python类nan()的实例源码

def test_unstack_to_series(self):
        # check reversibility
        data = self.frame.unstack()

        self.assertTrue(isinstance(data, Series))
        undo = data.unstack().T
        assert_frame_equal(undo, self.frame)

        # check NA handling
        data = DataFrame({'x': [1, 2, np.NaN], 'y': [3.0, 4, np.NaN]})
        data.index = Index(['a', 'b', 'c'])
        result = data.unstack()

        midx = MultiIndex(levels=[['x', 'y'], ['a', 'b', 'c']],
                          labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
        expected = Series([1, 2, np.NaN, 3, 4, np.NaN], index=midx)

        assert_series_equal(result, expected)

        # check composability of unstack
        old_data = data.copy()
        for _ in range(4):
            data = data.unstack()
        assert_frame_equal(old_data, data)

def test_rolling_skew_edge_cases(self):

        all_nan = Series([np.NaN] * 5)

        # yields all NaN (0 variance)
        d = Series([1] * 5)
        x = d.rolling(window=5).skew()
        assert_series_equal(all_nan, x)

        # yields all NaN (window too small)
        d = Series(np.random.randn(5))
        x = d.rolling(window=2).skew()
        assert_series_equal(all_nan, x)

        # yields [NaN, NaN, NaN, 0.177994, 1.548824]
        d = Series([-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401
                    ])
        expected = Series([np.NaN, np.NaN, np.NaN, 0.177994, 1.548824])
        x = d.rolling(window=4).skew()
        assert_series_equal(expected, x)

def test_rolling_kurt_edge_cases(self):

        all_nan = Series([np.NaN] * 5)

        # yields all NaN (0 variance)
        d = Series([1] * 5)
        x = d.rolling(window=5).kurt()
        assert_series_equal(all_nan, x)

        # yields all NaN (window too small)
        d = Series(np.random.randn(5))
        x = d.rolling(window=3).kurt()
        assert_series_equal(all_nan, x)

        # yields [NaN, NaN, NaN, 1.224307, 2.671499]
        d = Series([-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401
                    ])
        expected = Series([np.NaN, np.NaN, np.NaN, 1.224307, 2.671499])
        x = d.rolling(window=4).kurt()
        assert_series_equal(expected, x)

def test_interpolate(self):
        ts = Series(np.arange(len(self.ts), dtype=float), self.ts.index)

        ts_copy = ts.copy()
        ts_copy[5:10] = np.NaN

        linear_interp = ts_copy.interpolate(method='linear')
        self.assert_numpy_array_equal(linear_interp, ts)

        ord_ts = Series([d.toordinal() for d in self.ts.index],
                        index=self.ts.index).astype(float)

        ord_ts_copy = ord_ts.copy()
        ord_ts_copy[5:10] = np.NaN

        time_interp = ord_ts_copy.interpolate(method='time')
        self.assert_numpy_array_equal(time_interp, ord_ts)

        # try time interpolation on a non-TimeSeries
        # Only raises ValueError if there are NaNs.
        non_ts = self.series.copy()
        non_ts[0] = np.NaN
        self.assertRaises(ValueError, non_ts.interpolate, method='time')

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)
            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)}
        assertRaisesRegexp(TypeError, "NaN fill value", spf.homogenize, data)

def somenan(self, x, fun, p=0.1):
        """returns sometimes np.NaN, otherwise fun(x)"""
        if np.random.rand(1) < p:
            return np.NaN
        else:
            return fun(x)

def cornersphere(self, x):
        """Sphere (squared norm) test objective function constraint to the corner"""
        nconstr = len(x) - 0
        if any(x[:nconstr] < 1):
            return np.NaN
        return sum(x**2) - nconstr

def cornerelli(self, x):
        """ """
        if any(x < 1):
            return np.NaN
        return self.elli(x) - self.elli(np.ones(len(x)))

def cornerellirot(self, x):
        """ """
        if any(x < 1):
            return np.NaN
        return self.ellirot(x)

def lincon(self, x, theta=0.01):
        """ridge like linear function with one linear constraint"""
        if x[0] < 0:
            return np.NaN
        return theta * x[1] + x[0]