test_interpolate_pd_compat with range of fill_value's (#8189)

* ENH: test_interpolate_pd_compat with range of fill_value's
* add whats-new.rst entry

Author: kmuehlbauer

doc/whats-new.rst

@@ -108,6 +108,9 @@ Internal Changes
   than `.reduce`, as the start of a broader effort to move non-reducing
   functions away from ```.reduce``, (:pull:`8114`).
   By `Maximilian Roos <https://github.com/max-sixty>`_.
+- Test range of fill_value's in test_interpolate_pd_compat (:issue:`8146`, :pull:`8189`).
+  By `Kai Mühlbauer <https://github.com/kmuehlbauer>`_.
+
 
 .. _whats-new.2023.08.0:
 

xarray/tests/test_missing.py

@@ -92,26 +92,36 @@ def make_interpolate_example_data(shape, frac_nan, seed=12345, non_uniform=False
     return da, df
 
 
+@pytest.mark.parametrize("fill_value", [None, np.nan, 47.11])
+@pytest.mark.parametrize(
+    "method", ["linear", "nearest", "zero", "slinear", "quadratic", "cubic"]
+)
 @requires_scipy
-def test_interpolate_pd_compat():
+def test_interpolate_pd_compat(method, fill_value) -> None:
     shapes = [(8, 8), (1, 20), (20, 1), (100, 100)]
     frac_nans = [0, 0.5, 1]
-    methods = ["linear", "nearest", "zero", "slinear", "quadratic", "cubic"]
 
-    for shape, frac_nan, method in itertools.product(shapes, frac_nans, methods):
+    for shape, frac_nan in itertools.product(shapes, frac_nans):
         da, df = make_interpolate_example_data(shape, frac_nan)
 
         for dim in ["time", "x"]:
-            actual = da.interpolate_na(method=method, dim=dim, fill_value=np.nan)
+            actual = da.interpolate_na(method=method, dim=dim, fill_value=fill_value)
+            # need limit_direction="both" here, to let pandas fill
+            # in both directions instead of default forward direction only
             expected = df.interpolate(
                 method=method,
                 axis=da.get_axis_num(dim),
+                limit_direction="both",
+                fill_value=fill_value,
             )
-            # Note, Pandas does some odd things with the left/right fill_value
-            # for the linear methods. This next line inforces the xarray
-            # fill_value convention on the pandas output. Therefore, this test
-            # only checks that interpolated values are the same (not nans)
-            expected.values[pd.isnull(actual.values)] = np.nan
+
+            if method == "linear":
+                # Note, Pandas does not take left/right fill_value into account
+                # for the numpy linear methods.
+                # see https://github.com/pandas-dev/pandas/issues/55144
+                # This aligns the pandas output with the xarray output
+                expected.values[pd.isnull(actual.values)] = np.nan
+                expected.values[actual.values == fill_value] = fill_value
 
             np.testing.assert_allclose(actual.values, expected.values)