Fix pandas FutureWarnings (#1116)

webviz_subsurface/_components/tornado/_tornado_data.py

@@ -106,15 +106,15 @@ def _calculate_sensitivity_averages(
     ) -> List[Dict[str, Union[str, list, float]]]:
         avg_per_sensitivity = []
 
-        for sens_name, sens_name_df in dframe.groupby(["SENSNAME"]):
+        for sens_name, sens_name_df in dframe.groupby("SENSNAME"):
             # Excluding cases if `ref` is used as `SENSNAME`, and only one realization
             # is present for this `SENSNAME`
             if sens_name == "ref" and len(sens_name_df["REAL"].unique()) == 1:
                 continue
 
             # If `SENSTYPE` is scalar get the mean for each `SENSCASE`
             if (sens_name_df["SENSTYPE"] == "scalar").all():
-                for sens_case, sens_case_df in sens_name_df.groupby(["SENSCASE"]):
+                for sens_case, sens_case_df in sens_name_df.groupby("SENSCASE"):
                     avg_per_sensitivity.append(
                         {
                             "sensname": sens_name,
@@ -179,7 +179,7 @@ def _calculate_tornado_low_high_list(
     ) -> List[Dict[str, Union[str, list, float]]]:
         low_high_per_sensitivity = []
         for sensname, sens_name_df in pd.DataFrame(avg_per_sensitivity).groupby(
-            ["sensname"]
+            "sensname"
         ):
             low = sens_name_df.copy().loc[sens_name_df["values_ref"].idxmin()]
             high = sens_name_df.copy().loc[sens_name_df["values_ref"].idxmax()]
@@ -259,7 +259,7 @@ def low_high_realizations_list(self) -> Dict[str, Dict]:
                 "real_low": sens_name_df["low_reals"].tolist()[0],
                 "real_high": sens_name_df["high_reals"].tolist()[0],
             }
-            for sensname, sens_name_df in self.tornadotable.groupby(["sensname"])
+            for sensname, sens_name_df in self.tornadotable.groupby("sensname")
         }
 
     @staticmethod

webviz_subsurface/_models/inplace_volumes_model.py

@@ -309,7 +309,7 @@ def get_df(
             aggregations.update({x: "mean" for x in parameters})
 
             dframe = dframe.groupby(sum_over_groups).agg(aggregations).reset_index()
-            dframe = dframe.groupby(groups).mean().reset_index()
+            dframe = dframe.groupby(groups).mean(numeric_only=True).reset_index()
 
         dframe = self.compute_property_columns(dframe, properties)
         if "FLUID_ZONE" not in groups:

webviz_subsurface/plugins/_property_statistics/models/property_statistics_model.py

@@ -202,6 +202,9 @@ def delta_statistics(
             lambda x: " | ".join([f"{x[sel]}" for sel in self.selectors]), axis=1
         )
 
+        # Drop non-numerical columns before aggregations
+        df = df.drop(columns=["SOURCE", "PROPERTY"] + self.selectors)
+
         df = df[df["ENSEMBLE"].isin([ensemble, delta_ensemble])]
         df = df.pivot_table(columns=["ENSEMBLE"], index="label").reset_index()
 
@@ -311,6 +314,8 @@ def make_statistics_table(
         if selector_values is not None:
             df = self.filter_dataframe(df, self.selectors, selector_values)
 
+        # Drop non-numerical columns before aggregations
+        df = df.drop(columns=["SOURCE", "PROPERTY"] + self.selectors)
         df = df[df["ENSEMBLE"].isin(ensembles)]
         df = df.pivot_table(columns=["ENSEMBLE"], index="label").reset_index()
         return self.make_table(df)

webviz_subsurface/plugins/_volumetric_analysis/controllers/distribution_controllers.py

@@ -362,8 +362,13 @@ def make_tables(
         statcols = ["Mean", "Stddev", "P90", "P10", "Min", "Max"]
         groups = [x for x in groups if x != "REAL"]
         responses = [x for x in responses if x != "REAL" and x is not None]
-        df_groups = dframe.groupby(groups) if groups else [(None, dframe)]
 
+        if not groups:
+            df_groups = [("", dframe)]
+        elif len(groups) == 1:
+            df_groups = dframe.groupby(groups[0])
+        else:
+            df_groups = dframe.groupby(groups)
         data_properties = []
         data_volcols = []
         for response in responses:
@@ -383,10 +388,11 @@ def make_tables(
                     data.update(
                         FLUID_ZONE=(" + ").join(selections["filters"]["FLUID_ZONE"])
                     )
-
                 for idx, group in enumerate(groups):
                     data[group] = (
-                        name if not isinstance(name, tuple) else list(name)[idx]
+                        name
+                        if name is not None or name is not isinstance(name, str)
+                        else list(name)[idx]
                     )
                 if response in volumemodel.volume_columns:
                     data["Response"] = response

webviz_subsurface/plugins/_volumetric_analysis/views/selections_view.py

@@ -244,6 +244,8 @@ def histogram_options(uuid: str, tab: str) -> html.Div:
                 label="Histogram bins:",
                 id={"id": uuid, "tab": tab, "selector": "hist_bins"},
                 value=15,
+                step=1,
+                marks={1: 1, 10: 10, 20: 20, 30: 30},
                 min=1,
                 max=30,
             ),

webviz_subsurface/plugins/_volumetric_analysis/volume_validator_and_combinator.py

@@ -170,7 +170,7 @@ def create_set_dframe(self, volume_dfs: List[pd.DataFrame]) -> pd.DataFrame:
         """Sum Eclipse and RMS volumetrics over the common disjoints sets."""
         region_selectors = self.find_region_selectors()
         set_data_list = []
-        for set_idx, df in self.disjoint_set_df.groupby(["SET"]):
+        for set_idx, df in self.disjoint_set_df.groupby("SET"):
             for voldf in volume_dfs:
                 source = voldf["SOURCE"].unique()[0]
                 if "FIPNUM" in voldf.columns:
@@ -187,7 +187,7 @@ def create_set_dframe(self, volume_dfs: List[pd.DataFrame]) -> pd.DataFrame:
                     )
                 set_df = (
                     filtered_df.groupby(["ENSEMBLE", "REAL"])
-                    .sum()
+                    .sum(numeric_only=True)
                     .reset_index()
                     .drop(labels=["FIPNUM", "REGION", "ZONE"], errors="ignore")
                 )