Example #1
0
 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.delevel()
     self.assert_(com.is_integer_dtype(deleveled["prm1"]))
     self.assert_(com.is_float_dtype(deleveled["prm2"]))
Example #2
0
class TestMultiLevel(unittest.TestCase):
    def setUp(self):
        index = MultiIndex(
            levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
            labels=[[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=["A", "B", "C"])

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

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

    def test_append(self):
        a, b = self.frame[:5], self.frame[5:]

        result = a.append(b)
        tm.assert_frame_equal(result, self.frame)

        result = a["A"].append(b["A"])
        tm.assert_series_equal(result, self.frame["A"])

    def test_pickle(self):
        import cPickle

        def _test_roundtrip(frame):
            pickled = cPickle.dumps(frame)
            unpickled = cPickle.loads(pickled)
            assert_frame_equal(frame, unpickled)

        _test_roundtrip(self.frame)
        _test_roundtrip(self.frame.T)
        _test_roundtrip(self.ymd)
        _test_roundtrip(self.ymd.T)

    def test_reindex(self):
        reindexed = self.frame.ix[[("foo", "one"), ("bar", "one")]]
        expected = self.frame.ix[[0, 3]]
        assert_frame_equal(reindexed, expected)

    def test_reindex_preserve_levels(self):
        new_index = self.ymd.index[::10]
        chunk = self.ymd.reindex(new_index)
        self.assert_(chunk.index is new_index)

        chunk = self.ymd.ix[new_index]
        self.assert_(chunk.index is new_index)

        ymdT = self.ymd.T
        chunk = ymdT.reindex(columns=new_index)
        self.assert_(chunk.columns is new_index)

        chunk = ymdT.ix[:, new_index]
        self.assert_(chunk.columns is new_index)

    def test_repr_to_string(self):
        repr(self.frame)
        repr(self.ymd)
        repr(self.frame.T)
        repr(self.ymd.T)

        buf = StringIO()
        self.frame.to_string(buf=buf)
        self.ymd.to_string(buf=buf)
        self.frame.T.to_string(buf=buf)
        self.ymd.T.to_string(buf=buf)

    def test_getitem_simple(self):
        df = self.frame.T

        col = df["foo", "one"]
        assert_almost_equal(col.values, df.values[:, 0])
        self.assertRaises(KeyError, df.__getitem__, ("foo", "four"))
        self.assertRaises(KeyError, df.__getitem__, "foobar")

    def test_series_getitem(self):
        s = self.ymd["A"]

        result = s[2000, 3]
        result2 = s.ix[2000, 3]
        expected = s[42:65]
        expected.index = expected.index.droplevel(0).droplevel(0)
        assert_series_equal(result, expected)

        result = s[2000, 3, 10]
        expected = s[49]
        self.assertEquals(result, expected)

        # fancy
        result = s.ix[[(2000, 3, 10), (2000, 3, 13)]]
        expected = s[49:51]
        assert_series_equal(result, expected)

        # key error
        self.assertRaises(KeyError, s.__getitem__, (2000, 3, 4))

    def test_series_setitem(self):
        s = self.ymd["A"]

        s[2000, 3] = np.nan
        self.assert_(isnull(s[42:65]).all())
        self.assert_(notnull(s[:42]).all())
        self.assert_(notnull(s[65:]).all())

        s[2000, 3, 10] = np.nan
        self.assert_(isnull(s[49]))

    def test_series_slice_partial(self):
        pass

    def test_xs(self):
        xs = self.frame.xs(("bar", "two"))
        xs2 = self.frame.ix[("bar", "two")]

        assert_series_equal(xs, xs2)
        assert_almost_equal(xs.values, self.frame.values[4])

    def test_xs_partial(self):
        result = self.frame.xs("foo")
        result2 = self.frame.ix["foo"]
        expected = self.frame.T["foo"].T
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_fancy_2d(self):
        result = self.frame.ix["foo", "B"]
        expected = self.frame.xs("foo")["B"]
        assert_series_equal(result, expected)

        ft = self.frame.T
        result = ft.ix["B", "foo"]
        expected = ft.xs("B")["foo"]
        assert_series_equal(result, expected)

    def test_get_loc_single_level(self):
        s = Series(np.random.randn(len(self.single_level)), index=self.single_level)
        for k in self.single_level.values:
            s[k]

    def test_getitem_toplevel(self):
        df = self.frame.T

        result = df["foo"]
        expected = df.reindex(columns=df.columns[:3])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)

        result = df["bar"]
        result2 = df.ix[:, "bar"]

        expected = df.reindex(columns=df.columns[3:5])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_getitem_int(self):
        levels = [[0, 1], [0, 1, 2]]
        labels = [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]
        index = MultiIndex(levels=levels, labels=labels)

        frame = DataFrame(np.random.randn(6, 2), index=index)

        result = frame.ix[1]
        expected = frame[-3:]
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)

