import re import unicodedata import numpy as np import pytest import pandas.util._test_decorators as td from pandas.core.dtypes.common import is_integer_dtype import pandas as pd from pandas import ( ArrowDtype, Categorical, CategoricalDtype, CategoricalIndex, DataFrame, Index, RangeIndex, Series, SparseDtype, get_dummies, ) import pandas._testing as tm from pandas.core.arrays.sparse import SparseArray try: import pyarrow as pa except ImportError: pa = None 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): msg = "dtype=object is not a valid dtype for get_dummies" with pytest.raises(ValueError, match=msg): 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: if dtype.kind == "b": expected = expected.apply(SparseArray, fill_value=False) else: 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, using_infer_string): # 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}, name="count") 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) key = "string" if using_infer_string else "object" expected_counts = {"int64": 1, key: 1} expected_counts[dtype_name] = 3 + expected_counts.get(dtype_name, 0) expected = Series(expected_counts, name="count").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: if dtype.kind == "b": exp = exp.apply(SparseArray, fill_value=False) else: 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: if dtype.kind == "b": exp_na = exp_na.apply(SparseArray, fill_value=False) else: 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 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": [True, False, False], f"letter_{eacute}": [False, True, True]} ) if sparse: exp = exp.apply(SparseArray, fill_value=False) 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=bool, ) if sparse: expected = DataFrame( { "A_a": SparseArray([1, 0, 1], dtype="bool"), "A_b": SparseArray([0, 1, 0], dtype="bool"), "B_b": SparseArray([1, 1, 0], dtype="bool"), "B_c": SparseArray([0, 0, 1], dtype="bool"), } ) tm.assert_frame_equal(result, expected) def test_dataframe_dummies_string_dtype(self, df, using_infer_string): # GH44965 df = df[["A", "B"]] df = df.astype({"A": "object", "B": "string"}) result = get_dummies(df) expected = DataFrame( { "A_a": [1, 0, 1], "A_b": [0, 1, 0], "B_b": [1, 1, 0], "B_c": [0, 0, 1], }, dtype=bool, ) if not using_infer_string: # infer_string returns numpy bools expected[["B_b", "B_c"]] = expected[["B_b", "B_c"]].astype("boolean") 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 if dtype.kind == "b": typ = SparseDtype(dtype, False) else: 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": [True, False, True], "from_A_b": [False, True, False], "from_B_b": [True, True, False], "from_B_c": [False, False, True], }, ) 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, True, False, True, False], [2, False, True, True, False], [3, True, False, False, True], ], columns=["C"] + bad_columns, ) 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([True, False, True], name="bad_a", dtype="Sparse[bool]"), Series([False, True, False], name="bad_b", dtype="Sparse[bool]"), Series([True, True, False], name="bad_b", dtype="Sparse[bool]"), Series([False, False, True], name="bad_c", dtype="Sparse[bool]"), ], 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], }, ) cols = expected.columns expected[cols[1:]] = expected[cols[1:]].astype(bool) expected[["C"]] = df[["C"]] if sparse: cols = ["from_A_a", "from_A_b"] expected[cols] = expected[cols].astype(SparseDtype("bool", False)) 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": [True, False, True], "A..b": [False, True, False], "B..b": [True, True, False], "B..c": [False, False, True], }, ) 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("bool", False)) 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): msg = re.escape( "Length of 'prefix' (1) did not match the length of the columns being " "encoded (2)" ) with pytest.raises(ValueError, match=msg): get_dummies(df, prefix=["too few"], sparse=sparse) def test_dataframe_dummies_prefix_sep_bad_length(self, df, sparse): msg = re.escape( "Length of 'prefix_sep' (1) did not match the length of the columns being " "encoded (2)" ) with pytest.raises(ValueError, match=msg): 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(bool) if sparse: expected[columns] = expected[columns].astype(SparseDtype("bool", False)) 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 if dtype.kind == "b": typ = SparseDtype(dtype, False) else: 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 if dtype.kind == "b": typ = SparseDtype(dtype, False) else: 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": [True]}), ), ( {"data": DataFrame({"x": ["ä"]})}, DataFrame({"x_ä": [True]}), ), ( {"data": DataFrame({"x": ["a"]}), "prefix": "ä"}, DataFrame({"ä_a": [True]}), ), ( {"data": DataFrame({"x": ["a"]}), "prefix_sep": "ä"}, DataFrame({"xäa": [True]}), ), ], ) 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=bool) result = get_dummies(s_list, drop_first=True, sparse=sparse) if sparse: expected = expected.apply(SparseArray, fill_value=False) 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=RangeIndex(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=bool) if sparse: exp = exp.apply(SparseArray, fill_value=False) 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=bool).reindex( ["b", np.nan], axis=1 ) if sparse: exp_na = exp_na.apply(SparseArray, fill_value=False) 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=RangeIndex(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=bool) if sparse: expected = expected.apply(SparseArray, fill_value=False) 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(bool) 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(bool) 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=bool) 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=bool ) 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({"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, True, False, True, False], [2, False, True, True, False], [3, True, False, False, True], ], columns=["A", "A_a", "A_b", "A_b", "A_c"], ).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("bool", False) 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) def test_get_dummies_ea_dtype_series(self, any_numeric_ea_and_arrow_dtype): # GH#32430 ser = Series(list("abca")) result = get_dummies(ser, dtype=any_numeric_ea_and_arrow_dtype) expected = DataFrame( {"a": [1, 0, 0, 1], "b": [0, 1, 0, 0], "c": [0, 0, 1, 0]}, dtype=any_numeric_ea_and_arrow_dtype, ) tm.assert_frame_equal(result, expected) def test_get_dummies_ea_dtype_dataframe(self, any_numeric_ea_and_arrow_dtype): # GH#32430 df = DataFrame({"x": list("abca")}) result = get_dummies(df, dtype=any_numeric_ea_and_arrow_dtype) expected = DataFrame( {"x_a": [1, 0, 0, 1], "x_b": [0, 1, 0, 0], "x_c": [0, 0, 1, 0]}, dtype=any_numeric_ea_and_arrow_dtype, ) tm.assert_frame_equal(result, expected) @td.skip_if_no("pyarrow") def test_get_dummies_ea_dtype(self): # GH#56273 for dtype, exp_dtype in [ ("string[pyarrow]", "boolean"), ("string[pyarrow_numpy]", "bool"), (CategoricalDtype(Index(["a"], dtype="string[pyarrow]")), "boolean"), (CategoricalDtype(Index(["a"], dtype="string[pyarrow_numpy]")), "bool"), ]: df = DataFrame({"name": Series(["a"], dtype=dtype), "x": 1}) result = get_dummies(df) expected = DataFrame({"x": 1, "name_a": Series([True], dtype=exp_dtype)}) tm.assert_frame_equal(result, expected) @td.skip_if_no("pyarrow") def test_get_dummies_arrow_dtype(self): # GH#56273 df = DataFrame({"name": Series(["a"], dtype=ArrowDtype(pa.string())), "x": 1}) result = get_dummies(df) expected = DataFrame({"x": 1, "name_a": Series([True], dtype="bool[pyarrow]")}) tm.assert_frame_equal(result, expected) df = DataFrame( { "name": Series( ["a"], dtype=CategoricalDtype(Index(["a"], dtype=ArrowDtype(pa.string()))), ), "x": 1, } ) result = get_dummies(df) tm.assert_frame_equal(result, expected)