Python compat.range方法代码示例

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def apply(self, other):
        if self._use_relativedelta:
            other = as_datetime(other)

        if len(self.kwds) > 0:
            tzinfo = getattr(other, 'tzinfo', None)
            if tzinfo is not None and self._use_relativedelta:
                # perform calculation in UTC
                other = other.replace(tzinfo=None)

            if self.n > 0:
                for i in range(self.n):
                    other = other + self._offset
            else:
                for i in range(-self.n):
                    other = other - self._offset

            if tzinfo is not None and self._use_relativedelta:
                # bring tz back from UTC calculation
                other = conversion.localize_pydatetime(other, tzinfo)

            return as_timestamp(other)
        else:
            return other + timedelta(self.n) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def test_agg_consistency(self):

        df = DataFrame({'A': range(5), 'B': range(0, 10, 2)})
        r = df.rolling(window=3)

        result = r.agg([np.sum, np.mean]).columns
        expected = pd.MultiIndex.from_product([list('AB'), ['sum', 'mean']])
        tm.assert_index_equal(result, expected)

        result = r['A'].agg([np.sum, np.mean]).columns
        expected = Index(['sum', 'mean'])
        tm.assert_index_equal(result, expected)

        result = r.agg({'A': [np.sum, np.mean]}).columns
        expected = pd.MultiIndex.from_tuples([('A', 'sum'), ('A', 'mean')])
        tm.assert_index_equal(result, expected) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def test_rolling_axis(self, axis_frame):
        # see gh-23372.
        df = DataFrame(np.ones((10, 20)))
        axis = df._get_axis_number(axis_frame)

        if axis == 0:
            expected = DataFrame({
                i: [np.nan] * 2 + [3.0] * 8
                for i in range(20)
            })
        else:
            # axis == 1
            expected = DataFrame([
                [np.nan] * 2 + [3.0] * 18
            ] * 10)

        result = df.rolling(3, axis=axis_frame).sum()
        tm.assert_frame_equal(result, expected) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def test_expanding_axis(self, axis_frame):
        # see gh-23372.
        df = DataFrame(np.ones((10, 20)))
        axis = df._get_axis_number(axis_frame)

        if axis == 0:
            expected = DataFrame({
                i: [np.nan] * 2 + [float(j) for j in range(3, 11)]
                for i in range(20)
            })
        else:
            # axis == 1
            expected = DataFrame([
                [np.nan] * 2 + [float(i) for i in range(3, 21)]
            ] * 10)

        result = df.expanding(3, axis=axis_frame).sum()
        tm.assert_frame_equal(result, expected) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def test_cmov_window_special_linear_range(self, win_types_special):
        # GH 8238
        kwds = {
            'kaiser': {'beta': 1.},
            'gaussian': {'std': 1.},
            'general_gaussian': {'power': 2., 'width': 2.},
            'slepian': {'width': 0.5}}

        vals = np.array(range(10), dtype=np.float)
        xp = vals.copy()
        xp[:2] = np.nan
        xp[-2:] = np.nan
        xp = Series(xp)

        rs = Series(vals).rolling(
            5, win_type=win_types_special, center=True).mean(
            **kwds[win_types_special])
        tm.assert_series_equal(xp, rs) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def test_corr_sanity(self):
        # GH 3155
        df = DataFrame(np.array(
            [[0.87024726, 0.18505595], [0.64355431, 0.3091617],
             [0.92372966, 0.50552513], [0.00203756, 0.04520709],
             [0.84780328, 0.33394331], [0.78369152, 0.63919667]]))

        res = df[0].rolling(5, center=True).corr(df[1])
        assert all(np.abs(np.nan_to_num(x)) <= 1 for x in res)

        # and some fuzzing
        for _ in range(10):
            df = DataFrame(np.random.rand(30, 2))
            res = df[0].rolling(5, center=True).corr(df[1])
            try:
                assert all(np.abs(np.nan_to_num(x)) <= 1 for x in res)
            except AssertionError:
                print(res) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def test_rolling_functions_window_non_shrinkage(self, f):
        # GH 7764
        s = Series(range(4))
        s_expected = Series(np.nan, index=s.index)
        df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]], columns=['A', 'B'])
        df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)

        try:
            s_result = f(s)
            tm.assert_series_equal(s_result, s_expected)

            df_result = f(df)
            tm.assert_frame_equal(df_result, df_expected)
        except (ImportError):

            # scipy needed for rolling_window
            pytest.skip("scipy not available") 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def test_rolling_functions_window_non_shrinkage_binary(self):

        # corr/cov return a MI DataFrame
        df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]],
                       columns=Index(['A', 'B'], name='foo'),
                       index=Index(range(4), name='bar'))
        df_expected = DataFrame(
            columns=Index(['A', 'B'], name='foo'),
            index=pd.MultiIndex.from_product([df.index, df.columns],
                                             names=['bar', 'foo']),
            dtype='float64')
        functions = [lambda x: (x.rolling(window=10, min_periods=5)
                                .cov(x, pairwise=True)),
                     lambda x: (x.rolling(window=10, min_periods=5)
                                .corr(x, pairwise=True))]
        for f in functions:
            df_result = f(df)
            tm.assert_frame_equal(df_result, df_expected) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def test_expanding_corr_pairwise_diff_length(self):
        # GH 7512
        df1 = DataFrame([[1, 2], [3, 2], [3, 4]],
                        columns=['A', 'B'],
                        index=Index(range(3), name='bar'))
        df1a = DataFrame([[1, 2], [3, 4]],
                         index=Index([0, 2], name='bar'),
                         columns=['A', 'B'])
        df2 = DataFrame([[5, 6], [None, None], [2, 1]],
                        columns=['X', 'Y'],
                        index=Index(range(3), name='bar'))
        df2a = DataFrame([[5, 6], [2, 1]],
                         index=Index([0, 2], name='bar'),
                         columns=['X', 'Y'])
        result1 = df1.expanding().corr(df2, pairwise=True).loc[2]
        result2 = df1.expanding().corr(df2a, pairwise=True).loc[2]
        result3 = df1a.expanding().corr(df2, pairwise=True).loc[2]
        result4 = df1a.expanding().corr(df2a, pairwise=True).loc[2]
        expected = DataFrame([[-1.0, -1.0], [-1.0, -1.0]],
                             columns=['A', 'B'],
                             index=Index(['X', 'Y']))
        tm.assert_frame_equal(result1, expected)
        tm.assert_frame_equal(result2, expected)
        tm.assert_frame_equal(result3, expected)
        tm.assert_frame_equal(result4, expected) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def test_rolling_max_gh6297(self):
        """Replicate result expected in GH #6297"""

