def eia860_boiler_info_design(year):
expected_860_folder = join(data_dir, "eia860_{}".format(year))
if not os.path.exists(expected_860_folder):
print("Downloading EIA-860 files")
eia860_download(year=year, save_path=expected_860_folder)
eia860_path, eia860_name = find_file_in_folder(
folder_path=expected_860_folder,
file_pattern_match=["6_2_EnviroEquip", "xlsx"],
return_name=True,
)
eia = load_eia860_excel(eia860_path, "Boiler Info & Design Parameters",
1)
# Save as csv for easier access in future
csv_fn = eia860_name.split(".")[0] + "_boiler_info.csv"
csv_path = join(expected_860_folder, csv_fn)
eia.to_csv(csv_path, index=False)
else:
all_files = os.listdir(expected_860_folder)
# Check for both csv and year<_Final> in case multiple years
# or other csv files exist
csv_file = [
f for f in all_files if "_boiler_info.csv" in f
and "6_2_EnviroEquip_Y{}".format(year) in f
]
# Read and return the existing csv file if it exists
if csv_file:
print("Loading {} EIA-860 plant data from csv file".format(year))
fn = csv_file[0]
csv_path = join(expected_860_folder, fn)
eia = pd.read_csv(csv_path, dtype={"Plant Id": str})
else:
print("Loading data from previously downloaded excel file")
eia860_path, eia860_name = find_file_in_folder(
folder_path=expected_860_folder,
file_pattern_match=["6_2_EnviroEquip", "xlsx"],
return_name=True,
)
# # would be more elegent with glob but this works to identify the
# # Schedule_2_3_4_5 file
# for f in all_files:
# if '2___Plant' in f:
# plant_file = f
# eia860_path = join(expected_860_folder, plant_file)
eia = load_eia860_excel(eia860_path,
"Boiler Info & Design Parameters", 1)
csv_fn = eia860_name.split(".")[0] + "_boiler_info.csv"
csv_path = join(expected_860_folder, csv_fn)
eia.to_csv(csv_path, index=False)
eia = _clean_columns(eia)
return eia
def eia923_sched8_aec(year):
expected_923_folder = join(data_dir, "f923_{}".format(year))
if not os.path.exists(expected_923_folder):
print("Downloading EIA-923 files")
eia923_download(year=year, save_path=expected_923_folder)
eia923_path, eia923_name = find_file_in_folder(
folder_path=expected_923_folder,
file_pattern_match=["Schedule_8", "xlsx"],
return_name=True,
)
# Save as csv for easier access in future
csv_fn = eia923_name.split(".")[0] + "page_8c.csv"
csv_path = join(expected_923_folder, csv_fn)
eia = load_eia923_excel(expected_923_folder, page="8c")
eia.to_csv(csv_path, index=False)
else:
all_files = os.listdir(expected_923_folder)
# Check for both csv and year<_Final> in case multiple years
# or other csv files exist
csv_file = [
f
for f in all_files
if ".csv" in f and "{}_Final".format(year) in f and "page_8c" in f
]
# Read and return the existing csv file if it exists
if csv_file:
print("Loading {} EIA-923 data from csv file".format(year))
fn = csv_file[0]
csv_path = join(expected_923_folder, fn)
eia = pd.read_csv(
csv_path,
dtype={"Plant Id": str, "YEAR": str, "NAICS Code": str},
low_memory=False,
)
else:
eia923_path, eia923_name = find_file_in_folder(
folder_path=expected_923_folder,
file_pattern_match=["Schedule_8", "xlsx"],
return_name=True,
)
# # would be more elegent with glob but this works to identify the
# # Schedule_2_3_4_5 file
# for f in all_files:
# if '2_3_4_5' in f:
# gen_file = f
# eia923_path = join(expected_923_folder, gen_file)
eia = load_eia923_excel(eia923_path, page="8c")
csv_fn = eia923_name.split(".")[0] + "_page_8c.csv"
csv_path = join(expected_923_folder, csv_fn)
eia.to_csv(csv_path, index=False)
eia = _clean_columns(eia)
return eia
def read_eia923_fuel_receipts(year):
expected_923_folder = join(data_dir, 'f923_{}'.format(year))
if not os.path.exists(expected_923_folder):
print('Downloading EIA-923 files')
eia923_download(year=year, save_path=expected_923_folder)
eia923_path, eia923_name = find_file_in_folder(
folder_path=expected_923_folder,
