REF/TST: misplaced MultiIndex tests (#32314)

Author: jbrockmendel

pandas/tests/indexes/multi/test_constructors.py

@@ -1,3 +1,6 @@
+from datetime import date, datetime
+import itertools
+
 import numpy as np
 import pytest
 
@@ -6,7 +9,7 @@
 from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
 
 import pandas as pd
-from pandas import Index, MultiIndex, date_range
+from pandas import Index, MultiIndex, Series, date_range
 import pandas._testing as tm
 
 
@@ -723,3 +726,73 @@ def test_index_equal_empty_iterable():
     a = MultiIndex(levels=[[], []], codes=[[], []], names=["a", "b"])
     b = MultiIndex.from_arrays(arrays=[[], []], names=["a", "b"])
     tm.assert_index_equal(a, b)
+
+
+def test_raise_invalid_sortorder():
+    # Test that the MultiIndex constructor raise when a incorrect sortorder is given
+    # GH#28518
+
+    levels = [[0, 1], [0, 1, 2]]
+
+    # Correct sortorder
+    MultiIndex(
+        levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]], sortorder=2
+    )
+
+    with pytest.raises(ValueError, match=r".* sortorder 2 with lexsort_depth 1.*"):
+        MultiIndex(
+            levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]], sortorder=2,
+        )
+
+    with pytest.raises(ValueError, match=r".* sortorder 1 with lexsort_depth 0.*"):
+        MultiIndex(
+            levels=levels, codes=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]], sortorder=1,
+        )
+
+
+def test_datetimeindex():
+    idx1 = pd.DatetimeIndex(
+        ["2013-04-01 9:00", "2013-04-02 9:00", "2013-04-03 9:00"] * 2, tz="Asia/Tokyo",
+    )
+    idx2 = pd.date_range("2010/01/01", periods=6, freq="M", tz="US/Eastern")
+    idx = MultiIndex.from_arrays([idx1, idx2])
+
+    expected1 = pd.DatetimeIndex(
+        ["2013-04-01 9:00", "2013-04-02 9:00", "2013-04-03 9:00"], tz="Asia/Tokyo"
+    )
+
+    tm.assert_index_equal(idx.levels[0], expected1)
+    tm.assert_index_equal(idx.levels[1], idx2)
+
+    # from datetime combos
+    # GH 7888
+    date1 = date.today()
+    date2 = datetime.today()
+    date3 = Timestamp.today()
+
+    for d1, d2 in itertools.product([date1, date2, date3], [date1, date2, date3]):
+        index = MultiIndex.from_product([[d1], [d2]])
+        assert isinstance(index.levels[0], pd.DatetimeIndex)
+        assert isinstance(index.levels[1], pd.DatetimeIndex)
+
+
+def test_constructor_with_tz():
+
+    index = pd.DatetimeIndex(
+        ["2013/01/01 09:00", "2013/01/02 09:00"], name="dt1", tz="US/Pacific"
+    )
+    columns = pd.DatetimeIndex(
+        ["2014/01/01 09:00", "2014/01/02 09:00"], name="dt2", tz="Asia/Tokyo"
+    )
+
+    result = MultiIndex.from_arrays([index, columns])
+
+    assert result.names == ["dt1", "dt2"]
+    tm.assert_index_equal(result.levels[0], index)
+    tm.assert_index_equal(result.levels[1], columns)
+
+    result = MultiIndex.from_arrays([Series(index), Series(columns)])
+
+    assert result.names == ["dt1", "dt2"]
+    tm.assert_index_equal(result.levels[0], index)
+    tm.assert_index_equal(result.levels[1], columns)

