Fix pandas deprecation warnings

src/fmu/ensemble/realization.py

@@ -523,15 +523,14 @@ def load_status(self):
         status.fillna("", inplace=True)
 
         errorjobs = status[errorcolumns[0]] != ""
-
         # Merge any error strings:
         error_string = (
             status.loc[errorjobs, errorcolumns]
             .astype(str)
             .apply(" ".join, axis=1)
             .apply(str.strip)
         )
-        status["errorstring"] = np.nan
+        status["errorstring"] = pd.NA
         status.loc[errorjobs, "errorstring"] = error_string
         status.drop(errorcolumns, axis=1, inplace=True)
 

src/fmu/ensemble/virtualrealization.py

@@ -381,15 +381,10 @@ def get_smry(self, column_keys=None, time_index="monthly"):
         ]
         if cum_columns:
             smry[cum_columns] = (
-                smry[cum_columns]
-                .interpolate(method="time")
-                .fillna(method="ffill")
-                .fillna(method="bfill")
+                smry[cum_columns].interpolate(method="time").ffill().bfill()
             )
         if noncum_columns:
-            smry[noncum_columns] = (
-                smry[noncum_columns].fillna(method="bfill").fillna(value=0)
-            )
+            smry[noncum_columns] = smry[noncum_columns].bfill().fillna(value=0)
 
         smry.index = smry.index.set_names(["DATE"])
         return smry.loc[pd.to_datetime(time_index_dt)]

tests/test_ensemble.py

@@ -544,7 +544,8 @@ def test_ensemble_ecl():
     # For oil industry, p15 on FOPT should yield a larger value than p85.
     # But the quantiles we get out follows the rest of the world
     # so we check for the opposite.
-    assert df_stats["FOPT"]["p85"][-1] > df_stats["FOPT"]["p15"][-1]
+
+    assert df_stats["FOPT"]["p85"].iloc[-1] > df_stats["FOPT"]["p15"].iloc[-1]
 
     with pytest.raises(ValueError):
         reekensemble.get_smry_stats(

tests/test_realization.py

@@ -381,7 +381,9 @@ def test_volumetric_rates():
     # Calculate cumulative production from the computed volumetric daily rates:
     dayscum["FOPRcum"] = dayscum["FOPR"] * dayscum["DIFFDAYS"]
     # Check that this sum is equal to FOPT between first and last date:
-    assert dayscum["FOPRcum"].sum() == pytest.approx(dcum["FOPT"][-1] - dcum["FOPT"][0])
+    assert dayscum["FOPRcum"].sum() == pytest.approx(
+        dcum["FOPT"].iloc[-1] - dcum["FOPT"].iloc[0]
+    )
     # (here we could catch an error in case we don't support leap days)
 
     # Monthly rates between the random dates: