import random

import numpy as np
import pytest

import pandas as pd
from pandas import (
    Categorical,
    DataFrame,
    IntervalIndex,
    MultiIndex,
    NaT,
    Series,
    Timestamp,
    date_range,
)
from pandas.api.types import CategoricalDtype
from pandas.tests.frame.common import TestData
import pandas.util.testing as tm
from pandas.util.testing import assert_frame_equal, assert_series_equal


class TestDataFrameSorting(TestData):
    def test_sort_values(self):
        frame = DataFrame(
            [[1, 1, 2], [3, 1, 0], [4, 5, 6]], index=[1, 2, 3], columns=list("ABC")
        )

        # by column (axis=0)
        sorted_df = frame.sort_values(by="A")
        indexer = frame["A"].argsort().values
        expected = frame.loc[frame.index[indexer]]
        assert_frame_equal(sorted_df, expected)

        sorted_df = frame.sort_values(by="A", ascending=False)
        indexer = indexer[::-1]
        expected = frame.loc[frame.index[indexer]]
        assert_frame_equal(sorted_df, expected)

        sorted_df = frame.sort_values(by="A", ascending=False)
        assert_frame_equal(sorted_df, expected)

        # GH4839
        sorted_df = frame.sort_values(by=["A"], ascending=[False])
        assert_frame_equal(sorted_df, expected)

        # multiple bys
        sorted_df = frame.sort_values(by=["B", "C"])
        expected = frame.loc[[2, 1, 3]]
        assert_frame_equal(sorted_df, expected)

        sorted_df = frame.sort_values(by=["B", "C"], ascending=False)
        assert_frame_equal(sorted_df, expected[::-1])

        sorted_df = frame.sort_values(by=["B", "A"], ascending=[True, False])
        assert_frame_equal(sorted_df, expected)

        msg = "No axis named 2 for object type <class 'pandas.core.frame.DataFrame'>"
        with pytest.raises(ValueError, match=msg):
            frame.sort_values(by=["A", "B"], axis=2, inplace=True)

        # by row (axis=1): GH 10806
        sorted_df = frame.sort_values(by=3, axis=1)
        expected = frame
        assert_frame_equal(sorted_df, expected)

        sorted_df = frame.sort_values(by=3, axis=1, ascending=False)
        expected = frame.reindex(columns=["C", "B", "A"])
        assert_frame_equal(sorted_df, expected)

        sorted_df = frame.sort_values(by=[1, 2], axis="columns")
        expected = frame.reindex(columns=["B", "A", "C"])
        assert_frame_equal(sorted_df, expected)

        sorted_df = frame.sort_values(by=[1, 3], axis=1, ascending=[True, False])
        assert_frame_equal(sorted_df, expected)

        sorted_df = frame.sort_values(by=[1, 3], axis=1, ascending=False)
        expected = frame.reindex(columns=["C", "B", "A"])
        assert_frame_equal(sorted_df, expected)

        msg = r"Length of ascending \(5\) != length of by \(2\)"
        with pytest.raises(ValueError, match=msg):
            frame.sort_values(by=["A", "B"], axis=0, ascending=[True] * 5)

    def test_sort_values_inplace(self):
        frame = DataFrame(
            np.random.randn(4, 4), index=[1, 2, 3, 4], columns=["A", "B", "C", "D"]
        )

        sorted_df = frame.copy()
        sorted_df.sort_values(by="A", inplace=True)
        expected = frame.sort_values(by="A")
        assert_frame_equal(sorted_df, expected)

        sorted_df = frame.copy()
        sorted_df.sort_values(by=1, axis=1, inplace=True)
        expected = frame.sort_values(by=1, axis=1)
        assert_frame_equal(sorted_df, expected)

        sorted_df = frame.copy()
        sorted_df.sort_values(by="A", ascending=False, inplace=True)
        expected = frame.sort_values(by="A", ascending=False)
        assert_frame_equal(sorted_df, expected)

        sorted_df = frame.copy()
        sorted_df.sort_values(by=["A", "B"], ascending=False, inplace=True)
        expected = frame.sort_values(by=["A", "B"], ascending=False)
        assert_frame_equal(sorted_df, expected)

    def test_sort_nan(self):
        # GH3917
        nan = np.nan
        df = DataFrame({"A": [1, 2, nan, 1, 6, 8, 4], "B": [9, nan, 5, 2, 5, 4, 5]})

        # sort one column only
        expected = DataFrame(
            {"A": [nan, 1, 1, 2, 4, 6, 8], "B": [5, 9, 2, nan, 5, 5, 4]},
            index=[2, 0, 3, 1, 6, 4, 5],
        )
        sorted_df = df.sort_values(["A"], na_position="first")
        assert_frame_equal(sorted_df, expected)

        expected = DataFrame(
            {"A": [nan, 8, 6, 4, 2, 1, 1], "B": [5, 4, 5, 5, nan, 9, 2]},
            index=[2, 5, 4, 6, 1, 0, 3],
        )
        sorted_df = df.sort_values(["A"], na_position="first", ascending=False)
        assert_frame_equal(sorted_df, expected)

        expected = df.reindex(columns=["B", "A"])
        sorted_df = df.sort_values(by=1, axis=1, na_position="first")
        assert_frame_equal(sorted_df, expected)

        # na_position='last', order
        expected = DataFrame(
            {"A": [1, 1, 2, 4, 6, 8, nan], "B": [2, 9, nan, 5, 5, 4, 5]},
            index=[3, 0, 1, 6, 4, 5, 2],
        )
        sorted_df = df.sort_values(["A", "B"])
        assert_frame_equal(sorted_df, expected)

        # na_position='first', order
        expected = DataFrame(
            {"A": [nan, 1, 1, 2, 4, 6, 8], "B": [5, 2, 9, nan, 5, 5, 4]},
            index=[2, 3, 0, 1, 6, 4, 5],
        )
        sorted_df = df.sort_values(["A", "B"], na_position="first")
        assert_frame_equal(sorted_df, expected)

        # na_position='first', not order
        expected = DataFrame(
            {"A": [nan, 1, 1, 2, 4, 6, 8], "B": [5, 9, 2, nan, 5, 5, 4]},
            index=[2, 0, 3, 1, 6, 4, 5],
        )
        sorted_df = df.sort_values(["A", "B"], ascending=[1, 0], na_position="first")
        assert_frame_equal(sorted_df, expected)

        # na_position='last', not order
        expected = DataFrame(
            {"A": [8, 6, 4, 2, 1, 1, nan], "B": [4, 5, 5, nan, 2, 9, 5]},
            index=[5, 4, 6, 1, 3, 0, 2],
        )
        sorted_df = df.sort_values(["A", "B"], ascending=[0, 1], na_position="last")
        assert_frame_equal(sorted_df, expected)

        # Test DataFrame with nan label
        df = DataFrame(
            {"A": [1, 2, nan, 1, 6, 8, 4], "B": [9, nan, 5, 2, 5, 4, 5]},
            index=[1, 2, 3, 4, 5, 6, nan],
        )

        # NaN label, ascending=True, na_position='last'
        sorted_df = df.sort_index(kind="quicksort", ascending=True, na_position="last")
        expected = DataFrame(
            {"A": [1, 2, nan, 1, 6, 8, 4], "B": [9, nan, 5, 2, 5, 4, 5]},
            index=[1, 2, 3, 4, 5, 6, nan],
        )
        assert_frame_equal(sorted_df, expected)

        # NaN label, ascending=True, na_position='first'
        sorted_df = df.sort_index(na_position="first")
        expected = DataFrame(
            {"A": [4, 1, 2, nan, 1, 6, 8], "B": [5, 9, nan, 5, 2, 5, 4]},
            index=[nan, 1, 2, 3, 4, 5, 6],
        )
        assert_frame_equal(sorted_df, expected)