file_pattern_match=['2_3_4_5'],
return_name=True)
eia_fuel_receipts_df = pd.read_excel(
eia923_path,
sheet_name='Page 5 Fuel Receipts and Costs',
skiprows=4,
usecols="A:E,H:M,P:Q")
csv_fn = eia923_name.split('.')[0] + '_page_5_reduced.csv'
csv_path = join(expected_923_folder, csv_fn)
eia_fuel_receipts_df.to_csv(csv_path, index=False)
else:
# Check for both csv and year<_Final> in case multiple years
# or other csv files exist
print('Loading data from previously downloaded excel file')
all_files = os.listdir(expected_923_folder)
# Check for both csv and year<_Final> in case multiple years
# or other csv files exist
csv_file = [
f for f in all_files if '.csv' in f and '_page_5_reduced.csv' in f
]
if csv_file:
csv_path = os.path.join(expected_923_folder, csv_file[0])
eia_fuel_receipts_df = pd.read_csv(csv_path)
else:
eia923_path, eia923_name = find_file_in_folder(
folder_path=expected_923_folder,
file_pattern_match=['2_3_4_5'],
return_name=True)
eia_fuel_receipts_df = pd.read_excel(
eia923_path,
sheet_name='Page 5 Fuel Receipts and Costs',
skiprows=4,
usecols="A:E,H:M,P:Q")
csv_fn = eia923_name.split('.')[0] + '_page_5_reduced.csv'
csv_path = join(expected_923_folder, csv_fn)
eia_fuel_receipts_df.to_csv(csv_path, index=False)
_clean_columns(eia_fuel_receipts_df)
return eia_fuel_receipts_df
def eia923_download_extract(
year,
group_cols=[
"Plant Id",
"Plant Name",
"State",
"NAICS Code",
"Reported Prime Mover",
"Reported Fuel Type Code",
"YEAR",
],
):
"""
Download (if necessary) and extract a single year of generation/fuel
consumption data from EIA-923.
Data are grouped by plant level
Parameters
----------
year : int or str
Year of data to download/extract
group_cols : list, optional
The columns from EIA923 generation and fuel sheet to use when grouping
generation and fuel consumption data.
"""
expected_923_folder = join(data_dir, "f923_{}".format(year))
if not os.path.exists(expected_923_folder):
print("Downloading EIA-923 files")
eia923_download(year=year, save_path=expected_923_folder)
eia923_path, eia923_name = find_file_in_folder(
folder_path=expected_923_folder,
file_pattern_match=["2_3_4_5"],
return_name=True,
)
# eia923_files = os.listdir(expected_923_folder)
# # would be more elegent with glob but this works to identify the
# # Schedule_2_3_4_5 file
# for f in eia923_files:
# if '2_3_4_5' in f:
# gen_file = f
# eia923_path = join(expected_923_folder, gen_file)
# colstokeep = group_cols + sum_cols
eia = load_eia923_excel(eia923_path)
# Save as csv for easier access in future
csv_fn = eia923_name.split(".")[0] + "page_1.csv"
csv_path = join(expected_923_folder, csv_fn)
eia.to_csv(csv_path, index=False)
else:
all_files = os.listdir(expected_923_folder)
# Check for both csv and year<_Final> in case multiple years
# or other csv files exist
csv_file = [
f for f in all_files
if ".csv" in f and "{}_Final".format(year) in f and "page_1" in f
]
# Read and return the existing csv file if it exists
if csv_file:
print("Loading {} EIA-923 data from csv file".format(year))
fn = csv_file[0]
csv_path = join(expected_923_folder, fn)
eia = pd.read_csv(
csv_path,
dtype={
"Plant Id": str,
"YEAR": str,
"NAICS Code": str
},
)
else:
print(
"Loading data from previously downloaded excel file,",
" how did the csv file get deleted?",
)
eia923_path, eia923_name = find_file_in_folder(
folder_path=expected_923_folder,
file_pattern_match=["2_3_4_5", "xlsx"],
return_name=True,
)
# # would be more elegent with glob but this works to identify the
# # Schedule_2_3_4_5 file
# for f in all_files:
# if '2_3_4_5' in f:
# gen_file = f
# eia923_path = join(expected_923_folder, gen_file)
eia = load_eia923_excel(eia923_path)
csv_fn = eia923_name.split(".")[0] + "_page_1.csv"
csv_path = join(expected_923_folder, csv_fn)
eia.to_csv(csv_path, index=False)
