text: from collections import OrderedDict
  
  import numpy as np
  import pytest
  
  from pandas.core.dtypes.common import is_integer_dtype
  
  import pandas as pd
  from pandas import Categorical, CategoricalIndex, DataFrame, Series, get_dummies
  import pandas._testing as tm
  from pandas.core.arrays.sparse import SparseArray, SparseDtype
  
  
  class TestGetDummies:
      @pytest.fixture
      def df(self):
          return DataFrame({"A": ["a", "b", "a"], "B": ["b", "b", "c"], "C": [1, 2, 3]})
  
      @pytest.fixture(params=["uint8", "i8", np.float64, bool, None])
      def dtype(self, request):
          return np.dtype(request.param)
  
      @pytest.fixture(params=["dense", "sparse"])
      def sparse(self, request):
          # params are strings to simplify reading test results,
          # e.g. TestGetDummies::test_basic[uint8-sparse] instead of [uint8-True]
          return request.param == "sparse"
  
      def effective_dtype(self, dtype):
          if dtype is None:
              return np.uint8
          return dtype
  
      def test_get_dummies_raises_on_dtype_object(self, df):
          with pytest.raises(ValueError):
              get_dummies(df, dtype="object")
  
      def test_get_dummies_basic(self, sparse, dtype):
          s_list = list("abc")
          s_series = Series(s_list)
          s_series_index = Series(s_list, list("ABC"))
  
          expected = DataFrame(
              {"a": [1, 0, 0], "b": [0, 1, 0], "c": [0, 0, 1]},
              dtype=self.effective_dtype(dtype),
          )
          if sparse:
              expected = expected.apply(SparseArray, fill_value=0.0)
          result = get_dummies(s_list, sparse=sparse, dtype=dtype)
          tm.assert_frame_equal(result, expected)
  
          result = get_dummies(s_series, sparse=sparse, dtype=dtype)
          tm.assert_frame_equal(result, expected)
  
          expected.index = list("ABC")
          result = get_dummies(s_series_index, sparse=sparse, dtype=dtype)
          tm.assert_frame_equal(result, expected)
  
      def test_get_dummies_basic_types(self, sparse, dtype):
          # GH 10531
          s_list = list("abc")
          s_series = Series(s_list)
          s_df = DataFrame(
              {"a": [0, 1, 0, 1, 2], "b": ["A", "A", "B", "C", "C"], "c": [2, 3, 3, 3, 2]}
          )
  
          expected = DataFrame(
              {"a": [1, 0, 0], "b": [0, 1, 0], "c": [0, 0, 1]},
              dtype=self.effective_dtype(dtype),
              columns=list("abc"),
          )
          if sparse:
              if is_integer_dtype(dtype):
                  fill_value = 0
              elif dtype == bool:
                  fill_value = False
              else:
                  fill_value = 0.0
  
              expected = expected.apply(SparseArray, fill_value=fill_value)
          result = get_dummies(s_list, sparse=sparse, dtype=dtype)
          tm.assert_frame_equal(result, expected)
  
          result = get_dummies(s_series, sparse=sparse, dtype=dtype)
          tm.assert_frame_equal(result, expected)
  
          result = get_dummies(s_df, columns=s_df.columns, sparse=sparse, dtype=dtype)
          if sparse:
              dtype_name = f"Sparse[{self.effective_dtype(dtype).name}, {fill_value}]"
          else:
              dtype_name = self.effective_dtype(dtype).name
  
          expected = Series({dtype_name: 8})
          result = result.dtypes.value_counts()
          result.index = [str(i) for i in result.index]
          tm.assert_series_equal(result, expected)
  
          result = get_dummies(s_df, columns=["a"], sparse=sparse, dtype=dtype)
  
          expected_counts = {"int64": 1, "object": 1}
          expected_counts[dtype_name] = 3 + expected_counts.get(dtype_name, 0)
  
          expected = Series(expected_counts).sort_index()
          result = result.dtypes.value_counts()
          result.index = [str(i) for i in result.index]
          result = result.sort_index()
          tm.assert_series_equal(result, expected)
  
      def test_get_dummies_just_na(self, sparse):
          just_na_list = [np.nan]
          just_na_series = Series(just_na_list)
          just_na_series_index = Series(just_na_list, index=["A"])
  
