Python config.option_context方法代码示例

# 需要导入模块: from pandas.core import config [as 别名]
# 或者: from pandas.core.config import option_context [as 别名]
def test_sparse_mi_max_row(self):
        idx = pd.MultiIndex.from_tuples([('A', 0), ('A', 1), ('B', 0),
                                         ('C', 0), ('C', 1), ('C', 2)])
        s = pd.Series([1, np.nan, np.nan, 3, np.nan, np.nan],
                      index=idx).to_sparse()
        result = repr(s)
        dfm = self.dtype_format_for_platform
        exp = ("A  0    1.0\n   1    NaN\nB  0    NaN\n"
               "C  0    3.0\n   1    NaN\n   2    NaN\n"
               "dtype: Sparse[float64, nan]\nBlockIndex\n"
               "Block locations: array([0, 3]{0})\n"
               "Block lengths: array([1, 1]{0})".format(dfm))
        assert result == exp

        with option_context("display.max_rows", 3,
                            "display.show_dimensions", False):
            # GH 13144
            result = repr(s)
            exp = ("A  0    1.0\n       ... \nC  2    NaN\n"
                   "dtype: Sparse[float64, nan]\nBlockIndex\n"
                   "Block locations: array([0, 3]{0})\n"
                   "Block lengths: array([1, 1]{0})".format(dfm))
            assert result == exp 

# 需要导入模块: from pandas.core import config [as 别名]
# 或者: from pandas.core.config import option_context [as 别名]
def test_sparse_bool(self):
        # GH 13110
        s = pd.SparseSeries([True, False, False, True, False, False],
                            fill_value=False)
        result = repr(s)
        dtype = '' if use_32bit_repr else ', dtype=int32'
        exp = ("0     True\n1    False\n2    False\n"
               "3     True\n4    False\n5    False\n"
               "dtype: Sparse[bool, False]\nBlockIndex\n"
               "Block locations: array([0, 3]{0})\n"
               "Block lengths: array([1, 1]{0})".format(dtype))
        assert result == exp

        with option_context("display.max_rows", 3):
            result = repr(s)
            exp = ("0     True\n     ...  \n5    False\n"
                   "Length: 6, dtype: Sparse[bool, False]\nBlockIndex\n"
                   "Block locations: array([0, 3]{0})\n"
                   "Block lengths: array([1, 1]{0})".format(dtype))
            assert result == exp 

# 需要导入模块: from pandas.core import config [as 别名]
# 或者: from pandas.core.config import option_context [as 别名]
def test_sparse_int(self):
        # GH 13110
        s = pd.SparseSeries([0, 1, 0, 0, 1, 0], fill_value=False)

        result = repr(s)
        dtype = '' if use_32bit_repr else ', dtype=int32'
        exp = ("0    0\n1    1\n2    0\n3    0\n4    1\n"
               "5    0\ndtype: Sparse[int64, False]\nBlockIndex\n"
               "Block locations: array([1, 4]{0})\n"
               "Block lengths: array([1, 1]{0})".format(dtype))
        assert result == exp

        with option_context("display.max_rows", 3,
                            "display.show_dimensions", False):
            result = repr(s)
            exp = ("0    0\n    ..\n5    0\n"
                   "dtype: Sparse[int64, False]\nBlockIndex\n"
                   "Block locations: array([1, 4]{0})\n"
                   "Block lengths: array([1, 1]{0})".format(dtype))
            assert result == exp 

# 需要导入模块: from pandas.core import config [as 别名]
# 或者: from pandas.core.config import option_context [as 别名]
def test_option_no_warning(self):
        pytest.importorskip("matplotlib.pyplot")
        ctx = cf.option_context("plotting.matplotlib.register_converters",
                                False)
        plt = pytest.importorskip("matplotlib.pyplot")
        s = Series(range(12), index=date_range('2017', periods=12))
        _, ax = plt.subplots()

        converter._WARN = True
        # Test without registering first, no warning
        with ctx:
            with tm.assert_produces_warning(None) as w:
                ax.plot(s.index, s.values)

        assert len(w) == 0

        # Now test with registering
        converter._WARN = True
        register_matplotlib_converters()
        with ctx:
            with tm.assert_produces_warning(None) as w:
                ax.plot(s.index, s.values)

