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HTS.py
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HTS.py
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from datetime import datetime
import glob
import os
from sqlalchemy import ARRAY, Column, Float, ForeignKey, Integer, String
from sqlalchemy.ext.declarative import declarative_base
import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas.io.sql as sqlio
import psycopg2
import xarray as xr
from egon.data import db, subprocess
import egon.data.datasets.era5 as era
try:
from disaggregator import config, data, plot, spatial, temporal
except ImportError as e:
pass
from math import ceil
from egon.data.datasets import Dataset
import egon
class IdpProfiles:
def __init__(self, df_index, **kwargs):
index = pd.date_range(
datetime(2011, 1, 1, 0), periods=8760, freq="H"
)
self.df = pd.DataFrame(index=df_index)
self.temperature = kwargs.get("temperature")
def get_temperature_interval(self, how="geometric_series"):
index = pd.date_range(
datetime(2011, 1, 1, 0), periods=8760, freq="H"
)
"""Appoints the corresponding temperature interval to each temperature
in the temperature vector.
"""
self.df["temperature"] = self.temperature.values
temperature = (
self.df["temperature"]
.resample("D")
.mean()
.reindex(self.df.index)
.fillna(method="ffill")
.fillna(method="bfill")
)
if how == "geometric_series":
temperature_mean = (
temperature
+ 0.5 * np.roll(temperature, 24)
+ 0.25 * np.roll(temperature, 48)
+ 0.125 * np.roll(temperature, 72)
) / 1.875
elif how == "mean":
temperature_mean = temperature
else:
temperature_mean = None
self.df["temperature_geo"] = temperature_mean
temperature_rounded = []
for i in self.df["temperature_geo"]:
temperature_rounded.append(ceil(i))
intervals = {
-20: 1,
-19: 1,
-18: 1,
-17: 1,
-16: 1,
-15: 1,
-14: 2,
-13: 2,
-12: 2,
-11: 2,
-10: 2,
-9: 3,
-8: 3,
-7: 3,
-6: 3,
-5: 3,
-4: 4,
-3: 4,
-2: 4,
-1: 4,
0: 4,
1: 5,
2: 5,
3: 5,
4: 5,
5: 5,
6: 6,
7: 6,
8: 6,
9: 6,
10: 6,
11: 7,
12: 7,
13: 7,
14: 7,
15: 7,
16: 8,
17: 8,
18: 8,
19: 8,
20: 8,
21: 9,
22: 9,
23: 9,
24: 9,
25: 9,
26: 10,
27: 10,
28: 10,
29: 10,
30: 10,
31: 10,
32: 10,
33: 10,
34: 10,
35: 10,
36: 10,
37: 10,
38: 10,
39: 10,
40: 10,
}
temperature_interval = []
for i in temperature_rounded:
temperature_interval.append(intervals[i])
self.df["temperature_interval"] = temperature_interval
return self.df
def temperature_profile_extract():
"""
Description: Extract temperature data from atlite
Returns
-------
temperature_profile : pandas.DataFrame
Temperatur profile of all TRY Climate Zones 2011
"""
cutout = era.import_cutout(boundary="Europe")
coordinates_path = os.path.join(
os.getcwd(),
"data_bundle_egon_data",
"climate_zones_Germany",
"TRY_Climate_Zone",
)
station_location = pd.read_csv(
os.path.join(coordinates_path, "station_coordinates.csv")
)
weather_cells = db.select_geodataframe(
"""
SELECT geom FROM supply.egon_era5_weather_cells
""",
epsg=4326,
)
gdf = gpd.GeoDataFrame(
station_location,
geometry=gpd.points_from_xy(
station_location.Longitude, station_location.Latitude
),
)
selected_weather_cells = gpd.sjoin(weather_cells, gdf).set_index("Station")
temperature_profile = cutout.temperature(
shapes=selected_weather_cells.geom.values,
index=selected_weather_cells.index,
).to_pandas()
return temperature_profile
def temp_interval():
"""
Description: Create Dataframe with temperature data for TRY Climate Zones
Returns
-------
temperature_interval : pandas.DataFrame
Hourly temperature intrerval of all 15 TRY Climate station#s temperature profile
"""
index = pd.date_range(datetime(2011, 1, 1, 0), periods=8760, freq="H")
temperature_interval = pd.DataFrame()
temp_profile = temperature_profile_extract()
for x in range(len(temp_profile.columns)):
name_station = temp_profile.columns[x]
idp_this_station = IdpProfiles(
index, temperature=temp_profile[temp_profile.columns[x]]
