from datetime import datetime
import struct

import numpy as np
import pytest

from pandas._libs import (
    algos as libalgos,
    hashtable as ht,
)

from pandas.core.dtypes.common import (
    is_bool_dtype,
    is_complex_dtype,
    is_float_dtype,
    is_integer_dtype,
    is_object_dtype,
)
from pandas.core.dtypes.dtypes import (
    CategoricalDtype,
    DatetimeTZDtype,
)

import pandas as pd
from pandas import (
    Categorical,
    CategoricalIndex,
    DataFrame,
    DatetimeIndex,
    Index,
    IntervalIndex,
    MultiIndex,
    NaT,
    Period,
    PeriodIndex,
    Series,
    Timedelta,
    Timestamp,
    cut,
    date_range,
    timedelta_range,
    to_datetime,
    to_timedelta,
)
import pandas._testing as tm
import pandas.core.algorithms as algos
from pandas.core.arrays import (
    DatetimeArray,
    TimedeltaArray,
)
import pandas.core.common as com


class TestFactorize:
    def test_factorize_complex(self):
        # GH#17927
        array = np.array([1, 2, 2 + 1j], dtype=complex)
        labels, uniques = algos.factorize(array)

        expected_labels = np.array([0, 1, 2], dtype=np.intp)
        tm.assert_numpy_array_equal(labels, expected_labels)

        expected_uniques = np.array([(1 + 0j), (2 + 0j), (2 + 1j)], dtype=complex)
        tm.assert_numpy_array_equal(uniques, expected_uniques)

    def test_factorize(self, index_or_series_obj, sort):
        obj = index_or_series_obj
        result_codes, result_uniques = obj.factorize(sort=sort)

        constructor = Index
        if isinstance(obj, MultiIndex):
            constructor = MultiIndex.from_tuples
        expected_arr = obj.unique()
        if expected_arr.dtype == np.float16:
            expected_arr = expected_arr.astype(np.float32)
        expected_uniques = constructor(expected_arr)
        if (
            isinstance(obj, Index)
            and expected_uniques.dtype == bool
            and obj.dtype == object
        ):
            expected_uniques = expected_uniques.astype(object)

        if sort:
            expected_uniques = expected_uniques.sort_values()

        # construct an integer ndarray so that
        # `expected_uniques.take(expected_codes)` is equal to `obj`
        expected_uniques_list = list(expected_uniques)
        expected_codes = [expected_uniques_list.index(val) for val in obj]
        expected_codes = np.asarray(expected_codes, dtype=np.intp)

        tm.assert_numpy_array_equal(result_codes, expected_codes)
        tm.assert_index_equal(result_uniques, expected_uniques, exact=True)

    def test_series_factorize_use_na_sentinel_false(self):
        # GH#35667
        values = np.array([1, 2, 1, np.nan])
        ser = Series(values)
        codes, uniques = ser.factorize(use_na_sentinel=False)

        expected_codes = np.array([0, 1, 0, 2], dtype=np.intp)
        expected_uniques = Index([1.0, 2.0, np.nan])

        tm.assert_numpy_array_equal(codes, expected_codes)
        tm.assert_index_equal(uniques, expected_uniques)

    def test_basic(self):
        items = np.array(["a", "b", "b", "a", "a", "c", "c", "c"], dtype=object)
        codes, uniques = algos.factorize(items)
        tm.assert_numpy_array_equal(uniques, np.array(["a", "b", "c"], dtype=object))

        codes, uniques = algos.factorize(items, sort=True)
        exp = np.array([0, 1, 1, 0, 0, 2, 2, 2], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, exp)
        exp = np.array(["a", "b", "c"], dtype=object)
        tm.assert_numpy_array_equal(uniques, exp)

        arr = np.arange(5, dtype=np.intp)[::-1]

        codes, uniques = algos.factorize(arr)
        exp = np.array([0, 1, 2, 3, 4], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, exp)
        exp = np.array([4, 3, 2, 1, 0], dtype=arr.dtype)
        tm.assert_numpy_array_equal(uniques, exp)

        codes, uniques = algos.factorize(arr, sort=True)
        exp = np.array([4, 3, 2, 1, 0], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, exp)
        exp = np.array([0, 1, 2, 3, 4], dtype=arr.dtype)
        tm.assert_numpy_array_equal(uniques, exp)

        arr = np.arange(5.0)[::-1]

        codes, uniques = algos.factorize(arr)
        exp = np.array([0, 1, 2, 3, 4], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, exp)
        exp = np.array([4.0, 3.0, 2.0, 1.0, 0.0], dtype=arr.dtype)
        tm.assert_numpy_array_equal(uniques, exp)

        codes, uniques = algos.factorize(arr, sort=True)
        exp = np.array([4, 3, 2, 1, 0], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, exp)
        exp = np.array([0.0, 1.0, 2.0, 3.0, 4.0], dtype=arr.dtype)
        tm.assert_numpy_array_equal(uniques, exp)

    def test_mixed(self):
        # doc example reshaping.rst
        x = Series(["A", "A", np.nan, "B", 3.14, np.inf])
        codes, uniques = algos.factorize(x)

        exp = np.array([0, 0, -1, 1, 2, 3], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, exp)
        exp = Index(["A", "B", 3.14, np.inf])
        tm.assert_index_equal(uniques, exp)

        codes, uniques = algos.factorize(x, sort=True)
        exp = np.array([2, 2, -1, 3, 0, 1], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, exp)
        exp = Index([3.14, np.inf, "A", "B"])
        tm.assert_index_equal(uniques, exp)

    def test_factorize_datetime64(self):
        # M8
        v1 = Timestamp("20130101 09:00:00.00004")
        v2 = Timestamp("20130101")
        x = Series([v1, v1, v1, v2, v2, v1])
        codes, uniques = algos.factorize(x)

        exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, exp)
        exp = DatetimeIndex([v1, v2])
        tm.assert_index_equal(uniques, exp)

        codes, uniques = algos.factorize(x, sort=True)
        exp = np.array([1, 1, 1, 0, 0, 1], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, exp)
        exp = DatetimeIndex([v2, v1])
        tm.assert_index_equal(uniques, exp)

    def test_factorize_period(self):
        # period
        v1 = Period("201302", freq="M")
        v2 = Period("201303", freq="M")
        x = Series([v1, v1, v1, v2, v2, v1])

        # periods are not 'sorted' as they are converted back into an index
        codes, uniques = algos.factorize(x)
        exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, exp)
        tm.assert_index_equal(uniques, PeriodIndex([v1, v2]))

        codes, uniques = algos.factorize(x, sort=True)
        exp = np.array([0, 0, 0, 1, 1, 0], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, exp)
        tm.assert_index_equal(uniques, PeriodIndex([v1, v2]))

    def test_factorize_timedelta(self):
        # GH 5986
        v1 = to_timedelta("1 day 1 min")
        v2 = to_timedelta("1 day")
        x = Series([v1, v2, v1, v1, v2, v2, v1])
        codes, uniques = algos.factorize(x)
        exp = np.array([0, 1, 0, 0, 1, 1, 0], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, exp)
        tm.assert_index_equal(uniques, to_timedelta([v1, v2]))

        codes, uniques = algos.factorize(x, sort=True)
        exp = np.array([1, 0, 1, 1, 0, 0, 1], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, exp)
        tm.assert_index_equal(uniques, to_timedelta([v2, v1]))

    def test_factorize_nan(self):
        # nan should map to na_sentinel, not reverse_indexer[na_sentinel]
        # rizer.factorize should not raise an exception if na_sentinel indexes
        # outside of reverse_indexer
        key = np.array([1, 2, 1, np.nan], dtype="O")
        rizer = ht.ObjectFactorizer(len(key))
        for na_sentinel in (-1, 20):
            ids = rizer.factorize(key, na_sentinel=na_sentinel)
            expected = np.array([0, 1, 0, na_sentinel], dtype=np.intp)
            assert len(set(key)) == len(set(expected))
            tm.assert_numpy_array_equal(pd.isna(key), expected == na_sentinel)
            tm.assert_numpy_array_equal(ids, expected)

    def test_factorizer_with_mask(self):
        # GH#49549
        data = np.array([1, 2, 3, 1, 1, 0], dtype="int64")
        mask = np.array([False, False, False, False, False, True])
        rizer = ht.Int64Factorizer(len(data))
        result = rizer.factorize(data, mask=mask)
        expected = np.array([0, 1, 2, 0, 0, -1], dtype=np.intp)
        tm.assert_numpy_array_equal(result, expected)
        expected_uniques = np.array([1, 2, 3], dtype="int64")
        tm.assert_numpy_array_equal(rizer.uniques.to_array(), expected_uniques)

    def test_factorizer_object_with_nan(self):
        # GH#49549
        data = np.array([1, 2, 3, 1, np.nan])
        rizer = ht.ObjectFactorizer(len(data))
        result = rizer.factorize(data.astype(object))
        expected = np.array([0, 1, 2, 0, -1], dtype=np.intp)
        tm.assert_numpy_array_equal(result, expected)
        expected_uniques = np.array([1, 2, 3], dtype=object)
        tm.assert_numpy_array_equal(rizer.uniques.to_array(), expected_uniques)

