python类option_context()的实例源码

def summary(self):
        """
        Print the summary of the Process model.
        :return: None
        """
        with pd.option_context("precision", self.options["precision"]):
            full_model = self.outcome_models[self.iv]
            m_models = [self.outcome_models.get(med_name) for med_name in self.mediators]
            if self.options["detail"]:
                print("\n***************************** OUTCOME MODELS ****************************\n")
                print(full_model)
                print("\n-------------------------------------------------------------------------\n")
                for med_model in m_models:
                    print(med_model)
                    print("\n-------------------------------------------------------------------------\n")
            if self.indirect_model:
                print("\n********************** DIRECT AND INDIRECT EFFECTS **********************\n")
                print(self.direct_model)
                print(self.indirect_model)
            else:
                print("\n********************** CONDITIONAL EFFECTS **********************\n")
                print(self.direct_model)

def test_show_null_counts(self):

        df = DataFrame(1, columns=range(10), index=range(10))
        df.iloc[1, 1] = np.nan

        def check(null_counts, result):
            buf = StringIO()
            df.info(buf=buf, null_counts=null_counts)
            self.assertTrue(('non-null' in buf.getvalue()) is result)

        with option_context('display.max_info_rows', 20,
                            'display.max_info_columns', 20):
            check(None, True)
            check(True, True)
            check(False, False)

        with option_context('display.max_info_rows', 5,
                            'display.max_info_columns', 5):
            check(None, False)
            check(True, False)
            check(False, False)

def test_repr_truncation(self):
        max_len = 20
        with option_context("display.max_colwidth", max_len):
            df = DataFrame({'A': np.random.randn(10),
                            'B': [tm.rands(np.random.randint(
                                max_len - 1, max_len + 1)) for i in range(10)
            ]})
            r = repr(df)
            r = r[r.find('\n') + 1:]

            adj = fmt._get_adjustment()

            for line, value in lzip(r.split('\n'), df['B']):
                if adj.len(value) + 1 > max_len:
                    self.assertIn('...', line)
                else:
                    self.assertNotIn('...', line)

        with option_context("display.max_colwidth", 999999):
            self.assertNotIn('...', repr(df))

        with option_context("display.max_colwidth", max_len + 2):
            self.assertNotIn('...', repr(df))

def test_expand_frame_repr(self):
        df_small = DataFrame('hello', [0], [0])
        df_wide = DataFrame('hello', [0], lrange(10))
        df_tall = DataFrame('hello', lrange(30), lrange(5))

        with option_context('mode.sim_interactive', True):
            with option_context('display.max_columns', 10, 'display.width', 20,
                                'display.max_rows', 20,
                                'display.show_dimensions', True):
                with option_context('display.expand_frame_repr', True):
                    self.assertFalse(has_truncated_repr(df_small))
                    self.assertFalse(has_expanded_repr(df_small))
                    self.assertFalse(has_truncated_repr(df_wide))
                    self.assertTrue(has_expanded_repr(df_wide))
                    self.assertTrue(has_vertically_truncated_repr(df_tall))
                    self.assertTrue(has_expanded_repr(df_tall))

                with option_context('display.expand_frame_repr', False):
                    self.assertFalse(has_truncated_repr(df_small))
                    self.assertFalse(has_expanded_repr(df_small))
                    self.assertFalse(has_horizontally_truncated_repr(df_wide))
                    self.assertFalse(has_expanded_repr(df_wide))
                    self.assertTrue(has_vertically_truncated_repr(df_tall))
                    self.assertFalse(has_expanded_repr(df_tall))

def test_str_max_colwidth(self):
        # GH 7856
        df = pd.DataFrame([{'a': 'foo',
                            'b': 'bar',
                            'c': 'uncomfortably long line with lots of stuff',
                            'd': 1}, {'a': 'foo',
                                      'b': 'bar',
                                      'c': 'stuff',
                                      'd': 1}])
        df.set_index(['a', 'b', 'c'])
        self.assertTrue(
            str(df) ==
            '     a    b                                           c  d\n'
            '0  foo  bar  uncomfortably long line with lots of stuff  1\n'
            '1  foo  bar                                       stuff  1')
        with option_context('max_colwidth', 20):
            self.assertTrue(str(df) == '     a    b                    c  d\n'
                            '0  foo  bar  uncomfortably lo...  1\n'
                            '1  foo  bar                stuff  1')