          res_list = get_dummies(just_na_list, sparse=sparse)
          res_series = get_dummies(just_na_series, sparse=sparse)
          res_series_index = get_dummies(just_na_series_index, sparse=sparse)
  
          assert res_list.empty
          assert res_series.empty
          assert res_series_index.empty
  
          assert res_list.index.tolist() == [0]
          assert res_series.index.tolist() == [0]
          assert res_series_index.index.tolist() == ["A"]
  
      def test_get_dummies_include_na(self, sparse, dtype):
          s = ["a", "b", np.nan]
          res = get_dummies(s, sparse=sparse, dtype=dtype)
          exp = DataFrame(
              {"a": [1, 0, 0], "b": [0, 1, 0]}, dtype=self.effective_dtype(dtype)
          )
          if sparse:
              exp = exp.apply(SparseArray, fill_value=0.0)
          tm.assert_frame_equal(res, exp)
  
          # Sparse dataframes do not allow nan labelled columns, see #GH8822
          res_na = get_dummies(s, dummy_na=True, sparse=sparse, dtype=dtype)
          exp_na = DataFrame(
              {np.nan: [0, 0, 1], "a": [1, 0, 0], "b": [0, 1, 0]},
              dtype=self.effective_dtype(dtype),
          )
          exp_na = exp_na.reindex(["a", "b", np.nan], axis=1)
          # hack (NaN handling in assert_index_equal)
          exp_na.columns = res_na.columns
          if sparse:
              exp_na = exp_na.apply(SparseArray, fill_value=0.0)
          tm.assert_frame_equal(res_na, exp_na)
  
          res_just_na = get_dummies([np.nan], dummy_na=True, sparse=sparse, dtype=dtype)
          exp_just_na = DataFrame(
              Series(1, index=[0]), columns=[np.nan], dtype=self.effective_dtype(dtype)
          )
          tm.assert_numpy_array_equal(res_just_na.values, exp_just_na.values)
  
      def test_get_dummies_unicode(self, sparse):
          # See GH 6885 - get_dummies chokes on unicode values
          import unicodedata
  
          e = "e"
          eacute = unicodedata.lookup("LATIN SMALL LETTER E WITH ACUTE")
          s = [e, eacute, eacute]
          res = get_dummies(s, prefix="letter", sparse=sparse)
          exp = DataFrame(
              {"letter_e": [1, 0, 0], f"letter_{eacute}": [0, 1, 1]}, dtype=np.uint8
          )
          if sparse:
              exp = exp.apply(SparseArray, fill_value=0)
          tm.assert_frame_equal(res, exp)
  
      def test_dataframe_dummies_all_obj(self, df, sparse):
          df = df[["A", "B"]]
          result = get_dummies(df, sparse=sparse)
          expected = DataFrame(
              {"A_a": [1, 0, 1], "A_b": [0, 1, 0], "B_b": [1, 1, 0], "B_c": [0, 0, 1]},
              dtype=np.uint8,
          )
          if sparse:
              expected = DataFrame(
                  {
                      "A_a": SparseArray([1, 0, 1], dtype="uint8"),
                      "A_b": SparseArray([0, 1, 0], dtype="uint8"),
                      "B_b": SparseArray([1, 1, 0], dtype="uint8"),
                      "B_c": SparseArray([0, 0, 1], dtype="uint8"),
                  }
              )
  
          tm.assert_frame_equal(result, expected)
  
      def test_dataframe_dummies_mix_default(self, df, sparse, dtype):
          result = get_dummies(df, sparse=sparse, dtype=dtype)
          if sparse:
              arr = SparseArray
              typ = SparseDtype(dtype, 0)
          else:
              arr = np.array
              typ = dtype
          expected = DataFrame(
              {
                  "C": [1, 2, 3],
                  "A_a": arr([1, 0, 1], dtype=typ),
                  "A_b": arr([0, 1, 0], dtype=typ),
                  "B_b": arr([1, 1, 0], dtype=typ),
                  "B_c": arr([0, 0, 1], dtype=typ),
              }
          )
          expected = expected[["C", "A_a", "A_b", "B_b", "B_c"]]
          tm.assert_frame_equal(result, expected)
  
