Python MultiIndex.from_tuples方法代码示例

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def test_mixed_depth_pop(self):
        arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
                  ['', 'OD', 'OD', 'result1', 'result2', 'result1'],
                  ['', 'wx', 'wy', '', '', '']]

        tuples = sorted(zip(*arrays))
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(randn(4, 6), columns=index)

        df1 = df.copy()
        df2 = df.copy()
        result = df1.pop('a')
        expected = df2.pop(('a', '', ''))
        tm.assert_series_equal(expected, result, check_names=False)
        tm.assert_frame_equal(df1, df2)
        assert result.name == 'a'

        expected = df1['top']
        df1 = df1.drop(['top'], axis=1)
        result = df2.pop('top')
        tm.assert_frame_equal(expected, result)
        tm.assert_frame_equal(df1, df2) 

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def test_sort_ascending_list(self):
        # GH: 16934

        # Set up a Series with a three level MultiIndex
        arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
                  ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'],
                  [4, 3, 2, 1, 4, 3, 2, 1]]
        tuples = lzip(*arrays)
        mi = MultiIndex.from_tuples(tuples, names=['first', 'second', 'third'])
        s = Series(range(8), index=mi)

        # Sort with boolean ascending
        result = s.sort_index(level=['third', 'first'], ascending=False)
        expected = s.iloc[[4, 0, 5, 1, 6, 2, 7, 3]]
        tm.assert_series_equal(result, expected)

        # Sort with list of boolean ascending
        result = s.sort_index(level=['third', 'first'],
                              ascending=[False, True])
        expected = s.iloc[[0, 4, 1, 5, 2, 6, 3, 7]]
        tm.assert_series_equal(result, expected) 

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def test_sort_index_level(self):
        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
        s = Series([1, 2], mi)
        backwards = s.iloc[[1, 0]]

        res = s.sort_index(level='A')
        assert_series_equal(backwards, res)

        res = s.sort_index(level=['A', 'B'])
        assert_series_equal(backwards, res)

        res = s.sort_index(level='A', sort_remaining=False)
        assert_series_equal(s, res)

        res = s.sort_index(level=['A', 'B'], sort_remaining=False)
        assert_series_equal(s, res) 

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def _transform_index(index, func, level=None):
    """
    Apply function to all values found in index.

    This includes transforming multiindex entries separately.
    Only apply function to one level of the MultiIndex if level is specified.

    """
    if isinstance(index, MultiIndex):
        if level is not None:
            items = [tuple(func(y) if i == level else y
                           for i, y in enumerate(x)) for x in index]
        else:
            items = [tuple(func(y) for y in x) for x in index]
        return MultiIndex.from_tuples(items, names=index.names)
    else:
        items = [func(x) for x in index]
        return Index(items, name=index.name, tupleize_cols=False) 

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def test_xs_level(self):
        result = self.frame.xs('two', level='second')
        expected = self.frame[self.frame.index.get_level_values(1) == 'two']
        expected.index = expected.index.droplevel(1)

        tm.assert_frame_equal(result, expected)

        index = MultiIndex.from_tuples([('x', 'y', 'z'), ('a', 'b', 'c'), (
            'p', 'q', 'r')])
        df = DataFrame(np.random.randn(3, 5), index=index)
        result = df.xs('c', level=2)
        expected = df[1:2]
        expected.index = expected.index.droplevel(2)
        tm.assert_frame_equal(result, expected)

        # this is a copy in 0.14
        result = self.frame.xs('two', level='second')

        # setting this will give a SettingWithCopyError
        # as we are trying to write a view
        def f(x):
            x[:] = 10

        pytest.raises(com.SettingWithCopyError, f, result) 

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def test_alignment(self):
        x = Series(data=[1, 2, 3], index=MultiIndex.from_tuples([("A", 1), (
            "A", 2), ("B", 3)]))

        y = Series(data=[4, 5, 6], index=MultiIndex.from_tuples([("Z", 1), (
            "Z", 2), ("B", 3)]))

        res = x - y
        exp_index = x.index.union(y.index)
        exp = x.reindex(exp_index) - y.reindex(exp_index)
        tm.assert_series_equal(res, exp)

        # hit non-monotonic code path
        res = x[::-1] - y[::-1]
        exp_index = x.index.union(y.index)
        exp = x.reindex(exp_index) - y.reindex(exp_index)
        tm.assert_series_equal(res, exp) 

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def test_mixed_depth_get(self, unicode_strings):
        # If unicode_strings is True, the column labels in dataframe
        # construction will use unicode strings in Python 2 (pull request
        # #17099).

        arrays = [['a', 'top', 'top', 'routine1', 'routine1', 'routine2'],
                  ['', 'OD', 'OD', 'result1', 'result2', 'result1'],
                  ['', 'wx', 'wy', '', '', '']]

        if unicode_strings:
            arrays = [[u(s) for s in arr] for arr in arrays]

        tuples = sorted(zip(*arrays))
        index = MultiIndex.from_tuples(tuples)
        df = DataFrame(np.random.randn(4, 6), columns=index)

        result = df['a']
        expected = df['a', '', ''].rename('a')
        tm.assert_series_equal(result, expected)

        result = df['routine1', 'result1']
        expected = df['routine1', 'result1', '']
        expected = expected.rename(('routine1', 'result1'))
        tm.assert_series_equal(result, expected) 