def test_wide_repr(self):
        with option_context('mode.sim_interactive', True,
                            'display.show_dimensions', True):
            max_cols = get_option('display.max_columns')
            df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1)))
            set_option('display.expand_frame_repr', False)
            rep_str = repr(df)

            assert "10 rows x %d columns" % (max_cols - 1) in rep_str
            set_option('display.expand_frame_repr', True)
            wide_repr = repr(df)
            self.assertNotEqual(rep_str, wide_repr)

            with option_context('display.width', 120):
                wider_repr = repr(df)
                self.assertTrue(len(wider_repr) < len(wide_repr))

        reset_option('display.expand_frame_repr')

def test_wide_repr_named(self):
        with option_context('mode.sim_interactive', True):
            max_cols = get_option('display.max_columns')
            df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1)))
            df.index.name = 'DataFrame Index'
            set_option('display.expand_frame_repr', False)

            rep_str = repr(df)
            set_option('display.expand_frame_repr', True)
            wide_repr = repr(df)
            self.assertNotEqual(rep_str, wide_repr)

            with option_context('display.width', 150):
                wider_repr = repr(df)
                self.assertTrue(len(wider_repr) < len(wide_repr))

            for line in wide_repr.splitlines()[1::13]:
                self.assertIn('DataFrame Index', line)

        reset_option('display.expand_frame_repr')

def test_wide_repr_multiindex_cols(self):
        with option_context('mode.sim_interactive', True):
            max_cols = get_option('display.max_columns')
            midx = MultiIndex.from_arrays(tm.rands_array(5, size=(2, 10)))
            mcols = MultiIndex.from_arrays(tm.rands_array(3, size=(2, max_cols
                                                                   - 1)))
            df = DataFrame(tm.rands_array(25, (10, max_cols - 1)),
                           index=midx, columns=mcols)
            df.index.names = ['Level 0', 'Level 1']
            set_option('display.expand_frame_repr', False)
            rep_str = repr(df)
            set_option('display.expand_frame_repr', True)
            wide_repr = repr(df)
            self.assertNotEqual(rep_str, wide_repr)

        with option_context('display.width', 150):
            wider_repr = repr(df)
            self.assertTrue(len(wider_repr) < len(wide_repr))

        reset_option('display.expand_frame_repr')

def test_show_dimensions(self):
        df = DataFrame(123, lrange(10, 15), lrange(30))

        with option_context('display.max_rows', 10, 'display.max_columns', 40,
                            'display.width', 500, 'display.expand_frame_repr',
                            'info', 'display.show_dimensions', True):
            self.assertTrue('5 rows' in str(df))
            self.assertTrue('5 rows' in df._repr_html_())
        with option_context('display.max_rows', 10, 'display.max_columns', 40,
                            'display.width', 500, 'display.expand_frame_repr',
                            'info', 'display.show_dimensions', False):
            self.assertFalse('5 rows' in str(df))
            self.assertFalse('5 rows' in df._repr_html_())
        with option_context('display.max_rows', 2, 'display.max_columns', 2,
                            'display.width', 500, 'display.expand_frame_repr',
                            'info', 'display.show_dimensions', 'truncate'):
            self.assertTrue('5 rows' in str(df))
            self.assertTrue('5 rows' in df._repr_html_())
        with option_context('display.max_rows', 10, 'display.max_columns', 40,
                            'display.width', 500, 'display.expand_frame_repr',
                            'info', 'display.show_dimensions', 'truncate'):
            self.assertFalse('5 rows' in str(df))
            self.assertFalse('5 rows' in df._repr_html_())

def test_info_repr(self):
        max_rows = get_option('display.max_rows')
        max_cols = get_option('display.max_columns')
        # Long
        h, w = max_rows + 1, max_cols - 1
        df = DataFrame(dict((k, np.arange(1, 1 + h)) for k in np.arange(w)))
        assert has_vertically_truncated_repr(df)
        with option_context('display.large_repr', 'info'):
            assert has_info_repr(df)

