diff --git a/scripts/make_summary.py b/scripts/make_summary.py
index 9a6ff098..3f7ca991 100644
--- a/scripts/make_summary.py
+++ b/scripts/make_summary.py
@@ -63,7 +63,7 @@ def assign_locations(n):
 def calculate_nodal_cfs(n,label,nodal_cfs):
     #Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
     for c in n.iterate_components((n.branch_components^{"Line","Transformer"})|n.controllable_one_port_components^{"Load","StorageUnit"}):
-        capacities_c = c.df[opt_name.get(c.name,"p") + "_nom_opt"].groupby((c.df.location,c.df.carrier)).sum()
+        capacities_c = c.df.groupby(["location","carrier"])[opt_name.get(c.name,"p") + "_nom_opt"].sum()
 
         if c.name == "Link":
             p = c.pnl.p0.abs().mean()
@@ -74,7 +74,8 @@ def calculate_nodal_cfs(n,label,nodal_cfs):
         else:
             sys.exit()
 
-        p_c = p.groupby((c.df.location,c.df.carrier)).sum()
+        c.df["p"] = p
+        p_c = c.df.groupby(["location","carrier"])["p"].sum()
 
         cf_c = p_c/capacities_c
 
@@ -118,7 +119,8 @@ def calculate_cfs(n,label,cfs):
 def calculate_nodal_costs(n,label,nodal_costs):
     #Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
     for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
-        capital_costs = (c.df.capital_cost*c.df[opt_name.get(c.name,"p") + "_nom_opt"]).groupby((c.df.location,c.df.carrier)).sum()
+        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.loc[index,label] = capital_costs.values
@@ -139,7 +141,8 @@ def calculate_nodal_costs(n,label,nodal_costs):
             items = c.df.index[(c.df.carrier == "co2 stored") & (c.df.marginal_cost <= -100.)]
             c.df.loc[items,"marginal_cost"] = -20.
 
-        marginal_costs = (p*c.df.marginal_cost).groupby((c.df.location,c.df.carrier)).sum()
+        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.loc[index,label] = marginal_costs.values
@@ -206,7 +209,7 @@ def calculate_costs(n,label,costs):
 def calculate_nodal_capacities(n,label,nodal_capacities):
     #Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
     for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
-        nodal_capacities_c = c.df[opt_name.get(c.name,"p") + "_nom_opt"].groupby((c.df.location,c.df.carrier)).sum()
+        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.loc[index,label] = nodal_capacities_c.values
diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index 9baa07d4..1430b6a3 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -111,13 +111,17 @@ def plot_map(components=["links","stores","storage_units","generators"],bus_size
     costs = pd.DataFrame(index=n.buses.index)
 
     for comp in components:
-        getattr(n,comp)["nice_group"] = getattr(n,comp).carrier.map(rename_techs_tyndp)
+
+        df = getattr(n,comp)
+
+        df["nice_group"] = df.carrier.map(rename_techs_tyndp)
 
         attr = "e_nom_opt" if comp == "stores" else "p_nom_opt"
 
-        costs = pd.concat((costs,(getattr(n,comp).capital_cost*getattr(n,comp)[attr]).groupby((getattr(n,comp).location,getattr(n,comp).nice_group)).sum().unstack().fillna(0.)),axis=1)
+        df["costs"] = df.capital_cost*df[attr]
+
+        costs = pd.concat((costs,df.groupby(by=["location","nice_group"]).sum()["costs"].unstack().fillna(0.)),axis=1)
 
-        print(comp,costs)
     costs = costs.groupby(costs.columns,axis=1).sum()
 
 
@@ -127,13 +131,8 @@ def plot_map(components=["links","stores","storage_units","generators"],bus_size
 
     costs = costs[new_columns]
 
-    #print(costs)
-    #print(costs.sum())
-
     costs = costs.stack()#.sort_index()
 
-    #print(costs)
-
     fig, ax = plt.subplots(subplot_kw={"projection":ccrs.PlateCarree()})
 
     fig.set_size_inches(7,6)