From 9530d63e551f1cc19dd6847f10902fec3d59da0f Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Thu, 4 Jan 2024 18:57:09 +0100
Subject: [PATCH] cluster_heat_buses: performance boost and tidy code

---
 scripts/prepare_sector_network.py | 29 +++++++++++++----------------
 1 file changed, 13 insertions(+), 16 deletions(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 62bfa80a..26aa50c9 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -3332,24 +3332,24 @@ def limit_individual_line_extension(n, maxext):
 
 
 aggregate_dict = {
-    "p_nom": "sum",
-    "s_nom": "sum",
+    "p_nom": pd.Series.sum,
+    "s_nom": pd.Series.sum,
     "v_nom": "max",
     "v_mag_pu_max": "min",
     "v_mag_pu_min": "max",
-    "p_nom_max": "sum",
-    "s_nom_max": "sum",
-    "p_nom_min": "sum",
-    "s_nom_min": "sum",
+    "p_nom_max": pd.Series.sum,
+    "s_nom_max": pd.Series.sum,
+    "p_nom_min": pd.Series.sum,
+    "s_nom_min": pd.Series.sum,
     "v_ang_min": "max",
     "v_ang_max": "min",
     "terrain_factor": "mean",
     "num_parallel": "sum",
     "p_set": "sum",
     "e_initial": "sum",
-    "e_nom": "sum",
-    "e_nom_max": "sum",
-    "e_nom_min": "sum",
+    "e_nom": pd.Series.sum,
+    "e_nom_max": pd.Series.sum,
+    "e_nom_min": pd.Series.sum,
     "state_of_charge_initial": "sum",
     "state_of_charge_set": "sum",
     "inflow": "sum",
@@ -3411,13 +3411,10 @@ def cluster_heat_buses(n):
         pnl = c.pnl
         agg = define_clustering(pd.Index(pnl.keys()), aggregate_dict)
         for k in pnl.keys():
-            pnl[k].rename(
-                columns=lambda x: x.replace("residential ", "").replace(
-                    "services ", ""
-                ),
-                inplace=True,
-            )
-            pnl[k] = pnl[k].groupby(level=0, axis=1).agg(agg[k], **agg_group_kwargs)
+            def renamer(s):
+                return s.replace("residential ", "").replace("services ", "")
+
+            pnl[k] = pnl[k].groupby(renamer, axis=1).agg(agg[k], **agg_group_kwargs)
 
         # remove unclustered assets of service/residential
         to_drop = c.df.index.difference(df.index)