        # raises exception
        self.assertRaises(KeyError, frame.ix.__getitem__, 3)

        # however this will work
        result = self.frame.ix[2]
        expected = self.frame.xs(self.frame.index[2])
        assert_series_equal(result, expected)

    def test_getitem_partial(self):
        ymd = self.ymd.T
        result = ymd[2000, 2]

        expected = ymd.reindex(columns=ymd.columns[ymd.columns.labels[1] == 1])
        expected.columns = expected.columns.droplevel(0).droplevel(0)
        assert_frame_equal(result, expected)

    def test_setitem_change_dtype(self):
        dft = self.frame.T
        s = dft["foo", "two"]
        dft["foo", "two"] = s > s.median()
        assert_series_equal(dft["foo", "two"], s > s.median())
        self.assert_(isinstance(dft._data.blocks[1].items, MultiIndex))

        reindexed = dft.reindex(columns=[("foo", "two")])
        assert_series_equal(reindexed["foo", "two"], s > s.median())

    def test_fancy_slice_partial(self):
        result = self.frame.ix["bar":"baz"]
        expected = self.frame[3:7]
        assert_frame_equal(result, expected)

        result = self.ymd.ix[(2000, 2):(2000, 4)]
        lev = self.ymd.index.labels[1]
        expected = self.ymd[(lev >= 1) & (lev <= 3)]
        assert_frame_equal(result, expected)

    def test_sortlevel(self):
        df = self.frame.copy()
        df.index = np.arange(len(df))
        self.assertRaises(Exception, df.sortlevel, 0)

        # axis=1

        # series
        a_sorted = self.frame["A"].sortlevel(0)
        self.assertRaises(Exception, self.frame.delevel()["A"].sortlevel)

        # preserve names
        self.assertEquals(a_sorted.index.names, self.frame.index.names)

    def test_sortlevel_by_name(self):
        self.frame.index.names = ["first", "second"]
        result = self.frame.sortlevel(level="second")
        expected = self.frame.sortlevel(level=1)
        assert_frame_equal(result, expected)

    def test_sortlevel_mixed(self):
        sorted_before = self.frame.sortlevel(1)

        df = self.frame.copy()
        df["foo"] = "bar"
        sorted_after = df.sortlevel(1)
        assert_frame_equal(sorted_before, sorted_after.drop(["foo"], axis=1))

        dft = self.frame.T
        sorted_before = dft.sortlevel(1, axis=1)
        dft["foo", "three"] = "bar"

        sorted_after = dft.sortlevel(1, axis=1)
        assert_frame_equal(
            sorted_before.drop([("foo", "three")], axis=1), sorted_after.drop([("foo", "three")], axis=1)
        )

    def test_count_level(self):
        def _check_counts(frame, axis=0):
            index = frame._get_axis(axis)
            for i in range(index.nlevels):
                result = frame.count(axis=axis, level=i)
                expected = frame.groupby(axis=axis, level=i).count(axis=axis)

        _check_counts(self.frame)
        _check_counts(self.ymd)
        _check_counts(self.frame.T, axis=1)
        _check_counts(self.ymd.T, axis=1)

        # can't call with level on regular DataFrame
        df = tm.makeTimeDataFrame()
        self.assertRaises(Exception, df.count, level=0)

    def test_count_level_corner(self):
        s = self.frame["A"][:0]
        result = s.count(level=0)
        expected = Series(0, index=s.index.levels[0])
        assert_series_equal(result, expected)

        df = self.frame[:0]
        result = df.count(level=0)
        expected = DataFrame({}, index=s.index.levels[0], columns=df.columns).fillna(0).astype(int)
        assert_frame_equal(result, expected)

    def test_unstack(self):
        # just check that it works for now
        unstacked = self.ymd.unstack()
        unstacked2 = unstacked.unstack()

        # test that ints work
        unstacked = self.ymd.astype(int).unstack()

    def test_stack(self):
        # regular roundtrip
        unstacked = self.ymd.unstack()
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        unlexsorted = self.ymd.sortlevel(2)

        unstacked = unlexsorted.unstack(2)
        restacked = unstacked.stack()
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted[::-1]
        unstacked = unlexsorted.unstack(1)
        restacked = unstacked.stack().swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted.swaplevel(0, 1)
        unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
        restacked = unstacked.stack(0).swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        # columns unsorted
        unstacked = self.ymd.unstack()
        unstacked = unstacked.sort(axis=1, ascending=False)
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        # more than 2 levels in the columns
        unstacked = self.ymd.unstack(1).unstack(1)

        result = unstacked.stack(1)
        expected = self.ymd.unstack()
        assert_frame_equal(result, expected)

        result = unstacked.stack(2)
        expected = self.ymd.unstack(1)
        assert_frame_equal(result, expected)

        result = unstacked.stack(0)
        expected = self.ymd.stack().unstack(1).unstack(1)
        assert_frame_equal(result, expected)

