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 env-llmeval/lib/python3.10/site-packages/scipy/optimize/_highs/_highs_wrapper.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 env-llmeval/lib/python3.10/site-packages/scipy/io/_fast_matrix_market/_fmm_core.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 env-llmeval/lib/python3.10/site-packages/pandas/_libs/join.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+env-llmeval/lib/python3.10/site-packages/pandas/_libs/hashtable.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+env-llmeval/lib/python3.10/site-packages/pandas/_libs/interval.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+env-llmeval/lib/python3.10/site-packages/pandas/_libs/tslibs/offsets.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text

env-llmeval/lib/python3.10/site-packages/pandas/_libs/hashtable.cpython-310-x86_64-linux-gnu.so

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env-llmeval/lib/python3.10/site-packages/pandas/_libs/interval.cpython-310-x86_64-linux-gnu.so

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env-llmeval/lib/python3.10/site-packages/pandas/_libs/tslibs/offsets.cpython-310-x86_64-linux-gnu.so

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env-llmeval/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_api.py

@@ -0,0 +1,501 @@
+import re
+
+import numpy as np
+import pytest
+
+from pandas.compat import PY311
+
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    DataFrame,
+    Index,
+    Series,
+    StringDtype,
+)
+import pandas._testing as tm
+from pandas.core.arrays.categorical import recode_for_categories
+
+
+class TestCategoricalAPI:
+    def test_to_list_deprecated(self):
+        # GH#51254
+        cat1 = Categorical(list("acb"), ordered=False)
+        msg = "Categorical.to_list is deprecated and will be removed"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            cat1.to_list()
+
+    def test_ordered_api(self):
+        # GH 9347
+        cat1 = Categorical(list("acb"), ordered=False)
+        tm.assert_index_equal(cat1.categories, Index(["a", "b", "c"]))
+        assert not cat1.ordered
+
+        cat2 = Categorical(list("acb"), categories=list("bca"), ordered=False)
+        tm.assert_index_equal(cat2.categories, Index(["b", "c", "a"]))
+        assert not cat2.ordered
+
+        cat3 = Categorical(list("acb"), ordered=True)
+        tm.assert_index_equal(cat3.categories, Index(["a", "b", "c"]))
+        assert cat3.ordered
+
+        cat4 = Categorical(list("acb"), categories=list("bca"), ordered=True)
+        tm.assert_index_equal(cat4.categories, Index(["b", "c", "a"]))
+        assert cat4.ordered
+
+    def test_set_ordered(self):
+        cat = Categorical(["a", "b", "c", "a"], ordered=True)
+        cat2 = cat.as_unordered()
+        assert not cat2.ordered
+        cat2 = cat.as_ordered()
+        assert cat2.ordered
+
+        assert cat2.set_ordered(True).ordered
+        assert not cat2.set_ordered(False).ordered
+
+        # removed in 0.19.0
+        msg = (
+            "property 'ordered' of 'Categorical' object has no setter"
+            if PY311
+            else "can't set attribute"
+        )
+        with pytest.raises(AttributeError, match=msg):
+            cat.ordered = True
+        with pytest.raises(AttributeError, match=msg):
+            cat.ordered = False
+
+    def test_rename_categories(self):
+        cat = Categorical(["a", "b", "c", "a"])
+
+        # inplace=False: the old one must not be changed
+        res = cat.rename_categories([1, 2, 3])
+        tm.assert_numpy_array_equal(
+            res.__array__(), np.array([1, 2, 3, 1], dtype=np.int64)
+        )
+        tm.assert_index_equal(res.categories, Index([1, 2, 3]))
+
+        exp_cat = np.array(["a", "b", "c", "a"], dtype=np.object_)
+        tm.assert_numpy_array_equal(cat.__array__(), exp_cat)
+
+        exp_cat = Index(["a", "b", "c"])
+        tm.assert_index_equal(cat.categories, exp_cat)
+
+        # GH18862 (let rename_categories take callables)
+        result = cat.rename_categories(lambda x: x.upper())
+        expected = Categorical(["A", "B", "C", "A"])
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("new_categories", [[1, 2, 3, 4], [1, 2]])
+    def test_rename_categories_wrong_length_raises(self, new_categories):
+        cat = Categorical(["a", "b", "c", "a"])
+        msg = (
+            "new categories need to have the same number of items as the "
+            "old categories!"
+        )
+        with pytest.raises(ValueError, match=msg):
+            cat.rename_categories(new_categories)
+
+    def test_rename_categories_series(self):
+        # https://github.com/pandas-dev/pandas/issues/17981
+        c = Categorical(["a", "b"])
+        result = c.rename_categories(Series([0, 1], index=["a", "b"]))
+        expected = Categorical([0, 1])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_rename_categories_dict(self):
+        # GH 17336
+        cat = Categorical(["a", "b", "c", "d"])
+        res = cat.rename_categories({"a": 4, "b": 3, "c": 2, "d": 1})
+        expected = Index([4, 3, 2, 1])
+        tm.assert_index_equal(res.categories, expected)
+
+        # Test for dicts of smaller length
+        cat = Categorical(["a", "b", "c", "d"])
+        res = cat.rename_categories({"a": 1, "c": 3})
+
+        expected = Index([1, "b", 3, "d"])
+        tm.assert_index_equal(res.categories, expected)
+
+        # Test for dicts with bigger length
+        cat = Categorical(["a", "b", "c", "d"])
+        res = cat.rename_categories({"a": 1, "b": 2, "c": 3, "d": 4, "e": 5, "f": 6})
+        expected = Index([1, 2, 3, 4])
+        tm.assert_index_equal(res.categories, expected)
+
+        # Test for dicts with no items from old categories
+        cat = Categorical(["a", "b", "c", "d"])
+        res = cat.rename_categories({"f": 1, "g": 3})
+
+        expected = Index(["a", "b", "c", "d"])
+        tm.assert_index_equal(res.categories, expected)
+
+    def test_reorder_categories(self):
+        cat = Categorical(["a", "b", "c", "a"], ordered=True)
+        old = cat.copy()
+        new = Categorical(
+            ["a", "b", "c", "a"], categories=["c", "b", "a"], ordered=True
+        )
+
+        res = cat.reorder_categories(["c", "b", "a"])
+        # cat must be the same as before
+        tm.assert_categorical_equal(cat, old)
+        # only res is changed
+        tm.assert_categorical_equal(res, new)
+
+    @pytest.mark.parametrize(
+        "new_categories",
+        [
+            ["a"],  # not all "old" included in "new"
+            ["a", "b", "d"],  # still not all "old" in "new"
+            ["a", "b", "c", "d"],  # all "old" included in "new", but too long
+        ],
+    )
+    def test_reorder_categories_raises(self, new_categories):
+        cat = Categorical(["a", "b", "c", "a"], ordered=True)
+        msg = "items in new_categories are not the same as in old categories"
+        with pytest.raises(ValueError, match=msg):
+            cat.reorder_categories(new_categories)
+
+    def test_add_categories(self):
+        cat = Categorical(["a", "b", "c", "a"], ordered=True)
+        old = cat.copy()
+        new = Categorical(
+            ["a", "b", "c", "a"], categories=["a", "b", "c", "d"], ordered=True
+        )
+
+        res = cat.add_categories("d")
+        tm.assert_categorical_equal(cat, old)
+        tm.assert_categorical_equal(res, new)
+
+        res = cat.add_categories(["d"])
+        tm.assert_categorical_equal(cat, old)
+        tm.assert_categorical_equal(res, new)
+
+        # GH 9927
+        cat = Categorical(list("abc"), ordered=True)
+        expected = Categorical(list("abc"), categories=list("abcde"), ordered=True)
+        # test with Series, np.array, index, list
+        res = cat.add_categories(Series(["d", "e"]))
+        tm.assert_categorical_equal(res, expected)
+        res = cat.add_categories(np.array(["d", "e"]))
+        tm.assert_categorical_equal(res, expected)
+        res = cat.add_categories(Index(["d", "e"]))
+        tm.assert_categorical_equal(res, expected)
+        res = cat.add_categories(["d", "e"])
+        tm.assert_categorical_equal(res, expected)
+
+    def test_add_categories_existing_raises(self):
+        # new is in old categories
+        cat = Categorical(["a", "b", "c", "d"], ordered=True)
+        msg = re.escape("new categories must not include old categories: {'d'}")
+        with pytest.raises(ValueError, match=msg):
+            cat.add_categories(["d"])
+
+    def test_add_categories_losing_dtype_information(self):
+        # GH#48812
+        cat = Categorical(Series([1, 2], dtype="Int64"))
+        ser = Series([4], dtype="Int64")
+        result = cat.add_categories(ser)
+        expected = Categorical(
+            Series([1, 2], dtype="Int64"), categories=Series([1, 2, 4], dtype="Int64")
+        )
+        tm.assert_categorical_equal(result, expected)
+
+        cat = Categorical(Series(["a", "b", "a"], dtype=StringDtype()))
+        ser = Series(["d"], dtype=StringDtype())
+        result = cat.add_categories(ser)
+        expected = Categorical(
+            Series(["a", "b", "a"], dtype=StringDtype()),
+            categories=Series(["a", "b", "d"], dtype=StringDtype()),
+        )
+        tm.assert_categorical_equal(result, expected)
+
+    def test_set_categories(self):
+        cat = Categorical(["a", "b", "c", "a"], ordered=True)
+        exp_categories = Index(["c", "b", "a"])
+        exp_values = np.array(["a", "b", "c", "a"], dtype=np.object_)
+
+        cat = cat.set_categories(["c", "b", "a"])
+        res = cat.set_categories(["a", "b", "c"])
+        # cat must be the same as before
+        tm.assert_index_equal(cat.categories, exp_categories)
+        tm.assert_numpy_array_equal(cat.__array__(), exp_values)
+        # only res is changed
+        exp_categories_back = Index(["a", "b", "c"])
+        tm.assert_index_equal(res.categories, exp_categories_back)
+        tm.assert_numpy_array_equal(res.__array__(), exp_values)
+
+        # not all "old" included in "new" -> all not included ones are now
+        # np.nan
+        cat = Categorical(["a", "b", "c", "a"], ordered=True)
+        res = cat.set_categories(["a"])
+        tm.assert_numpy_array_equal(res.codes, np.array([0, -1, -1, 0], dtype=np.int8))
+
+        # still not all "old" in "new"
+        res = cat.set_categories(["a", "b", "d"])
+        tm.assert_numpy_array_equal(res.codes, np.array([0, 1, -1, 0], dtype=np.int8))
+        tm.assert_index_equal(res.categories, Index(["a", "b", "d"]))
+
+        # all "old" included in "new"
+        cat = cat.set_categories(["a", "b", "c", "d"])
+        exp_categories = Index(["a", "b", "c", "d"])
+        tm.assert_index_equal(cat.categories, exp_categories)
+
+        # internals...
+        c = Categorical([1, 2, 3, 4, 1], categories=[1, 2, 3, 4], ordered=True)
+        tm.assert_numpy_array_equal(c._codes, np.array([0, 1, 2, 3, 0], dtype=np.int8))
+        tm.assert_index_equal(c.categories, Index([1, 2, 3, 4]))
+
+        exp = np.array([1, 2, 3, 4, 1], dtype=np.int64)
+        tm.assert_numpy_array_equal(np.asarray(c), exp)
+
+        # all "pointers" to '4' must be changed from 3 to 0,...
+        c = c.set_categories([4, 3, 2, 1])
+
+        # positions are changed
+        tm.assert_numpy_array_equal(c._codes, np.array([3, 2, 1, 0, 3], dtype=np.int8))
+
+        # categories are now in new order
+        tm.assert_index_equal(c.categories, Index([4, 3, 2, 1]))
+
+        # output is the same
+        exp = np.array([1, 2, 3, 4, 1], dtype=np.int64)
+        tm.assert_numpy_array_equal(np.asarray(c), exp)
+        assert c.min() == 4
+        assert c.max() == 1
+
+        # set_categories should set the ordering if specified
+        c2 = c.set_categories([4, 3, 2, 1], ordered=False)
+        assert not c2.ordered
+
+        tm.assert_numpy_array_equal(np.asarray(c), np.asarray(c2))
+
+        # set_categories should pass thru the ordering
+        c2 = c.set_ordered(False).set_categories([4, 3, 2, 1])
+        assert not c2.ordered
+
+        tm.assert_numpy_array_equal(np.asarray(c), np.asarray(c2))
+
+    @pytest.mark.parametrize(
+        "values, categories, new_categories",
+        [
+            # No NaNs, same cats, same order
+            (["a", "b", "a"], ["a", "b"], ["a", "b"]),
+            # No NaNs, same cats, different order
+            (["a", "b", "a"], ["a", "b"], ["b", "a"]),
+            # Same, unsorted
+            (["b", "a", "a"], ["a", "b"], ["a", "b"]),
+            # No NaNs, same cats, different order
+            (["b", "a", "a"], ["a", "b"], ["b", "a"]),
+            # NaNs
+            (["a", "b", "c"], ["a", "b"], ["a", "b"]),
+            (["a", "b", "c"], ["a", "b"], ["b", "a"]),
+            (["b", "a", "c"], ["a", "b"], ["a", "b"]),
+            (["b", "a", "c"], ["a", "b"], ["a", "b"]),
+            # Introduce NaNs
+            (["a", "b", "c"], ["a", "b"], ["a"]),
+            (["a", "b", "c"], ["a", "b"], ["b"]),
+            (["b", "a", "c"], ["a", "b"], ["a"]),
+            (["b", "a", "c"], ["a", "b"], ["a"]),
+            # No overlap
+            (["a", "b", "c"], ["a", "b"], ["d", "e"]),
+        ],
+    )
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_set_categories_many(self, values, categories, new_categories, ordered):
+        c = Categorical(values, categories)
+        expected = Categorical(values, new_categories, ordered)
+        result = c.set_categories(new_categories, ordered=ordered)
+        tm.assert_categorical_equal(result, expected)
+
+    def test_set_categories_rename_less(self):
+        # GH 24675
+        cat = Categorical(["A", "B"])
+        result = cat.set_categories(["A"], rename=True)
+        expected = Categorical(["A", np.nan])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_set_categories_private(self):
+        cat = Categorical(["a", "b", "c"], categories=["a", "b", "c", "d"])
+        cat._set_categories(["a", "c", "d", "e"])
+        expected = Categorical(["a", "c", "d"], categories=list("acde"))
+        tm.assert_categorical_equal(cat, expected)
+
+        # fastpath
+        cat = Categorical(["a", "b", "c"], categories=["a", "b", "c", "d"])
+        cat._set_categories(["a", "c", "d", "e"], fastpath=True)
+        expected = Categorical(["a", "c", "d"], categories=list("acde"))
+        tm.assert_categorical_equal(cat, expected)
+
+    def test_remove_categories(self):
+        cat = Categorical(["a", "b", "c", "a"], ordered=True)
+        old = cat.copy()
+        new = Categorical(["a", "b", np.nan, "a"], categories=["a", "b"], ordered=True)
+
+        res = cat.remove_categories("c")
+        tm.assert_categorical_equal(cat, old)
+        tm.assert_categorical_equal(res, new)
+
+        res = cat.remove_categories(["c"])
+        tm.assert_categorical_equal(cat, old)
+        tm.assert_categorical_equal(res, new)
+
+    @pytest.mark.parametrize("removals", [["c"], ["c", np.nan], "c", ["c", "c"]])
+    def test_remove_categories_raises(self, removals):
+        cat = Categorical(["a", "b", "a"])
+        message = re.escape("removals must all be in old categories: {'c'}")
+
+        with pytest.raises(ValueError, match=message):
+            cat.remove_categories(removals)
+
+    def test_remove_unused_categories(self):
+        c = Categorical(["a", "b", "c", "d", "a"], categories=["a", "b", "c", "d", "e"])
+        exp_categories_all = Index(["a", "b", "c", "d", "e"])
+        exp_categories_dropped = Index(["a", "b", "c", "d"])
+
+        tm.assert_index_equal(c.categories, exp_categories_all)
+
+        res = c.remove_unused_categories()
+        tm.assert_index_equal(res.categories, exp_categories_dropped)
+        tm.assert_index_equal(c.categories, exp_categories_all)
+
+        # with NaN values (GH11599)
+        c = Categorical(["a", "b", "c", np.nan], categories=["a", "b", "c", "d", "e"])
+        res = c.remove_unused_categories()
+        tm.assert_index_equal(res.categories, Index(np.array(["a", "b", "c"])))
+        exp_codes = np.array([0, 1, 2, -1], dtype=np.int8)
+        tm.assert_numpy_array_equal(res.codes, exp_codes)
+        tm.assert_index_equal(c.categories, exp_categories_all)
+
+        val = ["F", np.nan, "D", "B", "D", "F", np.nan]
+        cat = Categorical(values=val, categories=list("ABCDEFG"))
+        out = cat.remove_unused_categories()
+        tm.assert_index_equal(out.categories, Index(["B", "D", "F"]))
+        exp_codes = np.array([2, -1, 1, 0, 1, 2, -1], dtype=np.int8)
+        tm.assert_numpy_array_equal(out.codes, exp_codes)
+        assert out.tolist() == val
+
+        alpha = list("abcdefghijklmnopqrstuvwxyz")
+        val = np.random.default_rng(2).choice(alpha[::2], 10000).astype("object")
+        val[np.random.default_rng(2).choice(len(val), 100)] = np.nan
+
+        cat = Categorical(values=val, categories=alpha)
+        out = cat.remove_unused_categories()
+        assert out.tolist() == val.tolist()
+
+
+class TestCategoricalAPIWithFactor:
+    def test_describe(self):
+        factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
+        # string type
+        desc = factor.describe()
+        assert factor.ordered
+        exp_index = CategoricalIndex(
+            ["a", "b", "c"], name="categories", ordered=factor.ordered
+        )
+        expected = DataFrame(
+            {"counts": [3, 2, 3], "freqs": [3 / 8.0, 2 / 8.0, 3 / 8.0]}, index=exp_index
+        )
+        tm.assert_frame_equal(desc, expected)
+
+        # check unused categories
+        cat = factor.copy()
+        cat = cat.set_categories(["a", "b", "c", "d"])
+        desc = cat.describe()
+
+        exp_index = CategoricalIndex(
+            list("abcd"), ordered=factor.ordered, name="categories"
+        )
+        expected = DataFrame(
+            {"counts": [3, 2, 3, 0], "freqs": [3 / 8.0, 2 / 8.0, 3 / 8.0, 0]},
+            index=exp_index,
+        )
+        tm.assert_frame_equal(desc, expected)
+
+        # check an integer one
+        cat = Categorical([1, 2, 3, 1, 2, 3, 3, 2, 1, 1, 1])
+        desc = cat.describe()
+        exp_index = CategoricalIndex([1, 2, 3], ordered=cat.ordered, name="categories")
+        expected = DataFrame(
+            {"counts": [5, 3, 3], "freqs": [5 / 11.0, 3 / 11.0, 3 / 11.0]},
+            index=exp_index,
+        )
+        tm.assert_frame_equal(desc, expected)
+
+        # https://github.com/pandas-dev/pandas/issues/3678
+        # describe should work with NaN
+        cat = Categorical([np.nan, 1, 2, 2])
+        desc = cat.describe()
+        expected = DataFrame(
+            {"counts": [1, 2, 1], "freqs": [1 / 4.0, 2 / 4.0, 1 / 4.0]},
+            index=CategoricalIndex(
+                [1, 2, np.nan], categories=[1, 2], name="categories"
+            ),
+        )
+        tm.assert_frame_equal(desc, expected)
+
+
+class TestPrivateCategoricalAPI:
+    def test_codes_immutable(self):
+        # Codes should be read only
+        c = Categorical(["a", "b", "c", "a", np.nan])
+        exp = np.array([0, 1, 2, 0, -1], dtype="int8")
+        tm.assert_numpy_array_equal(c.codes, exp)
+
+        # Assignments to codes should raise
+        msg = (
+            "property 'codes' of 'Categorical' object has no setter"
+            if PY311
+            else "can't set attribute"
+        )
+        with pytest.raises(AttributeError, match=msg):
+            c.codes = np.array([0, 1, 2, 0, 1], dtype="int8")
+
+        # changes in the codes array should raise
+        codes = c.codes
+
+        with pytest.raises(ValueError, match="assignment destination is read-only"):
+            codes[4] = 1
+
+        # But even after getting the codes, the original array should still be
+        # writeable!
+        c[4] = "a"
+        exp = np.array([0, 1, 2, 0, 0], dtype="int8")
+        tm.assert_numpy_array_equal(c.codes, exp)
+        c._codes[4] = 2
+        exp = np.array([0, 1, 2, 0, 2], dtype="int8")
+        tm.assert_numpy_array_equal(c.codes, exp)
+
+    @pytest.mark.parametrize(
+        "codes, old, new, expected",
+        [
+            ([0, 1], ["a", "b"], ["a", "b"], [0, 1]),
+            ([0, 1], ["b", "a"], ["b", "a"], [0, 1]),
+            ([0, 1], ["a", "b"], ["b", "a"], [1, 0]),
+            ([0, 1], ["b", "a"], ["a", "b"], [1, 0]),
+            ([0, 1, 0, 1], ["a", "b"], ["a", "b", "c"], [0, 1, 0, 1]),
+            ([0, 1, 2, 2], ["a", "b", "c"], ["a", "b"], [0, 1, -1, -1]),
+            ([0, 1, -1], ["a", "b", "c"], ["a", "b", "c"], [0, 1, -1]),
+            ([0, 1, -1], ["a", "b", "c"], ["b"], [-1, 0, -1]),
+            ([0, 1, -1], ["a", "b", "c"], ["d"], [-1, -1, -1]),
+            ([0, 1, -1], ["a", "b", "c"], [], [-1, -1, -1]),
+            ([-1, -1], [], ["a", "b"], [-1, -1]),
+            ([1, 0], ["b", "a"], ["a", "b"], [0, 1]),
+        ],
+    )
+    def test_recode_to_categories(self, codes, old, new, expected):
+        codes = np.asanyarray(codes, dtype=np.int8)
+        expected = np.asanyarray(expected, dtype=np.int8)
+        old = Index(old)
+        new = Index(new)
+        result = recode_for_categories(codes, old, new)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_recode_to_categories_large(self):
+        N = 1000
+        codes = np.arange(N)
+        old = Index(codes)
+        expected = np.arange(N - 1, -1, -1, dtype=np.int16)
+        new = Index(expected)
+        result = recode_for_categories(codes, old, new)
+        tm.assert_numpy_array_equal(result, expected)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_astype.py