        assert len(w) == 0 

# 需要导入模块: from pandas.core import config [as 别名]
# 或者: from pandas.core.config import option_context [as 别名]
def test_transform_mixed_type():
    index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3]
                                    ])
    df = DataFrame({'d': [1., 1., 1., 2., 2., 2.],
                    'c': np.tile(['a', 'b', 'c'], 2),
                    'v': np.arange(1., 7.)}, index=index)

    def f(group):
        group['g'] = group['d'] * 2
        return group[:1]

    grouped = df.groupby('c')
    result = grouped.apply(f)

    assert result['d'].dtype == np.float64

    # this is by definition a mutating operation!
    with option_context('mode.chained_assignment', None):
        for key, group in grouped:
            res = f(group)
            assert_frame_equal(res, result.loc[key]) 

# 需要导入模块: from pandas.core import config [as 别名]
# 或者: from pandas.core.config import option_context [as 别名]
def test_publishes(self):

        df = pd.DataFrame({"A": [1, 2]})
        objects = [df['A'], df, df]  # dataframe / series
        expected_keys = [
            {'text/plain', 'application/vnd.dataresource+json'},
            {'text/plain', 'text/html', 'application/vnd.dataresource+json'},
        ]

        opt = pd.option_context('display.html.table_schema', True)
        for obj, expected in zip(objects, expected_keys):
            with opt:
                formatted = self.display_formatter.format(obj)
            assert set(formatted[0].keys()) == expected

        with_latex = pd.option_context('display.latex.repr', True)

        with opt, with_latex:
            formatted = self.display_formatter.format(obj)

        expected = {'text/plain', 'text/html', 'text/latex',
                    'application/vnd.dataresource+json'}
        assert set(formatted[0].keys()) == expected 

# 需要导入模块: from pandas.core import config [as 别名]
# 或者: from pandas.core.config import option_context [as 别名]
def test_enable_data_resource_formatter(self):
        # GH 10491
        formatters = self.display_formatter.formatters
        mimetype = 'application/vnd.dataresource+json'

        with pd.option_context('display.html.table_schema', True):
            assert 'application/vnd.dataresource+json' in formatters
            assert formatters[mimetype].enabled

        # still there, just disabled
        assert 'application/vnd.dataresource+json' in formatters
        assert not formatters[mimetype].enabled

        # able to re-set
        with pd.option_context('display.html.table_schema', True):
            assert 'application/vnd.dataresource+json' in formatters
            assert formatters[mimetype].enabled
            # smoke test that it works
            self.display_formatter.format(cf) 

# 需要导入模块: from pandas.core import config [as 别名]
# 或者: from pandas.core.config import option_context [as 别名]
def test_sparse_max_row(self):
        s = pd.Series([1, np.nan, np.nan, 3, np.nan]).to_sparse()
        result = repr(s)
        dfm = self.dtype_format_for_platform
        exp = ("0    1.0\n1    NaN\n2    NaN\n3    3.0\n"
               "4    NaN\ndtype: float64\nBlockIndex\n"
               "Block locations: array([0, 3]{0})\n"
               "Block lengths: array([1, 1]{0})".format(dfm))
        assert result == exp

        with option_context("display.max_rows", 3):
            # GH 10560
            result = repr(s)
            exp = ("0    1.0\n    ... \n4    NaN\n"
                   "Length: 5, dtype: float64\nBlockIndex\n"
                   "Block locations: array([0, 3]{0})\n"
                   "Block lengths: array([1, 1]{0})".format(dfm))
            assert result == exp 

