DEPR: don't make Index instantiate Int64/Uint64/Flaot64Index

pandas/tests/apply/test_series_apply.py

@@ -186,7 +186,7 @@ def test_apply_datetimetz():
     # change dtype
     # GH 14506 : Returned dtype changed from int32 to int64
     result = s.apply(lambda x: x.hour)
-    exp = Series(list(range(24)) + [0], name="XX", dtype=np.int64)
+    exp = Series(list(range(24)) + [0], name="XX", dtype=np.int32)
     tm.assert_series_equal(result, exp)
 
     # not vectorized
@@ -766,7 +766,7 @@ def test_map_datetimetz():
     # change dtype
     # GH 14506 : Returned dtype changed from int32 to int64
     result = s.map(lambda x: x.hour)
-    exp = Series(list(range(24)) + [0], name="XX", dtype=np.int64)
+    exp = Series(list(range(24)) + [0], name="XX", dtype=np.int32)
     tm.assert_series_equal(result, exp)
 
     # not vectorized

pandas/tests/frame/indexing/test_indexing.py

@@ -736,7 +736,7 @@ def test_getitem_setitem_float_labels(self, using_array_manager):
 
         # positional slicing only via iloc!
         msg = (
-            "cannot do positional indexing on Float64Index with "
+            "cannot do positional indexing on NumericIndex with "
             r"these indexers \[1.0\] of type float"
         )
         with pytest.raises(TypeError, match=msg):

pandas/tests/frame/methods/test_set_index.py

@@ -159,7 +159,7 @@ def test_set_index_cast(self):
         df = DataFrame(
             {"A": [1.1, 2.2, 3.3], "B": [5.0, 6.1, 7.2]}, index=[2010, 2011, 2012]
         )
-        df2 = df.set_index(df.index.astype(np.int32))
+        df2 = df.set_index(df.index.astype(np.int64))
         tm.assert_frame_equal(df, df2)
 
     # A has duplicate values, C does not

pandas/tests/reshape/test_pivot.py

@@ -411,7 +411,14 @@ def test_pivot_no_values(self):
         res = df.pivot_table(index=df.index.month, columns=df.index.day)
 
         exp_columns = MultiIndex.from_tuples([("A", 1), ("A", 2)])
-        exp = DataFrame([[2.5, 4.0], [2.0, np.nan]], index=[1, 2], columns=exp_columns)
+        exp_columns = exp_columns.set_levels(
+            exp_columns.levels[1].astype(np.int32), level=1
+        )
+        exp = DataFrame(
+            [[2.5, 4.0], [2.0, np.nan]],
+            index=Index([1, 2], dtype=np.int32),
+            columns=exp_columns,
+        )
         tm.assert_frame_equal(res, exp)
 
         df = DataFrame(
@@ -424,7 +431,9 @@ def test_pivot_no_values(self):
         res = df.pivot_table(index=df.index.month, columns=Grouper(key="dt", freq="M"))
         exp_columns = MultiIndex.from_tuples([("A", pd.Timestamp("2011-01-31"))])
         exp_columns.names = [None, "dt"]
-        exp = DataFrame([3.25, 2.0], index=[1, 2], columns=exp_columns)
+        exp = DataFrame(
+            [3.25, 2.0], index=Index([1, 2], dtype=np.int32), columns=exp_columns
+        )
         tm.assert_frame_equal(res, exp)
 
         res = df.pivot_table(
@@ -1604,7 +1613,7 @@ def test_pivot_dtaccessor(self):
         expected = DataFrame(
             {7: [0, 3], 8: [1, 4], 9: [2, 5]},
             index=exp_idx,
-            columns=Index([7, 8, 9], name="dt1"),
+            columns=Index([7, 8, 9], dtype=np.int32, name="dt1"),
         )
         tm.assert_frame_equal(result, expected)
 
@@ -1614,8 +1623,8 @@ def test_pivot_dtaccessor(self):
 
         expected = DataFrame(
             {7: [0, 3], 8: [1, 4], 9: [2, 5]},
-            index=Index([1, 2], name="dt2"),
-            columns=Index([7, 8, 9], name="dt1"),
+            index=Index([1, 2], dtype=np.int32, name="dt2"),
+            columns=Index([7, 8, 9], dtype=np.int32, name="dt1"),
         )
         tm.assert_frame_equal(result, expected)
 
