- cullen merge

homeharvest/utils.py

@@ -1,38 +1,76 @@
-import re
+from .core.scrapers.models import Property
+import pandas as pd
+
+ordered_properties = [
+    "PropertyURL",
+    "MLS",
+    "MLS #",
+    "Status",
+    "Style",
+    "Street",
+    "Unit",
+    "City",
+    "State",
+    "Zip",
+    "Beds",
+    "FB",
+    "NumHB",
+    "EstSF",
+    "YrBlt",
+    "ListPrice",
+    "Lst Date",
+    "Sold Price",
+    "COEDate",
+    "LotSFApx",
+    "PrcSqft",
+    "LATITUDE",
+    "LONGITUDE",
+    "Stories",
+    "HOAFee",
+    "PrkgGar",
+    "Community",
+]
 
 
-def parse_address_one(street_address: str) -> tuple:
-    if not street_address:
-        return street_address, "#"
+def process_result(result: Property) -> pd.DataFrame:
+    prop_data = {prop: None for prop in ordered_properties}
+    prop_data.update(result.__dict__)
+    prop_data["PropertyURL"] = prop_data["property_url"]
+    prop_data["MLS"] = prop_data["mls"]
+    prop_data["MLS #"] = prop_data["mls_id"]
+    prop_data["Status"] = prop_data["status"]
+    prop_data["Style"] = prop_data["style"]
 
-    apt_match = re.search(
-        r"(APT\s*[\dA-Z]+|#[\dA-Z]+|UNIT\s*[\dA-Z]+|LOT\s*[\dA-Z]+|SUITE\s*[\dA-Z]+)$",
-        street_address,
-        re.I,
-    )
+    if "address" in prop_data:
+        address_data = prop_data["address"]
+        prop_data["Street"] = address_data.street
+        prop_data["Unit"] = address_data.unit
+        prop_data["City"] = address_data.city
+        prop_data["State"] = address_data.state
+        prop_data["Zip"] = address_data.zip
 
-    if apt_match:
-        apt_str = apt_match.group().strip()
-        cleaned_apt_str = re.sub(r"(APT\s*|UNIT\s*|LOT\s*|SUITE\s*)", "#", apt_str, flags=re.I)
+    prop_data["Community"] = prop_data["neighborhoods"]
+    prop_data["Beds"] = prop_data["beds"]
+    prop_data["FB"] = prop_data["baths_full"]
+    prop_data["NumHB"] = prop_data["baths_half"]
+    prop_data["EstSF"] = prop_data["est_sf"]
+    prop_data["ListPrice"] = prop_data["list_price"]
+    prop_data["Lst Date"] = prop_data["list_date"]
+    prop_data["Sold Price"] = prop_data["sold_price"]
+    prop_data["COEDate"] = prop_data["last_sold_date"]
+    prop_data["LotSFApx"] = prop_data["lot_sf"]
+    prop_data["HOAFee"] = prop_data["hoa_fee"]
 
-        main_address = street_address.replace(apt_str, "").strip()
-        return main_address, cleaned_apt_str
-    else:
-        return street_address, "#"
+    if prop_data.get("prc_sqft") is not None:
+        prop_data["PrcSqft"] = round(prop_data["prc_sqft"], 2)
 
+    prop_data["YrBlt"] = prop_data["yr_blt"]
+    prop_data["LATITUDE"] = prop_data["latitude"]
+    prop_data["LONGITUDE"] = prop_data["longitude"]
+    prop_data["Stories"] = prop_data["stories"]
+    prop_data["PrkgGar"] = prop_data["prkg_gar"]
 
-def parse_address_two(street_address: str):
-    if not street_address:
-        return "#"
-    apt_match = re.search(
-        r"(APT\s*[\dA-Z]+|#[\dA-Z]+|UNIT\s*[\dA-Z]+|LOT\s*[\dA-Z]+|SUITE\s*[\dA-Z]+)$",
-        street_address,
-        re.I,
-    )
+    properties_df = pd.DataFrame([prop_data])
+    properties_df = properties_df.reindex(columns=ordered_properties)
 
-    if apt_match:
-        apt_str = apt_match.group().strip()
-        apt_str = re.sub(r"(APT\s*|UNIT\s*|LOT\s*|SUITE\s*)", "#", apt_str, flags=re.I)
-        return apt_str
-    else:
-        return "#"
+    return properties_df[ordered_properties]