REF: misplaced sort_index tests (#33774)

Author: jbrockmendel

pandas/tests/frame/methods/test_sort_index.py

@@ -2,11 +2,156 @@
 import pytest
 
 import pandas as pd
-from pandas import CategoricalDtype, DataFrame, IntervalIndex, MultiIndex, Series
+from pandas import CategoricalDtype, DataFrame, Index, IntervalIndex, MultiIndex, Series
 import pandas._testing as tm
 
 
 class TestDataFrameSortIndex:
+    def test_sort_index_and_reconstruction_doc_example(self):
+        # doc example
+        df = DataFrame(
+            {"value": [1, 2, 3, 4]},
+            index=MultiIndex(
+                levels=[["a", "b"], ["bb", "aa"]], codes=[[0, 0, 1, 1], [0, 1, 0, 1]]
+            ),
+        )
+        assert df.index.is_lexsorted()
+        assert not df.index.is_monotonic
+
+        # sort it
+        expected = DataFrame(
+            {"value": [2, 1, 4, 3]},
+            index=MultiIndex(
+                levels=[["a", "b"], ["aa", "bb"]], codes=[[0, 0, 1, 1], [0, 1, 0, 1]]
+            ),
+        )
+        result = df.sort_index()
+        assert result.index.is_lexsorted()
+        assert result.index.is_monotonic
+
+        tm.assert_frame_equal(result, expected)
+
+        # reconstruct
+        result = df.sort_index().copy()
+        result.index = result.index._sort_levels_monotonic()
+        assert result.index.is_lexsorted()
+        assert result.index.is_monotonic
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_sort_index_non_existent_label_multiindex(self):
+        # GH#12261
+        df = DataFrame(0, columns=[], index=MultiIndex.from_product([[], []]))
+        df.loc["b", "2"] = 1
+        df.loc["a", "3"] = 1
+        result = df.sort_index().index.is_monotonic
+        assert result is True
+
+    def test_sort_index_reorder_on_ops(self):
+        # GH#15687
+        df = DataFrame(
+            np.random.randn(8, 2),
+            index=MultiIndex.from_product(
+                [["a", "b"], ["big", "small"], ["red", "blu"]],
+                names=["letter", "size", "color"],
+            ),
+            columns=["near", "far"],
+        )
+        df = df.sort_index()
+
+        def my_func(group):
+            group.index = ["newz", "newa"]
+            return group
+
+        result = df.groupby(level=["letter", "size"]).apply(my_func).sort_index()
+        expected = MultiIndex.from_product(
+            [["a", "b"], ["big", "small"], ["newa", "newz"]],
+            names=["letter", "size", None],
+        )
+
+        tm.assert_index_equal(result.index, expected)
+
+    def test_sort_index_nan_multiindex(self):
+        # GH#14784
+        # incorrect sorting w.r.t. nans
+        tuples = [[12, 13], [np.nan, np.nan], [np.nan, 3], [1, 2]]
+        mi = MultiIndex.from_tuples(tuples)
+
+        df = DataFrame(np.arange(16).reshape(4, 4), index=mi, columns=list("ABCD"))
+        s = Series(np.arange(4), index=mi)
+
+        df2 = DataFrame(
+            {
+                "date": pd.DatetimeIndex(
+                    [
+                        "20121002",
+                        "20121007",
+                        "20130130",
+                        "20130202",
+                        "20130305",
+                        "20121002",
+                        "20121207",
+                        "20130130",
+                        "20130202",
+                        "20130305",
+                        "20130202",
+                        "20130305",
+                    ]
+                ),
+                "user_id": [1, 1, 1, 1, 1, 3, 3, 3, 5, 5, 5, 5],
+                "whole_cost": [
+                    1790,
+                    np.nan,
+                    280,
+                    259,
+                    np.nan,
+                    623,
+                    90,
+                    312,
+                    np.nan,
+                    301,
+                    359,
+                    801,
+                ],
+                "cost": [12, 15, 10, 24, 39, 1, 0, np.nan, 45, 34, 1, 12],
+            }
+        ).set_index(["date", "user_id"])
+
+        # sorting frame, default nan position is last
+        result = df.sort_index()
+        expected = df.iloc[[3, 0, 2, 1], :]
+        tm.assert_frame_equal(result, expected)
+
+        # sorting frame, nan position last
+        result = df.sort_index(na_position="last")
+        expected = df.iloc[[3, 0, 2, 1], :]
+        tm.assert_frame_equal(result, expected)
+
+        # sorting frame, nan position first
+        result = df.sort_index(na_position="first")
+        expected = df.iloc[[1, 2, 3, 0], :]
+        tm.assert_frame_equal(result, expected)
+
+        # sorting frame with removed rows
+        result = df2.dropna().sort_index()
+        expected = df2.sort_index().dropna()
+        tm.assert_frame_equal(result, expected)
+
+        # sorting series, default nan position is last
+        result = s.sort_index()
+        expected = s.iloc[[3, 0, 2, 1]]
+        tm.assert_series_equal(result, expected)
+
+        # sorting series, nan position last
+        result = s.sort_index(na_position="last")
+        expected = s.iloc[[3, 0, 2, 1]]
+        tm.assert_series_equal(result, expected)
+
+        # sorting series, nan position first
+        result = s.sort_index(na_position="first")
+        expected = s.iloc[[1, 2, 3, 0]]
+        tm.assert_series_equal(result, expected)
+
     def test_sort_index_nan(self):
         # GH#3917
 
