Python util.cartesian_product方法代码示例

# 需要导入模块: from pandas.core.reshape import util [as 别名]
# 或者: from pandas.core.reshape.util import cartesian_product [as 别名]
def setup_method(self, method):
        import scipy.sparse
        # SparseSeries inputs used in tests, the tests rely on the order
        self.sparse_series = []
        s = pd.Series([3.0, nan, 1.0, 2.0, nan, nan])
        s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),
                                             (1, 2, 'a', 1),
                                             (1, 1, 'b', 0),
                                             (1, 1, 'b', 1),
                                             (2, 1, 'b', 0),
                                             (2, 1, 'b', 1)],
                                            names=['A', 'B', 'C', 'D'])
        self.sparse_series.append(s.to_sparse())

        ss = self.sparse_series[0].copy()
        ss.index.names = [3, 0, 1, 2]
        self.sparse_series.append(ss)

        ss = pd.Series([
            nan
        ] * 12, index=cartesian_product((range(3), range(4)))).to_sparse()
        for k, v in zip([(0, 0), (1, 2), (1, 3)], [3.0, 1.0, 2.0]):
            ss[k] = v
        self.sparse_series.append(ss)

        # results used in tests
        self.coo_matrices = []
        self.coo_matrices.append(scipy.sparse.coo_matrix(
            ([3.0, 1.0, 2.0], ([0, 1, 1], [0, 2, 3])), shape=(3, 4)))
        self.coo_matrices.append(scipy.sparse.coo_matrix(
            ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4)))
        self.coo_matrices.append(scipy.sparse.coo_matrix(
            ([3.0, 1.0, 2.0], ([0, 1, 1], [0, 0, 1])), shape=(3, 2)))
        self.ils = [[(1, 2), (1, 1), (2, 1)], [(1, 1), (1, 2), (2, 1)],
                    [(1, 2, 'a'), (1, 1, 'b'), (2, 1, 'b')]]
        self.jls = [[('a', 0), ('a', 1), ('b', 0), ('b', 1)], [0, 1]] 

# 需要导入模块: from pandas.core.reshape import util [as 别名]
# 或者: from pandas.core.reshape.util import cartesian_product [as 别名]
def test_simple(self):
        x, y = list('ABC'), [1, 22]
        result1, result2 = cartesian_product([x, y])
        expected1 = np.array(['A', 'A', 'B', 'B', 'C', 'C'])
        expected2 = np.array([1, 22, 1, 22, 1, 22])
        tm.assert_numpy_array_equal(result1, expected1)
        tm.assert_numpy_array_equal(result2, expected2) 

# 需要导入模块: from pandas.core.reshape import util [as 别名]
# 或者: from pandas.core.reshape.util import cartesian_product [as 别名]
def test_datetimeindex(self):
        # regression test for GitHub issue #6439
        # make sure that the ordering on datetimeindex is consistent
        x = date_range('2000-01-01', periods=2)
        result1, result2 = [Index(y).day for y in cartesian_product([x, x])]
        expected1 = Index([1, 1, 2, 2])
        expected2 = Index([1, 2, 1, 2])
        tm.assert_index_equal(result1, expected1)
        tm.assert_index_equal(result2, expected2) 

# 需要导入模块: from pandas.core.reshape import util [as 别名]
# 或者: from pandas.core.reshape.util import cartesian_product [as 别名]
def test_empty(self):
        # product of empty factors
        X = [[], [0, 1], []]
        Y = [[], [], ['a', 'b', 'c']]
        for x, y in zip(X, Y):
            expected1 = np.array([], dtype=np.asarray(x).dtype)
            expected2 = np.array([], dtype=np.asarray(y).dtype)
            result1, result2 = cartesian_product([x, y])
            tm.assert_numpy_array_equal(result1, expected1)
            tm.assert_numpy_array_equal(result2, expected2)

        # empty product (empty input):
        result = cartesian_product([])
        expected = []
        assert result == expected 

