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working on algorithm
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1 changed files with 14 additions and 1 deletions
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@ -1633,6 +1633,9 @@ def propsed_wave_3_sample():
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"- Archetyped V3.1.xlsx",
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header=4
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)
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# TODO: We drop 302 properties that are not priority postcodes - confirm w/ Stonewater
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asset_list = asset_list[asset_list["Archetype ID"] == "NOT PRIORITY POSTCODE"]
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# Clean address ids
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asset_list = asset_list[~pd.isnull(asset_list["Address ID"])]
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asset_list = asset_list[asset_list["Address ID"] != "Address ID"]
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@ -1674,6 +1677,7 @@ def propsed_wave_3_sample():
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# Tier 2: We have a property in the same archetype that was surveyed and is below EPC D
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#
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results = []
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for region in unique_postal_regions:
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# Take all of the properties in that region
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region_assets = asset_list[asset_list["Postal Region"] == region].copy()
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@ -1722,10 +1726,17 @@ def propsed_wave_3_sample():
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)
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region_assets["Current EPC Band"] = region_assets["Current EPC Band"].fillna(
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region_assets["Current EPC Band_method2"])
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region_assets["Current EPC Band_method2"].astype(str),
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)
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region_assets = region_assets.drop(columns=["Current EPC Band_method2"])
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# We label EPC C properties
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region_assets["Confidence Tier"] = np.where(
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region_assets["Current EPC Band"].isin(["C", "B", "A"]),
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"6 - EPC C or above", region_assets["Confidence Tier"]
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)
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missed_addressids = region_assets[pd.isnull(region_assets["Confidence Tier"])]["Address ID"].unique().tolist()
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# This means that this archetype was never surveyed and so we need to find a sufficiently similar property
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@ -1785,5 +1796,7 @@ def propsed_wave_3_sample():
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if pd.isnull(region_assets["Current EPC Band"]).sum():
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raise Exception("Something went wrong")
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results.append(region_assets)
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# if __name__ == "__main__":
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# main()
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