Replace pandas.append()
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445892dd87
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@ -223,7 +223,7 @@ def prepare_building_stock_data():
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usecols=[0, 1, 2, 3],
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usecols=[0, 1, 2, 3],
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encoding="ISO-8859-1",
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encoding="ISO-8859-1",
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)
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)
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area_tot = area_tot.append(area_missing.unstack(level=-1).dropna().stack())
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area_tot = pd.concat([area_tot, area_missing.unstack(level=-1).dropna().stack()])
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area_tot = area_tot.loc[~area_tot.index.duplicated(keep="last")]
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area_tot = area_tot.loc[~area_tot.index.duplicated(keep="last")]
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# for still missing countries calculate floor area by population size
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# for still missing countries calculate floor area by population size
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@ -246,7 +246,7 @@ def prepare_building_stock_data():
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averaged_data.index = index
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averaged_data.index = index
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averaged_data["estimated"] = 1
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averaged_data["estimated"] = 1
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if ct not in area_tot.index.levels[0]:
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if ct not in area_tot.index.levels[0]:
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area_tot = area_tot.append(averaged_data, sort=True)
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area_tot = pd.concat([area_tot, averaged_data], sort=True)
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else:
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else:
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area_tot.loc[averaged_data.index] = averaged_data
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area_tot.loc[averaged_data.index] = averaged_data
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@ -272,7 +272,7 @@ def prepare_building_stock_data():
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][x["bage"]].iloc[0],
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][x["bage"]].iloc[0],
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axis=1,
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axis=1,
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)
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)
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data_PL_final = data_PL_final.append(data_PL)
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data_PL_final = pd.concat([data_PL_final, data_PL])
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u_values = pd.concat([u_values, data_PL_final]).reset_index(drop=True)
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u_values = pd.concat([u_values, data_PL_final]).reset_index(drop=True)
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@ -966,7 +966,7 @@ def sample_dE_costs_area(
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.mean(level=1)
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.mean(level=1)
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.set_index(pd.MultiIndex.from_product([[ct], cost_dE.index.levels[1]]))
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.set_index(pd.MultiIndex.from_product([[ct], cost_dE.index.levels[1]]))
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)
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)
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cost_dE = cost_dE.append(averaged_data)
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cost_dE = pd.concat(cost_dE, averaged_data)
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# weights costs after construction index
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# weights costs after construction index
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if construction_index:
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if construction_index:
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@ -995,12 +995,12 @@ def sample_dE_costs_area(
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)
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)
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)
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)
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)
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)
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cost_dE = cost_dE.append(tot).unstack().stack()
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cost_dE = pd.concat(cost_dE, tot).unstack().stack()
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summed_area = pd.DataFrame(area_tot.groupby("country").sum()).set_index(
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summed_area = pd.DataFrame(area_tot.groupby("country").sum()).set_index(
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pd.MultiIndex.from_product([area_tot.index.unique(level="country"), ["tot"]])
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pd.MultiIndex.from_product([area_tot.index.unique(level="country"), ["tot"]])
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)
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)
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area_tot = area_tot.append(summed_area).unstack().stack()
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area_tot = pd.concat(area_tot, summed_area).unstack().stack()
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cost_per_saving = cost_dE["cost"] / (
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cost_per_saving = cost_dE["cost"] / (
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1 - cost_dE["dE"]
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1 - cost_dE["dE"]
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