# 需要导入模块: from pandas.core.reshape import util [as 别名]
# 或者: from pandas.core.reshape.util import cartesian_product [as 别名]
def test_invalid_input(self, X):
        msg = "Input must be a list-like of list-likes"

        with pytest.raises(TypeError, match=msg):
            cartesian_product(X=X) 

# 需要导入模块: from pandas.core.reshape import util [as 别名]
# 或者: from pandas.core.reshape.util import cartesian_product [as 别名]
def test_invalid_input(self):
        invalid_inputs = [1, [1], [1, 2], [[1], 2],
                          'a', ['a'], ['a', 'b'], [['a'], 'b']]
        msg = "Input must be a list-like of list-likes"
        for X in invalid_inputs:
            tm.assert_raises_regex(TypeError, msg, cartesian_product, X=X) 

# 需要导入模块: from pandas.core.reshape import util [as 别名]
# 或者: from pandas.core.reshape.util import cartesian_product [as 别名]
def from_product(cls, iterables, sortorder=None, names=None):
        """
        Make a MultiIndex from the cartesian product of multiple iterables

        Parameters
        ----------
        iterables : list / sequence of iterables
            Each iterable has unique labels for each level of the index.
        sortorder : int or None
            Level of sortedness (must be lexicographically sorted by that
            level).
        names : list / sequence of strings or None
            Names for the levels in the index.

        Returns
        -------
        index : MultiIndex

        Examples
        --------
        >>> numbers = [0, 1, 2]
        >>> colors = [u'green', u'purple']
        >>> MultiIndex.from_product([numbers, colors],
                                     names=['number', 'color'])
        MultiIndex(levels=[[0, 1, 2], [u'green', u'purple']],
                   labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
                   names=[u'number', u'color'])

        See Also
        --------
        MultiIndex.from_arrays : Convert list of arrays to MultiIndex
        MultiIndex.from_tuples : Convert list of tuples to MultiIndex
        """
        from pandas.core.categorical import _factorize_from_iterables
        from pandas.core.reshape.util import cartesian_product

        labels, levels = _factorize_from_iterables(iterables)
        labels = cartesian_product(labels)
        return MultiIndex(levels, labels, sortorder=sortorder, names=names) 

# 需要导入模块: from pandas.core.reshape import util [as 别名]
# 或者: from pandas.core.reshape.util import cartesian_product [as 别名]
def setup_method(self, method):
        tm._skip_if_no_scipy()
        import scipy.sparse
        # SparseSeries inputs used in tests, the tests rely on the order
        self.sparse_series = []
        s = pd.Series([3.0, nan, 1.0, 2.0, nan, nan])
        s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),
                                             (1, 2, 'a', 1),
                                             (1, 1, 'b', 0),
                                             (1, 1, 'b', 1),
                                             (2, 1, 'b', 0),
                                             (2, 1, 'b', 1)],
                                            names=['A', 'B', 'C', 'D'])
        self.sparse_series.append(s.to_sparse())

        ss = self.sparse_series[0].copy()
        ss.index.names = [3, 0, 1, 2]
        self.sparse_series.append(ss)

        ss = pd.Series([
            nan
        ] * 12, index=cartesian_product((range(3), range(4)))).to_sparse()
        for k, v in zip([(0, 0), (1, 2), (1, 3)], [3.0, 1.0, 2.0]):
            ss[k] = v
        self.sparse_series.append(ss)

        # results used in tests
        self.coo_matrices = []
        self.coo_matrices.append(scipy.sparse.coo_matrix(
            ([3.0, 1.0, 2.0], ([0, 1, 1], [0, 2, 3])), shape=(3, 4)))
        self.coo_matrices.append(scipy.sparse.coo_matrix(
            ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4)))
        self.coo_matrices.append(scipy.sparse.coo_matrix(
            ([3.0, 1.0, 2.0], ([0, 1, 1], [0, 0, 1])), shape=(3, 2)))
        self.ils = [[(1, 2), (1, 1), (2, 1)], [(1, 1), (1, 2), (2, 1)],
                    [(1, 2, 'a'), (1, 1, 'b'), (2, 1, 'b')]]
        self.jls = [[('a', 0), ('a', 1), ('b', 0), ('b', 1)], [0, 1]]