        # NaN label, ascending=False, na_position='last'
        sorted_df = df.sort_index(kind="quicksort", ascending=False)
        expected = DataFrame(
            {"A": [8, 6, 1, nan, 2, 1, 4], "B": [4, 5, 2, 5, nan, 9, 5]},
            index=[6, 5, 4, 3, 2, 1, nan],
        )
        assert_frame_equal(sorted_df, expected)

        # NaN label, ascending=False, na_position='first'
        sorted_df = df.sort_index(
            kind="quicksort", ascending=False, na_position="first"
        )
        expected = DataFrame(
            {"A": [4, 8, 6, 1, nan, 2, 1], "B": [5, 4, 5, 2, 5, nan, 9]},
            index=[nan, 6, 5, 4, 3, 2, 1],
        )
        assert_frame_equal(sorted_df, expected)

    def test_stable_descending_sort(self):
        # GH #6399
        df = DataFrame(
            [[2, "first"], [2, "second"], [1, "a"], [1, "b"]],
            columns=["sort_col", "order"],
        )
        sorted_df = df.sort_values(by="sort_col", kind="mergesort", ascending=False)
        assert_frame_equal(df, sorted_df)

    def test_stable_descending_multicolumn_sort(self):
        nan = np.nan
        df = DataFrame({"A": [1, 2, nan, 1, 6, 8, 4], "B": [9, nan, 5, 2, 5, 4, 5]})
        # test stable mergesort
        expected = DataFrame(
            {"A": [nan, 8, 6, 4, 2, 1, 1], "B": [5, 4, 5, 5, nan, 2, 9]},
            index=[2, 5, 4, 6, 1, 3, 0],
        )
        sorted_df = df.sort_values(
            ["A", "B"], ascending=[0, 1], na_position="first", kind="mergesort"
        )
        assert_frame_equal(sorted_df, expected)

        expected = DataFrame(
            {"A": [nan, 8, 6, 4, 2, 1, 1], "B": [5, 4, 5, 5, nan, 9, 2]},
            index=[2, 5, 4, 6, 1, 0, 3],
        )
        sorted_df = df.sort_values(
            ["A", "B"], ascending=[0, 0], na_position="first", kind="mergesort"
        )
        assert_frame_equal(sorted_df, expected)

    def test_sort_multi_index(self):
        # GH 25775, testing that sorting by index works with a multi-index.
        df = DataFrame(
            {"a": [3, 1, 2], "b": [0, 0, 0], "c": [0, 1, 2], "d": list("abc")}
        )
        result = df.set_index(list("abc")).sort_index(level=list("ba"))

        expected = DataFrame(
            {"a": [1, 2, 3], "b": [0, 0, 0], "c": [1, 2, 0], "d": list("bca")}
        )
        expected = expected.set_index(list("abc"))

        tm.assert_frame_equal(result, expected)

    def test_stable_categorial(self):
        # GH 16793
        df = DataFrame({"x": pd.Categorical(np.repeat([1, 2, 3, 4], 5), ordered=True)})
        expected = df.copy()
        sorted_df = df.sort_values("x", kind="mergesort")
        assert_frame_equal(sorted_df, expected)

    def test_sort_datetimes(self):

        # GH 3461, argsort / lexsort differences for a datetime column
        df = DataFrame(
            ["a", "a", "a", "b", "c", "d", "e", "f", "g"],
            columns=["A"],
            index=date_range("20130101", periods=9),
        )
        dts = [
            Timestamp(x)
            for x in [
                "2004-02-11",
                "2004-01-21",
                "2004-01-26",
                "2005-09-20",
                "2010-10-04",
                "2009-05-12",
                "2008-11-12",
                "2010-09-28",
                "2010-09-28",
            ]
        ]
        df["B"] = dts[::2] + dts[1::2]
        df["C"] = 2.0
        df["A1"] = 3.0

