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3 Commits

Author SHA1 Message Date
Cullen
c5b15e9be5 chore: version 2024-04-20 17:45:29 -05:00
joecryptotoo
7a525caeb8 added county, fips, and text desciption fields (#72) 2024-04-20 17:44:28 -05:00
Cullen Watson
7246703999 Schools (#69) 2024-04-16 20:01:20 -05:00
6 changed files with 65 additions and 16 deletions

View File

@@ -134,6 +134,13 @@ Property
├── Location Details:
│ ├── latitude
│ ├── longitude
│ ├── nearby_schools
├── Agent Info:
│ ├── agent
│ ├── broker
│ └── broker_phone
├── Agent Info:
│ ├── agent

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@@ -1,3 +1,4 @@
import uuid
from dataclasses import dataclass
import requests
import uuid

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@@ -23,6 +23,12 @@ class ListingType(Enum):
SOLD = "SOLD"
@dataclass
class Agent:
name: str | None = None
phone: str | None = None
class PropertyType(Enum):
APARTMENT = "APARTMENT"
BUILDING = "BUILDING"
@@ -67,6 +73,7 @@ class Description:
year_built: int | None = None
garage: float | None = None
stories: int | None = None
text: str | None = None
@dataclass
@@ -95,5 +102,7 @@ class Property:
latitude: float | None = None
longitude: float | None = None
neighborhoods: Optional[str] = None
county: Optional[str] = None
fips_code: Optional[str] = None
agents: list[Agent] = None
nearby_schools: list[str] = None

View File

@@ -142,7 +142,7 @@ class RealtorScraper(Scraper):
days_on_mls = None
property_id = property_info["details"]["permalink"]
agents = self.get_agents(property_id)
agents_schools = self.get_agents_schools(property_id)
listing = Property(
mls=mls,
mls_id=(
@@ -176,9 +176,11 @@ class RealtorScraper(Scraper):
year_built=property_info["details"].get("year_built"),
garage=property_info["details"].get("garage"),
stories=property_info["details"].get("stories"),
text=property_info["description"].get("text"),
),
days_on_mls=days_on_mls,
agents=agents,
agents=agents_schools["agents"],
nearby_schools=agents_schools["schools"],
)
return [listing]
@@ -272,7 +274,7 @@ class RealtorScraper(Scraper):
}"""
variables = {"property_id": property_id}
agents = self.get_agents(property_id)
agents_schools = self.get_agents_schools(property_id)
payload = {
"query": query,
@@ -290,7 +292,8 @@ class RealtorScraper(Scraper):
property_url=f"{self.PROPERTY_URL}{property_info['details']['permalink']}",
address=self._parse_address(property_info, search_type="handle_address"),
description=self._parse_description(property_info),
agents=agents,
agents=agents_schools["agents"],
nearby_schools=agents_schools["schools"],
)
]
@@ -328,6 +331,7 @@ class RealtorScraper(Scraper):
type
name
stories
text
}
source {
id
@@ -351,10 +355,17 @@ class RealtorScraper(Scraper):
lat
}
}
county {
name
fips_code
}
neighborhoods {
name
}
}
tax_record {
public_record_id
}
primary_photo {
href
}
@@ -510,7 +521,7 @@ class RealtorScraper(Scraper):
return
property_id = result["property_id"]
agents = self.get_agents(property_id)
agents_schools = self.get_agents_schools(property_id)
realty_property = Property(
mls=mls,
@@ -534,8 +545,12 @@ class RealtorScraper(Scraper):
longitude=result["location"]["address"]["coordinate"].get("lon") if able_to_get_lat_long else None,
address=self._parse_address(result, search_type="general_search"),
description=self._parse_description(result),
neighborhoods=self._parse_neighborhoods(result),
county=result["location"]["county"].get("name"),
fips_code=result["location"]["county"].get("fips_code"),
days_on_mls=self.calculate_days_on_mls(result),
agents=agents,
agents=agents_schools["agents"],
nearby_schools=agents_schools["schools"],
)
return realty_property
@@ -630,18 +645,25 @@ class RealtorScraper(Scraper):
return homes
def get_agents(self, property_id: str) -> list[Agent]:
payload = f'{{"query":"query GetHome($property_id: ID!) {{\\n home(property_id: $property_id) {{\\n __typename\\n\\n consumerAdvertisers: consumer_advertisers {{\\n __typename\\n type\\n advertiserId: advertiser_id\\n name\\n phone\\n type\\n href\\n slogan\\n photo {{\\n __typename\\n href\\n }}\\n showRealtorLogo: show_realtor_logo\\n hours\\n }}\\n\\n\\n }}\\n}}\\n","variables":{{"property_id":"{property_id}"}}}}'
def get_agents_schools(self, property_id: str) -> dict:
payload = f'{{"query":"query GetHome($property_id: ID!) {{\\n home(property_id: $property_id) {{\\n __typename\\n\\n consumerAdvertisers: consumer_advertisers {{\\n __typename\\n type\\n advertiserId: advertiser_id\\n name\\n phone\\n type\\n href\\n slogan\\n photo {{\\n __typename\\n href\\n }}\\n showRealtorLogo: show_realtor_logo\\n hours\\n }}\\n\\n\\n nearbySchools: nearby_schools(radius: 5.0, limit_per_level: 3) {{ __typename schools {{ district {{ __typename id name }} }} }}}}\\n}}\\n","variables":{{"property_id":"{property_id}"}}}}'
response = self.session.post(self.PROPERTY_GQL, data=payload)
data = response.json()
try:
ads = data["data"]["home"]["consumerAdvertisers"]
except (KeyError, TypeError):
return []
def get_key(keys: list):
try:
data = response.json()
for key in keys:
data = data[key]
return data
except (KeyError, TypeError):
return []
ads = get_key(["data", "home", "consumerAdvertisers"])
schools = get_key(["data", "home", "nearbySchools", "schools"])
agents = [Agent(name=ad["name"], phone=ad["phone"]) for ad in ads]
return agents
schools = [school["district"]["name"] for school in schools]
return {"agents": agents, "schools": schools}
@staticmethod
def _parse_neighborhoods(result: dict) -> Optional[str]:
@@ -715,6 +737,7 @@ class RealtorScraper(Scraper):
year_built=description_data.get("year_built"),
garage=description_data.get("garage"),
stories=description_data.get("stories"),
text=description_data.get("text"),
)
@staticmethod

View File

@@ -8,6 +8,7 @@ ordered_properties = [
"mls",
"mls_id",
"status",
"text",
"style",
"street",
"unit",
@@ -28,12 +29,16 @@ ordered_properties = [
"price_per_sqft",
"latitude",
"longitude",
"neighborhoods",
"county",
"fips_code",
"stories",
"hoa_fee",
"parking_garage",
"agent",
"broker",
"broker_phone",
"nearby_schools",
"primary_photo",
"alt_photos",
]
@@ -60,6 +65,8 @@ def process_result(result: Property) -> pd.DataFrame:
prop_data["broker_phone"] = agents[1].phone
prop_data["price_per_sqft"] = prop_data["prc_sqft"]
prop_data["nearby_schools"] = filter(None, prop_data["nearby_schools"]) if prop_data["nearby_schools"] else None
prop_data["nearby_schools"] = ", ".join(set(prop_data["nearby_schools"])) if prop_data["nearby_schools"] else None
description = result.description
prop_data["primary_photo"] = description.primary_photo
@@ -74,6 +81,8 @@ def process_result(result: Property) -> pd.DataFrame:
prop_data["year_built"] = description.year_built
prop_data["parking_garage"] = description.garage
prop_data["stories"] = description.stories
prop_data["text"] = description.text
properties_df = pd.DataFrame([prop_data])
properties_df = properties_df.reindex(columns=ordered_properties)

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@@ -1,6 +1,6 @@
[tool.poetry]
name = "homeharvest"
version = "0.3.17"
version = "0.3.19"
description = "Real estate scraping library"
authors = ["Zachary Hampton <zachary@bunsly.com>", "Cullen Watson <cullen@bunsly.com>"]
homepage = "https://github.com/Bunsly/HomeHarvest"