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57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
import pandas as pd
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from backend.utils.addressMatch import AddressMatch
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def df_has_single_uprn(df: pd.DataFrame, uprn: str, column: str = "uprn") -> bool:
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"""
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Returns True if all non-null UPRNs in df match the given uprn.
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Returns False otherwise.
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"""
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if column not in df.columns:
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return False
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uprns = df[column].dropna().astype(str).str.strip().unique()
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if len(uprns) == 0:
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return False
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return len(uprns) == 1 and uprns[0] == str(uprn)
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def get_uprn_candidates(
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df: pd.DataFrame,
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user_address: str,
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address_column: str = "address",
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uprn_column: str = "uprn",
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) -> pd.DataFrame:
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"""
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Annotate EPC results with lexicographical similarity scores and ranks.
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Returns a DataFrame sorted by descending lexiscore.
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DOES NOT choose or return a UPRN.
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"""
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if address_column not in df.columns:
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raise ValueError(f"Missing column: {address_column}")
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if uprn_column not in df.columns:
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raise ValueError(f"Missing column: {uprn_column}")
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out = df.copy()
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user_norm = AddressMatch.normalise_address(user_address)
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out["lexiscore"] = out[address_column].apply(
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lambda x: AddressMatch.levenshtein(user_norm, x)
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)
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out[uprn_column] = out[uprn_column].astype(str).str.replace(r"\.0$", "", regex=True)
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out["lexirank"] = out["lexiscore"].rank(method="dense", ascending=False).astype(int)
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return out.sort_values(
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["lexirank", "lexiscore"],
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ascending=[True, False],
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)
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