      def test_dataframe_dummies_prefix_list(self, df, sparse):
          prefixes = ["from_A", "from_B"]
          result = get_dummies(df, prefix=prefixes, sparse=sparse)
          expected = DataFrame(
              {
                  "C": [1, 2, 3],
                  "from_A_a": [1, 0, 1],
                  "from_A_b": [0, 1, 0],
                  "from_B_b": [1, 1, 0],
                  "from_B_c": [0, 0, 1],
              },
              dtype=np.uint8,
          )
          expected[["C"]] = df[["C"]]
          cols = ["from_A_a", "from_A_b", "from_B_b", "from_B_c"]
          expected = expected[["C"] + cols]
  
          typ = SparseArray if sparse else Series
          expected[cols] = expected[cols].apply(lambda x: typ(x))
          tm.assert_frame_equal(result, expected)
  
      def test_dataframe_dummies_prefix_str(self, df, sparse):
          # not that you should do this...
          result = get_dummies(df, prefix="bad", sparse=sparse)
          bad_columns = ["bad_a", "bad_b", "bad_b", "bad_c"]
          expected = DataFrame(
              [[1, 1, 0, 1, 0], [2, 0, 1, 1, 0], [3, 1, 0, 0, 1]],
              columns=["C"] + bad_columns,
              dtype=np.uint8,
          )
          expected = expected.astype({"C": np.int64})
          if sparse:
              # work around astyping & assigning with duplicate columns
              # https://github.com/pandas-dev/pandas/issues/14427
              expected = pd.concat(
                  [
                      Series([1, 2, 3], name="C"),
                      Series([1, 0, 1], name="bad_a", dtype="Sparse[uint8]"),
                      Series([0, 1, 0], name="bad_b", dtype="Sparse[uint8]"),
                      Series([1, 1, 0], name="bad_b", dtype="Sparse[uint8]"),
                      Series([0, 0, 1], name="bad_c", dtype="Sparse[uint8]"),
                  ],
                  axis=1,
              )
  
          tm.assert_frame_equal(result, expected)
  
      def test_dataframe_dummies_subset(self, df, sparse):
          result = get_dummies(df, prefix=["from_A"], columns=["A"], sparse=sparse)
          expected = DataFrame(
              {
                  "B": ["b", "b", "c"],
                  "C": [1, 2, 3],
                  "from_A_a": [1, 0, 1],
                  "from_A_b": [0, 1, 0],
              },
              dtype=np.uint8,
          )
          expected[["C"]] = df[["C"]]
          if sparse:
              cols = ["from_A_a", "from_A_b"]
              expected[cols] = expected[cols].astype(SparseDtype("uint8", 0))
          tm.assert_frame_equal(result, expected)
  
      def test_dataframe_dummies_prefix_sep(self, df, sparse):
          result = get_dummies(df, prefix_sep="..", sparse=sparse)
          expected = DataFrame(
              {
                  "C": [1, 2, 3],
                  "A..a": [1, 0, 1],
                  "A..b": [0, 1, 0],
                  "B..b": [1, 1, 0],
                  "B..c": [0, 0, 1],
              },
              dtype=np.uint8,
          )
          expected[["C"]] = df[["C"]]
          expected = expected[["C", "A..a", "A..b", "B..b", "B..c"]]
          if sparse:
              cols = ["A..a", "A..b", "B..b", "B..c"]
              expected[cols] = expected[cols].astype(SparseDtype("uint8", 0))
  
          tm.assert_frame_equal(result, expected)
  
          result = get_dummies(df, prefix_sep=["..", "__"], sparse=sparse)
          expected = expected.rename(columns={"B..b": "B__b", "B..c": "B__c"})
          tm.assert_frame_equal(result, expected)
  
          result = get_dummies(df, prefix_sep={"A": "..", "B": "__"}, sparse=sparse)
          tm.assert_frame_equal(result, expected)
  
      def test_dataframe_dummies_prefix_bad_length(self, df, sparse):
          with pytest.raises(ValueError):
              get_dummies(df, prefix=["too few"], sparse=sparse)
  
      def test_dataframe_dummies_prefix_sep_bad_length(self, df, sparse):
          with pytest.raises(ValueError):
              get_dummies(df, prefix_sep=["bad"], sparse=sparse)
  
      def test_dataframe_dummies_prefix_dict(self, sparse):
          prefixes = {"A": "from_A", "B": "from_B"}
          df = DataFrame({"C": [1, 2, 3], "A": ["a", "b", "a"], "B": ["b", "b", "c"]})
          result = get_dummies(df, prefix=prefixes, sparse=sparse)
  