@@ -0,0 +1,155 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+    CategoricalIndex,
+    DatetimeIndex,
+    Interval,
+    NaT,
+    Period,
+    Timestamp,
+    array,
+    to_datetime,
+)
+import pandas._testing as tm
+
+
+class TestAstype:
+    @pytest.mark.parametrize("cls", [Categorical, CategoricalIndex])
+    @pytest.mark.parametrize("values", [[1, np.nan], [Timestamp("2000"), NaT]])
+    def test_astype_nan_to_int(self, cls, values):
+        # GH#28406
+        obj = cls(values)
+
+        msg = "Cannot (cast|convert)"
+        with pytest.raises((ValueError, TypeError), match=msg):
+            obj.astype(int)
+
+    @pytest.mark.parametrize(
+        "expected",
+        [
+            array(["2019", "2020"], dtype="datetime64[ns, UTC]"),
+            array([0, 0], dtype="timedelta64[ns]"),
+            array([Period("2019"), Period("2020")], dtype="period[Y-DEC]"),
+            array([Interval(0, 1), Interval(1, 2)], dtype="interval"),
+            array([1, np.nan], dtype="Int64"),
+        ],
+    )
+    def test_astype_category_to_extension_dtype(self, expected):
+        # GH#28668
+        result = expected.astype("category").astype(expected.dtype)
+
+        tm.assert_extension_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype, expected",
+        [
+            (
+                "datetime64[ns]",
+                np.array(["2015-01-01T00:00:00.000000000"], dtype="datetime64[ns]"),
+            ),
+            (
+                "datetime64[ns, MET]",
+                DatetimeIndex([Timestamp("2015-01-01 00:00:00+0100", tz="MET")]).array,
+            ),
+        ],
+    )
+    def test_astype_to_datetime64(self, dtype, expected):
+        # GH#28448
+        result = Categorical(["2015-01-01"]).astype(dtype)
+        assert result == expected
+
+    def test_astype_str_int_categories_to_nullable_int(self):
+        # GH#39616
+        dtype = CategoricalDtype([str(i) for i in range(5)])
+        codes = np.random.default_rng(2).integers(5, size=20)
+        arr = Categorical.from_codes(codes, dtype=dtype)
+
+        res = arr.astype("Int64")
+        expected = array(codes, dtype="Int64")
+        tm.assert_extension_array_equal(res, expected)
+
+    def test_astype_str_int_categories_to_nullable_float(self):
+        # GH#39616
+        dtype = CategoricalDtype([str(i / 2) for i in range(5)])
+        codes = np.random.default_rng(2).integers(5, size=20)
+        arr = Categorical.from_codes(codes, dtype=dtype)
+
+        res = arr.astype("Float64")
+        expected = array(codes, dtype="Float64") / 2
+        tm.assert_extension_array_equal(res, expected)
+
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_astype(self, ordered):
+        # string
+        cat = Categorical(list("abbaaccc"), ordered=ordered)
+        result = cat.astype(object)
+        expected = np.array(cat)
+        tm.assert_numpy_array_equal(result, expected)
+
+        msg = r"Cannot cast object|string dtype to float64"
+        with pytest.raises(ValueError, match=msg):
+            cat.astype(float)
+
+        # numeric
+        cat = Categorical([0, 1, 2, 2, 1, 0, 1, 0, 2], ordered=ordered)
+        result = cat.astype(object)
+        expected = np.array(cat, dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = cat.astype(int)
+        expected = np.array(cat, dtype="int")
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = cat.astype(float)
+        expected = np.array(cat, dtype=float)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype_ordered", [True, False])
+    @pytest.mark.parametrize("cat_ordered", [True, False])
+    def test_astype_category(self, dtype_ordered, cat_ordered):
+        # GH#10696/GH#18593
+        data = list("abcaacbab")
+        cat = Categorical(data, categories=list("bac"), ordered=cat_ordered)
+
+        # standard categories
+        dtype = CategoricalDtype(ordered=dtype_ordered)
+        result = cat.astype(dtype)
+        expected = Categorical(data, categories=cat.categories, ordered=dtype_ordered)
+        tm.assert_categorical_equal(result, expected)
+
+        # non-standard categories
+        dtype = CategoricalDtype(list("adc"), dtype_ordered)
+        result = cat.astype(dtype)
+        expected = Categorical(data, dtype=dtype)
+        tm.assert_categorical_equal(result, expected)
+
+        if dtype_ordered is False:
+            # dtype='category' can't specify ordered, so only test once
+            result = cat.astype("category")
+            expected = cat
+            tm.assert_categorical_equal(result, expected)
+
+    def test_astype_object_datetime_categories(self):
+        # GH#40754
+        cat = Categorical(to_datetime(["2021-03-27", NaT]))
+        result = cat.astype(object)
+        expected = np.array([Timestamp("2021-03-27 00:00:00"), NaT], dtype="object")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_astype_object_timestamp_categories(self):
+        # GH#18024
+        cat = Categorical([Timestamp("2014-01-01")])
+        result = cat.astype(object)
+        expected = np.array([Timestamp("2014-01-01 00:00:00")], dtype="object")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_astype_category_readonly_mask_values(self):
+        # GH#53658
+        arr = array([0, 1, 2], dtype="Int64")
+        arr._mask.flags["WRITEABLE"] = False
+        result = arr.astype("category")
+        expected = array([0, 1, 2], dtype="Int64").astype("category")
+        tm.assert_extension_array_equal(result, expected)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_constructors.py