        # not all levels present in each echelon
        unstacked = self.ymd.unstack(2).ix[:, ::3]
        stacked = unstacked.stack().stack()
        ymd_stacked = self.ymd.stack()
        assert_series_equal(stacked, ymd_stacked.reindex(stacked.index))

    def test_stack_mixed_dtype(self):
        df = self.frame.T
        df["foo", "four"] = "foo"
        df = df.sortlevel(1, axis=1)

        stacked = df.stack()
        assert_series_equal(stacked["foo"], df["foo"].stack())
        self.assert_(stacked["bar"].dtype == np.float_)

    def test_unstack_bug(self):
        df = DataFrame(
            {
                "state": ["naive", "naive", "naive", "activ", "activ", "activ"],
                "exp": ["a", "b", "b", "b", "a", "a"],
                "barcode": [1, 2, 3, 4, 1, 3],
                "v": ["hi", "hi", "bye", "bye", "bye", "peace"],
                "extra": np.arange(6.0),
            }
        )

        result = df.groupby(["state", "exp", "barcode", "v"]).apply(len)
        unstacked = result.unstack()
        restacked = unstacked.stack()
        assert_series_equal(restacked, result.reindex(restacked.index).astype(float))

    def test_stack_unstack_preserve_names(self):
        unstacked = self.frame.unstack()
        self.assertEquals(unstacked.index.name, "first")
        self.assertEquals(unstacked.columns.names, [None, "second"])

        restacked = unstacked.stack()
        self.assertEquals(restacked.index.names, self.frame.index.names)

    def test_unstack_level_name(self):
        result = self.frame.unstack("second")
        expected = self.frame.unstack(level=1)
        assert_frame_equal(result, expected)

    def test_groupby_transform(self):
        s = self.frame["A"]
        grouper = s.index.get_level_values(0)

        grouped = s.groupby(grouper)

        applied = grouped.apply(lambda x: x * 2)
        expected = grouped.transform(lambda x: x * 2)
        assert_series_equal(applied.reindex(expected.index), expected)

    def test_join(self):
        a = self.frame.ix[:5, ["A"]]
        b = self.frame.ix[2:, ["B", "C"]]

        joined = a.join(b, how="outer").reindex(self.frame.index)
        expected = self.frame.copy()
        expected.values[np.isnan(joined.values)] = np.nan

        self.assert_(not np.isnan(joined.values).all())

        assert_frame_equal(joined, expected)

    def test_swaplevel(self):
        swapped = self.frame["A"].swaplevel(0, 1)
        self.assert_(not swapped.index.equals(self.frame.index))

        back = swapped.swaplevel(0, 1)
        self.assert_(back.index.equals(self.frame.index))

    def test_swaplevel_panel(self):
        panel = Panel({"ItemA": self.frame, "ItemB": self.frame * 2})

        result = panel.swaplevel(0, 1, axis="major")
        expected = panel.copy()
        expected.major_axis = expected.major_axis.swaplevel(0, 1)

    def test_insert_index(self):
        df = self.ymd[:5].T
        df[2000, 1, 10] = df[2000, 1, 7]
        self.assert_(isinstance(df.columns, MultiIndex))
        self.assert_((df[2000, 1, 10] == df[2000, 1, 7]).all())

    def test_alignment(self):
        pass

    def test_is_lexsorted(self):
        levels = [[0, 1], [0, 1, 2]]

        index = MultiIndex(levels=levels, labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
        self.assert_(index.is_lexsorted())

        index = MultiIndex(levels=levels, labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]])
        self.assert_(not index.is_lexsorted())

        index = MultiIndex(levels=levels, labels=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]])
        self.assert_(not index.is_lexsorted())
        self.assert_(index.lexsort_depth == 0)

    def test_frame_getitem_view(self):
        df = self.frame.T
        df["foo"].values[:] = 0
        self.assert_((df["foo"].values == 0).all())

