text: import itertools
  
  import numpy as np
  import pytest
  
  import pandas as pd
  from pandas.core.internals import ExtensionBlock
  
  from .base import BaseExtensionTests
  
  
  class BaseReshapingTests(BaseExtensionTests):
      """Tests for reshaping and concatenation."""
  
      @pytest.mark.parametrize("in_frame", [True, False])
      def test_concat(self, data, in_frame):
          wrapped = pd.Series(data)
          if in_frame:
              wrapped = pd.DataFrame(wrapped)
          result = pd.concat([wrapped, wrapped], ignore_index=True)
  
          assert len(result) == len(data) * 2
  
          if in_frame:
              dtype = result.dtypes[0]
          else:
              dtype = result.dtype
  
          assert dtype == data.dtype
          assert isinstance(result._mgr.blocks[0], ExtensionBlock)
  
      @pytest.mark.parametrize("in_frame", [True, False])
      def test_concat_all_na_block(self, data_missing, in_frame):
          valid_block = pd.Series(data_missing.take([1, 1]), index=[0, 1])
          na_block = pd.Series(data_missing.take([0, 0]), index=[2, 3])
          if in_frame:
              valid_block = pd.DataFrame({"a": valid_block})
              na_block = pd.DataFrame({"a": na_block})
          result = pd.concat([valid_block, na_block])
          if in_frame:
              expected = pd.DataFrame({"a": data_missing.take([1, 1, 0, 0])})
              self.assert_frame_equal(result, expected)
          else:
              expected = pd.Series(data_missing.take([1, 1, 0, 0]))
              self.assert_series_equal(result, expected)
  
      def test_concat_mixed_dtypes(self, data):
          # https://github.com/pandas-dev/pandas/issues/20762
          df1 = pd.DataFrame({"A": data[:3]})
          df2 = pd.DataFrame({"A": [1, 2, 3]})
          df3 = pd.DataFrame({"A": ["a", "b", "c"]}).astype("category")
          dfs = [df1, df2, df3]
  
          # dataframes
          result = pd.concat(dfs)
          expected = pd.concat([x.astype(object) for x in dfs])
          self.assert_frame_equal(result, expected)
  
          # series
          result = pd.concat([x["A"] for x in dfs])
          expected = pd.concat([x["A"].astype(object) for x in dfs])
          self.assert_series_equal(result, expected)
  
          # simple test for just EA and one other
          result = pd.concat([df1, df2])
          expected = pd.concat([df1.astype("object"), df2.astype("object")])
          self.assert_frame_equal(result, expected)
  
          result = pd.concat([df1["A"], df2["A"]])
          expected = pd.concat([df1["A"].astype("object"), df2["A"].astype("object")])
          self.assert_series_equal(result, expected)
  
      def test_concat_columns(self, data, na_value):
          df1 = pd.DataFrame({"A": data[:3]})
          df2 = pd.DataFrame({"B": [1, 2, 3]})
  
          expected = pd.DataFrame({"A": data[:3], "B": [1, 2, 3]})
          result = pd.concat([df1, df2], axis=1)
          self.assert_frame_equal(result, expected)
          result = pd.concat([df1["A"], df2["B"]], axis=1)
          self.assert_frame_equal(result, expected)
  
          # non-aligned
          df2 = pd.DataFrame({"B": [1, 2, 3]}, index=[1, 2, 3])
          expected = pd.DataFrame(
              {
                  "A": data._from_sequence(list(data[:3]) + [na_value], dtype=data.dtype),
                  "B": [np.nan, 1, 2, 3],
              }
          )
  
          result = pd.concat([df1, df2], axis=1)
          self.assert_frame_equal(result, expected)
          result = pd.concat([df1["A"], df2["B"]], axis=1)
          self.assert_frame_equal(result, expected)
  
      def test_concat_extension_arrays_copy_false(self, data, na_value):
          # GH 20756
          df1 = pd.DataFrame({"A": data[:3]})
          df2 = pd.DataFrame({"B": data[3:7]})
          expected = pd.DataFrame(
              {
                  "A": data._from_sequence(list(data[:3]) + [na_value], dtype=data.dtype),
                  "B": data[3:7],
              }
          )
          result = pd.concat([df1, df2], axis=1, copy=False)
          self.assert_frame_equal(result, expected)
  
      def test_align(self, data, na_value):
          a = data[:3]
          b = data[2:5]
          r1, r2 = pd.Series(a).align(pd.Series(b, index=[1, 2, 3]))
  
