address FutureWarning regarding set operations on pd.Index (#103)

scripts/build_industrial_energy_demand_per_country_today.py

@@ -98,7 +98,7 @@ for ct in eu28:
 
             for fuel in fuels:
                 summary.at[fuel,sub] = s[fuels[fuel]].sum()
-                summary.at['other',sub] = summary.at['all',sub] - summary.loc[summary.index^['all','other'],sub].sum()
+                summary.at['other',sub] = summary.at['all',sub] - summary.loc[summary.index.symmetric_difference(['all','other']),sub].sum()
 
     summary['Other Industrial Sectors'] = summary[ois_subs].sum(axis=1)
     summary.drop(columns=ois_subs,inplace=True)
@@ -128,7 +128,7 @@ output = pd.read_csv(snakemake.input.industrial_production_per_country,
 
 eu28_averages = final_summary.groupby(level=1,axis=1).sum().divide(output.loc[eu28].sum(),axis=1)
 
-non_eu28 = output.index^eu28
+non_eu28 = output.index.symmetric_difference(eu28)
 
 for ct in non_eu28:
     print(ct)

scripts/build_industrial_production_per_country.py

@@ -196,7 +196,7 @@ ammonia = pd.read_csv(snakemake.input.ammonia_production,
                       index_col=0)
 
 there = ammonia.index.intersection(countries_demand.index)
-missing = countries_demand.index^there
+missing = countries_demand.index.symmetric_difference(there)
 
 print("Following countries have no ammonia demand:", missing)
 

scripts/build_population_layouts.py

@@ -46,7 +46,7 @@ urban_fraction = pd.read_csv(snakemake.input.urban_percent,
 #fill missing Balkans values
 missing = ["AL","ME","MK"]
 reference = ["RS","BA"]
-urban_fraction = urban_fraction.reindex(urban_fraction.index|missing)
+urban_fraction = urban_fraction.reindex(urban_fraction.index.union(missing))
 urban_fraction.loc[missing] = urban_fraction[reference].mean()
 
 

scripts/build_retro_cost.py

@@ -194,7 +194,7 @@ def prepare_building_stock_data():
 
     area_per_pop = area_tot.unstack().reindex(index=ct_total.index).apply(lambda x: x / ct_total[x.index])
     missing_area_ct = ct_total.index.difference(area_tot.index.levels[0])
-    for ct in (missing_area_ct & ct_total.index):
+    for ct in missing_area_ct.intersection(ct_total.index):
         averaged_data = pd.DataFrame(
             area_per_pop.value.reindex(map_for_missings[ct]).mean()
             * ct_total[ct],
@@ -343,7 +343,7 @@ def calculate_cost_energy_curve(u_values, l_strength, l_weight, average_surface_
     res = res.reset_index().set_index(["country", "sector"])
 
     # map missing countries
-    for ct in pd.Index(map_for_missings.keys()) & countries:
+    for ct in pd.Index(map_for_missings.keys()).intersection(countries):
         averaged_data = res.reindex(index=map_for_missings[ct], level=0).mean(level=1)
         index = pd.MultiIndex.from_product([[ct], averaged_data.index.to_list()])
         averaged_data.index = index

scripts/make_summary.py

@@ -79,7 +79,7 @@ def calculate_nodal_cfs(n,label,nodal_cfs):
         cf_c = p_c/capacities_c
 
         index = pd.MultiIndex.from_tuples([(c.list_name,) + t for t in cf_c.index.to_list()])
-        nodal_cfs = nodal_cfs.reindex(index|nodal_cfs.index)
+        nodal_cfs = nodal_cfs.reindex(index.union(nodal_cfs.index))
         nodal_cfs.loc[index,label] = cf_c.values
 
     return nodal_cfs
@@ -106,7 +106,7 @@ def calculate_cfs(n,label,cfs):
 
         cf_c = pd.concat([cf_c], keys=[c.list_name])
 
