diff --git a/scripts/build_transport_demand.py b/scripts/build_transport_demand.py
index be8ad414..33c8faae 100644
--- a/scripts/build_transport_demand.py
+++ b/scripts/build_transport_demand.py
@@ -13,9 +13,7 @@ import logging
 import numpy as np
 import pandas as pd
 import xarray as xr
-
-from _helpers import configure_logging
-from _helpers import generate_periodic_profiles
+from _helpers import configure_logging, generate_periodic_profiles
 
 logger = logging.getLogger(__name__)
 
@@ -137,8 +135,10 @@ def bev_availability_profile(fn, snapshots, nodes, options):
     )
 
     if not avail[avail < 0].empty:
-        logger.warning("The BEV availability weekly profile has negative values which can "
-                       "lead to infeasibility.")
+        logger.warning(
+            "The BEV availability weekly profile has negative values which can "
+            "lead to infeasibility."
+        )
 
     return generate_periodic_profiles(
         dt_index=snapshots,
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index ba3f24c6..8bb9089b 100755
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1324,7 +1324,7 @@ def add_storage_and_grids(n, costs):
             n, "H2 pipeline ", carriers=["DC", "gas pipeline"]
         )
 
-        h2_pipes["p_nom"] = 0.
+        h2_pipes["p_nom"] = 0.0
 
         if snakemake.input.get("custom_h2_pipelines"):
             fn = snakemake.input.custom_h2_pipelines
@@ -1333,8 +1333,14 @@ def add_storage_and_grids(n, costs):
             h2_pipes = pd.concat([h2_pipes, wkn])
 
             # drop duplicates according to buses (order can be different) and keep pipe with highest p_nom
-            h2_pipes['buses_sorted'] = h2_pipes.apply(lambda row: tuple(sorted([row['bus0'], row['bus1']])), axis=1)
-            h2_pipes = h2_pipes.sort_values('p_nom').drop_duplicates(subset=['buses_sorted'], keep='last').drop(columns = 'buses_sorted')
+            h2_pipes["buses_sorted"] = h2_pipes.apply(
+                lambda row: tuple(sorted([row["bus0"], row["bus1"]])), axis=1
+            )
+            h2_pipes = (
+                h2_pipes.sort_values("p_nom")
+                .drop_duplicates(subset=["buses_sorted"], keep="last")
+                .drop(columns="buses_sorted")
+            )
 
         # TODO Add efficiency losses
         n.madd(