TST/REF: share/split index tests

pandas/tests/indexes/datetimelike.py

@@ -9,6 +9,18 @@
 
 
 class DatetimeLike(Base):
+    def test_isin(self, simple_index):
+        index = simple_index[:4]
+        result = index.isin(index)
+        assert result.all()
+
+        result = index.isin(list(index))
+        assert result.all()
+
+        result = index.isin([index[2], 5])
+        expected = np.array([False, False, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
     def test_argsort_matches_array(self, simple_index):
         idx = simple_index
         idx = idx.insert(1, pd.NaT)

pandas/tests/indexes/datetimes/test_datetime.py

@@ -154,18 +154,6 @@ def test_groupby_function_tuple_1677(self):
         result = monthly_group.mean()
         assert isinstance(result.index[0], tuple)
 
-    def test_isin(self):
-        index = tm.makeDateIndex(4)
-        result = index.isin(index)
-        assert result.all()
-
-        result = index.isin(list(index))
-        assert result.all()
-
-        tm.assert_almost_equal(
-            index.isin([index[2], 5]), np.array([False, False, True, False])
-        )
-
     def assert_index_parameters(self, index):
         assert index.freq == "40960N"
         assert index.inferred_freq == "40960N"

pandas/tests/indexes/datetimes/test_indexing.py

@@ -485,19 +485,23 @@ def test_get_loc(self):
         with pytest.raises(InvalidIndexError, match=r"slice\(None, 2, None\)"):
             idx.get_loc(slice(2))
 
-        idx = pd.to_datetime(["2000-01-01", "2000-01-04"])
+        idx = DatetimeIndex(["2000-01-01", "2000-01-04"])
         assert idx.get_loc("2000-01-02", method="nearest") == 0
         assert idx.get_loc("2000-01-03", method="nearest") == 1
         assert idx.get_loc("2000-01", method="nearest") == slice(0, 2)
 
+    def test_get_loc_time_obj(self):
         # time indexing
         idx = date_range("2000-01-01", periods=24, freq="H")
-        tm.assert_numpy_array_equal(
-            idx.get_loc(time(12)), np.array([12]), check_dtype=False
-        )
-        tm.assert_numpy_array_equal(
-            idx.get_loc(time(12, 30)), np.array([]), check_dtype=False
-        )
+
+        result = idx.get_loc(time(12))
+        expected = np.array([12])
+        tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+
+        result = idx.get_loc(time(12, 30))
+        expected = np.array([])
+        tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+
         msg = "cannot yet lookup inexact labels when key is a time object"
         with pytest.raises(NotImplementedError, match=msg):
             with tm.assert_produces_warning(FutureWarning, match="deprecated"):

pandas/tests/indexes/datetimes/test_misc.py

@@ -156,6 +156,15 @@ def test_range_edges9(self):
 
 
 class TestDatetime64:
+    def test_no_millisecond_field(self):
+        msg = "type object 'DatetimeIndex' has no attribute 'millisecond'"
+        with pytest.raises(AttributeError, match=msg):
+            DatetimeIndex.millisecond
+
+        msg = "'DatetimeIndex' object has no attribute 'millisecond'"
+        with pytest.raises(AttributeError, match=msg):
+            DatetimeIndex([]).millisecond
+
     def test_datetimeindex_accessors(self):
         dti_naive = date_range(freq="D", start=datetime(1998, 1, 1), periods=365)
         # GH#13303

pandas/tests/indexes/period/test_period.py

@@ -4,7 +4,6 @@
 from pandas._libs.tslibs.period import IncompatibleFrequency
 
 from pandas import (
-    DatetimeIndex,
     Index,
     NaT,
     Period,
@@ -49,15 +48,6 @@ def test_where(self):
         # This is handled in test_indexing
         pass
 
-    def test_no_millisecond_field(self):
-        msg = "type object 'DatetimeIndex' has no attribute 'millisecond'"
-        with pytest.raises(AttributeError, match=msg):
-            DatetimeIndex.millisecond
-
-        msg = "'DatetimeIndex' object has no attribute 'millisecond'"
-        with pytest.raises(AttributeError, match=msg):
-            DatetimeIndex([]).millisecond
-
     def test_make_time_series(self):
         index = period_range(freq="A", start="1/1/2001", end="12/1/2009")
         series = Series(1, index=index)

pandas/tests/indexes/timedeltas/test_pickle.py

@@ -0,0 +1,11 @@
+from pandas import timedelta_range
+import pandas._testing as tm
+
+
+class TestPickle:
+    def test_pickle_after_set_freq(self):
+        tdi = timedelta_range("1 day", periods=4, freq="s")
+        tdi = tdi._with_freq(None)
+
+        res = tm.round_trip_pickle(tdi)
+        tm.assert_index_equal(res, tdi)

pandas/tests/indexes/timedeltas/test_timedelta.py

@@ -7,10 +7,10 @@
 from pandas import (
     Index,
     Int64Index,
+    NaT,
     Series,
     Timedelta,
     TimedeltaIndex,
-    date_range,
     timedelta_range,
 )
 import pandas._testing as tm
@@ -42,26 +42,6 @@ def test_numeric_compat(self):
     def test_shift(self):
         pass  # this is handled in test_arithmetic.py
 
-    def test_pickle_after_set_freq(self):
-        tdi = timedelta_range("1 day", periods=4, freq="s")
-        tdi = tdi._with_freq(None)
-
-        res = tm.round_trip_pickle(tdi)
-        tm.assert_index_equal(res, tdi)
-
-    def test_isin(self):
-
-        index = tm.makeTimedeltaIndex(4)
-        result = index.isin(index)
-        assert result.all()
-
-        result = index.isin(list(index))
-        assert result.all()
-
-        tm.assert_almost_equal(
-            index.isin([index[2], 5]), np.array([False, False, True, False])
-        )
-
     def test_misc_coverage(self):
 
         rng = timedelta_range("1 day", periods=5)
@@ -140,11 +120,11 @@ def test_freq_conversion(self):
         # doc example
 
         # series
-        td = Series(date_range("20130101", periods=4)) - Series(
-            date_range("20121201", periods=4)
+        scalar = Timedelta(days=31)
+        td = Series(
+            [scalar, scalar, scalar + timedelta(minutes=5, seconds=3), NaT],
+            dtype="m8[ns]",
         )
-        td[2] += timedelta(minutes=5, seconds=3)
-        td[3] = np.nan
 
         result = td / np.timedelta64(1, "D")
         expected = Series([31, 31, (31 * 86400 + 5 * 60 + 3) / 86400.0, np.nan])