Fix more tests and make ruff

pyam/core.py

@@ -703,7 +703,7 @@ def interpolate(self, time, inplace=False, **kwargs):
         df.columns.name = ret.time_col
         df = df.stack(future_stack=True).dropna()  # wide data to pd.Series
         df.name = "value"
-        ret._data = df.sort_index()
+        ret._data = df
         ret._set_attributes()
 
         if not inplace:
@@ -816,12 +816,10 @@ def timeseries(self, iamc_index=False):
                 )
             s = self._data.droplevel(self.extra_cols)
             if s.index.has_duplicates:
-                raise ValueError(
-                    "Dropping non-IAMC-index causes duplicated index."
-                )
+                raise ValueError("Dropping non-IAMC-index causes duplicated index.")
 
         return (
-            s.unstack(level=self.time_col, sort=False)
+            s.unstack(level=self.time_col)
             .rename_axis(None, axis=1)
             .sort_index(
                 axis=1, key=compare_year_time if self.time_domain == "mixed" else None

pyam/utils.py

@@ -609,7 +609,9 @@ def print_list(x, n):
     [
         # set year lower than first timestep of that year (2010 < 2010-01-01 00:00)
         datetime(time, 1, 1, 0, 0, 0) - timedelta(0, 0.01)
-        if isinstance(time, int) else time for time in x
+        if isinstance(time, int)
+        else time
+        for time in x
     ]
 )
 

tests/test_cast_to_iamc.py

@@ -43,7 +43,7 @@ def test_cast_from_value_col_and_args(test_df_year):
         ],
         columns=[
             "scenario",
-            "iso",
+            "node",
             "unit",
             "year",
             "Primary Energy",
@@ -53,7 +53,7 @@ def test_cast_from_value_col_and_args(test_df_year):
     df = IamDataFrame(
         df_with_value_cols,
         model="model_a",
-        region="iso",
+        region="node",
         value=["Primary Energy", "Primary Energy|Coal"],
     )
     pdt.assert_series_equal(df._data, test_df_year._data, check_like=True)

tests/test_core.py

@@ -564,8 +564,7 @@ def test_timeseries_wide(test_pd_df, unsort):
     exp = test_pd_df.set_index(IAMC_IDX)
 
     if unsort:
-        obs = IamDataFrame(test_pd_df[IAMC_IDX + [2010, 2005]]
-        ).timeseries()
+        obs = IamDataFrame(test_pd_df[IAMC_IDX + [2010, 2005]]).timeseries()
     else:
         obs = IamDataFrame(test_pd_df).timeseries()
     pdt.assert_frame_equal(obs, exp, check_column_type=False)

tests/test_feature_append_concat.py

@@ -102,11 +102,10 @@ def test_concat(test_df, reverse, iterable):
     pdt.assert_frame_equal(obs.meta, EXP_META, check_like=True)
 
     # assert that appending data works as expected
-    ts = obs.timeseries()
-    print(ts)
+    ts = obs.timeseries().sort_index()
     pdt.assert_frame_equal(
-        ts.iloc[0:1].droplevel("scenario"),
-        ts.iloc[3:].droplevel("scenario")
+        ts.iloc[2:3].droplevel("scenario"),
+        ts.iloc[3:4].droplevel("scenario"),
     )
 
 
@@ -162,21 +161,24 @@ def test_concat_with_pd_dataframe(test_df, reverse):
 
     # merge with only the timeseries `data` DataFrame of `other`
     if reverse:
-        result = concat([other.data, test_df])
+        obs = concat([other.data, test_df])
     else:
-        result = concat([test_df, other.data])
+        obs = concat([test_df, other.data])
 
     # check that the original object is not updated
     assert test_df.scenario == ["scen_a", "scen_b"]
 
     # assert that merging meta from `other` is ignored
     exp_meta = META_DF.copy()
     exp_meta.loc[("model_a", "scen_c"), "number"] = np.nan
-    pdt.assert_frame_equal(result.meta, exp_meta[META_COLS])
+    pdt.assert_frame_equal(obs.meta, exp_meta[META_COLS])
 
     # assert that appending data works as expected
-    ts = result.timeseries()
-    npt.assert_array_equal(ts.iloc[2].values, ts.iloc[3].values)
+    ts = obs.timeseries().sort_index()
+    pdt.assert_frame_equal(
+        ts.iloc[2:3].droplevel("scenario"),
+        ts.iloc[3:4].droplevel("scenario"),
+    )
 
 
 def test_concat_all_pd_dataframe(test_df):
@@ -185,11 +187,14 @@ def test_concat_all_pd_dataframe(test_df):
     other = test_df.filter(scenario="scen_b").rename({"scenario": {"scen_b": "scen_c"}})
 
     # merge only the timeseries `data` DataFrame of both items
-    result = concat([test_df.data, other.data])
+    obs = concat([test_df.data, other.data])
 
     # assert that appending data works as expected
-    ts = result.timeseries()
-    npt.assert_array_equal(ts.iloc[2].values, ts.iloc[3].values)
+    ts = obs.timeseries().sort_index()
+    pdt.assert_frame_equal(
+        ts.iloc[2:3].droplevel("scenario"),
+        ts.iloc[3:4].droplevel("scenario"),
+    )
 
 
 @pytest.mark.parametrize("inplace", (True, False))

tests/test_iiasa.py

@@ -375,9 +375,7 @@ def test_lazy_read(tmpdir):
     assert df.model == ["model_a"]
     # This is read from the file, so the filter is not applied.
     df2 = lazy_read_iiasa(tmp_file, TEST_API)
-    assert (
-        df.data.sort_values(by=IAMC_IDX).reset_index(drop=True).equals(df2.data)
-    )
+    assert df.data.sort_values(by=IAMC_IDX).reset_index(drop=True).equals(df2.data)
     # If requesting with an inconsistent filter, get nothing back. Strings and filters
     # work interchangably.
     tmp_file = str(tmp_file)