          # Assumes that the ctor can take a list of scalars of the type
          e1 = pd.Series(data._from_sequence(list(a) + [na_value], dtype=data.dtype))
          e2 = pd.Series(data._from_sequence([na_value] + list(b), dtype=data.dtype))
          self.assert_series_equal(r1, e1)
          self.assert_series_equal(r2, e2)
  
      def test_align_frame(self, data, na_value):
          a = data[:3]
          b = data[2:5]
          r1, r2 = pd.DataFrame({"A": a}).align(pd.DataFrame({"A": b}, index=[1, 2, 3]))
  
          # Assumes that the ctor can take a list of scalars of the type
          e1 = pd.DataFrame(
              {"A": data._from_sequence(list(a) + [na_value], dtype=data.dtype)}
          )
          e2 = pd.DataFrame(
              {"A": data._from_sequence([na_value] + list(b), dtype=data.dtype)}
          )
          self.assert_frame_equal(r1, e1)
          self.assert_frame_equal(r2, e2)
  
      def test_align_series_frame(self, data, na_value):
          # https://github.com/pandas-dev/pandas/issues/20576
          ser = pd.Series(data, name="a")
          df = pd.DataFrame({"col": np.arange(len(ser) + 1)})
          r1, r2 = ser.align(df)
  
          e1 = pd.Series(
              data._from_sequence(list(data) + [na_value], dtype=data.dtype),
              name=ser.name,
          )
  
          self.assert_series_equal(r1, e1)
          self.assert_frame_equal(r2, df)
  
      def test_set_frame_expand_regular_with_extension(self, data):
          df = pd.DataFrame({"A": [1] * len(data)})
          df["B"] = data
          expected = pd.DataFrame({"A": [1] * len(data), "B": data})
          self.assert_frame_equal(df, expected)
  
      def test_set_frame_expand_extension_with_regular(self, data):
          df = pd.DataFrame({"A": data})
          df["B"] = [1] * len(data)
          expected = pd.DataFrame({"A": data, "B": [1] * len(data)})
          self.assert_frame_equal(df, expected)
  
      def test_set_frame_overwrite_object(self, data):
          # https://github.com/pandas-dev/pandas/issues/20555
          df = pd.DataFrame({"A": [1] * len(data)}, dtype=object)
          df["A"] = data
          assert df.dtypes["A"] == data.dtype
  
      def test_merge(self, data, na_value):
          # GH-20743
          df1 = pd.DataFrame({"ext": data[:3], "int1": [1, 2, 3], "key": [0, 1, 2]})
          df2 = pd.DataFrame({"int2": [1, 2, 3, 4], "key": [0, 0, 1, 3]})
  
          res = pd.merge(df1, df2)
          exp = pd.DataFrame(
              {
                  "int1": [1, 1, 2],
                  "int2": [1, 2, 3],
                  "key": [0, 0, 1],
                  "ext": data._from_sequence(
                      [data[0], data[0], data[1]], dtype=data.dtype
                  ),
              }
          )
          self.assert_frame_equal(res, exp[["ext", "int1", "key", "int2"]])
  
          res = pd.merge(df1, df2, how="outer")
          exp = pd.DataFrame(
              {
                  "int1": [1, 1, 2, 3, np.nan],
                  "int2": [1, 2, 3, np.nan, 4],
                  "key": [0, 0, 1, 2, 3],
                  "ext": data._from_sequence(
                      [data[0], data[0], data[1], data[2], na_value], dtype=data.dtype
                  ),
              }
          )
          self.assert_frame_equal(res, exp[["ext", "int1", "key", "int2"]])
  
      def test_merge_on_extension_array(self, data):
          # GH 23020
          a, b = data[:2]
          key = type(data)._from_sequence([a, b], dtype=data.dtype)
  
          df = pd.DataFrame({"key": key, "val": [1, 2]})
          result = pd.merge(df, df, on="key")
          expected = pd.DataFrame({"key": key, "val_x": [1, 2], "val_y": [1, 2]})
          self.assert_frame_equal(result, expected)
  