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def test_iloc_mi(self):
        # GH 13797
        # Test if iloc can handle integer locations in MultiIndexed DataFrame

        data = [['str00', 'str01'], ['str10', 'str11'], ['str20', 'srt21'],
                ['str30', 'str31'], ['str40', 'str41']]

        mi = MultiIndex.from_tuples(
            [('CC', 'A'), ('CC', 'B'), ('CC', 'B'), ('BB', 'a'), ('BB', 'b')])

        expected = DataFrame(data)
        df_mi = DataFrame(data, index=mi)

        result = DataFrame([[df_mi.iloc[r, c] for c in range(2)]
                            for r in range(5)])

        tm.assert_frame_equal(result, expected) 

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def _wrap_applied_output(self, keys, values, not_indexed_same=False):
        if len(keys) == 0:
            return Series([])

        def _get_index():
            if self.grouper.nkeys > 1:
                index = MultiIndex.from_tuples(keys, names=self.grouper.names)
            else:
                index = Index(keys, name=self.grouper.names[0])
            return index

        if isinstance(values[0], dict):
            # GH #823
            index = _get_index()
            return DataFrame(values, index=index).stack()

        if isinstance(values[0], (Series, dict)):
            return self._concat_objects(keys, values,
                                        not_indexed_same=not_indexed_same)
        elif isinstance(values[0], DataFrame):
            # possible that Series -> DataFrame by applied function
            return self._concat_objects(keys, values,
                                        not_indexed_same=not_indexed_same)
        else:
            return Series(values, index=_get_index()) 

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def setup_method(self, method):

        index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two',
                                                                  'three']],
                           codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                                  [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                           names=['first', 'second'])
        self.frame = DataFrame(np.random.randn(10, 3), index=index,
                               columns=Index(['A', 'B', 'C'], name='exp'))

        self.single_level = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux']],
                                       codes=[[0, 1, 2, 3]], names=['first'])

        # create test series object
        arrays = [['bar', 'bar', 'baz', 'baz', 'qux', 'qux', 'foo', 'foo'],
                  ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
        tuples = lzip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)
        s[3] = np.NaN
        self.series = s

        self.tdf = tm.makeTimeDataFrame(100)
        self.ymd = self.tdf.groupby([lambda x: x.year, lambda x: x.month,
                                     lambda x: x.day]).sum()

        # use Int64Index, to make sure things work
        self.ymd.index.set_levels([lev.astype('i8')
                                   for lev in self.ymd.index.levels],
                                  inplace=True)
        self.ymd.index.set_names(['year', 'month', 'day'], inplace=True) 

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def test_repr_name_coincide(self):
        index = MultiIndex.from_tuples([('a', 0, 'foo'), ('b', 1, 'bar')],
                                       names=['a', 'b', 'c'])

        df = DataFrame({'value': [0, 1]}, index=index)

        lines = repr(df).split('\n')
        assert lines[2].startswith('a 0 foo') 

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def test_delevel_infer_dtype(self):
        tuples = [tuple
                  for tuple in cart_product(
                      ['foo', 'bar'], [10, 20], [1.0, 1.1])]
        index = MultiIndex.from_tuples(tuples, names=['prm0', 'prm1', 'prm2'])
        df = DataFrame(np.random.randn(8, 3), columns=['A', 'B', 'C'],
                       index=index)
        deleveled = df.reset_index()
        assert is_integer_dtype(deleveled['prm1'])
        assert is_float_dtype(deleveled['prm2']) 

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def test_unstack_multiple_no_empty_columns(self):
        index = MultiIndex.from_tuples([(0, 'foo', 0), (0, 'bar', 0), (
            1, 'baz', 1), (1, 'qux', 1)])

        s = Series(np.random.randn(4), index=index)

        unstacked = s.unstack([1, 2])
        expected = unstacked.dropna(axis=1, how='all')
        tm.assert_frame_equal(unstacked, expected) 

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def test_groupby_level_no_obs(self):
        # #1697
        midx = MultiIndex.from_tuples([('f1', 's1'), ('f1', 's2'), (
            'f2', 's1'), ('f2', 's2'), ('f3', 's1'), ('f3', 's2')])
        df = DataFrame(
            [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], columns=midx)
        df1 = df.loc(axis=1)[df.columns.map(
            lambda u: u[0] in ['f2', 'f3'])]

        grouped = df1.groupby(axis=1, level=0)
        result = grouped.sum()
        assert (result.columns == ['f2', 'f3']).all() 

# 需要导入模块: from pandas.core.index import MultiIndex [as 别名]
# 或者: from pandas.core.index.MultiIndex import from_tuples [as 别名]
def test_stat_op_corner(self):
        obj = Series([10.0], index=MultiIndex.from_tuples([(2, 3)]))

        result = obj.sum(level=0)
        expected = Series([10.0], index=[2])
        tm.assert_series_equal(result, expected)