    @pytest.mark.parametrize(
        "data, expected_codes, expected_uniques",
        [
            (
                [(1, 1), (1, 2), (0, 0), (1, 2), "nonsense"],
                [0, 1, 2, 1, 3],
                [(1, 1), (1, 2), (0, 0), "nonsense"],
            ),
            (
                [(1, 1), (1, 2), (0, 0), (1, 2), (1, 2, 3)],
                [0, 1, 2, 1, 3],
                [(1, 1), (1, 2), (0, 0), (1, 2, 3)],
            ),
            ([(1, 1), (1, 2), (0, 0), (1, 2)], [0, 1, 2, 1], [(1, 1), (1, 2), (0, 0)]),
        ],
    )
    def test_factorize_tuple_list(self, data, expected_codes, expected_uniques):
        # GH9454
        data = com.asarray_tuplesafe(data, dtype=object)
        codes, uniques = pd.factorize(data)

        tm.assert_numpy_array_equal(codes, np.array(expected_codes, dtype=np.intp))

        expected_uniques_array = com.asarray_tuplesafe(expected_uniques, dtype=object)
        tm.assert_numpy_array_equal(uniques, expected_uniques_array)

    def test_complex_sorting(self):
        # gh 12666 - check no segfault
        x17 = np.array([complex(i) for i in range(17)], dtype=object)

        msg = "'[<>]' not supported between instances of .*"
        with pytest.raises(TypeError, match=msg):
            algos.factorize(x17[::-1], sort=True)

    def test_numeric_dtype_factorize(self, any_real_numpy_dtype):
        # GH41132
        dtype = any_real_numpy_dtype
        data = np.array([1, 2, 2, 1], dtype=dtype)
        expected_codes = np.array([0, 1, 1, 0], dtype=np.intp)
        expected_uniques = np.array([1, 2], dtype=dtype)

        codes, uniques = algos.factorize(data)
        tm.assert_numpy_array_equal(codes, expected_codes)
        tm.assert_numpy_array_equal(uniques, expected_uniques)

    def test_float64_factorize(self, writable):
        data = np.array([1.0, 1e8, 1.0, 1e-8, 1e8, 1.0], dtype=np.float64)
        data.setflags(write=writable)
        expected_codes = np.array([0, 1, 0, 2, 1, 0], dtype=np.intp)
        expected_uniques = np.array([1.0, 1e8, 1e-8], dtype=np.float64)

        codes, uniques = algos.factorize(data)
        tm.assert_numpy_array_equal(codes, expected_codes)
        tm.assert_numpy_array_equal(uniques, expected_uniques)

    def test_uint64_factorize(self, writable):
        data = np.array([2**64 - 1, 1, 2**64 - 1], dtype=np.uint64)
        data.setflags(write=writable)
        expected_codes = np.array([0, 1, 0], dtype=np.intp)
        expected_uniques = np.array([2**64 - 1, 1], dtype=np.uint64)

        codes, uniques = algos.factorize(data)
        tm.assert_numpy_array_equal(codes, expected_codes)
        tm.assert_numpy_array_equal(uniques, expected_uniques)

    def test_int64_factorize(self, writable):
        data = np.array([2**63 - 1, -(2**63), 2**63 - 1], dtype=np.int64)
        data.setflags(write=writable)
        expected_codes = np.array([0, 1, 0], dtype=np.intp)
        expected_uniques = np.array([2**63 - 1, -(2**63)], dtype=np.int64)

        codes, uniques = algos.factorize(data)
        tm.assert_numpy_array_equal(codes, expected_codes)
        tm.assert_numpy_array_equal(uniques, expected_uniques)

    def test_string_factorize(self, writable):
        data = np.array(["a", "c", "a", "b", "c"], dtype=object)
        data.setflags(write=writable)
        expected_codes = np.array([0, 1, 0, 2, 1], dtype=np.intp)
        expected_uniques = np.array(["a", "c", "b"], dtype=object)

        codes, uniques = algos.factorize(data)
        tm.assert_numpy_array_equal(codes, expected_codes)
        tm.assert_numpy_array_equal(uniques, expected_uniques)

    def test_object_factorize(self, writable):
        data = np.array(["a", "c", None, np.nan, "a", "b", NaT, "c"], dtype=object)
        data.setflags(write=writable)
        expected_codes = np.array([0, 1, -1, -1, 0, 2, -1, 1], dtype=np.intp)
        expected_uniques = np.array(["a", "c", "b"], dtype=object)

        codes, uniques = algos.factorize(data)
        tm.assert_numpy_array_equal(codes, expected_codes)
        tm.assert_numpy_array_equal(uniques, expected_uniques)

    def test_datetime64_factorize(self, writable):
        # GH35650 Verify whether read-only datetime64 array can be factorized
        data = np.array([np.datetime64("2020-01-01T00:00:00.000")], dtype="M8[ns]")
        data.setflags(write=writable)
        expected_codes = np.array([0], dtype=np.intp)
        expected_uniques = np.array(
            ["2020-01-01T00:00:00.000000000"], dtype="datetime64[ns]"
        )

        codes, uniques = pd.factorize(data)
        tm.assert_numpy_array_equal(codes, expected_codes)
        tm.assert_numpy_array_equal(uniques, expected_uniques)

    def test_factorize_rangeindex(self, sort):
        # increasing -> sort doesn't matter
        ri = pd.RangeIndex.from_range(range(10))
        expected = np.arange(10, dtype=np.intp), ri

        result = algos.factorize(ri, sort=sort)
        tm.assert_numpy_array_equal(result[0], expected[0])
        tm.assert_index_equal(result[1], expected[1], exact=True)

        result = ri.factorize(sort=sort)
        tm.assert_numpy_array_equal(result[0], expected[0])
        tm.assert_index_equal(result[1], expected[1], exact=True)

    def test_factorize_rangeindex_decreasing(self, sort):
        # decreasing -> sort matters
        ri = pd.RangeIndex.from_range(range(10))
        expected = np.arange(10, dtype=np.intp), ri

        ri2 = ri[::-1]
        expected = expected[0], ri2
        if sort:
            expected = expected[0][::-1], expected[1][::-1]

        result = algos.factorize(ri2, sort=sort)
        tm.assert_numpy_array_equal(result[0], expected[0])
        tm.assert_index_equal(result[1], expected[1], exact=True)

        result = ri2.factorize(sort=sort)
        tm.assert_numpy_array_equal(result[0], expected[0])
        tm.assert_index_equal(result[1], expected[1], exact=True)

    def test_deprecate_order(self):
        # gh 19727 - check warning is raised for deprecated keyword, order.
        # Test not valid once order keyword is removed.
        data = np.array([2**63, 1, 2**63], dtype=np.uint64)
        with pytest.raises(TypeError, match="got an unexpected keyword"):
            algos.factorize(data, order=True)
        with tm.assert_produces_warning(False):
            algos.factorize(data)

    @pytest.mark.parametrize(
        "data",
        [
            np.array([0, 1, 0], dtype="u8"),
            np.array([-(2**63), 1, -(2**63)], dtype="i8"),
            np.array(["__nan__", "foo", "__nan__"], dtype="object"),
        ],
    )
    def test_parametrized_factorize_na_value_default(self, data):
        # arrays that include the NA default for that type, but isn't used.
        codes, uniques = algos.factorize(data)
        expected_uniques = data[[0, 1]]
        expected_codes = np.array([0, 1, 0], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, expected_codes)
        tm.assert_numpy_array_equal(uniques, expected_uniques)

    @pytest.mark.parametrize(
        "data, na_value",
        [
            (np.array([0, 1, 0, 2], dtype="u8"), 0),
            (np.array([1, 0, 1, 2], dtype="u8"), 1),
            (np.array([-(2**63), 1, -(2**63), 0], dtype="i8"), -(2**63)),
            (np.array([1, -(2**63), 1, 0], dtype="i8"), 1),
            (np.array(["a", "", "a", "b"], dtype=object), "a"),
            (np.array([(), ("a", 1), (), ("a", 2)], dtype=object), ()),
            (np.array([("a", 1), (), ("a", 1), ("a", 2)], dtype=object), ("a", 1)),
        ],
    )
    def test_parametrized_factorize_na_value(self, data, na_value):
        codes, uniques = algos.factorize_array(data, na_value=na_value)
        expected_uniques = data[[1, 3]]
        expected_codes = np.array([-1, 0, -1, 1], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, expected_codes)
        tm.assert_numpy_array_equal(uniques, expected_uniques)

    @pytest.mark.parametrize(
        "data, uniques",
        [
            (
                np.array(["b", "a", None, "b"], dtype=object),
                np.array(["b", "a"], dtype=object),
            ),
            (
                pd.array([2, 1, pd.NA, 2], dtype="Int64"),
                pd.array([2, 1], dtype="Int64"),
            ),
        ],
        ids=["numpy_array", "extension_array"],
    )
    def test_factorize_use_na_sentinel(self, sort, data, uniques):
        codes, uniques = algos.factorize(data, sort=sort, use_na_sentinel=True)
        if sort:
            expected_codes = np.array([1, 0, -1, 1], dtype=np.intp)
            expected_uniques = algos.safe_sort(uniques)
        else:
            expected_codes = np.array([0, 1, -1, 0], dtype=np.intp)
            expected_uniques = uniques
        tm.assert_numpy_array_equal(codes, expected_codes)
        if isinstance(data, np.ndarray):
            tm.assert_numpy_array_equal(uniques, expected_uniques)
        else:
            tm.assert_extension_array_equal(uniques, expected_uniques)

    @pytest.mark.parametrize(
        "data, expected_codes, expected_uniques",
        [
            (
                ["a", None, "b", "a"],
                np.array([0, 1, 2, 0], dtype=np.dtype("intp")),
                np.array(["a", np.nan, "b"], dtype=object),
            ),
            (
                ["a", np.nan, "b", "a"],
                np.array([0, 1, 2, 0], dtype=np.dtype("intp")),
                np.array(["a", np.nan, "b"], dtype=object),
            ),
        ],
    )
    def test_object_factorize_use_na_sentinel_false(
        self, data, expected_codes, expected_uniques
    ):
        codes, uniques = algos.factorize(
            np.array(data, dtype=object), use_na_sentinel=False
        )

        tm.assert_numpy_array_equal(uniques, expected_uniques, strict_nan=True)
        tm.assert_numpy_array_equal(codes, expected_codes, strict_nan=True)

    @pytest.mark.parametrize(
        "data, expected_codes, expected_uniques",
        [
            (
                np.array([1, None, 1, 2], dtype=object),
                np.array([0, 1, 0, 2], dtype=np.dtype("intp")),
                np.array([1, np.nan, 2], dtype="O"),
            ),
            (
                np.array([1, np.nan, 1, 2], dtype=np.float64),
                np.array([0, 1, 0, 2], dtype=np.dtype("intp")),
                np.array([1, np.nan, 2], dtype=np.float64),
            ),
        ],
    )
    def test_int_factorize_use_na_sentinel_false(
        self, data, expected_codes, expected_uniques
    ):
        codes, uniques = algos.factorize(data, use_na_sentinel=False)

        tm.assert_numpy_array_equal(uniques, expected_uniques, strict_nan=True)
        tm.assert_numpy_array_equal(codes, expected_codes, strict_nan=True)

