sparse = orig.to_sparse()

        res = sparse.reindex(['A', 'E', 'C', 'D'])
        exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse()
        tm.assert_sp_series_equal(res, exp) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
def test_concat(self, kind):
        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=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, consolidate_block_indices=True)

        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, consolidate_block_indices=True) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
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)

            with tm.assert_produces_warning(PerformanceWarning):
                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)

            with tm.assert_produces_warning(PerformanceWarning):
                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) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
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')

        res = pd.concat([sparse1, sparse2])
        exp = pd.concat([pd.Series(val1), pd.Series(val2)])
        exp = pd.SparseSeries(exp, kind=sparse1.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=sparse2.kind)
        tm.assert_sp_series_equal(res, exp, consolidate_block_indices=True) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
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) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
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 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
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)) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
def test_unary_operators(self, values, op, fill_value):
        # https://github.com/pandas-dev/pandas/issues/22835
        values = np.asarray(values)
        if op is operator.invert:
            new_fill_value = not fill_value
        else:
            new_fill_value = op(fill_value)
        s = SparseSeries(values,
                         fill_value=fill_value,
                         index=['a', 'b', 'c', 'd'],
                         name='name')
        result = op(s)
        expected = SparseSeries(op(values),
                                fill_value=new_fill_value,
                                index=['a', 'b', 'c', 'd'],
                                name='name')
        tm.assert_sp_series_equal(result, expected) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
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,
                                      consolidate_block_indices=True) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
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)

            with tm.assert_produces_warning(PerformanceWarning):
                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)

            with tm.assert_produces_warning(PerformanceWarning):
                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) 

# 需要导入模块: from pandas.util import testing [as 别名]
# 或者: from pandas.util.testing import assert_sp_series_equal [as 别名]
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)

        with tm.assert_produces_warning(PerformanceWarning):
            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)

        with tm.assert_produces_warning(PerformanceWarning):
            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)