# EIA_923 = eia
# Grouping similar facilities together.
# group_cols = ['Plant Id', 'Plant Name', 'State', 'YEAR']
sum_cols = [
"Total Fuel Consumption MMBtu",
"Net Generation (Megawatthours)",
]
EIA_923_generation_data = eia.groupby(group_cols,
as_index=False)[sum_cols].sum()
return EIA_923_generation_data
def eia860_balancing_authority(year, regional_aggregation=None):
expected_860_folder = join(data_dir, "eia860_{}".format(year))
if not os.path.exists(expected_860_folder):
print("Downloading EIA-860 files")
eia860_download(year=year, save_path=expected_860_folder)
eia860_path, eia860_name = find_file_in_folder(
folder_path=expected_860_folder,
file_pattern_match=["2___Plant"],
return_name=True,
)
# eia860_files = os.listdir(expected_860_folder)
# # would be more elegent with glob but this works to identify the
# # Schedule_2_3_4_5 file
# for f in eia860_files:
# if '2___Plant' in f:
# plant_file = f
# eia860_path = join(expected_860_folder, plant_file)
# colstokeep = group_cols + sum_cols
eia = load_eia860_excel(eia860_path)
# Save as csv for easier access in future
csv_fn = eia860_name.split(".")[0] + ".csv"
csv_path = join(expected_860_folder, csv_fn)
eia.to_csv(csv_path, index=False)
else:
all_files = os.listdir(expected_860_folder)
# Check for both csv and year<_Final> in case multiple years
# or other csv files exist
csv_file = [
f for f in all_files
if ".csv" in f and "Plant_Y{}".format(year) in f
]
# Read and return the existing csv file if it exists
if csv_file:
print("Loading {} EIA-860 plant data from csv file".format(year))
fn = csv_file[0]
csv_path = join(expected_860_folder, fn)
eia = pd.read_csv(csv_path,
dtype={"Plant Id": str},
low_memory=False)
else:
print("Loading data from previously downloaded excel file")
eia860_path, eia860_name = find_file_in_folder(
folder_path=expected_860_folder,
file_pattern_match=["2___Plant"],
return_name=True,
)
# # would be more elegent with glob but this works to identify the
# # Schedule_2_3_4_5 file
# for f in all_files:
# if '2___Plant' in f:
# plant_file = f
# eia860_path = join(expected_860_folder, plant_file)
eia = load_eia860_excel(eia860_path)
csv_fn = eia860_name.split(".")[0] + ".csv"
csv_path = join(expected_860_folder, csv_fn)
eia.to_csv(csv_path, index=False)
ba_cols = [
"Plant Id",
"State",
"NERC Region",
"Balancing Authority Code",
"Balancing Authority Name",
]
eia_plant_ba_match = eia.loc[:, ba_cols].drop_duplicates()
# Map the balancing authority to a larger region (e.g. FERC or EIA)
if regional_aggregation:
region_map = create_ba_region_map(region_col=regional_aggregation)
eia_plant_ba_match[regional_aggregation] = eia_plant_ba_match[
"Balancing Authority Code"].map(region_map)
return eia_plant_ba_match
def generate_upstream_coal_map(year):
eia_fuel_receipts_df = read_eia923_fuel_receipts(year)
expected_7a_folder = join(data_dir, 'f7a_{}'.format(year))
if not os.path.exists(expected_7a_folder):
eia_7a_download(year, expected_7a_folder)
eia7a_path, eia7a_name = find_file_in_folder(
folder_path=expected_7a_folder,
file_pattern_match=['coalpublic'],
return_name=True)
else:
eia7a_path, eia7a_name = find_file_in_folder(
folder_path=expected_7a_folder,
file_pattern_match=['coalpublic'],
return_name=True)
eia7a_df = pd.read_excel(eia7a_path,
sheet_name='Hist_Coal_Prod',
skiprows=3)
_clean_columns(eia7a_df)
