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ha27 complete
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5b39cf138d
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1 changed files with 9 additions and 3 deletions
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@ -280,6 +280,12 @@ class DataLoader:
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asset_list["matching_postcode"] = asset_list['matching_address'].apply(
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lambda x: ' '.join(x.split()[-2:]) if pd.notnull(x) else x
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)
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elif ha_name == "HA27":
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asset_list["matching_address"] = (
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asset_list[" Address"].astype(str).str.lower().str.strip() + ", " +
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asset_list[" Postcode"].astype(str).str.lower().str.strip()
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)
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asset_list["matching_postcode"] = asset_list[" Postcode"].astype(str).str.lower().str.strip()
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elif ha_name == "HA28":
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asset_list["matching_address"] = (
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asset_list["House Number"].astype(str).str.lower().str.strip() + ", " +
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@ -582,7 +588,7 @@ class DataLoader:
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# For HA1 and HA25, there is an exception in the structure of the data. We don't have any survey or ciga
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# lists, and so
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# we can return the asset list now
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if ha_name in ["HA1"]:
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if ha_name in ["HA1", "HA27"]:
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return asset_list, pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
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# If we have ECO3 surveys, we need to match them, because any properties treated under ECO3 won't be
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@ -4966,13 +4972,13 @@ def app():
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# Add in:
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priority_has = [
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"HA1", "HA2", "HA6", "HA7", "HA9", "HA12", "HA13", "HA14", "HA15", "HA16", "HA18",
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"HA19", "HA24", "HA25", "HA28", "HA32",
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"HA19", "HA24", "HA25", "HA27", "HA28", "HA32",
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# "HA34",
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"HA35", "HA39", "HA41", "HA48", "HA50", "HA56", "HA63", "HA107", "HA117"
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]
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# Next HAs to do: 14 [DONE], 15[DONE], 32 [DONE], 33 [Input format is 4 parts and no eco4 jobs identified - come
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# back on this], 28 [DONE], 41 [DONE], 50 [DONE], 48 [DONE], 2 [DONE], 63 [DONE], 12 [DONE], 117 [DONE], 13 [DONE],
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# 35 [DONE], 56 [DONE], 19 [DONE]
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# 35 [DONE], 56 [DONE], 19 [DONE], 18 [DONE], 9 [DONE], 27 DONE
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#
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# Consider for ECO4:
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# Consider for GBIS:
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