Fix Python deprecation and future warnings in codebase

cea/demand/demand_writers.py

@@ -265,7 +265,7 @@ def write_to_hdf5(locator, list_buildings):
             if df is None:
                 df = pd.read_hdf(temporary_file, key='dataset')
             else:
-                df = df.append(pd.read_hdf(temporary_file, key='dataset'))
+                df = pd.concat([df, pd.read_hdf(temporary_file, key='dataset')], ignore_index=True)
         df.to_hdf(locator.get_total_demand('hdf'), key='dataset')
 
         """read saved data of monthly values and return as totals"""

cea/plugin.py

@@ -217,7 +217,9 @@ def calc_graph(self):
         raise AssertionError("cea.plots.PlotBase.calc_graph should not be part of the abstract interface")
 
     def calc_table(self):
-        raise DeprecationWarning("cea.plots.PlotBase.calc_table is not used anymore and will be removed in future")
+        import warnings
+        warnings.warn("cea.plots.PlotBase.calc_table is not used anymore and will be removed in future", 
+                     DeprecationWarning, stacklevel=2)
 
     @property
     def output_path(self):

cea/technologies/thermal_network/thermal_network.py

@@ -465,7 +465,8 @@ def thermal_network_main(locator, thermal_network, processes=1):
             # To do this, the initial dataset is repeated 4 times, the remaining values are filled with the average values of all above.
             edge_mass_flow_for_csv = pd.concat([edge_mass_flow_for_csv] * 4, ignore_index=True)
             while len(edge_mass_flow_for_csv.index) < HOURS_IN_YEAR:
-                edge_mass_flow_for_csv = edge_mass_flow_for_csv.append(edge_mass_flow_for_csv.mean(), ignore_index=True)
+                mean_row = edge_mass_flow_for_csv.mean().to_frame().T
+                edge_mass_flow_for_csv = pd.concat([edge_mass_flow_for_csv, mean_row], ignore_index=True)
             edge_mass_flow_for_csv.to_csv(
                 thermal_network.locator.get_nominal_edge_mass_flow_csv_file(thermal_network.network_type,
                                                                             thermal_network.network_name), index=False)
@@ -921,7 +922,8 @@ def extrapolate_datapoints_for_representative_weeks(representative_week_data):
     representative_week_df = pd.DataFrame(representative_week_data)
     representative_week_df = pd.concat([representative_week_df] * 4, ignore_index=True)
     while len(representative_week_df.index) < HOURS_IN_YEAR:
-        representative_week_df = representative_week_df.append(representative_week_df.mean(), ignore_index=True)
+        mean_row = representative_week_df.mean().to_frame().T
+        representative_week_df = pd.concat([representative_week_df, mean_row], ignore_index=True)
     return representative_week_df
 
 

cea/utilities/create_mixed_use_type.py

@@ -89,7 +89,7 @@ def calculate_mixed_loads(properties_df, loads_df, occupant_densities, list_uses
     calculated_loads_df['code'] = use_type_name
     # Remove rows that have the same `code` as new row
     clean_loads_df = loads_df[loads_df['code'] != use_type_name]
-    return clean_loads_df.append(calculated_loads_df)
+    return pd.concat([clean_loads_df, calculated_loads_df], ignore_index=True)
 
 
 def calculate_occupant_density(use_types, internal_loads_df):