@@ -0,0 +1,783 @@
+from datetime import (
+    date,
+    datetime,
+)
+
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+
+from pandas.core.dtypes.common import (
+    is_float_dtype,
+    is_integer_dtype,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    DatetimeIndex,
+    Index,
+    Interval,
+    IntervalIndex,
+    MultiIndex,
+    NaT,
+    Series,
+    Timestamp,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalConstructors:
+    def test_fastpath_deprecated(self):
+        codes = np.array([1, 2, 3])
+        dtype = CategoricalDtype(categories=["a", "b", "c", "d"], ordered=False)
+        msg = "The 'fastpath' keyword in Categorical is deprecated"
+        with tm.assert_produces_warning(DeprecationWarning, match=msg):
+            Categorical(codes, dtype=dtype, fastpath=True)
+
+    def test_categorical_from_cat_and_dtype_str_preserve_ordered(self):
+        # GH#49309 we should preserve orderedness in `res`
+        cat = Categorical([3, 1], categories=[3, 2, 1], ordered=True)
+
+        res = Categorical(cat, dtype="category")
+        assert res.dtype.ordered
+
+    def test_categorical_disallows_scalar(self):
+        # GH#38433
+        with pytest.raises(TypeError, match="Categorical input must be list-like"):
+            Categorical("A", categories=["A", "B"])
+
+    def test_categorical_1d_only(self):
+        # ndim > 1
+        msg = "> 1 ndim Categorical are not supported at this time"
+        with pytest.raises(NotImplementedError, match=msg):
+            Categorical(np.array([list("abcd")]))
+
+    def test_validate_ordered(self):
+        # see gh-14058
+        exp_msg = "'ordered' must either be 'True' or 'False'"
+        exp_err = TypeError
+
+        # This should be a boolean.
+        ordered = np.array([0, 1, 2])
+
+        with pytest.raises(exp_err, match=exp_msg):
+            Categorical([1, 2, 3], ordered=ordered)
+
+        with pytest.raises(exp_err, match=exp_msg):
+            Categorical.from_codes(
+                [0, 0, 1], categories=["a", "b", "c"], ordered=ordered
+            )
+
+    def test_constructor_empty(self):
+        # GH 17248
+        c = Categorical([])
+        expected = Index([])
+        tm.assert_index_equal(c.categories, expected)
+
+        c = Categorical([], categories=[1, 2, 3])
+        expected = Index([1, 2, 3], dtype=np.int64)
+        tm.assert_index_equal(c.categories, expected)
+
+    def test_constructor_empty_boolean(self):
+        # see gh-22702
+        cat = Categorical([], categories=[True, False])
+        categories = sorted(cat.categories.tolist())
+        assert categories == [False, True]
+
+    def test_constructor_tuples(self):
+        values = np.array([(1,), (1, 2), (1,), (1, 2)], dtype=object)
+        result = Categorical(values)
+        expected = Index([(1,), (1, 2)], tupleize_cols=False)
+        tm.assert_index_equal(result.categories, expected)
+        assert result.ordered is False
+
+    def test_constructor_tuples_datetimes(self):
+        # numpy will auto reshape when all of the tuples are the
+        # same len, so add an extra one with 2 items and slice it off
+        values = np.array(
+            [
+                (Timestamp("2010-01-01"),),
+                (Timestamp("2010-01-02"),),
+                (Timestamp("2010-01-01"),),
+                (Timestamp("2010-01-02"),),
+                ("a", "b"),
+            ],
+            dtype=object,
+        )[:-1]
+        result = Categorical(values)
+        expected = Index(
+            [(Timestamp("2010-01-01"),), (Timestamp("2010-01-02"),)],
+            tupleize_cols=False,
+        )
+        tm.assert_index_equal(result.categories, expected)
+
+    def test_constructor_unsortable(self):
+        # it works!
+        arr = np.array([1, 2, 3, datetime.now()], dtype="O")
+        factor = Categorical(arr, ordered=False)
+        assert not factor.ordered
+
+        # this however will raise as cannot be sorted
+        msg = (
+            "'values' is not ordered, please explicitly specify the "
+            "categories order by passing in a categories argument."
+        )
+        with pytest.raises(TypeError, match=msg):
+            Categorical(arr, ordered=True)
+
+    def test_constructor_interval(self):
+        result = Categorical(
+            [Interval(1, 2), Interval(2, 3), Interval(3, 6)], ordered=True
+        )
+        ii = IntervalIndex([Interval(1, 2), Interval(2, 3), Interval(3, 6)])
+        exp = Categorical(ii, ordered=True)
+        tm.assert_categorical_equal(result, exp)
+        tm.assert_index_equal(result.categories, ii)
+
+    def test_constructor(self):
+        exp_arr = np.array(["a", "b", "c", "a", "b", "c"], dtype=np.object_)
+        c1 = Categorical(exp_arr)
+        tm.assert_numpy_array_equal(c1.__array__(), exp_arr)
+        c2 = Categorical(exp_arr, categories=["a", "b", "c"])
+        tm.assert_numpy_array_equal(c2.__array__(), exp_arr)
+        c2 = Categorical(exp_arr, categories=["c", "b", "a"])
+        tm.assert_numpy_array_equal(c2.__array__(), exp_arr)
+
+        # categories must be unique
+        msg = "Categorical categories must be unique"
+        with pytest.raises(ValueError, match=msg):
+            Categorical([1, 2], [1, 2, 2])
+
+        with pytest.raises(ValueError, match=msg):
+            Categorical(["a", "b"], ["a", "b", "b"])
+
+        # The default should be unordered
+        c1 = Categorical(["a", "b", "c", "a"])
+        assert not c1.ordered
+
+        # Categorical as input
+        c1 = Categorical(["a", "b", "c", "a"])
+        c2 = Categorical(c1)
+        tm.assert_categorical_equal(c1, c2)
+
+        c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
+        c2 = Categorical(c1)
+        tm.assert_categorical_equal(c1, c2)
+
+        c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"])
+        c2 = Categorical(c1)
+        tm.assert_categorical_equal(c1, c2)
+
+        c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"])
+        c2 = Categorical(c1, categories=["a", "b", "c"])
+        tm.assert_numpy_array_equal(c1.__array__(), c2.__array__())
+        tm.assert_index_equal(c2.categories, Index(["a", "b", "c"]))
+
+        # Series of dtype category
+        c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
+        c2 = Categorical(Series(c1))
+        tm.assert_categorical_equal(c1, c2)
+
+        c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"])
+        c2 = Categorical(Series(c1))
+        tm.assert_categorical_equal(c1, c2)
+
+        # Series
+        c1 = Categorical(["a", "b", "c", "a"])
+        c2 = Categorical(Series(["a", "b", "c", "a"]))
+        tm.assert_categorical_equal(c1, c2)
+
+        c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
+        c2 = Categorical(Series(["a", "b", "c", "a"]), categories=["a", "b", "c", "d"])
+        tm.assert_categorical_equal(c1, c2)
+
+        # This should result in integer categories, not float!
+        cat = Categorical([1, 2, 3, np.nan], categories=[1, 2, 3])
+        assert is_integer_dtype(cat.categories)
+
+        # https://github.com/pandas-dev/pandas/issues/3678
+        cat = Categorical([np.nan, 1, 2, 3])
+        assert is_integer_dtype(cat.categories)
+
+        # this should result in floats
+        cat = Categorical([np.nan, 1, 2.0, 3])
+        assert is_float_dtype(cat.categories)
+
+        cat = Categorical([np.nan, 1.0, 2.0, 3.0])
+        assert is_float_dtype(cat.categories)
+
+        # This doesn't work -> this would probably need some kind of "remember
+        # the original type" feature to try to cast the array interface result
+        # to...
+
+        # vals = np.asarray(cat[cat.notna()])
+        # assert is_integer_dtype(vals)
+
+        # corner cases
+        cat = Categorical([1])
+        assert len(cat.categories) == 1
+        assert cat.categories[0] == 1
+        assert len(cat.codes) == 1
+        assert cat.codes[0] == 0
+
+        cat = Categorical(["a"])
+        assert len(cat.categories) == 1
+        assert cat.categories[0] == "a"
+        assert len(cat.codes) == 1
+        assert cat.codes[0] == 0
+
+        # two arrays
+        #  - when the first is an integer dtype and the second is not
+        #  - when the resulting codes are all -1/NaN
+        with tm.assert_produces_warning(None):
+            Categorical([0, 1, 2, 0, 1, 2], categories=["a", "b", "c"])
+
+        with tm.assert_produces_warning(None):
+            Categorical([0, 1, 2, 0, 1, 2], categories=[3, 4, 5])
+
+        # the next one are from the old docs
+        with tm.assert_produces_warning(None):
+            Categorical([0, 1, 2, 0, 1, 2], [1, 2, 3])
+            cat = Categorical([1, 2], categories=[1, 2, 3])
+
+        # this is a legitimate constructor
+        with tm.assert_produces_warning(None):
+            Categorical(np.array([], dtype="int64"), categories=[3, 2, 1], ordered=True)
+
+    def test_constructor_with_existing_categories(self):
+        # GH25318: constructing with pd.Series used to bogusly skip recoding
+        # categories
+        c0 = Categorical(["a", "b", "c", "a"])
+        c1 = Categorical(["a", "b", "c", "a"], categories=["b", "c"])
+
+        c2 = Categorical(c0, categories=c1.categories)
+        tm.assert_categorical_equal(c1, c2)
+
+        c3 = Categorical(Series(c0), categories=c1.categories)
+        tm.assert_categorical_equal(c1, c3)
+
+    def test_constructor_not_sequence(self):
+        # https://github.com/pandas-dev/pandas/issues/16022
+        msg = r"^Parameter 'categories' must be list-like, was"
+        with pytest.raises(TypeError, match=msg):
+            Categorical(["a", "b"], categories="a")
+
+    def test_constructor_with_null(self):
+        # Cannot have NaN in categories
+        msg = "Categorical categories cannot be null"
+        with pytest.raises(ValueError, match=msg):
+            Categorical([np.nan, "a", "b", "c"], categories=[np.nan, "a", "b", "c"])
+
+        with pytest.raises(ValueError, match=msg):
+            Categorical([None, "a", "b", "c"], categories=[None, "a", "b", "c"])
+
+        with pytest.raises(ValueError, match=msg):
+            Categorical(
+                DatetimeIndex(["nat", "20160101"]),
+                categories=[NaT, Timestamp("20160101")],
+            )
+
+    def test_constructor_with_index(self):
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
+        tm.assert_categorical_equal(ci.values, Categorical(ci))
+
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
+        tm.assert_categorical_equal(
+            ci.values, Categorical(ci.astype(object), categories=ci.categories)
+        )
+
+    def test_constructor_with_generator(self):
+        # This was raising an Error in isna(single_val).any() because isna
+        # returned a scalar for a generator
+
+        exp = Categorical([0, 1, 2])
+        cat = Categorical(x for x in [0, 1, 2])
+        tm.assert_categorical_equal(cat, exp)
+        cat = Categorical(range(3))
+        tm.assert_categorical_equal(cat, exp)
+
+        MultiIndex.from_product([range(5), ["a", "b", "c"]])
+
+        # check that categories accept generators and sequences
+        cat = Categorical([0, 1, 2], categories=(x for x in [0, 1, 2]))
+        tm.assert_categorical_equal(cat, exp)
+        cat = Categorical([0, 1, 2], categories=range(3))
+        tm.assert_categorical_equal(cat, exp)
+
+    def test_constructor_with_rangeindex(self):
+        # RangeIndex is preserved in Categories
+        rng = Index(range(3))
+
+        cat = Categorical(rng)
+        tm.assert_index_equal(cat.categories, rng, exact=True)
+
+        cat = Categorical([1, 2, 0], categories=rng)
+        tm.assert_index_equal(cat.categories, rng, exact=True)
+
+    @pytest.mark.parametrize(
+        "dtl",
+        [
+            date_range("1995-01-01 00:00:00", periods=5, freq="s"),
+            date_range("1995-01-01 00:00:00", periods=5, freq="s", tz="US/Eastern"),
+            timedelta_range("1 day", periods=5, freq="s"),
+        ],
+    )
+    def test_constructor_with_datetimelike(self, dtl):
+        # see gh-12077
+        # constructor with a datetimelike and NaT
+
+        s = Series(dtl)
+        c = Categorical(s)
+
+        expected = type(dtl)(s)
+        expected._data.freq = None
+
+        tm.assert_index_equal(c.categories, expected)
+        tm.assert_numpy_array_equal(c.codes, np.arange(5, dtype="int8"))
+
+        # with NaT
+        s2 = s.copy()
+        s2.iloc[-1] = NaT
+        c = Categorical(s2)
+
+        expected = type(dtl)(s2.dropna())
+        expected._data.freq = None
+
+        tm.assert_index_equal(c.categories, expected)
+
+        exp = np.array([0, 1, 2, 3, -1], dtype=np.int8)
+        tm.assert_numpy_array_equal(c.codes, exp)
+
+        result = repr(c)
+        assert "NaT" in result
+
+    def test_constructor_from_index_series_datetimetz(self):
+        idx = date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern")
+        idx = idx._with_freq(None)  # freq not preserved in result.categories
+        result = Categorical(idx)
+        tm.assert_index_equal(result.categories, idx)
+
+        result = Categorical(Series(idx))
+        tm.assert_index_equal(result.categories, idx)
+
+    def test_constructor_date_objects(self):
+        # we dont cast date objects to timestamps, matching Index constructor
+        v = date.today()
+
+        cat = Categorical([v, v])
+        assert cat.categories.dtype == object
+        assert type(cat.categories[0]) is date
+
+    def test_constructor_from_index_series_timedelta(self):
+        idx = timedelta_range("1 days", freq="D", periods=3)
+        idx = idx._with_freq(None)  # freq not preserved in result.categories
+        result = Categorical(idx)
+        tm.assert_index_equal(result.categories, idx)
+
+        result = Categorical(Series(idx))
+        tm.assert_index_equal(result.categories, idx)
+
+    def test_constructor_from_index_series_period(self):
+        idx = period_range("2015-01-01", freq="D", periods=3)
+        result = Categorical(idx)
+        tm.assert_index_equal(result.categories, idx)
+
+        result = Categorical(Series(idx))
+        tm.assert_index_equal(result.categories, idx)
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            np.array([1.0, 1.2, 1.8, np.nan]),
+            np.array([1, 2, 3], dtype="int64"),
+            ["a", "b", "c", np.nan],
+            [pd.Period("2014-01"), pd.Period("2014-02"), NaT],
+            [Timestamp("2014-01-01"), Timestamp("2014-01-02"), NaT],
+            [
+                Timestamp("2014-01-01", tz="US/Eastern"),
+                Timestamp("2014-01-02", tz="US/Eastern"),
+                NaT,
+            ],
+        ],
+    )
+    def test_constructor_invariant(self, values):
+        # GH 14190
+        c = Categorical(values)
+        c2 = Categorical(c)
+        tm.assert_categorical_equal(c, c2)
+
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_constructor_with_dtype(self, ordered):
+        categories = ["b", "a", "c"]
+        dtype = CategoricalDtype(categories, ordered=ordered)
+        result = Categorical(["a", "b", "a", "c"], dtype=dtype)
+        expected = Categorical(
+            ["a", "b", "a", "c"], categories=categories, ordered=ordered
+        )
+        tm.assert_categorical_equal(result, expected)
+        assert result.ordered is ordered
+
+    def test_constructor_dtype_and_others_raises(self):
+        dtype = CategoricalDtype(["a", "b"], ordered=True)
+        msg = "Cannot specify `categories` or `ordered` together with `dtype`."
+        with pytest.raises(ValueError, match=msg):
+            Categorical(["a", "b"], categories=["a", "b"], dtype=dtype)
+
+        with pytest.raises(ValueError, match=msg):
+            Categorical(["a", "b"], ordered=True, dtype=dtype)
+
+        with pytest.raises(ValueError, match=msg):
+            Categorical(["a", "b"], ordered=False, dtype=dtype)
+
+    @pytest.mark.parametrize("categories", [None, ["a", "b"], ["a", "c"]])
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_constructor_str_category(self, categories, ordered):
+        result = Categorical(
+            ["a", "b"], categories=categories, ordered=ordered, dtype="category"
+        )
+        expected = Categorical(["a", "b"], categories=categories, ordered=ordered)
+        tm.assert_categorical_equal(result, expected)
+
+    def test_constructor_str_unknown(self):
+        with pytest.raises(ValueError, match="Unknown dtype"):
+            Categorical([1, 2], dtype="foo")
+
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="Can't be NumPy strings")
+    def test_constructor_np_strs(self):
+        # GH#31499 Hashtable.map_locations needs to work on np.str_ objects
+        cat = Categorical(["1", "0", "1"], [np.str_("0"), np.str_("1")])
+        assert all(isinstance(x, np.str_) for x in cat.categories)
+
+    def test_constructor_from_categorical_with_dtype(self):
+        dtype = CategoricalDtype(["a", "b", "c"], ordered=True)
+        values = Categorical(["a", "b", "d"])
+        result = Categorical(values, dtype=dtype)
+        # We use dtype.categories, not values.categories
+        expected = Categorical(
+            ["a", "b", "d"], categories=["a", "b", "c"], ordered=True
+        )
+        tm.assert_categorical_equal(result, expected)
+
+    def test_constructor_from_categorical_with_unknown_dtype(self):
+        dtype = CategoricalDtype(None, ordered=True)
+        values = Categorical(["a", "b", "d"])
+        result = Categorical(values, dtype=dtype)
+        # We use values.categories, not dtype.categories
+        expected = Categorical(
+            ["a", "b", "d"], categories=["a", "b", "d"], ordered=True
+        )
+        tm.assert_categorical_equal(result, expected)
+
+    def test_constructor_from_categorical_string(self):
+        values = Categorical(["a", "b", "d"])
+        # use categories, ordered
+        result = Categorical(
+            values, categories=["a", "b", "c"], ordered=True, dtype="category"
+        )
+        expected = Categorical(
+            ["a", "b", "d"], categories=["a", "b", "c"], ordered=True
+        )
+        tm.assert_categorical_equal(result, expected)
+
+        # No string
+        result = Categorical(values, categories=["a", "b", "c"], ordered=True)
+        tm.assert_categorical_equal(result, expected)
+
+    def test_constructor_with_categorical_categories(self):
+        # GH17884
+        expected = Categorical(["a", "b"], categories=["a", "b", "c"])
+
+        result = Categorical(["a", "b"], categories=Categorical(["a", "b", "c"]))
+        tm.assert_categorical_equal(result, expected)
+
+        result = Categorical(["a", "b"], categories=CategoricalIndex(["a", "b", "c"]))
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("klass", [lambda x: np.array(x, dtype=object), list])
+    def test_construction_with_null(self, klass, nulls_fixture):
+        # https://github.com/pandas-dev/pandas/issues/31927
+        values = klass(["a", nulls_fixture, "b"])
+        result = Categorical(values)
+
+        dtype = CategoricalDtype(["a", "b"])
+        codes = [0, -1, 1]
+        expected = Categorical.from_codes(codes=codes, dtype=dtype)
+
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("validate", [True, False])
+    def test_from_codes_nullable_int_categories(self, any_numeric_ea_dtype, validate):
+        # GH#39649
+        cats = pd.array(range(5), dtype=any_numeric_ea_dtype)
+        codes = np.random.default_rng(2).integers(5, size=3)
+        dtype = CategoricalDtype(cats)
+        arr = Categorical.from_codes(codes, dtype=dtype, validate=validate)
+        assert arr.categories.dtype == cats.dtype
+        tm.assert_index_equal(arr.categories, Index(cats))
+
+    def test_from_codes_empty(self):
+        cat = ["a", "b", "c"]
+        result = Categorical.from_codes([], categories=cat)
+        expected = Categorical([], categories=cat)
+
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("validate", [True, False])
+    def test_from_codes_validate(self, validate):
+        # GH53122
+        dtype = CategoricalDtype(["a", "b"])
+        if validate:
+            with pytest.raises(ValueError, match="codes need to be between "):
+                Categorical.from_codes([4, 5], dtype=dtype, validate=validate)
+        else:
+            # passes, though has incorrect codes, but that's the user responsibility
+            Categorical.from_codes([4, 5], dtype=dtype, validate=validate)
+
+    def test_from_codes_too_few_categories(self):
+        dtype = CategoricalDtype(categories=[1, 2])
+        msg = "codes need to be between "
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes([1, 2], categories=dtype.categories)
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes([1, 2], dtype=dtype)
+
+    def test_from_codes_non_int_codes(self):
+        dtype = CategoricalDtype(categories=[1, 2])
+        msg = "codes need to be array-like integers"
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(["a"], categories=dtype.categories)
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(["a"], dtype=dtype)
+
+    def test_from_codes_non_unique_categories(self):
+        with pytest.raises(ValueError, match="Categorical categories must be unique"):
+            Categorical.from_codes([0, 1, 2], categories=["a", "a", "b"])
+
+    def test_from_codes_nan_cat_included(self):
+        with pytest.raises(ValueError, match="Categorical categories cannot be null"):
+            Categorical.from_codes([0, 1, 2], categories=["a", "b", np.nan])
+
+    def test_from_codes_too_negative(self):
+        dtype = CategoricalDtype(categories=["a", "b", "c"])
+        msg = r"codes need to be between -1 and len\(categories\)-1"
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes([-2, 1, 2], categories=dtype.categories)
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes([-2, 1, 2], dtype=dtype)
+
+    def test_from_codes(self):
+        dtype = CategoricalDtype(categories=["a", "b", "c"])
+        exp = Categorical(["a", "b", "c"], ordered=False)
+        res = Categorical.from_codes([0, 1, 2], categories=dtype.categories)
+        tm.assert_categorical_equal(exp, res)
+
+        res = Categorical.from_codes([0, 1, 2], dtype=dtype)
+        tm.assert_categorical_equal(exp, res)
+
+    @pytest.mark.parametrize("klass", [Categorical, CategoricalIndex])
+    def test_from_codes_with_categorical_categories(self, klass):
+        # GH17884
+        expected = Categorical(["a", "b"], categories=["a", "b", "c"])
+
+        result = Categorical.from_codes([0, 1], categories=klass(["a", "b", "c"]))
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("klass", [Categorical, CategoricalIndex])
+    def test_from_codes_with_non_unique_categorical_categories(self, klass):
+        with pytest.raises(ValueError, match="Categorical categories must be unique"):
+            Categorical.from_codes([0, 1], klass(["a", "b", "a"]))
+
+    def test_from_codes_with_nan_code(self):
+        # GH21767
+        codes = [1, 2, np.nan]
+        dtype = CategoricalDtype(categories=["a", "b", "c"])
+        with pytest.raises(ValueError, match="codes need to be array-like integers"):
+            Categorical.from_codes(codes, categories=dtype.categories)
+        with pytest.raises(ValueError, match="codes need to be array-like integers"):
+            Categorical.from_codes(codes, dtype=dtype)
+
+    @pytest.mark.parametrize("codes", [[1.0, 2.0, 0], [1.1, 2.0, 0]])
+    def test_from_codes_with_float(self, codes):
+        # GH21767
+        # float codes should raise even if values are equal to integers
+        dtype = CategoricalDtype(categories=["a", "b", "c"])
+
+        msg = "codes need to be array-like integers"
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(codes, dtype.categories)
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(codes, dtype=dtype)
+
+    def test_from_codes_with_dtype_raises(self):
+        msg = "Cannot specify"
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(
+                [0, 1], categories=["a", "b"], dtype=CategoricalDtype(["a", "b"])
+            )
+
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(
+                [0, 1], ordered=True, dtype=CategoricalDtype(["a", "b"])
+            )
+
+    def test_from_codes_neither(self):
+        msg = "Both were None"
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes([0, 1])
+
+    def test_from_codes_with_nullable_int(self):
+        codes = pd.array([0, 1], dtype="Int64")
+        categories = ["a", "b"]
+
+        result = Categorical.from_codes(codes, categories=categories)
+        expected = Categorical.from_codes(codes.to_numpy(int), categories=categories)
+
+        tm.assert_categorical_equal(result, expected)
+
+    def test_from_codes_with_nullable_int_na_raises(self):
+        codes = pd.array([0, None], dtype="Int64")
+        categories = ["a", "b"]
+
+        msg = "codes cannot contain NA values"
+        with pytest.raises(ValueError, match=msg):
+            Categorical.from_codes(codes, categories=categories)
+
+    @pytest.mark.parametrize("dtype", [None, "category"])
+    def test_from_inferred_categories(self, dtype):
+        cats = ["a", "b"]
+        codes = np.array([0, 0, 1, 1], dtype="i8")
+        result = Categorical._from_inferred_categories(cats, codes, dtype)
+        expected = Categorical.from_codes(codes, cats)
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [None, "category"])
+    def test_from_inferred_categories_sorts(self, dtype):
+        cats = ["b", "a"]
+        codes = np.array([0, 1, 1, 1], dtype="i8")
+        result = Categorical._from_inferred_categories(cats, codes, dtype)
+        expected = Categorical.from_codes([1, 0, 0, 0], ["a", "b"])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_from_inferred_categories_dtype(self):
+        cats = ["a", "b", "d"]
+        codes = np.array([0, 1, 0, 2], dtype="i8")
+        dtype = CategoricalDtype(["c", "b", "a"], ordered=True)
+        result = Categorical._from_inferred_categories(cats, codes, dtype)
+        expected = Categorical(
+            ["a", "b", "a", "d"], categories=["c", "b", "a"], ordered=True
+        )
+        tm.assert_categorical_equal(result, expected)
+
+    def test_from_inferred_categories_coerces(self):
+        cats = ["1", "2", "bad"]
+        codes = np.array([0, 0, 1, 2], dtype="i8")
+        dtype = CategoricalDtype([1, 2])
+        result = Categorical._from_inferred_categories(cats, codes, dtype)
+        expected = Categorical([1, 1, 2, np.nan])
+        tm.assert_categorical_equal(result, expected)
+
+    @pytest.mark.parametrize("ordered", [None, True, False])
+    def test_construction_with_ordered(self, ordered):
+        # GH 9347, 9190
+        cat = Categorical([0, 1, 2], ordered=ordered)
+        assert cat.ordered == bool(ordered)
+
+    def test_constructor_imaginary(self):
+        values = [1, 2, 3 + 1j]
+        c1 = Categorical(values)
+        tm.assert_index_equal(c1.categories, Index(values))
+        tm.assert_numpy_array_equal(np.array(c1), np.array(values))
+
+    def test_constructor_string_and_tuples(self):
+        # GH 21416
+        c = Categorical(np.array(["c", ("a", "b"), ("b", "a"), "c"], dtype=object))
+        expected_index = Index([("a", "b"), ("b", "a"), "c"])
+        assert c.categories.equals(expected_index)
+
+    def test_interval(self):
+        idx = pd.interval_range(0, 10, periods=10)
+        cat = Categorical(idx, categories=idx)
+        expected_codes = np.arange(10, dtype="int8")
+        tm.assert_numpy_array_equal(cat.codes, expected_codes)
+        tm.assert_index_equal(cat.categories, idx)
+
+        # infer categories
+        cat = Categorical(idx)
+        tm.assert_numpy_array_equal(cat.codes, expected_codes)
+        tm.assert_index_equal(cat.categories, idx)
+
+        # list values
+        cat = Categorical(list(idx))
+        tm.assert_numpy_array_equal(cat.codes, expected_codes)
+        tm.assert_index_equal(cat.categories, idx)
+
+        # list values, categories
+        cat = Categorical(list(idx), categories=list(idx))
+        tm.assert_numpy_array_equal(cat.codes, expected_codes)
+        tm.assert_index_equal(cat.categories, idx)
+
+        # shuffled
+        values = idx.take([1, 2, 0])
+        cat = Categorical(values, categories=idx)
+        tm.assert_numpy_array_equal(cat.codes, np.array([1, 2, 0], dtype="int8"))
+        tm.assert_index_equal(cat.categories, idx)
+
+        # extra
+        values = pd.interval_range(8, 11, periods=3)
+        cat = Categorical(values, categories=idx)
+        expected_codes = np.array([8, 9, -1], dtype="int8")
+        tm.assert_numpy_array_equal(cat.codes, expected_codes)
+        tm.assert_index_equal(cat.categories, idx)
+
+        # overlapping
+        idx = IntervalIndex([Interval(0, 2), Interval(0, 1)])
+        cat = Categorical(idx, categories=idx)
+        expected_codes = np.array([0, 1], dtype="int8")
+        tm.assert_numpy_array_equal(cat.codes, expected_codes)
+        tm.assert_index_equal(cat.categories, idx)
+
+    def test_categorical_extension_array_nullable(self, nulls_fixture):
+        # GH:
+        arr = pd.arrays.StringArray._from_sequence(
+            [nulls_fixture] * 2, dtype=pd.StringDtype()
+        )
+        result = Categorical(arr)
+        assert arr.dtype == result.categories.dtype
+        expected = Categorical(Series([pd.NA, pd.NA], dtype=arr.dtype))
+        tm.assert_categorical_equal(result, expected)
+
+    def test_from_sequence_copy(self):
+        cat = Categorical(np.arange(5).repeat(2))
+        result = Categorical._from_sequence(cat, dtype=cat.dtype, copy=False)
+
+        # more generally, we'd be OK with a view
+        assert result._codes is cat._codes
+
+        result = Categorical._from_sequence(cat, dtype=cat.dtype, copy=True)
+
+        assert not tm.shares_memory(result, cat)
+
+    def test_constructor_datetime64_non_nano(self):
+        categories = np.arange(10).view("M8[D]")
+        values = categories[::2].copy()
+
+        cat = Categorical(values, categories=categories)
+        assert (cat == values).all()
+
+    def test_constructor_preserves_freq(self):
+        # GH33830 freq retention in categorical
+        dti = date_range("2016-01-01", periods=5)
+
+        expected = dti.freq
+
+        cat = Categorical(dti)
+        result = cat.categories.freq
+
+        assert expected == result