        # but not if it's mixed-type
        df["foo", "four"] = "foo"
        df = df.sortlevel(0, axis=1)
        df["foo"]["one"] = 2
        self.assert_((df["foo", "one"] == 0).all())

    def test_frame_getitem_not_sorted(self):
        df = self.frame.T
        df["foo", "four"] = "foo"

        arrays = [np.array(x) for x in zip(*df.columns.get_tuple_index())]

        result = df["foo"]
        result2 = df.ix[:, "foo"]
        expected = df.reindex(columns=df.columns[arrays[0] == "foo"])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        df = df.T
        result = df.xs("foo")
        result2 = df.ix["foo"]
        expected = df.reindex(df.index[arrays[0] == "foo"])
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

    def test_series_getitem_not_sorted(self):
        arrays = [
            ["bar", "bar", "baz", "baz", "qux", "qux", "foo", "foo"],
            ["one", "two", "one", "two", "one", "two", "one", "two"],
        ]
        tuples = zip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)

        arrays = [np.array(x) for x in zip(*index.get_tuple_index())]

        result = s["qux"]
        result2 = s.ix["qux"]
        expected = s[arrays[0] == "qux"]
        expected.index = expected.index.droplevel(0)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)
Example #3
0
class TestMultiLevel(unittest.TestCase):
    def setUp(self):
        index = MultiIndex(
            levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
            labels=[[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"]], labels=[[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 = zip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)
        s[3] = np.NaN
        self.series = s

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

    def test_append(self):
        a, b = self.frame[:5], self.frame[5:]

        result = a.append(b)
        tm.assert_frame_equal(result, self.frame)

        result = a["A"].append(b["A"])
        tm.assert_series_equal(result, self.frame["A"])

    def test_pickle(self):
        import cPickle

        def _test_roundtrip(frame):
            pickled = cPickle.dumps(frame)
            unpickled = cPickle.loads(pickled)
            assert_frame_equal(frame, unpickled)

        _test_roundtrip(self.frame)
        _test_roundtrip(self.frame.T)
        _test_roundtrip(self.ymd)
        _test_roundtrip(self.ymd.T)

    def test_reindex(self):
        reindexed = self.frame.ix[[("foo", "one"), ("bar", "one")]]
        expected = self.frame.ix[[0, 3]]
        assert_frame_equal(reindexed, expected)

    def test_reindex_preserve_levels(self):
        new_index = self.ymd.index[::10]
        chunk = self.ymd.reindex(new_index)
        self.assert_(chunk.index is new_index)

        chunk = self.ymd.ix[new_index]
        self.assert_(chunk.index is new_index)

        ymdT = self.ymd.T
        chunk = ymdT.reindex(columns=new_index)
        self.assert_(chunk.columns is new_index)

        chunk = ymdT.ix[:, new_index]
        self.assert_(chunk.columns is new_index)

    def test_sort_index_preserve_levels(self):
        result = self.frame.sort_index()
        self.assertEquals(result.index.names, self.frame.index.names)

    def test_repr_to_string(self):
        repr(self.frame)
        repr(self.ymd)
        repr(self.frame.T)
        repr(self.ymd.T)

        buf = StringIO()
        self.frame.to_string(buf=buf)
        self.ymd.to_string(buf=buf)
        self.frame.T.to_string(buf=buf)
        self.ymd.T.to_string(buf=buf)

    def test_getitem_simple(self):
        df = self.frame.T

        col = df["foo", "one"]
        assert_almost_equal(col.values, df.values[:, 0])
        self.assertRaises(KeyError, df.__getitem__, ("foo", "four"))
        self.assertRaises(KeyError, df.__getitem__, "foobar")

    def test_series_getitem(self):
        s = self.ymd["A"]

        result = s[2000, 3]
        result2 = s.ix[2000, 3]
        expected = s[42:65]
        expected.index = expected.index.droplevel(0).droplevel(0)
        assert_series_equal(result, expected)

        result = s[2000, 3, 10]
        expected = s[49]
        self.assertEquals(result, expected)

        # fancy
        result = s.ix[[(2000, 3, 10), (2000, 3, 13)]]
        expected = s[49:51]
        assert_series_equal(result, expected)

        # key error
        self.assertRaises(KeyError, s.__getitem__, (2000, 3, 4))

    def test_series_setitem(self):
        s = self.ymd["A"]

        s[2000, 3] = np.nan
        self.assert_(isnull(s[42:65]).all())
        self.assert_(notnull(s[:42]).all())
        self.assert_(notnull(s[65:]).all())

        s[2000, 3, 10] = np.nan
        self.assert_(isnull(s[49]))

    def test_series_slice_partial(self):
        pass

    def test_xs(self):
        xs = self.frame.xs(("bar", "two"))
        xs2 = self.frame.ix[("bar", "two")]

        assert_series_equal(xs, xs2)
        assert_almost_equal(xs.values, self.frame.values[4])

    def test_xs_partial(self):
        result = self.frame.xs("foo")
        result2 = self.frame.ix["foo"]
        expected = self.frame.T["foo"].T
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_fancy_2d(self):
        result = self.frame.ix["foo", "B"]
        expected = self.frame.xs("foo")["B"]
        assert_series_equal(result, expected)

        ft = self.frame.T
        result = ft.ix["B", "foo"]
        expected = ft.xs("B")["foo"]
        assert_series_equal(result, expected)