          expected = DataFrame(
              {
                  "C": [1, 2, 3],
                  "from_A_a": [1, 0, 1],
                  "from_A_b": [0, 1, 0],
                  "from_B_b": [1, 1, 0],
                  "from_B_c": [0, 0, 1],
              }
          )
  
          columns = ["from_A_a", "from_A_b", "from_B_b", "from_B_c"]
          expected[columns] = expected[columns].astype(np.uint8)
          if sparse:
              expected[columns] = expected[columns].astype(SparseDtype("uint8", 0))
  
          tm.assert_frame_equal(result, expected)
  
      def test_dataframe_dummies_with_na(self, df, sparse, dtype):
          df.loc[3, :] = [np.nan, np.nan, np.nan]
          result = get_dummies(df, dummy_na=True, sparse=sparse, dtype=dtype).sort_index(
              axis=1
          )
  
          if sparse:
              arr = SparseArray
              typ = SparseDtype(dtype, 0)
          else:
              arr = np.array
              typ = dtype
  
          expected = DataFrame(
              {
                  "C": [1, 2, 3, np.nan],
                  "A_a": arr([1, 0, 1, 0], dtype=typ),
                  "A_b": arr([0, 1, 0, 0], dtype=typ),
                  "A_nan": arr([0, 0, 0, 1], dtype=typ),
                  "B_b": arr([1, 1, 0, 0], dtype=typ),
                  "B_c": arr([0, 0, 1, 0], dtype=typ),
                  "B_nan": arr([0, 0, 0, 1], dtype=typ),
              }
          ).sort_index(axis=1)
  
          tm.assert_frame_equal(result, expected)
  
          result = get_dummies(df, dummy_na=False, sparse=sparse, dtype=dtype)
          expected = expected[["C", "A_a", "A_b", "B_b", "B_c"]]
          tm.assert_frame_equal(result, expected)
  
      def test_dataframe_dummies_with_categorical(self, df, sparse, dtype):
          df["cat"] = Categorical(["x", "y", "y"])
          result = get_dummies(df, sparse=sparse, dtype=dtype).sort_index(axis=1)
          if sparse:
              arr = SparseArray
              typ = SparseDtype(dtype, 0)
          else:
              arr = np.array
              typ = dtype
  
          expected = DataFrame(
              {
                  "C": [1, 2, 3],
                  "A_a": arr([1, 0, 1], dtype=typ),
                  "A_b": arr([0, 1, 0], dtype=typ),
                  "B_b": arr([1, 1, 0], dtype=typ),
                  "B_c": arr([0, 0, 1], dtype=typ),
                  "cat_x": arr([1, 0, 0], dtype=typ),
                  "cat_y": arr([0, 1, 1], dtype=typ),
              }
          ).sort_index(axis=1)
  
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize(
          "get_dummies_kwargs,expected",
          [
              (
                  {"data": DataFrame(({"ä": ["a"]}))},
                  DataFrame({"ä_a": [1]}, dtype=np.uint8),
              ),
              (
                  {"data": DataFrame({"x": ["ä"]})},
                  DataFrame({"x_ä": [1]}, dtype=np.uint8),
              ),
              (
                  {"data": DataFrame({"x": ["a"]}), "prefix": "ä"},
                  DataFrame({"ä_a": [1]}, dtype=np.uint8),
              ),
              (
                  {"data": DataFrame({"x": ["a"]}), "prefix_sep": "ä"},
                  DataFrame({"xäa": [1]}, dtype=np.uint8),
              ),
          ],
      )
      def test_dataframe_dummies_unicode(self, get_dummies_kwargs, expected):
          # GH22084 get_dummies incorrectly encodes unicode characters
          # in dataframe column names
          result = get_dummies(**get_dummies_kwargs)
          tm.assert_frame_equal(result, expected)
  
      def test_get_dummies_basic_drop_first(self, sparse):
          # GH12402 Add a new parameter `drop_first` to avoid collinearity
          # Basic case
          s_list = list("abc")
          s_series = Series(s_list)
          s_series_index = Series(s_list, list("ABC"))
  