env-llmeval/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_dtypes.py

@@ -0,0 +1,139 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    Index,
+    IntervalIndex,
+    Series,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalDtypes:
+    def test_categories_match_up_to_permutation(self):
+        # test dtype comparisons between cats
+
+        c1 = Categorical(list("aabca"), categories=list("abc"), ordered=False)
+        c2 = Categorical(list("aabca"), categories=list("cab"), ordered=False)
+        c3 = Categorical(list("aabca"), categories=list("cab"), ordered=True)
+        assert c1._categories_match_up_to_permutation(c1)
+        assert c2._categories_match_up_to_permutation(c2)
+        assert c3._categories_match_up_to_permutation(c3)
+        assert c1._categories_match_up_to_permutation(c2)
+        assert not c1._categories_match_up_to_permutation(c3)
+        assert not c1._categories_match_up_to_permutation(Index(list("aabca")))
+        assert not c1._categories_match_up_to_permutation(c1.astype(object))
+        assert c1._categories_match_up_to_permutation(CategoricalIndex(c1))
+        assert c1._categories_match_up_to_permutation(
+            CategoricalIndex(c1, categories=list("cab"))
+        )
+        assert not c1._categories_match_up_to_permutation(
+            CategoricalIndex(c1, ordered=True)
+        )
+
+        # GH 16659
+        s1 = Series(c1)
+        s2 = Series(c2)
+        s3 = Series(c3)
+        assert c1._categories_match_up_to_permutation(s1)
+        assert c2._categories_match_up_to_permutation(s2)
+        assert c3._categories_match_up_to_permutation(s3)
+        assert c1._categories_match_up_to_permutation(s2)
+        assert not c1._categories_match_up_to_permutation(s3)
+        assert not c1._categories_match_up_to_permutation(s1.astype(object))
+
+    def test_set_dtype_same(self):
+        c = Categorical(["a", "b", "c"])
+        result = c._set_dtype(CategoricalDtype(["a", "b", "c"]))
+        tm.assert_categorical_equal(result, c)
+
+    def test_set_dtype_new_categories(self):
+        c = Categorical(["a", "b", "c"])
+        result = c._set_dtype(CategoricalDtype(list("abcd")))
+        tm.assert_numpy_array_equal(result.codes, c.codes)
+        tm.assert_index_equal(result.dtype.categories, Index(list("abcd")))
+
+    @pytest.mark.parametrize(
+        "values, categories, new_categories",
+        [
+            # No NaNs, same cats, same order
+            (["a", "b", "a"], ["a", "b"], ["a", "b"]),
+            # No NaNs, same cats, different order
+            (["a", "b", "a"], ["a", "b"], ["b", "a"]),
+            # Same, unsorted
+            (["b", "a", "a"], ["a", "b"], ["a", "b"]),
+            # No NaNs, same cats, different order
+            (["b", "a", "a"], ["a", "b"], ["b", "a"]),
+            # NaNs
+            (["a", "b", "c"], ["a", "b"], ["a", "b"]),
+            (["a", "b", "c"], ["a", "b"], ["b", "a"]),
+            (["b", "a", "c"], ["a", "b"], ["a", "b"]),
+            (["b", "a", "c"], ["a", "b"], ["a", "b"]),
+            # Introduce NaNs
+            (["a", "b", "c"], ["a", "b"], ["a"]),
+            (["a", "b", "c"], ["a", "b"], ["b"]),
+            (["b", "a", "c"], ["a", "b"], ["a"]),
+            (["b", "a", "c"], ["a", "b"], ["a"]),
+            # No overlap
+            (["a", "b", "c"], ["a", "b"], ["d", "e"]),
+        ],
+    )
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_set_dtype_many(self, values, categories, new_categories, ordered):
+        c = Categorical(values, categories)
+        expected = Categorical(values, new_categories, ordered)
+        result = c._set_dtype(expected.dtype)
+        tm.assert_categorical_equal(result, expected)
+
+    def test_set_dtype_no_overlap(self):
+        c = Categorical(["a", "b", "c"], ["d", "e"])
+        result = c._set_dtype(CategoricalDtype(["a", "b"]))
+        expected = Categorical([None, None, None], categories=["a", "b"])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_codes_dtypes(self):
+        # GH 8453
+        result = Categorical(["foo", "bar", "baz"])
+        assert result.codes.dtype == "int8"
+
+        result = Categorical([f"foo{i:05d}" for i in range(400)])
+        assert result.codes.dtype == "int16"
+
+        result = Categorical([f"foo{i:05d}" for i in range(40000)])
+        assert result.codes.dtype == "int32"
+
+        # adding cats
+        result = Categorical(["foo", "bar", "baz"])
+        assert result.codes.dtype == "int8"
+        result = result.add_categories([f"foo{i:05d}" for i in range(400)])
+        assert result.codes.dtype == "int16"
+
+        # removing cats
+        result = result.remove_categories([f"foo{i:05d}" for i in range(300)])
+        assert result.codes.dtype == "int8"
+
+    def test_iter_python_types(self):
+        # GH-19909
+        cat = Categorical([1, 2])
+        assert isinstance(next(iter(cat)), int)
+        assert isinstance(cat.tolist()[0], int)
+
+    def test_iter_python_types_datetime(self):
+        cat = Categorical([Timestamp("2017-01-01"), Timestamp("2017-01-02")])
+        assert isinstance(next(iter(cat)), Timestamp)
+        assert isinstance(cat.tolist()[0], Timestamp)
+
+    def test_interval_index_category(self):
+        # GH 38316
+        index = IntervalIndex.from_breaks(np.arange(3, dtype="uint64"))
+
+        result = CategoricalIndex(index).dtype.categories
+        expected = IntervalIndex.from_arrays(
+            [0, 1], [1, 2], dtype="interval[uint64, right]"
+        )
+        tm.assert_index_equal(result, expected)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_indexing.py