coal_criteria = eia_fuel_receipts_df['fuel_group'] == 'Coal'
eia_fuel_receipts_df = eia_fuel_receipts_df.loc[coal_criteria, :]
eia_fuel_receipts_df = eia_fuel_receipts_df.merge(
eia7a_df[['msha_id', 'coal_supply_region']],
how='left',
left_on='coalmine_msha_id',
right_on='msha_id',
)
eia_fuel_receipts_df.drop(columns=['msha_id'], inplace=True)
eia_fuel_receipts_df.rename(
columns={'coal_supply_region': 'eia_coal_supply_region'}, inplace=True)
state_region_map = pd.read_csv(data_dir + '/coal_state_to_basin.csv')
eia_fuel_receipts_df = eia_fuel_receipts_df.merge(
state_region_map[['state', 'basin1', 'basin2']],
left_on='coalmine_state',
right_on='state',
how='left')
eia_fuel_receipts_df.drop(columns=['state'], inplace=True)
eia_netl_basin = pd.read_csv(data_dir + '/eia_to_netl_basin.csv')
eia_fuel_receipts_df = eia_fuel_receipts_df.merge(
eia_netl_basin,
how='left',
left_on='eia_coal_supply_region',
right_on='eia_basin')
eia_fuel_receipts_df.drop(columns=['eia_basin'], inplace=True)
gulf_lignite = (
(eia_fuel_receipts_df['energy_source'] == 'LIG') &
(eia_fuel_receipts_df['eia_coal_supply_region'] == 'Interior'))
eia_fuel_receipts_df.loc[gulf_lignite, ['netl_basin']] = 'Gulf Lignite'
lignite = ((eia_fuel_receipts_df['energy_source'] == 'LIG') &
(eia_fuel_receipts_df['eia_coal_supply_region'] == 'Western'))
eia_fuel_receipts_df.loc[lignite, ['netl_basin']] = 'Lignite'
netl_na = (eia_fuel_receipts_df['netl_basin'].isna())
minimerge = pd.merge(left=eia_fuel_receipts_df,
right=eia_netl_basin,
left_on='basin1',
right_on='eia_basin',
how='left')
eia_fuel_receipts_df.loc[netl_na,
'netl_basin'] = (minimerge.loc[netl_na,
'netl_basin_y'])
eia_fuel_receipts_df[['netl_basin', 'energy_source', 'coalmine_type']]
eia_fuel_receipts_df.dropna(
subset=['netl_basin', 'energy_source', 'coalmine_type'], inplace=True)
eia_fuel_receipts_df['coal_source_code'] = eia_fuel_receipts_df.apply(
_coal_code, axis=1)
eia_fuel_receipts_df['heat_input'] = eia_fuel_receipts_df[
'quantity'] * eia_fuel_receipts_df['average_heat_content']
eia_fuel_receipts_df.drop_duplicates(inplace=True)
final_df = eia_fuel_receipts_df.groupby(
['plant_id', 'coal_source_code'], as_index=False)['quantity',
'heat_input'].sum()
return final_df
def generate_upstream_coal_map(year):
from electricitylci.globals import STATE_ABBREV
from electricitylci.eia923_generation import eia923_generation_and_fuel
eia_fuel_receipts_df = read_eia923_fuel_receipts(year)
expected_7a_folder = join(data_dir, 'f7a_{}'.format(year))
if not os.path.exists(expected_7a_folder):
eia_7a_download(year, expected_7a_folder)
eia7a_path, eia7a_name = find_file_in_folder(
folder_path=expected_7a_folder,
file_pattern_match=['coalpublic'],
return_name=True)
else:
eia7a_path, eia7a_name = find_file_in_folder(
folder_path=expected_7a_folder,
file_pattern_match=['coalpublic'],
return_name=True)
eia7a_df = pd.read_excel(eia7a_path,
sheet_name='Hist_Coal_Prod',
skiprows=3)
_clean_columns(eia7a_df)
coal_criteria = eia_fuel_receipts_df['fuel_group'] == 'Coal'
eia_fuel_receipts_df = eia_fuel_receipts_df.loc[coal_criteria, :]
eia_fuel_receipts_df = eia_fuel_receipts_df.merge(
eia7a_df[['msha_id', 'coal_supply_region']],
how='left',
left_on='coalmine_msha_id',
right_on='msha_id',
)
eia_fuel_receipts_df.drop(columns=['msha_id'], inplace=True)
eia_fuel_receipts_df.rename(
