Python compat.OrderedDict方法代码示例

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import OrderedDict [as 别名]
def test_aggregate_str_func(tsframe, groupbyfunc):
    grouped = tsframe.groupby(groupbyfunc)

    # single series
    result = grouped['A'].agg('std')
    expected = grouped['A'].std()
    tm.assert_series_equal(result, expected)

    # group frame by function name
    result = grouped.aggregate('var')
    expected = grouped.var()
    tm.assert_frame_equal(result, expected)

    # group frame by function dict
    result = grouped.agg(OrderedDict([['A', 'var'],
                                      ['B', 'std'],
                                      ['C', 'mean'],
                                      ['D', 'sem']]))
    expected = DataFrame(OrderedDict([['A', grouped['A'].var()],
                                      ['B', grouped['B'].std()],
                                      ['C', grouped['C'].mean()],
                                      ['D', grouped['D'].sem()]]))
    tm.assert_frame_equal(result, expected) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import OrderedDict [as 别名]
def _write_cell(self, s, kind='td', indent=0, tags=None):
        if tags is not None:
            start_tag = '<{kind} {tags}>'.format(kind=kind, tags=tags)
        else:
            start_tag = '<{kind}>'.format(kind=kind)

        if self.escape:
            # escape & first to prevent double escaping of &
            esc = OrderedDict([('&', r'&amp;'), ('<', r'&lt;'),
                               ('>', r'&gt;')])
        else:
            esc = {}

        rs = pprint_thing(s, escape_chars=esc).strip()

        if self.render_links and _is_url(rs):
            rs_unescaped = pprint_thing(s, escape_chars={}).strip()
            start_tag += '<a href="{url}" target="_blank">'.format(
                url=rs_unescaped)
            end_a = '</a>'
        else:
            end_a = ''

        self.write(u'{start}{rs}{end_a}</{kind}>'.format(
            start=start_tag, rs=rs, end_a=end_a, kind=kind), indent) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import OrderedDict [as 别名]
def test_ctor_orderedDict(self):
        keys = list(set(np.random.randint(0, 5000, 100)))[
            :50]  # unique random int  keys
        d = OrderedDict([(k, mkdf(10, 5)) for k in keys])
        p = Panel(d)
        assert list(p.items) == keys

        p = Panel.from_dict(d)
        assert list(p.items) == keys 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import OrderedDict [as 别名]
def test_more_flexible_frame_multi_function(df):
    grouped = df.groupby('A')

    exmean = grouped.agg(OrderedDict([['C', np.mean], ['D', np.mean]]))
    exstd = grouped.agg(OrderedDict([['C', np.std], ['D', np.std]]))

    expected = concat([exmean, exstd], keys=['mean', 'std'], axis=1)
    expected = expected.swaplevel(0, 1, axis=1).sort_index(level=0, axis=1)

    d = OrderedDict([['C', [np.mean, np.std]], ['D', [np.mean, np.std]]])
    result = grouped.aggregate(d)

    tm.assert_frame_equal(result, expected)

    # be careful
    result = grouped.aggregate(OrderedDict([['C', np.mean],
                                            ['D', [np.mean, np.std]]]))
    expected = grouped.aggregate(OrderedDict([['C', np.mean],
                                              ['D', [np.mean, np.std]]]))
    tm.assert_frame_equal(result, expected)

    def foo(x):
        return np.mean(x)

    def bar(x):
        return np.std(x, ddof=1)

    # this uses column selection & renaming
    with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
        d = OrderedDict([['C', np.mean],
                         ['D', OrderedDict([['foo', np.mean],
                                            ['bar', np.std]])]])
        result = grouped.aggregate(d)

    d = OrderedDict([['C', [np.mean]], ['D', [foo, bar]]])
    expected = grouped.aggregate(d)

    tm.assert_frame_equal(result, expected) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import OrderedDict [as 别名]
def test_multi_function_flexible_mix(df):
    # GH #1268
    grouped = df.groupby('A')

    # Expected
    d = OrderedDict([['C', OrderedDict([['foo', 'mean'], ['bar', 'std']])],
                     ['D', {'sum': 'sum'}]])
    # this uses column selection & renaming
    with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
        expected = grouped.aggregate(d)