).get_temperature_interval(how="geometric_series")
temperature_interval[name_station] = idp_this_station[
"temperature_interval"
]
return temperature_interval
def idp_pool_generator():
"""
Description: Create List of Dataframes for each temperature class for each household stock
Returns
-------
TYPE list
List of dataframes with each element representing a dataframe
for every combination of household stock and temperature class
"""
path = os.path.join(
os.getcwd(),
"data_bundle_egon_data",
"household_heat_demand_profiles",
"household_heat_demand_profiles.hdf5",
)
index = pd.date_range(datetime(2011, 1, 1, 0), periods=8760, freq="H")
sfh = pd.read_hdf(path, key="SFH")
mfh = pd.read_hdf(path, key="MFH")
temp = pd.read_hdf(path, key="temperature")
globals()["luebeck_sfh"] = sfh[sfh.filter(like="Luebeck").columns]
globals()["luebeck_mfh"] = mfh[mfh.filter(like="Luebeck").columns]
globals()["kassel_sfh"] = sfh[sfh.filter(like="Kassel").columns]
globals()["kassel_mfh"] = mfh[mfh.filter(like="Kassel").columns]
globals()["wuerzburg_sfh"] = sfh[sfh.filter(like="Wuerzburg").columns]
globals()["wuerzburg_mfh"] = mfh[mfh.filter(like="Wuerzburg").columns]
globals()["chemnitz_sfh"] = sfh[sfh.filter(like="Chemnitz").columns]
globals()["chemnitz_mfh"] = mfh[mfh.filter(like="Chemnitz").columns]
temp_daily = pd.DataFrame()
for column in temp.columns:
temp_current = (
temp[column]
.resample("D")
.mean()
.reindex(temp.index)
.fillna(method="ffill")
.fillna(method="bfill")
)
temp_current_geom = temp_current
temp_daily = pd.concat([temp_daily, temp_current_geom], axis=1)
def round_temperature(station):
"""
Description: Create dataframe to assign temperature class to each day of TRY climate zones
Parameters
----------
station : str
Name of the location
Returns
-------
temp_class : pandas.DataFrame
Each day assignd to their respective temperature class
"""
intervals = {
-20: 1,
-19: 1,
-18: 1,
-17: 1,
-16: 1,
-15: 1,
-14: 2,
-13: 2,
-12: 2,
-11: 2,
-10: 2,
-9: 3,
-8: 3,
-7: 3,
-6: 3,
-5: 3,
-4: 4,
-3: 4,
-2: 4,
-1: 4,
0: 4,
1: 5,
2: 5,
3: 5,
4: 5,
5: 5,
6: 6,
7: 6,
8: 6,
9: 6,
10: 6,
11: 7,
12: 7,
13: 7,
14: 7,
15: 7,
16: 8,
17: 8,
18: 8,
19: 8,
20: 8,
21: 9,
22: 9,
23: 9,
24: 9,
25: 9,
26: 10,
27: 10,
28: 10,
29: 10,
30: 10,
31: 10,
32: 10,
33: 10,
34: 10,
35: 10,
36: 10,
37: 10,
38: 10,
39: 10,
40: 10,
}
temperature_rounded = []
for i in temp_daily.loc[:, station]:
temperature_rounded.append(ceil(i))
temperature_interval = []
for i in temperature_rounded:
temperature_interval.append(intervals[i])
temp_class_dic = {f"Class_{station}": temperature_interval}
temp_class = pd.DataFrame.from_dict(temp_class_dic)
return temp_class
temp_class_luebeck = round_temperature("Luebeck")
temp_class_kassel = round_temperature("Kassel")
temp_class_wuerzburg = round_temperature("Wuerzburg")
temp_class_chemnitz = round_temperature("Chemnitz")
temp_class = pd.concat(
[
temp_class_luebeck,
temp_class_kassel,
temp_class_wuerzburg,
temp_class_chemnitz,
],
axis=1,
)
temp_class.set_index(index, inplace=True)
def unique_classes(station):
"""
Returns
-------
classes : list
Collection of temperature classes for each location
"""
classes = []
for x in temp_class[f"Class_{station}"]:
if x not in classes:
classes.append(x)
classes.sort()
return classes
globals()["luebeck_classes"] = unique_classes("Luebeck")
globals()["kassel_classes"] = unique_classes("Kassel")
globals()["wuerzburg_classes"] = unique_classes("Wuerzburg")
globals()["chemnitz_classes"] = unique_classes("Chemnitz")
stock = ["MFH", "SFH"]
class_list = [2, 3, 4, 5, 6, 7, 8, 9, 10]
for s in stock:
for m in class_list:
globals()[f"idp_collection_class_{m}_{s}"] = pd.DataFrame(
index=range(24)
)
def splitter(station, household_stock):
"""
Parameters
----------
station : str
Name of the location
household_stock : str
SFH or MFH
Returns
-------
None.
"""
this_classes = globals()["{}_classes".format(station.lower())]
for classes in this_classes:
# this_itteration = globals()[f'{station.lower()}_{household_stock.lower()}'].loc[temp_class[f'Class_{station}']==classes,:]
this_itteration = globals()[
"{}_{}".format(station.lower(), household_stock.lower())
].loc[temp_class["Class_{}".format(station)] == classes, :]
days = list(range(int(len(this_itteration) / 24)))
for day in days:
this_day = this_itteration[day * 24 : (day + 1) * 24]
this_day = this_day.reset_index(drop=True)
globals()[
f"idp_collection_class_{classes}_{household_stock}"
] = pd.concat(
[
globals()[
f"idp_collection_class_{classes}_{household_stock}"
],
this_day,
],
axis=1,
ignore_index=True,
)
splitter("Luebeck", "SFH")
splitter("Kassel", "SFH")
splitter("Wuerzburg", "SFH")
splitter("Chemnitz", "SFH")
splitter("Luebeck", "MFH")
splitter("Kassel", "MFH")
splitter("Chemnitz", "MFH")
def pool_normalize(x):
"""
Parameters
----------
x : pandas.Series
24-hour profiles of IDP pool
Returns
-------
TYPE : pandas.Series
Normalized to their daily total
"""
if x.sum() != 0:
c = x.sum()
return x / c
else:
return x
stock = ["MFH", "SFH"]
class_list = [2, 3, 4, 5, 6, 7, 8, 9, 10]
for s in stock:
for m in class_list:
df_name = globals()[f"idp_collection_class_{m}_{s}"]
globals()[f"idp_collection_class_{m}_{s}_norm"] = df_name.apply(
pool_normalize
)
return [
idp_collection_class_2_SFH_norm,
idp_collection_class_3_SFH_norm,
idp_collection_class_4_SFH_norm,
idp_collection_class_5_SFH_norm,
idp_collection_class_6_SFH_norm,
idp_collection_class_7_SFH_norm,
idp_collection_class_8_SFH_norm,
idp_collection_class_9_SFH_norm,
idp_collection_class_10_SFH_norm,
idp_collection_class_2_MFH_norm,
idp_collection_class_3_MFH_norm,
idp_collection_class_4_MFH_norm,
idp_collection_class_5_MFH_norm,
idp_collection_class_6_MFH_norm,
idp_collection_class_7_MFH_norm,
idp_collection_class_8_MFH_norm,
idp_collection_class_9_MFH_norm,
idp_collection_class_10_MFH_norm,
]
def idp_df_generator():
"""
Description: Create dataframe with all temprature classes, 24hr. profiles and household stock
Returns
-------
idp_df : pandas.DataFrame
All IDP pool as classified as per household stock and temperature class
"""
idp_list = idp_pool_generator()
stock = ["MFH", "SFH"]
class_list = [2, 3, 4, 5, 6, 7, 8, 9, 10]
idp_df = pd.DataFrame(columns=["idp", "house", "temperature_class"])
for s in stock:
for m in class_list:
if s == "SFH":
i = class_list.index(m)
if s == "MFH":
i = class_list.index(m) + 9
current_pool = idp_list[i]
idp_df = idp_df.append(
pd.DataFrame(
data={
"idp": current_pool.transpose().values.tolist(),
"house": s,
"temperature_class": m,
}
)
)
idp_df = idp_df.reset_index(drop=True)
idp_df.to_sql(
"heat_idp_pool",
con=db.engine(),
schema="demand",
if_exists="replace",
index=True,
dtype={
"index": Integer(),
"idp": ARRAY(Float()),
"house": String(),
"temperature_class": Integer(),
},
)
idp_df["idp"] = idp_df.idp.apply(lambda x: np.array(x))
idp_df.idp = idp_df.idp.apply(lambda x: x.astype(np.float32))
return idp_df
def psycop_df_AF(table_name):
"""
Description: Read tables from database into pandas dataframe
Parameters
----------
table_name : str
Name of the database table
Returns
-------
data : pandas.DataFrame
Imported database tables
"""
conn = db.engine()
sql = "SELECT * FROM {}".format(table_name)
data = sqlio.read_sql_query(sql, conn)
conn = None
return data
def psycop_gdf_AF(table_name, geom_column="geom"):
"""
Description: Read tables from database into geopandas dataframe
Parameters
----------
table_name : str
Name of the database table
geom_column : str, optional
Column name with geometry. The default is 'geom'.