        # Wide
        h, w = max_rows - 1, max_cols + 1
        df = DataFrame(dict((k, np.arange(1, 1 + h)) for k in np.arange(w)))
        assert has_horizontally_truncated_repr(df)
        with option_context('display.large_repr', 'info'):
            assert has_info_repr(df)

def test_info_repr_html(self):
        max_rows = get_option('display.max_rows')
        max_cols = get_option('display.max_columns')
        # Long
        h, w = max_rows + 1, max_cols - 1
        df = DataFrame(dict((k, np.arange(1, 1 + h)) for k in np.arange(w)))
        assert r'<class' not in df._repr_html_()
        with option_context('display.large_repr', 'info'):
            assert r'<class' in df._repr_html_()

        # Wide
        h, w = max_rows - 1, max_cols + 1
        df = DataFrame(dict((k, np.arange(1, 1 + h)) for k in np.arange(w)))
        assert '<class' not in df._repr_html_()
        with option_context('display.large_repr', 'info'):
            assert '&lt;class' in df._repr_html_()

def test_format_explicit(self):
        test_sers = self.gen_test_series()
        with option_context("display.max_rows", 4):
            res = repr(test_sers['onel'])
            exp = '0     a\n1     a\n     ..\n98    a\n99    a\ndtype: object'
            self.assertEqual(exp, res)
            res = repr(test_sers['twol'])
            exp = ('0     ab\n1     ab\n      ..\n98    ab\n99    ab\ndtype:'
                   ' object')
            self.assertEqual(exp, res)
            res = repr(test_sers['asc'])
            exp = ('0         a\n1        ab\n      ...  \n4     abcde\n5'
                   '    abcdef\ndtype: object')
            self.assertEqual(exp, res)
            res = repr(test_sers['desc'])
            exp = ('5    abcdef\n4     abcde\n      ...  \n1        ab\n0'
                   '         a\ndtype: object')
            self.assertEqual(exp, res)

def df_to_html(df, border=0, classes=('table', 'table-striped', 'table-hover'),
               **kwargs):
    """Convert a dataframe to HTML without truncating contents.

    pandas will truncate cell contents that exceed 50 characters by default.
    Use this function to avoid this truncation behavior.

    This function uses different default parameters than `DataFrame.to_html` to
    give uniform styling to HTML tables that are compatible with q2template
    themes. These parameters can be overridden, and they (along with any other
    parameters) will be passed through to `DataFrame.to_html`.

    Parameters
    ----------
    df : pd.DataFrame
        DataFrame to convert to HTML.
    kwargs : dict
        Parameters passed through to `pd.DataFrame.to_html`.

    Returns
    -------
    str
        DataFrame converted to HTML.

    References
    ----------
    .. [1] https://stackoverflow.com/q/26277757/3776794
    .. [2] https://github.com/pandas-dev/pandas/issues/1852

    """
    with pd.option_context('display.max_colwidth', -1):
        return df.to_html(border=border, classes=classes, **kwargs)

def test_representation(self):

        idx = []
        idx.append(DatetimeIndex([], freq='D'))
        idx.append(DatetimeIndex(['2011-01-01'], freq='D'))
        idx.append(DatetimeIndex(['2011-01-01', '2011-01-02'], freq='D'))
        idx.append(DatetimeIndex(
            ['2011-01-01', '2011-01-02', '2011-01-03'], freq='D'))
        idx.append(DatetimeIndex(
            ['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00'
             ], freq='H', tz='Asia/Tokyo'))
        idx.append(DatetimeIndex(
            ['2011-01-01 09:00', '2011-01-01 10:00', pd.NaT], tz='US/Eastern'))
        idx.append(DatetimeIndex(
            ['2011-01-01 09:00', '2011-01-01 10:00', pd.NaT], tz='UTC'))