    # Test 1
    d = OrderedDict([['C', OrderedDict([['foo', 'mean'], ['bar', 'std']])],
                     ['D', 'sum']])
    # this uses column selection & renaming
    with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
        result = grouped.aggregate(d)
    tm.assert_frame_equal(result, expected)

    # Test 2
    d = OrderedDict([['C', OrderedDict([['foo', 'mean'], ['bar', 'std']])],
                     ['D', ['sum']]])
    # this uses column selection & renaming
    with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
        result = grouped.aggregate(d)
    tm.assert_frame_equal(result, expected) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import OrderedDict [as 别名]
def _init_dict(self, data, axes, dtype=None):
        haxis = axes.pop(self._info_axis_number)

        # prefilter if haxis passed
        if haxis is not None:
            haxis = ensure_index(haxis)
            data = OrderedDict((k, v)
                               for k, v in compat.iteritems(data)
                               if k in haxis)
        else:
            keys = com.dict_keys_to_ordered_list(data)
            haxis = Index(keys)

        for k, v in compat.iteritems(data):
            if isinstance(v, dict):
                data[k] = self._constructor_sliced(v)

        # extract axis for remaining axes & create the slicemap
        raxes = [self._extract_axis(self, data, axis=i) if a is None else a
                 for i, a in enumerate(axes)]
        raxes_sm = self._extract_axes_for_slice(self, raxes)

        # shallow copy
        arrays = []
        haxis_shape = [len(a) for a in raxes]
        for h in haxis:
            v = values = data.get(h)
            if v is None:
                values = np.empty(haxis_shape, dtype=dtype)
                values.fill(np.nan)
            elif isinstance(v, self._constructor_sliced):
                d = raxes_sm.copy()
                d['copy'] = False
                v = v.reindex(**d)
                if dtype is not None:
                    v = v.astype(dtype)
                values = v.values
            arrays.append(values)

        return self._init_arrays(arrays, haxis, [haxis] + raxes) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import OrderedDict [as 别名]
def from_dict(cls, data, intersect=False, orient='items', dtype=None):
        """
        Construct Panel from dict of DataFrame objects.

        Parameters
        ----------
        data : dict
            {field : DataFrame}
        intersect : boolean
            Intersect indexes of input DataFrames
        orient : {'items', 'minor'}, default 'items'
            The "orientation" of the data. If the keys of the passed dict
            should be the items of the result panel, pass 'items'
            (default). Otherwise if the columns of the values of the passed
            DataFrame objects should be the items (which in the case of
            mixed-dtype data you should do), instead pass 'minor'
        dtype : dtype, default None
            Data type to force, otherwise infer

        Returns
        -------
        Panel
        """
        from collections import defaultdict

        orient = orient.lower()
        if orient == 'minor':
            new_data = defaultdict(OrderedDict)
            for col, df in compat.iteritems(data):
                for item, s in compat.iteritems(df):
                    new_data[item][col] = s
            data = new_data
        elif orient != 'items':  # pragma: no cover
            raise ValueError('Orientation must be one of {items, minor}.')

        d = cls._homogenize_dict(cls, data, intersect=intersect, dtype=dtype)
        ks = list(d['data'].keys())
        if not isinstance(d['data'], OrderedDict):
            ks = list(sorted(ks))
        d[cls._info_axis_name] = Index(ks)
        return cls(**d) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import OrderedDict [as 别名]
def dict_keys_to_ordered_list(mapping):
    # when pandas drops support for Python < 3.6, this function
    # can be replaced by a simple list(mapping.keys())
    if PY36 or isinstance(mapping, OrderedDict):
        keys = list(mapping.keys())
    else:
        keys = try_sort(mapping)
    return keys 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import OrderedDict [as 别名]
def test_ctor_orderedDict(self):
        with catch_warnings(record=True):
            keys = list(set(np.random.randint(0, 5000, 100)))[
                :50]  # unique random int  keys
            d = OrderedDict([(k, mkdf(10, 5)) for k in keys])
            p = Panel(d)
            assert list(p.items) == keys

            p = Panel.from_dict(d)
            assert list(p.items) == keys 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import OrderedDict [as 别名]
def _dict_keys_to_ordered_list(mapping):
    # when pandas drops support for Python < 3.6, this function
    # can be replaced by a simple list(mapping.keys())
    if PY36 or isinstance(mapping, OrderedDict):
        keys = list(mapping.keys())
    else:
        keys = _try_sort(mapping)
    return keys 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import OrderedDict [as 别名]
def _to_recarray(self, data, columns):
        dtypes = []
        o = compat.OrderedDict()