        df1 = df.sort_values(by="A")
        df2 = df.sort_values(by=["A"])
        assert_frame_equal(df1, df2)

        df1 = df.sort_values(by="B")
        df2 = df.sort_values(by=["B"])
        assert_frame_equal(df1, df2)

        df1 = df.sort_values(by="B")

        df2 = df.sort_values(by=["C", "B"])
        assert_frame_equal(df1, df2)

    def test_frame_column_inplace_sort_exception(self):
        s = self.frame["A"]
        with pytest.raises(ValueError, match="This Series is a view"):
            s.sort_values(inplace=True)

        cp = s.copy()
        cp.sort_values()  # it works!

    def test_sort_nat_values_in_int_column(self):

        # GH 14922: "sorting with large float and multiple columns incorrect"

        # cause was that the int64 value NaT was considered as "na". Which is
        # only correct for datetime64 columns.

        int_values = (2, int(NaT))
        float_values = (2.0, -1.797693e308)

        df = DataFrame(
            dict(int=int_values, float=float_values), columns=["int", "float"]
        )

        df_reversed = DataFrame(
            dict(int=int_values[::-1], float=float_values[::-1]),
            columns=["int", "float"],
            index=[1, 0],
        )

        # NaT is not a "na" for int64 columns, so na_position must not
        # influence the result:
        df_sorted = df.sort_values(["int", "float"], na_position="last")
        assert_frame_equal(df_sorted, df_reversed)

        df_sorted = df.sort_values(["int", "float"], na_position="first")
        assert_frame_equal(df_sorted, df_reversed)

        # reverse sorting order
        df_sorted = df.sort_values(["int", "float"], ascending=False)
        assert_frame_equal(df_sorted, df)

        # and now check if NaT is still considered as "na" for datetime64
        # columns:
        df = DataFrame(
            dict(datetime=[Timestamp("2016-01-01"), NaT], float=float_values),
            columns=["datetime", "float"],
        )

        df_reversed = DataFrame(
            dict(datetime=[NaT, Timestamp("2016-01-01")], float=float_values[::-1]),
            columns=["datetime", "float"],
            index=[1, 0],
        )

        df_sorted = df.sort_values(["datetime", "float"], na_position="first")
        assert_frame_equal(df_sorted, df_reversed)

        df_sorted = df.sort_values(["datetime", "float"], na_position="last")
        assert_frame_equal(df_sorted, df)

        # Ascending should not affect the results.
        df_sorted = df.sort_values(["datetime", "float"], ascending=False)
        assert_frame_equal(df_sorted, df)

    def test_sort_nat(self):

        # GH 16836

        d1 = [Timestamp(x) for x in ["2016-01-01", "2015-01-01", np.nan, "2016-01-01"]]
        d2 = [
            Timestamp(x)
            for x in ["2017-01-01", "2014-01-01", "2016-01-01", "2015-01-01"]
        ]
        df = pd.DataFrame({"a": d1, "b": d2}, index=[0, 1, 2, 3])

        d3 = [Timestamp(x) for x in ["2015-01-01", "2016-01-01", "2016-01-01", np.nan]]
        d4 = [
            Timestamp(x)
            for x in ["2014-01-01", "2015-01-01", "2017-01-01", "2016-01-01"]
        ]
        expected = pd.DataFrame({"a": d3, "b": d4}, index=[1, 3, 0, 2])
        sorted_df = df.sort_values(by=["a", "b"])
        tm.assert_frame_equal(sorted_df, expected)


class TestDataFrameSortIndexKinds(TestData):
    def test_sort_index_multicolumn(self):
        A = np.arange(5).repeat(20)
        B = np.tile(np.arange(5), 20)
        random.shuffle(A)
        random.shuffle(B)
        frame = DataFrame({"A": A, "B": B, "C": np.random.randn(100)})