Returns
-------
data : Tandas.DataFrame
Imported database tables
"""
conn = db.engine()
sql = "SELECT * FROM {}".format(table_name)
data = gpd.read_postgis(sql, conn, geom_column)
conn = None
return data
def annual_demand_generator():
"""
Description: Create dataframe with annual demand and household count for each zensus cell
Returns
-------
demand_count: pandas.DataFrame
Annual demand of all zensus cell with MFH and SFH count and
respective associated Station
"""
demand = psycop_df_AF("demand.egon_peta_heat")
a_pha = psycop_df_AF("society.egon_destatis_zensus_apartment_per_ha")
b_pha = psycop_df_AF("society.egon_destatis_zensus_building_per_ha")
h_pha = psycop_df_AF("society.egon_destatis_zensus_household_per_ha")
zp_pha = psycop_gdf_AF("society.destatis_zensus_population_per_ha")
demand = demand[demand["scenario"] == "eGon2035"]
demand = demand[demand["sector"] == "residential"]
a_pha_1 = a_pha[["zensus_population_id", "grid_id"]]
a_pha_1 = a_pha_1.drop_duplicates(subset="zensus_population_id")
b_pha_1 = b_pha[["zensus_population_id", "grid_id"]]
b_pha_1 = b_pha_1.drop_duplicates(subset="zensus_population_id")
h_pha_1 = h_pha[["zensus_population_id", "grid_id"]]
h_pha_1 = h_pha_1.drop_duplicates(subset="zensus_population_id")
all_grid = a_pha_1.append(b_pha_1).append(h_pha_1)
all_grid = all_grid.drop_duplicates(subset="zensus_population_id")
demand_grid = pd.merge(
demand, all_grid, how="inner", on="zensus_population_id"
)
all_geom = zp_pha.drop(
zp_pha.columns.difference(["grid_id", "geom"]), axis=1
)
demand_geom = pd.merge(demand_grid, all_geom, how="inner", on="grid_id")
demand_geom = gpd.GeoDataFrame(demand_geom, geometry="geom")
demand_geom = demand_geom.drop(
demand_geom.columns.difference(
["demand", "grid_id", "geom", "zensus_population_id"]
),
1,
)
demand_geom["geom"] = demand_geom["geom"].to_crs(epsg=4326)
temperature_zones = gpd.read_file(
os.path.join(
os.getcwd(),
"data_bundle_egon_data",
"climate_zones_Germany",
"TRY_Climate_Zone",
"Climate_Zone.shp",
)
)
temperature_zones.sort_values("Zone", inplace=True)
temperature_zones.reset_index(inplace=True)
temperature_zones.drop(columns=["index", "Id"], inplace=True, axis=0)
demand_zone = gpd.sjoin(
demand_geom, temperature_zones, how="inner", op="intersects"
)
demand_zone.drop_duplicates(["grid_id"], inplace=True)
sfh_chartext = [
"Freistehendes Einfamilienhaus",
"Einfamilienhaus: Doppelhaushälfte",
"Einfamilienhaus: Reihenhaus",
"Freistehendes Zweifamilienhaus",
"Zweifamilienhaus: Doppelhaushälfte",
"Zweifamilienhaus: Reihenhaus",
]
mfh_chartext = [
"Mehrfamilienhaus: 3-6 Wohnungen",
"Mehrfamilienhaus: 7-12 Wohnungen",
"Mehrfamilienhaus: 13 und mehr Wohnungen",
"Anderer Gebäudetyp",
]
bg_pha = b_pha[b_pha["attribute"] == "GEBTYPGROESSE"]
def household_stock(x):
"""
Parameters
----------
x : str
household characteristics
Returns
-------
output : str
Categorized to either SFH or MFH
"""
if x in sfh_chartext:
output = "SFH"
if x in mfh_chartext:
output = "MFH"
return output
bg_pha["Household Stock"] = bg_pha["characteristics_text"].apply(
household_stock
)
house_count = bg_pha[
["zensus_population_id", "quantity", "Household Stock"]
]
house_count = house_count.groupby(
["zensus_population_id", "Household Stock"]
).sum("quantity")
house_count = house_count.reset_index()
house_count = house_count.pivot_table(
values="quantity",
index="zensus_population_id",
columns="Household Stock",
)
house_count = house_count.fillna(0)
demand_count = pd.merge(