    @pytest.mark.parametrize(
        "data, expected_codes, expected_uniques",
        [
            (
                Index(Categorical(["a", "a", "b"])),
                np.array([0, 0, 1], dtype=np.intp),
                CategoricalIndex(["a", "b"], categories=["a", "b"], dtype="category"),
            ),
            (
                Series(Categorical(["a", "a", "b"])),
                np.array([0, 0, 1], dtype=np.intp),
                CategoricalIndex(["a", "b"], categories=["a", "b"], dtype="category"),
            ),
            (
                Series(DatetimeIndex(["2017", "2017"], tz="US/Eastern")),
                np.array([0, 0], dtype=np.intp),
                DatetimeIndex(["2017"], tz="US/Eastern"),
            ),
        ],
    )
    def test_factorize_mixed_values(self, data, expected_codes, expected_uniques):
        # GH 19721
        codes, uniques = algos.factorize(data)
        tm.assert_numpy_array_equal(codes, expected_codes)
        tm.assert_index_equal(uniques, expected_uniques)

    def test_factorize_interval_non_nano(self, unit):
        # GH#56099
        left = DatetimeIndex(["2016-01-01", np.nan, "2015-10-11"]).as_unit(unit)
        right = DatetimeIndex(["2016-01-02", np.nan, "2015-10-15"]).as_unit(unit)
        idx = IntervalIndex.from_arrays(left, right)
        codes, cats = idx.factorize()
        assert cats.dtype == f"interval[datetime64[{unit}], right]"

        ts = Timestamp(0).as_unit(unit)
        idx2 = IntervalIndex.from_arrays(left - ts, right - ts)
        codes2, cats2 = idx2.factorize()
        assert cats2.dtype == f"interval[timedelta64[{unit}], right]"

        idx3 = IntervalIndex.from_arrays(
            left.tz_localize("US/Pacific"), right.tz_localize("US/Pacific")
        )
        codes3, cats3 = idx3.factorize()
        assert cats3.dtype == f"interval[datetime64[{unit}, US/Pacific], right]"


class TestUnique:
    def test_ints(self):
        arr = np.random.default_rng(2).integers(0, 100, size=50)

        result = algos.unique(arr)
        assert isinstance(result, np.ndarray)

    def test_objects(self):
        arr = np.random.default_rng(2).integers(0, 100, size=50).astype("O")

        result = algos.unique(arr)
        assert isinstance(result, np.ndarray)

    def test_object_refcount_bug(self):
        lst = np.array(["A", "B", "C", "D", "E"], dtype=object)
        for i in range(1000):
            len(algos.unique(lst))

    def test_index_returned(self, index):
        # GH#57043
        index = index.repeat(2)
        result = algos.unique(index)

        # dict.fromkeys preserves the order
        unique_values = list(dict.fromkeys(index.values))
        if isinstance(index, MultiIndex):
            expected = MultiIndex.from_tuples(unique_values, names=index.names)
        else:
            expected = Index(unique_values, dtype=index.dtype)
            if isinstance(index.dtype, DatetimeTZDtype):
                expected = expected.normalize()
        tm.assert_index_equal(result, expected, exact=True)

    def test_factorize_multiindex_empty(self):
        # GH#57517
        mi = MultiIndex.from_product(
            [Index([], name="a", dtype=object), Index([], name="i", dtype="f4")]
        )
        codes, uniques = mi.factorize()
        exp_codes = np.array([], dtype=np.intp)
        tm.assert_numpy_array_equal(codes, exp_codes)
        tm.assert_index_equal(uniques, mi[:0])

    def test_dtype_preservation(self, any_numpy_dtype):
        # GH 15442
        if any_numpy_dtype in (tm.BYTES_DTYPES + tm.STRING_DTYPES):
            data = [1, 2, 2]
            uniques = [1, 2]
        elif is_integer_dtype(any_numpy_dtype):
            data = [1, 2, 2]
            uniques = [1, 2]
        elif is_float_dtype(any_numpy_dtype):
            data = [1, 2, 2]
            uniques = [1.0, 2.0]
        elif is_complex_dtype(any_numpy_dtype):
            data = [complex(1, 0), complex(2, 0), complex(2, 0)]
            uniques = [complex(1, 0), complex(2, 0)]
        elif is_bool_dtype(any_numpy_dtype):
            data = [True, True, False]
            uniques = [True, False]
        elif is_object_dtype(any_numpy_dtype):
            data = ["A", "B", "B"]
            uniques = ["A", "B"]
        else:
            # datetime64[ns]/M8[ns]/timedelta64[ns]/m8[ns] tested elsewhere
            data = [1, 2, 2]
            uniques = [1, 2]

        result = Series(data, dtype=any_numpy_dtype).unique()
        expected = np.array(uniques, dtype=any_numpy_dtype)

        if any_numpy_dtype in tm.STRING_DTYPES:
            expected = expected.astype(object)

        if expected.dtype.kind in ["m", "M"]:
            # We get TimedeltaArray/DatetimeArray
            assert isinstance(result, (DatetimeArray, TimedeltaArray))
            result = np.array(result)
        tm.assert_numpy_array_equal(result, expected)

    def test_datetime64_dtype_array_returned(self):
        # GH 9431
        dt_arr = np.array(
            [
                "2015-01-03T00:00:00.000000000",
                "2015-01-01T00:00:00.000000000",
            ],
            dtype="M8[ns]",
        )

        dt_index = to_datetime(
            [
                "2015-01-03T00:00:00.000000000",
                "2015-01-01T00:00:00.000000000",
                "2015-01-01T00:00:00.000000000",
            ]
        )
        result = algos.unique(dt_index)
        expected = to_datetime(dt_arr)
        tm.assert_index_equal(result, expected, exact=True)

        s = Series(dt_index)
        result = algos.unique(s)
        tm.assert_numpy_array_equal(result, dt_arr)
        assert result.dtype == dt_arr.dtype

        arr = s.values
        result = algos.unique(arr)
        tm.assert_numpy_array_equal(result, dt_arr)
        assert result.dtype == dt_arr.dtype

    def test_datetime_non_ns(self):
        a = np.array(["2000", "2000", "2001"], dtype="datetime64[s]")
        result = pd.unique(a)
        expected = np.array(["2000", "2001"], dtype="datetime64[s]")
        tm.assert_numpy_array_equal(result, expected)

    def test_timedelta_non_ns(self):
        a = np.array(["2000", "2000", "2001"], dtype="timedelta64[s]")
        result = pd.unique(a)
        expected = np.array([2000, 2001], dtype="timedelta64[s]")
        tm.assert_numpy_array_equal(result, expected)

    def test_timedelta64_dtype_array_returned(self):
        # GH 9431
        td_arr = np.array([31200, 45678, 10000], dtype="m8[ns]")

        td_index = to_timedelta([31200, 45678, 31200, 10000, 45678])
        result = algos.unique(td_index)
        expected = to_timedelta(td_arr)
        tm.assert_index_equal(result, expected)
        assert result.dtype == expected.dtype

        s = Series(td_index)
        result = algos.unique(s)
        tm.assert_numpy_array_equal(result, td_arr)
        assert result.dtype == td_arr.dtype

        arr = s.values
        result = algos.unique(arr)
        tm.assert_numpy_array_equal(result, td_arr)
        assert result.dtype == td_arr.dtype

    def test_uint64_overflow(self):
        s = Series([1, 2, 2**63, 2**63], dtype=np.uint64)
        exp = np.array([1, 2, 2**63], dtype=np.uint64)
        tm.assert_numpy_array_equal(algos.unique(s), exp)

    def test_nan_in_object_array(self):
        duplicated_items = ["a", np.nan, "c", "c"]
        result = pd.unique(np.array(duplicated_items, dtype=object))
        expected = np.array(["a", np.nan, "c"], dtype=object)
        tm.assert_numpy_array_equal(result, expected)

    def test_categorical(self):
        # we are expecting to return in the order
        # of appearance
        expected = Categorical(list("bac"))

        # we are expecting to return in the order
        # of the categories
        expected_o = Categorical(list("bac"), categories=list("abc"), ordered=True)

        # GH 15939
        c = Categorical(list("baabc"))
        result = c.unique()
        tm.assert_categorical_equal(result, expected)

        result = algos.unique(c)
        tm.assert_categorical_equal(result, expected)

        c = Categorical(list("baabc"), ordered=True)
        result = c.unique()
        tm.assert_categorical_equal(result, expected_o)

        result = algos.unique(c)
        tm.assert_categorical_equal(result, expected_o)

        # Series of categorical dtype
        s = Series(Categorical(list("baabc")), name="foo")
        result = s.unique()
        tm.assert_categorical_equal(result, expected)

        result = pd.unique(s)
        tm.assert_categorical_equal(result, expected)