        # use .sort_values #9816
        with tm.assert_produces_warning(FutureWarning):
            frame.sort_index(by=["A", "B"])
        result = frame.sort_values(by=["A", "B"])
        indexer = np.lexsort((frame["B"], frame["A"]))
        expected = frame.take(indexer)
        assert_frame_equal(result, expected)

        # use .sort_values #9816
        with tm.assert_produces_warning(FutureWarning):
            frame.sort_index(by=["A", "B"], ascending=False)
        result = frame.sort_values(by=["A", "B"], ascending=False)
        indexer = np.lexsort(
            (frame["B"].rank(ascending=False), frame["A"].rank(ascending=False))
        )
        expected = frame.take(indexer)
        assert_frame_equal(result, expected)

        # use .sort_values #9816
        with tm.assert_produces_warning(FutureWarning):
            frame.sort_index(by=["B", "A"])
        result = frame.sort_values(by=["B", "A"])
        indexer = np.lexsort((frame["A"], frame["B"]))
        expected = frame.take(indexer)
        assert_frame_equal(result, expected)

    def test_sort_index_inplace(self):
        frame = DataFrame(
            np.random.randn(4, 4), index=[1, 2, 3, 4], columns=["A", "B", "C", "D"]
        )

        # axis=0
        unordered = frame.loc[[3, 2, 4, 1]]
        a_id = id(unordered["A"])
        df = unordered.copy()
        df.sort_index(inplace=True)
        expected = frame
        assert_frame_equal(df, expected)
        assert a_id != id(df["A"])

        df = unordered.copy()
        df.sort_index(ascending=False, inplace=True)
        expected = frame[::-1]
        assert_frame_equal(df, expected)

        # axis=1
        unordered = frame.loc[:, ["D", "B", "C", "A"]]
        df = unordered.copy()
        df.sort_index(axis=1, inplace=True)
        expected = frame
        assert_frame_equal(df, expected)

        df = unordered.copy()
        df.sort_index(axis=1, ascending=False, inplace=True)
        expected = frame.iloc[:, ::-1]
        assert_frame_equal(df, expected)

    def test_sort_index_different_sortorder(self):
        A = np.arange(20).repeat(5)
        B = np.tile(np.arange(5), 20)

        indexer = np.random.permutation(100)
        A = A.take(indexer)
        B = B.take(indexer)

        df = DataFrame({"A": A, "B": B, "C": np.random.randn(100)})

        # use .sort_values #9816
        with tm.assert_produces_warning(FutureWarning):
            df.sort_index(by=["A", "B"], ascending=[1, 0])
        result = df.sort_values(by=["A", "B"], ascending=[1, 0])

        ex_indexer = np.lexsort((df.B.max() - df.B, df.A))
        expected = df.take(ex_indexer)
        assert_frame_equal(result, expected)

        # test with multiindex, too
        idf = df.set_index(["A", "B"])

        result = idf.sort_index(ascending=[1, 0])
        expected = idf.take(ex_indexer)
        assert_frame_equal(result, expected)

        # also, Series!
        result = idf["C"].sort_index(ascending=[1, 0])
        assert_series_equal(result, expected["C"])

    def test_sort_index_duplicates(self):

        # with 9816, these are all translated to .sort_values

        df = DataFrame([range(5, 9), range(4)], columns=["a", "a", "b", "b"])

        with pytest.raises(ValueError, match="not unique"):
            # use .sort_values #9816
            with tm.assert_produces_warning(FutureWarning):
                df.sort_index(by="a")
        with pytest.raises(ValueError, match="not unique"):
            df.sort_values(by="a")

        with pytest.raises(ValueError, match="not unique"):
            # use .sort_values #9816
            with tm.assert_produces_warning(FutureWarning):
                df.sort_index(by=["a"])
        with pytest.raises(ValueError, match="not unique"):
            df.sort_values(by=["a"])

        with pytest.raises(ValueError, match="not unique"):
            # use .sort_values #9816
            with tm.assert_produces_warning(FutureWarning):
                # multi-column 'by' is separate codepath
                df.sort_index(by=["a", "b"])
        with pytest.raises(ValueError, match="not unique"):
            # multi-column 'by' is separate codepath
            df.sort_values(by=["a", "b"])