demand_zone, house_count, how="inner", on="zensus_population_id"
)
demand_count.drop("index_right", axis=1, inplace=True)
demand_count.drop(
demand_count.columns.difference(
["zensus_population_id", "demand", "SFH", "MFH", "Station"]
),
axis=1,
inplace=True,
)
return demand_count
def profile_selector():
"""
Description: Random assignment of profiles to each day based on their temeprature class
and household stock count
Returns
-------
annual_demand : pandas.DataFrame
Annual demand of all zensus cell with MFH and SFH count and
respective associated Station
idp_df : pandas.DataFrame
All IDP pool as classified as per household stock and temperature class
selected_idp_names: pandas.DataFrame
Each cell of the table assigned with the column number (int) with the value
corresponding to the idp_df row number. This indicates the assignmnet of
24 hr. array to the day.
"""
idp_df = idp_df_generator()
annual_demand = annual_demand_generator()
all_temperature_interval = temp_interval()
all_temperature_interval = all_temperature_interval.resample("D").max()
all_temperature_interval.reset_index(drop=True, inplace=True)
all_temperature_interval = all_temperature_interval.loc[
:, annual_demand.Station.unique()
]
Temperature_interval = all_temperature_interval.transpose()
np.random.seed(
seed=egon.data.config.settings()["egon-data"]["--random-seed"]
)
if os.path.isfile("selected_profiles.csv"):
os.remove("selected_profiles.csv")
selected_idp_names = pd.DataFrame()
length = 0
for station in Temperature_interval.index:
result_SFH = pd.DataFrame(columns=Temperature_interval.columns)
result_MFH = pd.DataFrame(columns=Temperature_interval.columns)
for day in Temperature_interval.columns:
t_class = Temperature_interval.loc[station, day].astype(int)
array_SFH = np.array(
idp_df[
(idp_df.temperature_class == t_class)
& (idp_df.house == "SFH")
].index.values
)
array_MFH = np.array(
idp_df[
(idp_df.temperature_class == t_class)
& (idp_df.house == "MFH")
].index.values
)
result_SFH[day] = np.random.choice(
array_SFH,
int(annual_demand[annual_demand.Station == station].SFH.sum()),
)
result_MFH[day] = np.random.choice(
array_MFH,
int(annual_demand[annual_demand.Station == station].MFH.sum()),
)
result_SFH["zensus_population_id"] = (
annual_demand[annual_demand.Station == station]
.loc[
annual_demand[annual_demand.Station == station].index.repeat(
annual_demand[annual_demand.Station == station].SFH.astype(
int
)
)
]
.zensus_population_id.values
)
result_MFH["zensus_population_id"] = (
annual_demand[annual_demand.Station == station]
.loc[
annual_demand[annual_demand.Station == station].index.repeat(
annual_demand[annual_demand.Station == station].MFH.astype(
int
)
)
]
.zensus_population_id.values
)
result_SFH.set_index("zensus_population_id", inplace=True)
result_MFH.set_index("zensus_population_id", inplace=True)
selected_idp_names = selected_idp_names.append(result_SFH)
selected_idp_names = selected_idp_names.append(result_MFH)
selected_idp_names = selected_idp_names.apply(
lambda x: x.astype(np.int32)
)
new_length = len(selected_idp_names)
selected_this_station = selected_idp_names.iloc[length:new_length, :]
selected_this_station[
"selected_idp"
] = selected_this_station.values.tolist()
selected_this_station = selected_this_station.selected_idp
selected_this_station = selected_this_station.reset_index()
selected_this_station.index = range(length, new_length)
if os.path.isfile("selected_profiles.csv"):
selected_this_station.to_csv(
"selected_profiles.csv", mode="a", header=False