@@ -0,0 +1,388 @@
+import math
+
+import numpy as np
+import pytest
+
+from pandas import (
+    NA,
+    Categorical,
+    CategoricalIndex,
+    Index,
+    Interval,
+    IntervalIndex,
+    NaT,
+    PeriodIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+)
+import pandas._testing as tm
+import pandas.core.common as com
+
+
+class TestCategoricalIndexingWithFactor:
+    def test_getitem(self):
+        factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
+        assert factor[0] == "a"
+        assert factor[-1] == "c"
+
+        subf = factor[[0, 1, 2]]
+        tm.assert_numpy_array_equal(subf._codes, np.array([0, 1, 1], dtype=np.int8))
+
+        subf = factor[np.asarray(factor) == "c"]
+        tm.assert_numpy_array_equal(subf._codes, np.array([2, 2, 2], dtype=np.int8))
+
+    def test_setitem(self):
+        factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
+        # int/positional
+        c = factor.copy()
+        c[0] = "b"
+        assert c[0] == "b"
+        c[-1] = "a"
+        assert c[-1] == "a"
+
+        # boolean
+        c = factor.copy()
+        indexer = np.zeros(len(c), dtype="bool")
+        indexer[0] = True
+        indexer[-1] = True
+        c[indexer] = "c"
+        expected = Categorical(["c", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
+
+        tm.assert_categorical_equal(c, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [Categorical(["b", "a"]), Categorical(["b", "a"], categories=["b", "a"])],
+    )
+    def test_setitem_same_but_unordered(self, other):
+        # GH-24142
+        target = Categorical(["a", "b"], categories=["a", "b"])
+        mask = np.array([True, False])
+        target[mask] = other[mask]
+        expected = Categorical(["b", "b"], categories=["a", "b"])
+        tm.assert_categorical_equal(target, expected)
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            Categorical(["b", "a"], categories=["b", "a", "c"]),
+            Categorical(["b", "a"], categories=["a", "b", "c"]),
+            Categorical(["a", "a"], categories=["a"]),
+            Categorical(["b", "b"], categories=["b"]),
+        ],
+    )
+    def test_setitem_different_unordered_raises(self, other):
+        # GH-24142
+        target = Categorical(["a", "b"], categories=["a", "b"])
+        mask = np.array([True, False])
+        msg = "Cannot set a Categorical with another, without identical categories"
+        with pytest.raises(TypeError, match=msg):
+            target[mask] = other[mask]
+
+    @pytest.mark.parametrize(
+        "other",
+        [
+            Categorical(["b", "a"]),
+            Categorical(["b", "a"], categories=["b", "a"], ordered=True),
+            Categorical(["b", "a"], categories=["a", "b", "c"], ordered=True),
+        ],
+    )
+    def test_setitem_same_ordered_raises(self, other):
+        # Gh-24142
+        target = Categorical(["a", "b"], categories=["a", "b"], ordered=True)
+        mask = np.array([True, False])
+        msg = "Cannot set a Categorical with another, without identical categories"
+        with pytest.raises(TypeError, match=msg):
+            target[mask] = other[mask]
+
+    def test_setitem_tuple(self):
+        # GH#20439
+        cat = Categorical([(0, 1), (0, 2), (0, 1)])
+
+        # This should not raise
+        cat[1] = cat[0]
+        assert cat[1] == (0, 1)
+
+    def test_setitem_listlike(self):
+        # GH#9469
+        # properly coerce the input indexers
+
+        cat = Categorical(
+            np.random.default_rng(2).integers(0, 5, size=150000).astype(np.int8)
+        ).add_categories([-1000])
+        indexer = np.array([100000]).astype(np.int64)
+        cat[indexer] = -1000
+
+        # we are asserting the code result here
+        # which maps to the -1000 category
+        result = cat.codes[np.array([100000]).astype(np.int64)]
+        tm.assert_numpy_array_equal(result, np.array([5], dtype="int8"))
+
+
+class TestCategoricalIndexing:
+    def test_getitem_slice(self):
+        cat = Categorical(["a", "b", "c", "d", "a", "b", "c"])
+        sliced = cat[3]
+        assert sliced == "d"
+
+        sliced = cat[3:5]
+        expected = Categorical(["d", "a"], categories=["a", "b", "c", "d"])
+        tm.assert_categorical_equal(sliced, expected)
+
+    def test_getitem_listlike(self):
+        # GH 9469
+        # properly coerce the input indexers
+
+        c = Categorical(
+            np.random.default_rng(2).integers(0, 5, size=150000).astype(np.int8)
+        )
+        result = c.codes[np.array([100000]).astype(np.int64)]
+        expected = c[np.array([100000]).astype(np.int64)].codes
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_periodindex(self):
+        idx1 = PeriodIndex(
+            ["2014-01", "2014-01", "2014-02", "2014-02", "2014-03", "2014-03"],
+            freq="M",
+        )
+
+        cat1 = Categorical(idx1)
+        str(cat1)
+        exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.int8)
+        exp_idx = PeriodIndex(["2014-01", "2014-02", "2014-03"], freq="M")
+        tm.assert_numpy_array_equal(cat1._codes, exp_arr)
+        tm.assert_index_equal(cat1.categories, exp_idx)
+
+        idx2 = PeriodIndex(
+            ["2014-03", "2014-03", "2014-02", "2014-01", "2014-03", "2014-01"],
+            freq="M",
+        )
+        cat2 = Categorical(idx2, ordered=True)
+        str(cat2)
+        exp_arr = np.array([2, 2, 1, 0, 2, 0], dtype=np.int8)
+        exp_idx2 = PeriodIndex(["2014-01", "2014-02", "2014-03"], freq="M")
+        tm.assert_numpy_array_equal(cat2._codes, exp_arr)
+        tm.assert_index_equal(cat2.categories, exp_idx2)
+
+        idx3 = PeriodIndex(
+            [
+                "2013-12",
+                "2013-11",
+                "2013-10",
+                "2013-09",
+                "2013-08",
+                "2013-07",
+                "2013-05",
+            ],
+            freq="M",
+        )
+        cat3 = Categorical(idx3, ordered=True)
+        exp_arr = np.array([6, 5, 4, 3, 2, 1, 0], dtype=np.int8)
+        exp_idx = PeriodIndex(
+            [
+                "2013-05",
+                "2013-07",
+                "2013-08",
+                "2013-09",
+                "2013-10",
+                "2013-11",
+                "2013-12",
+            ],
+            freq="M",
+        )
+        tm.assert_numpy_array_equal(cat3._codes, exp_arr)
+        tm.assert_index_equal(cat3.categories, exp_idx)
+
+    @pytest.mark.parametrize(
+        "null_val",
+        [None, np.nan, NaT, NA, math.nan, "NaT", "nat", "NAT", "nan", "NaN", "NAN"],
+    )
+    def test_periodindex_on_null_types(self, null_val):
+        # GH 46673
+        result = PeriodIndex(["2022-04-06", "2022-04-07", null_val], freq="D")
+        expected = PeriodIndex(["2022-04-06", "2022-04-07", "NaT"], dtype="period[D]")
+        assert result[2] is NaT
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("new_categories", [[1, 2, 3, 4], [1, 2]])
+    def test_categories_assignments_wrong_length_raises(self, new_categories):
+        cat = Categorical(["a", "b", "c", "a"])
+        msg = (
+            "new categories need to have the same number of items "
+            "as the old categories!"
+        )
+        with pytest.raises(ValueError, match=msg):
+            cat.rename_categories(new_categories)
+
+    # Combinations of sorted/unique:
+    @pytest.mark.parametrize(
+        "idx_values", [[1, 2, 3, 4], [1, 3, 2, 4], [1, 3, 3, 4], [1, 2, 2, 4]]
+    )
+    # Combinations of missing/unique
+    @pytest.mark.parametrize("key_values", [[1, 2], [1, 5], [1, 1], [5, 5]])
+    @pytest.mark.parametrize("key_class", [Categorical, CategoricalIndex])
+    @pytest.mark.parametrize("dtype", [None, "category", "key"])
+    def test_get_indexer_non_unique(self, idx_values, key_values, key_class, dtype):
+        # GH 21448
+        key = key_class(key_values, categories=range(1, 5))
+
+        if dtype == "key":
+            dtype = key.dtype
+
+        # Test for flat index and CategoricalIndex with same/different cats:
+        idx = Index(idx_values, dtype=dtype)
+        expected, exp_miss = idx.get_indexer_non_unique(key_values)
+        result, res_miss = idx.get_indexer_non_unique(key)
+
+        tm.assert_numpy_array_equal(expected, result)
+        tm.assert_numpy_array_equal(exp_miss, res_miss)
+
+        exp_unique = idx.unique().get_indexer(key_values)
+        res_unique = idx.unique().get_indexer(key)
+        tm.assert_numpy_array_equal(res_unique, exp_unique)
+
+    def test_where_unobserved_nan(self):
+        ser = Series(Categorical(["a", "b"]))
+        result = ser.where([True, False])
+        expected = Series(Categorical(["a", None], categories=["a", "b"]))
+        tm.assert_series_equal(result, expected)
+
+        # all NA
+        ser = Series(Categorical(["a", "b"]))
+        result = ser.where([False, False])
+        expected = Series(Categorical([None, None], categories=["a", "b"]))
+        tm.assert_series_equal(result, expected)
+
+    def test_where_unobserved_categories(self):
+        ser = Series(Categorical(["a", "b", "c"], categories=["d", "c", "b", "a"]))
+        result = ser.where([True, True, False], other="b")
+        expected = Series(Categorical(["a", "b", "b"], categories=ser.cat.categories))
+        tm.assert_series_equal(result, expected)
+
+    def test_where_other_categorical(self):
+        ser = Series(Categorical(["a", "b", "c"], categories=["d", "c", "b", "a"]))
+        other = Categorical(["b", "c", "a"], categories=["a", "c", "b", "d"])
+        result = ser.where([True, False, True], other)
+        expected = Series(Categorical(["a", "c", "c"], dtype=ser.dtype))
+        tm.assert_series_equal(result, expected)
+
+    def test_where_new_category_raises(self):
+        ser = Series(Categorical(["a", "b", "c"]))
+        msg = "Cannot setitem on a Categorical with a new category"
+        with pytest.raises(TypeError, match=msg):
+            ser.where([True, False, True], "d")
+
+    def test_where_ordered_differs_rasies(self):
+        ser = Series(
+            Categorical(["a", "b", "c"], categories=["d", "c", "b", "a"], ordered=True)
+        )
+        other = Categorical(
+            ["b", "c", "a"], categories=["a", "c", "b", "d"], ordered=True
+        )
+        with pytest.raises(TypeError, match="without identical categories"):
+            ser.where([True, False, True], other)
+
+
+class TestContains:
+    def test_contains(self):
+        # GH#21508
+        cat = Categorical(list("aabbca"), categories=list("cab"))
+
+        assert "b" in cat
+        assert "z" not in cat
+        assert np.nan not in cat
+        with pytest.raises(TypeError, match="unhashable type: 'list'"):
+            assert [1] in cat
+
+        # assert codes NOT in index
+        assert 0 not in cat
+        assert 1 not in cat
+
+        cat = Categorical(list("aabbca") + [np.nan], categories=list("cab"))
+        assert np.nan in cat
+
+    @pytest.mark.parametrize(
+        "item, expected",
+        [
+            (Interval(0, 1), True),
+            (1.5, True),
+            (Interval(0.5, 1.5), False),
+            ("a", False),
+            (Timestamp(1), False),
+            (Timedelta(1), False),
+        ],
+        ids=str,
+    )
+    def test_contains_interval(self, item, expected):
+        # GH#23705
+        cat = Categorical(IntervalIndex.from_breaks(range(3)))
+        result = item in cat
+        assert result is expected
+
+    def test_contains_list(self):
+        # GH#21729
+        cat = Categorical([1, 2, 3])
+
+        assert "a" not in cat
+
+        with pytest.raises(TypeError, match="unhashable type"):
+            ["a"] in cat
+
+        with pytest.raises(TypeError, match="unhashable type"):
+            ["a", "b"] in cat
+
+
+@pytest.mark.parametrize("index", [True, False])
+def test_mask_with_boolean(index):
+    ser = Series(range(3))
+    idx = Categorical([True, False, True])
+    if index:
+        idx = CategoricalIndex(idx)
+
+    assert com.is_bool_indexer(idx)
+    result = ser[idx]
+    expected = ser[idx.astype("object")]
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("index", [True, False])
+def test_mask_with_boolean_na_treated_as_false(index):
+    # https://github.com/pandas-dev/pandas/issues/31503
+    ser = Series(range(3))
+    idx = Categorical([True, False, None])
+    if index:
+        idx = CategoricalIndex(idx)
+
+    result = ser[idx]
+    expected = ser[idx.fillna(False)]
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.fixture
+def non_coercible_categorical(monkeypatch):
+    """
+    Monkeypatch Categorical.__array__ to ensure no implicit conversion.
+
+    Raises
+    ------
+    ValueError
+        When Categorical.__array__ is called.
+    """
+
+    # TODO(Categorical): identify other places where this may be
+    # useful and move to a conftest.py
+    def array(self, dtype=None):
+        raise ValueError("I cannot be converted.")
+
+    with monkeypatch.context() as m:
+        m.setattr(Categorical, "__array__", array)
+        yield
+
+
+def test_series_at():
+    arr = Categorical(["a", "b", "c"])
+    ser = Series(arr)
+    result = ser.at[0]
+    assert result == "a"