        # CI -> return CI
        ci = CategoricalIndex(Categorical(list("baabc"), categories=list("abc")))
        expected = CategoricalIndex(expected)
        result = ci.unique()
        tm.assert_index_equal(result, expected)

        result = pd.unique(ci)
        tm.assert_index_equal(result, expected)

    def test_datetime64tz_aware(self, unit):
        # GH 15939

        dti = Index(
            [
                Timestamp("20160101", tz="US/Eastern"),
                Timestamp("20160101", tz="US/Eastern"),
            ]
        ).as_unit(unit)
        ser = Series(dti)

        result = ser.unique()
        expected = dti[:1]._data
        tm.assert_extension_array_equal(result, expected)

        result = dti.unique()
        expected = dti[:1]
        tm.assert_index_equal(result, expected)

        result = pd.unique(ser)
        expected = dti[:1]._data
        tm.assert_extension_array_equal(result, expected)

        result = pd.unique(dti)
        expected = dti[:1]
        tm.assert_index_equal(result, expected)

    def test_order_of_appearance(self):
        # 9346
        # light testing of guarantee of order of appearance
        # these also are the doc-examples
        result = pd.unique(Series([2, 1, 3, 3]))
        tm.assert_numpy_array_equal(result, np.array([2, 1, 3], dtype="int64"))

        result = pd.unique(Series([2] + [1] * 5))
        tm.assert_numpy_array_equal(result, np.array([2, 1], dtype="int64"))

        data = np.array(["a", "a", "b", "c"], dtype=object)
        result = pd.unique(data)
        expected = np.array(["a", "b", "c"], dtype=object)
        tm.assert_numpy_array_equal(result, expected)

        result = pd.unique(Series(Categorical(list("aabc"))))
        expected = Categorical(list("abc"))
        tm.assert_categorical_equal(result, expected)

    def test_order_of_appearance_dt64(self, unit):
        ser = Series([Timestamp("20160101"), Timestamp("20160101")]).dt.as_unit(unit)
        result = pd.unique(ser)
        expected = np.array(["2016-01-01T00:00:00.000000000"], dtype=f"M8[{unit}]")
        tm.assert_numpy_array_equal(result, expected)

    def test_order_of_appearance_dt64tz(self, unit):
        dti = DatetimeIndex(
            [
                Timestamp("20160101", tz="US/Eastern"),
                Timestamp("20160101", tz="US/Eastern"),
            ]
        ).as_unit(unit)
        result = pd.unique(dti)
        expected = DatetimeIndex(
            ["2016-01-01 00:00:00"], dtype=f"datetime64[{unit}, US/Eastern]", freq=None
        )
        tm.assert_index_equal(result, expected)

    @pytest.mark.parametrize(
        "arg ,expected",
        [
            (("1", "1", "2"), np.array(["1", "2"], dtype=object)),
            (("foo",), np.array(["foo"], dtype=object)),
        ],
    )
    def test_tuple_with_strings(self, arg, expected):
        # see GH 17108
        arg = com.asarray_tuplesafe(arg, dtype=object)
        result = pd.unique(arg)
        tm.assert_numpy_array_equal(result, expected)

    def test_obj_none_preservation(self):
        # GH 20866
        arr = np.array(["foo", None], dtype=object)
        result = pd.unique(arr)
        expected = np.array(["foo", None], dtype=object)

        tm.assert_numpy_array_equal(result, expected, strict_nan=True)

    def test_signed_zero(self):
        # GH 21866
        a = np.array([-0.0, 0.0])
        result = pd.unique(a)
        expected = np.array([-0.0])  # 0.0 and -0.0 are equivalent
        tm.assert_numpy_array_equal(result, expected)

    def test_different_nans(self):
        # GH 21866
        # create different nans from bit-patterns:
        NAN1 = struct.unpack("d", struct.pack("=Q", 0x7FF8000000000000))[0]
        NAN2 = struct.unpack("d", struct.pack("=Q", 0x7FF8000000000001))[0]
        assert NAN1 != NAN1
        assert NAN2 != NAN2
        a = np.array([NAN1, NAN2])  # NAN1 and NAN2 are equivalent
        result = pd.unique(a)
        expected = np.array([np.nan])
        tm.assert_numpy_array_equal(result, expected)

    @pytest.mark.parametrize("el_type", [np.float64, object])
    def test_first_nan_kept(self, el_type):
        # GH 22295
        # create different nans from bit-patterns:
        bits_for_nan1 = 0xFFF8000000000001
        bits_for_nan2 = 0x7FF8000000000001
        NAN1 = struct.unpack("d", struct.pack("=Q", bits_for_nan1))[0]
        NAN2 = struct.unpack("d", struct.pack("=Q", bits_for_nan2))[0]
        assert NAN1 != NAN1
        assert NAN2 != NAN2
        a = np.array([NAN1, NAN2], dtype=el_type)
        result = pd.unique(a)
        assert result.size == 1
        # use bit patterns to identify which nan was kept:
        result_nan_bits = struct.unpack("=Q", struct.pack("d", result[0]))[0]
        assert result_nan_bits == bits_for_nan1

    def test_do_not_mangle_na_values(self, unique_nulls_fixture, unique_nulls_fixture2):
        # GH 22295
        if unique_nulls_fixture is unique_nulls_fixture2:
            return  # skip it, values not unique
        a = np.array([unique_nulls_fixture, unique_nulls_fixture2], dtype=object)
        result = pd.unique(a)
        assert result.size == 2
        assert a[0] is unique_nulls_fixture
        assert a[1] is unique_nulls_fixture2

    def test_unique_masked(self, any_numeric_ea_dtype):
        # GH#48019
        ser = Series([1, pd.NA, 2] * 3, dtype=any_numeric_ea_dtype)
        result = pd.unique(ser)
        expected = pd.array([1, pd.NA, 2], dtype=any_numeric_ea_dtype)
        tm.assert_extension_array_equal(result, expected)

    def test_unique_NumpyExtensionArray(self):
        arr_complex = pd.array(
            [1 + 1j, 2, 3]
        )  # NumpyEADtype('complex128') => NumpyExtensionArray
        result = pd.unique(arr_complex)
        expected = pd.array([1 + 1j, 2 + 0j, 3 + 0j])
        tm.assert_extension_array_equal(result, expected)


def test_nunique_ints(index_or_series_or_array):
    # GH#36327
    values = index_or_series_or_array(np.random.default_rng(2).integers(0, 20, 30))
    result = algos.nunique_ints(values)
    expected = len(algos.unique(values))
    assert result == expected


class TestIsin:
    def test_invalid(self):
        msg = (
            r"only list-like objects are allowed to be passed to isin\(\), "
            r"you passed a `int`"
        )
        with pytest.raises(TypeError, match=msg):
            algos.isin(1, 1)
        with pytest.raises(TypeError, match=msg):
            algos.isin(1, [1])
        with pytest.raises(TypeError, match=msg):
            algos.isin([1], 1)

    def test_basic(self):
        result = algos.isin(np.array([1, 2]), [1])
        expected = np.array([True, False])
        tm.assert_numpy_array_equal(result, expected)

        result = algos.isin(Series([1, 2]), [1])
        expected = np.array([True, False])
        tm.assert_numpy_array_equal(result, expected)

        result = algos.isin(Series([1, 2]), Series([1]))
        expected = np.array([True, False])
        tm.assert_numpy_array_equal(result, expected)

        result = algos.isin(Series([1, 2]), {1})
        expected = np.array([True, False])
        tm.assert_numpy_array_equal(result, expected)

        arg = np.array(["a", "b"], dtype=object)
        result = algos.isin(arg, ["a"])
        expected = np.array([True, False])
        tm.assert_numpy_array_equal(result, expected)

        result = algos.isin(Series(arg), Series(["a"]))
        expected = np.array([True, False])
        tm.assert_numpy_array_equal(result, expected)

        result = algos.isin(Series(arg), {"a"})
        expected = np.array([True, False])
        tm.assert_numpy_array_equal(result, expected)

        result = algos.isin(arg, [1])
        expected = np.array([False, False])
        tm.assert_numpy_array_equal(result, expected)

    def test_i8(self):
        arr = date_range("20130101", periods=3).values
        result = algos.isin(arr, [arr[0]])
        expected = np.array([True, False, False])
        tm.assert_numpy_array_equal(result, expected)

        result = algos.isin(arr, arr[0:2])
        expected = np.array([True, True, False])
        tm.assert_numpy_array_equal(result, expected)

        result = algos.isin(arr, set(arr[0:2]))
        expected = np.array([True, True, False])
        tm.assert_numpy_array_equal(result, expected)

        arr = timedelta_range("1 day", periods=3).values
        result = algos.isin(arr, [arr[0]])
        expected = np.array([True, False, False])
        tm.assert_numpy_array_equal(result, expected)

        result = algos.isin(arr, arr[0:2])
        expected = np.array([True, True, False])
        tm.assert_numpy_array_equal(result, expected)

        result = algos.isin(arr, set(arr[0:2]))
        expected = np.array([True, True, False])
        tm.assert_numpy_array_equal(result, expected)

    @pytest.mark.parametrize("dtype1", ["m8[ns]", "M8[ns]", "M8[ns, UTC]", "period[D]"])
    @pytest.mark.parametrize("dtype", ["i8", "f8", "u8"])
    def test_isin_datetimelike_values_numeric_comps(self, dtype, dtype1):
        # Anything but object and we get all-False shortcut

        dta = date_range("2013-01-01", periods=3)._values
        arr = Series(dta.view("i8")).array.view(dtype1)

        comps = arr.view("i8").astype(dtype)

        result = algos.isin(comps, arr)
        expected = np.zeros(comps.shape, dtype=bool)
        tm.assert_numpy_array_equal(result, expected)

    def test_large(self):
        s = date_range("20000101", periods=2000000, freq="s").values
        result = algos.isin(s, s[0:2])
        expected = np.zeros(len(s), dtype=bool)
        expected[0] = True
        expected[1] = True
        tm.assert_numpy_array_equal(result, expected)

    @pytest.mark.parametrize("dtype", ["m8[ns]", "M8[ns]", "M8[ns, UTC]", "period[D]"])
    def test_isin_datetimelike_all_nat(self, dtype):
        # GH#56427
        dta = date_range("2013-01-01", periods=3)._values
        arr = Series(dta.view("i8")).array.view(dtype)

        arr[0] = NaT
        result = algos.isin(arr, [NaT])
        expected = np.array([True, False, False], dtype=bool)
        tm.assert_numpy_array_equal(result, expected)

