diff --git a/scripts/make_summary_perfect.py b/scripts/make_summary_perfect.py
index d401b28ec..064db454e 100644
--- a/scripts/make_summary_perfect.py
+++ b/scripts/make_summary_perfect.py
@@ -246,8 +246,9 @@ def calculate_energy(n, label, energy):
                 .groupby(level=0)
                 .sum()
                 .multiply(c.df.sign)
-                .groupby(c.df.carrier, axis=1)
+                .T.groupby(c.df.carrier)
                 .sum()
+                .T
             )
         else:
             c_energies = pd.DataFrame(
@@ -394,16 +395,9 @@ def calculate_supply_energy(n, label, supply_energy):
                 if len(items) == 0:
                     continue
 
-                s = (
-                    (-1)
-                    * c.pnl["p" + end]
-                    .reindex(items, axis=1)
-                    .multiply(n.snapshot_weightings.objective, axis=0)
-                    .groupby(level=0)
-                    .sum()
-                    .groupby(c.df.loc[items, "carrier"], axis=1)
-                    .sum()
-                ).T
+                s = (-1) * c.pnl["p" + end].reindex(items, axis=1).multiply(
+                    n.snapshot_weightings.objective, axis=0
+                ).groupby(level=0).sum().T.groupby(c.df.loc[items, "carrier"]).sum()
                 s.index = s.index + end
                 s = pd.concat([s], keys=[c.list_name])
                 s = pd.concat([s], keys=[i])
@@ -498,7 +492,7 @@ def calculate_weighted_prices(n, label, weighted_prices):
         else:
             suffix = " " + carrier
 
-        buses = n.buses.index[n.buses.index.str[2:] == suffix]
+        buses = n.buses.index[n.buses.index.str[5:] == suffix]
 
         if buses.empty:
             continue
@@ -509,14 +503,14 @@ def calculate_weighted_prices(n, label, weighted_prices):
             else n.loads_t.p_set.reindex(buses, axis=1)
         )
         for tech in value:
-            names = n.links.index[n.links.index.to_series().str[-len(tech) :] == tech]
+            names = n.links.index[
+                n.links.index.to_series().str[-len(tech) - 5 : -5] == tech
+            ]
 
             if names.empty:
                 continue
 
-            load += (
-                n.links_t.p0[names].groupby(n.links.loc[names, "bus0"], axis=1).sum()
-            )
+            load += n.links_t.p0[names].T.groupby(n.links.loc[names, "bus0"]).sum().T
 
         # Add H2 Store when charging
         # if carrier == "H2":
@@ -557,9 +551,9 @@ def calculate_market_values(n, label, market_values):
 
         dispatch = (
             n.generators_t.p[gens]
-            .groupby(n.generators.loc[gens, "bus"], axis=1)
+            .T.groupby(n.generators.loc[gens, "bus"])
             .sum()
-            .reindex(columns=buses, fill_value=0.0)
+            .T.reindex(columns=buses, fill_value=0.0)
         )
 
         revenue = dispatch * n.buses_t.marginal_price[buses]
@@ -583,9 +577,9 @@ def calculate_market_values(n, label, market_values):
 
             dispatch = (
                 n.links_t["p" + i][links]
-                .groupby(n.links.loc[links, "bus" + i], axis=1)
+                .T.groupby(n.links.loc[links, "bus" + i])
                 .sum()
-                .reindex(columns=buses, fill_value=0.0)
+                .T.reindex(columns=buses, fill_value=0.0)
             )
 
             revenue = dispatch * n.buses_t.marginal_price[buses]
@@ -652,7 +646,7 @@ def calculate_co2_emissions(n, label, df):
         emitted = n.generators_t.p[gens.index].mul(em_pu)
 
         emitted_grouped = (
-            emitted.groupby(level=0).sum().groupby(n.generators.carrier, axis=1).sum().T
+            emitted.groupby(level=0).sum().T.groupby(n.generators.carrier).sum()
         )
 
         df = df.reindex(emitted_grouped.index.union(df.index))
diff --git a/scripts/prepare_perfect_foresight.py b/scripts/prepare_perfect_foresight.py
index 3d72cd8cc..cf0135772 100644
--- a/scripts/prepare_perfect_foresight.py
+++ b/scripts/prepare_perfect_foresight.py
@@ -56,7 +56,9 @@ def get_investment_weighting(time_weighting, r=0.01):
     end = time_weighting.cumsum()
     start = time_weighting.cumsum().shift().fillna(0)
     return pd.concat([start, end], axis=1).apply(
-        lambda x: sum(get_social_discount(t, r) for t in range(int(x[0]), int(x[1]))),
+        lambda x: sum(
+            get_social_discount(t, r) for t in range(int(x.iloc[0]), int(x.iloc[1]))
+        ),
         axis=1,
     )
 
@@ -188,7 +190,7 @@ def concat_networks(years):
             pnl = getattr(n, component.list_name + "_t")
             for k in iterkeys(component.pnl):
                 pnl_year = component.pnl[k].copy().reindex(snapshots, level=1)
-                if pnl_year.empty and ~(component.name == "Load" and k == "p_set"):
+                if pnl_year.empty and (not (component.name == "Load" and k == "p_set")):
                     continue
                 if component.name == "Load":
                     static_load = network.loads.loc[network.loads.p_set != 0]