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49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
# Get units for postcodes WF17 8RA, WF17 8RB
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import os
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import pandas as pd
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from epc_api.client import EpcClient
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from dotenv import load_dotenv
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load_dotenv(dotenv_path="backend/.env")
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EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
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postcodes = [
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"WF17 8RA",
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"WF17 8RB",
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]
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client = EpcClient(auth_token=EPC_AUTH_TOKEN)
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data = []
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for postcode in postcodes:
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resp = client.domestic.search(
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params={"postcode": postcode, "address": None, "local-authority": None, "property-type": None,
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"floor-area": None,
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"energy-band": None, "from-month": None, "from-year": None, "to-month": None, "to-year": None,
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'constituency': None},
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size=1000
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)
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data.extend(resp["rows"])
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df = pd.DataFrame(data)
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# Get newest field by UPRN, inspection-date
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df["inspection-date"] = pd.to_datetime(df["inspection-date"])
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df = df.sort_values(by=["uprn", "inspection-date"], ascending=[True, False])
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df = df.drop_duplicates(subset=["uprn"], keep="first")
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df.to_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Blakeridge Mill/blakeridge_mill_epc_data.xlsx", index=False
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)
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df = df[df["address"] != "The Tower Blakeridge Mill, Upper Blakeridge Lane"]
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df["walls-description"].value_counts()
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df["roof-description"].value_counts()
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df["total-floor-area"].astype(float).mean()
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df["current-energy-efficiency"] = pd.to_numeric(df["current-energy-efficiency"], errors='coerce')
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df.groupby("transaction-type")["current-energy-efficiency"].mean()
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df["transaction-type"].value_counts()
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df[df["transaction-type"] == "rental"]["built-form"].value_counts()
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