columns={'coal_supply_region': 'eia_coal_supply_region'}, inplace=True)
eia_fuel_receipts_na = eia_fuel_receipts_df.loc[
eia_fuel_receipts_df["eia_coal_supply_region"].isnull(), :]
eia_fuel_receipts_good = eia_fuel_receipts_df.loc[
~eia_fuel_receipts_df["eia_coal_supply_region"].isnull(), :]
county_basin = eia7a_df.groupby(
by=["mine_state", "mine_county", "coal_supply_region"],
as_index=False)["production_short_tons"].count()
county_basin["mine_state"] = county_basin["mine_state"].str.replace(
r" \(.*\)", "")
county_basin["mine_state_abv"] = county_basin["mine_state"].str.lower(
).map(STATE_ABBREV).str.upper()
county_basin["mine_county"] = county_basin["mine_county"].str.lower()
fips_codes = pd.read_csv(f"{data_dir}/fips_codes.csv", )
_clean_columns(fips_codes)
fips_codes["gu_name"] = fips_codes["gu_name"].str.lower()
fips_codes["county_fips_code"] = fips_codes["county_fips_code"].astype(
str).str.replace(".0", "")
county_basin = county_basin.merge(
right=fips_codes[["state_abbreviation", "county_fips_code",
"gu_name"]],
left_on=["mine_state_abv", "mine_county"],
right_on=["state_abbreviation", "gu_name"],
how="left")
county_basin.drop_duplicates(subset=["mine_state_abv", "county_fips_code"],
inplace=True)
eia_fuel_receipts_na = eia_fuel_receipts_na.merge(
right=county_basin[[
"mine_state_abv", "county_fips_code", "coal_supply_region"
]],
left_on=["coalmine_state", "coalmine_county"],
right_on=["mine_state_abv", "county_fips_code"],
how="left")
eia_fuel_receipts_na["eia_coal_supply_region"] = eia_fuel_receipts_na[
"coal_supply_region"]
eia_fuel_receipts_made_good = eia_fuel_receipts_na.loc[
~eia_fuel_receipts_na["eia_coal_supply_region"].isnull(
), :].reset_index(drop=True)
eia_fuel_receipts_na = eia_fuel_receipts_na.loc[
eia_fuel_receipts_na["eia_coal_supply_region"].isnull(
), :].reset_index(drop=True)
eia_fuel_receipts_made_good.drop(
columns=["mine_state_abv", "county_fips_code", "coal_supply_region"],
inplace=True)
eia_fuel_receipts_good = pd.concat(
[eia_fuel_receipts_good, eia_fuel_receipts_made_good],
ignore_index=True,
sort=False)
eia_netl_basin = pd.read_csv(data_dir + '/eia_to_netl_basin.csv')
eia_fuel_receipts_good = eia_fuel_receipts_good.merge(
eia_netl_basin,
left_on='eia_coal_supply_region',
right_on="eia_basin",
how="left").reset_index(drop=True)
state_region_map = pd.read_csv(data_dir + '/coal_state_to_basin.csv')
eia_fuel_receipts_na = eia_fuel_receipts_na.merge(
state_region_map[['state', 'basin1', 'basin2']],
left_on='coalmine_state',
right_on='state',
how='left')
eia_fuel_receipts_na.drop(columns=['state'], inplace=True)
eia_fuel_receipts_na = eia_fuel_receipts_na.merge(
eia_netl_basin,
how='left',
left_on='eia_coal_supply_region',
right_on='eia_basin')
eia_fuel_receipts_na.drop(columns=['eia_basin'], inplace=True)
netl_na = (eia_fuel_receipts_na['netl_basin'].isna())
minimerge = pd.merge(left=eia_fuel_receipts_na,
right=eia_netl_basin,
left_on='basin1',
right_on='eia_basin',
how='left')
minimerge.drop(columns=[
"mine_state_abv", "county_fips_code", "coal_supply_region", "basin1",
"basin2", "netl_basin_x", "eia_basin"
],
inplace=True)
minimerge.rename(columns={"netl_basin_y": "netl_basin"}, inplace=True)
eia_fuel_receipts_good = pd.concat([eia_fuel_receipts_good, minimerge],
ignore_index=True,
sort=False)
gulf_lignite = (
(eia_fuel_receipts_good['energy_source'] == 'LIG') &
(eia_fuel_receipts_good['eia_coal_supply_region'] == 'Interior'))
eia_fuel_receipts_good.loc[gulf_lignite, ['netl_basin']] = 'Gulf Lignite'
lignite = ((eia_fuel_receipts_good['energy_source'] == 'LIG') &
(eia_fuel_receipts_good['eia_coal_supply_region'] == 'Western'))
eia_fuel_receipts_good.loc[lignite, ['netl_basin']] = 'Lignite'
# eia_fuel_receipts_df.loc[netl_na,'netl_basin']=(
# minimerge.loc[netl_na,'netl_basin_y'])
# eia_fuel_receipts_df[['netl_basin','energy_source','coalmine_type']]
eia_fuel_receipts_good.dropna(
subset=['netl_basin', 'energy_source', 'coalmine_type'], inplace=True)
eia_fuel_receipts_good['coal_source_code'] = eia_fuel_receipts_good.apply(
_coal_code, axis=1)
eia_fuel_receipts_good['heat_input'] = eia_fuel_receipts_good[
'quantity'] * eia_fuel_receipts_good['average_heat_content']
eia_fuel_receipts_good.drop_duplicates(inplace=True)
eia_fuel_receipts_good["coal_type"] = eia_fuel_receipts_good[
"energy_source"].map(coal_type_codes)
final_df = eia_fuel_receipts_good.groupby(
['plant_id', 'coal_type', 'coal_source_code'], )[[
'quantity', 'heat_input'
]].sum()
final_df["mass_fraction"] = final_df[["quantity"]].div(
final_df[["quantity"]].groupby(level=[0, 1]).transform('sum'))
final_df["energy_fraction"] = final_df[["heat_input"]].div(
final_df[["heat_input"]].groupby(level=[0, 1]).transform('sum'))
final_df["mass_fraction_total"] = final_df[["quantity"]].div(
final_df[["quantity"]].groupby(level=[0]).transform('sum'))
final_df["energy_fraction_total"] = final_df[["heat_input"]].div(
final_df[["heat_input"]].groupby(level=[0]).transform('sum'))
eia_923_gen_fuel = eia923_generation_and_fuel(year)
eia_923_gen_fuel = eia_923_gen_fuel[[
"plant_id", "reported_fuel_type_code",
"electric_fuel_consumption_quantity", "elec_fuel_consumption_mmbtu"
]]
eia_923_gen_fuel["coal_type"] = eia_923_gen_fuel[
"reported_fuel_type_code"].map(coal_type_codes)
eia_923_gen_fuel["plant_id"] = eia_923_gen_fuel["plant_id"].astype(int)
eia_923_grouped = eia_923_gen_fuel.groupby(by=["plant_id", "coal_type"])[[
"electric_fuel_consumption_quantity", "elec_fuel_consumption_mmbtu"
]].sum()
final_df.reset_index(inplace=True)
eia_923_grouped.reset_index(inplace=True)
final_df = final_df.merge(eia_923_grouped,
on=["plant_id", "coal_type"],
how="left")
rc_coal = eia_923_grouped.loc[eia_923_grouped["coal_type"] ==
"RC", :].set_index("plant_id")
final_df["rc_coal_quantity"] = final_df["plant_id"].map(
rc_coal["electric_fuel_consumption_quantity"])
final_df["rc_coal_mmbtu"] = final_df["plant_id"].map(
rc_coal["elec_fuel_consumption_mmbtu"])
final_df["new_quantity"] = final_df["mass_fraction"] * final_df[
"electric_fuel_consumption_quantity"]
final_df["new_heat_input"] = final_df["energy_fraction"] * final_df[
"elec_fuel_consumption_mmbtu"]
final_df.loc[~final_df["rc_coal_quantity"].isna(),
"new_quantity"] = final_df["new_quantity"] + final_df[
"mass_fraction_total"] * final_df["rc_coal_quantity"]
final_df.loc[~final_df["rc_coal_mmbtu"].isna(),
"new_heat_input"] = final_df["new_heat_input"] + final_df[
"energy_fraction"] * final_df["rc_coal_mmbtu"]
final_df = final_df[[
"plant_id", "coal_source_code", "new_quantity", "new_heat_input"
]]
final_df = final_df.rename(columns={
"new_quantity": "quantity",
"new_heat_input": "heat_input"
})
return final_df