    def test_get_loc_single_level(self):
        s = Series(np.random.randn(len(self.single_level)), index=self.single_level)
        for k in self.single_level.values:
            s[k]

    def test_getitem_toplevel(self):
        df = self.frame.T

        result = df["foo"]
        expected = df.reindex(columns=df.columns[:3])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)

        result = df["bar"]
        result2 = df.ix[:, "bar"]

        expected = df.reindex(columns=df.columns[3:5])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result, result2)

    def test_getitem_int(self):
        levels = [[0, 1], [0, 1, 2]]
        labels = [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]
        index = MultiIndex(levels=levels, labels=labels)

        frame = DataFrame(np.random.randn(6, 2), index=index)

        result = frame.ix[1]
        expected = frame[-3:]
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)

        # raises exception
        self.assertRaises(KeyError, frame.ix.__getitem__, 3)

        # however this will work
        result = self.frame.ix[2]
        expected = self.frame.xs(self.frame.index[2])
        assert_series_equal(result, expected)

    def test_getitem_partial(self):
        ymd = self.ymd.T
        result = ymd[2000, 2]

        expected = ymd.reindex(columns=ymd.columns[ymd.columns.labels[1] == 1])
        expected.columns = expected.columns.droplevel(0).droplevel(0)
        assert_frame_equal(result, expected)

    def test_setitem_change_dtype(self):
        dft = self.frame.T
        s = dft["foo", "two"]
        dft["foo", "two"] = s > s.median()
        assert_series_equal(dft["foo", "two"], s > s.median())
        self.assert_(isinstance(dft._data.blocks[1].items, MultiIndex))

        reindexed = dft.reindex(columns=[("foo", "two")])
        assert_series_equal(reindexed["foo", "two"], s > s.median())

    def test_fancy_slice_partial(self):
        result = self.frame.ix["bar":"baz"]
        expected = self.frame[3:7]
        assert_frame_equal(result, expected)

        result = self.ymd.ix[(2000, 2):(2000, 4)]
        lev = self.ymd.index.labels[1]
        expected = self.ymd[(lev >= 1) & (lev <= 3)]
        assert_frame_equal(result, expected)

    def test_sortlevel(self):
        df = self.frame.copy()
        df.index = np.arange(len(df))
        self.assertRaises(Exception, df.sortlevel, 0)

        # axis=1

        # series
        a_sorted = self.frame["A"].sortlevel(0)
        self.assertRaises(Exception, self.frame.delevel()["A"].sortlevel)

        # preserve names
        self.assertEquals(a_sorted.index.names, self.frame.index.names)

    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.delevel()
        self.assert_(com.is_integer_dtype(deleveled["prm1"]))
        self.assert_(com.is_float_dtype(deleveled["prm2"]))

    def test_sortlevel_by_name(self):
        self.frame.index.names = ["first", "second"]
        result = self.frame.sortlevel(level="second")
        expected = self.frame.sortlevel(level=1)
        assert_frame_equal(result, expected)

    def test_sortlevel_mixed(self):
        sorted_before = self.frame.sortlevel(1)

        df = self.frame.copy()
        df["foo"] = "bar"
        sorted_after = df.sortlevel(1)
        assert_frame_equal(sorted_before, sorted_after.drop(["foo"], axis=1))

        dft = self.frame.T
        sorted_before = dft.sortlevel(1, axis=1)
        dft["foo", "three"] = "bar"

        sorted_after = dft.sortlevel(1, axis=1)
        assert_frame_equal(
            sorted_before.drop([("foo", "three")], axis=1), sorted_after.drop([("foo", "three")], axis=1)
        )

    def test_count_level(self):
        def _check_counts(frame, axis=0):
            index = frame._get_axis(axis)
            for i in range(index.nlevels):
                result = frame.count(axis=axis, level=i)
                expected = frame.groupby(axis=axis, level=i).count(axis=axis)
                expected = expected.reindex_like(result).astype("i8")
                assert_frame_equal(result, expected)

        self.frame.ix[1, [1, 2]] = np.nan
        self.frame.ix[7, [0, 1]] = np.nan
        self.ymd.ix[1, [1, 2]] = np.nan
        self.ymd.ix[7, [0, 1]] = np.nan

        _check_counts(self.frame)
        _check_counts(self.ymd)
        _check_counts(self.frame.T, axis=1)
        _check_counts(self.ymd.T, axis=1)

        # can't call with level on regular DataFrame
        df = tm.makeTimeDataFrame()
        self.assertRaises(Exception, df.count, level=0)

        self.frame["D"] = "foo"
        result = self.frame.count(level=0, numeric_only=True)
        assert_almost_equal(result.columns, ["A", "B", "C"])

    def test_count_level_series(self):
        index = MultiIndex(
            levels=[["foo", "bar", "baz"], ["one", "two", "three", "four"]], labels=[[0, 0, 0, 2, 2], [2, 0, 1, 1, 2]]
        )

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

        result = s.count(level=0)
        expected = s.groupby(level=0).count()
        assert_series_equal(result.astype("f8"), expected.reindex(result.index).fillna(0))

        result = s.count(level=1)
        expected = s.groupby(level=1).count()
        assert_series_equal(result.astype("f8"), expected.reindex(result.index).fillna(0))

    def test_count_level_corner(self):
        s = self.frame["A"][:0]
        result = s.count(level=0)
        expected = Series(0, index=s.index.levels[0])
        assert_series_equal(result, expected)

        df = self.frame[:0]
        result = df.count(level=0)
        expected = DataFrame({}, index=s.index.levels[0], columns=df.columns).fillna(0).astype(int)
        assert_frame_equal(result, expected)

    def test_unstack(self):
        # just check that it works for now
        unstacked = self.ymd.unstack()
        unstacked2 = unstacked.unstack()

        # test that ints work
        unstacked = self.ymd.astype(int).unstack()

    def test_stack(self):
        # regular roundtrip
        unstacked = self.ymd.unstack()
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        unlexsorted = self.ymd.sortlevel(2)

        unstacked = unlexsorted.unstack(2)
        restacked = unstacked.stack()
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted[::-1]
        unstacked = unlexsorted.unstack(1)
        restacked = unstacked.stack().swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        unlexsorted = unlexsorted.swaplevel(0, 1)
        unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
        restacked = unstacked.stack(0).swaplevel(1, 2)
        assert_frame_equal(restacked.sortlevel(0), self.ymd)

        # columns unsorted
        unstacked = self.ymd.unstack()
        unstacked = unstacked.sort(axis=1, ascending=False)
        restacked = unstacked.stack()
        assert_frame_equal(restacked, self.ymd)

        # more than 2 levels in the columns
        unstacked = self.ymd.unstack(1).unstack(1)

        result = unstacked.stack(1)
        expected = self.ymd.unstack()
        assert_frame_equal(result, expected)

        result = unstacked.stack(2)
        expected = self.ymd.unstack(1)
        assert_frame_equal(result, expected)

        result = unstacked.stack(0)
        expected = self.ymd.stack().unstack(1).unstack(1)
        assert_frame_equal(result, expected)

        # not all levels present in each echelon
        unstacked = self.ymd.unstack(2).ix[:, ::3]
        stacked = unstacked.stack().stack()
        ymd_stacked = self.ymd.stack()
        assert_series_equal(stacked, ymd_stacked.reindex(stacked.index))

        # stack with negative number
        result = self.ymd.unstack(0).stack(-2)
        expected = self.ymd.unstack(0).stack(0)

    def test_stack_mixed_dtype(self):
        df = self.frame.T
        df["foo", "four"] = "foo"
        df = df.sortlevel(1, axis=1)

        stacked = df.stack()
        assert_series_equal(stacked["foo"], df["foo"].stack())
        self.assert_(stacked["bar"].dtype == np.float_)

    def test_unstack_bug(self):
        df = DataFrame(
            {
                "state": ["naive", "naive", "naive", "activ", "activ", "activ"],
                "exp": ["a", "b", "b", "b", "a", "a"],
                "barcode": [1, 2, 3, 4, 1, 3],
                "v": ["hi", "hi", "bye", "bye", "bye", "peace"],
                "extra": np.arange(6.0),
            }
        )

        result = df.groupby(["state", "exp", "barcode", "v"]).apply(len)
        unstacked = result.unstack()
        restacked = unstacked.stack()
        assert_series_equal(restacked, result.reindex(restacked.index).astype(float))

    def test_stack_unstack_preserve_names(self):
        unstacked = self.frame.unstack()
        self.assertEquals(unstacked.index.name, "first")
        self.assertEquals(unstacked.columns.names, ["exp", "second"])

        restacked = unstacked.stack()
        self.assertEquals(restacked.index.names, self.frame.index.names)

    def test_unstack_level_name(self):
        result = self.frame.unstack("second")
        expected = self.frame.unstack(level=1)
        assert_frame_equal(result, expected)

    def test_stack_level_name(self):
        unstacked = self.frame.unstack("second")
        result = unstacked.stack("exp")
        expected = self.frame.unstack().stack(0)
        assert_frame_equal(result, expected)

        result = self.frame.stack("exp")
        expected = self.frame.stack()
        assert_series_equal(result, expected)

    def test_stack_unstack_multiple(self):
        unstacked = self.ymd.unstack(["year", "month"])
        expected = self.ymd.unstack("year").unstack("month")
        assert_frame_equal(unstacked, expected)
        self.assertEquals(unstacked.columns.names, expected.columns.names)