          # order
          result = pd.merge(df.iloc[[1, 0]], df, on="key")
          expected = expected.iloc[[1, 0]].reset_index(drop=True)
          self.assert_frame_equal(result, expected)
  
      def test_merge_on_extension_array_duplicates(self, data):
          # GH 23020
          a, b = data[:2]
          key = type(data)._from_sequence([a, b, a], dtype=data.dtype)
          df1 = pd.DataFrame({"key": key, "val": [1, 2, 3]})
          df2 = pd.DataFrame({"key": key, "val": [1, 2, 3]})
  
          result = pd.merge(df1, df2, on="key")
          expected = pd.DataFrame(
              {
                  "key": key.take([0, 0, 0, 0, 1]),
                  "val_x": [1, 1, 3, 3, 2],
                  "val_y": [1, 3, 1, 3, 2],
              }
          )
          self.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize(
          "columns",
          [
              ["A", "B"],
              pd.MultiIndex.from_tuples(
                  [("A", "a"), ("A", "b")], names=["outer", "inner"]
              ),
          ],
      )
      def test_stack(self, data, columns):
          df = pd.DataFrame({"A": data[:5], "B": data[:5]})
          df.columns = columns
          result = df.stack()
          expected = df.astype(object).stack()
          # we need a second astype(object), in case the constructor inferred
          # object -> specialized, as is done for period.
          expected = expected.astype(object)
  
          if isinstance(expected, pd.Series):
              assert result.dtype == df.iloc[:, 0].dtype
          else:
              assert all(result.dtypes == df.iloc[:, 0].dtype)
  
          result = result.astype(object)
          self.assert_equal(result, expected)
  
      @pytest.mark.parametrize(
          "index",
          [
              # Two levels, uniform.
              pd.MultiIndex.from_product(([["A", "B"], ["a", "b"]]), names=["a", "b"]),
              # non-uniform
              pd.MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "b")]),
              # three levels, non-uniform
              pd.MultiIndex.from_product([("A", "B"), ("a", "b", "c"), (0, 1, 2)]),
              pd.MultiIndex.from_tuples(
                  [
                      ("A", "a", 1),
                      ("A", "b", 0),
                      ("A", "a", 0),
                      ("B", "a", 0),
                      ("B", "c", 1),
                  ]
              ),
          ],
      )
      @pytest.mark.parametrize("obj", ["series", "frame"])
      def test_unstack(self, data, index, obj):
          data = data[: len(index)]
          if obj == "series":
              ser = pd.Series(data, index=index)
          else:
              ser = pd.DataFrame({"A": data, "B": data}, index=index)
  
          n = index.nlevels
          levels = list(range(n))
          # [0, 1, 2]
          # [(0,), (1,), (2,), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
          combinations = itertools.chain.from_iterable(
              itertools.permutations(levels, i) for i in range(1, n)
          )
  
          for level in combinations:
              result = ser.unstack(level=level)
              assert all(
                  isinstance(result[col].array, type(data)) for col in result.columns
              )
              expected = ser.astype(object).unstack(level=level)
              result = result.astype(object)
  
              self.assert_frame_equal(result, expected)
  
      def test_ravel(self, data):
          # as long as EA is 1D-only, ravel is a no-op
          result = data.ravel()
          assert type(result) == type(data)
  
          # Check that we have a view, not a copy
          result[0] = result[1]
          assert data[0] == data[1]
  
      def test_transpose(self, data):
          df = pd.DataFrame({"A": data[:4], "B": data[:4]}, index=["a", "b", "c", "d"])
          result = df.T
          expected = pd.DataFrame(
              {
                  "a": type(data)._from_sequence([data[0]] * 2, dtype=data.dtype),
                  "b": type(data)._from_sequence([data[1]] * 2, dtype=data.dtype),
                  "c": type(data)._from_sequence([data[2]] * 2, dtype=data.dtype),
                  "d": type(data)._from_sequence([data[3]] * 2, dtype=data.dtype),
              },
              index=["A", "B"],
          )
          self.assert_frame_equal(result, expected)
          self.assert_frame_equal(np.transpose(np.transpose(df)), df)
          self.assert_frame_equal(np.transpose(np.transpose(df[["A"]])), df[["A"]])
  