58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
from pathlib import Path
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import pandas as pd
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from rec.query 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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# remove all entries where we didnt calculate transit time (probably due to a too far distance)
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# df2 = df[df.travel_time_fastest.notna()]
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df2 = df
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# drop columns
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# dropcolumns = ['distance_per_transit', 'duration_static', 'distance']
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# s1 = df2['travel_time_fastest'].apply(pd.Series).drop(dropcolumns, axis=1)
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# s1 = df2
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# fill in gap values for service charge and lease left. This is for excel so we can use filters better there
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if "service_charge" not in df2.columns:
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df2.loc[:, "service_charge"] = -1
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df2.loc[:, "service_charge"] = df2.service_charge.fillna(-1)
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if "lease_left" not in df2.columns:
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df2.loc[:, "lease_left"] = -1
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df2.loc[:, "lease_left"] = df2.lease_left.fillna(-1)
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if "square_meters" not in df2.columns:
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df2.loc[:, "square_meters"] = -1
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df2.loc[:, "square_meters"] = df2.square_meters.fillna(-1)
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df3 = df2
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# df3 = pd.concat([df2.drop(['travel_time_fastest', 'travel_time_second'], axis=1), s1], axis=1)
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# df3.loc[:, 'duration'] = (df3.loc[:, ['duration']].min(axis=1) / 60).round()
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df3.shape
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df4 = df3
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# df5 = df4[columns]
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# Add some interesting columns
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df4.loc[:, "price_per_sqm"] = df4.price / df4.square_meters
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df5 = df4
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df6 = df5.sort_values(by=["price_per_sqm"], ascending=True)
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df6.to_csv(str(output_file), index=False)
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