)
else:
selected_this_station.to_csv("selected_profiles.csv")
length = new_length
Base = declarative_base()
class EgonHeatTimeseries(Base):
__tablename__ = "heat_timeseries_selected_profiles"
__table_args__ = {"schema": "demand"}
ID = Column(Integer, primary_key=True)
zensus_population_id = Column(Integer, primary_key=True)
selected_idp_profiles = Column(String)
engine = db.engine()
EgonHeatTimeseries.__table__.drop(bind=engine, checkfirst=True)
EgonHeatTimeseries.__table__.create(bind=engine, checkfirst=True)
heat_selected_profiles = {
"schema": "demand",
"table": "heat_timeseries_selected_profiles",
}
filename_insert = "selected_profiles.csv"
docker_db_config = db.credentials()
selected_profiles_table = (
f"{heat_selected_profiles['schema']}"
f".{heat_selected_profiles['table']}"
)
host = ["-h", f"{docker_db_config['HOST']}"]
port = ["-p", f"{docker_db_config['PORT']}"]
pgdb = ["-d", f"{docker_db_config['POSTGRES_DB']}"]
user = ["-U", f"{docker_db_config['POSTGRES_USER']}"]
command = [
"-c",
rf"\copy {selected_profiles_table}"
rf" FROM '{filename_insert}' DELIMITER ',' CSV HEADER;",
]
subprocess.run(
["psql"] + host + port + pgdb + user + command,
env={"PGPASSWORD": docker_db_config["POSTGRES_PASSWORD"]},
)
os.remove(filename_insert)
return annual_demand, idp_df, selected_idp_names
def h_value():
"""
Description: Assignment of daily demand scaling factor to each day of all TRY Climate Zones
Returns
-------
h : pandas.DataFrame
Hourly factor values for each station corresponding to the temperature profile.
Extracted from demandlib.
"""
index = pd.date_range(datetime(2011, 1, 1, 0), periods=8760, freq="H")
a = 3.0469695
b = -37.1833141
c = 5.6727847
d = 0.1163157
temp_profile = temperature_profile_extract()
temperature_profile_res = (
temp_profile.resample("D")
.mean()
.reindex(index)
.fillna(method="ffill")
.fillna(method="bfill")
)
temp_profile_geom = (
(
temperature_profile_res.transpose()
+ 0.5 * np.roll(temperature_profile_res.transpose(), 24, axis=1)
+ 0.25 * np.roll(temperature_profile_res.transpose(), 48, axis=1)
+ 0.125 * np.roll(temperature_profile_res.transpose(), 72, axis=1)
)
/ 1.875
).transpose()
h = a / (1 + (b / (temp_profile_geom - 40)) ** c) + d
return h
def profile_generator(aggregation_level):
"""
Descriptiion: Aggregation of profiles either based on district heating network and medium voltage
grid or zensus cell
Parameters
----------
aggregation_level : str
if further processing is to be done in zensus cell level 'other'
else 'dsitrict'
Returns
-------
annual_demand : pandas.DataFrame
Annual demand of all zensus cell with MFH and SFH count and
respective associated Station
heat_profile_dist : pandas.DataFrame
if aggreation_level = 'district'
heat profiles for every distric heating id
else
0
heat_profile_idp : pandas.DataFrame
if aggreation_level = 'district'
heat profiles for every mv grid bus_id
else
heat profiles for every zensus_poppulation_id
"""
annual_demand, idp_df, selected_profiles = profile_selector()
y = idp_df["idp"].reset_index()
heat_profile_idp = pd.DataFrame(
index=selected_profiles.index.sort_values()
)
for i in selected_profiles.columns:
col = selected_profiles[i]
col = col.reset_index()
y.rename(columns={"index": i}, inplace=True)
col = pd.merge(col, y[[i, "idp"]], how="left", on=i)
col[i] = col["idp"]
col.drop("idp", axis=1, inplace=True)
col.set_index("zensus_population_id", inplace=True)
heat_profile_idp[i] = col[i].values
y.rename(columns={i: "index"}, inplace=True)