        # with multi-index
        # GH4370
        df = DataFrame(
            np.random.randn(4, 2), columns=MultiIndex.from_tuples([("a", 0), ("a", 1)])
        )
        with pytest.raises(ValueError, match="level"):
            # use .sort_values #9816
            with tm.assert_produces_warning(FutureWarning):
                df.sort_index(by="a")
        with pytest.raises(ValueError, match="level"):
            df.sort_values(by="a")

        # convert tuples to a list of tuples
        # use .sort_values #9816
        with tm.assert_produces_warning(FutureWarning):
            df.sort_index(by=[("a", 1)])
        expected = df.sort_values(by=[("a", 1)])

        # use .sort_values #9816
        with tm.assert_produces_warning(FutureWarning):
            df.sort_index(by=("a", 1))
        result = df.sort_values(by=("a", 1))
        assert_frame_equal(result, expected)

    def test_sort_index_level(self):
        mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))
        df = DataFrame([[1, 2], [3, 4]], mi)

        result = df.sort_index(level="A", sort_remaining=False)
        expected = df
        assert_frame_equal(result, expected)

        result = df.sort_index(level=["A", "B"], sort_remaining=False)
        expected = df
        assert_frame_equal(result, expected)

        # Error thrown by sort_index when
        # first index is sorted last (#26053)
        result = df.sort_index(level=["C", "B", "A"])
        expected = df.iloc[[1, 0]]
        assert_frame_equal(result, expected)

        result = df.sort_index(level=["B", "C", "A"])
        expected = df.iloc[[1, 0]]
        assert_frame_equal(result, expected)

        result = df.sort_index(level=["C", "A"])
        expected = df.iloc[[1, 0]]
        assert_frame_equal(result, expected)

    def test_sort_index_categorical_index(self):

        df = DataFrame(
            {
                "A": np.arange(6, dtype="int64"),
                "B": Series(list("aabbca")).astype(CategoricalDtype(list("cab"))),
            }
        ).set_index("B")

        result = df.sort_index()
        expected = df.iloc[[4, 0, 1, 5, 2, 3]]
        assert_frame_equal(result, expected)

        result = df.sort_index(ascending=False)
        expected = df.iloc[[2, 3, 0, 1, 5, 4]]
        assert_frame_equal(result, expected)

    def test_sort_index(self):
        # GH13496

        frame = DataFrame(
            np.arange(16).reshape(4, 4),
            index=[1, 2, 3, 4],
            columns=["A", "B", "C", "D"],
        )

        # axis=0 : sort rows by index labels
        unordered = frame.loc[[3, 2, 4, 1]]
        result = unordered.sort_index(axis=0)
        expected = frame
        assert_frame_equal(result, expected)

        result = unordered.sort_index(ascending=False)
        expected = frame[::-1]
        assert_frame_equal(result, expected)

        # axis=1 : sort columns by column names
        unordered = frame.iloc[:, [2, 1, 3, 0]]
        result = unordered.sort_index(axis=1)
        assert_frame_equal(result, frame)

        result = unordered.sort_index(axis=1, ascending=False)
        expected = frame.iloc[:, ::-1]
        assert_frame_equal(result, expected)

    @pytest.mark.parametrize("level", ["A", 0])  # GH 21052
    def test_sort_index_multiindex(self, level):
        # GH13496

        # sort rows by specified level of multi-index
        mi = MultiIndex.from_tuples(
            [[2, 1, 3], [2, 1, 2], [1, 1, 1]], names=list("ABC")
        )
        df = DataFrame([[1, 2], [3, 4], [5, 6]], index=mi)

        expected_mi = MultiIndex.from_tuples(
            [[1, 1, 1], [2, 1, 2], [2, 1, 3]], names=list("ABC")
        )
        expected = pd.DataFrame([[5, 6], [3, 4], [1, 2]], index=expected_mi)
        result = df.sort_index(level=level)
        assert_frame_equal(result, expected)

