address recent deprecations (#235)

* address recent deprecations

* address recent deprecations in pd.read_csv

scripts/add_existing_baseyear.py

@@ -153,8 +153,8 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
     df_agg.Fueltype = df_agg.Fueltype.map(rename_fuel)
 
     # assign clustered bus
-    busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0, squeeze=True)
+    busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0).squeeze()
-    busmap = pd.read_csv(snakemake.input.busmap, index_col=0, squeeze=True)
+    busmap = pd.read_csv(snakemake.input.busmap, index_col=0).squeeze()
 
     inv_busmap = {}
     for k, v in busmap.iteritems():

scripts/build_biomass_potentials.py

@@ -149,7 +149,7 @@ def build_nuts2_shapes():
 
     nuts2.rename(index={"ME00": "ME", "MK00": "MK"}, inplace=True)
 
-    return nuts2.append(missing)
+    return pd.concat([nuts2, missing])
 
 
 def area(gdf):

scripts/build_gas_input_locations.py

@@ -26,7 +26,7 @@ def build_gas_input_locations(lng_fn, planned_lng_fn, entry_fn, prod_fn, countri
     planned_lng = pd.read_csv(planned_lng_fn)
     planned_lng.geometry = planned_lng.geometry.apply(wkt.loads)
     planned_lng = gpd.GeoDataFrame(planned_lng, crs=4326)
-    lng = lng.append(planned_lng, ignore_index=True)
+    lng = pd.concat([lng, planned_lng], ignore_index=True)
 
     # Entry points from outside the model scope
     entry = read_scigrid_gas(entry_fn)

scripts/build_industrial_production_per_country.py

@@ -115,14 +115,14 @@ def get_energy_ratio(country):
         # estimate physical output, energy consumption in the sector and country
         fn = f"{eurostat_dir}/{eb_names[country]}.XLSX"
         df = pd.read_excel(fn, sheet_name='2016', index_col=2,
-                           header=0, skiprows=1, squeeze=True)
+                           header=0, skiprows=1).squeeze('columns')
         e_country = df.loc[eb_sectors.keys(
         ), 'Total all products'].rename(eb_sectors)
 
     fn = f'{jrc_dir}/JRC-IDEES-2015_Industry_EU28.xlsx'
 
     df = pd.read_excel(fn, sheet_name='Ind_Summary',
-                       index_col=0, header=0, squeeze=True)
+                       index_col=0, header=0).squeeze('columns')
 
     assert df.index[48] == "by sector"
     year_i = df.columns.get_loc(year)
@@ -142,7 +142,7 @@ def industry_production_per_country(country):
         fn = f'{jrc_dir}/JRC-IDEES-2015_Industry_{jrc_country}.xlsx'
         sheet = sub_sheet_name_dict[sector]
         df = pd.read_excel(fn, sheet_name=sheet,
-                           index_col=0, header=0, squeeze=True)
+                           index_col=0, header=0).squeeze('columns')
 
         year_i = df.columns.get_loc(year)
         df = df.iloc[find_physical_output(df), year_i]

scripts/build_industry_sector_ratios.py

@@ -78,9 +78,8 @@ def load_idees_data(sector, country="EU28"):
         sheet_name=list(sheets.values()),
         index_col=0,
         header=0,
-        squeeze=True,
         usecols=usecols,
-    )
+    ).squeeze('columns')
 
     for k, v in sheets.items():
         idees[k] = idees.pop(v)

scripts/build_population_layouts.py

@@ -33,7 +33,7 @@ if __name__ == '__main__':
 
     urban_fraction = pd.read_csv(snakemake.input.urban_percent,
                                 header=None, index_col=0,
-                                names=['fraction'], squeeze=True) / 100.
+                                names=['fraction']).squeeze() / 100.
 
     # fill missing Balkans values
     missing = ["AL", "ME", "MK"]

scripts/prepare_sector_network.py

@@ -279,7 +279,7 @@ def create_network_topology(n, prefix, carriers=["DC"], connector=" -> ", bidire
         topo_reverse = topo.copy()
         topo_reverse.rename(columns=swap_buses, inplace=True)
         topo_reverse.index = topo_reverse.apply(make_index, axis=1)
-        topo = topo.append(topo_reverse)
+        topo = pd.concat([topo, topo_reverse])
 
     return topo
 
@@ -686,7 +686,7 @@ def prepare_data(n):
 
     ## Get overall demand curve for all vehicles
 
-    traffic = pd.read_csv(snakemake.input.traffic_data_KFZ, skiprows=2, usecols=["count"], squeeze=True)
+    traffic = pd.read_csv(snakemake.input.traffic_data_KFZ, skiprows=2, usecols=["count"]).squeeze()
 
     #Generate profiles
     transport_shape = generate_periodic_profiles(
@@ -741,7 +741,7 @@ def prepare_data(n):
 
     ## derive plugged-in availability for PKW's (cars)
 
-    traffic = pd.read_csv(snakemake.input.traffic_data_Pkw, skiprows=2, usecols=["count"], squeeze=True)
+    traffic = pd.read_csv(snakemake.input.traffic_data_Pkw, skiprows=2, usecols=["count"]).squeeze()
 
     avail_max = options.get("bev_avail_max", 0.95)
     avail_mean = options.get("bev_avail_mean", 0.8)
@@ -1888,8 +1888,7 @@ def add_biomass(n, costs):
         transport_costs = pd.read_csv(
             snakemake.input.biomass_transport_costs,
             index_col=0,
-            squeeze=True
+        ).squeeze()
-        )
 
         # add biomass transport
         biomass_transport = create_network_topology(n, "biomass transport ", bidirectional=False)
@@ -2521,7 +2520,7 @@ if __name__ == "__main__":
         fn = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/carbon_budget_distribution.csv'
         if not os.path.exists(fn):
             build_carbon_budget(o, fn)
-        co2_cap = pd.read_csv(fn, index_col=0, squeeze=True)
+        co2_cap = pd.read_csv(fn, index_col=0).squeeze()
         limit = co2_cap[investment_year]
         break
     for o in opts: