REGR: Interpolate with method=index

pandas/core/missing.py

@@ -314,16 +314,7 @@ def get_interp_index(method, index: Index) -> Index:
         # prior default
         from pandas import Index
 
-        if isinstance(index.dtype, DatetimeTZDtype) or lib.is_np_dtype(
-            index.dtype, "mM"
-        ):
-            # Convert datetime-like indexes to int64
-            index = Index(index.view("i8"))
-
-        elif not is_numeric_dtype(index.dtype):
-            # We keep behavior consistent with prior versions of pandas for
-            # non-numeric, non-datetime indexes
-            index = Index(range(len(index)))
+        index = Index(np.arange(len(index)))
     else:
         methods = {"index", "values", "nearest", "time"}
         is_numeric_or_datetime = (

pandas/tests/resample/test_base.py

@@ -123,20 +123,22 @@ def test_resample_interpolate_regular_sampling_off_grid(
     ser = Series(np.arange(5.0), index)
 
     method = all_1d_no_arg_interpolation_methods
-    # Resample to 1 hour sampling and interpolate with the given method
-    ser_resampled = ser.resample("1h").interpolate(method)
+    result = ser.resample("1h").interpolate(method)
 
-    # Check that none of the resampled values are NaN, except the first one
-    # which lies 1 minute before the first actual data point
-    assert np.isnan(ser_resampled.iloc[0])
-    assert not ser_resampled.iloc[1:].isna().any()
-
-    if method not in ["nearest", "zero"]:
-        # Check that the resampled values are close to the expected values
-        # except for methods with known inaccuracies
-        assert np.all(
-            np.isclose(ser_resampled.values[1:], np.arange(0.5, 4.5, 0.5), rtol=1.0e-1)
+    if method == "linear":
+        values = np.repeat(np.arange(0.0, 4.0), 2) + np.tile(
+            np.arange(1 / 3, 0.7, 1 / 3), 4
         )
+    elif method == "nearest":
+        values = np.repeat(np.arange(0.0, 5.0), 2)[1:-1]
+    elif method == "zero":
+        values = np.repeat(np.arange(0.0, 4.0), 2)
+    else:
+        values = 0.491667 + np.arange(0.0, 4.0, 0.5)
+    values = np.insert(values, 0, np.nan)
+    index = date_range("2000-01-01 00:00:00", periods=9, freq="1h")
+    expected = Series(values, index=index)
+    tm.assert_series_equal(result, expected)
 
 
 def test_resample_interpolate_irregular_sampling(all_1d_no_arg_interpolation_methods):

pandas/tests/resample/test_time_grouper.py

@@ -430,13 +430,7 @@ def test_groupby_resample_interpolate_with_apply_syntax_off_grid(groupy_test_df)
     )
 
     expected = DataFrame(
-        data={
-            "price": [
-                10.0,
-                9.21131,
-                11.0,
-            ]
-        },
+        data={"price": [10.0, 9.5, 11.0]},
         index=expected_ind,
     )
     tm.assert_frame_equal(result, expected, check_names=False)

pandas/tests/series/methods/test_interpolate.py

@@ -270,7 +270,7 @@ def test_nan_interpolate(self, kwargs):
     def test_nan_irregular_index(self):
         s = Series([1, 2, np.nan, 4], index=[1, 3, 5, 9])
         result = s.interpolate()
-        expected = Series([1.0, 2.0, 2.6666666666666665, 4.0], index=[1, 3, 5, 9])
+        expected = Series([1.0, 2.0, 3.0, 4.0], index=[1, 3, 5, 9])
         tm.assert_series_equal(result, expected)
 
     def test_nan_str_index(self):