-        cfs = cfs.reindex(cf_c.index|cfs.index)
+        cfs = cfs.reindex(cf_c.index.union(cfs.index))
 
         cfs.loc[cf_c.index,label] = cf_c
 
@@ -121,7 +121,7 @@ def calculate_nodal_costs(n,label,nodal_costs):
         c.df["capital_costs"] = c.df.capital_cost*c.df[opt_name.get(c.name,"p") + "_nom_opt"]
         capital_costs = c.df.groupby(["location","carrier"])["capital_costs"].sum()
         index = pd.MultiIndex.from_tuples([(c.list_name,"capital") + t for t in capital_costs.index.to_list()])
-        nodal_costs = nodal_costs.reindex(index|nodal_costs.index)
+        nodal_costs = nodal_costs.reindex(index.union(nodal_costs.index))
         nodal_costs.loc[index,label] = capital_costs.values
 
         if c.name == "Link":
@@ -143,7 +143,7 @@ def calculate_nodal_costs(n,label,nodal_costs):
         c.df["marginal_costs"] = p*c.df.marginal_cost
         marginal_costs = c.df.groupby(["location","carrier"])["marginal_costs"].sum()
         index = pd.MultiIndex.from_tuples([(c.list_name,"marginal") + t for t in marginal_costs.index.to_list()])
-        nodal_costs = nodal_costs.reindex(index|nodal_costs.index)
+        nodal_costs = nodal_costs.reindex(index.union(nodal_costs.index))
         nodal_costs.loc[index,label] = marginal_costs.values
 
     return nodal_costs
@@ -158,7 +158,7 @@ def calculate_costs(n,label,costs):
         capital_costs_grouped = pd.concat([capital_costs_grouped], keys=["capital"])
         capital_costs_grouped = pd.concat([capital_costs_grouped], keys=[c.list_name])
 
-        costs = costs.reindex(capital_costs_grouped.index|costs.index)
+        costs = costs.reindex(capital_costs_grouped.index.union(costs.index))
 
         costs.loc[capital_costs_grouped.index,label] = capital_costs_grouped
 
@@ -185,7 +185,7 @@ def calculate_costs(n,label,costs):
         marginal_costs_grouped = pd.concat([marginal_costs_grouped], keys=["marginal"])
         marginal_costs_grouped = pd.concat([marginal_costs_grouped], keys=[c.list_name])
 
-        costs = costs.reindex(marginal_costs_grouped.index|costs.index)
+        costs = costs.reindex(marginal_costs_grouped.index.union(costs.index))
 
         costs.loc[marginal_costs_grouped.index,label] = marginal_costs_grouped
 
@@ -220,7 +220,7 @@ def calculate_nodal_capacities(n,label,nodal_capacities):
     for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
         nodal_capacities_c = c.df.groupby(["location","carrier"])[opt_name.get(c.name,"p") + "_nom_opt"].sum()
         index = pd.MultiIndex.from_tuples([(c.list_name,) + t for t in nodal_capacities_c.index.to_list()])
-        nodal_capacities = nodal_capacities.reindex(index|nodal_capacities.index)
+        nodal_capacities = nodal_capacities.reindex(index.union(nodal_capacities.index))
         nodal_capacities.loc[index,label] = nodal_capacities_c.values
 
     return nodal_capacities
@@ -234,7 +234,7 @@ def calculate_capacities(n,label,capacities):
         capacities_grouped = c.df[opt_name.get(c.name,"p") + "_nom_opt"].groupby(c.df.carrier).sum()
         capacities_grouped = pd.concat([capacities_grouped], keys=[c.list_name])
 
-        capacities = capacities.reindex(capacities_grouped.index|capacities.index)
+        capacities = capacities.reindex(capacities_grouped.index.union(capacities.index))
 
         capacities.loc[capacities_grouped.index,label] = capacities_grouped
 
@@ -267,7 +267,7 @@ def calculate_energy(n,label,energy):
 
         c_energies = pd.concat([c_energies], keys=[c.list_name])
 
