Python pandas.util.testing.assert_sp_frame_equal() Examples

The following are 30 code examples of pandas.util.testing.assert_sp_frame_equal(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module pandas.util.testing , or try the search function .
Example #1
Source File: test_indexing.py    From vnpy_crypto with MIT License 6 votes vote down vote up
def test_take_fill_value(self):
        orig = pd.DataFrame([[1, np.nan, 0],
                             [2, 3, np.nan],
                             [0, np.nan, 4],
                             [0, np.nan, 5]],
                            columns=list('xyz'))
        sparse = orig.to_sparse(fill_value=0)

        exp = orig.take([0]).to_sparse(fill_value=0)
        exp._default_fill_value = np.nan
        tm.assert_sp_frame_equal(sparse.take([0]), exp)

        exp = orig.take([0, 1]).to_sparse(fill_value=0)
        exp._default_fill_value = np.nan
        tm.assert_sp_frame_equal(sparse.take([0, 1]), exp)

        exp = orig.take([-1, -2]).to_sparse(fill_value=0)
        exp._default_fill_value = np.nan
        tm.assert_sp_frame_equal(sparse.take([-1, -2]), exp) 
Example #2
Source File: test_indexing.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_reindex(self):
        orig = pd.DataFrame([[1, np.nan, 0],
                             [2, 3, np.nan],
                             [0, np.nan, 4],
                             [0, np.nan, 5]],
                            index=list('ABCD'), columns=list('xyz'))
        sparse = orig.to_sparse()

        res = sparse.reindex(['A', 'C', 'B'])
        exp = orig.reindex(['A', 'C', 'B']).to_sparse()
        tm.assert_sp_frame_equal(res, exp)

        orig = pd.DataFrame([[np.nan, np.nan, np.nan],
                             [np.nan, np.nan, np.nan],
                             [np.nan, np.nan, np.nan],
                             [np.nan, np.nan, np.nan]],
                            index=list('ABCD'), columns=list('xyz'))
        sparse = orig.to_sparse()

        res = sparse.reindex(['A', 'C', 'B'])
        exp = orig.reindex(['A', 'C', 'B']).to_sparse()
        tm.assert_sp_frame_equal(res, exp) 
Example #3
Source File: test_indexing.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_take_fill_value(self):
        orig = pd.DataFrame([[1, np.nan, 0],
                             [2, 3, np.nan],
                             [0, np.nan, 4],
                             [0, np.nan, 5]],
                            columns=list('xyz'))
        sparse = orig.to_sparse(fill_value=0)

        exp = orig.take([0]).to_sparse(fill_value=0)
        exp._default_fill_value = np.nan
        tm.assert_sp_frame_equal(sparse.take([0]), exp)

        exp = orig.take([0, 1]).to_sparse(fill_value=0)
        exp._default_fill_value = np.nan
        tm.assert_sp_frame_equal(sparse.take([0, 1]), exp)

        exp = orig.take([-1, -2]).to_sparse(fill_value=0)
        exp._default_fill_value = np.nan
        tm.assert_sp_frame_equal(sparse.take([-1, -2]), exp) 
Example #4
Source File: test_indexing.py    From vnpy_crypto with MIT License 6 votes vote down vote up
def test_reindex(self):
        orig = pd.DataFrame([[1, np.nan, 0],
                             [2, 3, np.nan],
                             [0, np.nan, 4],
                             [0, np.nan, 5]],
                            index=list('ABCD'), columns=list('xyz'))
        sparse = orig.to_sparse()

        res = sparse.reindex(['A', 'C', 'B'])
        exp = orig.reindex(['A', 'C', 'B']).to_sparse()
        tm.assert_sp_frame_equal(res, exp)

        orig = pd.DataFrame([[np.nan, np.nan, np.nan],
                             [np.nan, np.nan, np.nan],
                             [np.nan, np.nan, np.nan],
                             [np.nan, np.nan, np.nan]],
                            index=list('ABCD'), columns=list('xyz'))
        sparse = orig.to_sparse()

        res = sparse.reindex(['A', 'C', 'B'])
        exp = orig.reindex(['A', 'C', 'B']).to_sparse()
        tm.assert_sp_frame_equal(res, exp) 
Example #5
Source File: test_series.py    From vnpy_crypto with MIT License 6 votes vote down vote up
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) 
Example #6
Source File: test_subclass.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_subclass_sparse_slice(self):
        rows = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
        ssdf = tm.SubclassedSparseDataFrame(rows)
        ssdf.testattr = "testattr"

        tm.assert_sp_frame_equal(ssdf.loc[:2],
                                 tm.SubclassedSparseDataFrame(rows[:3]))
        tm.assert_sp_frame_equal(ssdf.iloc[:2],
                                 tm.SubclassedSparseDataFrame(rows[:2]))
        tm.assert_sp_frame_equal(ssdf[:2],
                                 tm.SubclassedSparseDataFrame(rows[:2]))
        assert ssdf.loc[:2].testattr == "testattr"
        assert ssdf.iloc[:2].testattr == "testattr"
        assert ssdf[:2].testattr == "testattr"

        tm.assert_sp_series_equal(ssdf.loc[1],
                                  tm.SubclassedSparseSeries(rows[1]),
                                  check_names=False,
                                  check_kind=False)
        tm.assert_sp_series_equal(ssdf.iloc[1],
                                  tm.SubclassedSparseSeries(rows[1]),
                                  check_names=False,
                                  check_kind=False) 
Example #7
Source File: test_indexing.py    From vnpy_crypto with MIT License 6 votes vote down vote up
def test_getitem(self):
        orig = pd.DataFrame([[1, np.nan, np.nan],
                             [2, 3, np.nan],
                             [np.nan, np.nan, 4],
                             [0, np.nan, 5]],
                            columns=list('xyz'))
        sparse = orig.to_sparse()

        tm.assert_sp_series_equal(sparse['x'], orig['x'].to_sparse())
        tm.assert_sp_frame_equal(sparse[['x']], orig[['x']].to_sparse())
        tm.assert_sp_frame_equal(sparse[['z', 'x']],
                                 orig[['z', 'x']].to_sparse())

        tm.assert_sp_frame_equal(sparse[[True, False, True, True]],
                                 orig[[True, False, True, True]].to_sparse())

        tm.assert_sp_frame_equal(sparse.iloc[[1, 2]],
                                 orig.iloc[[1, 2]].to_sparse()) 
Example #8
Source File: test_series.py    From recruit with Apache License 2.0 6 votes vote down vote up
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) 
Example #9
Source File: test_subclass.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_subclass_sparse_to_frame(self):
        s = tm.SubclassedSparseSeries([1, 2], index=list('ab'), name='xxx')
        res = s.to_frame()

        exp_arr = pd.SparseArray([1, 2], dtype=np.int64, kind='block',
                                 fill_value=0)
        exp = tm.SubclassedSparseDataFrame({'xxx': exp_arr},
                                           index=list('ab'),
                                           default_fill_value=0)
        tm.assert_sp_frame_equal(res, exp)

        # create from int dict
        res = tm.SubclassedSparseDataFrame({'xxx': [1, 2]},
                                           index=list('ab'),
                                           default_fill_value=0)
        tm.assert_sp_frame_equal(res, exp)

        s = tm.SubclassedSparseSeries([1.1, 2.1], index=list('ab'),
                                      name='xxx')
        res = s.to_frame()
        exp = tm.SubclassedSparseDataFrame({'xxx': [1.1, 2.1]},
                                           index=list('ab'))
        tm.assert_sp_frame_equal(res, exp) 
Example #10
Source File: test_to_from_scipy.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_from_scipy_correct_ordering(spmatrix):
    # GH 16179
    arr = np.arange(1, 5).reshape(2, 2)
    try:
        spm = spmatrix(arr)
        assert spm.dtype == arr.dtype
    except (TypeError, AssertionError):
        # If conversion to sparse fails for this spmatrix type and arr.dtype,
        # then the combination is not currently supported in NumPy, so we
        # can just skip testing it thoroughly
        return

    sdf = SparseDataFrame(spm)
    expected = SparseDataFrame(arr)
    tm.assert_sp_frame_equal(sdf, expected)
    tm.assert_frame_equal(sdf.to_dense(), expected.to_dense()) 
Example #11
Source File: test_frame.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_constructor_ndarray(self, float_frame):
        # no index or columns
        sp = SparseDataFrame(float_frame.values)

        # 1d
        sp = SparseDataFrame(float_frame['A'].values, index=float_frame.index,
                             columns=['A'])
        tm.assert_sp_frame_equal(sp, float_frame.reindex(columns=['A']))

        # raise on level argument
        pytest.raises(TypeError, float_frame.reindex, columns=['A'],
                      level=1)

        # wrong length index / columns
        with pytest.raises(ValueError, match="^Index length"):
            SparseDataFrame(float_frame.values, index=float_frame.index[:-1])

        with pytest.raises(ValueError, match="^Column length"):
            SparseDataFrame(float_frame.values,
                            columns=float_frame.columns[:-1])

    # GH 9272 
Example #12
Source File: test_frame.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_notna(self):
        # GH 8276
        df = pd.SparseDataFrame({'A': [np.nan, np.nan, 1, 2, np.nan],
                                 'B': [0, np.nan, np.nan, 2, np.nan]})

        res = df.notna()
        exp = pd.SparseDataFrame({'A': [False, False, True, True, False],
                                  'B': [True, False, False, True, False]},
                                 default_fill_value=False)
        exp._default_fill_value = np.nan
        tm.assert_sp_frame_equal(res, exp)

        # if fill_value is not nan, True can be included in sp_values
        df = pd.SparseDataFrame({'A': [0, 0, 1, 2, np.nan],
                                 'B': [0, np.nan, 0, 2, np.nan]},
                                default_fill_value=0.)
        res = df.notna()
        assert isinstance(res, pd.SparseDataFrame)
        exp = pd.DataFrame({'A': [True, True, True, True, False],
                            'B': [True, False, True, True, False]})
        tm.assert_frame_equal(res.to_dense(), exp) 
Example #13
Source File: test_frame.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_isna(self):
        # GH 8276
        df = pd.SparseDataFrame({'A': [np.nan, np.nan, 1, 2, np.nan],
                                 'B': [0, np.nan, np.nan, 2, np.nan]})

        res = df.isna()
        exp = pd.SparseDataFrame({'A': [True, True, False, False, True],
                                  'B': [False, True, True, False, True]},
                                 default_fill_value=True)
        exp._default_fill_value = np.nan
        tm.assert_sp_frame_equal(res, exp)

        # if fill_value is not nan, True can be included in sp_values
        df = pd.SparseDataFrame({'A': [0, 0, 1, 2, np.nan],
                                 'B': [0, np.nan, 0, 2, np.nan]},
                                default_fill_value=0.)
        res = df.isna()
        assert isinstance(res, pd.SparseDataFrame)
        exp = pd.DataFrame({'A': [False, False, False, False, True],
                            'B': [False, True, False, False, True]})
        tm.assert_frame_equal(res.to_dense(), exp) 
Example #14
Source File: test_frame.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_astype_bool(self):
        sparse = pd.SparseDataFrame({'A': SparseArray([0, 2, 0, 4],
                                                      fill_value=0,
                                                      dtype=np.int64),
                                     'B': SparseArray([0, 5, 0, 7],
                                                      fill_value=0,
                                                      dtype=np.int64)},
                                    default_fill_value=0)
        assert sparse['A'].dtype == SparseDtype(np.int64)
        assert sparse['B'].dtype == SparseDtype(np.int64)

        res = sparse.astype(SparseDtype(bool, False))
        exp = pd.SparseDataFrame({'A': SparseArray([False, True, False, True],
                                                   dtype=np.bool,
                                                   fill_value=False,
                                                   kind='integer'),
                                  'B': SparseArray([False, True, False, True],
                                                   dtype=np.bool,
                                                   fill_value=False,
                                                   kind='integer')},
                                 default_fill_value=False)
        tm.assert_sp_frame_equal(res, exp)
        assert res['A'].dtype == SparseDtype(np.bool)
        assert res['B'].dtype == SparseDtype(np.bool) 
Example #15
Source File: test_frame.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_transpose(self, float_frame, float_frame_int_kind,
                       float_frame_dense,
                       float_frame_fill0, float_frame_fill0_dense,
                       float_frame_fill2, float_frame_fill2_dense):

        def _check(frame, orig):
            transposed = frame.T
            untransposed = transposed.T
            tm.assert_sp_frame_equal(frame, untransposed)

            tm.assert_frame_equal(frame.T.to_dense(), orig.T)
            tm.assert_frame_equal(frame.T.T.to_dense(), orig.T.T)
            tm.assert_sp_frame_equal(frame, frame.T.T, exact_indices=False)