# 需要导入模块: from pandas.core import config [as 别名]
# 或者: from pandas.core.config import option_context [as 别名]
def test_sparse_mi_max_row(self):
        idx = pd.MultiIndex.from_tuples([('A', 0), ('A', 1), ('B', 0),
                                         ('C', 0), ('C', 1), ('C', 2)])
        s = pd.Series([1, np.nan, np.nan, 3, np.nan, np.nan],
                      index=idx).to_sparse()
        result = repr(s)
        dfm = self.dtype_format_for_platform
        exp = ("A  0    1.0\n   1    NaN\nB  0    NaN\n"
               "C  0    3.0\n   1    NaN\n   2    NaN\n"
               "dtype: float64\nBlockIndex\n"
               "Block locations: array([0, 3]{0})\n"
               "Block lengths: array([1, 1]{0})".format(dfm))
        assert result == exp

        with option_context("display.max_rows", 3,
                            "display.show_dimensions", False):
            # GH 13144
            result = repr(s)
            exp = ("A  0    1.0\n       ... \nC  2    NaN\n"
                   "dtype: float64\nBlockIndex\n"
                   "Block locations: array([0, 3]{0})\n"
                   "Block lengths: array([1, 1]{0})".format(dfm))
            assert result == exp 

# 需要导入模块: from pandas.core import config [as 别名]
# 或者: from pandas.core.config import option_context [as 别名]
def test_sparse_bool(self):
        # GH 13110
        s = pd.SparseSeries([True, False, False, True, False, False],
                            fill_value=False)
        result = repr(s)
        dtype = '' if use_32bit_repr else ', dtype=int32'
        exp = ("0     True\n1    False\n2    False\n"
               "3     True\n4    False\n5    False\n"
               "dtype: bool\nBlockIndex\n"
               "Block locations: array([0, 3]{0})\n"
               "Block lengths: array([1, 1]{0})".format(dtype))
        assert result == exp

        with option_context("display.max_rows", 3):
            result = repr(s)
            exp = ("0     True\n     ...  \n5    False\n"
                   "Length: 6, dtype: bool\nBlockIndex\n"
                   "Block locations: array([0, 3]{0})\n"
                   "Block lengths: array([1, 1]{0})".format(dtype))
            assert result == exp 

# 需要导入模块: from pandas.core import config [as 别名]
# 或者: from pandas.core.config import option_context [as 别名]
def test_sparse_int(self):
        # GH 13110
        s = pd.SparseSeries([0, 1, 0, 0, 1, 0], fill_value=False)

        result = repr(s)
        dtype = '' if use_32bit_repr else ', dtype=int32'
        exp = ("0    0\n1    1\n2    0\n3    0\n4    1\n"
               "5    0\ndtype: int64\nBlockIndex\n"
               "Block locations: array([1, 4]{0})\n"
               "Block lengths: array([1, 1]{0})".format(dtype))
        assert result == exp

        with option_context("display.max_rows", 3,
                            "display.show_dimensions", False):
            result = repr(s)
            exp = ("0    0\n    ..\n5    0\n"
                   "dtype: int64\nBlockIndex\n"
                   "Block locations: array([1, 4]{0})\n"
                   "Block lengths: array([1, 1]{0})".format(dtype))
            assert result == exp 

# 需要导入模块: from pandas.core import config [as 别名]
# 或者: from pandas.core.config import option_context [as 别名]
def apply(self, func, *args, **kwargs):

        func = self._is_builtin_func(func)

        # this is needed so we don't try and wrap strings. If we could
        # resolve functions to their callable functions prior, this
        # wouldn't be needed
        if args or kwargs:
            if callable(func):

                @wraps(func)
                def f(g):
                    with np.errstate(all='ignore'):
                        return func(g, *args, **kwargs)
            else:
                raise ValueError('func must be a callable if args or '
                                 'kwargs are supplied')
        else:
            f = func

        # ignore SettingWithCopy here in case the user mutates
        with option_context('mode.chained_assignment', None):
            return self._python_apply_general(f) 

# 需要导入模块: from pandas.core import config [as 别名]
# 或者: from pandas.core.config import option_context [as 别名]
def test_transform_mixed_type(self):
        index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3]
                                        ])
        df = DataFrame({'d': [1., 1., 1., 2., 2., 2.],
                        'c': np.tile(['a', 'b', 'c'], 2),
                        'v': np.arange(1., 7.)}, index=index)

        def f(group):
            group['g'] = group['d'] * 2
            return group[:1]

        grouped = df.groupby('c')
        result = grouped.apply(f)

        assert result['d'].dtype == np.float64

        # this is by definition a mutating operation!
        with option_context('mode.chained_assignment', None):
            for key, group in grouped:
                res = f(group)
                assert_frame_equal(res, result.loc[key])