env-llmeval/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_map.py

@@ -0,0 +1,154 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture(params=[None, "ignore"])
+def na_action(request):
+    return request.param
+
+
+@pytest.mark.parametrize(
+    "data, categories",
+    [
+        (list("abcbca"), list("cab")),
+        (pd.interval_range(0, 3).repeat(3), pd.interval_range(0, 3)),
+    ],
+    ids=["string", "interval"],
+)
+def test_map_str(data, categories, ordered, na_action):
+    # GH 31202 - override base class since we want to maintain categorical/ordered
+    cat = Categorical(data, categories=categories, ordered=ordered)
+    result = cat.map(str, na_action=na_action)
+    expected = Categorical(
+        map(str, data), categories=map(str, categories), ordered=ordered
+    )
+    tm.assert_categorical_equal(result, expected)
+
+
+def test_map(na_action):
+    cat = Categorical(list("ABABC"), categories=list("CBA"), ordered=True)
+    result = cat.map(lambda x: x.lower(), na_action=na_action)
+    exp = Categorical(list("ababc"), categories=list("cba"), ordered=True)
+    tm.assert_categorical_equal(result, exp)
+
+    cat = Categorical(list("ABABC"), categories=list("BAC"), ordered=False)
+    result = cat.map(lambda x: x.lower(), na_action=na_action)
+    exp = Categorical(list("ababc"), categories=list("bac"), ordered=False)
+    tm.assert_categorical_equal(result, exp)
+
+    # GH 12766: Return an index not an array
+    result = cat.map(lambda x: 1, na_action=na_action)
+    exp = Index(np.array([1] * 5, dtype=np.int64))
+    tm.assert_index_equal(result, exp)
+
+    # change categories dtype
+    cat = Categorical(list("ABABC"), categories=list("BAC"), ordered=False)
+
+    def f(x):
+        return {"A": 10, "B": 20, "C": 30}.get(x)
+
+    result = cat.map(f, na_action=na_action)
+    exp = Categorical([10, 20, 10, 20, 30], categories=[20, 10, 30], ordered=False)
+    tm.assert_categorical_equal(result, exp)
+
+    mapper = Series([10, 20, 30], index=["A", "B", "C"])
+    result = cat.map(mapper, na_action=na_action)
+    tm.assert_categorical_equal(result, exp)
+
+    result = cat.map({"A": 10, "B": 20, "C": 30}, na_action=na_action)
+    tm.assert_categorical_equal(result, exp)
+
+
+@pytest.mark.parametrize(
+    ("data", "f", "expected"),
+    (
+        ([1, 1, np.nan], pd.isna, Index([False, False, True])),
+        ([1, 2, np.nan], pd.isna, Index([False, False, True])),
+        ([1, 1, np.nan], {1: False}, Categorical([False, False, np.nan])),
+        ([1, 2, np.nan], {1: False, 2: False}, Index([False, False, np.nan])),
+        (
+            [1, 1, np.nan],
+            Series([False, False]),
+            Categorical([False, False, np.nan]),
+        ),
+        (
+            [1, 2, np.nan],
+            Series([False] * 3),
+            Index([False, False, np.nan]),
+        ),
+    ),
+)
+def test_map_with_nan_none(data, f, expected):  # GH 24241
+    values = Categorical(data)
+    result = values.map(f, na_action=None)
+    if isinstance(expected, Categorical):
+        tm.assert_categorical_equal(result, expected)
+    else:
+        tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    ("data", "f", "expected"),
+    (
+        ([1, 1, np.nan], pd.isna, Categorical([False, False, np.nan])),
+        ([1, 2, np.nan], pd.isna, Index([False, False, np.nan])),
+        ([1, 1, np.nan], {1: False}, Categorical([False, False, np.nan])),
+        ([1, 2, np.nan], {1: False, 2: False}, Index([False, False, np.nan])),
+        (
+            [1, 1, np.nan],
+            Series([False, False]),
+            Categorical([False, False, np.nan]),
+        ),
+        (
+            [1, 2, np.nan],
+            Series([False, False, False]),
+            Index([False, False, np.nan]),
+        ),
+    ),
+)
+def test_map_with_nan_ignore(data, f, expected):  # GH 24241
+    values = Categorical(data)
+    result = values.map(f, na_action="ignore")
+    if data[1] == 1:
+        tm.assert_categorical_equal(result, expected)
+    else:
+        tm.assert_index_equal(result, expected)
+
+
+def test_map_with_dict_or_series(na_action):
+    orig_values = ["a", "B", 1, "a"]
+    new_values = ["one", 2, 3.0, "one"]
+    cat = Categorical(orig_values)
+
+    mapper = Series(new_values[:-1], index=orig_values[:-1])
+    result = cat.map(mapper, na_action=na_action)
+
+    # Order of categories in result can be different
+    expected = Categorical(new_values, categories=[3.0, 2, "one"])
+    tm.assert_categorical_equal(result, expected)
+
+    mapper = dict(zip(orig_values[:-1], new_values[:-1]))
+    result = cat.map(mapper, na_action=na_action)
+    # Order of categories in result can be different
+    tm.assert_categorical_equal(result, expected)
+
+
+def test_map_na_action_no_default_deprecated():
+    # GH51645
+    cat = Categorical(["a", "b", "c"])
+    msg = (
+        "The default value of 'ignore' for the `na_action` parameter in "
+        "pandas.Categorical.map is deprecated and will be "
+        "changed to 'None' in a future version. Please set na_action to the "
+        "desired value to avoid seeing this warning"
+    )
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        cat.map(lambda x: x)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_operators.py

@@ -0,0 +1,414 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalOpsWithFactor:
+    def test_categories_none_comparisons(self):
+        factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
+        tm.assert_categorical_equal(factor, factor)
+
+    def test_comparisons(self):
+        factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
+        result = factor[factor == "a"]
+        expected = factor[np.asarray(factor) == "a"]
+        tm.assert_categorical_equal(result, expected)
+
+        result = factor[factor != "a"]
+        expected = factor[np.asarray(factor) != "a"]
+        tm.assert_categorical_equal(result, expected)
+
+        result = factor[factor < "c"]
+        expected = factor[np.asarray(factor) < "c"]
+        tm.assert_categorical_equal(result, expected)
+
+        result = factor[factor > "a"]
+        expected = factor[np.asarray(factor) > "a"]
+        tm.assert_categorical_equal(result, expected)
+
+        result = factor[factor >= "b"]
+        expected = factor[np.asarray(factor) >= "b"]
+        tm.assert_categorical_equal(result, expected)
+
+        result = factor[factor <= "b"]
+        expected = factor[np.asarray(factor) <= "b"]
+        tm.assert_categorical_equal(result, expected)
+
+        n = len(factor)
+
+        other = factor[np.random.default_rng(2).permutation(n)]
+        result = factor == other
+        expected = np.asarray(factor) == np.asarray(other)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = factor == "d"
+        expected = np.zeros(len(factor), dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # comparisons with categoricals
+        cat_rev = Categorical(["a", "b", "c"], categories=["c", "b", "a"], ordered=True)
+        cat_rev_base = Categorical(
+            ["b", "b", "b"], categories=["c", "b", "a"], ordered=True
+        )
+        cat = Categorical(["a", "b", "c"], ordered=True)
+        cat_base = Categorical(["b", "b", "b"], categories=cat.categories, ordered=True)
+
+        # comparisons need to take categories ordering into account
+        res_rev = cat_rev > cat_rev_base
+        exp_rev = np.array([True, False, False])
+        tm.assert_numpy_array_equal(res_rev, exp_rev)
+
+        res_rev = cat_rev < cat_rev_base
+        exp_rev = np.array([False, False, True])
+        tm.assert_numpy_array_equal(res_rev, exp_rev)
+
+        res = cat > cat_base
+        exp = np.array([False, False, True])
+        tm.assert_numpy_array_equal(res, exp)
+
+        # Only categories with same categories can be compared
+        msg = "Categoricals can only be compared if 'categories' are the same"
+        with pytest.raises(TypeError, match=msg):
+            cat > cat_rev
+
+        cat_rev_base2 = Categorical(["b", "b", "b"], categories=["c", "b", "a", "d"])
+
+        with pytest.raises(TypeError, match=msg):
+            cat_rev > cat_rev_base2
+
+        # Only categories with same ordering information can be compared
+        cat_unordered = cat.set_ordered(False)
+        assert not (cat > cat).any()
+
+        with pytest.raises(TypeError, match=msg):
+            cat > cat_unordered
+
+        # comparison (in both directions) with Series will raise
+        s = Series(["b", "b", "b"], dtype=object)
+        msg = (
+            "Cannot compare a Categorical for op __gt__ with type "
+            r"<class 'numpy\.ndarray'>"
+        )
+        with pytest.raises(TypeError, match=msg):
+            cat > s
+        with pytest.raises(TypeError, match=msg):
+            cat_rev > s
+        with pytest.raises(TypeError, match=msg):
+            s < cat
+        with pytest.raises(TypeError, match=msg):
+            s < cat_rev
+
+        # comparison with numpy.array will raise in both direction, but only on
+        # newer numpy versions
+        a = np.array(["b", "b", "b"], dtype=object)
+        with pytest.raises(TypeError, match=msg):
+            cat > a
+        with pytest.raises(TypeError, match=msg):
+            cat_rev > a
+
+        # Make sure that unequal comparison take the categories order in
+        # account
+        cat_rev = Categorical(list("abc"), categories=list("cba"), ordered=True)
+        exp = np.array([True, False, False])
+        res = cat_rev > "b"
+        tm.assert_numpy_array_equal(res, exp)
+
+        # check that zero-dim array gets unboxed
+        res = cat_rev > np.array("b")
+        tm.assert_numpy_array_equal(res, exp)
+
+
+class TestCategoricalOps:
+    @pytest.mark.parametrize(
+        "categories",
+        [["a", "b"], [0, 1], [Timestamp("2019"), Timestamp("2020")]],
+    )
+    def test_not_equal_with_na(self, categories):
+        # https://github.com/pandas-dev/pandas/issues/32276
+        c1 = Categorical.from_codes([-1, 0], categories=categories)
+        c2 = Categorical.from_codes([0, 1], categories=categories)
+
+        result = c1 != c2
+
+        assert result.all()
+
+    def test_compare_frame(self):
+        # GH#24282 check that Categorical.__cmp__(DataFrame) defers to frame
+        data = ["a", "b", 2, "a"]
+        cat = Categorical(data)
+
+        df = DataFrame(cat)
+
+        result = cat == df.T
+        expected = DataFrame([[True, True, True, True]])
+        tm.assert_frame_equal(result, expected)
+
+        result = cat[::-1] != df.T
+        expected = DataFrame([[False, True, True, False]])
+        tm.assert_frame_equal(result, expected)
+
+    def test_compare_frame_raises(self, comparison_op):
+        # alignment raises unless we transpose
+        op = comparison_op
+        cat = Categorical(["a", "b", 2, "a"])
+        df = DataFrame(cat)
+        msg = "Unable to coerce to Series, length must be 1: given 4"
+        with pytest.raises(ValueError, match=msg):
+            op(cat, df)
+
+    def test_datetime_categorical_comparison(self):
+        dt_cat = Categorical(date_range("2014-01-01", periods=3), ordered=True)
+        tm.assert_numpy_array_equal(dt_cat > dt_cat[0], np.array([False, True, True]))
+        tm.assert_numpy_array_equal(dt_cat[0] < dt_cat, np.array([False, True, True]))
+
+    def test_reflected_comparison_with_scalars(self):
+        # GH8658
+        cat = Categorical([1, 2, 3], ordered=True)
+        tm.assert_numpy_array_equal(cat > cat[0], np.array([False, True, True]))
+        tm.assert_numpy_array_equal(cat[0] < cat, np.array([False, True, True]))
+
+    def test_comparison_with_unknown_scalars(self):
+        # https://github.com/pandas-dev/pandas/issues/9836#issuecomment-92123057
+        # and following comparisons with scalars not in categories should raise
+        # for unequal comps, but not for equal/not equal
+        cat = Categorical([1, 2, 3], ordered=True)
+
+        msg = "Invalid comparison between dtype=category and int"
+        with pytest.raises(TypeError, match=msg):
+            cat < 4
+        with pytest.raises(TypeError, match=msg):
+            cat > 4
+        with pytest.raises(TypeError, match=msg):
+            4 < cat
+        with pytest.raises(TypeError, match=msg):
+            4 > cat
+
+        tm.assert_numpy_array_equal(cat == 4, np.array([False, False, False]))
+        tm.assert_numpy_array_equal(cat != 4, np.array([True, True, True]))
+
+    def test_comparison_with_tuple(self):
+        cat = Categorical(np.array(["foo", (0, 1), 3, (0, 1)], dtype=object))
+
+        result = cat == "foo"
+        expected = np.array([True, False, False, False], dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = cat == (0, 1)
+        expected = np.array([False, True, False, True], dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = cat != (0, 1)
+        tm.assert_numpy_array_equal(result, ~expected)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    def test_comparison_of_ordered_categorical_with_nan_to_scalar(
+        self, compare_operators_no_eq_ne
+    ):
+        # https://github.com/pandas-dev/pandas/issues/26504
+        # BUG: fix ordered categorical comparison with missing values (#26504 )
+        # and following comparisons with scalars in categories with missing
+        # values should be evaluated as False
+
+        cat = Categorical([1, 2, 3, None], categories=[1, 2, 3], ordered=True)
+        scalar = 2
+        expected = getattr(np.array(cat), compare_operators_no_eq_ne)(scalar)
+        actual = getattr(cat, compare_operators_no_eq_ne)(scalar)
+        tm.assert_numpy_array_equal(actual, expected)
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    def test_comparison_of_ordered_categorical_with_nan_to_listlike(
+        self, compare_operators_no_eq_ne
+    ):
+        # https://github.com/pandas-dev/pandas/issues/26504
+        # and following comparisons of missing values in ordered Categorical
+        # with listlike should be evaluated as False
+
+        cat = Categorical([1, 2, 3, None], categories=[1, 2, 3], ordered=True)
+        other = Categorical([2, 2, 2, 2], categories=[1, 2, 3], ordered=True)
+        expected = getattr(np.array(cat), compare_operators_no_eq_ne)(2)
+        actual = getattr(cat, compare_operators_no_eq_ne)(other)
+        tm.assert_numpy_array_equal(actual, expected)
+
+    @pytest.mark.parametrize(
+        "data,reverse,base",
+        [(list("abc"), list("cba"), list("bbb")), ([1, 2, 3], [3, 2, 1], [2, 2, 2])],
+    )
+    def test_comparisons(self, data, reverse, base):
+        cat_rev = Series(Categorical(data, categories=reverse, ordered=True))
+        cat_rev_base = Series(Categorical(base, categories=reverse, ordered=True))
+        cat = Series(Categorical(data, ordered=True))
+        cat_base = Series(
+            Categorical(base, categories=cat.cat.categories, ordered=True)
+        )
+        s = Series(base, dtype=object if base == list("bbb") else None)
+        a = np.array(base)
+
+        # comparisons need to take categories ordering into account
+        res_rev = cat_rev > cat_rev_base
+        exp_rev = Series([True, False, False])
+        tm.assert_series_equal(res_rev, exp_rev)
+
+        res_rev = cat_rev < cat_rev_base
+        exp_rev = Series([False, False, True])
+        tm.assert_series_equal(res_rev, exp_rev)
+
+        res = cat > cat_base
+        exp = Series([False, False, True])
+        tm.assert_series_equal(res, exp)
+
+        scalar = base[1]
+        res = cat > scalar
+        exp = Series([False, False, True])
+        exp2 = cat.values > scalar
+        tm.assert_series_equal(res, exp)
+        tm.assert_numpy_array_equal(res.values, exp2)
+        res_rev = cat_rev > scalar
+        exp_rev = Series([True, False, False])
+        exp_rev2 = cat_rev.values > scalar
+        tm.assert_series_equal(res_rev, exp_rev)
+        tm.assert_numpy_array_equal(res_rev.values, exp_rev2)
+
+        # Only categories with same categories can be compared
+        msg = "Categoricals can only be compared if 'categories' are the same"
+        with pytest.raises(TypeError, match=msg):
+            cat > cat_rev
+
+        # categorical cannot be compared to Series or numpy array, and also
+        # not the other way around
+        msg = (
+            "Cannot compare a Categorical for op __gt__ with type "
+            r"<class 'numpy\.ndarray'>"
+        )
+        with pytest.raises(TypeError, match=msg):
+            cat > s
+        with pytest.raises(TypeError, match=msg):
+            cat_rev > s
+        with pytest.raises(TypeError, match=msg):
+            cat > a
+        with pytest.raises(TypeError, match=msg):
+            cat_rev > a
+
+        with pytest.raises(TypeError, match=msg):
+            s < cat
+        with pytest.raises(TypeError, match=msg):
+            s < cat_rev
+
+        with pytest.raises(TypeError, match=msg):
+            a < cat
+        with pytest.raises(TypeError, match=msg):
+            a < cat_rev
+
+    @pytest.mark.parametrize(
+        "ctor",
+        [
+            lambda *args, **kwargs: Categorical(*args, **kwargs),
+            lambda *args, **kwargs: Series(Categorical(*args, **kwargs)),
+        ],
+    )
+    def test_unordered_different_order_equal(self, ctor):
+        # https://github.com/pandas-dev/pandas/issues/16014
+        c1 = ctor(["a", "b"], categories=["a", "b"], ordered=False)
+        c2 = ctor(["a", "b"], categories=["b", "a"], ordered=False)
+        assert (c1 == c2).all()
+
+        c1 = ctor(["a", "b"], categories=["a", "b"], ordered=False)
+        c2 = ctor(["b", "a"], categories=["b", "a"], ordered=False)
+        assert (c1 != c2).all()
+
+        c1 = ctor(["a", "a"], categories=["a", "b"], ordered=False)
+        c2 = ctor(["b", "b"], categories=["b", "a"], ordered=False)
+        assert (c1 != c2).all()
+
+        c1 = ctor(["a", "a"], categories=["a", "b"], ordered=False)
+        c2 = ctor(["a", "b"], categories=["b", "a"], ordered=False)
+        result = c1 == c2
+        tm.assert_numpy_array_equal(np.array(result), np.array([True, False]))
+
+    def test_unordered_different_categories_raises(self):
+        c1 = Categorical(["a", "b"], categories=["a", "b"], ordered=False)
+        c2 = Categorical(["a", "c"], categories=["c", "a"], ordered=False)
+
+        with pytest.raises(TypeError, match=("Categoricals can only be compared")):
+            c1 == c2
+
+    def test_compare_different_lengths(self):
+        c1 = Categorical([], categories=["a", "b"])
+        c2 = Categorical([], categories=["a"])
+
+        msg = "Categoricals can only be compared if 'categories' are the same."
+        with pytest.raises(TypeError, match=msg):
+            c1 == c2
+
+    def test_compare_unordered_different_order(self):
+        # https://github.com/pandas-dev/pandas/issues/16603#issuecomment-
+        # 349290078
+        a = Categorical(["a"], categories=["a", "b"])
+        b = Categorical(["b"], categories=["b", "a"])
+        assert not a.equals(b)
+
+    def test_numeric_like_ops(self):
+        df = DataFrame({"value": np.random.default_rng(2).integers(0, 10000, 100)})
+        labels = [f"{i} - {i + 499}" for i in range(0, 10000, 500)]
+        cat_labels = Categorical(labels, labels)
+
+        df = df.sort_values(by=["value"], ascending=True)
+        df["value_group"] = pd.cut(
+            df.value, range(0, 10500, 500), right=False, labels=cat_labels
+        )
+
+        # numeric ops should not succeed
+        for op, str_rep in [
+            ("__add__", r"\+"),
+            ("__sub__", "-"),
+            ("__mul__", r"\*"),
+            ("__truediv__", "/"),
+        ]:
+            msg = f"Series cannot perform the operation {str_rep}|unsupported operand"
+            with pytest.raises(TypeError, match=msg):
+                getattr(df, op)(df)
+
+        # reduction ops should not succeed (unless specifically defined, e.g.
+        # min/max)
+        s = df["value_group"]
+        for op in ["kurt", "skew", "var", "std", "mean", "sum", "median"]:
+            msg = f"does not support reduction '{op}'"
+            with pytest.raises(TypeError, match=msg):
+                getattr(s, op)(numeric_only=False)
+
+    def test_numeric_like_ops_series(self):
+        # numpy ops
+        s = Series(Categorical([1, 2, 3, 4]))
+        with pytest.raises(TypeError, match="does not support reduction 'sum'"):
+            np.sum(s)
+
+    @pytest.mark.parametrize(
+        "op, str_rep",
+        [
+            ("__add__", r"\+"),
+            ("__sub__", "-"),
+            ("__mul__", r"\*"),
+            ("__truediv__", "/"),
+        ],
+    )
+    def test_numeric_like_ops_series_arith(self, op, str_rep):
+        # numeric ops on a Series
+        s = Series(Categorical([1, 2, 3, 4]))
+        msg = f"Series cannot perform the operation {str_rep}|unsupported operand"
+        with pytest.raises(TypeError, match=msg):
+            getattr(s, op)(2)
+
+    def test_numeric_like_ops_series_invalid(self):
+        # invalid ufunc
+        s = Series(Categorical([1, 2, 3, 4]))
+        msg = "Object with dtype category cannot perform the numpy op log"
+        with pytest.raises(TypeError, match=msg):
+            np.log(s)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_sorting.py