        _check(float_frame, float_frame_dense)
        _check(float_frame_int_kind, float_frame_dense)
        _check(float_frame_fill0, float_frame_fill0_dense)
        _check(float_frame_fill2, float_frame_fill2_dense) 
Example #16
Source File: test_indexing.py    From recruit with Apache License 2.0 6 votes vote down vote up
def test_getitem(self):
        orig = pd.DataFrame([[1, np.nan, np.nan],
                             [2, 3, np.nan],
                             [np.nan, np.nan, 4],
                             [0, np.nan, 5]],
                            columns=list('xyz'))
        sparse = orig.to_sparse()

        tm.assert_sp_series_equal(sparse['x'], orig['x'].to_sparse())
        tm.assert_sp_frame_equal(sparse[['x']], orig[['x']].to_sparse())
        tm.assert_sp_frame_equal(sparse[['z', 'x']],
                                 orig[['z', 'x']].to_sparse())

        tm.assert_sp_frame_equal(sparse[[True, False, True, True]],
                                 orig[[True, False, True, True]].to_sparse())

        tm.assert_sp_frame_equal(sparse.iloc[[1, 2]],
                                 orig.iloc[[1, 2]].to_sparse()) 
Example #17
Source File: test_indexing.py    From vnpy_crypto with MIT License 5 votes vote down vote up
def test_reindex_fill_value(self):
        orig = pd.DataFrame([[1, np.nan, 0],
                             [2, 3, np.nan],
                             [0, np.nan, 4],
                             [0, np.nan, 5]],
                            index=list('ABCD'), columns=list('xyz'))
        sparse = orig.to_sparse(fill_value=0)

        res = sparse.reindex(['A', 'C', 'B'])
        exp = orig.reindex(['A', 'C', 'B']).to_sparse(fill_value=0)
        tm.assert_sp_frame_equal(res, exp)

        # all missing
        orig = pd.DataFrame([[np.nan, np.nan, np.nan],
                             [np.nan, np.nan, np.nan],
                             [np.nan, np.nan, np.nan],
                             [np.nan, np.nan, np.nan]],
                            index=list('ABCD'), columns=list('xyz'))
        sparse = orig.to_sparse(fill_value=0)

        res = sparse.reindex(['A', 'C', 'B'])
        exp = orig.reindex(['A', 'C', 'B']).to_sparse(fill_value=0)
        tm.assert_sp_frame_equal(res, exp)

        # all fill_value
        orig = pd.DataFrame([[0, 0, 0],
                             [0, 0, 0],
                             [0, 0, 0],
                             [0, 0, 0]],
                            index=list('ABCD'), columns=list('xyz'))
        sparse = orig.to_sparse(fill_value=0)

        res = sparse.reindex(['A', 'C', 'B'])
        exp = orig.reindex(['A', 'C', 'B']).to_sparse(fill_value=0)
        tm.assert_sp_frame_equal(res, exp) 
Example #18
Source File: test_indexing.py    From vnpy_crypto with MIT License 5 votes vote down vote up
def test_loc_slice(self):
        orig = pd.DataFrame([[1, np.nan, np.nan],
                             [2, 3, np.nan],
                             [np.nan, np.nan, 4]],
                            columns=list('xyz'))
        sparse = orig.to_sparse()
        tm.assert_sp_frame_equal(sparse.loc[2:], orig.loc[2:].to_sparse()) 
Example #19
Source File: test_frame.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_assign_with_sparse_frame(self):
        # GH 19163
        df = pd.DataFrame({"a": [1, 2, 3]})
        res = df.to_sparse(fill_value=False).assign(newcol=False)
        exp = df.assign(newcol=False).to_sparse(fill_value=False)

        tm.assert_sp_frame_equal(res, exp)

        for column in res.columns:
            assert type(res[column]) is SparseSeries 
Example #20
Source File: test_frame.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_numpy_cumsum(self, float_frame):
        result = np.cumsum(float_frame)
        expected = SparseDataFrame(float_frame.to_dense().cumsum())
        tm.assert_sp_frame_equal(result, expected)

        msg = "the 'dtype' parameter is not supported"
        with pytest.raises(ValueError, match=msg):
            np.cumsum(float_frame, dtype=np.int64)

        msg = "the 'out' parameter is not supported"
        with pytest.raises(ValueError, match=msg):
            np.cumsum(float_frame, out=result) 
Example #21
Source File: test_frame.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_cumsum(self, float_frame):
        expected = SparseDataFrame(float_frame.to_dense().cumsum())

        result = float_frame.cumsum()
        tm.assert_sp_frame_equal(result, expected)

        result = float_frame.cumsum(axis=None)
        tm.assert_sp_frame_equal(result, expected)

