40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
from pathlib import Path
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import pandas as pd
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from models.listing import QueryParameters
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from repositories.listing_repository import ListingRepository
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async def export_to_csv(
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repository: ListingRepository,
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output_file: Path,
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query_parameters: QueryParameters | None = None,
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) -> None:
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listings = await repository.get_listings(query_parameters=query_parameters)
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ds = [listing.__dict__ for listing in listings]
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df = pd.DataFrame(ds)
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# read decisions on file
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decisions_path = "data/decisions.json"
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decisions = pd.read_json(decisions_path)
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df.loc[:, "decision"] = df.id.apply(lambda x: decisions.get(x))
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# remove _sa_instance_state column
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drop_columns = ["_sa_instance_state", "additional_info"]
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df = df.drop(columns=drop_columns)
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# fill in gap values for service charge and lease left for Excel filters
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if "service_charge" not in df.columns:
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df.loc[:, "service_charge"] = -1
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df.loc[:, "service_charge"] = df.service_charge.fillna(-1)
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if "lease_left" not in df.columns:
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df.loc[:, "lease_left"] = -1
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df.loc[:, "lease_left"] = df.lease_left.fillna(-1)
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if "square_meters" not in df.columns:
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df.loc[:, "square_meters"] = -1
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df.loc[:, "square_meters"] = df.square_meters.fillna(-1)
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# Add price per sqm column
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df.loc[:, "price_per_sqm"] = df.price / df.square_meters
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df = df.sort_values(by=["price_per_sqm"], ascending=True)
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df.to_csv(str(output_file), index=False)
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