minimal fixes

climada/engine/calibration_opt.py

@@ -91,7 +91,7 @@ def calib_instance(
             years_in_common = df_out.loc[
                 df_out["year"].isin(np.sort(list((iys.keys())))), "year"
             ]
-            for cnt_, year in years_in_common.iteritems():
+            for cnt_, year in years_in_common.items():
                 df_out.loc[df_out["year"] == year, "impact_CLIMADA"] = iys[year]
 
     else:  # impact per event
@@ -403,7 +403,7 @@ def calib_all(
         if df_result is None:
             df_result = copy.deepcopy(df_out)
         else:
-            df_result = df_result.append(df_out, input)
+            df_result = pd.concat([df_result, df_out], ignore_index=True)
 
     return df_result
 

climada/engine/impact_data.py

@@ -410,7 +410,7 @@ def hit_country_per_hazard(intensity_path, names_path, reg_id_path, date_path):
         all_hits.append(hits)
 
     # create data frame for output
-    hit_countries = pd.DataFrame(columns=["hit_country", "Date_start", "ibtracsID"])
+    hit_countries = []
     for track, _ in enumerate(names):
         # Check if track has hit any country else go to the next track
         if len(all_hits[track]) > 0:
@@ -419,16 +419,17 @@ def hit_country_per_hazard(intensity_path, names_path, reg_id_path, date_path):
                 # Hit country ISO
                 ctry_iso = u_coord.country_to_iso(all_hits[track][hit], "alpha3")
                 # create entry for each country a hazard has hit
-                hit_countries = hit_countries.append(
+                hit_countries.append(
                     {
                         "hit_country": ctry_iso,
                         "Date_start": date[track],
                         "ibtracsID": names[track],
-                    },
-                    ignore_index=True,
+                    }
                 )
     # retrun data frame with all hit countries per hazard
-    return hit_countries
+    return pd.DataFrame(
+        hit_countries, columns=["hit_country", "Date_start", "ibtracsID"]
+    )
 
 
 def create_lookup(emdat_data, start, end, disaster_subtype="Tropical cyclone"):

climada/entity/exposures/litpop/litpop.py

@@ -632,17 +632,18 @@ def from_shape_and_countries(
             # works if shape is Polygon or MultiPolygon
             gdf = exp.gdf.loc[exp.gdf.geometry.within(shape)]
         elif isinstance(shape, (geopandas.GeoSeries, list)):
-            gdf = geopandas.GeoDataFrame(columns=exp.gdf.columns)
+            idx = np.array([False] * exp.gdf.shape[0], dtype=bool)
             for shp in shape:
                 if isinstance(
                     shp, (shapely.geometry.MultiPolygon, shapely.geometry.Polygon)
                 ):
-                    gdf = gdf.append(exp.gdf.loc[exp.gdf.geometry.within(shp)])
+                    idx |= exp.gdf.geometry.within(shp)
                 else:
                     raise NotImplementedError(
                         "Not implemented for list or GeoSeries containing "
                         f"objects of type {type(shp)} as `shape`"
                     )
+            gdf = exp.gdf.loc[idx]
         else:
             raise NotImplementedError(
                 "Not implemented for `shape` of type {type(shape)}"

climada/test/test_litpop_integr.py

@@ -195,7 +195,7 @@ def test_from_shape_and_countries_zurich_pass(self):
         with from_shape_and_countries()"""
 
         ent = lp.LitPop.from_shape_and_countries(
-            shape, "Switzerland", res_arcsec=30, reference_year=2016
+            [shape, shape], "Switzerland", res_arcsec=30, reference_year=2016
         )
         self.assertEqual(ent.value.min(), 0.0)
         self.assertEqual(ent.region_id.min(), 756)
@@ -217,6 +217,8 @@ def test_from_shape_and_countries_zurich_pass(self):
             ],
             8.529166666666658,
         )
+        # must be the same as for shape=`shape` or shape=`[shape]`
+        self.assertEqual(ent.gdf.shape, (1050, 5))
 
     def test_Liechtenstein_15_lit_pass(self):
         """Create Nightlights entity for Liechtenstein 2016:"""