        exp = []
        exp.append("""DatetimeIndex([], dtype='datetime64[ns]', freq='D')""")
        exp.append("DatetimeIndex(['2011-01-01'], dtype='datetime64[ns]', "
                   "freq='D')")
        exp.append("DatetimeIndex(['2011-01-01', '2011-01-02'], "
                   "dtype='datetime64[ns]', freq='D')")
        exp.append("DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03'], "
                   "dtype='datetime64[ns]', freq='D')")
        exp.append("DatetimeIndex(['2011-01-01 09:00:00+09:00', "
                   "'2011-01-01 10:00:00+09:00', '2011-01-01 11:00:00+09:00']"
                   ", dtype='datetime64[ns, Asia/Tokyo]', freq='H')")
        exp.append("DatetimeIndex(['2011-01-01 09:00:00-05:00', "
                   "'2011-01-01 10:00:00-05:00', 'NaT'], "
                   "dtype='datetime64[ns, US/Eastern]', freq=None)")
        exp.append("DatetimeIndex(['2011-01-01 09:00:00+00:00', "
                   "'2011-01-01 10:00:00+00:00', 'NaT'], "
                   "dtype='datetime64[ns, UTC]', freq=None)""")

        with pd.option_context('display.width', 300):
            for indx, expected in zip(idx, exp):
                for func in ['__repr__', '__unicode__', '__str__']:
                    result = getattr(indx, func)()
                    self.assertEqual(result, expected)

def test_representation(self):
        idx1 = TimedeltaIndex([], freq='D')
        idx2 = TimedeltaIndex(['1 days'], freq='D')
        idx3 = TimedeltaIndex(['1 days', '2 days'], freq='D')
        idx4 = TimedeltaIndex(['1 days', '2 days', '3 days'], freq='D')
        idx5 = TimedeltaIndex(['1 days 00:00:01', '2 days', '3 days'])

        exp1 = """TimedeltaIndex([], dtype='timedelta64[ns]', freq='D')"""

        exp2 = ("TimedeltaIndex(['1 days'], dtype='timedelta64[ns]', "
                "freq='D')")

        exp3 = ("TimedeltaIndex(['1 days', '2 days'], "
                "dtype='timedelta64[ns]', freq='D')")

        exp4 = ("TimedeltaIndex(['1 days', '2 days', '3 days'], "
                "dtype='timedelta64[ns]', freq='D')")

        exp5 = ("TimedeltaIndex(['1 days 00:00:01', '2 days 00:00:00', "
                "'3 days 00:00:00'], dtype='timedelta64[ns]', freq=None)")

        with pd.option_context('display.width', 300):
            for idx, expected in zip([idx1, idx2, idx3, idx4, idx5],
                                     [exp1, exp2, exp3, exp4, exp5]):
                for func in ['__repr__', '__unicode__', '__str__']:
                    result = getattr(idx, func)()
                    self.assertEqual(result, expected)

def test_representation_to_series(self):
        idx1 = TimedeltaIndex([], freq='D')
        idx2 = TimedeltaIndex(['1 days'], freq='D')
        idx3 = TimedeltaIndex(['1 days', '2 days'], freq='D')
        idx4 = TimedeltaIndex(['1 days', '2 days', '3 days'], freq='D')
        idx5 = TimedeltaIndex(['1 days 00:00:01', '2 days', '3 days'])

        exp1 = """Series([], dtype: timedelta64[ns])"""

        exp2 = """0   1 days
dtype: timedelta64[ns]"""

        exp3 = """0   1 days
1   2 days
dtype: timedelta64[ns]"""

        exp4 = """0   1 days
1   2 days
2   3 days
dtype: timedelta64[ns]"""

        exp5 = """0   1 days 00:00:01
1   2 days 00:00:00
2   3 days 00:00:00
dtype: timedelta64[ns]"""

        with pd.option_context('display.width', 300):
            for idx, expected in zip([idx1, idx2, idx3, idx4, idx5],
                                     [exp1, exp2, exp3, exp4, exp5]):
                result = repr(pd.Series(idx))
                self.assertEqual(result, expected)