          expected = DataFrame({"b": [0, 1, 0], "c": [0, 0, 1]}, dtype=np.uint8)
  
          result = get_dummies(s_list, drop_first=True, sparse=sparse)
          if sparse:
              expected = expected.apply(SparseArray, fill_value=0)
          tm.assert_frame_equal(result, expected)
  
          result = get_dummies(s_series, drop_first=True, sparse=sparse)
          tm.assert_frame_equal(result, expected)
  
          expected.index = list("ABC")
          result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
          tm.assert_frame_equal(result, expected)
  
      def test_get_dummies_basic_drop_first_one_level(self, sparse):
          # Test the case that categorical variable only has one level.
          s_list = list("aaa")
          s_series = Series(s_list)
          s_series_index = Series(s_list, list("ABC"))
  
          expected = DataFrame(index=np.arange(3))
  
          result = get_dummies(s_list, drop_first=True, sparse=sparse)
          tm.assert_frame_equal(result, expected)
  
          result = get_dummies(s_series, drop_first=True, sparse=sparse)
          tm.assert_frame_equal(result, expected)
  
          expected = DataFrame(index=list("ABC"))
          result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
          tm.assert_frame_equal(result, expected)
  
      def test_get_dummies_basic_drop_first_NA(self, sparse):
          # Test NA handling together with drop_first
          s_NA = ["a", "b", np.nan]
          res = get_dummies(s_NA, drop_first=True, sparse=sparse)
          exp = DataFrame({"b": [0, 1, 0]}, dtype=np.uint8)
          if sparse:
              exp = exp.apply(SparseArray, fill_value=0)
  
          tm.assert_frame_equal(res, exp)
  
          res_na = get_dummies(s_NA, dummy_na=True, drop_first=True, sparse=sparse)
          exp_na = DataFrame({"b": [0, 1, 0], np.nan: [0, 0, 1]}, dtype=np.uint8).reindex(
              ["b", np.nan], axis=1
          )
          if sparse:
              exp_na = exp_na.apply(SparseArray, fill_value=0)
          tm.assert_frame_equal(res_na, exp_na)
  
          res_just_na = get_dummies(
              [np.nan], dummy_na=True, drop_first=True, sparse=sparse
          )
          exp_just_na = DataFrame(index=np.arange(1))
          tm.assert_frame_equal(res_just_na, exp_just_na)
  
      def test_dataframe_dummies_drop_first(self, df, sparse):
          df = df[["A", "B"]]
          result = get_dummies(df, drop_first=True, sparse=sparse)
          expected = DataFrame({"A_b": [0, 1, 0], "B_c": [0, 0, 1]}, dtype=np.uint8)
          if sparse:
              expected = expected.apply(SparseArray, fill_value=0)
          tm.assert_frame_equal(result, expected)
  
      def test_dataframe_dummies_drop_first_with_categorical(self, df, sparse, dtype):
          df["cat"] = Categorical(["x", "y", "y"])
          result = get_dummies(df, drop_first=True, sparse=sparse)
          expected = DataFrame(
              {"C": [1, 2, 3], "A_b": [0, 1, 0], "B_c": [0, 0, 1], "cat_y": [0, 1, 1]}
          )
          cols = ["A_b", "B_c", "cat_y"]
          expected[cols] = expected[cols].astype(np.uint8)
          expected = expected[["C", "A_b", "B_c", "cat_y"]]
          if sparse:
              for col in cols:
                  expected[col] = SparseArray(expected[col])
          tm.assert_frame_equal(result, expected)
  
      def test_dataframe_dummies_drop_first_with_na(self, df, sparse):
          df.loc[3, :] = [np.nan, np.nan, np.nan]
          result = get_dummies(
              df, dummy_na=True, drop_first=True, sparse=sparse
          ).sort_index(axis=1)
          expected = DataFrame(
              {
                  "C": [1, 2, 3, np.nan],
                  "A_b": [0, 1, 0, 0],
                  "A_nan": [0, 0, 0, 1],
                  "B_c": [0, 0, 1, 0],
                  "B_nan": [0, 0, 0, 1],
              }
          )
          cols = ["A_b", "A_nan", "B_c", "B_nan"]
          expected[cols] = expected[cols].astype(np.uint8)
          expected = expected.sort_index(axis=1)
          if sparse:
              for col in cols:
                  expected[col] = SparseArray(expected[col])
  
