chore: lint modules

src/data_cleaning.py

@@ -1,7 +1,5 @@
 import pandas as pd
 import numpy as np
-import os
-import sqlalchemy as sa
 
 import pudl.analysis.allocate_gen_fuel as allocate_gen_fuel
 import pudl.analysis.epacamd_eia as epacamd_eia
@@ -12,7 +10,7 @@
 import emissions
 from emissions import CLEAN_FUELS
 from column_checks import get_dtypes, apply_dtypes
-from filepaths import manual_folder, outputs_folder, downloads_folder
+from filepaths import manual_folder, outputs_folder
 from logging_util import get_logger
 
 logger = get_logger(__name__)

src/eia930.py

@@ -12,7 +12,7 @@
 # Tell gridemissions where to find config before we load gridemissions
 os.environ["GRIDEMISSIONS_CONFIG_FILE_PATH"] = top_folder("config/gridemissions.json")
 
-from gridemissions.workflows import make_dataset
+from gridemissions.workflows import make_dataset  # noqa E402
 
 logger = get_logger(__name__)
 

src/gross_to_net_generation.py

@@ -2,7 +2,6 @@
 import os
 import pandas as pd
 import statsmodels.formula.api as smf
-import sqlalchemy as sa
 import warnings
 
 # import pudl packages
@@ -13,7 +12,7 @@
 import data_cleaning
 import validation
 from column_checks import get_dtypes
-from filepaths import outputs_folder, downloads_folder
+from filepaths import outputs_folder
 from logging_util import get_logger
 
 logger = get_logger(__name__)

src/load_data.py

@@ -1,6 +1,5 @@
 import pandas as pd
 import numpy as np
-import os
 import sqlalchemy as sa
 import warnings
 from pathlib import Path

src/output_data.py

@@ -462,7 +462,7 @@ def write_power_sector_results(ba_fuel_data, path_prefix, skip_outputs):
 
     if not skip_outputs:
         for ba in list(ba_fuel_data.ba_code.unique()):
-            if type(ba) is not str:
+            if not isinstance(ba, str):
                 logger.warning(
                     f"not aggregating {sum(ba_fuel_data.ba_code.isna())} plants with numeric BA {ba}"
                 )

src/validation.py

@@ -220,7 +220,7 @@ def check_for_orphaned_cc_part_in_subplant(subplant_crosswalk):
         "prime_mover_code"
     ].agg(["unique"])
     cc_subplants["unique_cc_pms"] = [
-        ",".join(map(str, l)) for l in cc_subplants["unique"]
+        ",".join(map(str, L)) for L in cc_subplants["unique"]
     ]
     cc_subplants = cc_subplants.drop(columns="unique")
     # identify where there are subplants that only contain a single CC part