        # series
        s = self.ymd["A"]
        s_unstacked = s.unstack(["year", "month"])
        assert_frame_equal(s_unstacked, expected["A"])

        restacked = unstacked.stack(["year", "month"])
        restacked = restacked.swaplevel(0, 1).swaplevel(1, 2)
        restacked = restacked.sortlevel(0)

        assert_frame_equal(restacked, self.ymd)
        self.assertEquals(restacked.index.names, self.ymd.index.names)

        # GH #451
        unstacked = self.ymd.unstack([1, 2])
        expected = self.ymd.unstack(1).unstack(1)
        assert_frame_equal(unstacked, expected)

        unstacked = self.ymd.unstack([2, 1])
        expected = self.ymd.unstack(2).unstack(1)
        assert_frame_equal(unstacked, expected)

    def test_groupby_transform(self):
        s = self.frame["A"]
        grouper = s.index.get_level_values(0)

        grouped = s.groupby(grouper)

        applied = grouped.apply(lambda x: x * 2)
        expected = grouped.transform(lambda x: x * 2)
        assert_series_equal(applied.reindex(expected.index), expected)

    def test_join(self):
        a = self.frame.ix[:5, ["A"]]
        b = self.frame.ix[2:, ["B", "C"]]

        joined = a.join(b, how="outer").reindex(self.frame.index)
        expected = self.frame.copy()
        expected.values[np.isnan(joined.values)] = np.nan

        self.assert_(not np.isnan(joined.values).all())

        assert_frame_equal(joined, expected)

    def test_swaplevel(self):
        swapped = self.frame["A"].swaplevel(0, 1)
        swapped2 = self.frame["A"].swaplevel("first", "second")
        self.assert_(not swapped.index.equals(self.frame.index))
        assert_series_equal(swapped, swapped2)

        back = swapped.swaplevel(0, 1)
        back2 = swapped.swaplevel("second", "first")
        self.assert_(back.index.equals(self.frame.index))
        assert_series_equal(back, back2)

        ft = self.frame.T
        swapped = ft.swaplevel("first", "second", axis=1)
        exp = self.frame.swaplevel("first", "second").T
        assert_frame_equal(swapped, exp)

    def test_swaplevel_panel(self):
        panel = Panel({"ItemA": self.frame, "ItemB": self.frame * 2})

        result = panel.swaplevel(0, 1, axis="major")
        expected = panel.copy()
        expected.major_axis = expected.major_axis.swaplevel(0, 1)
        tm.assert_panel_equal(result, expected)

    def test_insert_index(self):
        df = self.ymd[:5].T
        df[2000, 1, 10] = df[2000, 1, 7]
        self.assert_(isinstance(df.columns, MultiIndex))
        self.assert_((df[2000, 1, 10] == df[2000, 1, 7]).all())

    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)
        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)
        assert_series_equal(res, exp)

    def test_is_lexsorted(self):
        levels = [[0, 1], [0, 1, 2]]

        index = MultiIndex(levels=levels, labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
        self.assert_(index.is_lexsorted())

        index = MultiIndex(levels=levels, labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]])
        self.assert_(not index.is_lexsorted())

        index = MultiIndex(levels=levels, labels=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]])
        self.assert_(not index.is_lexsorted())
        self.assert_(index.lexsort_depth == 0)

    def test_frame_getitem_view(self):
        df = self.frame.T
        df["foo"].values[:] = 0
        self.assert_((df["foo"].values == 0).all())

        # but not if it's mixed-type
        df["foo", "four"] = "foo"
        df = df.sortlevel(0, axis=1)
        df["foo"]["one"] = 2
        self.assert_((df["foo", "one"] == 0).all())

    def test_frame_getitem_not_sorted(self):
        df = self.frame.T
        df["foo", "four"] = "foo"

        arrays = [np.array(x) for x in zip(*df.columns.get_tuple_index())]

        result = df["foo"]
        result2 = df.ix[:, "foo"]
        expected = df.reindex(columns=df.columns[arrays[0] == "foo"])
        expected.columns = expected.columns.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

        df = df.T
        result = df.xs("foo")
        result2 = df.ix["foo"]
        expected = df.reindex(df.index[arrays[0] == "foo"])
        expected.index = expected.index.droplevel(0)
        assert_frame_equal(result, expected)
        assert_frame_equal(result2, expected)

    def test_series_getitem_not_sorted(self):
        arrays = [
            ["bar", "bar", "baz", "baz", "qux", "qux", "foo", "foo"],
            ["one", "two", "one", "two", "one", "two", "one", "two"],
        ]
        tuples = zip(*arrays)
        index = MultiIndex.from_tuples(tuples)
        s = Series(randn(8), index=index)

        arrays = [np.array(x) for x in zip(*index.get_tuple_index())]

        result = s["qux"]
        result2 = s.ix["qux"]
        expected = s[arrays[0] == "qux"]
        expected.index = expected.index.droplevel(0)
        assert_series_equal(result, expected)
        assert_series_equal(result2, expected)

