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How to use test_random_partitions method in stestr

Best Python code snippet using stestr_python

test_dataframe.py

Source:test_dataframe.py

...805    loads = pickle.loads806    e = d.groupby(d.a).b.sum()807    f = loads(dumps(e))808    assert eq(e, f)809def test_random_partitions():810    a, b = d.random_split([0.5, 0.5])811    assert isinstance(a, dd.DataFrame)812    assert isinstance(b, dd.DataFrame)813    assert len(a.compute()) + len(b.compute()) == len(full)814def test_series_nunique():815    ps = pd.Series(list('aaabbccccdddeee'), name='a')816    s = dd.from_pandas(ps, npartitions=3)817    assert eq(s.nunique(), ps.nunique())818def test_set_partition_2():819    df = pd.DataFrame({'x': [1, 2, 3, 4, 5, 6], 'y': list('abdabd')})820    ddf = dd.from_pandas(df, 2)821    result = ddf.set_partition('y', ['a', 'c', 'd'])822    assert result.divisions == ('a', 'c', 'd')823    assert list(result.compute(get=get_sync).index[-2:]) == ['d', 'd']...

test_scheduler.py

Source:test_scheduler.py

...43        self.assertTrue('fast1' in partitions[1])44        self.assertTrue('fast2' in partitions[1])45        self.assertEqual(3, len(partitions[0]))46        self.assertEqual(4, len(partitions[1]))47    def test_random_partitions(self):48        repo = memory.RepositoryFactory().initialise('memory:')49        test_ids = frozenset(['a_test', 'b_test', 'c_test', 'd_test'])50        random_parts = scheduler.partition_tests(test_ids, 2, repo, None,51                                                 randomize=True)52        # NOTE(masayukig): We can't test this randomness. So just checking53        # what we should get here.54        self.assertEqual(2, len(random_parts))55        self.assertTrue(isinstance(random_parts, list))56        self.assertTrue(isinstance(random_parts[0], list))57        self.assertTrue(isinstance(random_parts[1], list))58        flatten_random_parts = []59        for i, j in random_parts:60            flatten_random_parts.append(i)61            flatten_random_parts.append(j)...

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