test_reshape.py

from datetime import datetime
import itertools

import numpy as np
import pytest

import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Period, Series, Timedelta, date_range
from pandas.tests.frame.common import TestData
import pandas.util.testing as tm
from pandas.util.testing import assert_frame_equal, assert_series_equal


class TestDataFrameReshape(TestData):
    def test_pivot(self):
        data = {
            "index": ["A", "B", "C", "C", "B", "A"],
            "columns": ["One", "One", "One", "Two", "Two", "Two"],
            "values": [1.0, 2.0, 3.0, 3.0, 2.0, 1.0],
        }

        frame = DataFrame(data)
        pivoted = frame.pivot(index="index", columns="columns", values="values")

        expected = DataFrame(
            {
                "One": {"A": 1.0, "B": 2.0, "C": 3.0},
                "Two": {"A": 1.0, "B": 2.0, "C": 3.0},
            }
        )

        expected.index.name, expected.columns.name = "index", "columns"
        tm.assert_frame_equal(pivoted, expected)

        # name tracking
        assert pivoted.index.name == "index"
        assert pivoted.columns.name == "columns"

        # don't specify values
        pivoted = frame.pivot(index="index", columns="columns")
        assert pivoted.index.name == "index"
        assert pivoted.columns.names == (None, "columns")

    def test_pivot_duplicates(self):
        data = DataFrame(
            {
                "a": ["bar", "bar", "foo", "foo", "foo"],
                "b": ["one", "two", "one", "one", "two"],
                "c": [1.0, 2.0, 3.0, 3.0, 4.0],
            }
        )
        with pytest.raises(ValueError, match="duplicate entries"):
            data.pivot("a", "b", "c")

    def test_pivot_empty(self):
        df = DataFrame(columns=["a", "b", "c"])
        result = df.pivot("a", "b", "c")
        expected = DataFrame()
        tm.assert_frame_equal(result, expected, check_names=False)

    def test_pivot_integer_bug(self):
        df = DataFrame(data=[("A", "1", "A1"), ("B", "2", "B2")])

        result = df.pivot(index=1, columns=0, values=2)
        repr(result)
        tm.assert_index_equal(result.columns, Index(["A", "B"], name=0))

    def test_pivot_index_none(self):
        # gh-3962
        data = {
            "index": ["A", "B", "C", "C", "B", "A"],
            "columns": ["One", "One", "One", "Two", "Two", "Two"],
            "values": [1.0, 2.0, 3.0, 3.0, 2.0, 1.0],
        }

        frame = DataFrame(data).set_index("index")
        result = frame.pivot(columns="columns", values="values")
        expected = DataFrame(
            {
                "One": {"A": 1.0, "B": 2.0, "C": 3.0},
                "Two": {"A": 1.0, "B": 2.0, "C": 3.0},
            }
        )

        expected.index.name, expected.columns.name = "index", "columns"
        assert_frame_equal(result, expected)

        # omit values
        result = frame.pivot(columns="columns")

        expected.columns = pd.MultiIndex.from_tuples(
            [("values", "One"), ("values", "Two")], names=[None, "columns"]
        )
        expected.index.name = "index"
        tm.assert_frame_equal(result, expected, check_names=False)
        assert result.index.name == "index"
        assert result.columns.names == (None, "columns")
        expected.columns = expected.columns.droplevel(0)
        result = frame.pivot(columns="columns", values="values")

        expected.columns.name = "columns"
        tm.assert_frame_equal(result, expected)

    def test_stack_unstack(self):
        df = self.frame.copy()
        df[:] = np.arange(np.prod(df.shape)).reshape(df.shape)

        stacked = df.stack()
        stacked_df = DataFrame({"foo": stacked, "bar": stacked})

        unstacked = stacked.unstack()
        unstacked_df = stacked_df.unstack()

        assert_frame_equal(unstacked, df)
        assert_frame_equal(unstacked_df["bar"], df)

        unstacked_cols = stacked.unstack(0)
        unstacked_cols_df = stacked_df.unstack(0)
        assert_frame_equal(unstacked_cols.T, df)
        assert_frame_equal(unstacked_cols_df["bar"].T, df)

    def test_stack_mixed_level(self):
        # GH 18310
        levels = [range(3), [3, "a", "b"], [1, 2]]

        # flat columns:
        df = DataFrame(1, index=levels[0], columns=levels[1])
        result = df.stack()
        expected = Series(1, index=MultiIndex.from_product(levels[:2]))
        assert_series_equal(result, expected)

        # MultiIndex columns:
        df = DataFrame(1, index=levels[0], columns=MultiIndex.from_product(levels[1:]))
        result = df.stack(1)
        expected = DataFrame(
            1, index=MultiIndex.from_product([levels[0], levels[2]]), columns=levels[1]
        )
        assert_frame_equal(result, expected)

        # as above, but used labels in level are actually of homogeneous type
        result = df[["a", "b"]].stack(1)
        expected = expected[["a", "b"]]
        assert_frame_equal(result, expected)

    def test_unstack_fill(self):

