diff --git a/climada_petals/entity/exposures/openstreetmap/osm_dataloader.py b/climada_petals/entity/exposures/openstreetmap/osm_dataloader.py
index 8237a73ae..3005d9fe6 100644
--- a/climada_petals/entity/exposures/openstreetmap/osm_dataloader.py
+++ b/climada_petals/entity/exposures/openstreetmap/osm_dataloader.py
@@ -26,7 +26,7 @@
 import urllib.request
 
 import geopandas as gpd
-import numpy as np
+import pandas as pd
 from osgeo import ogr, gdal
 import overpy
 import shapely
@@ -417,28 +417,29 @@ def retrieve_cis(self, ci_type):
         """
         # features consisting in points and multipolygon results:
         if ci_type in ['healthcare','education','food']:
-            gdf = self.retrieve('points', DICT_CIS_OSM[ci_type]['osm_keys'],
-                                 DICT_CIS_OSM[ci_type]['osm_query'])
-            gdf = gdf.append(
+            gdf = pd.concat([
+                self.retrieve('points', DICT_CIS_OSM[ci_type]['osm_keys'],
+                              DICT_CIS_OSM[ci_type]['osm_query']),
                 self.retrieve('multipolygons', DICT_CIS_OSM[ci_type]['osm_keys'],
-                              DICT_CIS_OSM[ci_type]['osm_query']))
+                              DICT_CIS_OSM[ci_type]['osm_query']),
+            ])
 
         # features consisting in multipolygon results:
         elif ci_type in ['air']:
             gdf = self.retrieve('multipolygons', DICT_CIS_OSM[ci_type]['osm_keys'],
-                                 DICT_CIS_OSM[ci_type]['osm_query'])
+                                DICT_CIS_OSM[ci_type]['osm_query'])
 
         # features consisting in points, multipolygons and lines:
         elif ci_type in ['gas','oil','telecom','water','wastewater','power',
                          'rail','road']:
-            gdf = self.retrieve('points', DICT_CIS_OSM[ci_type]['osm_keys'],
-                                 DICT_CIS_OSM[ci_type]['osm_query'])
-            gdf = gdf.append(
+            gdf = pd.concat([
+                self.retrieve('points', DICT_CIS_OSM[ci_type]['osm_keys'],
+                              DICT_CIS_OSM[ci_type]['osm_query']),
                 self.retrieve('multipolygons', DICT_CIS_OSM[ci_type]['osm_keys'],
-                                 DICT_CIS_OSM[ci_type]['osm_query']))
-            gdf = gdf.append(
+                              DICT_CIS_OSM[ci_type]['osm_query']),
                 self.retrieve('lines', DICT_CIS_OSM[ci_type]['osm_keys'],
-                                 DICT_CIS_OSM[ci_type]['osm_query']))
+                              DICT_CIS_OSM[ci_type]['osm_query']),
+            ])
         else:
             LOGGER.warning('feature not in DICT_CIS_OSM. Returning empty gdf')
             gdf = gpd.GeoDataFrame()
diff --git a/climada_petals/entity/exposures/test/test_spamagrar_unit.py b/climada_petals/entity/exposures/test/test_spamagrar_unit.py
index 3b778d686..6e7c7bd7a 100644
--- a/climada_petals/entity/exposures/test/test_spamagrar_unit.py
+++ b/climada_petals/entity/exposures/test/test_spamagrar_unit.py
@@ -108,7 +108,7 @@ def init_testdata():
     @staticmethod
     def assert_pd_frame_equal(df1, df2, **kwds):
         """Assert that two dataframes are equal, ignoring ordering of columns"""
-        from pandas.util.testing import assert_frame_equal
+        from pandas.testing import assert_frame_equal
         return assert_frame_equal(df1.sort_index(axis=1), df2.sort_index(axis=1),
                                   check_names=True, **kwds)
 
diff --git a/climada_petals/hazard/emulator/stats.py b/climada_petals/hazard/emulator/stats.py
index e6aa089ba..9ffd02872 100644
--- a/climada_petals/hazard/emulator/stats.py
+++ b/climada_petals/hazard/emulator/stats.py
@@ -71,7 +71,7 @@ def seasonal_average(data, season):
         data = data[(data['year'] > year_min) & (data['year'] <= year_max)]
     data = data.reset_index(drop=True)
     data = data.groupby('year').mean().reset_index()
-    return data.drop('month', 1)
+    return data.drop('month', axis=1)
 
 
 def seasonal_statistics(events, season):
diff --git a/climada_petals/hazard/low_flow.py b/climada_petals/hazard/low_flow.py
index cd8b5eae9..ec686d7f3 100644
--- a/climada_petals/hazard/low_flow.py
+++ b/climada_petals/hazard/low_flow.py
@@ -27,10 +27,12 @@
 import datetime as dt
 from pathlib import Path
 import cftime
-import xarray as xr
 import geopandas as gpd
 import numpy as np
 import numba
+import pandas as pd
+import xarray as xr
+
 
 from sklearn.cluster import DBSCAN
 from sklearn.neighbors import BallTree
@@ -754,7 +756,7 @@ def data_preprocessing_percentile(percentile, yearrange, yearrange_ref,
                 dataf = _xarray_to_geopandas(data_chunk)
                 first_file = False
             else:
-                dataf = dataf.append(_xarray_to_geopandas(data_chunk))
+                dataf = pd.concat([dataf, _xarray_to_geopandas(data_chunk)])
     del data_chunk
     dataf = dataf.sort_values(['lat', 'lon', 'dtime'], ascending=[True, True, True])
     return dataf.reset_index(drop=True), centroids
diff --git a/climada_petals/hazard/wildfire.py b/climada_petals/hazard/wildfire.py
index 79ec5a277..cca16d246 100644
--- a/climada_petals/hazard/wildfire.py
+++ b/climada_petals/hazard/wildfire.py
@@ -606,7 +606,7 @@ def _clean_firms_df(self, df_firms):
                     df_firms_viirs = df_firms_viirs.drop(df_firms_viirs[ \
                         df_firms_viirs.confidence == 'l'].index)
                     df_firms_viirs = df_firms_viirs.rename(columns={'bright_ti4':'brightness'})
-                    temp = temp.append(df_firms_viirs, sort=True)
+                    temp = pd.concat([temp, df_firms_viirs], sort=True)
                     temp = temp.drop(columns=['bright_ti4'])
 
                 df_firms = temp