def test_repr_max_seq_item_setting(self):
        # GH10182
        idx = self.create_index()
        idx = idx.repeat(50)
        with pd.option_context("display.max_seq_items", None):
            repr(idx)
            self.assertFalse('...' in str(idx))

def test_isnull_for_inf(self):
        s = Series(['a', np.inf, np.nan, 1.0])
        with pd.option_context('mode.use_inf_as_null', True):
            r = s.isnull()
            dr = s.dropna()
        e = Series([False, True, True, False])
        de = Series(['a', 1.0], index=[0, 3])
        tm.assert_series_equal(r, e)
        tm.assert_series_equal(dr, de)

def test_repr_chop_threshold(self):
        df = DataFrame([[0.1, 0.5], [0.5, -0.1]])
        pd.reset_option("display.chop_threshold")  # default None
        self.assertEqual(repr(df), '     0    1\n0  0.1  0.5\n1  0.5 -0.1')

        with option_context("display.chop_threshold", 0.2):
            self.assertEqual(repr(df), '     0    1\n0  0.0  0.5\n1  0.5  0.0')

        with option_context("display.chop_threshold", 0.6):
            self.assertEqual(repr(df), '     0    1\n0  0.0  0.0\n1  0.0  0.0')

        with option_context("display.chop_threshold", None):
            self.assertEqual(repr(df), '     0    1\n0  0.1  0.5\n1  0.5 -0.1')

def test_repr_obeys_max_seq_limit(self):
        with option_context("display.max_seq_items", 2000):
            self.assertTrue(len(com.pprint_thing(lrange(1000))) > 1000)

        with option_context("display.max_seq_items", 5):
            self.assertTrue(len(com.pprint_thing(lrange(1000))) < 100)

def test_repr_non_interactive(self):
        # in non interactive mode, there can be no dependency on the
        # result of terminal auto size detection
        df = DataFrame('hello', lrange(1000), lrange(5))

        with option_context('mode.sim_interactive', False, 'display.width', 0,
                            'display.height', 0, 'display.max_rows', 5000):
            self.assertFalse(has_truncated_repr(df))
            self.assertFalse(has_expanded_repr(df))

def test_auto_detect(self):
        term_width, term_height = get_terminal_size()
        fac = 1.05  # Arbitrary large factor to exceed term widht
        cols = range(int(term_width * fac))
        index = range(10)
        df = DataFrame(index=index, columns=cols)
        with option_context('mode.sim_interactive', True):
            with option_context('max_rows', None):
                with option_context('max_columns', None):
                    # Wrap around with None
                    self.assertTrue(has_expanded_repr(df))
            with option_context('max_rows', 0):
                with option_context('max_columns', 0):
                    # Truncate with auto detection.
                    self.assertTrue(has_horizontally_truncated_repr(df))

            index = range(int(term_height * fac))
            df = DataFrame(index=index, columns=cols)
            with option_context('max_rows', 0):
                with option_context('max_columns', None):
                    # Wrap around with None
                    self.assertTrue(has_expanded_repr(df))
                    # Truncate vertically
                    self.assertTrue(has_vertically_truncated_repr(df))

            with option_context('max_rows', None):
                with option_context('max_columns', 0):
                    self.assertTrue(has_horizontally_truncated_repr(df))

def test_to_string_truncate_indices(self):
        for index in [tm.makeStringIndex, tm.makeUnicodeIndex, tm.makeIntIndex,
                      tm.makeDateIndex, tm.makePeriodIndex]:
            for column in [tm.makeStringIndex]:
                for h in [10, 20]:
                    for w in [10, 20]:
                        with option_context("display.expand_frame_repr",
                                            False):
                            df = DataFrame(index=index(h), columns=column(w))
                            with option_context("display.max_rows", 15):
                                if h == 20:
                                    self.assertTrue(
                                        has_vertically_truncated_repr(df))
                                else:
                                    self.assertFalse(
                                        has_vertically_truncated_repr(df))
                            with option_context("display.max_columns", 15):
                                if w == 20:
                                    self.assertTrue(
                                        has_horizontally_truncated_repr(df))
                                else:
                                    self.assertFalse(
                                        has_horizontally_truncated_repr(df))
                            with option_context("display.max_rows", 15,
                                                "display.max_columns", 15):
                                if h == 20 and w == 20:
                                    self.assertTrue(has_doubly_truncated_repr(
                                        df))
                                else:
                                    self.assertFalse(has_doubly_truncated_repr(
                                        df))