-        energy = energy.reindex(c_energies.index|energy.index)
+        energy = energy.reindex(c_energies.index.union(energy.index))
 
         energy.loc[c_energies.index,label] = c_energies
 
@@ -294,7 +294,7 @@ def calculate_supply(n,label,supply):
             s = pd.concat([s], keys=[c.list_name])
             s = pd.concat([s], keys=[i])
 
-            supply = supply.reindex(s.index|supply.index)
+            supply = supply.reindex(s.index.union(supply.index))
             supply.loc[s.index,label] = s
 
 
@@ -313,7 +313,7 @@ def calculate_supply(n,label,supply):
                 s = pd.concat([s], keys=[c.list_name])
                 s = pd.concat([s], keys=[i])
 
-                supply = supply.reindex(s.index|supply.index)
+                supply = supply.reindex(s.index.union(supply.index))
                 supply.loc[s.index,label] = s
 
     return supply
@@ -339,7 +339,7 @@ def calculate_supply_energy(n,label,supply_energy):
             s = pd.concat([s], keys=[c.list_name])
             s = pd.concat([s], keys=[i])
 
-            supply_energy = supply_energy.reindex(s.index|supply_energy.index)
+            supply_energy = supply_energy.reindex(s.index.union(supply_energy.index))
             supply_energy.loc[s.index,label] = s
 
 
@@ -357,7 +357,7 @@ def calculate_supply_energy(n,label,supply_energy):
                 s = pd.concat([s], keys=[c.list_name])
                 s = pd.concat([s], keys=[i])
 
-                supply_energy = supply_energy.reindex(s.index|supply_energy.index)
+                supply_energy = supply_energy.reindex(s.index.union(supply_energy.index))
 
                 supply_energy.loc[s.index,label] = s
 
@@ -366,7 +366,7 @@ def calculate_supply_energy(n,label,supply_energy):
 
 def calculate_metrics(n,label,metrics):
 
-    metrics = metrics.reindex(pd.Index(["line_volume","line_volume_limit","line_volume_AC","line_volume_DC","line_volume_shadow","co2_shadow"])|metrics.index)
+    metrics = metrics.reindex(pd.Index(["line_volume","line_volume_limit","line_volume_AC","line_volume_DC","line_volume_shadow","co2_shadow"]).union(metrics.index))
 
     metrics.at["line_volume_DC",label] = (n.links.length*n.links.p_nom_opt)[n.links.carrier == "DC"].sum()
     metrics.at["line_volume_AC",label] = (n.lines.length*n.lines.s_nom_opt).sum()
@@ -384,7 +384,7 @@ def calculate_metrics(n,label,metrics):
 
 def calculate_prices(n,label,prices):
 
-    prices = prices.reindex(prices.index|n.buses.carrier.unique())
+    prices = prices.reindex(prices.index.union(n.buses.carrier.unique()))
 
     #WARNING: this is time-averaged, see weighted_prices for load-weighted average
     prices[label] = n.buses_t.marginal_price.mean().groupby(n.buses.carrier).mean()
@@ -467,7 +467,7 @@ def calculate_market_values(n, label, market_values):
 
     techs = n.generators.loc[generators,"carrier"].value_counts().index
 
-    market_values = market_values.reindex(market_values.index | techs)
+    market_values = market_values.reindex(market_values.index.union(techs))
 
 
     for tech in techs:
@@ -488,7 +488,7 @@ def calculate_market_values(n, label, market_values):
 
         techs = n.links.loc[all_links,"carrier"].value_counts().index
 
-        market_values = market_values.reindex(market_values.index | techs)
+        market_values = market_values.reindex(market_values.index.union(techs))
 
         for tech in techs:
             links = all_links[n.links.loc[all_links,"carrier"] == tech]
@@ -505,7 +505,7 @@ def calculate_market_values(n, label, market_values):
 def calculate_price_statistics(n, label, price_statistics):
 