        # sort_remaining=False
        expected_mi = MultiIndex.from_tuples(
            [[1, 1, 1], [2, 1, 3], [2, 1, 2]], names=list("ABC")
        )
        expected = pd.DataFrame([[5, 6], [1, 2], [3, 4]], index=expected_mi)
        result = df.sort_index(level=level, sort_remaining=False)
        assert_frame_equal(result, expected)

    def test_sort_index_intervalindex(self):
        # this is a de-facto sort via unstack
        # confirming that we sort in the order of the bins
        y = Series(np.random.randn(100))
        x1 = Series(np.sign(np.random.randn(100)))
        x2 = pd.cut(Series(np.random.randn(100)), bins=[-3, -0.5, 0, 0.5, 3])
        model = pd.concat([y, x1, x2], axis=1, keys=["Y", "X1", "X2"])

        result = model.groupby(["X1", "X2"], observed=True).mean().unstack()
        expected = IntervalIndex.from_tuples(
            [(-3.0, -0.5), (-0.5, 0.0), (0.0, 0.5), (0.5, 3.0)], closed="right"
        )
        result = result.columns.levels[1].categories
        tm.assert_index_equal(result, expected)

    def test_sort_index_na_position_with_categories(self):
        # GH 22556
        # Positioning missing value properly when column is Categorical.
        categories = ["A", "B", "C"]
        category_indices = [0, 2, 4]
        list_of_nans = [np.nan, np.nan]
        na_indices = [1, 3]
        na_position_first = "first"
        na_position_last = "last"
        column_name = "c"

        reversed_categories = sorted(categories, reverse=True)
        reversed_category_indices = sorted(category_indices, reverse=True)
        reversed_na_indices = sorted(na_indices)

        df = pd.DataFrame(
            {
                column_name: pd.Categorical(
                    ["A", np.nan, "B", np.nan, "C"], categories=categories, ordered=True
                )
            }
        )
        # sort ascending with na first
        result = df.sort_values(
            by=column_name, ascending=True, na_position=na_position_first
        )
        expected = DataFrame(
            {
                column_name: Categorical(
                    list_of_nans + categories, categories=categories, ordered=True
                )
            },
            index=na_indices + category_indices,
        )

        assert_frame_equal(result, expected)

        # sort ascending with na last
        result = df.sort_values(
            by=column_name, ascending=True, na_position=na_position_last
        )
        expected = DataFrame(
            {
                column_name: Categorical(
                    categories + list_of_nans, categories=categories, ordered=True
                )
            },
            index=category_indices + na_indices,
        )

        assert_frame_equal(result, expected)

        # sort descending with na first
        result = df.sort_values(
            by=column_name, ascending=False, na_position=na_position_first
        )
        expected = DataFrame(
            {
                column_name: Categorical(
                    list_of_nans + reversed_categories,
                    categories=categories,
                    ordered=True,
                )
            },
            index=reversed_na_indices + reversed_category_indices,
        )

        assert_frame_equal(result, expected)

        # sort descending with na last
        result = df.sort_values(
            by=column_name, ascending=False, na_position=na_position_last
        )
        expected = DataFrame(
            {
                column_name: Categorical(
                    reversed_categories + list_of_nans,
                    categories=categories,
                    ordered=True,
                )
            },
            index=reversed_category_indices + reversed_na_indices,
        )

        assert_frame_equal(result, expected)

    def test_sort_index_na_position_with_categories_raises(self):
        df = pd.DataFrame(
            {
                "c": pd.Categorical(
                    ["A", np.nan, "B", np.nan, "C"],
                    categories=["A", "B", "C"],
                    ordered=True,
                )
            }
        )

        with pytest.raises(ValueError):
            df.sort_values(by="c", ascending=False, na_position="bad_position")