    @pytest.mark.parametrize("dtype", ["m8[ns]", "M8[ns]", "M8[ns, UTC]"])
    def test_isin_datetimelike_strings_returns_false(self, dtype):
        # GH#53111
        dta = date_range("2013-01-01", periods=3)._values
        arr = Series(dta.view("i8")).array.view(dtype)

        vals = [str(x) for x in arr]
        res = algos.isin(arr, vals)
        assert not res.any()

        vals2 = np.array(vals, dtype=str)
        res2 = algos.isin(arr, vals2)
        assert not res2.any()

    def test_isin_dt64tz_with_nat(self):
        # the all-NaT values used to get inferred to tznaive, which was evaluated
        #  as non-matching GH#56427
        dti = date_range("2016-01-01", periods=3, tz="UTC")
        ser = Series(dti)
        ser[0] = NaT

        res = algos.isin(ser._values, [NaT])
        exp = np.array([True, False, False], dtype=bool)
        tm.assert_numpy_array_equal(res, exp)

    def test_categorical_from_codes(self):
        # GH 16639
        vals = np.array([0, 1, 2, 0])
        cats = ["a", "b", "c"]
        Sd = Series(Categorical([1]).from_codes(vals, cats))
        St = Series(Categorical([1]).from_codes(np.array([0, 1]), cats))
        expected = np.array([True, True, False, True])
        result = algos.isin(Sd, St)
        tm.assert_numpy_array_equal(expected, result)

    def test_categorical_isin(self):
        vals = np.array([0, 1, 2, 0])
        cats = ["a", "b", "c"]
        cat = Categorical([1]).from_codes(vals, cats)
        other = Categorical([1]).from_codes(np.array([0, 1]), cats)

        expected = np.array([True, True, False, True])
        result = algos.isin(cat, other)
        tm.assert_numpy_array_equal(expected, result)

    def test_same_nan_is_in(self):
        # GH 22160
        # nan is special, because from " a is b" doesn't follow "a == b"
        # at least, isin() should follow python's "np.nan in [nan] == True"
        # casting to -> np.float64 -> another float-object somewhere on
        # the way could lead jeopardize this behavior
        comps = np.array([np.nan], dtype=object)  # could be casted to float64
        values = [np.nan]
        expected = np.array([True])
        result = algos.isin(comps, values)
        tm.assert_numpy_array_equal(expected, result)

    def test_same_nan_is_in_large(self):
        # https://github.com/pandas-dev/pandas/issues/22205
        s = np.tile(1.0, 1_000_001)
        s[0] = np.nan
        result = algos.isin(s, np.array([np.nan, 1]))
        expected = np.ones(len(s), dtype=bool)
        tm.assert_numpy_array_equal(result, expected)

    def test_same_nan_is_in_large_series(self):
        # https://github.com/pandas-dev/pandas/issues/22205
        s = np.tile(1.0, 1_000_001)
        series = Series(s)
        s[0] = np.nan
        result = series.isin(np.array([np.nan, 1]))
        expected = Series(np.ones(len(s), dtype=bool))
        tm.assert_series_equal(result, expected)

    def test_same_object_is_in(self):
        # GH 22160
        # there could be special treatment for nans
        # the user however could define a custom class
        # with similar behavior, then we at least should
        # fall back to usual python's behavior: "a in [a] == True"
        class LikeNan:
            def __eq__(self, other) -> bool:
                return False

            def __hash__(self):
                return 0

        a, b = LikeNan(), LikeNan()

        arg = np.array([a], dtype=object)

        # same object -> True
        tm.assert_numpy_array_equal(algos.isin(arg, [a]), np.array([True]))
        # different objects -> False
        tm.assert_numpy_array_equal(algos.isin(arg, [b]), np.array([False]))

    def test_different_nans(self):
        # GH 22160
        # all nans are handled as equivalent

        comps = [float("nan")]
        values = [float("nan")]
        assert comps[0] is not values[0]  # different nan-objects

        # as list of python-objects:
        result = algos.isin(np.array(comps), values)
        tm.assert_numpy_array_equal(np.array([True]), result)

        # as object-array:
        result = algos.isin(
            np.asarray(comps, dtype=object), np.asarray(values, dtype=object)
        )
        tm.assert_numpy_array_equal(np.array([True]), result)

        # as float64-array:
        result = algos.isin(
            np.asarray(comps, dtype=np.float64), np.asarray(values, dtype=np.float64)
        )
        tm.assert_numpy_array_equal(np.array([True]), result)

    def test_no_cast(self):
        # GH 22160
        # ensure 42 is not casted to a string
        comps = np.array(["ss", 42], dtype=object)
        values = ["42"]
        expected = np.array([False, False])

        result = algos.isin(comps, values)
        tm.assert_numpy_array_equal(expected, result)

    @pytest.mark.parametrize("empty", [[], Series(dtype=object), np.array([])])
    def test_empty(self, empty):
        # see gh-16991
        vals = Index(["a", "b"])
        expected = np.array([False, False])

        result = algos.isin(vals, empty)
        tm.assert_numpy_array_equal(expected, result)

    def test_different_nan_objects(self):
        # GH 22119
        comps = np.array(["nan", np.nan * 1j, float("nan")], dtype=object)
        vals = np.array([float("nan")], dtype=object)
        expected = np.array([False, False, True])
        result = algos.isin(comps, vals)
        tm.assert_numpy_array_equal(expected, result)

    def test_different_nans_as_float64(self):
        # GH 21866
        # create different nans from bit-patterns,
        # these nans will land in different buckets in the hash-table
        # if no special care is taken
        NAN1 = struct.unpack("d", struct.pack("=Q", 0x7FF8000000000000))[0]
        NAN2 = struct.unpack("d", struct.pack("=Q", 0x7FF8000000000001))[0]
        assert NAN1 != NAN1
        assert NAN2 != NAN2

        # check that NAN1 and NAN2 are equivalent:
        arr = np.array([NAN1, NAN2], dtype=np.float64)
        lookup1 = np.array([NAN1], dtype=np.float64)
        result = algos.isin(arr, lookup1)
        expected = np.array([True, True])
        tm.assert_numpy_array_equal(result, expected)

        lookup2 = np.array([NAN2], dtype=np.float64)
        result = algos.isin(arr, lookup2)
        expected = np.array([True, True])
        tm.assert_numpy_array_equal(result, expected)

    def test_isin_int_df_string_search(self):
        """Comparing df with int`s (1,2) with a string at isin() ("1")
        -> should not match values because int 1 is not equal str 1"""
        df = DataFrame({"values": [1, 2]})
        result = df.isin(["1"])
        expected_false = DataFrame({"values": [False, False]})
        tm.assert_frame_equal(result, expected_false)

    def test_isin_nan_df_string_search(self):
        """Comparing df with nan value (np.nan,2) with a string at isin() ("NaN")
        -> should not match values because np.nan is not equal str NaN"""
        df = DataFrame({"values": [np.nan, 2]})
        result = df.isin(np.array(["NaN"], dtype=object))
        expected_false = DataFrame({"values": [False, False]})
        tm.assert_frame_equal(result, expected_false)

    def test_isin_float_df_string_search(self):
        """Comparing df with floats (1.4245,2.32441) with a string at isin() ("1.4245")
        -> should not match values because float 1.4245 is not equal str 1.4245"""
        df = DataFrame({"values": [1.4245, 2.32441]})
        result = df.isin(np.array(["1.4245"], dtype=object))
        expected_false = DataFrame({"values": [False, False]})
        tm.assert_frame_equal(result, expected_false)

    def test_isin_unsigned_dtype(self):
        # GH#46485
        ser = Series([1378774140726870442], dtype=np.uint64)
        result = ser.isin([1378774140726870528])
        expected = Series(False)
        tm.assert_series_equal(result, expected)


class TestValueCounts:
    def test_value_counts(self):
        arr = np.random.default_rng(1234).standard_normal(4)
        factor = cut(arr, 4)

        # assert isinstance(factor, n)
        result = algos.value_counts_internal(factor)
        breaks = [-1.606, -1.018, -0.431, 0.155, 0.741]
        index = IntervalIndex.from_breaks(breaks).astype(CategoricalDtype(ordered=True))
        expected = Series([1, 0, 2, 1], index=index, name="count")
        tm.assert_series_equal(result.sort_index(), expected.sort_index())

    def test_value_counts_bins(self):
        s = [1, 2, 3, 4]
        result = algos.value_counts_internal(s, bins=1)
        expected = Series(
            [4], index=IntervalIndex.from_tuples([(0.996, 4.0)]), name="count"
        )
        tm.assert_series_equal(result, expected)

        result = algos.value_counts_internal(s, bins=2, sort=False)
        expected = Series(
            [2, 2],
            index=IntervalIndex.from_tuples([(0.996, 2.5), (2.5, 4.0)]),
            name="count",
        )
        tm.assert_series_equal(result, expected)

    def test_value_counts_dtypes(self):
        result = algos.value_counts_internal(np.array([1, 1.0]))
        assert len(result) == 1

        result = algos.value_counts_internal(np.array([1, 1.0]), bins=1)
        assert len(result) == 1

        result = algos.value_counts_internal(Series([1, 1.0, "1"]))  # object
        assert len(result) == 2

        msg = "bins argument only works with numeric data"
        with pytest.raises(TypeError, match=msg):
            algos.value_counts_internal(np.array(["1", 1], dtype=object), bins=1)

    def test_value_counts_nat(self):
        td = Series([np.timedelta64(10000), NaT], dtype="timedelta64[ns]")
        dt = to_datetime(["NaT", "2014-01-01"])

        for ser in [td, dt]:
            vc = algos.value_counts_internal(ser)
            vc_with_na = algos.value_counts_internal(ser, dropna=False)
            assert len(vc) == 1
            assert len(vc_with_na) == 2

        exp_dt = Series({Timestamp("2014-01-01 00:00:00"): 1}, name="count")
        result_dt = algos.value_counts_internal(dt)
        tm.assert_series_equal(result_dt, exp_dt)

        exp_td = Series([1], index=[np.timedelta64(10000)], name="count")
        result_td = algos.value_counts_internal(td)
        tm.assert_series_equal(result_td, exp_td)

    @pytest.mark.parametrize("dtype", [object, "M8[us]"])
    def test_value_counts_datetime_outofbounds(self, dtype):
        # GH 13663
        ser = Series(
            [
                datetime(3000, 1, 1),
                datetime(5000, 1, 1),
                datetime(5000, 1, 1),
                datetime(6000, 1, 1),
                datetime(3000, 1, 1),
                datetime(3000, 1, 1),
            ],
            dtype=dtype,
        )

        res = ser.value_counts()

        exp_index = Index(
            [datetime(3000, 1, 1), datetime(5000, 1, 1), datetime(6000, 1, 1)],
            dtype=dtype,
        )
        exp = Series([3, 2, 1], index=exp_index, name="count")
        tm.assert_series_equal(res, exp)

    def test_categorical(self):
        s = Series(Categorical(list("aaabbc")))
        result = s.value_counts()
        expected = Series(
            [3, 2, 1], index=CategoricalIndex(["a", "b", "c"]), name="count"
        )

        tm.assert_series_equal(result, expected, check_index_type=True)

        # preserve order?
        s = s.cat.as_ordered()
        result = s.value_counts()
        expected.index = expected.index.as_ordered()
        tm.assert_series_equal(result, expected, check_index_type=True)

    def test_categorical_nans(self):
        s = Series(Categorical(list("aaaaabbbcc")))  # 4,3,2,1 (nan)
        s.iloc[1] = np.nan
        result = s.value_counts()
        expected = Series(
            [4, 3, 2],
            index=CategoricalIndex(["a", "b", "c"], categories=["a", "b", "c"]),
            name="count",
        )
        tm.assert_series_equal(result, expected, check_index_type=True)
        result = s.value_counts(dropna=False)
        expected = Series(
            [4, 3, 2, 1], index=CategoricalIndex(["a", "b", "c", np.nan]), name="count"
        )
        tm.assert_series_equal(result, expected, check_index_type=True)

        # out of order
        s = Series(
            Categorical(list("aaaaabbbcc"), ordered=True, categories=["b", "a", "c"])
        )
        s.iloc[1] = np.nan
        result = s.value_counts()
        expected = Series(
            [4, 3, 2],
            index=CategoricalIndex(
                ["a", "b", "c"],
                categories=["b", "a", "c"],
                ordered=True,
            ),
            name="count",
        )
        tm.assert_series_equal(result, expected, check_index_type=True)

        result = s.value_counts(dropna=False)
        expected = Series(
            [4, 3, 2, 1],
            index=CategoricalIndex(
                ["a", "b", "c", np.nan], categories=["b", "a", "c"], ordered=True
            ),
            name="count",
        )
        tm.assert_series_equal(result, expected, check_index_type=True)

    def test_categorical_zeroes(self):
        # keep the `d` category with 0
        s = Series(Categorical(list("bbbaac"), categories=list("abcd"), ordered=True))
        result = s.value_counts()
        expected = Series(
            [3, 2, 1, 0],
            index=Categorical(
                ["b", "a", "c", "d"], categories=list("abcd"), ordered=True
            ),
            name="count",
        )
        tm.assert_series_equal(result, expected, check_index_type=True)

    def test_value_counts_dropna(self):
        # https://github.com/pandas-dev/pandas/issues/9443#issuecomment-73719328

        tm.assert_series_equal(
            Series([True, True, False]).value_counts(dropna=True),
            Series([2, 1], index=[True, False], name="count"),
        )
        tm.assert_series_equal(
            Series([True, True, False]).value_counts(dropna=False),
            Series([2, 1], index=[True, False], name="count"),
        )

        tm.assert_series_equal(
            Series([True] * 3 + [False] * 2 + [None] * 5).value_counts(dropna=True),
            Series([3, 2], index=Index([True, False], dtype=object), name="count"),
        )
        tm.assert_series_equal(
            Series([True] * 5 + [False] * 3 + [None] * 2).value_counts(dropna=False),
            Series([5, 3, 2], index=[True, False, None], name="count"),
        )
        tm.assert_series_equal(
            Series([10.3, 5.0, 5.0]).value_counts(dropna=True),
            Series([2, 1], index=[5.0, 10.3], name="count"),
        )
        tm.assert_series_equal(
            Series([10.3, 5.0, 5.0]).value_counts(dropna=False),
            Series([2, 1], index=[5.0, 10.3], name="count"),
        )

        tm.assert_series_equal(
            Series([10.3, 5.0, 5.0, None]).value_counts(dropna=True),
            Series([2, 1], index=[5.0, 10.3], name="count"),
        )

        result = Series([10.3, 10.3, 5.0, 5.0, 5.0, None]).value_counts(dropna=False)
        expected = Series([3, 2, 1], index=[5.0, 10.3, None], name="count")
        tm.assert_series_equal(result, expected)

    @pytest.mark.parametrize("dtype", (np.float64, object, "M8[ns]"))
    def test_value_counts_normalized(self, dtype):
        # GH12558
        s = Series([1] * 2 + [2] * 3 + [np.nan] * 5)
        s_typed = s.astype(dtype)
        result = s_typed.value_counts(normalize=True, dropna=False)
        expected = Series(
            [0.5, 0.3, 0.2],
            index=Series([np.nan, 2.0, 1.0], dtype=dtype),
            name="proportion",
        )
        tm.assert_series_equal(result, expected)

        result = s_typed.value_counts(normalize=True, dropna=True)
        expected = Series(
            [0.6, 0.4], index=Series([2.0, 1.0], dtype=dtype), name="proportion"
        )
        tm.assert_series_equal(result, expected)

    def test_value_counts_uint64(self):
        arr = np.array([2**63], dtype=np.uint64)
        expected = Series([1], index=[2**63], name="count")
        result = algos.value_counts_internal(arr)

        tm.assert_series_equal(result, expected)

        arr = np.array([-1, 2**63], dtype=object)
        expected = Series([1, 1], index=[-1, 2**63], name="count")
        result = algos.value_counts_internal(arr)

        tm.assert_series_equal(result, expected)

    def test_value_counts_series(self):
        # GH#54857
        values = np.array([3, 1, 2, 3, 4, np.nan])
        result = Series(values).value_counts(bins=3)
        expected = Series(
            [2, 2, 1],
            index=IntervalIndex.from_tuples(
                [(0.996, 2.0), (2.0, 3.0), (3.0, 4.0)], dtype="interval[float64, right]"
            ),
            name="count",
        )
        tm.assert_series_equal(result, expected)

    def test_value_counts_stability(self):
        # GH 63155
        arr = np.random.default_rng(2).integers(0, 32, 64)
        result = algos.value_counts_internal(arr, sort=True)

        value_counts = Series(arr).value_counts(sort=False)
        expected = value_counts.sort_values(ascending=False, kind="stable")
        tm.assert_series_equal(result, expected)

        unstable_sorted = value_counts.sort_values(ascending=False, kind="quicksort")
        with pytest.raises(AssertionError):
            tm.assert_series_equal(result, unstable_sorted)


class TestDuplicated:
    def test_duplicated_with_nas(self):
        keys = np.array([0, 1, np.nan, 0, 2, np.nan], dtype=object)

        result = algos.duplicated(keys)
        expected = np.array([False, False, False, True, False, True])
        tm.assert_numpy_array_equal(result, expected)

        result = algos.duplicated(keys, keep="first")
        expected = np.array([False, False, False, True, False, True])
        tm.assert_numpy_array_equal(result, expected)

        result = algos.duplicated(keys, keep="last")
        expected = np.array([True, False, True, False, False, False])
        tm.assert_numpy_array_equal(result, expected)

        result = algos.duplicated(keys, keep=False)
        expected = np.array([True, False, True, True, False, True])
        tm.assert_numpy_array_equal(result, expected)

        keys = np.empty(8, dtype=object)
        for i, t in enumerate(
            zip([0, 0, np.nan, np.nan] * 2, [0, np.nan, 0, np.nan] * 2, strict=True)
        ):
            keys[i] = t

        result = algos.duplicated(keys)
        falses = [False] * 4
        trues = [True] * 4
        expected = np.array(falses + trues)
        tm.assert_numpy_array_equal(result, expected)

        result = algos.duplicated(keys, keep="last")
        expected = np.array(trues + falses)
        tm.assert_numpy_array_equal(result, expected)

        result = algos.duplicated(keys, keep=False)
        expected = np.array(trues + trues)
        tm.assert_numpy_array_equal(result, expected)

    @pytest.mark.parametrize(
        "case",
        [
            np.array([1, 2, 1, 5, 3, 2, 4, 1, 5, 6]),
            np.array([1.1, 2.2, 1.1, np.nan, 3.3, 2.2, 4.4, 1.1, np.nan, 6.6]),
            np.array(
                [
                    1 + 1j,
                    2 + 2j,
                    1 + 1j,
                    5 + 5j,
                    3 + 3j,
                    2 + 2j,
                    4 + 4j,
                    1 + 1j,
                    5 + 5j,
                    6 + 6j,
                ]
            ),
            np.array(["a", "b", "a", "e", "c", "b", "d", "a", "e", "f"], dtype=object),
            np.array([1, 2**63, 1, 3**5, 10, 2**63, 39, 1, 3**5, 7], dtype=np.uint64),
        ],
    )
    def test_numeric_object_likes(self, case):
        exp_first = np.array(
            [False, False, True, False, False, True, False, True, True, False]
        )
        exp_last = np.array(
            [True, True, True, True, False, False, False, False, False, False]
        )
        exp_false = exp_first | exp_last

        res_first = algos.duplicated(case, keep="first")
        tm.assert_numpy_array_equal(res_first, exp_first)

        res_last = algos.duplicated(case, keep="last")
        tm.assert_numpy_array_equal(res_last, exp_last)

        res_false = algos.duplicated(case, keep=False)
        tm.assert_numpy_array_equal(res_false, exp_false)

        # index
        for idx in [Index(case), Index(case, dtype="category")]:
            res_first = idx.duplicated(keep="first")
            tm.assert_numpy_array_equal(res_first, exp_first)

            res_last = idx.duplicated(keep="last")
            tm.assert_numpy_array_equal(res_last, exp_last)

            res_false = idx.duplicated(keep=False)
            tm.assert_numpy_array_equal(res_false, exp_false)

        # series
        for s in [Series(case), Series(case, dtype="category")]:
            res_first = s.duplicated(keep="first")
            tm.assert_series_equal(res_first, Series(exp_first))

            res_last = s.duplicated(keep="last")
            tm.assert_series_equal(res_last, Series(exp_last))

            res_false = s.duplicated(keep=False)
            tm.assert_series_equal(res_false, Series(exp_false))

    def test_datetime_likes(self):
        dt = [
            "2011-01-01",
            "2011-01-02",
            "2011-01-01",
            "NaT",
            "2011-01-03",
            "2011-01-02",
            "2011-01-04",
            "2011-01-01",
            "NaT",
            "2011-01-06",
        ]
        td = [
            "1 days",
            "2 days",
            "1 days",
            "NaT",
            "3 days",
            "2 days",
            "4 days",
            "1 days",
            "NaT",
            "6 days",
        ]

        cases = [
            np.array([Timestamp(d) for d in dt]),
            np.array([Timestamp(d, tz="US/Eastern") for d in dt]),
            np.array([Period(d, freq="D") for d in dt]),
            np.array([np.datetime64(d) for d in dt]),
            np.array([Timedelta(d) for d in td]),
        ]

        exp_first = np.array(
            [False, False, True, False, False, True, False, True, True, False]
        )
        exp_last = np.array(
            [True, True, True, True, False, False, False, False, False, False]
        )
        exp_false = exp_first | exp_last

        for case in cases:
            res_first = algos.duplicated(case, keep="first")
            tm.assert_numpy_array_equal(res_first, exp_first)

            res_last = algos.duplicated(case, keep="last")
            tm.assert_numpy_array_equal(res_last, exp_last)

            res_false = algos.duplicated(case, keep=False)
            tm.assert_numpy_array_equal(res_false, exp_false)

            # index
            for idx in [
                Index(case),
                Index(case, dtype="category"),
                Index(case, dtype=object),
            ]:
                res_first = idx.duplicated(keep="first")
                tm.assert_numpy_array_equal(res_first, exp_first)

                res_last = idx.duplicated(keep="last")
                tm.assert_numpy_array_equal(res_last, exp_last)

                res_false = idx.duplicated(keep=False)
                tm.assert_numpy_array_equal(res_false, exp_false)

            # series
            for s in [
                Series(case),
                Series(case, dtype="category"),
                Series(case, dtype=object),
            ]:
                res_first = s.duplicated(keep="first")
                tm.assert_series_equal(res_first, Series(exp_first))

                res_last = s.duplicated(keep="last")
                tm.assert_series_equal(res_last, Series(exp_last))

                res_false = s.duplicated(keep=False)
                tm.assert_series_equal(res_false, Series(exp_false))

    @pytest.mark.parametrize("case", [Index([1, 2, 3]), pd.RangeIndex(0, 3)])
    def test_unique_index(self, case):
        assert case.is_unique is True
        tm.assert_numpy_array_equal(case.duplicated(), np.array([False, False, False]))

    @pytest.mark.parametrize(
        "arr, uniques",
        [
            (
                [(0, 0), (0, 1), (1, 0), (1, 1), (0, 0), (0, 1), (1, 0), (1, 1)],
                [(0, 0), (0, 1), (1, 0), (1, 1)],
            ),
            (
                [("b", "c"), ("a", "b"), ("a", "b"), ("b", "c")],
                [("b", "c"), ("a", "b")],
            ),
            ([("a", 1), ("b", 2), ("a", 3), ("a", 1)], [("a", 1), ("b", 2), ("a", 3)]),
        ],
    )
    def test_unique_tuples(self, arr, uniques):
        # https://github.com/pandas-dev/pandas/issues/16519
        expected = np.empty(len(uniques), dtype=object)
        expected[:] = uniques

        msg = (
            r"unique requires a Series, Index, ExtensionArray, np.ndarray "
            r"or NumpyExtensionArray got list"
        )
        with pytest.raises(TypeError, match=msg):
            # GH#52986
            pd.unique(arr)

        res = pd.unique(com.asarray_tuplesafe(arr, dtype=object))
        tm.assert_numpy_array_equal(res, expected)

    @pytest.mark.parametrize(
        "array,expected",
        [
            (
                [1 + 1j, 0, 1, 1j, 1 + 2j, 1 + 2j],
                np.array([(1 + 1j), 0j, (1 + 0j), 1j, (1 + 2j)], dtype=complex),
            )
        ],
    )
    def test_unique_complex_numbers(self, array, expected):
        # GH 17927
        msg = (
            r"unique requires a Series, Index, ExtensionArray, np.ndarray "
            r"or NumpyExtensionArray got list"
        )

        with pytest.raises(TypeError, match=msg):
            # GH#52986
            pd.unique(array)

        res = pd.unique(np.array(array))
        tm.assert_numpy_array_equal(res, expected)


class TestHashTable:
    @pytest.mark.parametrize(
        "htable, data",
        [
            (
                ht.PyObjectHashTable,
                np.array([f"foo_{i}" for i in range(1000)], dtype=object),
            ),
            (
                ht.StringHashTable,
                np.array([f"foo_{i}" for i in range(1000)], dtype=object),
            ),
            (ht.Float64HashTable, np.arange(1000, dtype=np.float64)),
            (ht.Int64HashTable, np.arange(1000, dtype=np.int64)),
            (ht.UInt64HashTable, np.arange(1000, dtype=np.uint64)),
        ],
    )
    def test_hashtable_unique(self, htable, data, writable):
        # output of maker has guaranteed unique elements
        s = Series(data, dtype=data.dtype)
        if htable == ht.Float64HashTable:
            # add NaN for float column
            s.loc[500] = np.nan
        elif htable == ht.PyObjectHashTable:
            # use different NaN types for object column
            s.loc[500:502] = [np.nan, None, NaT]

        # create duplicated selection
        s_duplicated = s.sample(frac=3, replace=True).reset_index(drop=True)
        s_duplicated.values.setflags(write=writable)

        # drop_duplicates has own cython code (hash_table_func_helper.pxi)
        # and is tested separately; keeps first occurrence like ht.unique()
        expected_unique = s_duplicated.drop_duplicates(keep="first").values
        result_unique = htable().unique(s_duplicated.values)
        tm.assert_numpy_array_equal(result_unique, expected_unique)

        # test return_inverse=True
        # reconstruction can only succeed if the inverse is correct
        result_unique, result_inverse = htable().unique(
            s_duplicated.values, return_inverse=True
        )
        tm.assert_numpy_array_equal(result_unique, expected_unique)
        reconstr = result_unique[result_inverse]
        tm.assert_numpy_array_equal(reconstr, s_duplicated.values)

    @pytest.mark.parametrize(
        "htable, data",
        [
            (
                ht.PyObjectHashTable,
                np.array([f"foo_{i}" for i in range(1000)], dtype=object),
            ),
            (
                ht.StringHashTable,
                np.array([f"foo_{i}" for i in range(1000)], dtype=object),
            ),
            (ht.Float64HashTable, np.arange(1000, dtype=np.float64)),
            (ht.Int64HashTable, np.arange(1000, dtype=np.int64)),
            (ht.UInt64HashTable, np.arange(1000, dtype=np.uint64)),
        ],
    )
    def test_hashtable_factorize(self, htable, writable, data):
        # output of maker has guaranteed unique elements
        s = Series(data, dtype=data.dtype)
        if htable == ht.Float64HashTable:
            # add NaN for float column
            s.loc[500] = np.nan
        elif htable == ht.PyObjectHashTable:
            # use different NaN types for object column
            s.loc[500:502] = [np.nan, None, NaT]

        # create duplicated selection
        s_duplicated = s.sample(frac=3, replace=True).reset_index(drop=True)
        s_duplicated.values.setflags(write=writable)
        na_mask = s_duplicated.isna().values

        result_unique, result_inverse = htable().factorize(s_duplicated.values)

        # drop_duplicates has own cython code (hash_table_func_helper.pxi)
        # and is tested separately; keeps first occurrence like ht.factorize()
        # since factorize removes all NaNs, we do the same here
        expected_unique = s_duplicated.dropna().drop_duplicates().values
        tm.assert_numpy_array_equal(result_unique, expected_unique)

        # reconstruction can only succeed if the inverse is correct. Since
        # factorize removes the NaNs, those have to be excluded here as well
        result_reconstruct = result_unique[result_inverse[~na_mask]]
        expected_reconstruct = s_duplicated.dropna().values
        tm.assert_numpy_array_equal(result_reconstruct, expected_reconstruct)


class TestRank:
    @pytest.mark.parametrize(
        "arr",
        [
            [np.nan, np.nan, 5.0, 5.0, 5.0, np.nan, 1, 2, 3, np.nan],
            [4.0, np.nan, 5.0, 5.0, 5.0, np.nan, 1, 2, 4.0, np.nan],
        ],
    )
    def test_scipy_compat(self, arr):
        sp_stats = pytest.importorskip("scipy.stats")

        arr = np.array(arr)

        mask = ~np.isfinite(arr)
        result = libalgos.rank_1d(arr)
        arr[mask] = np.inf
        exp = sp_stats.rankdata(arr)
        exp[mask] = np.nan
        tm.assert_almost_equal(result, exp)

    def test_basic(self, writable, any_int_numpy_dtype):
        exp = np.array([1, 2], dtype=np.float64)

        data = np.array([1, 100], dtype=any_int_numpy_dtype)
        data.setflags(write=writable)
        ser = Series(data)
        result = algos.rank(ser)
        tm.assert_numpy_array_equal(result, exp)

    @pytest.mark.parametrize("dtype", [np.float64, np.uint64])
    def test_uint64_overflow(self, dtype):
        exp = np.array([1, 2], dtype=np.float64)

        s = Series([1, 2**63], dtype=dtype)
        tm.assert_numpy_array_equal(algos.rank(s), exp)