pandas/tests/indexes/multi/test_duplicates.py

@@ -274,3 +274,29 @@ def test_duplicated2():
             tm.assert_numpy_array_equal(
                 mi.duplicated(), np.zeros(len(mi), dtype="bool")
             )
+
+
+def test_duplicated_drop_duplicates():
+    # GH#4060
+    idx = MultiIndex.from_arrays(([1, 2, 3, 1, 2, 3], [1, 1, 1, 1, 2, 2]))
+
+    expected = np.array([False, False, False, True, False, False], dtype=bool)
+    duplicated = idx.duplicated()
+    tm.assert_numpy_array_equal(duplicated, expected)
+    assert duplicated.dtype == bool
+    expected = MultiIndex.from_arrays(([1, 2, 3, 2, 3], [1, 1, 1, 2, 2]))
+    tm.assert_index_equal(idx.drop_duplicates(), expected)
+
+    expected = np.array([True, False, False, False, False, False])
+    duplicated = idx.duplicated(keep="last")
+    tm.assert_numpy_array_equal(duplicated, expected)
+    assert duplicated.dtype == bool
+    expected = MultiIndex.from_arrays(([2, 3, 1, 2, 3], [1, 1, 1, 2, 2]))
+    tm.assert_index_equal(idx.drop_duplicates(keep="last"), expected)
+
+    expected = np.array([True, False, False, True, False, False])
+    duplicated = idx.duplicated(keep=False)
+    tm.assert_numpy_array_equal(duplicated, expected)
+    assert duplicated.dtype == bool
+    expected = MultiIndex.from_arrays(([2, 3, 2, 3], [1, 1, 2, 2]))
+    tm.assert_index_equal(idx.drop_duplicates(keep=False), expected)

pandas/tests/indexes/multi/test_format.py

@@ -1,9 +1,10 @@
 import warnings
 
+import numpy as np
 import pytest
 
 import pandas as pd
-from pandas import MultiIndex
+from pandas import Index, MultiIndex
 import pandas._testing as tm
 
 
@@ -76,6 +77,17 @@ def test_repr_max_seq_item_setting(idx):
 
 
 class TestRepr:
+    def test_unicode_repr_issues(self):
+        levels = [Index(["a/\u03c3", "b/\u03c3", "c/\u03c3"]), Index([0, 1])]
+        codes = [np.arange(3).repeat(2), np.tile(np.arange(2), 3)]
+        index = MultiIndex(levels=levels, codes=codes)
+
+        repr(index.levels)
+
+        # FIXME: dont leave commented-out
+        # NumPy bug
+        # repr(index.get_level_values(1))
+
     def test_repr(self, idx):
         result = idx[:1].__repr__()
         expected = """\

pandas/tests/indexes/multi/test_indexing.py

@@ -498,3 +498,41 @@ def test_slice_indexer_with_missing_value(index_arr, expected, start_idx, end_id
     idx = MultiIndex.from_arrays(index_arr)
     result = idx.slice_indexer(start=start_idx, end=end_idx)
     assert result == expected
+
+
+def test_pyint_engine():
+    # GH#18519 : when combinations of codes cannot be represented in 64
+    # bits, the index underlying the MultiIndex engine works with Python
+    # integers, rather than uint64.
+    N = 5
+    keys = [
+        tuple(l)
+        for l in [
+            [0] * 10 * N,
+            [1] * 10 * N,
+            [2] * 10 * N,
+            [np.nan] * N + [2] * 9 * N,
+            [0] * N + [2] * 9 * N,
+            [np.nan] * N + [2] * 8 * N + [0] * N,
+        ]
+    ]
+    # Each level contains 4 elements (including NaN), so it is represented
+    # in 2 bits, for a total of 2*N*10 = 100 > 64 bits. If we were using a
+    # 64 bit engine and truncating the first levels, the fourth and fifth
+    # keys would collide; if truncating the last levels, the fifth and
+    # sixth; if rotating bits rather than shifting, the third and fifth.
+
+    for idx in range(len(keys)):
+        index = MultiIndex.from_tuples(keys)
+        assert index.get_loc(keys[idx]) == idx
+
+        expected = np.arange(idx + 1, dtype=np.intp)
+        result = index.get_indexer([keys[i] for i in expected])
+        tm.assert_numpy_array_equal(result, expected)
+
+    # With missing key:
+    idces = range(len(keys))
+    expected = np.array([-1] + list(idces), dtype=np.intp)
+    missing = tuple([0, 1] * 5 * N)
+    result = index.get_indexer([missing] + [keys[i] for i in idces])
+    tm.assert_numpy_array_equal(result, expected)