        result = float_frame.cumsum(axis=0)
        tm.assert_sp_frame_equal(result, expected) 
Example #22
Source File: test_frame.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_numeric_op_scalar(self):
        df = pd.DataFrame({'A': [nan, nan, 0, 1, ],
                           'B': [0, 1, 2, nan],
                           'C': [1., 2., 3., 4.],
                           'D': [nan, nan, nan, nan]})
        sparse = df.to_sparse()

        tm.assert_sp_frame_equal(sparse + 1, (df + 1).to_sparse()) 
Example #23
Source File: test_combine_concat.py    From vnpy_crypto with MIT License 5 votes vote down vote up
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) 
Example #24
Source File: test_combine_concat.py    From vnpy_crypto with MIT License 5 votes vote down vote up
def test_concat_different_fill_value(self):
        # 1st fill_value will be used
        sparse = self.dense1.to_sparse()
        sparse2 = self.dense2.to_sparse(fill_value=0)

        res = pd.concat([sparse, sparse2])
        exp = pd.concat([self.dense1, self.dense2]).to_sparse()
        tm.assert_sp_frame_equal(res, exp)

        res = pd.concat([sparse2, sparse])
        exp = pd.concat([self.dense2, self.dense1]).to_sparse(fill_value=0)
        exp._default_fill_value = np.nan
        tm.assert_sp_frame_equal(res, exp) 
Example #25
Source File: test_frame.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_combine_first_with_dense(self):
        # We could support this if we allow
        # pd.core.dtypes.cast.find_common_type to special case SparseDtype
        # but I don't think that's worth it.
        df = self.frame

        result = df[::2].combine_first(df.to_dense())
        expected = df[::2].to_dense().combine_first(df.to_dense())
        expected = expected.to_sparse(fill_value=df.default_fill_value)

        tm.assert_sp_frame_equal(result, expected) 
Example #26
Source File: test_frame.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_combine_first(self, float_frame):
        df = float_frame

        result = df[::2].combine_first(df)

        expected = df[::2].to_dense().combine_first(df.to_dense())
        expected = expected.to_sparse(fill_value=df.default_fill_value)

        tm.assert_sp_frame_equal(result, expected) 
Example #27
Source File: test_combine_concat.py    From vnpy_crypto with MIT License 5 votes vote down vote up
def test_concat_series(self):
        # fill_value = np.nan
        sparse = self.dense1.to_sparse()
        sparse2 = self.dense2.to_sparse()

        for col in ['A', 'D']:
            res = pd.concat([sparse, sparse2[col]])
            exp = pd.concat([self.dense1, self.dense2[col]]).to_sparse()
            tm.assert_sp_frame_equal(res, exp)

            res = pd.concat([sparse2[col], sparse])
            exp = pd.concat([self.dense2[col], self.dense1]).to_sparse()
            tm.assert_sp_frame_equal(res, exp)

        # fill_value = 0
        sparse = self.dense1.to_sparse(fill_value=0)
        sparse2 = self.dense2.to_sparse(fill_value=0)

        for col in ['C', 'D']:
            res = pd.concat([sparse, sparse2[col]])
            exp = pd.concat([self.dense1,
                             self.dense2[col]]).to_sparse(fill_value=0)
            exp._default_fill_value = np.nan
            tm.assert_sp_frame_equal(res, exp)

            res = pd.concat([sparse2[col], sparse])
            exp = pd.concat([self.dense2[col],
                             self.dense1]).to_sparse(fill_value=0)
            exp._default_fill_value = np.nan
            tm.assert_sp_frame_equal(res, exp) 
Example #28
Source File: test_frame.py    From recruit with Apache License 2.0 5 votes vote down vote up
def test_numpy_transpose(self):
        sdf = SparseDataFrame([1, 2, 3], index=[1, 2, 3], columns=['a'])
        result = np.transpose(np.transpose(sdf))
        tm.assert_sp_frame_equal(result, sdf)

        msg = "the 'axes' parameter is not supported"
        with pytest.raises(ValueError, match=msg):
            np.transpose(sdf, axes=1) 
Example #29
Source File: test_series.py    From vnpy_crypto with MIT License 5 votes vote down vote up
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) 
Example #30
Source File: test_format.py    From vnpy_crypto with MIT License 5 votes vote down vote up
def test_sparse_repr_after_set(self):
        # GH 15488
        sdf = pd.SparseDataFrame([[np.nan, 1], [2, np.nan]])
        res = sdf.copy()

        # Ignore the warning
        with pd.option_context('mode.chained_assignment', None):
            sdf[0][1] = 2  # This line triggers the bug

        repr(sdf)
        tm.assert_sp_frame_equal(sdf, res)