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make retro script work with newer version of pandas

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This commit is contained in:
lisazeyen 2020-12-02 13:34:33 +01:00
parent de33ed3eb5
commit 0187d4d1d4
1 changed files with 20 additions and 13 deletions
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scripts/build_retro_cost.py
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@ -23,7 +23,6 @@ Structure:
import pandas as pd import pandas as pd
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
pd.options.mode.chained_assignment = None
#%% ************ FUCNTIONS *************************************************** #%% ************ FUCNTIONS ***************************************************
@ -175,9 +174,9 @@ def prepare_building_stock_data():
area = building_data[(building_data.type == 'Heated area [Mm²]') & area = building_data[(building_data.type == 'Heated area [Mm²]') &
(building_data.subsector != "Total")] (building_data.subsector != "Total")]
area_tot = area.groupby(["country", "sector"]).sum() area_tot = area.groupby(["country", "sector"]).sum()
area["weight"] = area.apply(lambda x: x.value / area = pd.concat([area, area.apply(lambda x: x.value /
area_tot.value.loc[(x.country, x.sector)], area_tot.value.loc[(x.country, x.sector)],
axis=1) axis=1).rename("weight")],axis=1)
area = area.groupby(['country', 'sector', 'subsector', 'bage']).sum() area = area.groupby(['country', 'sector', 'subsector', 'bage']).sum()
area_tot.rename(index=country_iso_dic, inplace=True) area_tot.rename(index=country_iso_dic, inplace=True)
@ -192,9 +191,9 @@ def prepare_building_stock_data():
pop_layout["ct"] = pop_layout.index.str[:2] pop_layout["ct"] = pop_layout.index.str[:2]
ct_total = pop_layout.total.groupby(pop_layout["ct"]).sum() ct_total = pop_layout.total.groupby(pop_layout["ct"]).sum()
area_per_pop = area_tot.unstack().apply(lambda x: x / ct_total[x.index]) area_per_pop = area_tot.unstack().reindex(index=ct_total.index).apply(lambda x: x / ct_total[x.index])
missing_area_ct = ct_total.index.difference(area_tot.index.levels[0]) missing_area_ct = ct_total.index.difference(area_tot.index.levels[0])
for ct in missing_area_ct: for ct in (missing_area_ct & ct_total.index):
averaged_data = pd.DataFrame( averaged_data = pd.DataFrame(
area_per_pop.value.reindex(map_for_missings[ct]).mean() area_per_pop.value.reindex(map_for_missings[ct]).mean()
* ct_total[ct], * ct_total[ct],
@ -233,7 +232,7 @@ def prepare_building_stock_data():
# smallest possible today u values for windows 0.8 (passive house standard) # smallest possible today u values for windows 0.8 (passive house standard)
# maybe the u values for the glass and not the whole window including frame # maybe the u values for the glass and not the whole window including frame
# for those types assumed in the dataset # for those types assumed in the dataset
u_values[(u_values.type=="Windows") & (u_values.value<0.8)]["value"] = 0.8 u_values.loc[(u_values.type=="Windows") & (u_values.value<0.8), "value"] = 0.8
# drop unnecessary columns # drop unnecessary columns
u_values.drop(['topic', 'feature','detail', 'estimated','unit'], u_values.drop(['topic', 'feature','detail', 'estimated','unit'],
axis=1, inplace=True, errors="ignore") axis=1, inplace=True, errors="ignore")
@ -314,8 +313,12 @@ def calculate_cost_energy_curve(u_values, l_strength, l_weight, average_surface_
for l in l_strength: for l in l_strength:
u_values[l] = calculate_new_u(u_values, l, l_weight) u_values[l] = calculate_new_u(u_values, l, l_weight)
energy_saved[l] = calculate_dE(u_values, l, average_surface_w) energy_saved = pd.concat([energy_saved,
costs[l] = calculate_costs(u_values, l, cost_retro, average_surface) calculate_dE(u_values, l, average_surface_w).rename(l)],
axis=1)
costs = pd.concat([costs,
calculate_costs(u_values, l, cost_retro, average_surface).rename(l)],
axis=1)
# energy and costs per country, sector, subsector and year # energy and costs per country, sector, subsector and year
e_tot = energy_saved.groupby(['country', 'sector', 'subsector', 'bage']).sum() e_tot = energy_saved.groupby(['country', 'sector', 'subsector', 'bage']).sum()
@ -334,11 +337,13 @@ def calculate_cost_energy_curve(u_values, l_strength, l_weight, average_surface_
axis=1, keys=["dE", "cost"]) axis=1, keys=["dE", "cost"])
res.rename(index=country_iso_dic, inplace=True) res.rename(index=country_iso_dic, inplace=True)
res = res.loc[countries] res = res.reindex(index=countries, level=0)
# reset index because otherwise not considered countries still in index.levels[0]
res = res.reset_index().set_index(["country", "sector"])
# map missing countries # map missing countries
for ct in map_for_missings.keys(): for ct in pd.Index(map_for_missings.keys()) & countries:
averaged_data = pd.DataFrame(res.loc[map_for_missings[ct], :].mean(level=1)) averaged_data = res.reindex(index=map_for_missings[ct], level=0).mean(level=1)
index = pd.MultiIndex.from_product([[ct], averaged_data.index.to_list()]) index = pd.MultiIndex.from_product([[ct], averaged_data.index.to_list()])
averaged_data.index = index averaged_data.index = index
if ct not in res.index.levels[0]: if ct not in res.index.levels[0]:
@ -436,12 +441,14 @@ if __name__ == "__main__":
# for missing weighting of surfaces of building types assume MultiFamily houses # for missing weighting of surfaces of building types assume MultiFamily houses
u_values["assumed_subsector"] = u_values.subsector u_values["assumed_subsector"] = u_values.subsector
u_values.assumed_subsector[ u_values.loc[~u_values.subsector.isin(average_surface.index),
~u_values.subsector.isin(average_surface.index)] = 'Multifamily houses' "assumed_subsector"] = 'Multifamily houses'
dE_and_cost = calculate_cost_energy_curve(u_values, l_strength, l_weight, dE_and_cost = calculate_cost_energy_curve(u_values, l_strength, l_weight,
average_surface_w, average_surface, area, average_surface_w, average_surface, area,
country_iso_dic, countries) country_iso_dic, countries)
# reset index because otherwise not considered countries still in index.levels[0]
dE_and_cost = dE_and_cost.reset_index().set_index(["country", "sector"])
# weights costs after construction index # weights costs after construction index
if construction_index: if construction_index: