Python random.randn方法代碼示例

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
def test_errors(self):
        x = np.random.randn(1, 2, 3)
        assert_raises_regex(np.AxisError, 'source.*out of bounds',
                            np.moveaxis, x, 3, 0)
        assert_raises_regex(np.AxisError, 'source.*out of bounds',
                            np.moveaxis, x, -4, 0)
        assert_raises_regex(np.AxisError, 'destination.*out of bounds',
                            np.moveaxis, x, 0, 5)
        assert_raises_regex(ValueError, 'repeated axis in `source`',
                            np.moveaxis, x, [0, 0], [0, 1])
        assert_raises_regex(ValueError, 'repeated axis in `destination`',
                            np.moveaxis, x, [0, 1], [1, 1])
        assert_raises_regex(ValueError, 'must have the same number',
                            np.moveaxis, x, 0, [0, 1])
        assert_raises_regex(ValueError, 'must have the same number',
                            np.moveaxis, x, [0, 1], [0]) 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
def test_window_with_args(self):
        # make sure that we are aggregating window functions correctly with arg
        r = Series(np.random.randn(100)).rolling(window=10, min_periods=1,
                                                 win_type='gaussian')
        expected = concat([r.mean(std=10), r.mean(std=.01)], axis=1)
        expected.columns = ['<lambda>', '<lambda>']
        result = r.aggregate([lambda x: x.mean(std=10),
                              lambda x: x.mean(std=.01)])
        tm.assert_frame_equal(result, expected)

        def a(x):
            return x.mean(std=10)

        def b(x):
            return x.mean(std=0.01)

        expected = concat([r.mean(std=10), r.mean(std=.01)], axis=1)
        expected.columns = ['a', 'b']
        result = r.aggregate([a, b])
        tm.assert_frame_equal(result, expected) 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
def test_cmov_window_corner(self):
        # GH 8238
        # all nan
        vals = pd.Series([np.nan] * 10)
        result = vals.rolling(5, center=True, win_type='boxcar').mean()
        assert np.isnan(result).all()

        # empty
        vals = pd.Series([])
        result = vals.rolling(5, center=True, win_type='boxcar').mean()
        assert len(result) == 0

        # shorter than window
        vals = pd.Series(np.random.randn(5))
        result = vals.rolling(10, win_type='boxcar').mean()
        assert np.isnan(result).all()
        assert len(result) == 5 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
def test_flex_binary_frame(self, method):
        series = self.frame[1]

        res = getattr(series.rolling(window=10), method)(self.frame)
        res2 = getattr(self.frame.rolling(window=10), method)(series)
        exp = self.frame.apply(lambda x: getattr(
            series.rolling(window=10), method)(x))

        tm.assert_frame_equal(res, exp)
        tm.assert_frame_equal(res2, exp)

        frame2 = self.frame.copy()
        frame2.values[:] = np.random.randn(*frame2.shape)

        res3 = getattr(self.frame.rolling(window=10), method)(frame2)
        exp = DataFrame({k: getattr(self.frame[k].rolling(
            window=10), method)(frame2[k]) for k in self.frame})
        tm.assert_frame_equal(res3, exp) 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
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()
        tm.assert_series_equal(all_nan, x)

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

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
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()
        tm.assert_series_equal(all_nan, x)

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

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
def test_count_level_series(self):
        index = MultiIndex(levels=[['foo', 'bar', 'baz'], ['one', 'two',
                                                           'three', 'four']],
                           codes=[[0, 0, 0, 2, 2], [2, 0, 1, 1, 2]])

        s = Series(np.random.randn(len(index)), index=index)

        result = s.count(level=0)
        expected = s.groupby(level=0).count()
        tm.assert_series_equal(
            result.astype('f8'), expected.reindex(result.index).fillna(0))

        result = s.count(level=1)
        expected = s.groupby(level=1).count()
        tm.assert_series_equal(
            result.astype('f8'), expected.reindex(result.index).fillna(0)) 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
def test_std_var_pass_ddof(self):
        index = MultiIndex.from_arrays([np.arange(5).repeat(10), np.tile(
            np.arange(10), 5)])
        df = DataFrame(np.random.randn(len(index), 5), index=index)

        for meth in ['var', 'std']:
            ddof = 4
            alt = lambda x: getattr(x, meth)(ddof=ddof)

            result = getattr(df[0], meth)(level=0, ddof=ddof)
            expected = df[0].groupby(level=0).agg(alt)
            tm.assert_series_equal(result, expected)

            result = getattr(df, meth)(level=0, ddof=ddof)
            expected = df.groupby(level=0).agg(alt)
            tm.assert_frame_equal(result, expected) 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
def test_mixed_depth_pop(self):
        arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
                  ['', 'OD', 'OD', 'result1', 'result2', 'result1'],
                  ['', 'wx', 'wy', '', '', '']]

        tuples = sorted(zip(*arrays))
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4, 6), columns=index)

        df1 = df.copy()
        df2 = df.copy()
        result = df1.pop('a')
        expected = df2.pop(('a', '', ''))
        tm.assert_series_equal(expected, result, check_names=False)
        tm.assert_frame_equal(df1, df2)
        assert result.name == 'a'

        expected = df1['top']
        df1 = df1.drop(['top'], axis=1)
        result = df2.pop('top')
        tm.assert_frame_equal(expected, result)
        tm.assert_frame_equal(df1, df2) 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
def test_sort_index_level_large_cardinality(self):

        # #2684 (int64)
        index = MultiIndex.from_arrays([np.arange(4000)] * 3)
        df = DataFrame(np.random.randn(4000), index=index, dtype=np.int64)

        # it works!
        result = df.sort_index(level=0)
        assert result.index.lexsort_depth == 3

        # #2684 (int32)
        index = MultiIndex.from_arrays([np.arange(4000)] * 3)
        df = DataFrame(np.random.randn(4000), index=index, dtype=np.int32)

        # it works!
        result = df.sort_index(level=0)
        assert (result.dtypes.values == df.dtypes.values).all()
        assert result.index.lexsort_depth == 3 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
def test_concat_dict(self):
        frames = {'foo': DataFrame(np.random.randn(4, 3)),
                  'bar': DataFrame(np.random.randn(4, 3)),
                  'baz': DataFrame(np.random.randn(4, 3)),
                  'qux': DataFrame(np.random.randn(4, 3))}

        sorted_keys = sorted(frames)

        result = concat(frames)
        expected = concat([frames[k] for k in sorted_keys], keys=sorted_keys)
        tm.assert_frame_equal(result, expected)

        result = concat(frames, axis=1)
        expected = concat([frames[k] for k in sorted_keys], keys=sorted_keys,
                          axis=1)
        tm.assert_frame_equal(result, expected)

        keys = ['baz', 'foo', 'bar']
        result = concat(frames, keys=keys)
        expected = concat([frames[k] for k in keys], keys=keys)
        tm.assert_frame_equal(result, expected) 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
def test_crossed_dtypes_weird_corner(self):
        columns = ['A', 'B', 'C', 'D']
        df1 = DataFrame({'A': np.array([1, 2, 3, 4], dtype='f8'),
                         'B': np.array([1, 2, 3, 4], dtype='i8'),
                         'C': np.array([1, 2, 3, 4], dtype='f8'),
                         'D': np.array([1, 2, 3, 4], dtype='i8')},
                        columns=columns)

        df2 = DataFrame({'A': np.array([1, 2, 3, 4], dtype='i8'),
                         'B': np.array([1, 2, 3, 4], dtype='f8'),
                         'C': np.array([1, 2, 3, 4], dtype='i8'),
                         'D': np.array([1, 2, 3, 4], dtype='f8')},
                        columns=columns)

        appended = df1.append(df2, ignore_index=True)
        expected = DataFrame(np.concatenate([df1.values, df2.values], axis=0),
                             columns=columns)
        tm.assert_frame_equal(appended, expected)

        df = DataFrame(np.random.randn(1, 3), index=['a'])
        df2 = DataFrame(np.random.randn(1, 4), index=['b'])
        result = concat(
            [df, df2], keys=['one', 'two'], names=['first', 'second'])
        assert result.index.names == ('first', 'second') 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
def setup_method(self):
        self.index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
                                        ['one', 'two', 'three']],
                                codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                                       [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                                names=['first', 'second'])
        self.to_join = DataFrame(np.random.randn(10, 3), index=self.index,
                                 columns=['j_one', 'j_two', 'j_three'])

        # a little relevant example with NAs
        key1 = ['bar', 'bar', 'bar', 'foo', 'foo', 'baz', 'baz', 'qux',
                'qux', 'snap']
        key2 = ['two', 'one', 'three', 'one', 'two', 'one', 'two', 'two',
                'three', 'one']

        data = np.random.randn(len(key1))
        self.data = DataFrame({'key1': key1, 'key2': key2,
                               'data': data}) 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
def setup_method(self, method):
        # aggregate multiple columns
        self.df = DataFrame({'key1': get_test_data(),
                             'key2': get_test_data(),
                             'data1': np.random.randn(N),
                             'data2': np.random.randn(N)})

        # exclude a couple keys for fun
        self.df = self.df[self.df['key2'] > 1]

        self.df2 = DataFrame({'key1': get_test_data(n=N // 5),
                              'key2': get_test_data(ngroups=NGROUPS // 2,
                                                    n=N // 5),
                              'value': np.random.randn(N // 5)})

        index, data = tm.getMixedTypeDict()
        self.target = DataFrame(data, index=index)

        # Join on string value
        self.source = DataFrame({'MergedA': data['A'], 'MergedD': data['D']},
                                index=data['C']) 

# 需要導入模塊: from numpy import random [as 別名]
# 或者: from numpy.random import randn [as 別名]
def test_regularize_matrix():
    """Test whether function regularizes matrix correctly."""
    # Generate test matrix
    A = rnd.randn(3)

    # Check for inappropriate input argument
    with pytest.raises(ValueError):
        regularize_matrix(A, a=-1.0)