示例#1
0
def test_set_options():
    def get_setting():
        return settings.formatting.get('nrows')
    assert get_setting() is None
    val = 13
    with set_options(formatting={'nrows': val}):
        assert get_setting() == val
    assert get_setting() is None
示例#2
0
def test_set_options():
    def get_setting():
        return settings.formatting.get('nrows')

    assert get_setting() is None
    val = 13
    with set_options(formatting={'nrows': val}):
        assert get_setting() == val
    assert get_setting() is None
示例#3
0
def test_dataframe_to_string():
    with set_options(formatting={'nrows': 5, 'ncols': 8}):
        # Test basic
        df = DataFrame([('a', [1, 2, 3, 4, 5, 6]),
                        ('b', [11, 12, 13, 14, 15, 16])])
        string = str(df)
        print(string)
        assert string.splitlines()[-1] == '[1 more rows]'

        # Test skipped columns
        df = DataFrame([('a', [1, 2, 3, 4, 5, 6]),
                        ('b', [11, 12, 13, 14, 15, 16]),
                        ('c', [11, 12, 13, 14, 15, 16]),
                        ('d', [11, 12, 13, 14, 15, 16])])
        string = df.to_string(ncols=3)
        print(string)
        assert string.splitlines()[-2] == '[1 more rows]'
        assert string.splitlines()[-1] == '[1 more columns]'

        # Test masked
        df = DataFrame([('a', [1, 2, 3, 4, 5, 6]),
                        ('b', [11, 12, 13, 14, 15, 16])])

        data = np.arange(6)
        mask = np.zeros(1, dtype=np.uint8)
        mask[0] = 0b00101101

        masked = Series.from_masked_array(data, mask)
        assert masked.null_count == 2
        df['c'] = masked

        # check data
        values = list(masked)
        validids = [0, 2, 3, 5]
        densearray = masked.to_array()
        np.testing.assert_equal(data[validids], densearray)
        # valid position is corret
        for i in validids:
            assert data[i] == values[i]
        # null position is correct
        for i in range(len(values)):
            if i not in validids:
                assert values[i] is None

        got = df.to_string(nrows=None)
        print(got)
        expect = '''
  a b  c
0 1 11 0
1 2 12
2 3 13 2
3 4 14 3
4 5 15
5 6 16 5
'''
        # values should match despite whitespace difference
        assert got.split() == expect.split()