    AGG_FUNCTIONS = ["sum", "prod", "min", "max", "median", "mean", "skew", "mad", "std", "var"]

    def test_series_group_min_max(self):
        for op, level, skipna in cart_product(self.AGG_FUNCTIONS, range(2), [False, True]):
            grouped = self.series.groupby(level=level)
            aggf = lambda x: getattr(x, op)(skipna=skipna)
            # skipna=True
            leftside = grouped.agg(aggf)
            rightside = getattr(self.series, op)(level=level, skipna=skipna)
            assert_series_equal(leftside, rightside)

    def test_frame_group_ops(self):
        self.frame.ix[1, [1, 2]] = np.nan
        self.frame.ix[7, [0, 1]] = np.nan

        for op, level, axis, skipna in cart_product(self.AGG_FUNCTIONS, range(2), range(2), [False, True]):
            if axis == 0:
                frame = self.frame
            else:
                frame = self.frame.T

            grouped = frame.groupby(level=level, axis=axis)

            aggf = lambda x: getattr(x, op)(skipna=skipna, axis=axis)
            leftside = grouped.agg(aggf)
            rightside = getattr(frame, op)(level=level, axis=axis, skipna=skipna)
            assert_frame_equal(leftside, rightside)

    def test_groupby_multilevel(self):
        result = self.ymd.groupby(level=[0, 1]).mean()

        k1 = self.ymd.index.get_level_values(0)
        k2 = self.ymd.index.get_level_values(1)

        expected = self.ymd.groupby([k1, k2]).mean()

        assert_frame_equal(result, expected)
        self.assertEquals(result.index.names, self.ymd.index.names[:2])

        result2 = self.ymd.groupby(level=self.ymd.index.names[:2]).mean()
        assert_frame_equal(result, result2)

    def test_groupby_multilevel_with_transform(self):
        pass

    def test_multilevel_consolidate(self):
        index = MultiIndex.from_tuples([("foo", "one"), ("foo", "two"), ("bar", "one"), ("bar", "two")])
        df = DataFrame(np.random.randn(4, 4), index=index, columns=index)
        df["Totals", ""] = df.sum(1)
        df = df.consolidate()

    def test_ix_preserve_names(self):
        result = self.ymd.ix[2000]
        result2 = self.ymd["A"].ix[2000]
        self.assertEquals(result.index.names, self.ymd.index.names[1:])
        self.assertEquals(result2.index.names, self.ymd.index.names[1:])

        result = self.ymd.ix[2000, 2]
        result2 = self.ymd["A"].ix[2000, 2]
        self.assertEquals(result.index.name, self.ymd.index.names[2])
        self.assertEquals(result2.index.name, self.ymd.index.names[2])

    def test_partial_set(self):
        # GH #397
        df = self.ymd.copy()
        exp = self.ymd.copy()
        df.ix[2000, 4] = 0
        exp.ix[2000, 4].values[:] = 0
        assert_frame_equal(df, exp)

        df["A"].ix[2000, 4] = 1
        exp["A"].ix[2000, 4].values[:] = 1
        assert_frame_equal(df, exp)

        df.ix[2000] = 5
        exp.ix[2000].values[:] = 5
        assert_frame_equal(df, exp)

        # this works...for now
        df["A"].ix[14] = 5
        self.assertEquals(df["A"][14], 5)

    def test_unstack_preserve_types(self):
        # GH #403
        self.ymd["E"] = "foo"
        self.ymd["F"] = 2

        unstacked = self.ymd.unstack("month")
        self.assert_(unstacked["A", 1].dtype == np.float64)
        self.assert_(unstacked["E", 1].dtype == np.object_)
        self.assert_(unstacked["F", 1].dtype == np.float64)

    def test_partial_ix_missing(self):
        result = self.ymd.ix[2000, 0]
        expected = self.ymd.ix[2000]["A"]
        assert_series_equal(result, expected)

        # need to put in some work here

        # self.ymd.ix[2000, 0] = 0
        # self.assert_((self.ymd.ix[2000]['A'] == 0).all())

        self.assertRaises(Exception, self.ymd.ix.__getitem__, (2000, 6))
        self.assertRaises(Exception, self.ymd.ix.__getitem__, (2000, 6), 0)

    def test_to_html(self):
        self.ymd.columns.name = "foo"
        self.ymd.to_html()
        self.ymd.T.to_html()