def test_to_string_truncate_multilevel(self):
        arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
                  ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        df = DataFrame(index=arrays, columns=arrays)
        with option_context("display.max_rows", 7, "display.max_columns", 7):
            self.assertTrue(has_doubly_truncated_repr(df))

def test_wide_repr_wide_columns(self):
        with option_context('mode.sim_interactive', True):
            df = DataFrame(randn(5, 3), columns=['a' * 90, 'b' * 90, 'c' * 90])
            rep_str = repr(df)

            self.assertEqual(len(rep_str.splitlines()), 20)

def test_wide_repr_unicode(self):
        with option_context('mode.sim_interactive', True):
            max_cols = get_option('display.max_columns')
            df = DataFrame(tm.rands_array(25, size=(10, max_cols - 1)))
            set_option('display.expand_frame_repr', False)
            rep_str = repr(df)
            set_option('display.expand_frame_repr', True)
            wide_repr = repr(df)
            self.assertNotEqual(rep_str, wide_repr)

            with option_context('display.width', 150):
                wider_repr = repr(df)
                self.assertTrue(len(wider_repr) < len(wide_repr))

        reset_option('display.expand_frame_repr')

def test_wide_repr_wide_long_columns(self):
        with option_context('mode.sim_interactive', True):
            df = DataFrame({'a': ['a' * 30, 'b' * 30],
                            'b': ['c' * 70, 'd' * 80]})

            result = repr(df)
            self.assertTrue('ccccc' in result)
            self.assertTrue('ddddd' in result)

def test_max_multi_index_display(self):
        # GH 7101

        # doc example (indexing.rst)

        # multi-index
        arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
                  ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        tuples = list(zip(*arrays))
        index = MultiIndex.from_tuples(tuples, names=['first', 'second'])
        s = Series(randn(8), index=index)

        with option_context("display.max_rows", 10):
            self.assertEqual(len(str(s).split('\n')), 10)
        with option_context("display.max_rows", 3):
            self.assertEqual(len(str(s).split('\n')), 5)
        with option_context("display.max_rows", 2):
            self.assertEqual(len(str(s).split('\n')), 5)
        with option_context("display.max_rows", 1):
            self.assertEqual(len(str(s).split('\n')), 4)
        with option_context("display.max_rows", 0):
            self.assertEqual(len(str(s).split('\n')), 10)

        # index
        s = Series(randn(8), None)

        with option_context("display.max_rows", 10):
            self.assertEqual(len(str(s).split('\n')), 9)
        with option_context("display.max_rows", 3):
            self.assertEqual(len(str(s).split('\n')), 4)
        with option_context("display.max_rows", 2):
            self.assertEqual(len(str(s).split('\n')), 4)
        with option_context("display.max_rows", 1):
            self.assertEqual(len(str(s).split('\n')), 3)
        with option_context("display.max_rows", 0):
            self.assertEqual(len(str(s).split('\n')), 9)

    # Make sure #8532 is fixed

def test_consistent_format(self):
        s = pd.Series([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.9999, 1, 1] * 10)
        with option_context("display.max_rows", 10):
            res = repr(s)
        exp = ('0      1.0000\n1      1.0000\n2      1.0000\n3      '
               '1.0000\n4      1.0000\n        ...  \n125    '
               '1.0000\n126    1.0000\n127    0.9999\n128    '
               '1.0000\n129    1.0000\ndtype: float64')
        self.assertEqual(res, exp)

def chck_ncols(self, s):
        with option_context("display.max_rows", 10):
            res = repr(s)
        lines = res.split('\n')
        lines = [line for line in repr(s).split('\n')
                 if not re.match('[^\.]*\.+', line)][:-1]
        ncolsizes = len(set(len(line.strip()) for line in lines))
        self.assertEqual(ncolsizes, 1)