@@ -318,3 +463,196 @@ def test_sort_index_ignore_index_multi_index(
 
         tm.assert_frame_equal(result_df, expected_df)
         tm.assert_frame_equal(df, DataFrame(original_dict, index=mi))
+
+    def test_sort_index_categorical_multiindex(self):
+        # GH#15058
+        df = DataFrame(
+            {
+                "a": range(6),
+                "l1": pd.Categorical(
+                    ["a", "a", "b", "b", "c", "c"],
+                    categories=["c", "a", "b"],
+                    ordered=True,
+                ),
+                "l2": [0, 1, 0, 1, 0, 1],
+            }
+        )
+        result = df.set_index(["l1", "l2"]).sort_index()
+        expected = DataFrame(
+            [4, 5, 0, 1, 2, 3],
+            columns=["a"],
+            index=MultiIndex(
+                levels=[
+                    pd.CategoricalIndex(
+                        ["c", "a", "b"],
+                        categories=["c", "a", "b"],
+                        ordered=True,
+                        name="l1",
+                        dtype="category",
+                    ),
+                    [0, 1],
+                ],
+                codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
+                names=["l1", "l2"],
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_sort_index_and_reconstruction(self):
+
+        # GH#15622
+        # lexsortedness should be identical
+        # across MultiIndex construction methods
+
+        df = DataFrame([[1, 1], [2, 2]], index=list("ab"))
+        expected = DataFrame(
+            [[1, 1], [2, 2], [1, 1], [2, 2]],
+            index=MultiIndex.from_tuples(
+                [(0.5, "a"), (0.5, "b"), (0.8, "a"), (0.8, "b")]
+            ),
+        )
+        assert expected.index.is_lexsorted()
+
+        result = DataFrame(
+            [[1, 1], [2, 2], [1, 1], [2, 2]],
+            index=MultiIndex.from_product([[0.5, 0.8], list("ab")]),
+        )
+        result = result.sort_index()
+        assert result.index.is_lexsorted()
+        assert result.index.is_monotonic
+
+        tm.assert_frame_equal(result, expected)
+
+        result = DataFrame(
+            [[1, 1], [2, 2], [1, 1], [2, 2]],
+            index=MultiIndex(
+                levels=[[0.5, 0.8], ["a", "b"]], codes=[[0, 0, 1, 1], [0, 1, 0, 1]]
+            ),
+        )
+        result = result.sort_index()
+        assert result.index.is_lexsorted()
+
+        tm.assert_frame_equal(result, expected)
+
+        concatted = pd.concat([df, df], keys=[0.8, 0.5])
+        result = concatted.sort_index()
+
+        assert result.index.is_lexsorted()
+        assert result.index.is_monotonic
+
+        tm.assert_frame_equal(result, expected)
+
+        # GH#14015
+        df = DataFrame(
+            [[1, 2], [6, 7]],
+            columns=MultiIndex.from_tuples(
+                [(0, "20160811 12:00:00"), (0, "20160809 12:00:00")],
+                names=["l1", "Date"],
+            ),
+        )
+
+        df.columns.set_levels(
+            pd.to_datetime(df.columns.levels[1]), level=1, inplace=True
+        )
+        assert not df.columns.is_lexsorted()
+        assert not df.columns.is_monotonic
+        result = df.sort_index(axis=1)
+        assert result.columns.is_lexsorted()
+        assert result.columns.is_monotonic
+        result = df.sort_index(axis=1, level=1)
+        assert result.columns.is_lexsorted()
+        assert result.columns.is_monotonic
+
+    # TODO: better name, de-duplicate with test_sort_index_level above
+    def test_sort_index_level2(self):
+        mi = MultiIndex(
+            levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
+            codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
+            names=["first", "second"],
+        )
+        frame = DataFrame(
+            np.random.randn(10, 3),
+            index=mi,