        indices = [datetime(1975, 1, i) for i in range(1, 6)]
        # So that we can have 2 datapoints on one of the days
        indices.append(datetime(1975, 1, 3, 6, 0))
        series = Series(range(1, 7), index=indices)
        # Use floats instead of ints as values
        series = series.map(lambda x: float(x))
        # Sort chronologically
        series = series.sort_index()

        expected = Series([1.0, 2.0, 6.0, 4.0, 5.0],
                          index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
        x = series.resample('D').max().rolling(window=1).max()
        tm.assert_series_equal(expected, x) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def test_rolling_min_resample(self):

        indices = [datetime(1975, 1, i) for i in range(1, 6)]
        # So that we can have 3 datapoints on last day (4, 10, and 20)
        indices.append(datetime(1975, 1, 5, 1))
        indices.append(datetime(1975, 1, 5, 2))
        series = Series(list(range(0, 5)) + [10, 20], index=indices)
        # Use floats instead of ints as values
        series = series.map(lambda x: float(x))
        # Sort chronologically
        series = series.sort_index()

        # Default how should be min
        expected = Series([0.0, 1.0, 2.0, 3.0, 4.0],
                          index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
        r = series.resample('D').min().rolling(window=1)
        tm.assert_series_equal(expected, r.min()) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def test_rolling_median_resample(self):

        indices = [datetime(1975, 1, i) for i in range(1, 6)]
        # So that we can have 3 datapoints on last day (4, 10, and 20)
        indices.append(datetime(1975, 1, 5, 1))
        indices.append(datetime(1975, 1, 5, 2))
        series = Series(list(range(0, 5)) + [10, 20], index=indices)
        # Use floats instead of ints as values
        series = series.map(lambda x: float(x))
        # Sort chronologically
        series = series.sort_index()

        # Default how should be median
        expected = Series([0.0, 1.0, 2.0, 3.0, 10],
                          index=[datetime(1975, 1, i, 0) for i in range(1, 6)])
        x = series.resample('D').median().rolling(window=1).median()
        tm.assert_series_equal(expected, x) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def test_monotonic_on(self):

        # on/index must be monotonic
        df = DataFrame({'A': pd.date_range('20130101',
                                           periods=5,
                                           freq='s'),
                        'B': range(5)})

        assert df.A.is_monotonic
        df.rolling('2s', on='A').sum()

        df = df.set_index('A')
        assert df.index.is_monotonic
        df.rolling('2s').sum()

        # non-monotonic
        df.index = reversed(df.index.tolist())
        assert not df.index.is_monotonic

        with pytest.raises(ValueError):
            df.rolling('2s').sum()

        df = df.reset_index()
        with pytest.raises(ValueError):
            df.rolling('2s', on='A').sum() 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def test_construction_with_dtype(self):

        # specify dtype
        ci = self.create_index(categories=list('abc'))

        result = Index(np.array(ci), dtype='category')
        tm.assert_index_equal(result, ci, exact=True)

        result = Index(np.array(ci).tolist(), dtype='category')
        tm.assert_index_equal(result, ci, exact=True)

        # these are generally only equal when the categories are reordered
        ci = self.create_index()

        result = Index(
            np.array(ci), dtype='category').reorder_categories(ci.categories)
        tm.assert_index_equal(result, ci, exact=True)

        # make sure indexes are handled
        expected = CategoricalIndex([0, 1, 2], categories=[0, 1, 2],
                                    ordered=True)
        idx = Index(range(3))
        result = CategoricalIndex(idx, categories=idx, ordered=True)
        tm.assert_index_equal(result, expected, exact=True) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import range [as 别名]
def test_index_groupby(self):
        int_idx = Index(range(6))
        float_idx = Index(np.arange(0, 0.6, 0.1))
        obj_idx = Index('A B C D E F'.split())
        dt_idx = pd.date_range('2013-01-01', freq='M', periods=6)

        for idx in [int_idx, float_idx, obj_idx, dt_idx]:
            to_groupby = np.array([1, 2, np.nan, np.nan, 2, 1])
            tm.assert_dict_equal(idx.groupby(to_groupby),
                                 {1.0: idx[[0, 5]], 2.0: idx[[1, 4]]})

            to_groupby = Index([datetime(2011, 11, 1),
                                datetime(2011, 12, 1),
                                pd.NaT,
                                pd.NaT,
                                datetime(2011, 12, 1),
                                datetime(2011, 11, 1)],
                               tz='UTC').values

            ex_keys = [Timestamp('2011-11-01'), Timestamp('2011-12-01')]
            expected = {ex_keys[0]: idx[[0, 5]],
                        ex_keys[1]: idx[[1, 4]]}
            tm.assert_dict_equal(idx.groupby(to_groupby), expected)