pandas/tests/indexes/multi/test_lexsort.py

@@ -0,0 +1,46 @@
+from pandas import MultiIndex
+
+
+class TestIsLexsorted:
+    def test_is_lexsorted(self):
+        levels = [[0, 1], [0, 1, 2]]
+
+        index = MultiIndex(
+            levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]
+        )
+        assert index.is_lexsorted()
+
+        index = MultiIndex(
+            levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]]
+        )
+        assert not index.is_lexsorted()
+
+        index = MultiIndex(
+            levels=levels, codes=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]]
+        )
+        assert not index.is_lexsorted()
+        assert index.lexsort_depth == 0
+
+
+class TestLexsortDepth:
+    def test_lexsort_depth(self):
+        # Test that lexsort_depth return the correct sortorder
+        # when it was given to the MultiIndex const.
+        # GH#28518
+
+        levels = [[0, 1], [0, 1, 2]]
+
+        index = MultiIndex(
+            levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]], sortorder=2
+        )
+        assert index.lexsort_depth == 2
+
+        index = MultiIndex(
+            levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]], sortorder=1
+        )
+        assert index.lexsort_depth == 1
+
+        index = MultiIndex(
+            levels=levels, codes=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]], sortorder=0
+        )
+        assert index.lexsort_depth == 0

pandas/tests/indexes/multi/test_missing.py

@@ -141,3 +141,13 @@ def test_nan_stays_float():
     assert pd.isna(df0.index.get_level_values(1)).all()
     # the following failed in 0.14.1
     assert pd.isna(dfm.index.get_level_values(1)[:-1]).all()
+
+
+def test_tuples_have_na():
+    index = MultiIndex(
+        levels=[[1, 0], [0, 1, 2, 3]],
+        codes=[[1, 1, 1, 1, -1, 0, 0, 0], [0, 1, 2, 3, 0, 1, 2, 3]],
+    )
+
+    assert pd.isna(index[4][0])
+    assert pd.isna(index.values[4][0])

pandas/tests/indexes/multi/test_reshape.py

@@ -1,5 +1,8 @@
+from datetime import datetime
+
 import numpy as np
 import pytest
+import pytz
 
 import pandas as pd
 from pandas import Index, MultiIndex
@@ -95,6 +98,53 @@ def test_append(idx):
     assert result.equals(idx)
 
 
+def test_append_index():
+    idx1 = Index([1.1, 1.2, 1.3])
+    idx2 = pd.date_range("2011-01-01", freq="D", periods=3, tz="Asia/Tokyo")
+    idx3 = Index(["A", "B", "C"])
+
+    midx_lv2 = MultiIndex.from_arrays([idx1, idx2])
+    midx_lv3 = MultiIndex.from_arrays([idx1, idx2, idx3])
+
+    result = idx1.append(midx_lv2)
+
+    # see gh-7112
+    tz = pytz.timezone("Asia/Tokyo")
+    expected_tuples = [
+        (1.1, tz.localize(datetime(2011, 1, 1))),
+        (1.2, tz.localize(datetime(2011, 1, 2))),
+        (1.3, tz.localize(datetime(2011, 1, 3))),
+    ]
+    expected = Index([1.1, 1.2, 1.3] + expected_tuples)
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv2.append(idx1)
+    expected = Index(expected_tuples + [1.1, 1.2, 1.3])
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv2.append(midx_lv2)
+    expected = MultiIndex.from_arrays([idx1.append(idx1), idx2.append(idx2)])
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv2.append(midx_lv3)
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv3.append(midx_lv2)
+    expected = Index._simple_new(
+        np.array(
+            [
+                (1.1, tz.localize(datetime(2011, 1, 1)), "A"),
+                (1.2, tz.localize(datetime(2011, 1, 2)), "B"),
+                (1.3, tz.localize(datetime(2011, 1, 3)), "C"),
+            ]
+            + expected_tuples,
+            dtype=object,
+        ),
+        None,
+    )
+    tm.assert_index_equal(result, expected)
+
+
 def test_repeat():
     reps = 2
     numbers = [1, 2, 3]