text: 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
  import pandas._testing as tm
  
  
  class TestDataFrameReshape:
      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"
          tm.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, float_frame):
          df = float_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()
  
          tm.assert_frame_equal(unstacked, df)
          tm.assert_frame_equal(unstacked_df["bar"], df)
  
          unstacked_cols = stacked.unstack(0)
          unstacked_cols_df = stacked_df.unstack(0)
          tm.assert_frame_equal(unstacked_cols.T, df)
          tm.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]))
          tm.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]
          )
          tm.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"]]
          tm.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
          )
          tm.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
          )
          tm.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)
          tm.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]
          tm.assert_frame_equal(result, df)
  
          # From a series
          s = df["w"]
          result = s.unstack(["x", "y"], fill_value=0)
          expected = unstacked["w"]
          tm.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")]
          )
          tm.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)
          tm.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")]
          )
          tm.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"],
          )
          tm.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"],
          )
          tm.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"],
          )
          tm.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"],
          )
          tm.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"],
          )
          tm.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"],
          )
          tm.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"),
          )
          tm.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"),
          )
          tm.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)
              tm.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)
  
          tm.assert_frame_equal(df.stack(level=[1, 2]), df.stack(level=1).stack(level=1))
          tm.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)
  
          tm.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]
          tm.assert_frame_equal(
              df2.stack(level=["animal", 1]), animal_hair_stacked, check_names=False
          )
          tm.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]
          tm.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]
          tm.assert_frame_equal(
              df2.stack(level=[1, 2]), animal_hair_stacked, check_names=False
          )
          tm.assert_frame_equal(
              df2.stack(level=[0, 1]), exp_animal_stacked, check_names=False
          )
          tm.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]
          tm.assert_frame_equal(
              df3.stack(level=[0, 1]), animal_hair_stacked, check_names=False
          )
          tm.assert_frame_equal(
              df3.stack(level=[2, 0]), exp_animal_stacked, check_names=False
          )
          tm.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"]]),
          )
          tm.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"),
          )
  
          tm.assert_frame_equal(result, expected)
  
      def test_unstack_to_series(self, float_frame):
          # check reversibility
          data = float_frame.unstack()
  
          assert isinstance(data, Series)
          undo = data.unstack().T
          tm.assert_frame_equal(undo, float_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)
  
          tm.assert_series_equal(result, expected)
  
          # check composability of unstack
          old_data = data.copy()
          for _ in range(4):
              data = data.unstack()
          tm.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"))
          tm.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")
              ),
          )
          tm.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")
              ),
          )
          tm.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")
              ),
          )
          tm.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(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
          tm.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)
          tm.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)
          tm.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)
          tm.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)
          tm.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)
          tm.assert_frame_equal(left, right)
  
          left = df.loc[17264:].copy().set_index(["s_id", "dosage", "agent"])
          tm.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)
          tm.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):
                          tm.assert_series_equal(result, expected)
                      else:
                          tm.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):
                      tm.assert_series_equal(result, expected)
                  else:
                      tm.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],
          )
          tm.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")
      )
      tm.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")
      )
      tm.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"],
          ),
      )
      tm.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]]
          ),
      )
      tm.assert_series_equal(result, expected)
  
  
  def test_unstacking_multi_index_df():
      # see gh-30740
      df = DataFrame(
          {
              "name": ["Alice", "Bob"],
              "score": [9.5, 8],
              "employed": [False, True],
              "kids": [0, 0],
              "gender": ["female", "male"],
          }
      )
      df = df.set_index(["name", "employed", "kids", "gender"])
      df = df.unstack(["gender"], fill_value=0)
      expected = df.unstack("employed", fill_value=0).unstack("kids", fill_value=0)
      result = df.unstack(["employed", "kids"], fill_value=0)
      expected = DataFrame(
          [[9.5, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 8.0]],
          index=Index(["Alice", "Bob"], name="name"),
          columns=MultiIndex.from_tuples(
              [
                  ("score", "female", False, 0),
                  ("score", "female", True, 0),
                  ("score", "male", False, 0),
                  ("score", "male", True, 0),
              ],
              names=[None, "gender", "employed", "kids"],
          ),
      )
      tm.assert_frame_equal(result, expected)
  