          tm.assert_frame_equal(result, expected)
  
          result = get_dummies(df, dummy_na=False, drop_first=True, sparse=sparse)
          expected = expected[["C", "A_b", "B_c"]]
          tm.assert_frame_equal(result, expected)
  
      def test_get_dummies_int_int(self):
          data = Series([1, 2, 1])
          result = get_dummies(data)
          expected = DataFrame([[1, 0], [0, 1], [1, 0]], columns=[1, 2], dtype=np.uint8)
          tm.assert_frame_equal(result, expected)
  
          data = Series(Categorical(["a", "b", "a"]))
          result = get_dummies(data)
          expected = DataFrame(
              [[1, 0], [0, 1], [1, 0]], columns=Categorical(["a", "b"]), dtype=np.uint8
          )
          tm.assert_frame_equal(result, expected)
  
      def test_get_dummies_int_df(self, dtype):
          data = DataFrame(
              {
                  "A": [1, 2, 1],
                  "B": Categorical(["a", "b", "a"]),
                  "C": [1, 2, 1],
                  "D": [1.0, 2.0, 1.0],
              }
          )
          columns = ["C", "D", "A_1", "A_2", "B_a", "B_b"]
          expected = DataFrame(
              [[1, 1.0, 1, 0, 1, 0], [2, 2.0, 0, 1, 0, 1], [1, 1.0, 1, 0, 1, 0]],
              columns=columns,
          )
          expected[columns[2:]] = expected[columns[2:]].astype(dtype)
          result = get_dummies(data, columns=["A", "B"], dtype=dtype)
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize("ordered", [True, False])
      def test_dataframe_dummies_preserve_categorical_dtype(self, dtype, ordered):
          # GH13854
          cat = Categorical(list("xy"), categories=list("xyz"), ordered=ordered)
          result = get_dummies(cat, dtype=dtype)
  
          data = np.array([[1, 0, 0], [0, 1, 0]], dtype=self.effective_dtype(dtype))
          cols = CategoricalIndex(
              cat.categories, categories=cat.categories, ordered=ordered
          )
          expected = DataFrame(data, columns=cols, dtype=self.effective_dtype(dtype))
  
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize("sparse", [True, False])
      def test_get_dummies_dont_sparsify_all_columns(self, sparse):
          # GH18914
          df = DataFrame.from_dict(
              OrderedDict([("GDP", [1, 2]), ("Nation", ["AB", "CD"])])
          )
          df = get_dummies(df, columns=["Nation"], sparse=sparse)
          df2 = df.reindex(columns=["GDP"])
  
          tm.assert_frame_equal(df[["GDP"]], df2)
  
      def test_get_dummies_duplicate_columns(self, df):
          # GH20839
          df.columns = ["A", "A", "A"]
          result = get_dummies(df).sort_index(axis=1)
  
          expected = DataFrame(
              [[1, 1, 0, 1, 0], [2, 0, 1, 1, 0], [3, 1, 0, 0, 1]],
              columns=["A", "A_a", "A_b", "A_b", "A_c"],
              dtype=np.uint8,
          ).sort_index(axis=1)
  
          expected = expected.astype({"A": np.int64})
  
          tm.assert_frame_equal(result, expected)
  
      def test_get_dummies_all_sparse(self):
          df = DataFrame({"A": [1, 2]})
          result = get_dummies(df, columns=["A"], sparse=True)
          dtype = SparseDtype("uint8", 0)
          expected = DataFrame(
              {
                  "A_1": SparseArray([1, 0], dtype=dtype),
                  "A_2": SparseArray([0, 1], dtype=dtype),
              }
          )
          tm.assert_frame_equal(result, expected)
  
      @pytest.mark.parametrize("values", ["baz"])
      def test_get_dummies_with_string_values(self, values):
          # issue #28383
          df = DataFrame(
              {
                  "bar": [1, 2, 3, 4, 5, 6],
                  "foo": ["one", "one", "one", "two", "two", "two"],
                  "baz": ["A", "B", "C", "A", "B", "C"],
                  "zoo": ["x", "y", "z", "q", "w", "t"],
              }
          )
  
          msg = "Input must be a list-like for parameter `columns`"
  
          with pytest.raises(TypeError, match=msg):
              get_dummies(df, columns=values)
  