@@ -0,0 +1,128 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    Index,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalSort:
+    def test_argsort(self):
+        c = Categorical([5, 3, 1, 4, 2], ordered=True)
+
+        expected = np.array([2, 4, 1, 3, 0])
+        tm.assert_numpy_array_equal(
+            c.argsort(ascending=True), expected, check_dtype=False
+        )
+
+        expected = expected[::-1]
+        tm.assert_numpy_array_equal(
+            c.argsort(ascending=False), expected, check_dtype=False
+        )
+
+    def test_numpy_argsort(self):
+        c = Categorical([5, 3, 1, 4, 2], ordered=True)
+
+        expected = np.array([2, 4, 1, 3, 0])
+        tm.assert_numpy_array_equal(np.argsort(c), expected, check_dtype=False)
+
+        tm.assert_numpy_array_equal(
+            np.argsort(c, kind="mergesort"), expected, check_dtype=False
+        )
+
+        msg = "the 'axis' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.argsort(c, axis=0)
+
+        msg = "the 'order' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.argsort(c, order="C")
+
+    def test_sort_values(self):
+        # unordered cats are sortable
+        cat = Categorical(["a", "b", "b", "a"], ordered=False)
+        cat.sort_values()
+
+        cat = Categorical(["a", "c", "b", "d"], ordered=True)
+
+        # sort_values
+        res = cat.sort_values()
+        exp = np.array(["a", "b", "c", "d"], dtype=object)
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, cat.categories)
+
+        cat = Categorical(
+            ["a", "c", "b", "d"], categories=["a", "b", "c", "d"], ordered=True
+        )
+        res = cat.sort_values()
+        exp = np.array(["a", "b", "c", "d"], dtype=object)
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, cat.categories)
+
+        res = cat.sort_values(ascending=False)
+        exp = np.array(["d", "c", "b", "a"], dtype=object)
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, cat.categories)
+
+        # sort (inplace order)
+        cat1 = cat.copy()
+        orig_codes = cat1._codes
+        cat1.sort_values(inplace=True)
+        assert cat1._codes is orig_codes
+        exp = np.array(["a", "b", "c", "d"], dtype=object)
+        tm.assert_numpy_array_equal(cat1.__array__(), exp)
+        tm.assert_index_equal(res.categories, cat.categories)
+
+        # reverse
+        cat = Categorical(["a", "c", "c", "b", "d"], ordered=True)
+        res = cat.sort_values(ascending=False)
+        exp_val = np.array(["d", "c", "c", "b", "a"], dtype=object)
+        exp_categories = Index(["a", "b", "c", "d"])
+        tm.assert_numpy_array_equal(res.__array__(), exp_val)
+        tm.assert_index_equal(res.categories, exp_categories)
+
+    def test_sort_values_na_position(self):
+        # see gh-12882
+        cat = Categorical([5, 2, np.nan, 2, np.nan], ordered=True)
+        exp_categories = Index([2, 5])
+
+        exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan])
+        res = cat.sort_values()  # default arguments
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, exp_categories)
+
+        exp = np.array([np.nan, np.nan, 2.0, 2.0, 5.0])
+        res = cat.sort_values(ascending=True, na_position="first")
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, exp_categories)
+
+        exp = np.array([np.nan, np.nan, 5.0, 2.0, 2.0])
+        res = cat.sort_values(ascending=False, na_position="first")
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, exp_categories)
+
+        exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan])
+        res = cat.sort_values(ascending=True, na_position="last")
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, exp_categories)
+
+        exp = np.array([5.0, 2.0, 2.0, np.nan, np.nan])
+        res = cat.sort_values(ascending=False, na_position="last")
+        tm.assert_numpy_array_equal(res.__array__(), exp)
+        tm.assert_index_equal(res.categories, exp_categories)
+
+        cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True)
+        res = cat.sort_values(ascending=False, na_position="last")
+        exp_val = np.array(["d", "c", "b", "a", np.nan], dtype=object)
+        exp_categories = Index(["a", "b", "c", "d"])
+        tm.assert_numpy_array_equal(res.__array__(), exp_val)
+        tm.assert_index_equal(res.categories, exp_categories)
+
+        cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True)
+        res = cat.sort_values(ascending=False, na_position="first")
+        exp_val = np.array([np.nan, "d", "c", "b", "a"], dtype=object)
+        exp_categories = Index(["a", "b", "c", "d"])
+        tm.assert_numpy_array_equal(res.__array__(), exp_val)
+        tm.assert_index_equal(res.categories, exp_categories)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_subclass.py

@@ -0,0 +1,26 @@
+from pandas import Categorical
+import pandas._testing as tm
+
+
+class SubclassedCategorical(Categorical):
+    pass
+
+
+class TestCategoricalSubclassing:
+    def test_constructor(self):
+        sc = SubclassedCategorical(["a", "b", "c"])
+        assert isinstance(sc, SubclassedCategorical)
+        tm.assert_categorical_equal(sc, Categorical(["a", "b", "c"]))
+
+    def test_from_codes(self):
+        sc = SubclassedCategorical.from_codes([1, 0, 2], ["a", "b", "c"])
+        assert isinstance(sc, SubclassedCategorical)
+        exp = Categorical.from_codes([1, 0, 2], ["a", "b", "c"])
+        tm.assert_categorical_equal(sc, exp)
+
+    def test_map(self):
+        sc = SubclassedCategorical(["a", "b", "c"])
+        res = sc.map(lambda x: x.upper(), na_action=None)
+        assert isinstance(res, SubclassedCategorical)
+        exp = Categorical(["A", "B", "C"])
+        tm.assert_categorical_equal(res, exp)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_take.py

@@ -0,0 +1,89 @@
+import numpy as np
+import pytest
+
+from pandas import Categorical
+import pandas._testing as tm
+
+
+@pytest.fixture(params=[True, False])
+def allow_fill(request):
+    """Boolean 'allow_fill' parameter for Categorical.take"""
+    return request.param
+
+
+class TestTake:
+    # https://github.com/pandas-dev/pandas/issues/20664
+
+    def test_take_default_allow_fill(self):
+        cat = Categorical(["a", "b"])
+        with tm.assert_produces_warning(None):
+            result = cat.take([0, -1])
+
+        assert result.equals(cat)
+
+    def test_take_positive_no_warning(self):
+        cat = Categorical(["a", "b"])
+        with tm.assert_produces_warning(None):
+            cat.take([0, 0])
+
+    def test_take_bounds(self, allow_fill):
+        # https://github.com/pandas-dev/pandas/issues/20664
+        cat = Categorical(["a", "b", "a"])
+        if allow_fill:
+            msg = "indices are out-of-bounds"
+        else:
+            msg = "index 4 is out of bounds for( axis 0 with)? size 3"
+        with pytest.raises(IndexError, match=msg):
+            cat.take([4, 5], allow_fill=allow_fill)
+
+    def test_take_empty(self, allow_fill):
+        # https://github.com/pandas-dev/pandas/issues/20664
+        cat = Categorical([], categories=["a", "b"])
+        if allow_fill:
+            msg = "indices are out-of-bounds"
+        else:
+            msg = "cannot do a non-empty take from an empty axes"
+        with pytest.raises(IndexError, match=msg):
+            cat.take([0], allow_fill=allow_fill)
+
+    def test_positional_take(self, ordered):
+        cat = Categorical(["a", "a", "b", "b"], categories=["b", "a"], ordered=ordered)
+        result = cat.take([0, 1, 2], allow_fill=False)
+        expected = Categorical(
+            ["a", "a", "b"], categories=cat.categories, ordered=ordered
+        )
+        tm.assert_categorical_equal(result, expected)
+
+    def test_positional_take_unobserved(self, ordered):
+        cat = Categorical(["a", "b"], categories=["a", "b", "c"], ordered=ordered)
+        result = cat.take([1, 0], allow_fill=False)
+        expected = Categorical(["b", "a"], categories=cat.categories, ordered=ordered)
+        tm.assert_categorical_equal(result, expected)
+
+    def test_take_allow_fill(self):
+        # https://github.com/pandas-dev/pandas/issues/23296
+        cat = Categorical(["a", "a", "b"])
+        result = cat.take([0, -1, -1], allow_fill=True)
+        expected = Categorical(["a", np.nan, np.nan], categories=["a", "b"])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_take_fill_with_negative_one(self):
+        # -1 was a category
+        cat = Categorical([-1, 0, 1])
+        result = cat.take([0, -1, 1], allow_fill=True, fill_value=-1)
+        expected = Categorical([-1, -1, 0], categories=[-1, 0, 1])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_take_fill_value(self):
+        # https://github.com/pandas-dev/pandas/issues/23296
+        cat = Categorical(["a", "b", "c"])
+        result = cat.take([0, 1, -1], fill_value="a", allow_fill=True)
+        expected = Categorical(["a", "b", "a"], categories=["a", "b", "c"])
+        tm.assert_categorical_equal(result, expected)
+
+    def test_take_fill_value_new_raises(self):
+        # https://github.com/pandas-dev/pandas/issues/23296
+        cat = Categorical(["a", "b", "c"])
+        xpr = r"Cannot setitem on a Categorical with a new category \(d\)"
+        with pytest.raises(TypeError, match=xpr):
+            cat.take([0, 1, -1], fill_value="d", allow_fill=True)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_warnings.py

@@ -0,0 +1,19 @@
+import pytest
+
+import pandas._testing as tm
+
+
+class TestCategoricalWarnings:
+    def test_tab_complete_warning(self, ip):
+        # https://github.com/pandas-dev/pandas/issues/16409
+        pytest.importorskip("IPython", minversion="6.0.0")
+        from IPython.core.completer import provisionalcompleter
+
+        code = "import pandas as pd; c = pd.Categorical([])"
+        ip.run_cell(code)
+
+        # GH 31324 newer jedi version raises Deprecation warning;
+        #  appears resolved 2021-02-02
+        with tm.assert_produces_warning(None, raise_on_extra_warnings=False):
+            with provisionalcompleter("ignore"):
+                list(ip.Completer.completions("c.", 1))

env-llmeval/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_constructors.py