 
-    price_statistics = price_statistics.reindex(price_statistics.index|pd.Index(["zero_hours","mean","standard_deviation"]))
+    price_statistics = price_statistics.reindex(price_statistics.index.union(pd.Index(["zero_hours","mean","standard_deviation"])))
 
     buses = n.buses.index[n.buses.carrier == "AC"]
 

scripts/plot_network.py

@@ -130,7 +130,7 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
 
     costs.drop(list(costs.columns[(costs == 0.).all()]), axis=1, inplace=True)
 
-    new_columns = ((preferred_order & costs.columns)
+    new_columns = (preferred_order.intersection(costs.columns)
                    .append(costs.columns.difference(preferred_order)))
     costs = costs[new_columns]
 
@@ -147,7 +147,7 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
         n.links.carrier != "B2B")], inplace=True)
 
     # drop non-bus
-    to_drop = costs.index.levels[0] ^ n.buses.index
+    to_drop = costs.index.levels[0].symmetric_difference(n.buses.index)
     if len(to_drop) != 0:
         print("dropping non-buses", to_drop)
         costs.drop(to_drop, level=0, inplace=True, axis=0)
@@ -463,7 +463,7 @@ def plot_series(network, carrier="AC", name="test"):
                                 "battery storage",
                                 "hot water storage"])
 
-    new_columns = ((preferred_order & supply.columns)
+    new_columns = (preferred_order.intersection(supply.columns)
                    .append(supply.columns.difference(preferred_order)))
 
     supply =  supply.groupby(supply.columns, axis=1).sum()

scripts/plot_summary.py

@@ -82,7 +82,7 @@ def plot_costs():
 
     print(df.sum())
 
-    new_index = (preferred_order&df.index).append(df.index.difference(preferred_order))
+    new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
 
     new_columns = df.sum().sort_values().index
 
@@ -136,7 +136,7 @@ def plot_energy():
 
     print(df)
 
-    new_index = (preferred_order&df.index).append(df.index.difference(preferred_order))
+    new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
 
     new_columns = df.columns.sort_values()
     #new_columns = df.sum().sort_values().index
@@ -177,7 +177,7 @@ def plot_balances():
     balances_df = pd.read_csv(snakemake.input.balances,index_col=list(range(3)),header=list(range(n_header)))
 
     balances = {i.replace(" ","_") : [i] for i in balances_df.index.levels[0]}
-    balances["energy"] = balances_df.index.levels[0]^co2_carriers
+    balances["energy"] = balances_df.index.levels[0].symmetric_difference(co2_carriers)
 
     for k,v in balances.items():
 
@@ -205,7 +205,7 @@ def plot_balances():
         if df.empty:
             continue
 
-        new_index = (preferred_order&df.index).append(df.index.difference(preferred_order))
+        new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
 
         new_columns = df.columns.sort_values()
 

scripts/prepare_sector_network.py

@@ -240,7 +240,7 @@ def remove_elec_base_techs(n):
 
     for c in n.iterate_components(snakemake.config["pypsa_eur"]):
         to_keep = snakemake.config["pypsa_eur"][c.name]
-        to_remove = pd.Index(c.df.carrier.unique())^to_keep
+        to_remove = pd.Index(c.df.carrier.unique()).symmetric_difference(to_keep)
         print("Removing",c.list_name,"with carrier",to_remove)
         names = c.df.index[c.df.carrier.isin(to_remove)]
         print(names)
@@ -921,7 +921,7 @@ def add_storage(network):
 
     # hydrogen stored overground
     h2_capital_cost = costs.at["hydrogen storage tank", "fixed"]
-    nodes_overground = nodes ^ cavern_nodes.index
+    nodes_overground = nodes.symmetric_difference(cavern_nodes.index)
 
     network.madd("Store",
                  nodes_overground + " H2 Store",
@@ -1484,7 +1484,7 @@ def create_nodes_for_heat_sector():
         else:
             nodes[sector + " urban decentral"] = pop_layout.index
     # for central nodes, residential and services are aggregated
-    nodes["urban central"] = pop_layout.index ^ nodes["residential urban decentral"]
+    nodes["urban central"] = pop_layout.index.symmetric_difference(nodes["residential urban decentral"])
     return nodes