@@ -1627,10 +1636,16 @@ def test_pivot_dtaccessor(self):
         )
 
         exp_col = MultiIndex.from_arrays(
-            [[7, 7, 8, 8, 9, 9], [1, 2] * 3], names=["dt1", "dt2"]
+            [
+                np.array([7, 7, 8, 8, 9, 9], dtype=np.int32),
+                np.array([1, 2] * 3, dtype=np.int32),
+            ],
+            names=["dt1", "dt2"],
         )
         expected = DataFrame(
-            np.array([[0, 3, 1, 4, 2, 5]], dtype="int64"), index=[2013], columns=exp_col
+            np.array([[0, 3, 1, 4, 2, 5]], dtype="int64"),
+            index=Index([2013], dtype=np.int32),
+            columns=exp_col,
         )
         tm.assert_frame_equal(result, expected)
 

pandas/tests/reshape/test_util.py

@@ -23,8 +23,8 @@ def test_datetimeindex(self):
         # make sure that the ordering on datetimeindex is consistent
         x = date_range("2000-01-01", periods=2)
         result1, result2 = (Index(y).day for y in cartesian_product([x, x]))
-        expected1 = Index([1, 1, 2, 2])
-        expected2 = Index([1, 2, 1, 2])
+        expected1 = Index([1, 1, 2, 2], dtype=np.int32)
+        expected2 = Index([1, 2, 1, 2], dtype=np.int32)
         tm.assert_index_equal(result1, expected1)
         tm.assert_index_equal(result2, expected2)
 

pandas/tests/series/accessors/test_dt_accessor.py

@@ -229,14 +229,14 @@ def test_dt_namespace_accessor_index_and_values(self):
         dti = date_range("20140204", periods=3, freq="s")
         ser = Series(dti, index=index, name="xxx")
         exp = Series(
-            np.array([2014, 2014, 2014], dtype="int64"), index=index, name="xxx"
+            np.array([2014, 2014, 2014], dtype="int32"), index=index, name="xxx"
         )
         tm.assert_series_equal(ser.dt.year, exp)
 
-        exp = Series(np.array([2, 2, 2], dtype="int64"), index=index, name="xxx")
+        exp = Series(np.array([2, 2, 2], dtype="int32"), index=index, name="xxx")
         tm.assert_series_equal(ser.dt.month, exp)
 
-        exp = Series(np.array([0, 1, 2], dtype="int64"), index=index, name="xxx")
+        exp = Series(np.array([0, 1, 2], dtype="int32"), index=index, name="xxx")
         tm.assert_series_equal(ser.dt.second, exp)
 
         exp = Series([ser[0]] * 3, index=index, name="xxx")
@@ -386,7 +386,7 @@ def test_dt_namespace_accessor_categorical(self):
         dti = DatetimeIndex(["20171111", "20181212"]).repeat(2)
         ser = Series(pd.Categorical(dti), name="foo")
         result = ser.dt.year
-        expected = Series([2017, 2017, 2018, 2018], name="foo")
+        expected = Series([2017, 2017, 2018, 2018], dtype="int32", name="foo")
         tm.assert_series_equal(result, expected)
 
     def test_dt_tz_localize_categorical(self, tz_aware_fixture):
@@ -748,6 +748,7 @@ def test_hour_index(self):
         result = dt_series.dt.hour
         expected = Series(
             [0, 1, 2, 3, 4],
+            dtype="int32",
             index=[2, 6, 7, 8, 11],
         )
         tm.assert_series_equal(result, expected)

pandas/tests/series/indexing/test_setitem.py

@@ -1159,7 +1159,7 @@ def expected(self, val):
         return Series(res_values)
 
 
-@pytest.mark.parametrize("val", [512, np.int16(512)])
+@pytest.mark.parametrize("val", [np.int16(512), np.int16(512)])
 class TestSetitemIntoIntegerSeriesNeedsUpcast(SetitemCastingEquivalents):
     @pytest.fixture
     def obj(self):
@@ -1174,7 +1174,7 @@ def expected(self):
         return Series([1, 512, 3], dtype=np.int16)
 
 
-@pytest.mark.parametrize("val", [2**33 + 1.0, 2**33 + 1.1, 2**62])
+@pytest.mark.parametrize("val", [2**30 + 1.0, 2**33 + 1.1, 2**62])
 class TestSmallIntegerSetitemUpcast(SetitemCastingEquivalents):
     # https://github.com/pandas-dev/pandas/issues/39584#issuecomment-941212124
     @pytest.fixture
@@ -1187,10 +1187,12 @@ def key(self):
 
     @pytest.fixture
     def expected(self, val):
-        if val % 1 != 0:
+        if val > np.iinfo(np.int64).max:
             dtype = "f8"
-        else:
+        elif val > np.iinfo(np.int32).max:
             dtype = "i8"
+        else:
+            dtype = "i4"
         return Series([val, 2, 3], dtype=dtype)