        # GH #9746: fill_value keyword argument for Series
        # and DataFrame unstack

        # From a series
        data = Series([1, 2, 4, 5], dtype=np.int16)
        data.index = MultiIndex.from_tuples(
            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
        )

        result = data.unstack(fill_value=-1)
        expected = DataFrame(
            {"a": [1, -1, 5], "b": [2, 4, -1]}, index=["x", "y", "z"], dtype=np.int16
        )
        assert_frame_equal(result, expected)

        # From a series with incorrect data type for fill_value
        result = data.unstack(fill_value=0.5)
        expected = DataFrame(
            {"a": [1, 0.5, 5], "b": [2, 4, 0.5]}, index=["x", "y", "z"], dtype=np.float
        )
        assert_frame_equal(result, expected)

        # GH #13971: fill_value when unstacking multiple levels:
        df = DataFrame(
            {"x": ["a", "a", "b"], "y": ["j", "k", "j"], "z": [0, 1, 2], "w": [0, 1, 2]}
        ).set_index(["x", "y", "z"])
        unstacked = df.unstack(["x", "y"], fill_value=0)
        key = ("w", "b", "j")
        expected = unstacked[key]
        result = pd.Series([0, 0, 2], index=unstacked.index, name=key)
        assert_series_equal(result, expected)

        stacked = unstacked.stack(["x", "y"])
        stacked.index = stacked.index.reorder_levels(df.index.names)
        # Workaround for GH #17886 (unnecessarily casts to float):
        stacked = stacked.astype(np.int64)
        result = stacked.loc[df.index]
        assert_frame_equal(result, df)

        # From a series
        s = df["w"]
        result = s.unstack(["x", "y"], fill_value=0)
        expected = unstacked["w"]
        assert_frame_equal(result, expected)

    def test_unstack_fill_frame(self):

        # From a dataframe
        rows = [[1, 2], [3, 4], [5, 6], [7, 8]]
        df = DataFrame(rows, columns=list("AB"), dtype=np.int32)
        df.index = MultiIndex.from_tuples(
            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
        )

        result = df.unstack(fill_value=-1)

        rows = [[1, 3, 2, 4], [-1, 5, -1, 6], [7, -1, 8, -1]]
        expected = DataFrame(rows, index=list("xyz"), dtype=np.int32)
        expected.columns = MultiIndex.from_tuples(
            [("A", "a"), ("A", "b"), ("B", "a"), ("B", "b")]
        )
        assert_frame_equal(result, expected)

        # From a mixed type dataframe
        df["A"] = df["A"].astype(np.int16)
        df["B"] = df["B"].astype(np.float64)

        result = df.unstack(fill_value=-1)
        expected["A"] = expected["A"].astype(np.int16)
        expected["B"] = expected["B"].astype(np.float64)
        assert_frame_equal(result, expected)

        # From a dataframe with incorrect data type for fill_value
        result = df.unstack(fill_value=0.5)

        rows = [[1, 3, 2, 4], [0.5, 5, 0.5, 6], [7, 0.5, 8, 0.5]]
        expected = DataFrame(rows, index=list("xyz"), dtype=np.float)
        expected.columns = MultiIndex.from_tuples(
            [("A", "a"), ("A", "b"), ("B", "a"), ("B", "b")]
        )
        assert_frame_equal(result, expected)

    def test_unstack_fill_frame_datetime(self):

        # Test unstacking with date times
        dv = pd.date_range("2012-01-01", periods=4).values
        data = Series(dv)
        data.index = MultiIndex.from_tuples(
            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
        )

        result = data.unstack()
        expected = DataFrame(
            {"a": [dv[0], pd.NaT, dv[3]], "b": [dv[1], dv[2], pd.NaT]},
            index=["x", "y", "z"],
        )
        assert_frame_equal(result, expected)

        result = data.unstack(fill_value=dv[0])
        expected = DataFrame(
            {"a": [dv[0], dv[0], dv[3]], "b": [dv[1], dv[2], dv[0]]},
            index=["x", "y", "z"],
        )
        assert_frame_equal(result, expected)

    def test_unstack_fill_frame_timedelta(self):

        # Test unstacking with time deltas
        td = [Timedelta(days=i) for i in range(4)]
        data = Series(td)
        data.index = MultiIndex.from_tuples(
            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
        )

        result = data.unstack()
        expected = DataFrame(
            {"a": [td[0], pd.NaT, td[3]], "b": [td[1], td[2], pd.NaT]},
            index=["x", "y", "z"],
        )
        assert_frame_equal(result, expected)

        result = data.unstack(fill_value=td[1])
        expected = DataFrame(
            {"a": [td[0], td[1], td[3]], "b": [td[1], td[2], td[1]]},
            index=["x", "y", "z"],
        )
        assert_frame_equal(result, expected)

    def test_unstack_fill_frame_period(self):

        # Test unstacking with period
        periods = [
            Period("2012-01"),
            Period("2012-02"),
            Period("2012-03"),
            Period("2012-04"),
        ]
        data = Series(periods)
        data.index = MultiIndex.from_tuples(
            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
        )

        result = data.unstack()
        expected = DataFrame(
            {"a": [periods[0], None, periods[3]], "b": [periods[1], periods[2], None]},
            index=["x", "y", "z"],
        )
        assert_frame_equal(result, expected)

        result = data.unstack(fill_value=periods[1])
        expected = DataFrame(
            {
                "a": [periods[0], periods[1], periods[3]],
                "b": [periods[1], periods[2], periods[1]],
            },
            index=["x", "y", "z"],
        )
        assert_frame_equal(result, expected)

    def test_unstack_fill_frame_categorical(self):

        # Test unstacking with categorical
        data = pd.Series(["a", "b", "c", "a"], dtype="category")
        data.index = pd.MultiIndex.from_tuples(
            [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
        )

        # By default missing values will be NaN
        result = data.unstack()
        expected = DataFrame(
            {
                "a": pd.Categorical(list("axa"), categories=list("abc")),
                "b": pd.Categorical(list("bcx"), categories=list("abc")),
            },
            index=list("xyz"),
        )
        assert_frame_equal(result, expected)

        # Fill with non-category results in a TypeError
        msg = r"'fill_value' \('d'\) is not in"
        with pytest.raises(TypeError, match=msg):
            data.unstack(fill_value="d")

        # Fill with category value replaces missing values as expected
        result = data.unstack(fill_value="c")
        expected = DataFrame(
            {
                "a": pd.Categorical(list("aca"), categories=list("abc")),
                "b": pd.Categorical(list("bcc"), categories=list("abc")),
            },
            index=list("xyz"),
        )
        assert_frame_equal(result, expected)

    def test_unstack_preserve_dtypes(self):
        # Checks fix for #11847
        df = pd.DataFrame(
            dict(
                state=["IL", "MI", "NC"],
                index=["a", "b", "c"],
                some_categories=pd.Series(["a", "b", "c"]).astype("category"),
                A=np.random.rand(3),
                B=1,
                C="foo",
                D=pd.Timestamp("20010102"),
                E=pd.Series([1.0, 50.0, 100.0]).astype("float32"),
                F=pd.Series([3.0, 4.0, 5.0]).astype("float64"),
                G=False,
                H=pd.Series([1, 200, 923442], dtype="int8"),
            )
        )

        def unstack_and_compare(df, column_name):
            unstacked1 = df.unstack([column_name])
            unstacked2 = df.unstack(column_name)
            assert_frame_equal(unstacked1, unstacked2)

        df1 = df.set_index(["state", "index"])
        unstack_and_compare(df1, "index")

        df1 = df.set_index(["state", "some_categories"])
        unstack_and_compare(df1, "some_categories")

        df1 = df.set_index(["F", "C"])
        unstack_and_compare(df1, "F")

        df1 = df.set_index(["G", "B", "state"])
        unstack_and_compare(df1, "B")

        df1 = df.set_index(["E", "A"])
        unstack_and_compare(df1, "E")

        df1 = df.set_index(["state", "index"])
        s = df1["A"]
        unstack_and_compare(s, "index")

    def test_stack_ints(self):
        columns = MultiIndex.from_tuples(list(itertools.product(range(3), repeat=3)))
        df = DataFrame(np.random.randn(30, 27), columns=columns)

        assert_frame_equal(df.stack(level=[1, 2]), df.stack(level=1).stack(level=1))
        assert_frame_equal(df.stack(level=[-2, -1]), df.stack(level=1).stack(level=1))

        df_named = df.copy()
        df_named.columns.set_names(range(3), inplace=True)

        assert_frame_equal(
            df_named.stack(level=[1, 2]), df_named.stack(level=1).stack(level=1)
        )

    def test_stack_mixed_levels(self):
        columns = MultiIndex.from_tuples(
            [
                ("A", "cat", "long"),
                ("B", "cat", "long"),
                ("A", "dog", "short"),
                ("B", "dog", "short"),
            ],
            names=["exp", "animal", "hair_length"],
        )
        df = DataFrame(np.random.randn(4, 4), columns=columns)

        animal_hair_stacked = df.stack(level=["animal", "hair_length"])
        exp_hair_stacked = df.stack(level=["exp", "hair_length"])

        # GH #8584: Need to check that stacking works when a number
        # is passed that is both a level name and in the range of
        # the level numbers
        df2 = df.copy()
        df2.columns.names = ["exp", "animal", 1]
        assert_frame_equal(
            df2.stack(level=["animal", 1]), animal_hair_stacked, check_names=False
        )
        assert_frame_equal(
            df2.stack(level=["exp", 1]), exp_hair_stacked, check_names=False
        )

        # When mixed types are passed and the ints are not level
        # names, raise
        msg = (
            "level should contain all level names or all level numbers, not"
            " a mixture of the two"
        )
        with pytest.raises(ValueError, match=msg):
            df2.stack(level=["animal", 0])

        # GH #8584: Having 0 in the level names could raise a
        # strange error about lexsort depth
        df3 = df.copy()
        df3.columns.names = ["exp", "animal", 0]
        assert_frame_equal(
            df3.stack(level=["animal", 0]), animal_hair_stacked, check_names=False
        )

    def test_stack_int_level_names(self):
        columns = MultiIndex.from_tuples(
            [
                ("A", "cat", "long"),
                ("B", "cat", "long"),
                ("A", "dog", "short"),
                ("B", "dog", "short"),
            ],
            names=["exp", "animal", "hair_length"],
        )
        df = DataFrame(np.random.randn(4, 4), columns=columns)

        exp_animal_stacked = df.stack(level=["exp", "animal"])
        animal_hair_stacked = df.stack(level=["animal", "hair_length"])
        exp_hair_stacked = df.stack(level=["exp", "hair_length"])

        df2 = df.copy()
        df2.columns.names = [0, 1, 2]
        assert_frame_equal(
            df2.stack(level=[1, 2]), animal_hair_stacked, check_names=False
        )
        assert_frame_equal(
            df2.stack(level=[0, 1]), exp_animal_stacked, check_names=False
        )
        assert_frame_equal(df2.stack(level=[0, 2]), exp_hair_stacked, check_names=False)

        # Out-of-order int column names
        df3 = df.copy()
        df3.columns.names = [2, 0, 1]
        assert_frame_equal(
            df3.stack(level=[0, 1]), animal_hair_stacked, check_names=False
        )
        assert_frame_equal(
            df3.stack(level=[2, 0]), exp_animal_stacked, check_names=False
        )
        assert_frame_equal(df3.stack(level=[2, 1]), exp_hair_stacked, check_names=False)

    def test_unstack_bool(self):
        df = DataFrame(
            [False, False],
            index=MultiIndex.from_arrays([["a", "b"], ["c", "l"]]),
            columns=["col"],
        )
        rs = df.unstack()
        xp = DataFrame(
            np.array([[False, np.nan], [np.nan, False]], dtype=object),
            index=["a", "b"],
            columns=MultiIndex.from_arrays([["col", "col"], ["c", "l"]]),
        )
        assert_frame_equal(rs, xp)

    def test_unstack_level_binding(self):
        # GH9856
        mi = pd.MultiIndex(
            levels=[["foo", "bar"], ["one", "two"], ["a", "b"]],
            codes=[[0, 0, 1, 1], [0, 1, 0, 1], [1, 0, 1, 0]],
            names=["first", "second", "third"],
        )
        s = pd.Series(0, index=mi)
        result = s.unstack([1, 2]).stack(0)

        expected_mi = pd.MultiIndex(
            levels=[["foo", "bar"], ["one", "two"]],
            codes=[[0, 0, 1, 1], [0, 1, 0, 1]],
            names=["first", "second"],
        )

        expected = pd.DataFrame(
            np.array(
                [[np.nan, 0], [0, np.nan], [np.nan, 0], [0, np.nan]], dtype=np.float64
            ),
            index=expected_mi,
            columns=pd.Index(["a", "b"], name="third"),
        )

        assert_frame_equal(result, expected)

    def test_unstack_to_series(self):
        # check reversibility
        data = self.frame.unstack()

        assert isinstance(data, Series)
        undo = data.unstack().T
        assert_frame_equal(undo, self.frame)

        # check NA handling
        data = DataFrame({"x": [1, 2, np.NaN], "y": [3.0, 4, np.NaN]})
        data.index = Index(["a", "b", "c"])
        result = data.unstack()

        midx = MultiIndex(
            levels=[["x", "y"], ["a", "b", "c"]],
            codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],
        )
        expected = Series([1, 2, np.NaN, 3, 4, np.NaN], index=midx)

        assert_series_equal(result, expected)

        # check composability of unstack
        old_data = data.copy()
        for _ in range(4):
            data = data.unstack()
        assert_frame_equal(old_data, data)

    def test_unstack_dtypes(self):

        # GH 2929
        rows = [[1, 1, 3, 4], [1, 2, 3, 4], [2, 1, 3, 4], [2, 2, 3, 4]]

        df = DataFrame(rows, columns=list("ABCD"))
        result = df.dtypes
        expected = Series([np.dtype("int64")] * 4, index=list("ABCD"))
        assert_series_equal(result, expected)

        # single dtype
        df2 = df.set_index(["A", "B"])
        df3 = df2.unstack("B")
        result = df3.dtypes
        expected = Series(
            [np.dtype("int64")] * 4,
            index=pd.MultiIndex.from_arrays(
                [["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B")
            ),
        )
        assert_series_equal(result, expected)

        # mixed
        df2 = df.set_index(["A", "B"])
        df2["C"] = 3.0
        df3 = df2.unstack("B")
        result = df3.dtypes
        expected = Series(
            [np.dtype("float64")] * 2 + [np.dtype("int64")] * 2,
            index=pd.MultiIndex.from_arrays(
                [["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B")
            ),
        )
        assert_series_equal(result, expected)
        df2["D"] = "foo"
        df3 = df2.unstack("B")
        result = df3.dtypes
        expected = Series(
            [np.dtype("float64")] * 2 + [np.dtype("object")] * 2,
            index=pd.MultiIndex.from_arrays(
                [["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B")
            ),
        )
        assert_series_equal(result, expected)

        # GH7405
        for c, d in (
            (np.zeros(5), np.zeros(5)),
            (np.arange(5, dtype="f8"), np.arange(5, 10, dtype="f8")),
        ):

            df = DataFrame(
                {
                    "A": ["a"] * 5,
                    "C": c,
                    "D": d,
                    "B": pd.date_range("2012-01-01", periods=5),
                }
            )

            right = df.iloc[:3].copy(deep=True)

            df = df.set_index(["A", "B"])
            df["D"] = df["D"].astype("int64")

            left = df.iloc[:3].unstack(0)
            right = right.set_index(["A", "B"]).unstack(0)
            right[("D", "a")] = right[("D", "a")].astype("int64")

            assert left.shape == (3, 2)
            tm.assert_frame_equal(left, right)

    def test_unstack_non_unique_index_names(self):
        idx = MultiIndex.from_tuples([("a", "b"), ("c", "d")], names=["c1", "c1"])
        df = DataFrame([1, 2], index=idx)
        with pytest.raises(ValueError):
            df.unstack("c1")

        with pytest.raises(ValueError):
            df.T.stack("c1")

    def test_unstack_unused_levels(self):
        # GH 17845: unused codes in index make unstack() cast int to float
        idx = pd.MultiIndex.from_product([["a"], ["A", "B", "C", "D"]])[:-1]
        df = pd.DataFrame([[1, 0]] * 3, index=idx)

        result = df.unstack()
        exp_col = pd.MultiIndex.from_product([[0, 1], ["A", "B", "C"]])
        expected = pd.DataFrame([[1, 1, 1, 0, 0, 0]], index=["a"], columns=exp_col)
        tm.assert_frame_equal(result, expected)
        assert (result.columns.levels[1] == idx.levels[1]).all()

        # Unused items on both levels
        levels = [[0, 1, 7], [0, 1, 2, 3]]
        codes = [[0, 0, 1, 1], [0, 2, 0, 2]]
        idx = pd.MultiIndex(levels, codes)
        block = np.arange(4).reshape(2, 2)
        df = pd.DataFrame(np.concatenate([block, block + 4]), index=idx)
        result = df.unstack()
        expected = pd.DataFrame(
            np.concatenate([block * 2, block * 2 + 1], axis=1), columns=idx
        )
        tm.assert_frame_equal(result, expected)
        assert (result.columns.levels[1] == idx.levels[1]).all()

        # With mixed dtype and NaN
        levels = [["a", 2, "c"], [1, 3, 5, 7]]
        codes = [[0, -1, 1, 1], [0, 2, -1, 2]]
        idx = pd.MultiIndex(levels, codes)
        data = np.arange(8)
        df = pd.DataFrame(data.reshape(4, 2), index=idx)

        cases = (
            (0, [13, 16, 6, 9, 2, 5, 8, 11], [np.nan, "a", 2], [np.nan, 5, 1]),
            (1, [8, 11, 1, 4, 12, 15, 13, 16], [np.nan, 5, 1], [np.nan, "a", 2]),
        )
        for level, idces, col_level, idx_level in cases:
            result = df.unstack(level=level)
            exp_data = np.zeros(18) * np.nan
            exp_data[idces] = data
            cols = pd.MultiIndex.from_product([[0, 1], col_level])
            expected = pd.DataFrame(
                exp_data.reshape(3, 6), index=idx_level, columns=cols
            )
            tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize("cols", [["A", "C"], slice(None)])
    def test_unstack_unused_level(self, cols):
        # GH 18562 : unused codes on the unstacked level
        df = pd.DataFrame(
            [[2010, "a", "I"], [2011, "b", "II"]], columns=["A", "B", "C"]
        )

        ind = df.set_index(["A", "B", "C"], drop=False)
        selection = ind.loc[(slice(None), slice(None), "I"), cols]
        result = selection.unstack()

        expected = ind.iloc[[0]][cols]
        expected.columns = MultiIndex.from_product(
            [expected.columns, ["I"]], names=[None, "C"]
        )
        expected.index = expected.index.droplevel("C")
        tm.assert_frame_equal(result, expected)

    def test_unstack_nan_index(self):  # GH7466
        cast = lambda val: "{0:1}".format("" if val != val else val)

        def verify(df):
            mk_list = lambda a: list(a) if isinstance(a, tuple) else [a]
            rows, cols = df.notna().values.nonzero()
            for i, j in zip(rows, cols):
                left = sorted(df.iloc[i, j].split("."))
                right = mk_list(df.index[i]) + mk_list(df.columns[j])
                right = sorted(list(map(cast, right)))
                assert left == right

        df = DataFrame(
            {
                "jim": ["a", "b", np.nan, "d"],
                "joe": ["w", "x", "y", "z"],
                "jolie": ["a.w", "b.x", " .y", "d.z"],
            }
        )

        left = df.set_index(["jim", "joe"]).unstack()["jolie"]
        right = df.set_index(["joe", "jim"]).unstack()["jolie"].T
        assert_frame_equal(left, right)

        for idx in itertools.permutations(df.columns[:2]):
            mi = df.set_index(list(idx))
            for lev in range(2):
                udf = mi.unstack(level=lev)
                assert udf.notna().values.sum() == len(df)
                verify(udf["jolie"])

        df = DataFrame(
            {
                "1st": ["d"] * 3
                + [np.nan] * 5
                + ["a"] * 2
                + ["c"] * 3
                + ["e"] * 2
                + ["b"] * 5,
                "2nd": ["y"] * 2
                + ["w"] * 3
                + [np.nan] * 3
                + ["z"] * 4
                + [np.nan] * 3
                + ["x"] * 3
                + [np.nan] * 2,
                "3rd": [
                    67,
                    39,
                    53,
                    72,
                    57,
                    80,
                    31,
                    18,
                    11,
                    30,
                    59,
                    50,
                    62,
                    59,
                    76,
                    52,
                    14,
                    53,
                    60,
                    51,
                ],
            }
        )

        df["4th"], df["5th"] = (
            df.apply(lambda r: ".".join(map(cast, r)), axis=1),
            df.apply(lambda r: ".".join(map(cast, r.iloc[::-1])), axis=1),
        )

        for idx in itertools.permutations(["1st", "2nd", "3rd"]):
            mi = df.set_index(list(idx))
            for lev in range(3):
                udf = mi.unstack(level=lev)
                assert udf.notna().values.sum() == 2 * len(df)
                for col in ["4th", "5th"]:
                    verify(udf[col])

        # GH7403
        df = pd.DataFrame({"A": list("aaaabbbb"), "B": range(8), "C": range(8)})
        df.iloc[3, 1] = np.NaN
        left = df.set_index(["A", "B"]).unstack(0)

        vals = [
            [3, 0, 1, 2, np.nan, np.nan, np.nan, np.nan],
            [np.nan, np.nan, np.nan, np.nan, 4, 5, 6, 7],
        ]
        vals = list(map(list, zip(*vals)))
        idx = Index([np.nan, 0, 1, 2, 4, 5, 6, 7], name="B")
        cols = MultiIndex(
            levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"]
        )

        right = DataFrame(vals, columns=cols, index=idx)
        assert_frame_equal(left, right)

        df = DataFrame({"A": list("aaaabbbb"), "B": list(range(4)) * 2, "C": range(8)})
        df.iloc[2, 1] = np.NaN
        left = df.set_index(["A", "B"]).unstack(0)

        vals = [[2, np.nan], [0, 4], [1, 5], [np.nan, 6], [3, 7]]
        cols = MultiIndex(
            levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"]
        )
        idx = Index([np.nan, 0, 1, 2, 3], name="B")
        right = DataFrame(vals, columns=cols, index=idx)
        assert_frame_equal(left, right)

        df = pd.DataFrame(
            {"A": list("aaaabbbb"), "B": list(range(4)) * 2, "C": range(8)}
        )
        df.iloc[3, 1] = np.NaN
        left = df.set_index(["A", "B"]).unstack(0)

        vals = [[3, np.nan], [0, 4], [1, 5], [2, 6], [np.nan, 7]]
        cols = MultiIndex(
            levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"]
        )
        idx = Index([np.nan, 0, 1, 2, 3], name="B")
        right = DataFrame(vals, columns=cols, index=idx)
        assert_frame_equal(left, right)

        # GH7401
        df = pd.DataFrame(
            {
                "A": list("aaaaabbbbb"),
                "B": (date_range("2012-01-01", periods=5).tolist() * 2),
                "C": np.arange(10),
            }
        )

        df.iloc[3, 1] = np.NaN
        left = df.set_index(["A", "B"]).unstack()

        vals = np.array([[3, 0, 1, 2, np.nan, 4], [np.nan, 5, 6, 7, 8, 9]])
        idx = Index(["a", "b"], name="A")
        cols = MultiIndex(
            levels=[["C"], date_range("2012-01-01", periods=5)],
            codes=[[0, 0, 0, 0, 0, 0], [-1, 0, 1, 2, 3, 4]],
            names=[None, "B"],
        )

        right = DataFrame(vals, columns=cols, index=idx)
        assert_frame_equal(left, right)

        # GH4862
        vals = [
            ["Hg", np.nan, np.nan, 680585148],
            ["U", 0.0, np.nan, 680585148],
            ["Pb", 7.07e-06, np.nan, 680585148],
            ["Sn", 2.3614e-05, 0.0133, 680607017],
            ["Ag", 0.0, 0.0133, 680607017],
            ["Hg", -0.00015, 0.0133, 680607017],
        ]
        df = DataFrame(
            vals,
            columns=["agent", "change", "dosage", "s_id"],
            index=[17263, 17264, 17265, 17266, 17267, 17268],
        )

        left = df.copy().set_index(["s_id", "dosage", "agent"]).unstack()

        vals = [
            [np.nan, np.nan, 7.07e-06, np.nan, 0.0],
            [0.0, -0.00015, np.nan, 2.3614e-05, np.nan],
        ]

        idx = MultiIndex(
            levels=[[680585148, 680607017], [0.0133]],
            codes=[[0, 1], [-1, 0]],
            names=["s_id", "dosage"],
        )

        cols = MultiIndex(
            levels=[["change"], ["Ag", "Hg", "Pb", "Sn", "U"]],
            codes=[[0, 0, 0, 0, 0], [0, 1, 2, 3, 4]],
            names=[None, "agent"],
        )

        right = DataFrame(vals, columns=cols, index=idx)
        assert_frame_equal(left, right)

        left = df.loc[17264:].copy().set_index(["s_id", "dosage", "agent"])
        assert_frame_equal(left.unstack(), right)

        # GH9497 - multiple unstack with nulls
        df = DataFrame(
            {
                "1st": [1, 2, 1, 2, 1, 2],
                "2nd": pd.date_range("2014-02-01", periods=6, freq="D"),
                "jim": 100 + np.arange(6),
                "joe": (np.random.randn(6) * 10).round(2),
            }
        )

        df["3rd"] = df["2nd"] - pd.Timestamp("2014-02-02")
        df.loc[1, "2nd"] = df.loc[3, "2nd"] = np.nan
        df.loc[1, "3rd"] = df.loc[4, "3rd"] = np.nan

        left = df.set_index(["1st", "2nd", "3rd"]).unstack(["2nd", "3rd"])
        assert left.notna().values.sum() == 2 * len(df)

        for col in ["jim", "joe"]:
            for _, r in df.iterrows():
                key = r["1st"], (col, r["2nd"], r["3rd"])
                assert r[col] == left.loc[key]

    def test_stack_datetime_column_multiIndex(self):
        # GH 8039
        t = datetime(2014, 1, 1)
        df = DataFrame([1, 2, 3, 4], columns=MultiIndex.from_tuples([(t, "A", "B")]))
        result = df.stack()

        eidx = MultiIndex.from_product([(0, 1, 2, 3), ("B",)])
        ecols = MultiIndex.from_tuples([(t, "A")])
        expected = DataFrame([1, 2, 3, 4], index=eidx, columns=ecols)
        assert_frame_equal(result, expected)

    def test_stack_partial_multiIndex(self):
        # GH 8844
        def _test_stack_with_multiindex(multiindex):
            df = DataFrame(
                np.arange(3 * len(multiindex)).reshape(3, len(multiindex)),
                columns=multiindex,
            )
            for level in (-1, 0, 1, [0, 1], [1, 0]):
                result = df.stack(level=level, dropna=False)

                if isinstance(level, int):
                    # Stacking a single level should not make any all-NaN rows,
                    # so df.stack(level=level, dropna=False) should be the same
                    # as df.stack(level=level, dropna=True).
                    expected = df.stack(level=level, dropna=True)
                    if isinstance(expected, Series):
                        assert_series_equal(result, expected)
                    else:
                        assert_frame_equal(result, expected)

                df.columns = MultiIndex.from_tuples(
                    df.columns.to_numpy(), names=df.columns.names
                )
                expected = df.stack(level=level, dropna=False)
                if isinstance(expected, Series):
                    assert_series_equal(result, expected)
                else:
                    assert_frame_equal(result, expected)

        full_multiindex = MultiIndex.from_tuples(
            [("B", "x"), ("B", "z"), ("A", "y"), ("C", "x"), ("C", "u")],
            names=["Upper", "Lower"],
        )
        for multiindex_columns in (
            [0, 1, 2, 3, 4],
            [0, 1, 2, 3],
            [0, 1, 2, 4],
            [0, 1, 2],
            [1, 2, 3],
            [2, 3, 4],
            [0, 1],
            [0, 2],
            [0, 3],
            [0],
            [2],
            [4],
        ):
            _test_stack_with_multiindex(full_multiindex[multiindex_columns])
            if len(multiindex_columns) > 1:
                multiindex_columns.reverse()
                _test_stack_with_multiindex(full_multiindex[multiindex_columns])

        df = DataFrame(np.arange(6).reshape(2, 3), columns=full_multiindex[[0, 1, 3]])
        result = df.stack(dropna=False)
        expected = DataFrame(
            [[0, 2], [1, np.nan], [3, 5], [4, np.nan]],
            index=MultiIndex(
                levels=[[0, 1], ["u", "x", "y", "z"]],
                codes=[[0, 0, 1, 1], [1, 3, 1, 3]],
                names=[None, "Lower"],
            ),
            columns=Index(["B", "C"], name="Upper"),
            dtype=df.dtypes[0],
        )
        assert_frame_equal(result, expected)

    @pytest.mark.parametrize("ordered", [False, True])
    @pytest.mark.parametrize("labels", [list("yxz"), list("yxy")])
    def test_stack_preserve_categorical_dtype(self, ordered, labels):
        # GH13854
        cidx = pd.CategoricalIndex(labels, categories=list("xyz"), ordered=ordered)
        df = DataFrame([[10, 11, 12]], columns=cidx)
        result = df.stack()

        # `MultiIndex.from_product` preserves categorical dtype -
        # it's tested elsewhere.
        midx = pd.MultiIndex.from_product([df.index, cidx])
        expected = Series([10, 11, 12], index=midx)

        tm.assert_series_equal(result, expected)

    def test_stack_preserve_categorical_dtype_values(self):
        # GH-23077
        cat = pd.Categorical(["a", "a", "b", "c"])
        df = pd.DataFrame({"A": cat, "B": cat})
        result = df.stack()
        index = pd.MultiIndex.from_product([[0, 1, 2, 3], ["A", "B"]])
        expected = pd.Series(
            pd.Categorical(["a", "a", "a", "a", "b", "b", "c", "c"]), index=index
        )
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize(
        "index, columns",
        [
            ([0, 0, 1, 1], pd.MultiIndex.from_product([[1, 2], ["a", "b"]])),
            ([0, 0, 2, 3], pd.MultiIndex.from_product([[1, 2], ["a", "b"]])),
            ([0, 1, 2, 3], pd.MultiIndex.from_product([[1, 2], ["a", "b"]])),
        ],
    )
    def test_stack_multi_columns_non_unique_index(self, index, columns):
        # GH-28301
        df = pd.DataFrame(index=index, columns=columns).fillna(1)
        stacked = df.stack()
        new_index = pd.MultiIndex.from_tuples(stacked.index.to_numpy())
        expected = pd.DataFrame(
            stacked.to_numpy(), index=new_index, columns=stacked.columns
        )
        tm.assert_frame_equal(stacked, expected)
        stacked_codes = np.asarray(stacked.index.codes)
        expected_codes = np.asarray(new_index.codes)
        tm.assert_numpy_array_equal(stacked_codes, expected_codes)

    @pytest.mark.parametrize("level", [0, 1])
    def test_unstack_mixed_extension_types(self, level):
        index = pd.MultiIndex.from_tuples(
            [("A", 0), ("A", 1), ("B", 1)], names=["a", "b"]
        )
        df = pd.DataFrame(
            {
                "A": pd.core.arrays.integer_array([0, 1, None]),
                "B": pd.Categorical(["a", "a", "b"]),
            },
            index=index,
        )

        result = df.unstack(level=level)
        expected = df.astype(object).unstack(level=level)

        expected_dtypes = pd.Series(
            [df.A.dtype] * 2 + [df.B.dtype] * 2, index=result.columns
        )
        tm.assert_series_equal(result.dtypes, expected_dtypes)
        tm.assert_frame_equal(result.astype(object), expected)

    @pytest.mark.parametrize("level", [0, "baz"])
    def test_unstack_swaplevel_sortlevel(self, level):
        # GH 20994
        mi = pd.MultiIndex.from_product([[0], ["d", "c"]], names=["bar", "baz"])
        df = pd.DataFrame([[0, 2], [1, 3]], index=mi, columns=["B", "A"])
        df.columns.name = "foo"

        expected = pd.DataFrame(
            [[3, 1, 2, 0]],
            columns=pd.MultiIndex.from_tuples(
                [("c", "A"), ("c", "B"), ("d", "A"), ("d", "B")], names=["baz", "foo"]
            ),
        )
        expected.index.name = "bar"

        result = df.unstack().swaplevel(axis=1).sort_index(axis=1, level=level)
        tm.assert_frame_equal(result, expected)


def test_unstack_fill_frame_object():
    # GH12815 Test unstacking with object.
    data = pd.Series(["a", "b", "c", "a"], dtype="object")
    data.index = pd.MultiIndex.from_tuples(
        [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")]
    )

    # By default missing values will be NaN
    result = data.unstack()
    expected = pd.DataFrame(
        {"a": ["a", np.nan, "a"], "b": ["b", "c", np.nan]}, index=list("xyz")
    )
    assert_frame_equal(result, expected)

    # Fill with any value replaces missing values as expected
    result = data.unstack(fill_value="d")
    expected = pd.DataFrame(
        {"a": ["a", "d", "a"], "b": ["b", "c", "d"]}, index=list("xyz")
    )
    assert_frame_equal(result, expected)


def test_unstack_timezone_aware_values():
    # GH 18338
    df = pd.DataFrame(
        {
            "timestamp": [pd.Timestamp("2017-08-27 01:00:00.709949+0000", tz="UTC")],
            "a": ["a"],
            "b": ["b"],
            "c": ["c"],
        },
        columns=["timestamp", "a", "b", "c"],
    )
    result = df.set_index(["a", "b"]).unstack()
    expected = pd.DataFrame(
        [[pd.Timestamp("2017-08-27 01:00:00.709949+0000", tz="UTC"), "c"]],
        index=pd.Index(["a"], name="a"),
        columns=pd.MultiIndex(
            levels=[["timestamp", "c"], ["b"]],
            codes=[[0, 1], [0, 0]],
            names=[None, "b"],
        ),
    )
    assert_frame_equal(result, expected)


def test_stack_timezone_aware_values():
    # GH 19420
    ts = pd.date_range(
        freq="D", start="20180101", end="20180103", tz="America/New_York"
    )
    df = pd.DataFrame({"A": ts}, index=["a", "b", "c"])
    result = df.stack()
    expected = pd.Series(
        ts,
        index=pd.MultiIndex(
            levels=[["a", "b", "c"], ["A"]], codes=[[0, 1, 2], [0, 0, 0]]
        ),
    )
    assert_series_equal(result, expected)