+            columns=Index(["A", "B", "C"], name="exp"),
+        )
+
+        df = frame.copy()
+        df.index = np.arange(len(df))
+
+        # axis=1
+
+        # series
+        a_sorted = frame["A"].sort_index(level=0)
+
+        # preserve names
+        assert a_sorted.index.names == frame.index.names
+
+        # inplace
+        rs = frame.copy()
+        rs.sort_index(level=0, inplace=True)
+        tm.assert_frame_equal(rs, frame.sort_index(level=0))
+
+    def test_sort_index_level_large_cardinality(self):
+
+        # GH#2684 (int64)
+        index = MultiIndex.from_arrays([np.arange(4000)] * 3)
+        df = DataFrame(np.random.randn(4000), index=index, dtype=np.int64)
+
+        # it works!
+        result = df.sort_index(level=0)
+        assert result.index.lexsort_depth == 3
+
+        # GH#2684 (int32)
+        index = MultiIndex.from_arrays([np.arange(4000)] * 3)
+        df = DataFrame(np.random.randn(4000), index=index, dtype=np.int32)
+
+        # it works!
+        result = df.sort_index(level=0)
+        assert (result.dtypes.values == df.dtypes.values).all()
+        assert result.index.lexsort_depth == 3
+
+    def test_sort_index_level_by_name(self):
+        mi = MultiIndex(
+            levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
+            codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
+            names=["first", "second"],
+        )
+        frame = DataFrame(
+            np.random.randn(10, 3),
+            index=mi,
+            columns=Index(["A", "B", "C"], name="exp"),
+        )
+
+        frame.index.names = ["first", "second"]
+        result = frame.sort_index(level="second")
+        expected = frame.sort_index(level=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_sort_index_level_mixed(self):
+        mi = MultiIndex(
+            levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
+            codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
+            names=["first", "second"],
+        )
+        frame = DataFrame(
+            np.random.randn(10, 3),
+            index=mi,
+            columns=Index(["A", "B", "C"], name="exp"),
+        )
+
+        sorted_before = frame.sort_index(level=1)
+
+        df = frame.copy()
+        df["foo"] = "bar"
+        sorted_after = df.sort_index(level=1)
+        tm.assert_frame_equal(sorted_before, sorted_after.drop(["foo"], axis=1))
+
+        dft = frame.T
+        sorted_before = dft.sort_index(level=1, axis=1)
+        dft["foo", "three"] = "bar"
+
+        sorted_after = dft.sort_index(level=1, axis=1)
+        tm.assert_frame_equal(
+            sorted_before.drop([("foo", "three")], axis=1),
+            sorted_after.drop([("foo", "three")], axis=1),
+        )

pandas/tests/series/methods/test_sort_index.py

@@ -170,3 +170,26 @@ def test_sort_index_ignore_index(
 
         tm.assert_series_equal(result_ser, expected)
         tm.assert_series_equal(ser, Series(original_list))
+
+    def test_sort_index_ascending_list(self):
+        # GH#16934
+
+        # Set up a Series with a three level MultiIndex
+        arrays = [
+            ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
+            ["one", "two", "one", "two", "one", "two", "one", "two"],
+            [4, 3, 2, 1, 4, 3, 2, 1],
+        ]
+        tuples = zip(*arrays)
+        mi = MultiIndex.from_tuples(tuples, names=["first", "second", "third"])
+        ser = Series(range(8), index=mi)
+
+        # Sort with boolean ascending
+        result = ser.sort_index(level=["third", "first"], ascending=False)
+        expected = ser.iloc[[4, 0, 5, 1, 6, 2, 7, 3]]
+        tm.assert_series_equal(result, expected)
+
+        # Sort with list of boolean ascending
+        result = ser.sort_index(level=["third", "first"], ascending=[False, True])
+        expected = ser.iloc[[0, 4, 1, 5, 2, 6, 3, 7]]
+        tm.assert_series_equal(result, expected)