        # use the columns to "order" the keys
        # in the unordered 'data' dictionary
        for col in columns:
            dtypes.append((str(col), data[col].dtype))
            o[col] = data[col]

        tuples = lzip(*o.values())
        return np.array(tuples, dtypes) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import OrderedDict [as 别名]
def test_aggregate_str_func(self):
        def _check_results(grouped):
            # single series
            result = grouped['A'].agg('std')
            expected = grouped['A'].std()
            assert_series_equal(result, expected)

            # group frame by function name
            result = grouped.aggregate('var')
            expected = grouped.var()
            assert_frame_equal(result, expected)

            # group frame by function dict
            result = grouped.agg(OrderedDict([['A', 'var'], ['B', 'std'],
                                              ['C', 'mean'], ['D', 'sem']]))
            expected = DataFrame(OrderedDict([['A', grouped['A'].var(
            )], ['B', grouped['B'].std()], ['C', grouped['C'].mean()],
                ['D', grouped['D'].sem()]]))
            assert_frame_equal(result, expected)

        by_weekday = self.tsframe.groupby(lambda x: x.weekday())
        _check_results(by_weekday)

        by_mwkday = self.tsframe.groupby([lambda x: x.month,
                                          lambda x: x.weekday()])
        _check_results(by_mwkday) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import OrderedDict [as 别名]
def test_more_flexible_frame_multi_function(self):

        grouped = self.df.groupby('A')

        exmean = grouped.agg(OrderedDict([['C', np.mean], ['D', np.mean]]))
        exstd = grouped.agg(OrderedDict([['C', np.std], ['D', np.std]]))

        expected = concat([exmean, exstd], keys=['mean', 'std'], axis=1)
        expected = expected.swaplevel(0, 1, axis=1).sort_index(level=0, axis=1)

        d = OrderedDict([['C', [np.mean, np.std]], ['D', [np.mean, np.std]]])
        result = grouped.aggregate(d)

        assert_frame_equal(result, expected)

        # be careful
        result = grouped.aggregate(OrderedDict([['C', np.mean],
                                                ['D', [np.mean, np.std]]]))
        expected = grouped.aggregate(OrderedDict([['C', np.mean],
                                                  ['D', [np.mean, np.std]]]))
        assert_frame_equal(result, expected)

        def foo(x):
            return np.mean(x)

        def bar(x):
            return np.std(x, ddof=1)

        # this uses column selection & renaming
        with tm.assert_produces_warning(FutureWarning,
                                        check_stacklevel=False):
            d = OrderedDict([['C', np.mean], ['D', OrderedDict(
                [['foo', np.mean], ['bar', np.std]])]])
            result = grouped.aggregate(d)

        d = OrderedDict([['C', [np.mean]], ['D', [foo, bar]]])
        expected = grouped.aggregate(d)

        assert_frame_equal(result, expected) 

# 需要导入模块: from pandas import compat [as 别名]
# 或者: from pandas.compat import OrderedDict [as 别名]
def test_multi_function_flexible_mix(self):
        # GH #1268
        grouped = self.df.groupby('A')

        d = OrderedDict([['C', OrderedDict([['foo', 'mean'], [
            'bar', 'std'
        ]])], ['D', 'sum']])

        # this uses column selection & renaming
        with tm.assert_produces_warning(FutureWarning,
                                        check_stacklevel=False):
            result = grouped.aggregate(d)

        d2 = OrderedDict([['C', OrderedDict([['foo', 'mean'], [
            'bar', 'std'
        ]])], ['D', ['sum']]])

        # this uses column selection & renaming
        with tm.assert_produces_warning(FutureWarning,
                                        check_stacklevel=False):
            result2 = grouped.aggregate(d2)

        d3 = OrderedDict([['C', OrderedDict([['foo', 'mean'], [
            'bar', 'std'
        ]])], ['D', {'sum': 'sum'}]])

        # this uses column selection & renaming
        with tm.assert_produces_warning(FutureWarning,
                                        check_stacklevel=False):
            expected = grouped.aggregate(d3)

        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)