@@ -0,0 +1,284 @@
+import numpy as np
+import pytest
+
+from pandas._libs import iNaT
+
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.arrays import DatetimeArray
+
+
+class TestDatetimeArrayConstructor:
+    def test_from_sequence_invalid_type(self):
+        mi = pd.MultiIndex.from_product([np.arange(5), np.arange(5)])
+        with pytest.raises(TypeError, match="Cannot create a DatetimeArray"):
+            DatetimeArray._from_sequence(mi, dtype="M8[ns]")
+
+    def test_only_1dim_accepted(self):
+        arr = np.array([0, 1, 2, 3], dtype="M8[h]").astype("M8[ns]")
+
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="Only 1-dimensional"):
+                # 3-dim, we allow 2D to sneak in for ops purposes GH#29853
+                DatetimeArray(arr.reshape(2, 2, 1))
+
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="Only 1-dimensional"):
+                # 0-dim
+                DatetimeArray(arr[[0]].squeeze())
+
+    def test_freq_validation(self):
+        # GH#24623 check that invalid instances cannot be created with the
+        #  public constructor
+        arr = np.arange(5, dtype=np.int64) * 3600 * 10**9
+
+        msg = (
+            "Inferred frequency h from passed values does not "
+            "conform to passed frequency W-SUN"
+        )
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match=msg):
+                DatetimeArray(arr, freq="W")
+
+    @pytest.mark.parametrize(
+        "meth",
+        [
+            DatetimeArray._from_sequence,
+            pd.to_datetime,
+            pd.DatetimeIndex,
+        ],
+    )
+    def test_mixing_naive_tzaware_raises(self, meth):
+        # GH#24569
+        arr = np.array([pd.Timestamp("2000"), pd.Timestamp("2000", tz="CET")])
+
+        msg = (
+            "Cannot mix tz-aware with tz-naive values|"
+            "Tz-aware datetime.datetime cannot be converted "
+            "to datetime64 unless utc=True"
+        )
+
+        for obj in [arr, arr[::-1]]:
+            # check that we raise regardless of whether naive is found
+            #  before aware or vice-versa
+            with pytest.raises(ValueError, match=msg):
+                meth(obj)
+
+    def test_from_pandas_array(self):
+        arr = pd.array(np.arange(5, dtype=np.int64)) * 3600 * 10**9
+
+        result = DatetimeArray._from_sequence(arr, dtype="M8[ns]")._with_freq("infer")
+
+        expected = pd.date_range("1970-01-01", periods=5, freq="h")._data
+        tm.assert_datetime_array_equal(result, expected)
+
+    def test_mismatched_timezone_raises(self):
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            arr = DatetimeArray(
+                np.array(["2000-01-01T06:00:00"], dtype="M8[ns]"),
+                dtype=DatetimeTZDtype(tz="US/Central"),
+            )
+        dtype = DatetimeTZDtype(tz="US/Eastern")
+        msg = r"dtype=datetime64\[ns.*\] does not match data dtype datetime64\[ns.*\]"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(TypeError, match=msg):
+                DatetimeArray(arr, dtype=dtype)
+
+        # also with mismatched tzawareness
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(TypeError, match=msg):
+                DatetimeArray(arr, dtype=np.dtype("M8[ns]"))
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(TypeError, match=msg):
+                DatetimeArray(arr.tz_localize(None), dtype=arr.dtype)
+
+    def test_non_array_raises(self):
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="list"):
+                DatetimeArray([1, 2, 3])
+
+    def test_bool_dtype_raises(self):
+        arr = np.array([1, 2, 3], dtype="bool")
+
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+        msg = "Unexpected value for 'dtype': 'bool'. Must be"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match=msg):
+                DatetimeArray(arr)
+
+        msg = r"dtype bool cannot be converted to datetime64\[ns\]"
+        with pytest.raises(TypeError, match=msg):
+            DatetimeArray._from_sequence(arr, dtype="M8[ns]")
+
+        with pytest.raises(TypeError, match=msg):
+            pd.DatetimeIndex(arr)
+
+        with pytest.raises(TypeError, match=msg):
+            pd.to_datetime(arr)
+
+    def test_incorrect_dtype_raises(self):
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="Unexpected value for 'dtype'."):
+                DatetimeArray(np.array([1, 2, 3], dtype="i8"), dtype="category")
+
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="Unexpected value for 'dtype'."):
+                DatetimeArray(np.array([1, 2, 3], dtype="i8"), dtype="m8[s]")
+
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="Unexpected value for 'dtype'."):
+                DatetimeArray(np.array([1, 2, 3], dtype="i8"), dtype="M8[D]")
+
+    def test_mismatched_values_dtype_units(self):
+        arr = np.array([1, 2, 3], dtype="M8[s]")
+        dtype = np.dtype("M8[ns]")
+        msg = "Values resolution does not match dtype."
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match=msg):
+                DatetimeArray(arr, dtype=dtype)
+
+        dtype2 = DatetimeTZDtype(tz="UTC", unit="ns")
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match=msg):
+                DatetimeArray(arr, dtype=dtype2)
+
+    def test_freq_infer_raises(self):
+        depr_msg = "DatetimeArray.__init__ is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=depr_msg):
+            with pytest.raises(ValueError, match="Frequency inference"):
+                DatetimeArray(np.array([1, 2, 3], dtype="i8"), freq="infer")
+
+    def test_copy(self):
+        data = np.array([1, 2, 3], dtype="M8[ns]")
+        arr = DatetimeArray._from_sequence(data, copy=False)
+        assert arr._ndarray is data
+
+        arr = DatetimeArray._from_sequence(data, copy=True)
+        assert arr._ndarray is not data
+
+    @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
+    def test_numpy_datetime_unit(self, unit):
+        data = np.array([1, 2, 3], dtype=f"M8[{unit}]")
+        arr = DatetimeArray._from_sequence(data)
+        assert arr.unit == unit
+        assert arr[0].unit == unit
+
+
+class TestSequenceToDT64NS:
+    def test_tz_dtype_mismatch_raises(self):
+        arr = DatetimeArray._from_sequence(
+            ["2000"], dtype=DatetimeTZDtype(tz="US/Central")
+        )
+        with pytest.raises(TypeError, match="data is already tz-aware"):
+            DatetimeArray._from_sequence(arr, dtype=DatetimeTZDtype(tz="UTC"))
+
+    def test_tz_dtype_matches(self):
+        dtype = DatetimeTZDtype(tz="US/Central")
+        arr = DatetimeArray._from_sequence(["2000"], dtype=dtype)
+        result = DatetimeArray._from_sequence(arr, dtype=dtype)
+        tm.assert_equal(arr, result)
+
+    @pytest.mark.parametrize("order", ["F", "C"])
+    def test_2d(self, order):
+        dti = pd.date_range("2016-01-01", periods=6, tz="US/Pacific")
+        arr = np.array(dti, dtype=object).reshape(3, 2)
+        if order == "F":
+            arr = arr.T
+
+        res = DatetimeArray._from_sequence(arr, dtype=dti.dtype)
+        expected = DatetimeArray._from_sequence(arr.ravel(), dtype=dti.dtype).reshape(
+            arr.shape
+        )
+        tm.assert_datetime_array_equal(res, expected)
+
+
+# ----------------------------------------------------------------------------
+# Arrow interaction
+
+
+EXTREME_VALUES = [0, 123456789, None, iNaT, 2**63 - 1, -(2**63) + 1]
+FINE_TO_COARSE_SAFE = [123_000_000_000, None, -123_000_000_000]
+COARSE_TO_FINE_SAFE = [123, None, -123]
+
+
+@pytest.mark.parametrize(
+    ("pa_unit", "pd_unit", "pa_tz", "pd_tz", "data"),
+    [
+        ("s", "s", "UTC", "UTC", EXTREME_VALUES),
+        ("ms", "ms", "UTC", "Europe/Berlin", EXTREME_VALUES),
+        ("us", "us", "US/Eastern", "UTC", EXTREME_VALUES),
+        ("ns", "ns", "US/Central", "Asia/Kolkata", EXTREME_VALUES),
+        ("ns", "s", "UTC", "UTC", FINE_TO_COARSE_SAFE),
+        ("us", "ms", "UTC", "Europe/Berlin", FINE_TO_COARSE_SAFE),
+        ("ms", "us", "US/Eastern", "UTC", COARSE_TO_FINE_SAFE),
+        ("s", "ns", "US/Central", "Asia/Kolkata", COARSE_TO_FINE_SAFE),
+    ],
+)
+def test_from_arrow_with_different_units_and_timezones_with(
+    pa_unit, pd_unit, pa_tz, pd_tz, data
+):
+    pa = pytest.importorskip("pyarrow")
+
+    pa_type = pa.timestamp(pa_unit, tz=pa_tz)
+    arr = pa.array(data, type=pa_type)
+    dtype = DatetimeTZDtype(unit=pd_unit, tz=pd_tz)
+
+    result = dtype.__from_arrow__(arr)
+    expected = DatetimeArray._from_sequence(data, dtype=f"M8[{pa_unit}, UTC]").astype(
+        dtype, copy=False
+    )
+    tm.assert_extension_array_equal(result, expected)
+
+    result = dtype.__from_arrow__(pa.chunked_array([arr]))
+    tm.assert_extension_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    ("unit", "tz"),
+    [
+        ("s", "UTC"),
+        ("ms", "Europe/Berlin"),
+        ("us", "US/Eastern"),
+        ("ns", "Asia/Kolkata"),
+        ("ns", "UTC"),
+    ],
+)
+def test_from_arrow_from_empty(unit, tz):
+    pa = pytest.importorskip("pyarrow")
+
+    data = []
+    arr = pa.array(data)
+    dtype = DatetimeTZDtype(unit=unit, tz=tz)
+
+    result = dtype.__from_arrow__(arr)
+    expected = DatetimeArray._from_sequence(np.array(data, dtype=f"datetime64[{unit}]"))
+    expected = expected.tz_localize(tz=tz)
+    tm.assert_extension_array_equal(result, expected)
+
+    result = dtype.__from_arrow__(pa.chunked_array([arr]))
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_from_arrow_from_integers():
+    pa = pytest.importorskip("pyarrow")
+
+    data = [0, 123456789, None, 2**63 - 1, iNaT, -123456789]
+    arr = pa.array(data)
+    dtype = DatetimeTZDtype(unit="ns", tz="UTC")
+
+    result = dtype.__from_arrow__(arr)
+    expected = DatetimeArray._from_sequence(np.array(data, dtype="datetime64[ns]"))
+    expected = expected.tz_localize("UTC")
+    tm.assert_extension_array_equal(result, expected)
+
+    result = dtype.__from_arrow__(pa.chunked_array([arr]))
+    tm.assert_extension_array_equal(result, expected)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_cumulative.py

@@ -0,0 +1,44 @@
+import pytest
+
+import pandas._testing as tm
+from pandas.core.arrays import DatetimeArray
+
+
+class TestAccumulator:
+    def test_accumulators_freq(self):
+        # GH#50297
+        arr = DatetimeArray._from_sequence(
+            [
+                "2000-01-01",
+                "2000-01-02",
+                "2000-01-03",
+            ],
+            dtype="M8[ns]",
+        )._with_freq("infer")
+        result = arr._accumulate("cummin")
+        expected = DatetimeArray._from_sequence(["2000-01-01"] * 3, dtype="M8[ns]")
+        tm.assert_datetime_array_equal(result, expected)
+
+        result = arr._accumulate("cummax")
+        expected = DatetimeArray._from_sequence(
+            [
+                "2000-01-01",
+                "2000-01-02",
+                "2000-01-03",
+            ],
+            dtype="M8[ns]",
+        )
+        tm.assert_datetime_array_equal(result, expected)
+
+    @pytest.mark.parametrize("func", ["cumsum", "cumprod"])
+    def test_accumulators_disallowed(self, func):
+        # GH#50297
+        arr = DatetimeArray._from_sequence(
+            [
+                "2000-01-01",
+                "2000-01-02",
+            ],
+            dtype="M8[ns]",
+        )._with_freq("infer")
+        with pytest.raises(TypeError, match=f"Accumulation {func}"):
+            arr._accumulate(func)

env-llmeval/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_reductions.py

@@ -0,0 +1,183 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+
+import pandas as pd
+from pandas import NaT
+import pandas._testing as tm
+from pandas.core.arrays import DatetimeArray
+
+
+class TestReductions:
+    @pytest.fixture(params=["s", "ms", "us", "ns"])
+    def unit(self, request):
+        return request.param
+
+    @pytest.fixture
+    def arr1d(self, tz_naive_fixture):
+        """Fixture returning DatetimeArray with parametrized timezones"""
+        tz = tz_naive_fixture
+        dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
+        arr = DatetimeArray._from_sequence(
+            [
+                "2000-01-03",
+                "2000-01-03",
+                "NaT",
+                "2000-01-02",
+                "2000-01-05",
+                "2000-01-04",
+            ],
+            dtype=dtype,
+        )
+        return arr
+
+    def test_min_max(self, arr1d, unit):
+        arr = arr1d
+        arr = arr.as_unit(unit)
+        tz = arr.tz
+
+        result = arr.min()
+        expected = pd.Timestamp("2000-01-02", tz=tz).as_unit(unit)
+        assert result == expected
+        assert result.unit == expected.unit
+
+        result = arr.max()
+        expected = pd.Timestamp("2000-01-05", tz=tz).as_unit(unit)
+        assert result == expected
+        assert result.unit == expected.unit
+
+        result = arr.min(skipna=False)
+        assert result is NaT
+
+        result = arr.max(skipna=False)
+        assert result is NaT
+
+    @pytest.mark.parametrize("tz", [None, "US/Central"])
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_min_max_empty(self, skipna, tz):
+        dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
+        arr = DatetimeArray._from_sequence([], dtype=dtype)
+        result = arr.min(skipna=skipna)
+        assert result is NaT
+
+        result = arr.max(skipna=skipna)
+        assert result is NaT
+
+    @pytest.mark.parametrize("tz", [None, "US/Central"])
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_median_empty(self, skipna, tz):
+        dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
+        arr = DatetimeArray._from_sequence([], dtype=dtype)
+        result = arr.median(skipna=skipna)
+        assert result is NaT
+
+        arr = arr.reshape(0, 3)
+        result = arr.median(axis=0, skipna=skipna)
+        expected = type(arr)._from_sequence([NaT, NaT, NaT], dtype=arr.dtype)
+        tm.assert_equal(result, expected)
+
+        result = arr.median(axis=1, skipna=skipna)
+        expected = type(arr)._from_sequence([], dtype=arr.dtype)
+        tm.assert_equal(result, expected)
+
+    def test_median(self, arr1d):
+        arr = arr1d
+
+        result = arr.median()
+        assert result == arr[0]
+        result = arr.median(skipna=False)
+        assert result is NaT
+
+        result = arr.dropna().median(skipna=False)
+        assert result == arr[0]
+
+        result = arr.median(axis=0)
+        assert result == arr[0]
+
+    def test_median_axis(self, arr1d):
+        arr = arr1d
+        assert arr.median(axis=0) == arr.median()
+        assert arr.median(axis=0, skipna=False) is NaT
+
+        msg = r"abs\(axis\) must be less than ndim"
+        with pytest.raises(ValueError, match=msg):
+            arr.median(axis=1)
+
+    @pytest.mark.filterwarnings("ignore:All-NaN slice encountered:RuntimeWarning")
+    def test_median_2d(self, arr1d):
+        arr = arr1d.reshape(1, -1)
+
+        # axis = None
+        assert arr.median() == arr1d.median()
+        assert arr.median(skipna=False) is NaT
+
+        # axis = 0
+        result = arr.median(axis=0)
+        expected = arr1d
+        tm.assert_equal(result, expected)
+
+        # Since column 3 is all-NaT, we get NaT there with or without skipna
+        result = arr.median(axis=0, skipna=False)
+        expected = arr1d
+        tm.assert_equal(result, expected)
+
+        # axis = 1
+        result = arr.median(axis=1)
+        expected = type(arr)._from_sequence([arr1d.median()], dtype=arr.dtype)
+        tm.assert_equal(result, expected)
+
+        result = arr.median(axis=1, skipna=False)
+        expected = type(arr)._from_sequence([NaT], dtype=arr.dtype)
+        tm.assert_equal(result, expected)
+
+    def test_mean(self, arr1d):
+        arr = arr1d
+
+        # manually verified result
+        expected = arr[0] + 0.4 * pd.Timedelta(days=1)
+
+        result = arr.mean()
+        assert result == expected
+        result = arr.mean(skipna=False)
+        assert result is NaT
+
+        result = arr.dropna().mean(skipna=False)
+        assert result == expected
+
+        result = arr.mean(axis=0)
+        assert result == expected
+
+    def test_mean_2d(self):
+        dti = pd.date_range("2016-01-01", periods=6, tz="US/Pacific")
+        dta = dti._data.reshape(3, 2)
+
+        result = dta.mean(axis=0)
+        expected = dta[1]
+        tm.assert_datetime_array_equal(result, expected)
+
+        result = dta.mean(axis=1)
+        expected = dta[:, 0] + pd.Timedelta(hours=12)
+        tm.assert_datetime_array_equal(result, expected)
+
+        result = dta.mean(axis=None)
+        expected = dti.mean()
+        assert result == expected
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_mean_empty(self, arr1d, skipna):
+        arr = arr1d[:0]
+
+        assert arr.mean(skipna=skipna) is NaT
+
+        arr2d = arr.reshape(0, 3)
+        result = arr2d.mean(axis=0, skipna=skipna)
+        expected = DatetimeArray._from_sequence([NaT, NaT, NaT], dtype=arr.dtype)
+        tm.assert_datetime_array_equal(result, expected)
+
+        result = arr2d.mean(axis=1, skipna=skipna)
+        expected = arr  # i.e. 1D, empty
+        tm.assert_datetime_array_equal(result, expected)
+
+        result = arr2d.mean(axis=None, skipna=skipna)
+        assert result is NaT