    @pytest.mark.parametrize("method", ["average", "min", "max"])
    def test_rank_tiny_values(self, method):
        # GH62036: regression test for ranking with tiny float values
        exp = np.array([4.0, 1.0, 3.0, np.nan, 2.0], dtype=np.float64)
        s = Series(
            [5.4954145e29, -9.791984e-21, 9.3715776e-26, pd.NA, 1.8790257e-28],
            dtype="Float64",
        )
        s = s.astype(object)
        result = algos.rank(s, method=method)
        tm.assert_numpy_array_equal(result, exp)

    def test_too_many_ndims(self):
        arr = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]])
        msg = "Array with ndim > 2 are not supported"

        with pytest.raises(TypeError, match=msg):
            algos.rank(arr)

    @pytest.mark.single_cpu
    def test_pct_max_many_rows(self):
        # GH 18271
        values = np.arange(2**24 + 1)
        result = algos.rank(values, pct=True).max()
        assert result == 1

        values = np.arange(2**25 + 2).reshape(2**24 + 1, 2)
        result = algos.rank(values, pct=True).max()
        assert result == 1


class TestMode:
    def test_no_mode(self):
        exp = Series([], dtype=np.float64, index=Index([], dtype=int))
        result, _ = algos.mode(np.array([]))
        tm.assert_numpy_array_equal(result, exp.values)

    def test_mode_single(self, any_real_numpy_dtype):
        # GH 15714
        exp_single = [1]
        data_single = [1]

        exp_multi = [1]
        data_multi = [1, 1]

        ser = Series(data_single, dtype=any_real_numpy_dtype)
        exp = Series(exp_single, dtype=any_real_numpy_dtype)
        result, _ = algos.mode(ser.values)
        tm.assert_numpy_array_equal(result, exp.values)
        tm.assert_series_equal(ser.mode(), exp)

        ser = Series(data_multi, dtype=any_real_numpy_dtype)
        exp = Series(exp_multi, dtype=any_real_numpy_dtype)
        result, _ = algos.mode(ser.values)
        tm.assert_numpy_array_equal(result, exp.values)
        tm.assert_series_equal(ser.mode(), exp)

    def test_mode_obj_int(self):
        exp = Series([1], dtype=int)
        result, _ = algos.mode(exp.values)
        tm.assert_numpy_array_equal(result, exp.values)

        exp = Series(["a", "b", "c"], dtype=object)
        result, _ = algos.mode(exp.values)
        tm.assert_numpy_array_equal(result, exp.values)

    def test_number_mode(self, any_real_numpy_dtype):
        exp_single = [1]
        data_single = [1] * 5 + [2] * 3

        exp_multi = [1, 3]
        data_multi = [1] * 5 + [2] * 3 + [3] * 5

        ser = Series(data_single, dtype=any_real_numpy_dtype)
        exp = Series(exp_single, dtype=any_real_numpy_dtype)
        result, _ = algos.mode(ser.values)
        tm.assert_numpy_array_equal(result, exp.values)
        tm.assert_series_equal(ser.mode(), exp)

        ser = Series(data_multi, dtype=any_real_numpy_dtype)
        exp = Series(exp_multi, dtype=any_real_numpy_dtype)
        result, _ = algos.mode(ser.values)
        tm.assert_numpy_array_equal(result, exp.values)
        tm.assert_series_equal(ser.mode(), exp)

    def test_strobj_mode(self):
        exp = ["b"]
        data = ["a"] * 2 + ["b"] * 3

        ser = Series(data, dtype="c")
        exp = Series(exp, dtype="c")
        result, _ = algos.mode(ser.values)
        tm.assert_numpy_array_equal(result, exp.values)
        tm.assert_series_equal(ser.mode(), exp)

    @pytest.mark.parametrize("dt", [str, object])
    def test_strobj_multi_char(self, dt, using_infer_string):
        exp = ["bar"]
        data = ["foo"] * 2 + ["bar"] * 3

        ser = Series(data, dtype=dt)
        exp = Series(exp, dtype=dt)
        result, _ = algos.mode(ser.values)
        if using_infer_string and dt is str:
            tm.assert_extension_array_equal(result, exp.values)
        else:
            tm.assert_numpy_array_equal(result, exp.values)
        tm.assert_series_equal(ser.mode(), exp)

    def test_datelike_mode(self):
        exp = Series(["1900-05-03", "2011-01-03", "2013-01-02"], dtype="M8[ns]")
        ser = Series(["2011-01-03", "2013-01-02", "1900-05-03"], dtype="M8[ns]")
        tm.assert_extension_array_equal(algos.mode(ser.values), exp._values)
        tm.assert_series_equal(ser.mode(), exp)

        exp = Series(["2011-01-03", "2013-01-02"], dtype="M8[ns]")
        ser = Series(
            ["2011-01-03", "2013-01-02", "1900-05-03", "2011-01-03", "2013-01-02"],
            dtype="M8[ns]",
        )
        tm.assert_extension_array_equal(algos.mode(ser.values), exp._values)
        tm.assert_series_equal(ser.mode(), exp)

    def test_timedelta_mode(self):
        exp = Series(["-1 days", "0 days", "1 days"], dtype="timedelta64[ns]")
        ser = Series(["1 days", "-1 days", "0 days"], dtype="timedelta64[ns]")
        tm.assert_extension_array_equal(algos.mode(ser.values), exp._values)
        tm.assert_series_equal(ser.mode(), exp)

        exp = Series(["2 min", "1 day"], dtype="timedelta64[ns]")
        ser = Series(
            ["1 day", "1 day", "-1 day", "-1 day 2 min", "2 min", "2 min"],
            dtype="timedelta64[ns]",
        )
        tm.assert_extension_array_equal(algos.mode(ser.values), exp._values)
        tm.assert_series_equal(ser.mode(), exp)

    def test_mixed_dtype(self):
        exp = Series(["foo"], dtype=object)
        ser = Series([1, "foo", "foo"])
        result, _ = algos.mode(ser.values)
        tm.assert_numpy_array_equal(result, exp.values)
        tm.assert_series_equal(ser.mode(), exp)

    def test_uint64_overflow(self):
        exp = Series([2**63], dtype=np.uint64)
        ser = Series([1, 2**63, 2**63], dtype=np.uint64)
        result, _ = algos.mode(ser.values)
        tm.assert_numpy_array_equal(result, exp.values)
        tm.assert_series_equal(ser.mode(), exp)

        exp = Series([1, 2**63], dtype=np.uint64)
        ser = Series([1, 2**63], dtype=np.uint64)
        result, _ = algos.mode(ser.values)
        tm.assert_numpy_array_equal(result, exp.values)
        tm.assert_series_equal(ser.mode(), exp)

    def test_categorical(self):
        c = Categorical([1, 2])
        exp = c
        res = Series(c).mode()._values
        tm.assert_categorical_equal(res, exp)

        c = Categorical([1, "a", "a"])
        exp = Categorical(["a"], categories=[1, "a"])
        res = Series(c).mode()._values
        tm.assert_categorical_equal(res, exp)

        c = Categorical([1, 1, 2, 3, 3])
        exp = Categorical([1, 3], categories=[1, 2, 3])
        res = Series(c).mode()._values
        tm.assert_categorical_equal(res, exp)

    def test_index(self):
        idx = Index([1, 2, 3])
        exp = Series([1, 2, 3], dtype=np.int64)
        result, _ = algos.mode(idx)
        tm.assert_numpy_array_equal(result, exp.values)

        idx = Index([1, "a", "a"])
        exp = Series(["a"], dtype=object)
        result, _ = algos.mode(idx)
        tm.assert_numpy_array_equal(result, exp.values)

        idx = Index([1, 1, 2, 3, 3])
        exp = Series([1, 3], dtype=np.int64)
        result, _ = algos.mode(idx)
        tm.assert_numpy_array_equal(result, exp.values)

        idx = Index(
            ["1 day", "1 day", "-1 day", "-1 day 2 min", "2 min", "2 min"],
            dtype="timedelta64[ns]",
        )
        with pytest.raises(AttributeError, match="TimedeltaIndex"):
            # algos.mode expects Arraylike, does *not* unwrap TimedeltaIndex
            algos.mode(idx)

    def test_ser_mode_with_name(self):
        # GH 46737
        ser = Series([1, 1, 3], name="foo")
        result = ser.mode()
        expected = Series([1], name="foo")
        tm.assert_series_equal(result, expected)


class TestDiff:
    @pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
    def test_diff_datetimelike_nat(self, dtype):
        # NaT - NaT is NaT, not 0
        arr = np.arange(12).astype(np.int64).view(dtype).reshape(3, 4)
        arr[:, 2] = arr.dtype.type("NaT", "ns")
        result = algos.diff(arr, 1, axis=0)

        expected = np.ones(arr.shape, dtype="timedelta64[ns]") * 4
        expected[:, 2] = np.timedelta64("NaT", "ns")
        expected[0, :] = np.timedelta64("NaT", "ns")

        tm.assert_numpy_array_equal(result, expected)

        result = algos.diff(arr.T, 1, axis=1)
        tm.assert_numpy_array_equal(result, expected.T)

    def test_diff_ea_axis(self):
        dta = date_range("2016-01-01", periods=3, tz="US/Pacific")._data

        msg = "cannot diff DatetimeArray on axis=1"
        with pytest.raises(ValueError, match=msg):
            algos.diff(dta, 1, axis=1)

    @pytest.mark.parametrize("dtype", ["int8", "int16"])
    def test_diff_low_precision_int(self, dtype):
        arr = np.array([0, 1, 1, 0, 0], dtype=dtype)
        result = algos.diff(arr, 1)
        expected = np.array([np.nan, 1, 0, -1, 0], dtype="float32")
        tm.assert_numpy_array_equal(result, expected)


@pytest.mark.parametrize("op", [np.array, pd.array])
def test_union_with_duplicates(op):
    # GH#36289
    lvals = op([3, 1, 3, 4])
    rvals = op([2, 3, 1, 1])
    expected = op([3, 3, 1, 1, 4, 2])
    if isinstance(expected, np.ndarray):
        result = algos.union_with_duplicates(lvals, rvals)
        tm.assert_numpy_array_equal(result, expected)
    else:
        result = algos.union_with_duplicates(lvals, rvals)
        tm.assert_extension_array_equal(result, expected)
